[Εξώφυλλο]

Study of Atmosphere-Wildland Fires Interactions, using Numerical Models, in Greece = Μελέτης της αλληλεπίδρασης ατμόσφαιρας - δασικών πυρκαγιών με τη βοήθεια αριθμητικών μοντέλων.

Stergios Vasileios Kartsios

Περίληψη


The present PhD dissertation investigated several aspects of atmosphere-wildland fire interactions by utilizing an online coupled atmosphere – fire numerical model (WRF-SFIRE), which is a combination of a numerical weather prediction (NWP) model with a semi-empirical numerical fire spread model. Subsequently, this PhD dissertation exploited the capabilities of WRF-SFIRE modelling system across several spatial scales, from mesoscale analysis on fire weather conditions during high-impact fire events in Greece to microscale analysis on highly idealized experiments, in Large Eddy Simulation (LES) mode. Additionally, this manuscript addressed the performance of the coupled model by utilizing a number of surface observational data from the Hellenic National Meteorological Service (HNMS) and several EO data from the Meteosat second generation (SEVIRI), SENTINEL-2 (MSI), Aqua, Terra (MODIS) and Suomi-NPP (VIIRS) satellites, respectively.
The first study investigated the influence of the released heat fluxes from a surface fire on its characteristics (e.g. rate of spread, fire area), flow dynamics in the vicinity and plume properties. Specifically, the role of the extinction depth or e-folding depth parameter, zex (the height at which the fluxes are equal to 36% of their initial value), was assessed throughout eight highly idealized experiments, which were performed in LES mode. Results indicated that the choice of the zext parameter not only affected the vertical distribution of the fluxes but also the amount of the released energy from the surface fire. The higher the zext value, the higher the percentage of the released energy that resided on the first theta model level. Moreover, the calculated burn probabilities revealed that under identical initial atmospheric conditions but different e-folding depths discrepancies might occur in the resulted fire area. The coupled model was able to reproduce certain flow characteristics such as the convergence region ahead of the fire front and the descending rear inflow to the updraft’s base, in all experiments albeit structural differences were observed. In general, an increase of the zext parameter led to weaker time-averaged potential temperature anomalies both close to the ground and in the top of the convective plume. However, the temporal peaks in theta anomalies did not follow any linearity and their occurrence varied both in time and space. The analysis on near surface dynamics revealed discrepancies in the patterns and the magnitude of vertical vorticity and divergence fields, in the shape of the fire perimeter and the location of the fire head between the experiments. Low e-folding depth values produced more organized and intense counter-rotating vertical vorticity pairs and regions of vorticity along the fire flanks and in front of the active fire head, whilst in the sensitivities with zext greater than 50 m, this vorticity was less organized and more transient. The vorticity equation budget analysis showed that the solenoidal term was up to twelve orders of magnitude less than the other terms. The horizontal advection of vertical vorticity contributed the most to the increase of vorticity, while the tilting/twisting term was dominant at the early stages of the fire, where the ambient shear-generated horizontal vorticity, ωy, was oriented into vertical due to buoyant gradients from the surface fire.
The second study analyzed the prevailing weather conditions on 23rd of July 2018, assessed the performance of the WRF-SFIRE modelling system, investigated the role of the complex terrain to the mean flow and fire behavior and examined the uncertainty of ignition features during two high-impact fire events that occurred in Attica Region, Central Greece (Mati and Kineta fire events). The synoptic analysis revealed the presence of a positively tilted trough over the Central Mediterranean, moving eastwards and interacting with the subtropical jet, resulting in a strong westerly flow over Greece. The AWS in Penteli Mt. recorded gusts reaching 25 m s-1 between 1230 and 1430 UTC, while several HNMS surface stations in the wider area recorded wind gusts exceeding 20 m s-1 between 1200 and 1730 UTC. The coupled model validated in terms of temperature, relative humidity and wind speed against the available HNMS surface observations by applying the Inverse Distance Weighting (IDW) method, the Gressman method and a 4-grid point method. Although the model performed satisfactory, the air temperature (2 m) and wind speed (10 m) were overestimated, whilst the relative humidity (2m) was underestimated. The predicted fire perimeters were in satisfactory agreement with the observed ones, but there were time lags in the initial development of the fires’ momentum and subsequently discrepancies on the temporal evolution of the modeled fires occurred. Moreover, simulations revealed the presence of induced orographic waves, paths of high winds on the lee-slopes, transient resemblance of a hydraulic jump downstream of Penteli Mt., while indicated a downward transport of energy and momentum during the maximum wind speed occurrences. The turbulent and dynamically unstable conditions on the lee-slopes of Gerania Mts. (Kineta) and Penteli Mt. (Mati) contributed to the flow kinetic energy, while vorticity provided additional forcing into the fire spread rates. Quite different influences of topography in each fire event were found, where the isolated Gerania Mts contributed to warmer, drier and windier conditions leeward, while Penteli Mt. had a lesser impact on atmospheric variables downstream. In addition, the sensitivity experiments showed that the type of ignition along with the rate of spread during ignition influenced the most the fire propagation at the early stages of the fire at Mati event. Finally, fuel description had a lesser impact on the simulated rate of spreads during the early stages of the fire but influenced its behavior later.

Η παρούσα Διδακτορική Διατριβή διερεύνησε τις αλληλεπιδράσεις ατμόσφαιρας – πυρός με τη βοήθεια ενός άμεσα συζευγμένου αριθμητικού μοντέλου (WRF-SFIRE), το οποίο αποτελεί συνδυασμό ενός αριθμητικού μοντέλου πρόγνωσης καιρού με ένα ημι-εμπειρικό μοντέλο διάδοσης του πυρός. Η αξιοποίηση των δυνατοτήτων του εν λόγω συστήματος επέτρεψε την ανάλυση των αναδράσεων ατμόσφαιρας-πυρός σε διαφορετικές χωρικές κλίμακες, από τη μέση κλίμακα και κατά τη διάρκεια πυρκαγιών με ακραία συμπεριφορά στον Ελλαδικό χώρο, έως τη μικροκλίμακα, μέσα από ιδεατά πειράματα με τη τεχνική Large Eddy Simulation (LES). Επιπρόσθετα, ελέγχθηκε η απόδοση του αριθμητικού συνδυασμού σε σύγκριση με παρατηρησιακά δεδομένα επιφανείας από την Εθνική Μετεωρολογική Υπηρεσία (ΕΜΥ) και δεδομένα τηλεπισκόπησης από τους δορυφόρους Meteosat (SEVIRI), SENTINEL-2 (MSI), Aqua, Terra (MODIS) και Suomi-NPP (VIIRS).
Στο πρώτο μέρος της ερευνητικής διεργασίας μελετήθηκε η επίδραση των εκλυόμενων ροών θερμότητας του πυρός στις ιδιότητές του (ρυθμός διάδοσης, καμένη έκταση κτλ.) και στα χαρακτηριστικά της ατμοσφαιρικής ροής και της επαγωγικής στήλης θερμότητας (πλούμιο), αντίστοιχα. Πιο συγκεκριμένα, εκτιμήθηκε ο ρόλος της παραμέτρου heat extinction depth ή e-folding depth (zex , ύψος στο οποίο οι ροές θερμότητας αποκτούν το 36% της αρχικής τους τιμής), μέσα από οχτώ ιδεατά αριθμητικά πειράματα, τα οποία πραγματοποιήθηκαν με τη μέθοδο LES. Σύμφωνα με τα αποτελέσματα, η τιμή του zex επηρέασε την καθ’ ύψος κατανομή των εκλυόμενων ροών αλλά επίσης και την ποσότητα της εκλυόμενης ενέργειας που «εισχωρεί» στο ατμοσφαιρικό μοντέλο. Όσο μεγαλύτερη ήταν η τιμή του zex τόσο μεγαλύτερο υπήρξε και το ποσοστό της ενέργειας που ήταν διαθέσιμο στο πρώτο θ επίπεδο του μοντέλου. Επιπρόσθετα, διαφορετικές τιμές zex κάτω από ίδιες αρχικές ατμοσφαιρικές συνθήκες είχαν ως αποτέλεσμα διαφορετικές καμένες περιοχές (σχήμα και έκταση). Αν και παρατηρήθηκαν διαφορές τόσο στη δομή όσο και στην ένταση τους, ο αριθμητικός συνδυασμός αναπαρήγαγε συγκεκριμένα χαρακτηριστικά της ατμοσφαιρικής ροής, όπως τη ζώνη σύγκλισης μπροστά από το μέτωπο και την εκ των όπισθεν καθοδική εισροή αέρα προς τη βάση της επαγωγικής στήλης θερμότητας σε όλα τα πειράματα. Εν γένει, η αύξηση της παραμέτρου zex οδήγησε σε μικρότερες χρονικά-μέσες ανωμαλίες της δυνητικής θερμοκρασίας, κοντά στο έδαφος αλλά και στην κορυφή του πλουμίου. Ωστόσο, τα μέγιστα των ανωμαλιών αυτών δεν ακολούθησαν κάποια γραμμικότητα και η εμφάνισή τους διέφερε χωρο-χρονικά ανάμεσα στα πειράματα. Όσον αφορά τα δυναμικά χαρακτηριστικά της ροής, διαφορές παρατηρήθηκαν τόσο στην ένταση όσο και στα μοτίβα της κάθετης συνιστώσας του στροβιλισμού (οριζόντιος στροβιλισμός) και της απόκλισης κοντά στην επιφάνεια, επηρεάζοντας το σχήμα της καμένης έκτασης και τη θέση της κεφαλής. Μικρές τιμές του zex  οδήγησαν στην παραγωγή περισσότερο οργανωμένων και ενισχυμένων ζευγών στροβίλων, κυκλωνικής και αντικυκλωνικής φοράς αντίστοιχα, περιοχών έντονου οριζόντιου στροβιλισμού (θετικού ή αρνητικού) κατά μήκος των πλευρικών ορίων και έμπροσθεν της κεφαλής. Αντιθέτως στα πειράματα όπου η τιμή του zex  ήταν μεγαλύτερη των 50 m, ο οριζόντιος στροβιλισμός ήταν λιγότερο οργανωμένος και παροδικός. Ο όρος του σωληνοειδούς στην εξίσωση του στροβιλισμού (οριζόντιος) βρέθηκε έως και δώδεκα φορές μικρότερος σε σύγκριση με τους υπόλοιπους όρους, ενώ ο όρος της οριζόντιας μεταφοράς συνείσφερε θετικότερα στην αύξηση του οριζόντιου στροβιλισμού. Ο όρος της κλίσης/συστροφής βρέθηκε μεγαλύτερος κατά τα πρώιμα στάδια της φωτιάς, όπου η παραγόμενη λόγω κατακόρυφης διάτμησης του ανέμου, ψ συνιστώσα του στροβιλισμού προσανατολίστηκε κατακόρυφα εξαιτίας της έντονης ανωμεταφοράς από την φωτιά επιφανείας.
Στο δεύτερο μέρος της ερευνητικής διεργασίας πραγματοποιήθηκε η συνοπτική ανάλυση, παρουσιάστηκαν οι επικρατούσες ατμοσφαιρικές συνθήκες στην επιφάνεια, διερευνήθηκε η επίδραση της τοπογραφίας στη μέση ροή και στη συμπεριφορά του πυρός και ελέγχθηκε η επίδραση των παραμέτρων της ανάφλεξης (τοποθεσία, χρόνος, είδος) στη καμένη έκταση, κατά τη διάρκεια των γεγονότων της 23ης Ιουλίου 2018, όπου εκδηλώθηκαν δύο πυρκαγιές με ακραία συμπεριφορά, σε Δυτική (περιοχή Κινέτα) και Ανατολική (περιοχή Μάτι) Αττική. Σύμφωνα με τη συνοπτική ανάλυση, η παρουσία ενός αυλώνα στην ανώτερη τροπόσφαιρα με θετική κλίση πάνω από την Κεντρική Μεσόγειο, η κίνησή του προς τα ανατολικά και η αλληλεπίδρασή του με τον υποτροπικό αεροχείμαρρο, οδήγησαν σε έντονη δυτική κυκλοφορία πάνω από τον Ελλαδικό χώρο. Ο αυτόματος μετεωρολογικός σταθμός στο Πεντελικό Όρος κατέγραψε ριπαίο άνεμο έως 25 m s-1, μεταξύ 1230 και 1430 UTC, ενώ αρκετοί σταθμοί επιφανείας (συνοπτικοί και δευτερεύοντες) της Εθνικής Μετεωρολογικής Υπηρεσίας (ΕΜΥ), στην ευρύτερη περιοχή της Αττικής, κατέγραψαν ριπές ανέμου μεγαλύτερες των 20 m s-1, μεταξύ 1200 και 1730 UTC. Ο αριθμητικός συνδυασμός αξιολογήθηκε ως προς την θερμοκρασία και υγρασία του αέρα και την ταχύτητα του ανέμου με βάση τα δεδομένα των σταθμών επιφανείας της ΕΜΥ, χρησιμοποιώντας τις μεθόδους χωρικής παρεμβολής Inverse Distance Weighting (IDW), Gressman και 4-grid point. Η απόδοση του κρίθηκε ικανοποιητική, αν και βρέθηκε να υπερεκτιμά τη θερμοκρασία στα 2 m και τη ταχύτητα του ανέμου στα 10 m, και να υποεκτιμά τη σχετική υγρασία στα 2 m. Η προσομοίωση της καμένης έκτασης και στα δύο γεγονότα υπό μελέτη ήταν σε σχετική συμφωνία με τη εκάστοτε πραγματική, ωστόσο και στις δύο περιπτώσεις υπήρξε καθυστέρηση στην ανάπτυξη της δυναμικής τους, με αποτέλεσμα να προκύψουν διαφορές ως προς την εξέλιξή τους χρονικά. Επιπλέον, η ανάλυση των αποτελεσμάτων της αριθμητικής προσομοίωσης υπέδειξε την παρουσία επαγόμενων κυμάνσεων λόγω ορεογραφίας στην ευρύτερη περιοχή, μονοπάτια ατμοσφαιρικής ροής μεγάλης ταχύτητας στα υπήνεμα των ορεινών εμποδίων, παροδική εμφάνιση χαρακτηριστικών ενός τυρβώδους υδραυλικού άλματος στα κατάντη του Όρους Πεντέλη και κατακόρυφη μεταφορά ενέργειας και ορμής προς τα έδαφος, κατά τη διάρκεια εμφάνισης των μέγιστων ταχυτήτων ανέμου. Η τύρβη και οι δυναμικά ασταθείς συνθήκες στα υπήνεμα των Γεράνειων Ορέων (περιοχή Κινέτα) και του Πεντελικού Όρους (περιοχή Μάτι) συνέβαλαν στην αύξηση της κινητικής ενέργειας της ροής, ενώ το πεδίο του στροβιλισμού εισήγαγε επιπλέον δυναμικό εξαναγκασμό στους ρυθμούς εξάπλωσης των πυρκαγιών. Η επίδραση της ορεογραφίας στην εκάστοτε πυρκαγιά βρέθηκε διαφορετική καθώς, η παρουσία των απομονωμένων Γεράνειων Ορέων οδήγησε σε θερμότερες και ξηρότερες συνθήκες, με ισχυρότερες ταχύτητες ανέμου στα κατάντη, ενώ το Όρος Πεντέλη είχε μικρότερη επίδραση στις ατμοσφαιρικές συνθήκες στην υπήνεμη πλευρά. Επιπλέον, τα πειράματα ευαισθησίας έδειξαν πως ο τύπος ανάφλεξης στο πυρικό μοντέλο μαζί με το ρυθμό εξάπλωσης κατά την ανάφλεξη επηρέασαν περισσότερο την εξάπλωση του πυρός κατά τα πρώτα στάδια της πυρκαγιάς στο Μάτι, σε σχέση με τις άλλες υπό διερεύνηση παραμέτρους. Τέλος, η κατηγοριοποίηση της καύσιμης ύλης δεν επηρέασε τόσο τους ρυθμούς εξάπλωσης κατά τα πρώτα στάδια της πυρκαγιάς όσο αργότερα.

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Αναφορές


Achtemeier, G.L., 2003. “Rabit Rules” - an application of Stephen Wolfram’s “New Kind of Science” to fire spread modelling, in: Fifth Symposium on Fire and Forest Meteorology. American Meteorological Society: Boston, MA, 16-20 November, Orlando, FL.

Adelabu, S.A., Adepoju, K.A., Mofokeng, O.D., 2020. Estimation of fire potential index in mountainous protected region using remote sensing. Geocarto Int. 35, 29–46. https://doi.org/10.1080/10106049.2018.1499818

Albini, F.A., 1982. Response of Free-Burning Fires to Nonsteady Wind. Combust. Sci. Technol. 29, 225–241. https://doi.org/10.1080/00102208208923599

Amraoui, M., Liberato, M.L.R., Calado, T.J., DaCamara, C.C., Coelho, L.P., Trigo, R.M., Gouveia, C.M., 2013. Fire activity over Mediterranean Europe based on information from Meteosat-8. For. Ecol. Manage. 294, 62–75. https://doi.org/10.1016/J.FORECO.2012.08.032

Amraoui, M., Pereira, M.G., DaCamara, C.C., Calado, T.J., 2015. Atmospheric conditions associated with extreme fire activity in the Western Mediterranean region. Sci. Total Environ. 524–525, 32–39. https://doi.org/10.1016/j.scitotenv.2015.04.032

Anderson, H.E., 1982. Aids to determining fuel models for estimating fire behavior. Bark Beetles, Fuels, Fire Bibliogr. 1–22. https://doi.org/citeulike-article-id:12114185

Anderson, H.E., 1970. Forest fuel ignitibility. Fire Technol. 6, 312–319. https://doi.org/10.1007/BF02588932

Anderson, H.E., 1969. Heat transfer and fire spread, Res. Pap. INT-RP-69. Ogden, Utah. https://doi.org/10.5962/bhl.title.69024

Andrews, P.L., 2014. Current status and future needs of the BehavePlus Fire Modeling System. Int. J. Wildl. Fire 23. https://doi.org/10.1071/WF12167

Andrews, P.L., 2007. BehavePlus fire modeling system: past, present, and future, in: 7th Symposium on Fire and Forest Meteorological Society., 13.

Ångström, A., 1949. Swedish meteorological research 1939–1948. Tellus 1, 60–64.

Ångström, A., 1942. The risks for forest fires and their relation to weather and climate [Riskerna för Skogsbrand och deras beroende ar väder och klimat]. Svenka skogrårdsföreningens

Tidskr. 4, 18.

Arnold, R.K., Buck, C.C., 1954. Blow-up fires - silviculutre or weather problems? J. For. 52, 408–411.

Arpaci, A., Eastaugh, C.S., Vacik, H., 2013. Selecting the best performing fire weather indices for Austrian ecoregions. Theor. Appl. Climatol. 114, 393–406. https://doi.org/10.1007/s00704-013-0839-7

Bak, P., Chen, K., Tang, C., 1990. A forest-fire model and some thoughts on turbulence. Phys. Lett. A 147, 297–300. https://doi.org/10.1016/0375-9601(90)90451-S

Bakhshaii, A., Johnson, E.A., 2019. A review of a new generation of wildfire–atmosphere modeling. Can. J. For. Res. 565–574. https://doi.org/10.1139/cjfr-2018-0138

Balbi, J.H., Morandini, F., Silvani, X., Filippi, J.B., Rinieri, F., 2009. A physical model for wildland fires. Combust. Flame 156, 2217–2230. https://doi.org/10.1016/j.combustflame.2009.07.010

Bampzelis, D., Spiridonov, V., Kartsios, S., Pytharoulis, I., Tegoulias, I., Karacostas, T., 2015. Numerical Simulation of Airborne Cloud Seeding for the DAPHNE Precipitation Enhancement

Project in Central Greece, in: 95th AMS Annual Meeting, 20th Conference on Planned and Inadvertent Weather. Phoenix, Arizona, USA.

Banta, R.M., Olivier, L.D., Holloway, E.T., Kropfli, R.A., Bartram, B.W., Cupp, R.E., Post, M.J., 1992. Smoke-Column Observations from Two Forest Fires Using Doppler Lidar and Doppler

Radar. J. Appl. Meteorol. 31, 1328–1349. https://doi.org/10.1175/1520-0450(1992)031<1328:SCOFTF>2.0.CO;2

Barbero, R., Curt, T., Ganteaume, A., Maillé, E., Jappiot, M., Bellet, A., 2019. Simulating the effects of weather and climate on large wildfires in France. Nat. Hazards Earth Syst. Sci. 19, 441–454. https://doi.org/10.5194/nhess-19-441-2019

Barry, R.G., 1992. Mountain Weather and Climate, 2nd ed. Routledge, New York.

Batchelor, G.K., 1954. Heat convection and buoyancy effects in fluids. Q. J. R. Meteorol. Soc. 80, 339–358. https://doi.org/10.1002/qj.49708034504

Bates, C.G., 1923. Evaporation as a simple index to weather conditions. Mon. Weather Rev. 51, 570–571. https://doi.org/10.1175/1520-0493(1923)51<570:EAASIT>2.0.CO;2

Baughman, R.G., Albini, F.A., 1980. Estimating Midflame Windspeeds, in: Sixth Conference on Fire and Forest Meteorology. Seatle. WA, pp. 88–92.

Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M., Rodwell, M.J., Vitart, F., Balsamo, G., 2008. Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales. Q. J. R. Meteorol. Soc. 134, 1337–1351. https://doi.org/10.1002/qj.289

Bedia, J., Golding, N., Casanueva, A., Iturbide, M., Buontempo, C., Gutiérrez, J.M., 2018. Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe. Clim. Serv. 9, 101–110. https://doi.org/10.1016/j.cliser.2017.04.001

Bedia, J., Herrera, S., Gutiérrez, J.M., Benali, A., Brands, S., Mota, B., Moreno, J.M., 2015. Global patterns in the sensitivity of burned area to fire-weather: Implications for climate change.

Agric. For. Meteorol. 214–215, 369–379. https://doi.org/10.1016/j.agrformet.2015.09.002

Beer, T., 1990. Percolation Theory and Fire Spread. Combust. Sci. Technol. 72, 297–304. https://doi.org/10.1080/00102209008951653

Beer, T., 1974. Atmospheric Waves. Wiley, New York.

Benali, A., Ervilha, A.R., Sá, A.C.L., Fernandes, P.M., Pinto, R.M.S., Trigo, R.M., Pereira, J.M.C., 2016. Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations. Sci. Total Environ. 569–570, 73–85. https://doi.org/10.1016/j.scitotenv.2016.06.112

Benech, B., 1976. Experimental Study of an Artificial Convective Plume Initiated from the Ground. J. Appl. Meteorol. 15, 127–137. https://doi.org/10.1175/1520-0450(1976)015<0127:ESOAAC>2.0.CO;2

Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R.J., Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J.W., Kinne, S., Mangold, A., Razinger, M., Simmons, A.J., Suttie, M., 2009. Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation. J. Geophys. Res. 114, D13205. https://doi.org/10.1029/2008JD011115

Bluestein, H.B., 1992. Synoptic-dynamic Meteorology in Midlatitudes: Principles of kinematics and dynamics. Oxford University Press, New York.

Bovio, G., Quaglino, A., Nosenzo, A., 1984. Individuazione di un indice di previsione per il Pericolo di Incendi Boschivi. Monti e Boschi Anno 35, 39–44.

Bowman, D.M.J.S., Balch, J.K., Artaxo, P., Bond, W.J., Carlson, J.M., Cochrane, M.A., D’Antonio, C.M., DeFries, R.S., Doyle, J.C., Harrison, S.P., Johnston, F.H., Keeley, J.E., Krawchuk, M.A.,

Kull, C.A., Marston, J.B., Moritz, M.A., Prentice, I.C., Roos, C.I., Scott, A.C., Swetnam, T.W., van der Werf, G.R., Pyne, S.J., 2009. Fire in the Earth System. Science (80-. ). 324, 481–484. https://doi.org/10.1126/science.1163886

Bromwich, D.H., Hines, K.M., Bai, L., 2009. Development and testing of Polar Weather Research and Forecasting model: 2. Arctic Ocean. J. Geophys. Res. 114, D08122. https://doi.org/10.1029/2008JD010300

Bromwich, D.H., Otieno, F.O., Hines, K.M., Manning, K.W., Shilo, E., 2013. Comprehensive evaluation of polar weather research and forecasting model performance in the Antarctic. J. Geophys. Res. Atmos. 118, 274–292. https://doi.org/10.1029/2012JD018139

Brown, J.K., 1974. Handbook for Inventorying Downed Woody Material. Gen. Tech. Rep. INT-16.

Brown, J.K., 1970. Ratios of Surface Area to Volume for Common Fine Fuels. For. Sci. 16, 101–105. https://doi.org/10.1093/FORESTSCIENCE/16.1.101

Buizza, R., Milleer, M., Palmer, T.N., 2007. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 125, 2887–2908. https://doi.org/10.1002/qj.49712556006

Burgan, R.E., 1988. 1988 Revisions to the 1978 National Fire-Danger Rating System. https://doi.org/10.2737/SE-RP-273

Burgan, R.E., Hartford, R.A., 1993. Monitoring vegetation greenness with satellite data. Ogden, UT.

Burgan, R.E., Klaver, R.W., Klarer, J.M., 1998. Fuel models and fire potential from satellite and surface observations. Int. J. Wildl. Fire 8, 159–170. https://doi.org/10.1071/WF9980159

Bush, A.F., Leonard, J.J., Yundt, W.H., 1969. Gas analysis in large fire experiments. In “Project Flambeau: an Investigation of Mass Fire (1964-1967): Final Report - Volume III”. Berkeley, CA.

Butler, B.W., Bartlette, R.A., Bradshaw, L.S., Cohen, J.D., Andrews, P.L., Putnam, T., Mangan, R.J., 1998. Fire behavior associated with the 1994 South Canyon fire on Storm King Mountain, Colorado. Ogden, UT. https://doi.org/10.2737/RMRS-RP-9

Byram, G.M., 1959. Combustion of forest fuels, in: Davis, K.P., McGraw-Hill (Eds.), Forest Fires: Control and Use. New York, pp. 61–89.

Byram, G.M., 1954. Atmospheric conditions related to blowup fires.

Byram, G.M., Nelson, R.M., 1951. The possible relation of air turbulence to erratic fire behavior in the Southeast. Fire Contol Notes 12, 1–8.

Caldarelli, G., Frondoni, R., Gabrielli, A., Montuori, M., Retzlaff, R., Ricotta, C., 2001. Percolation in real wildfires. Europhys. Lett. 56, 510–516. https://doi.org/10.1209/epl/i2001-00549-4

Carrega, P., 1991. A Meteorological Index of Forest Fire Hazard in Mediterranean France. Int. J. Wildl. Fire 1, 79–86.

Catchpole, E.A., Hatton, T.J., Catchpole, W.R., 1989. Fire spread through nonhomogeneous fuel modelled as a Markov process. Ecol. Modell. 48, 101–112. https://doi.org/10.1016

/0304-3800(89)90062-8

Chandler, C., Cheney, P., Thomas, P., Trabaud, L., Williams, D., 1983. Fire in Forestry, Volume 1: Forest Fire Behavior and Effects, 1st ed. John Wiley & Sons, Inc., New York, NY.

Chandler, C.C., Storey, T.G., Tangren, C.D., 1963. Prediction of fire spread following nuclear explosions. Berkeley, CA.

Chen, F., Dudhia, J., 2002. Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part II: Preliminary Model Validation. Mon. Weather Rev. 129, 587–604. https://doi.org/10.1175/1520-0493(2001)129<0587:caalsh>2.0.co;2

Cheney, N., Gould, J., Catchpole, W., 1998. Prediction of Fire Spread in Grasslands. Int. J. Wildl. Fire 8, 1. https://doi.org/10.1071/WF9980001

Church, C.R., Snow, J.T., Dessens, J., 1980. Intense Atmospheric Vortices Associated with a 1000 MW Fire. Bull. Am. Meteorol. Soc. 61, 682–694. https://doi.org/10.1175/1520-0477(1980)061<0682:iavawa>2.0.co;2

Clark, T., Jenkins, M., Coen, J., Packham, D., 1996a. A Coupled Atmosphere-Fire Model: Role of the Convective Froude Number and Dynamic Fingering at the Fireline. Int. J. Wildl. Fire 6, 177. https://doi.org/10.1071/WF9960177

Clark, T., Jenkins, M., Coen, J., Packham, D., 1996b. A Coupled Atmosphere-Fire Model: Convective Feedback on Fire-Line Dynamics. J. Appl. Meteorol. 35, 875–901. https://doi.org/10.1175/1520-0450(1996)035<0875:ACAMCF>2.0.CO;2

Clark, T.L., Coen, J., Latham, D., 2004. Description of a coupled atmosphere - fire model. Int. J. Wildl. Fire 13, 49. https://doi.org/10.1071/WF03043

Clark, T.L., Farley, R.D., 1984. Severe Downslope Windstorm Calculations in Two and Three Spatial Dimensions Using Anelastic Interactive Grid Nesting: A Possible Mechanism for Gustiness. J. Atmos. Sci. 41, 329–350. https://doi.org/10.1175/1520-0469(1984)041<0329:SDWCIT>2.0.CO;2

Clark, T.L., Peltier, W.R., 1984. Critical Level Reflection and the Resonant Growth of Nonlinear Mountain Waves. J. Atmos. Sci. 41, 3122–3134. https://doi.org/10.1175/1520-0469(1984)041<3122:CLRATR>2.0.CO;2

Clark, T.L., Peltier, W.R., 1977. On the Evolution and Stability of Finite-Amplitude Mountain Waves. J. Atmos. Sci. 34, 1715–1730. https://doi.org/10.1175/1520-0469(1977)034<1715:OTEASO>2.0.CO;2

Clarke, K.C., Brass, J.A., Riggan, P.J., 1994. A cellular automaton model of wildfire propagation and extinction. Photogramm. Eng. Remote Sensing 60, 1355–1367.

Clements, C.B., 2010. Thermodynamic structure of a grass fire plume. Int. J. Wildl. Fire 19, 895. https://doi.org/10.1071/WF09009

Clements, C.B., Kochanski, A.K., Seto, D., Davis, B., Camacho, C., Lareau, N.P., Contezac, J., Restaino, J., Heilman, W.E., Krueger, S.K., Butler, B., Ottmar, R.D., Vihnanek, R., Flynn, J.,

Filippi, J.-B., Barboni, T., Hall, D.E., Mandel, J., Jenkins, M.A., O’Brien, J., Hornsby, B., Teske, C., 2019. The FireFlux II experiment: a model-guided field experiment to improve understanding of fire–atmosphere interactions and fire spread. Int. J. Wildl. Fire 28, 308–326. https://doi.org/10.1071/WF18089

Clements, C.B., Lareau, N.P., Kingsmill, D.E., Bowers, C.L., Camacho, C.P., Bagley, R., Davis, B., 2018. The Rapid Deployments to Wildfires Experiment (RaDFIRE): Observations from the

Fire Zone. Bull. Am. Meteorol. Soc. 99, 2539–2559. https://doi.org/10.1175/BAMS-D-17-0230.1

Clements, C.B., Potter, B.E., Zhong, S., 2006. In situ measurements of water vapor, heat, and CO2 fluxes within a prescribed grass fire. Int. J. Wildl. Fire 15, 299. https://doi.org/10.1071/WF05101

Clements, C.B., Zhong, S., Bian, X., Heilman, W.E., Byun, D.W., 2008. First observations of turbulence generated by grass fires. J. Geophys. Res. 113, D22102. https://doi.org/10.1029/2008JD010014

Clements, C.B., Zhong, S., Goodrick, S., Li, J., Potter, B.E., Bian, X., Heilman, W.E., Charney, J.J., Perna, R., Jang, M., Lee, D., Patel, M., Street, S., Aumann, G., 2007. Observing the dynamics of wildland grass fires: FireFlux - A field validation experiment. Bull. Am. Meteorol. Soc. 88, 1369–1382. https://doi.org/10.1175/BAMS-88-9-1369

Clyne, D.J., Mininni, P., Norton, A., Rast, M., 2007. Interactive desktop analysis of high resolution simulations: Application to turbulent plume dynamics and current sheet formation. New J. Phys. 9. https://doi.org/10.1088/1367-2630/9/8/301

Coen, J., 2018. Some Requirements for Simulating Wildland Fire Behavior Using Insight from Coupled Weather—Wildland Fire Models. Fire 1, 6. https://doi.org/10.3390/fire1010006

Coen, J., Mahalingam, S., Daily, J., Coen, J., Mahalingam, S., Daily, J., 2004. Infrared Imagery of Crown-Fire Dynamics during FROSTFIRE. J. Appl. Meteorol. 43, 1241–1259. https://doi.org/10.1175/1520-0450(2004)043<1241:IIOCDD>2.0.CO;2

Coen, J., Schroeder, W., Quayle, B., Coen, J.L., Schroeder, W., Quayle, B., 2018. The Generation and Forecast of Extreme Winds during the Origin and Progression of the 2017 Tubbs Fire. Atmosphere (Basel). 9, 462. https://doi.org/10.3390/atmos9120462

Coen, J.L., 2013. Modeling Wildland Fires : of the Coupled Atmosphere- Wildland Fire Environment Model (CAWFE). NCAR Tech. Note NCAR/TN-500+STR 38. https://doi.org/10.5065/D6K64G2G

Coen, J.L., Cameron, M., Michalakes, J., Patton, E.G., Riggan, P.J., Yedinak, K.M., 2013. Wrf-fire: Coupled weather-wildland fire modeling with the weather research and forecasting model. J. Appl. Meteorol. Climatol. https://doi.org/10.1175/JAMC-D-12-023.1

Colle, B.A., Westrick, K.J., Mass, C.F., Colle, B.A., Westrick, K.J., Mass, C.F., 1999. Evaluation of MM5 and Eta-10 Precipitation Forecasts over the Pacific Northwest during the Cool Season. Weather Forecast. 14, 137–154. https://doi.org/10.1175/1520-0434(1999)014<0137:EOMAEP>2.0.CO;2

Conedera, M., Marxer, P., Ambrosetti, P., Della Bruna, G., Spinedi, F., 1998. The 1997 forest fire season in Switzerland. Int. For. Fires News 18, 85–88.

Coppola, E., Sobolowski, S., Pichelli, E., Raffaele, F., Ahrens, B., Anders, I., Ban, N., Bastin, S., Belda, M., Belusic, D., Caldas-Alvarez, A., Cardoso, R.M., Davolio, S., Dobler, A., Fernandez, J., Fita, L., Fumiere, Q., Giorgi, F., Goergen, K., Güttler, I., Halenka, T., Heinzeller, D., Hodnebrog, Ø., Jacob, D., Kartsios, S., Katragkou, E., Kendon, E., Khodayar, S., Kunstmann, H., Knist,

S., Lavín-Gullón, A., Lind, P., Lorenz, T., Maraun, D., Marelle, L., van Meijgaard, E., Milovac, J., Myhre, G., Panitz, H.-J., Piazza, M., Raffa, M., Raub, T., Rockel, B., Schär, C., Sieck, K., Soares, P.M.M., Somot, S., Srnec, L., Stocchi, P., Tölle, M.H., Truhetz, H., Vautard, R., de Vries, H., Warrach-Sagi, K., 2018. A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean. Clim. Dyn. 27. https://doi.org/10.1007/s00382-018-4521-8

Countryman, C.M., 1969. Project Flambeau: an investigation of mass fire (1964-1967): final report - Volume I. Berkeley, CA.

Countryman, C.M., 1964. Mass fires and fire behavior.

Cressman, G.P., 1959. An Operational Objective Analysis System. Mon. Weather Rev. 87, 367–374. https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2

Cunningham, P., Goodrick, S.L., Hussaini, M.Y., Linn, R.R., 2005. Coherent vortical structures in numerical simulations of buoyant plumes from wildland fires. Int. J. Wildl. Fire 14, 61. https://doi.org/10.1071/WF04044

Cunningham, P., Linn, R.R., 2007. Numerical simulations of grass fires using a coupled atmosphere-fire model: Dynamics of fire spread. J. Geophys. Res. 112, D05108. https://doi.org/10.1029/2006JD007638

Curry, J.R., Fons, W.L., 1940. Forest-fire behavior studies. Mech. Eng. 62, 219–225.

Curry, J.R., Fons, W.L., 1938. Rate of spread of surface fires in the Ponderosa pine type of California. J. Agric. Res. 57, 239–267.

Dahl, N., Xue, H., Hu, X., Xue, M., 2015. Coupled fire–atmosphere modeling of wildland fire spread using DEVS-FIRE and ARPS. Nat. Hazards 77. https://doi.org/10.1007/s11069-015-1640-y

Davin, E.L., Rechid, D., Breil, M., Cardoso, R.M., Coppola, E., Hoffmann, P., Jach, L.L., Katragkou, E., de Noblet-Ducoudré, N., Radtke, K., Raffa, M., Soares, P.M.M., Sofiadis, G., Strada, S., Strandberg, G., Tölle, M.H., Warrach-Sagi, K., Wulfmeyer, V., 2019. Biogeophysical impacts of forestation in Europe: First results from the LUCAS Regional Climate Model intercomparison. Earth Syst. Dyn. Discuss. 1–31. https://doi.org/10.5194/esd-2019-4

Deeming, J.E., Burgan, R.E., Cohen, J.D., 1977. The National Fire-danger Rating System-1978.

Dimitrakopoulos, A.P., 2009. Forest Fires. University Publications, AUTh, Thessaloniki, Greece (In Greek).

Dimitrakopoulos, A.P., 2002. Mediterannean fuel models and potential fire behaviour in Greece. Int. J. Wildl. Fire 11, 127. https://doi.org/10.1071/WF02018

Dimitrakopoulos, A.P., Bemmerzouk, A.M., 2003. Predicting live herbaceous moisturecontent from a seasonal drought index. Int. J. Biometeorol. 47, 73–79. https://doi.org/10.1007/s00484-002-0151-1

Dimitrakopoulos, A.P., Bemmerzouk, A.M., Mitsopoulos, I.D., 2011a. Evaluation of the Canadian fire weather index system in an eastern Mediterranean environment. Meteorol. Appl. 18, 83–93. https://doi.org/10.1002/met.214

Dimitrakopoulos, A.P., Gogi, C., Stamatelos, G., Mitsopoulos, I., 2011b. Statistical Analysis of the Fire Environment of Large Forest Fires (>1000 ha) in Greece. Polish J. Environ. Stud. 20, 327–332.

Dimitrakopoulos, A.P., Panov, P.I., 2001. Pyric properties of some dominant Mediterranean vegetation species. Int. J. Wildl. Fire 10, 23. https://doi.org/10.1071/WF01003

Dimitrakopoulos, A.P., Vlahou, M., Anagnostopoulou, C.G., Mitsopoulos, I.D., 2011c. Impact of drought on wildland fires in Greece: Implications of climatic change? Clim. Change 109, 331–347. https://doi.org/10.1007/s10584-011-0026-8

Dobrinkova, N., Jordanov, G., Mandel, J., 2011. WRF-fire applied in Bulgaria, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-642-18466-6_15

Dolling, K., Chu, P.S., Fujioka, F., 2005. A climatological study of the Keetch/Byram drought index and fire activity in the Hawaiian Islands, in: Agricultural and Forest Meteorology. pp. 17–27. https://doi.org/10.1016/j.agrformet.2005.07.016

Dowdy, A.J., Mills, G. a, Finkele, K., Groot, W. De, 2009. Australian fire weather as represented by the McArthur Forest Fire Danger Index and the Canadian Forest Fire Weather Index Australian fire weather as represented by the McArthur Forest Fire Danger Index and the Canadian Forest Fire Weather Index, CAWCR technical report. Victoria.

Doyle, J.D., Durran, D.R., 2004. THE MAP ROOM: Recent Developments in the Theory of Atmospheric Rotors. Bull. Am. Meteorol. Soc. 85, 337–342. https://doi.org/10.1175/BAMS-85-3-337

Doyle, J.D., Jiang, Q., 2006. Observations and numerical simulations of mountain waves in the presence of directional wind shear. Q. J. R. Meteorol. Soc. 132, 1877–1905. https://doi.org/10.1256/qj.05.140

Drakou, E.G., Kallimanis, A.S., Mazaris, A.D., Apostolopoulou, E., Pantis, J.D., 2011. Habitat type richness associations with environmental variables: a case study in the Greek Natura 2000 aquatic ecosystems. Biodivers. Conserv. 20, 929–943. https://doi.org/10.1007/s10531-011-0005-4

Drouet, J.C., Sol, B., 1990. Mise au point d’un Indice numérique de risque météorologique d’incendie. Rev. Génerale Secur. 92.

Drusch, M., Scipal, K., de Rosnay, P., Balsamo, G., Andersson, E., Bougeault, P., Viterbo, P., 2009. Towards a Kalman Filter based soil moisture analysis system for the operational ECMWF Integrated Forecast System. Geophys. Res. Lett. 36, L10401. https://doi.org/10.1029/2009GL037716

Duane, A., Brotons, L., 2018. Synoptic weather conditions and changing fire regimes in a Mediterranean environment. Agric. For. Meteorol. 253–254, 190–202. https://doi.org/10.1016/j.agrformet.2018.02.014

Duane, A., Piqué, M., Castellnou, M., Brotons, L., 2015. Predictive modelling of fire occurrences from different fire spread patterns in Mediterranean landscapes. Int. J. Wildl. Fire 24, 407. https://doi.org/10.1071/WF14040

Duarte, J.A.M.S., 1997. Bushfire Automata and Their Phase Transitions. Int. J. Mod. Phys. C 08, 171–189. https://doi.org/10.1142/S0129183197000175

Dudhia, J., 1996. A multi-layer soil temperature model for MM5, in: 6th PSU/NCAR Mesoscale Model Users’ Workshop. NCAR, Boulder, CO, USA.

Duguy, B., Alloza, J.A., Röder, A., Vallejo, R., Pastor, F., 2007. Modelling the effects of landscape fuel treatments on fire growth and behaviour in a Mediterranean landscape (eastern Spain). Int. J. Wildl. Fire 16, 619. https://doi.org/10.1071/WF06101

Dunn, A., Milne, G., 2004. Modelling wildfire dynamics via interacting automata, in: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (Eds.), Cellular Automata, 6th International Conference on Cellular Automata for Research and Industry. Springer-Verlag, Berlin, Germany, Amsterdam, The Netherlands, October 25-28, pp. 395–404.

Durran, D.R., 1990. Mountain Waves and Downslope Winds, in: Atmospheric Processes over Complex Terrain. American Meteorological Society, Boston, MA, pp. 59–81. https://doi.org/10.1007/978-1-935704-25-6_4

Durran, D.R., 1986. Another Look at Downslope Windstorms. Part I: The Development of Analogs to Supercritical Flow in an Infinitely Deep, Continuously Stratified Fluid. J. Atmos. Sci. 43, 2527–2543. https://doi.org/10.1175/1520-0469(1986)043<2527:ALADWP>2.0.CO;2

Durran, D.R., Klemp, J.B., 1987. Another Look at Downslope Winds. Part II: Nonlinear Amplification beneath Wave-Overturning Layers. J. Atmos. Sci. 44, 3402–3412. https://doi.org/10.1175/1520-0469(1987)044<3402:ALADWP>2.0.CO;2

Eastaugh, C.S., Hasenauer, H., 2014. Deriving forest fire ignition risk with biogeochemical process modelling. Environ. Model. Softw. 55, 132–142. https://doi.org/10.1016/j.envsoft.2014.01.018

Edgar, R.A., Sharples, J.J., Sidhu, H.S., 2015. Revisiting the King’s Cross underground disaster with implications for modelling wildfire eruption, in: Weber, T., McPhee, M.J., Anderssen, R.S. (Eds.), MODSIM2015, 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Broadbeach, Old., pp. 215–221.

Egorova, V.N., Trucchia, A., Pagnini, G., 2019. Fire-spotting generated fires. Part I: The role of atmospheric stability. Appl. Math. Model. https://doi.org/10.1016/J.APM.2019.02.010

Elhag, M., Boteva, S., 2017. Assessment of Forest Fire Rating Systems in Typical Mediterranean Forest, Crete, Greece. Nat. Hazards Earth Syst. Sci. Discuss. 1–27. https://doi.org/10.5194/nhess-2017-318

Etling, D., Brown, R.A., 1993. Roll vortices in the planetary boundary layer: A review. Boundary-Layer Meteorol. 65, 215–248. https://doi.org/10.1007/BF00705527

Falk, D.A., Miller, C., McKenzie, D., Black, A.E., 2007. Cross-Scale Analysis of Fire Regimes. Ecosystems 10, 809–823. https://doi.org/10.1007/s10021-007-9070-7

Farguell, À., Cortés, A., Margalef, T., Miro, J.R., Mercader, J., 2017. Data resolution effects on a coupled data driven system for forest fire propagation prediction. Procedia Comput. Sci. 108, 1562–1571.

Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., Alsdorf, D., 2007. The Shuttle Radar Topography Mission. Rev. Geophys. 45, RG2004. https://doi.org/10.1029/2005RG000183

Fast, J.D., Gustafson, W.I., Easter, R.C., Zaveri, R.A., Barnard, J.C., Chapman, E.G., Grell, G.A., Peckham, S.E., 2006. Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model. J. Geophys. Res. 111, D21305. https://doi.org/10.1029/2005JD006721

Fastie, C.L., Lloyd, A.H., Doak, P., 2002. Fire history and postfire forest development in an upland watershed of interior Alaska. J. Geophys. Res. 108, 8150. https://doi.org/10.1029/2001JD000570

Favier, C., 2004. Percolation model of fire dynamic. Phys. Lett. A 330, 396–401. https://doi.org/10.1016/j.physleta.2004.07.053

Fernandes, P.M., 2009. Combining forest structure data and fuel modelling to classify fire hazard in Portugal. Ann. For. Sci. 66, 415–415. https://doi.org/10.1051/forest/2009013

Fernandez-Pello, A.C., 2017. Wildland fire spot ignition by sparks and firebrands. Fire Saf. J. 91, 2–10. https://doi.org/10.1016/J.FIRESAF.2017.04.040

Filippi, J.-B., Bosseur, F., Mari, C., Lac, C., 2018. Simulation of a Large Wildfire in a Coupled Fire-Atmosphere Model. Atmosphere (Basel). 9, 218. https://doi.org/10.3390/atmos9060218

Filippi, J.-B., Bosseur, F., Pialat, X., Santoni, P.-A., Strada, S., Mari, C., 2011. Simulation of Coupled Fire/Atmosphere Interaction with the MesoNH-ForeFire Models. J. Combust. 2011, 1–13. https://doi.org/10.1155/2011/540390

Filippi, J.-B., Pialat, X., Clements, C.B., 2013. Assessment of ForeFire/Meso-NH for wildland fire/atmosphere coupled simulation of the FireFlux experiment. Proc. Combust. Inst. 34, 2633–2640. https://doi.org/10.1016/J.PROCI.2012.07.022

Filippi, J.B., Bosseur, F., Mari, C., Lac, C., Le Moigne, P., Cuenot, B., Veynante, D., Cariolle, D., Balbi, J.-H., 2009. Coupled atmosphere–wildland fire modelling. J. Adv. Model. Earth Syst. 2, 11. https://doi.org/10.3894/JAMES.2009.1.11

Filippopoulos, I., 2012. Managing forest fires with i-protect fire simulation module. PhD Thesis. University of Thessaly, School of Engineering (in Greek). https://doi.org/10.12681/eadd/29344

Finney, M.A., 1998. FARSITE: Fire Area Simulator-model development and evaluation.

Foley, J.C., 1947. A study of meteorological conditions assosiated with bush and grass fires and fire protection strategy in Australia.

Fons, W.L., 1946. Analysis of Fire Spread in Light Forest Fuels. J. Agric. Res. 72, 92–121.

Forestry Canada Fire Danger Group, 1992. Development and structure of the Canadian Forest Fire Behavior Prediction System.

Forthofer, J.M., Goodrick, S.L., 2011. Review of Vortices in Wildland Fire. J. Combust. 2011, 1–14. https://doi.org/10.1155/2011/984363

Fosberg, M.A., 1983. Weather in wildland fire management: the Fire Weather Index, in: Conference on Sierra Nevada Meteorology. Lake Tahoe, CA, USA.

Fosberg, M.A., Deeming, J.E., 1971. Derivation of the 1- and 10-hour timelag fuel moisture calculations of fire-danger.

Founda, D., Giannakopoulos, C., 2009. The exceptionally hot summer of 2007 in Athens, Greece — A typical summer in the future climate? Glob. Planet. Change 67, 227–236. https://doi.org/10.1016/j.gloplacha.2009.03.013

Freeborn, P.H., Wooster, M.J., Roy, D.P., Cochrane, M.A., 2014. Quantification of MODIS fire radiative power (FRP) measurement uncertainty for use in satellite-based active fire characterization and biomass burning estimation. Geophys. Res. Lett. 41, 1988–1994. https://doi.org/10.1002/2013GL059086

Fudeyasu, H., Kuwagata, T., Ohashi, Y., Suzuki, S., Kiyohara, Y., Hozumi, Y., 2008. Numerical Study of the Local Downslope Wind “Hirodo-Kaze” in Japan. Mon. Weather Rev. 136, 27–40.

https://doi.org/10.1175/2007MWR2049.1

Ganteaume, A., Jappiot, M., 2013. What causes large fires in Southern France. For. Ecol. Manage. 294, 76–85. https://doi.org/10.1016/j.foreco.2012.06.055

Giannaros, T.M., Kotroni, V., Lagouvardos, K., 2019. IRIS – Rapid response fire spread forecasting system: Development, calibration and evaluation. Agric. For. Meteorol. 279, 107745. https://doi.org/10.1016/J.AGRFORMET.2019.107745

Gibbs, J.A., Fedorovich, E., Gibbs, J.A., Fedorovich, E., 2014. Comparison of Convective Boundary Layer Velocity Spectra Retrieved from Large- Eddy-Simulation and Weather Research and

Forecasting Model Data. J. Appl. Meteorol. Climatol. 53, 377–394. https://doi.org/10.1175/JAMC-D-13-033.1

Giglio, L., Descloitres, J., Justice, C.O., Kaufman, Y.J., 2003. An Enhanced Contextual Fire Detection Algorithm for MODIS. Remote Sens. Environ. 87, 273–282. https://doi.org/10.1016/S0034-4257(03)00184-6

Gill, A.E., 1982. Atmosphere-ocean dynamics, International Geophysics Series, Vol. 30. Academic Press, New York.

Girardin, M.P., Wotton, B.M., 2009. Summer Moisture and Wildfire Risks across Canada. J. Appl. Meteorol. Climatol. 48, 517–533. https://doi.org/10.1175/2008JAMC1996.1

Gisborne, H.T., 1929. The Complicated Controls of Fire Behaviour. J. For. 27, 311–312.

Gisborne, H.T., 1928. Measuring forest fire danger in northern Idaho. Washington, DC.

Gisborne, H.T., 1927a. Meteorological factors in the Quartz Creek forest fire. Mon. Weather Rev. 55, 56–60. https://doi.org/10.1175/1520-0493(1927)55<56:MFITQC>2.0.CO;2

Gisborne, H.T., 1927b. The Objectives of Forest Fire-Weather Research. J. For. 25, 452–456. https://doi.org/10.1093/jof/25.4.452

Gitelson, A.A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., Derry, D., 2002. Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction. Int. J. Remote Sens. 23, 2537–2562. https://doi.org/10.1080/01431160110107806

Gochis, D.J., Yu, W., Yates, D.N., 2015. The WRF- Hydro Model technical description and user’s guide, version 3.0. NCAR Tech. Doc 123.

Good, P., Moriondo, M., Giannakopoulos, C., Bindi, M., 2008. The meteorological conditions associated with extreme fire risk in Italy and Greece: relevance to climate model studies. Int. J.

Wildl. Fire 17, 155. https://doi.org/10.1071/WF07001

Goodrick, S.L., 2002. Modification of the Fosberg fire weather index to include drought. Int. J. Wildl. Fire 11, 205. https://doi.org/10.1071/WF02005

Grell, G.A., Peckham, S.E., Schmitz, R., McKeen, S.A., Frost, G., Skamarock, W.C., Eder, B., 2005. Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39, 6957–6975. https://doi.org/10.1016/j.atmosenv.2005.04.027

Griffiths, D., 1999. Improved Formula for the Drought Factor in McArthur’s Forest Fire Danger Meter. Aust. For. 62, 202–206. https://doi.org/10.1080/00049158.1999.10674783

Groisman, P.Y., Sherstyukov, B.G., Razuvaev, V.N., Knight, R.W., Enloe, J.G., Stroumentova, N.S., Whitfield, P.H., Førland, E., Hannsen-Bauer, I., Tuomenvirta, H., Aleksandersson, H., Mescherskaya, A. V., Karl, T.R., 2007. Potential forest fire danger over Northern Eurasia: Changes during the 20th century. Glob. Planet. Change 56, 371–386. https://doi.org/10.1016

/j.gloplacha.2006.07.029

Haines, D.A., 1988. A lower atmosphere severity index for wildlife fires. Natl. Weather Dig. 13, 23–27.

Haines, D.A., Lyon, L.J., 1990. Horizontal roll vortices in complex terrain. Fire Manag. notes U.S. Dep. Agric. For. Serv.

Haines, D.A., Main, W.A., Frost, J.S., Simard, A.J., 1983. Fire-danger rating and wildfire occurrence in the northeastern United States. For. Sci. 29, 679–696. https://doi.org/10.1093/forestscience/29.4.679

Haines, D.A., Smith, M.C., 1987. Three Types of Horizontal Vortices Observed in Wildland Mass and Crown Fires. J. Clim. Appl. Meteorol. 26, 1624–1637. https://doi.org/10.1175/1520-0450(1987)026<1624:TTOHVO>2.0.CO;2

Hardy, C.C., 2005. Wildland fire hazard and risk: Problems, definitions, and context, in: Forest Ecology and Management. pp. 73–82. https://doi.org/10.1016/j.foreco.2005.01.029

Hargrove, W., Gardner, R., Turner, M., Romme, W., Despain, D., 2000. Simulating fire patterns in heterogeneous landscapes. Ecol. Modell. 135, 243–263. https://doi.org/10.1016/S0304-3800(00)00368-9

Hawley, L., 1926. Theoretical Considerations Regarding Factors which Influence Forest Fires. J. For. 24, 756–763. https://doi.org/10.1093/jof/24.7.756

Haylock, M.R., Hofstra, N., Klein Tank, A.M.G., Klok, E.J., Jones, P.D., New, M., 2008. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res. 113, D20119. https://doi.org/10.1029/2008JD010201

Heilman, W., Fast, J., 1992. Simulations of Horizontal Roll Vortex Development Above Lines of Extreme Surface Heating. Int. J. Wildl. Fire 2, 55. https://doi.org/10.1071/WF9920055

Helmis, C.G., Flocas, H.A., Kalogiros, J.A., Asimakopoulos, D.N., 2000. Strong downslope winds and application of hydraulic-like theory. J. Geophys. Res. Atmos. 105, 18039–18051. https://doi.org/10.1029/2000JD900246

Hines, K.M., Bromwich, D.H., 2008. Development and Testing of Polar Weather Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet Meteorology. Mon. Weather Rev. 136, 1971–1989. https://doi.org/10.1175/2007MWR2112.1

Hinzman, L.D., 2003. FROSTFIRE: An experimental approach to predicting the climate feedbacks from the changing boreal fire regime. J. Geophys. Res. 108, 8153. https://doi.org/10.1029/2001JD000415

Holton, J.R., 2004. An introduction to dynamic meteorology. Elsevier Academic Press.

Holton, J.R., Hakim, G.J., 2012. Geostrophic Approximation and Geostrophic Wind, in: An Introduction to Dynamic Meteorology. Academic Press, pp. 42–43.

Houze, Robert A., J., 1993. Cloud Dynamics. Academic Press, San Diego.

Hu, X., Sun, Y., Ntaimo, L., 2012. DEVS-FIRE: design and application of formal discrete event wildfire spread and suppression models. Simulation 88, 259–279. https://doi.org/10.1177/0037549711414592

Hunt, G.R., van den Bremer, T.S., 2011. Classical plume theory: 1937-2010 and beyond. IMA J. Appl. Math. 76, 424–448. https://doi.org/10.1093/imamat/hxq056

Iacono, M.J., Delamere, J.S., Mlawer, E.J., Shephard, M.W., Clough, S.A., Collins, W.D., 2008. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res. 113, D13103. https://doi.org/10.1029/2008JD009944

ICONA, 1988. Experimentation de un nuevo sistema para determina- cion del peligro de incendios forestales derivado de los combustibles: instrucciones de calculo. Madrid, Spain.

Iliopoulos, N., 2013. Fire-weather, wildfires and climate change. PhD Thesis. University of Aegean (in Greek).

INMG, 1988. Nota explicativa sobre o Índice de Risco Meteorológico de Incêndios Rurais. Lisbon, Portugal.

Isaaks, E.H., Srivastava, R.M., 1989. An Introduction to Applied Geostatistics.

Janis, M.J., Johnson, M.B., Forthun, G., 2002. Near-real time mapping of Keetch-Byram drought index in the south-eastern United States. Int. J. Wildl. Fire 11, 281. https://doi.org/10.1071/wf02013

Janjić, Z.I., 2002. Nonsingular implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso model.

Janjić, Z.I., 1996. The surface layer in the NCEP Eta Model, in: 11th Conference on Numerical Weather Prediction. Amer Meteor Soc, Boston, MA, Norfolk, VA, pp. 354–355.

Janjić, Z.I., 1994. The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes. Mon. Weather Rev. 122, 927–945. https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2

Jenkins, M.A., 2004. Investigating the Haines Index using parcel model theory. Int. J. Wildl. Fire 13, 297. https://doi.org/10.1071/WF03055

Jenkins, M.A., Kochanski, A.K., Krueger, S.K., 2011. The fluid dynamics of steady-state fireline propagation, in: Ninth Symposium on Fire and Forest Meteorology. Palm Springs, CA, October 18-20.

Jiménez, P.A., Dudhia, J., González-Rouco, J.F., Navarro, J., Montávez, J.P., García-Bustamante, E., 2012. A Revised Scheme for the WRF Surface Layer Formulation. Mon. Weather Rev. 140, 898–918. https://doi.org/10.1175/MWR-D-11-00056.1

Jimenez, P.A., Hacker, J.P., Dudhia, J., Haupt, S.E., Ruiz-Arias, J.A., Gueymard, C.A., Thompson, G., Eidhammer, T., Deng, A., 2016. WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction. Bull. Am. Meteorol. Soc. 97, 1249–1264. https://doi.org/10.1175/BAMS-D-14-00279.1

Jordanov, G., Beezley, J.D., Dobrinkova, N., Kochanski, A.K., Mandel, J., Sousedík, B., 2012. Simulation of the 2009 Harmanli Fire (Bulgaria), in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 291–298. https://doi.org/10.1007/978-3-642-29843-1_33

Josipa, M., Joachim, I., Kirsten, W.-S., 2014. Soil texture forcing data for the whole world for the Weather Research and Forecasting (WRF) Model of the University of Hohenheim (UHOH) based on the Harmonized World Soil Database (HWSD) at 30 arc-second horizontal resolution. World Data Center for Clima.

Kalabokidis, K., Iliopoulos, N., Gliglinos, D., 2012. Pyro-Meteorology and wildfires behavior in an enviroment under change. IWN, Athens (In Greek).

Kambezidis, H.D., Kalliampakos, G.K., 2016. Fire-Risk Assessment in Northern Greece Using a Modified Fosberg Fire-Weather Index That Includes Forest Coverage. Int. J. Atmos. Sci. 2016,

–8. https://doi.org/10.1155/2016/8108691

Karacostas, T., 2008. Notes on dynamic meteorology. University Publications, AUTh, Thessaloniki, Greece (In Greek).

Karacostas, T., 2003. Synoptic, dynamic and cloud microphysical characteristics related to precipitation enhancement projects, in: Regional Seminar on Cloud Physics and Weather

Modification. World Meteorological Organization, WMP No. 42, WMO-TD, pp. 194–200.

Karacostas, T., Flocas, A.A., Flocas, H.A., Kakaliagou, O., Rizou, C., 1992. A study of the synoptic situations over the area of Eastern Mediterranean, in: 1st Greek Conference On Meteorology-Climatology-Physics of the Atmosphere. Thessaloniki, Greece.

Karacostas, T., Kartsios, S., Pytharoulis, I., Tegoulias, I., Bampzelis, D., 2018. Observations and modelling of the characteristics of convective activity related to a potential rain enhancement program in central Greece. Atmos. Res. 208, 218–228. https://doi.org/10.1016/j.atmosres.2017.08.014

Karafyllidis, I., 1999. Acceleration of cellular automata algorithms using genetic algorithms. Adv. Eng. Softw. 30, 419–437. https://doi.org/10.1016/S0965-9978(98)00091-X

Karali, A., Hatzaki, M., Giannakopoulos, C., Roussos, A., Xanthopoulos, G., Tenentes, V., 2014. Sensitivity and evaluation of current fire risk and future projections due to climate change: the case study of Greece. Nat. Hazards Earth Syst. Sci. 14, 143–153. https://doi.org/10.5194/nhess-14-143-2014

Kartsios, S., 2013. Online coupling between Atmosphere – Fire Models for investigation of Wildland Fires. MSc Thesis. Faculty of Sciences, School of Geology, Aristotle University of Thessaloniki (in Greek).

Kartsios, S., Karacostas, T., Pytharoulis, I., Dimitrakopoulos, A.P., 2014a. Coupled Weather – Wildland Fire Model for fire behaviour interpretation, in: 12th International Conference on Meteorology, Climatology and Atmospheric Physics (COMECAP 2014). Herakleion, Greece.

Kartsios, S., Karacostas, T., Pytharoulis, I., Dimitrakopoulos, A.P., 2014b. Simulating Atmosphere-Fire Interactions using a Coupled Weather – Wildland Fire Model, in: 10th Congress of the Hellenic Geographical Society. Thessaloniki, Greece.

Kartsios, S., Karacostas, T.S., Pytharoulis, I., Dimitrakopoulos, A.P., 2017. The Role of Heat Extinction Depth Concept to Fire Behavior: An Application to WRF-SFIRE Model, in: Karacostas, T., Bais, A., Nastos, P.T. (Eds.), Perspectives on Atmospheric Sciences. Springer International Publishing, Cham, pp. 137–142. https://doi.org/10.1007/978-3-319-35095-0_20

Kartsios, S., Kotsopoulos, S., Karacostas, T.S., Tegoulias, I., Pytharoulis, I., Bampzelis, D., 2015. Statistical evaluation of the simulated convective activity over Central Greece, in: Geophysical Research Abstracts EGU General Assembly. pp. 2015–8418.

Katragkou, E., Garciá-Diéz, M., Vautard, R., Sobolowski, S., Zanis, P., Alexandri, G., Cardoso, R.M., Colette, A., Fernandez, J., Gobiet, A., Goergen, K., Karacostas, T., Knist, S., Mayer, S.,

Soares, P.M.M., Pytharoulis, I., Tegoulias, I., Tsikerdekis, A., Jacob, D., 2015. Regional climate hindcast simulations within EURO-CORDEX: Evaluation of a WRF multi-physics ensemble.

Geosci. Model Dev. 8, 603–618. https://doi.org/10.5194/gmd-8-603-2015

Katragkou, E., Gkotovou, I., Kartsios, S., Pavlidis, V., Tsigaridis, K., Trail, M., Nazarenko, L., Karacostas, T.S., 2017. AUTH Regional Climate Model Contributions to EURO-CORDEX, in:

Karacostas, T., Bais, A., Nastos, P. (Eds.), Springer Atmospheric Sciences. Springer, Cham, pp. 741–746. https://doi.org/10.1007/978-3-319-35095-0_106

Kaufman, Y.J., Justice, C.O., Flynn, L.P., Kendall, J.D., Prins, E.M., Giglio, L., Ward, D.E., Menzel, W.P., Setzer, A.W., 1998. Potential global fire monitoring from EOS‐MODIS. J. Geophys. Res. Atmos. 103, 32215–32238. https://doi.org/10.1029/98JD01644@10.1002/(ISSN)2169-8996.EOSAM1

Keeley, J.E., Fotheringham, C.J., 2001. History and Management of Crown-Fire Ecosystems: a Summary and Response, Conservation Biology 15, 1561-1567. https:// 10.1046/j.1523-1739.2001.t01-1-00186.x

Keetch, J., Byram, B., 1968. A Drought Index for Forest Fire Control. https://doi.org/10.1016/j.accpm.2015.04.007

Kiefer, M.T., Heilman, W.E., Zhong, S., Charney, J.J., Bian, X., 2015. Mean and Turbulent Flow Downstream of a Low-Intensity Fire: Influence of Canopy and Background Atmospheric Conditions. J. Appl. Meteorol. Climatol. 54, 42–57. https://doi.org/10.1175/JAMC-D-14-0058.1

Kiefer, M.T., Lin, Y.-L., Charney, J.J., 2008. A Study of Two-Dimensional Dry Convective Plume Modes with Variable Critical Level Height. J. Atmos. Sci. 65, 448–469. https://doi.org/10.1175/2007JAS2301.1

Kiefer, M.T., Parker, M.D., Charney, J.J., 2009. Regimes of Dry Convection above Wildfires: Idealized Numerical Simulations and Dimensional Analysis. J. Atmos. Sci. 66, 806–836. https://doi.org/10.1175/2008JAS2896.1

Klein Tank, A.M.G., Wijngaard, J.B., Können, G.P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., Heino, R., Bessemoulin, P., Müller-

Westermeier, G., Tzanakou, M., Szalai, S., Pálsdóttir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., van Engelen, A.F. V., Forland, E., Mietus, M.,

Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T., Antonio López, J., Dahlström, B., Moberg, A., Kirchhofer, W., Ceylan, A., Pachaliuk, O., Alexander, L. V., Petrovic, P., 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol. 22, 1441–1453. https://doi.org/10.1002/joc.773

Klemp, J.B., Dudhia, J., Hassiotis, A.D., 2008. An Upper Gravity-Wave Absorbing Layer for NWP Applications. Mon. Weather Rev. 136, 3987–4004. https://doi.org/10.1175/2008MWR2596.1

Klemp, J.B., Durran, D.R., 1987. Numerical modelling of Bora winds. Meteorol. Atmos. Phys. 36, 215–227. https://doi.org/10.1007/BF01045150

Klemp, J.B., Lilly, D.R., 1975. The Dynamics of Wave-Induced Downslope Winds. J. Atmos. Sci. 32, 320–339. https://doi.org/10.1175/1520-0469(1975)032<0320:TDOWID>2.0.CO;2

Knievel, J.C., Bryan, G.H., Hacker, J.P., 2007. Explicit Numerical Diffusion in the WRF Model. https://doi.org/10.1175/2007MWR2100.1

Knist, S., Goergen, K., Buonomo, E., Christensen, O.B., Colette, A., Cardoso, R.M., Fealy, R., Fernández, J., García-Díez, M., Jacob, D., Kartsios, S., Katragkou, E., Keuler, K., Mayer, S., Van

Meijgaard, E., Nikulin, G., Soares, P.M.M., Sobolowski, S., Szepszo, G., Teichmann, C., Vautard, R., Warrach-Sagi, K., Wulfmeyer, V., Simmer, C., 2017. Land-atmosphere coupling in EURO-

CORDEX evaluation experiments. J. Geophys. Res. 122, 79–103. https://doi.org/10.1002/2016JD025476

Knist, S., Goergen, K., Simmer, C., 2018. Effects of land surface inhomogeneity on convection-permitting WRF simulations over Central Europe. Meteorol. Atmos. Phys. 0, 2. https://doi.org/10.1007/s00382-018-4147-x

Knorr, W., Pytharoulis, I., Petropoulos, G.P., Gobron, N., 2011. Combined use of weather forecasting and satellite remote sensing information for fire risk , fire and fire impact monitoring. Comput. Ecol. Softw. 1, 112–120.

Kochanski, A.K., Jenkins, M.A., Mandel, J., Beezley, J.D., Clements, C.B., Krueger, S., 2013a. Evaluation of WRF-SFIRE performance with field observations from the FireFlux experiment. Geosci. Model Dev. 6, 1109–1126. https://doi.org/10.5194/gmd-6-1109-2013

Kochanski, A.K., Jenkins, M.A., Mandel, J., Beezley, J.D., Krueger, S.K., 2013b. Real time simulation of 2007 Santa Ana fires. For. Ecol. Manage. 294, 136–149. https://doi.org/10.1016/j.foreco.2012.12.014

Kochanski, A.K., Jenkins, M.A., Sun, R., Krueger, S., Abedi, S., Charney, J., 2013c. The importance of low-level environmental vertical wind shear to wildfire propagation: Proof of concept. J. Geophys. Res. Atmos. 118, 8238–8252. https://doi.org/10.1002/jgrd.50436

Kochanski, A.K., Jenkins, M.A., Yedinak, K., Mandel, J., Beezley, J., Lamb, B., 2016. Toward an integrated system for fire, smoke and air quality simulations. Int. J. Wildl. Fire 25, 534. https://doi.org/10.1071/WF14074

Kochanski, A.K., Mallia, D. V., Fearon, M.G., Mandel, J., Souri, A.H., Brown, T., 2019. Modeling wildfire smoke feedback mechanisms using a coupled fire‐atmosphere model with a radiatively active aerosol scheme. J. Geophys. Res. Atmos. 2019JD030558. https://doi.org/10.1029/2019JD030558

Koletsis, I., Giannaros, T.M., Lagouvardos, K., Kotroni, V., 2016. Observational and numerical study of the Vardaris wind regime in northern Greece. Atmos. Res. 171, 107–120. https://doi.org/10.1016/j.atmosres.2015.12.011

Koletsis, I., Lagouvardos, K., Kotroni, V., Bartzokas, A., 2009. Numerical study of a downslope windstorm in Northwestern Greece. Atmos. Res. 94, 178–193. https://doi.org/10.1016/j.atmosres.2009.05.012

Kontoes, C., Papoutsis, I., Herekakis, T., Sifakis, N., 2013. Wildfire Rapid Detection and Mapping and Post-fire Damage Assessment in Greece. Earthzine.

Kotroni, V., Lagouvardos, K., 2004. Evaluation of MM5 High-Resolution Real-Time Forecasts over the Urban Area of Athens, Greece. J. Appl. Meteorol. 43, 1666–1678. https://doi.org/10.1175/jam2170.1

Kotroni, V., Lagouvardos, K., Lykoudis, S., 2014. High-resolution model-based wind atlas for Greece. Renew. Sustain. Energy Rev. 30, 479–489. https://doi.org/10.1016/J.RSER.2013.10.016

Koutsias, N., Arianoutsou, M., Kallimanis, A.S., Mallinis, G., Halley, J.M., Dimopoulos, P., 2012. Where did the fires burn in Peloponnisos, Greece the summer of 2007? Evidence for a synergy of fuel and weather. Agric. For. Meteorol. 156, 41–53. https://doi.org/10.1016/j.agrformet.2011.12.006

Kraaij, T., Cowling, R.M., van Wilgen, B.W., 2013. Lightning and fire weather in eastern coastal fynbos shrublands: seasonality and long-term trends. Int. J. Wildl. Fire 22, 288. https://doi.org/10.1071/WF11167

Krestenitis, Y., Pytharoulis, I., Karacostas, T.S., Androulidakis, Y., Makris, C., Kombiadou, K., Tegoulias, I., Baltikas, V., Kotsopoulos, S., Kartsios, S., 2017. Severe Weather Events and Sea

Level Variability Over the Mediterranean Sea: The WaveForUs Operational Platform, in: Karacostas, T., Bais, A., Nastos, P.T. (Eds.), Perspectives on Atmospheric Sciences. Springer

International Publishing, Cham, pp. 63–68. https://doi.org/10.1007/978-3-319-35095-0_9

Lafore, J.P., Stein, J., Asencio, N., Bougeault, P., Ducrocq, V., Duron, J., Fischer, C., Héreil, P., Mascart, P., Masson, V., Pinty, J.P., Redelsperger, J.L., Richard, E., Vilà-Guerau de Arellano, J., 1998. The Meso-NH Atmospheric Simulation System. Part I: adiabatic formulation and control simulations. Ann. Geophys. 16, 90–109. https://doi.org/10.1007/s00585-997-0090-6

Lagouvardos, K., Kotroni, V., Defer, E., Bousquet, O., 2013. Study of a heavy precipitation event over southern france, in the frame of HYMEX project: Observational analysis and model results using assimilation of lightning. Atmos. Res. 134, 45–55. https://doi.org/10.1016/j.atmosres.2013.07.003

Lagouvardos, K., Kotroni, V., Giannaros, T.M., Dafis, S., 2019. Meteorological Conditions Conducive to the Rapid Spread of the Deadly Wildfire in Eastern Attica, Greece. Bull. Am. Meteorol. Soc. 100, 2137–2145. https://doi.org/10.1175/BAMS-D-18-0231.1

Laprise, R., 1992. The Euler Equations of Motion with Hydrostatic Pressure as an Independent Variable. Mon. Weather Rev. 120, 197–207. https://doi.org/10.1175/1520-0493(1992)120<0197:TEEOMW>2.0.CO;2

Lareau, N.P., Clements, C.B., 2016. Environmental controls on pyrocumulus and pyrocumulonimbus initiation and development. Atmos. Chem. Phys. 16, 4005–4022. https://doi.org/10.5194/acp-16-4005-2016

Le Page, Y., Oom, D., Silva, J.M.N., Jönsson, P., Pereira, J.M.C., 2010. Seasonality of vegetation fires as modified by human action: observing the deviation from eco-climatic fire regimes. Glob. Ecol. Biogeogr. https://doi.org/10.1111/j.1466-8238.2010.00525.x

Li, J., Heap, A.D., 2014. Spatial interpolation methods applied in the environmental sciences: A review. Environ. Model. Softw. 53, 173–189. https://doi.org/10.1016/j.envsoft.2013.12.008

Li, S., Jaroszynski, S., Pearse, S., Orf, L., Clyne, J., 2019. VAPOR: A Visualization Package Tailored to Analyze Simulation Data in Earth System Science. Atmosphere (Basel). 10, 488. https://doi.org/10.3390/atmos10090488

Li, X., Magill, W., 2000. Modeling fire spread under environmental influence using a cellular automaton approach. Complex. Int. 08.

Linn, R., Reisner, J., Colman, J.J., Winterkamp, J., 2002. Studying wildfire behavior using FIRETEC. Int. J. Wildl. Fire 11, 233. https://doi.org/10.1071/WF02007

Linn, R., Winterkamp, J., Edminster, C., Colman, J.J., Smith, W.S., 2007. Coupled influences of topography and wind on wildland fire behaviour. Int. J. Wildl. Fire 16, 183. https://doi.org/10.1071/WF06078

Linn, R.R., Cunningham, P., 2005. Numerical simulations of grass fires using a coupled atmosphere–fire model: Basic fire behavior and dependence on wind speed. J. Geophys. Res. 110, D13107. https://doi.org/10.1029/2004JD005597

Long, R.R., 1955. Some Aspects of the Flow of Stratified Fluids: III. Continuous Density Gradients. Tellus 7, 341–357. https://doi.org/10.1111/j.2153-3490.1955.tb01171.x

Long, R.R., 1953. Some Aspects of the Flow of Stratified Fluids: I. A Theoretical Investigation. Tellus 5, 42–58. https://doi.org/10.3402/tellusa.v5i1.8563

Mallia, D., Kochanski, A., Urbanski, S., Lin, J., 2018. Optimizing Smoke and Plume Rise Modeling Approaches at Local Scales. Atmosphere (Basel). 9, 166. https://doi.org/10.3390/atmos9050166

Mandel, J., Amram, S., Beezley, J.D., Kelman, G., Kochanski, A.K., Kondratenko, V.Y., Lynn, B.H., Regev, B., Vejmelka, M., 2014. Recent advances and applications of WRF-SFIRE. Nat. Hazards Earth Syst. Sci. 14, 2829–2845. https://doi.org/10.5194/nhess-14-2829-2014

Mandel, J., Beezley, J.D., Kochanski, A.K., 2011. Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011. Geosci. Model Dev. 4, 591–610. https://doi.org/10.5194/gmd-4-591-2011

Mandel, J., Beezley, J.D., Kochanski, A.K., Kondratenko, V.Y., Kim, M., 2012. Assimilation of perimeter data and coupling with fuel moisture in a wildland fire - Atmosphere DDDAS, in: Procedia Computer Science. pp. 1100–1109. https://doi.org/10.1016/j.procs.2012.04.119

Margerit, J., Sero-Guillaume, O., 1998. Richards’ model, Hamilton-Jacobi equations and temperature field equations of forest fires, in: Viegas, D.X. (Ed.), III International Conference on Forest Fire Research and 14th Conference on Fire and Forest Meterorology. Viegas DX, Coimbra, Portugal, 16-20 November, Luso, Portugal, pp. 281–294.

Matsangouras, I.T., Nastos, P.T., Pytharoulis, I., 2016. Study of the tornado event in Greece on March 25, 2009: Synoptic analysis and numerical modeling using modified topography. Atmos. Res. 169, 566–583. https://doi.org/10.1016/j.atmosres.2015.08.010

Matsangouras, I.T., Pytharoulis, I., Nastos, P.T., 2014. Numerical modeling and analysis of the effect of complex Greek topography on tornadogenesis. Hazards Earth Syst. Sci 14, 1905–1919. https://doi.org/10.5194/nhess-14-1905-2014

Matthews, S., 2006. A process-based model of fine fuel moisture. Int. J. Wildl. Fire 15, 155. https://doi.org/10.1071/WF05063

McArthur, A.G., 1977. Grassland Fire Danger Meter, Mk 5. Melbourne, Australia.

McArthur, A.G., 1967. Fire behaviour in eucalypt forests.

McArthur, A.G., 1966. Weather and grassland fire behaviour. Commonwealth of Australia Department of National Development, Forestry and Timber Bureau, Canberra, Australia.

McCaffrey, B.J.J., 1983. Momentum implications for buoyant diffusion flames. Combust. Flame 52, 149–167. https://doi.org/10.1016/0010-2180(83)90129-3

McCarthy, E.F., 1923. Forest fire weather in the southern Appalachians. Mon. Weather Rev. 51, 182–185. https://doi.org/10.1175/1520-0493(1923)51<182:FFWITS>2.0.CO;2

McCaw, L., Marchetti, P., Elliott, G., Reader, G., 2007. Bushfire weather climatology of the Haines Index in southwestern Australia. Aust. Meteorol. Mag. 56, 75–80.

McCormick, R.J., Brandner, T.A., Allen, T.F.H., 1999. Towards a theory of meso-scale wildfire modeling -- a complex systems approach using artificial neural networks, in: Neuenschwander,

L., Ryan, K. (Eds.), Proceedings of the Joint Fire Science Conference and Workshop. University of Idaho and International Association of Wildland Fire, Moscow, ID, 15-17 June,Boise, ID.

McCutchan, M.H., Fox, D.G., 1986. Effect of Elevation and Aspect on Wind, Temperature and Humidity. J. Clim. Appl. Meteorol. 25, 1996–2013. https://doi.org/10.1175/1520-0450(1986)025<1996:EOEAAO>2.0.CO;2

McCutchan, M.H., Main, W.A., 1989. The relationship between mean monthly fire potential indices and monthly fire severity, in: Maiver, D.C., Auld, H., Whitewood, R. (Eds.), Proceedings of the 10th Conference on Fire and Forest Meteorology. Ottawa, Ontario, Canada, pp. 430–435.

McGrattan, K.B., 2004. Fire Dynamics Simulator Version 4: Technical Reference Guide.

McRae, D.J., Conard, S.G., Ivanova, G.A., Sukhinin, A.I., Baker, S.P., Samsonov, Y.N., Blake, T.W., Ivanov, V.A., Ivanov, A. V., Churkina, T. V., Hao, W.M., Koutzenogij, K.P., Kovaleva, N., 2006. Variability of fire behavior, fire effects, and emissions in Scotch pine forests of central Siberia. Mitig. Adapt. Strateg. Glob. Chang. 11, 45–74. https://doi.org/10.1007/s11027-006-1008-4

Mell, W., Jenkins, M.A., Gould, J., Cheney, P., 2007. A physics-based approach to modelling grassland fires. Int. J. Wildl. Fire 16, 1. https://doi.org/10.1071/WF06002

Méndez, V., Llebot, J., 1997. Hyperbolic reaction-diffusion equations for a forest fire model. Phys. Rev. E 56, 6557–6563. https://doi.org/10.1103/PhysRevE.56.6557

Mestre, M., Allue, M., Peral, C., Santamaría, R., Lazcan, M., 2009. Operational Fire Danger Rating System in Spain, in: International Workshop on Advances in Operational Weather Systems

for Fire Danger Rating. Edmonton, Canada.

Millán, M.M., Estrela, M.J., Badenas, C., 1998. Meteorological Processes Relevant to Forest Fire Dynamics on the Spanish Mediterranean Coast. J. Appl. Meteorol. 37, 83–100. https://doi.org/10.1175/1520-0450(1998)037<0083:MPRTFF>2.0.CO;2

Miller, N.L., Schlegel, N.J., 2006. Climate change projected fire weather sensitivity: California Santa Ana wind occurrence. Geophys. Res. Lett. 33, L15711. https://doi.org/10.1029/2006GL025808

Minnich, R.A., 2001. An Integrated Model of Two Fire Regimes. Conserv. Biol. 15, 1549–1553. https://doi.org/10.1046/j.1523-1739.2001.01067.x

Mirocha, J.D., Lundquist, J.K., Kosović, B., 2010. Implementation of a Nonlinear Subfilter Turbulence Stress Model for Large-Eddy Simulation in the Advanced Research WRF Model. Mon. Weather Rev. 138, 4212–4228. https://doi.org/10.1175/2010MWR3286.1

Moeng, C.-H., Dudhia, J., Klemp, J., Sullivan, P., Moeng, C.-H., Dudhia, J., Klemp, J., Sullivan, P., 2007. Examining Two-Way Grid Nesting for Large Eddy Simulation of the PBL Using the WRF Model. Mon. Weather Rev. 135, 2295–2311. https://doi.org/10.1175/MWR3406.1

Moisseeva, N., Stull, R., 2019. Capturing Plume Rise and Dispersion with a Coupled Large-Eddy Simulation: Case Study of a Prescribed Burn. Atmosphere (Basel). 10, 579. https://doi.org/10.3390/atmos10100579

Monin, A.S., Obukhov, A.M., 1954. Basic laws of turbulent mixing in the atmosphere near the ground. Tr. Akad. Nauk SSSR Geofiz. Inst 24, 163–187.

Morcrette, J.-J., Beljaars, A., Benedetti, A., Jones, L., Boucher, O., 2008. Sea-salt and dust aerosols in the ECMWF IFS model. Geophys. Res. Lett. 35, L24813. https://doi.org/10.1029/2008GL036041

Morcrette, J.-J., Boucher, O., Jones, L., Salmond, D., Bechtold, P., Beljaars, A., Benedetti, A., Bonet, A., Kaiser, J.W., Razinger, M., Schulz, M., Serrar, S., Simmons, A.J., Sofiev, M., Suttie, M., Tompkins, A.M., Untch, A., 2009. Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: Forward modeling. J. Geophys.

Res. 114, D06206. https://doi.org/10.1029/2008JD011235

Moreira, F., Russo, D., 2007. Modelling the impact of agricultural abandonment and wildfires on vertebrate diversity in Mediterranean Europe. Landsc. Ecol. 22, 1461–1476. https://doi.org/10.1007/s10980-007-9125-3

Mori, A.S., Johnson, E.A., 2013. Assessing possible shifts in wildfire regimes under a changing climate in mountainous landscapes. For. Ecol. Manage. 310, 875–886. https://doi.org/10.1016/j.foreco.2013.09.036

Moriondo, M., Good, P., Durao, R., Bindi, M., Giannakopoulos, C., Corte-Real, J., 2006. Potential impact of climate change on fire risk in the Mediterranean area. Clim. Res. 31, 85–95. https://doi.org/10.3354/cr031085

Moritz, M.A., 1997. Analyzing extreme disturbance events: Fire in Los Padres National Forest. Ecol. Appl. 7, 1252–1262. https://doi.org/10.1890/1051-0761(1997)007[1252:AEDEFI]2.0.CO;2

Moritz, M.A., Moody, T.J., Krawchuk, M.A., Hughes, M., Hall, A., 2010. Spatial variation in extreme winds predicts large wildfire locations in chaparral ecosystems. Geophys. Res. Lett. 37. https://doi.org/10.1029/2009GL041735

Morton, B.R., Taylor, G., Turner, J.S., 1956. Turbulent gravitational convection from maintained and instantaneous sources. Proc. R. Soc. London. Ser. A. Math. Phys. Sci. 234, 1–23. https://doi.org/10.1098/rspa.1956.0011

Morvan, D., 2011. Physical Phenomena and Length Scales Governing the Behaviour of Wildfires: A Case for Physical Modelling. Fire Technol. 47, 437–460. https://doi.org/10.1007/s10694-010-0160-2

Morvan, D., Dupuy, J.L., 2004. Modeling the propagation of a wildfire through a Mediterranean shrub using a multiphase formulation. Combust. Flame 138, 199–210. https://doi.org/10.1016/J.COMBUSTFLAME.2004.05.001

Morvan, D., Dupuy, J.L., 2001. Modeling of fire spread through a forest fuel bed using a multiphase formulation. Combust. Flame 127, 1981–1994. https://doi.org/10.1016/S0010-2180(01)00302-9

Morvan, D., Meradji, S., Accary, G., 2008. Wildfire Behavior Study in a Mediterranean Pine Stand Using a Physically Based Model. Combust. Sci. Technol. 180, 230–248. https://doi.org/10.1080/00102200701600978

Morvan, D., Méradji, S., Accary, G., 2009. Physical modelling of fire spread in Grasslands. Fire Saf. J. 44, 50–61. https://doi.org/10.1016/j.firesaf.2008.03.004

Mraz, M., Zimic, N., Virant, J., 1999. Intelligent bush fire spread prediction using fuzzy cellular automata. J. Intell. Fuzzy Syst. 7, 203–207.

Mukherjee, S., Schalkwijk, J., Jonker, H.J.J., Mukherjee, S., Schalkwijk, J., Jonker, H.J.J., 2016. Predictability of Dry Convective Boundary Layers: An LES Study. J. Atmos. Sci. 73, 2715–2727. https://doi.org/10.1175/JAS-D-15-0206.1

Munger, T.T., 1916. Graphic method of representing and comparing drought intensities. Mon. Weather Rev. 44, 642–643. https://doi.org/10.1175/1520-0493(1916)44<642:gmorac>2.0.co;2

Munns, E.N., 1921. Evaporation and forest fires. Mon. Weather Rev. 49, 149–152. https://doi.org/10.1175/1520-0493(1921)49<149:EAFF>2.0.CO;2

Muzy, A., Innocenti, E., Aiello, A., Santucci, J.-F., Wainer, G., 2002. Methods for Special Applications: Cell-DEVS Quantization Techniques in a Fire Spreading Application, in: Snowdon, J., Charnes, J. (Eds.), Proceedings of the 34th Conference on Winter Simulation: Exploring New Frontiers, WSC ’02. Winter Simulation Conference, December 8-11, San Diego, CA, USA, pp. 542–549.

Nahmias, J., Téphany, H., Duarte, J., Letaconnoux, S., 2000. Fire spreading experiments on heterogeneous fuel beds. Applications of percolation theory. Can. J. For. Res. 30, 1318–1328. https://doi.org/10.1139/cjfr-30-8-1318

NASA, J., 2013. NASA Shuttle Radar Topography Mission Global 1 arc second. https://doi.org/https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003

Nauslar, N., Abatzoglou, J., Marsh, P., 2018. The 2017 North Bay and Southern California Fires: A Case Study. Fire 1, 18. https://doi.org/10.3390/fire1010018

Nelson Jr, R.M., 2000. Prediction of diurnal change in 10-h fuel stick moisture content. Can. J. For. Res. 30, 1071–1087. https://doi.org/10.1139/x00-032

Nesterov, V.G., 1949. Flammability of the Forest and Methods of its Determination, USSR State Industry Press. Moscow.

Noble, I.R., Gill, A.M., Bary, G.A.V., 1980. McArthur’s fire‐danger meters expressed as equations. Aust. J. Ecol. 5, 201–203. https://doi.org/10.1111/j.1442-9993.1980.tb01243.x

Ntaimo, L., Zeigler, B.P., Vasconcelos, M.J., Khargharia, B., 2004. Forest Fire Spread and Suppression in DEVS. Simulation 80, 479–500. https://doi.org/10.1177/0037549704050918

Nunes, S.A., DaCamara, C.C., Turkman, K.F., Calado, T.J., Trigo, R.M., Turkman, M.A.A., 2019. Wildland fire potential outlooks for Portugal using meteorological indices of fire danger. Nat.

Hazards Earth Syst. Sci. 19, 1459–1470. https://doi.org/10.5194/nhess-19-1459-2019

Ogura, Y., Phillips, N.A., 1962. Scale Analysis of Deep and Shallow Convection in the Atmosphere. J. Atmos. Sci. 19, 173–179. https://doi.org/10.1175/1520-0469(1962)019<0173:saodas>2.0.co;2

Onderka, M., Melicherčik, I., 2010. Fire-prone areas delineated from a combination of the Nesterov fire-risk rating Index with multispectral satellite data. Appl. Geomatics 2, 1–7. https://doi.org/10.1007/s12518-009-0014-0

Osher, S., Fedkiw, R., 2003. Level Set Methods and Dynamic Implicit Surfaces, Applied Mathematical Sciences. Springer New York, New York, NY. https://doi.org/10.1007/b98879

Palmer, T.Y., 1981. Large fire winds, gases and smoke. Atmos. Environ. 15, 2079–2090. https://doi.org/10.1016/0004-6981(81)90241-9

Papadopoulos, A., Katsafados, P., 2009. Verification of operational weather forecasts from the POSEIDON system across the Eastern Mediterranean. Nat. Hazards Earth Syst. Sci. 9, 1299–1306. https://doi.org/10.5194/nhess-9-1299-2009

Papadopoulos, G.D., Pavlidou, F.-N., 2011. A Comparative Review on Wildfire Simulators. IEEE Syst. J. 5, 233–243. https://doi.org/10.1109/JSYST.2011.2125230

Paschalidou, A.K., Kassomenos, P.A., 2016. What are the most fire-dangerous atmospheric circulations in the Eastern-Mediterranean? Analysis of the synoptic wildfire climatology. Sci. Total Environ. 539, 536–545. https://doi.org/10.1016/J.SCITOTENV.2015.09.039

Patton, E.G., Coen, J.L., 2004. WRF-Fire: A Coupled Atmosphere-Fire Module for WRF, in: Joint MM5/Weather Research and Forecasting Model Users’ Workshop. NCAR, Boulder, CO, USA.

Pausas, J.G., Llovet, J., Rodrigo, A., Vallejo, R., 2008. Are wildfires a disaster in the Mediterranean basin? - A review. Int. J. Wildl. Fire 17, 713. https://doi.org/10.1071/WF07151

Pavlidis, V., Katragkou, E., Prein, A., Georgoulias, A.K., Kartsios, S., Zanis, P., Karacostas, T., 2019. Investigating the sensitivity to resolving aerosol interactions in downscaling regional model experiments with WRFv3.8.1 over Europe. Geosci. Model Dev. Discuss. 1–34. https://doi.org/10.5194/gmd-2019-161

Peace, M., Mattner, T., Mills, G., 2011. The Kangaroo Island bushfires of 2007. A meteorological case study and WRF-fire simulation, in: 9th Symposium on Fire and Forest Meteorology, American Meteorological Society, October 17-21, USA, pp 228-234.

Peltier, W.R., Clark, T.L., 1979. The Evolution and Stability of Finite-Amplitude Mountain Waves. Part II: Surface Wave Drag and Severe Downslope Windstorms. J. Atmos. Sci. 36, 1498–1529. https://doi.org/10.1175/1520-0469(1979)036<1498:TEASOF>2.0.CO;2

Pereira, M.G., Trigo, R.M., da Camara, C.C., Pereira, J.M.C., Leite, S.M., 2005. Synoptic patterns associated with large summer forest fires in Portugal. Agric. For. Meteorol. 129, 11–25. https://doi.org/10.1016/J.AGRFORMET.2004.12.007

Petritsch, R., Hasenauer, H., 2014. Climate input parameters for real-time online risk assessment. Nat. Hazards 70, 1749–1762. https://doi.org/10.1007/s11069-011-9880-y

Potter, B., 2018. The Haines Index – it’s time to revise it or replace it. Int. J. Wildl. Fire 27, 437. https://doi.org/10.1071/WF18015

Potter, B., 1996. Atmospheric Properties Associated With Large Wildfires. Int. J. Wildl. Fire 6, 71. https://doi.org/10.1071/WF9960071

Potter, B.E., 2012a. Atmospheric interactions with wildland fire behaviour - I. Basic surface interactions, vertical profiles and synoptic structures. Int. J. Wildl. Fire 21, 779. https://doi.org/10.1071/WF11128

Potter, B.E., 2012b. Atmospheric interactions with wildland fire behaviour - II. Plume and vortex dynamics. Int. J. Wildl. Fire 21, 802. https://doi.org/10.1071/WF11129

Potter, B.E., 2005. The role of released moisture in the atmospheric dynamics associated with wildland fires. Int. J. Wildl. Fire 14, 77. https://doi.org/10.1071/WF04045

Powers, J.G., Klemp, J.B., Skamarock, W.C., Davis, C.A., Dudhia, J., Gill, D.O., Coen, J.L., Gochis, D.J., Ahmadov, R., Peckham, S.E., Grell, G.A., Michalakes, J., Trahan, S., Benjamin, S.G., Alexander, C.R., Dimego, G.J., Wang, W., Schwartz, C.S., Romine, G.S., Liu, Z., Snyder, C., Chen, F., Barlage, M.J., Yu, W., Duda, M.G., 2017. The weather research and forecasting model: Overview, system efforts, and future directions. Bull. Am. Meteorol. Soc. 98, 1717–1737. https://doi.org/10.1175/BAMS-D-15-00308.1

Prein, A.F., Gobiet, A., 2017. Impacts of uncertainties in European gridded precipitation observations on regional climate analysis. Int. J. Climatol. 37, 305–327. https://doi.org/10.1002/joc.4706

Purton, C.M., 1982. Equations for the McArthur mark 4 grassland fire danger meters. Melbourne, Australia.

Pytharoulis, I., 2018. Analysis of a Mediterranean tropical-like cyclone and its sensitivity to the sea surface temperatures. Atmos. Res. 208, 167–179. https://doi.org/10.1016/j.atmosres.2017.08.009

Pytharoulis, I., Kartsios, S., Tegoulias, I., Feidas, H., Miglietta, M., Matsangouras, I., Karacostas, T., 2018. Sensitivity of a Mediterranean Tropical-Like Cyclone to Physical

Parameterizations. Atmosphere (Basel). 9, 436. https://doi.org/10.3390/atmos9110436

Pytharoulis, I., Kotsopoulos, S., Tegoulias, I., Kartsios, S., Bampzelis, D., Karacostas, T., 2016. Numerical modeling of an intense precipitation event and its associated lightning activity over northern Greece. Atmos. Res. 169, 523–538. https://doi.org/10.1016/j.atmosres.2015.06.019

Pytharoulis, I., Tegoulias, I., Kotsopoulos, S., Bampzelis, D., Karacostas, T., Katragkou, E., 2015. Verification of the operational high-resolution WRF forecasts produced by WaveForUs project, in: 16th Annual WRF Users’ Workshop. Boulder, CO, USA.

Rabier, F., Järvinen, H., Klinker, E., Mahfouf, J.-F., Simmons, A., 2007. The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Q. J. R. Meteorol. Soc. 126, 1143–1170. https://doi.org/10.1002/qj.49712656415

Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A., Gratzki, A., 2013. A Central European precipitation climatology; Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorol. Zeitschrift 22, 235–256. https://doi.org/10.1127/0941-2948/2013/0436

Reid, D.G., Vines, R.G., 1972. Radar study of the smoke plume from a forest fire.

Reinhard, M., Rebetez, M., Schlaepfer, R., 2005. Recent climate change: Rethinking drought in the context of Forest Fire Research in Ticino, South of Switzerland. Theor. Appl. Climatol. 82, 17–25. https://doi.org/10.1007/s00704-005-0123-6

Reisner, J., Linn, R., Bossert, J., 1998. Comparison of a diagnostic wildfire modeling system (HIGRAD/BEHAVE) with a self-determining wildfire modeling system (HIGRAD/FIRETEC). Los Alamos, NM. https://doi.org/10.2172/314170

Reisner, J., Wynne, S., Margolin, L., Linn, R., 2000. Coupled Atmospheric–Fire Modeling Employing the Method of Averages. Mon. Weather Rev. 128, 3683–3691. https://doi.org/10.1175/1520-0493(2001)129<3683:CAFMET>2.0.CO;2

Ricotta, C., Retzlaff, R., 2000. Self-similar spatial clustering of wildland fires: The example of a large wildfire in Spain. Int. J. Remote Sens. 21, 2113–2118. https://doi.org/10.1080/01431160050021330

Roads, J., Tripp, P., Juang, H., Wang, J., Chen, S., Fujioka, F., 2008. ECPC/NCEP March 2008 seasonal fire danger forecasts, Experimental Long-Lead Forecasts Bulletin.

Roads, J.O., Ueyoshi, K., Chen, S.C., Alpert, J., Fujioka, F., 1991. Medium-range fire weather forecasts. Int. J. Wildl. Fire 1, 159–176. https://doi.org/10.1071/WF9910159

Rogers, E., Black, T., Ferrier, B., Lin, Y., Parrish, D., DiMego, G., 2001. Changes to the NCEP Meso Eta Analysis and Forecast System: Increase in Resolution, New Cloud Microphysics,

Modified Precipitation Assimilation, Modified 3DVAR Analysis, NOAA/NWS Technical Procedures Bulletin 488; Washington, DC, USA. Washington, DC, USA.

Rolinski, T., Capps, S.B., Fovell, R.G., Cao, Y., D’Agostino, B.J., Vanderburg, S., Rolinski, T., Capps, S.B., Fovell, R.G., Cao, Y., D’Agostino, B.J., Vanderburg, S., 2016. The Santa Ana Wildfire Threat Index: Methodology and Operational Implementation. Weather Forecast. 31, 1881–1897. https://doi.org/10.1175/WAF-D-15-0141.1

Rothermel, R.C., 1993. Mann Gulch Fire: A Race That Couldn’t Be Won.

Rothermel, R.C., 1972. A mathematical model for predicting fire spread in wildland fuels, USDA Forest Service Research Paper INT-116 USA.

Rouse, J., Haas, R., Schell, J., Deering, D., 1973. Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symp.

Sá, A.C.L., Benali, A., Fernandes, P.M., Pinto, R.M.S., Trigo, R.M., Salis, M., Russo, A., Jerez, S., Soares, P.M.M., Schroeder, W., Pereira, J.M.C., 2017. Evaluating fire growth simulations using satellite active fire data. Remote Sens. Environ. 190, 302–317. https://doi.org/10.1016/j.rse.2016.12.023

Saltenberger, J., Barker, T., 1993. Weather related unusual fire behavior in the Awbrey Hall fire. Natl. Weather Dig. 18, 20–29.

San-Miguel-Ayanz, J., Carlson, J.D., Alexander, M., Tolhurst, K., Morgan, G., Sneeuwjagt, R., Dudley, M., 2003. Current Methods to Assess Fire Danger Potential, in: Chuvieco, E. (Ed.),

Wildland Fire Danger Estimation and Mapping - The Role of Remote Sensing Data. World Scientific Publishing Cp. Pte. Ltd., pp. 21–61. https://doi.org/10.1142/9789812791177_0002

San-Miguel-Ayanz, J., Durrant, T., Boca, R., Libertà, G., Branco, A., De Rigo, D., Ferrari, D., Maianti, P., Artés Vivancos, T., Pfeiffer, H., Nuitjen, D., 2019. Advance EFFIS Report on Forest Fires in Europe, Middle East and North Africa 2018. Ispra. https://doi.org/10.2760/262459

San Jose, R., Luis Perez, J., Perez, L., Maria Gonzalez, R., Pecci, J., Palacios, M., 2015. Forest fire forecasting tool for air quality modelling systems. Fis. La Tierra 27, 69–90. https://doi.org/http://dx.doi.org/10.5209/rev_FITE.2015.v27.51194

Sawyer, J.S., 1960. Numerical calculation of the displacements of a stratified airstream crossing a ridge of small height. Q. J. R. Meteorol. Soc. 86, 326–345. https://doi.org/10.1002/qj.49708636905

Schneider, P., Roberts, D.A., Kyriakidis, P.C., 2008. A VARI-based relative greenness from MODIS data for computing the Fire Potential Index. Remote Sens. Environ. 112, 1151–1167. https://doi.org/10.1016/j.rse.2007.07.010

Schroeder, W., Oliva, P., Giglio, L., Csiszar, I.A., 2014. The New VIIRS 375m active fire detection data product: Algorithm description and initial assessment. Remote Sens. Environ. 143, 85–96. https://doi.org/10.1016/j.rse.2013.12.008

Scorer, R.S., 1949. Theory of waves in the lee of mountains. Q. J. R. Meteorol. Soc. 75, 41–56. https://doi.org/10.1002/qj.49707532308

Scorer, R.S., Klieforth, H., 1959. Theory of mountain waves of large amplitude. Q. J. R. Meteorol. Soc. 85, 131–143. https://doi.org/10.1002/qj.49708536406

Scott, J.H., Burgan, R.E., 2005. Standard fire behavior fuel models: A comprehensive set for use with Rothermel’s surface fire spread model. USDA For. Serv. - Gen. Tech. Rep. RMRS-GTR. https://doi.org/10.2737/RMRS-GTR-153

Sebastián López, A., San-Miguel-Ayanz, J., Burgan, R.E., 2002. Integration of satellite sensor data, fuel type maps and meteorological observations for evaluation of forest fire risk at the pan-European scale. Int. J. Remote Sens. 23, 2713–2719. https://doi.org/10.1080/01431160110107761

Shabbar, A., Skinner, W., Flannigan, M.D., 2011. Prediction of Seasonal Forest Fire Severity in Canada from Large-Scale Climate Patterns. J. Appl. Meteorol. Climatol. 50, 785–799. https://doi.org/10.1175/2010JAMC2547.1

Sharples, J.J., 2009. An overview of mountain meteorological effects relevant to fire behaviour and bushfire risk. Int. J. Wildl. Fire 18, 737. https://doi.org/10.1071/WF08041

Sharples, J.J., Kiss, A.E., Raposo, J., Viegas, D.X., Simpson, C.C., 2015. Pyrogenic vorticity from windward and lee slope fires. MODSIM2015, 21st International Congress of Modelling and Simulation.

Sharples, J.J., McRae, R.H.D., Weber, R.O., Gill, A.M., 2009a. A simple index for assessing fire danger rating. Environ. Model. Softw. 24, 764–774. https://doi.org/10.1016/j.envsoft.2008.11.004

Sharples, J.J., McRae, R.H.D., Weber, R.O., Gill, A.M., 2009b. A simple index for assessing fuel moisture content. Environ. Model. Softw. 24, 637–646. https://doi.org/10.1016/j.envsoft.2008.10.012

Sharples, J.J., McRae, R.H.D., Wilkes, S.R., 2012. Wind - terrain effects on the propagation of wildfires in rugged terrain: fire channelling. Int. J. Wildl. Fire 21, 282. https://doi.org/10.1071/WF10055

Sharples, J.J., Mills, G.A., McRae, R.H.D., Weber, R.O., 2010. Foehn-Like Winds and Elevated Fire Danger Conditions in Southeastern Australia. J. Appl. Meteorol. Climatol. 49, 1067–1095. https://doi.org/10.1175/2010JAMC2219.1

Sharples, J.J., Simpson, C.C., Evans, J.P., 2013. Examination of wind speed thresholds of vorticity-driven lateral fire spread, in: Piantadosi, J., Anderssen, R., Boland, J. (Eds.), 20th International Congress of Modelling and Simulation.

Simard, A.J., 1968. The moisture content of forest fuels – 1. A review of the basic concepts. Ottawa, Ontario, Canada.

Simpson, C.C., Sharples, J.J., Evans, J.P., 2016. Sensitivity of atypical lateral fire spread to wind and slope. Geophys. Res. Lett. 43, 1744-1751. https://doi.org/10.1002/2015GL067343

Simpson, C.C., Sharples, J.J., Evans, J.P., 2014. Resolving vorticity-driven lateral fire spread using the WRF-Fire coupled atmosphere&ndash;fire numerical model. Nat. Hazards Earth Syst. Sci. 14, 2359–2371. https://doi.org/10.5194/nhess-14-2359-2014

Simpson, C.C., Sharples, J.J., Evans, J.P., McCabe, M.F., 2013a. Large eddy simulation of atypical wildland fire spread on leeward slopes. Int. J. Wildl. Fire 22, 599. https://doi.org/10.1071/WF12072

Simpson, C.C., Sturman, A., Zawar-Reza, P., Pearce, G., 2013b. Assessment of fire weather during a Foehn event in South Island, New Zealand.

Sindosi, O.A., Bartzokas, A., Kotroni, V., Lagouvardos, K., 2015. Influence of orography on precipitation amount and distribution in NW Greece; A case study. Atmos. Res. 152, 105–122. https://doi.org/10.1016/j.atmosres.2014.06.013

Sindosi, O.A., Bartzokas, A., Kotroni, V., Lagouvardos, K., 2012. Verification of precipitation forecasts of MM5 model over Epirus, NW Greece, for various convective parameterization schemes. Nat. Hazards Earth Syst. Sci. 12, 1393–1405. https://doi.org/10.5194/nhess-12-1393-2012

Skamarock, W.C., 2004. Evaluating Mesoscale NWP Models Using Kinetic Energy Spectra. Mon. Weather Rev. 132, 3019–3032. https://doi.org/10.1175/MWR2830.1

Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.-Y., Wang, W., Powers, J.G., 2008. A Description of the Advanced Research WRF Version 3. https://doi.org/10.5065/D68S4MVH

Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Zhiquan, L., Berner, J., Wang, W., Powers, J.G., Duda, M.G., Barker, D.M., Huang, X.-Y., 2019. A Description of the Advanced Research

WRF Model Version 4 NCAR Technical Note. Natl. Cent. Atmos. Res. 145. https://doi.org/10.5065/1dfh-6p97

Škvarenina, J., Mindáš, J., Holécy, J., Tuček, J., 2004. An analysis of the meteorological conditions during two largest forest fire events in the Slovak Paradise National Park. Meteorol. J. 7, 167–171.

Smith, R.B., 1985. On Severe Downslope Winds. J. Atmos. Sci. 42, 2597–2603. https://doi.org/10.1175/1520-0469(1985)042<2597:OSDW>2.0.CO;2

Smolarkiewicz, P.K., Rotunno, R., 1989. Low Froude Number Flow Past Three-Dimensional Obstacles. Part I: Baroclinically Generated Lee Vortices. J. Atmos. Sci. 46, 1154–1164.

https://doi.org/10.1175/1520-0469(1989)046<1154:LFNFPT>2.0.CO;2

Sneeuwjagt, R.J., Peet, G.B., 1985. Forest fire behaviour tables for western Australia. Perth.

Sol, B., 1989. Risque numérique météorologique d’incendies de forêt en Région Méditerranéenne: dépouillement du test de lèté 1988 et propositions d’améliorations.

Srock, A., Charney, J., Potter, B., Goodrick, S., 2018. The Hot-Dry-Windy Index: A New Fire Weather Index. Atmosphere (Basel). 9, 279. https://doi.org/10.3390/atmos9070279

Steiner, J.T., 1976. Blowup fires - the Byram wind profile. Aust. Meteorol. Mag. 24, 139–142.

Stolaki, S., Pytharoulis, I., Karacostas, T., 2012. A study of fog characteristics using a coupled WRF-COBEL model over Thessaloniki Airport, Greece. Pure Appl. Geophys. 169, 961–981. https://doi.org/10.1007/s00024-011-0393-0

Stull, R., 1994. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, Dordrecht. https://doi.org/10.1007/978-94-009-3027-8

Sullivan, A.L., 2009a. Wildland surface fire spread modelling, 1990 - 2007. 1: Physical and quasi-physical models. Int. J. Wildl. Fire 18, 349. https://doi.org/10.1071/WF06143

Sullivan, A.L., 2009b. Wildland surface fire spread modelling, 1990 - 2007. 2: Empirical and quasi-empirical models. Int. J. Wildl. Fire 18, 369. https://doi.org/10.1071/wf06142

Sullivan, A.L., 2009c. Wildland surface fire spread modelling, 1990 - 2007. 3: Simulation and mathematical analogue models. Int. J. Wildl. Fire 18, 387. https://doi.org/10.1071/WF06144

Sullivan, P.P., Patton, E.G., Sullivan, P.P., Patton, E.G., 2011. The Effect of Mesh Resolution on Convective Boundary Layer Statistics and Structures Generated by Large-Eddy Simulation. J. Atmos. Sci. 68, 2395–2415. https://doi.org/10.1175/JAS-D-10-05010.1

Sun, R., Jenkins, M.A., Krueger, S.K., Mell, W., Charney, J.J., 2006. An evaluation of fire-plume properties simulated with the Fire Dynamics Simulator (FDS) and the Clark coupled wildfire model. Can. J. For. Res. 36, 2894–2908. https://doi.org/10.1139/x06-138

Sun, R., Krueger, S.K., Jenkins, M.A., Zulauf, M.A., Charney, J.J., 2009. The importance of fire - atmosphere coupling and boundary-layer turbulence to wildfire spread. Int. J. Wildl. Fire 18, 50. https://doi.org/10.1071/WF07072

Sun, W.-Y., 2013. Numerical study of severe downslope windstorm. Weather Clim. Extrem. 2, 22–30. https://doi.org/10.1016/j.wace.2013.10.002

Tallapragada, V., Coauthors, 2014. Hurricane Weather Research and Forecasting (HWRF) Model.

Tatli, H., Türkeş, M., 2014. Climatological evaluation of haines forest fire weather index over the Mediterranean Basin. Meteorol. Appl. 21, 545–552. https://doi.org/10.1002/met.1367

Taylor, R.J., Bethwaite, F.D., Packham, D.R., Vines, R.G., 1968. A meso- meteorological investigation of five forest fires.

Taylor, R.J., Corke, D.G., King, N.K., MacArthur, D.A., Packham, D.R., Vines, R.G., 1971. Some meteorological aspects of three intense forest fires.

Tegoulias, I., Kartsios, S., Pytharoulis, I., Kotsopoulos, S., Karacostas, T.S., 2017. The Influence of WRF Parameterisation Schemes on High Resolution Simulations Over Greece, in:

Karacostas, T., Bais, A., Nastos, P. (Eds.), Perspectives on Atmospheric Sciences. Springer International Publishing, Cham, pp. 3–8. https://doi.org/10.1007/978-3-319-35095-0_1

Tegoulias, I., Pytharoulis, I., Kotsopoulos, S., Bampzelis, D., Kartsios, S., Karacostas, T., 2014a. The influence of WRF parameterisation schemes on high resolution simulations over Central Greece, in: 15th Annual WRF Users’ Workshop. Boulder, CO, USA.

Tegoulias, I., Pytharoulis, I., Kotsopoulos, S., Karacostas, T., 2014b. Numerical weather prediction sensitivity to sea-surface tempeatures, in: 12th International Conference on Meteorology, Climatology and Atmospheric Physics (COMECAP2014). Herakleion, Crete, Greece, pp. 203–208.

Tewari, M., Chen, F., Wang, W., Dudhia, J., Lemone, M.A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., Cuenca, R.H., 2004. Implementation and verification of the unified NOAH land surface

model in the WRF model, in: 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction. pp. 11–15.

The NCAR Command Language (Version 6.4.0) [Software]. (2016). Boulder, Colorado: UCAR/NCAR/CISL/TDD. http://dx.doi.org/10.5065/D6WD3XH5

The NCAR Command Language (Version 6.6.2) [Software]. (2019). Boulder, Colorado: UCAR/NCAR/CISL/TDD. http://dx.doi.org/10.5065/D6WD3XH5

Thomas, C.M., Sharples, J.J., Evans, J.P., 2017. Modelling the dynamic behaviour of junction fires with a coupled atmosphere–fire model. Int. J. Wildl. Fire 26, 331. https://doi.org/10.1071/WF16079

Tian, X., McRae, D.J., Jin, J., Shu, L., Zhao, F., Wang, M., 2011. Wildfires and the Canadian Forest Fire Weather Index system for the Daxing’anling region of China. Int. J. Wildl. Fire 20, 963. https://doi.org/10.1071/WF09120

Tohidi, A., Gollner, M.J., Xiao, H., 2018. Fire Whirls. Annu. Rev. Fluid Mech. 50, 187–213. https://doi.org/10.1146/annurev-fluid-122316-045209

Tolika, K., Maheras, P., Tegoulias, I., 2009. Extreme temperatures in Greece during 2007: Could this be a “return to the future”? Geophys. Res. Lett. 36, L10813. https://doi.org/10.1029/2009GL038538

Trouet, V., Taylor, A.H., Carleton, A.M., Skinner, C.N., 2009. Interannual variations in fire weather, fire extent, and synoptic-scale circulation patterns in northern California and Oregon.

Theor. Appl. Climatol. 95, 349–360. https://doi.org/10.1007/s00704-008-0012-x

Tsagari, K., Karetsos, G., Prokopos, N., 2011. Forest Fires of Greece, 1983-2008. WWF Hellas and NAGREG-IMDO & TDP.

Tsinko, Y., Bakhshaii, A., Johnson, E.A., Martin, Y.E., 2018. Comparisons of fire weather indices using Canadian raw and homogenized weather data. Agric. For. Meteorol. 262, 110–119. https://doi.org/10.1016/j.agrformet.2018.07.005

Ulmer, F.-G., Balss, U., 2016. Spin-up time research on the weather research and forecasting model for atmospheric delay mitigations of electromagnetic waves. J. Appl. Remote Sens. 10, 016027. https://doi.org/10.1117/1.JRS.10.016027

Vakalis, D., Sarimveis, H., Kiranoudis, C., Alexandridis, A., Bafas, G., 2004. A GIS based operational system for wildland fire crisis management I. Mathematical modelling and simulation. Appl. Math. Model. 28, 389–410. https://doi.org/10.1016/j.apm.2003.10.005

Van Wagner, C.E., 1987. Development and structure of the Canadian Forest Fire Weather Index System. Canadian Forestry Service.

Van Wagner, C.E., 1979. A laboratory study of weather effects on the drying rate of jack pine litter. Can. J. For. Res. 9, 267–275. https://doi.org/10.1139/x79-044

Van Wagner, C.E., 1967. Calculations of Forest Fire Spread by Flame Radiation.

Van Wagner, C.E., Pickett, T.L., 1985. Equations and FORTRAN program for the Canadian Forest Fire Weather Index System, Forestry Technical Report. Canadian Forestry Service, Ottawa, Ontario, Canada.

Vázquez, A., Pérez, B., Fernández‐González, F., Moreno, J.M., 2002. Recent fire regime characteristics and potential natural vegetation relationships in Spain. J. Veg. Sci. 13, 663–676. https://doi.org/10.1111/j.1654-1103.2002.tb02094.x

Vejmelka, M., Kochanski, A.K., Mandel, J., 2016. Data assimilation of dead fuel moisture observations from remote automated weather stations. Int. J. Wildl. Fire 25. https://doi.org/10.1071/WF14085

Viegas, D., Neto, L., 1991. Wall Shear-Stress as a Parameter to Correlate the Rate of Spread of a Wind Induced Forest Fire. Int. J. Wildl. Fire 1, 177. https://doi.org/10.1071/WF9910177

Viegas, D.X., 2009. Recent Forest Fire Related Accidents in Europe. Ispra. https://doi.org/10.2788/50781

Viegas, D.X., Bovio, G., Ferreira, A., Nosenzo, A., Sol, B., 1999. Comparative study of various methods of fire danger evaluation in southern Europe. Int. J. Wildl. Fire 9, 235.

https://doi.org/10.1071/WF00015

Viney, N., 1991. A Review of Fine Fuel Moisture Modelling. Int. J. Wildl. Fire 1, 215. https://doi.org/10.1071/WF9910215

Wallenius, T.H., Pennanen, J., Burton, P.J., 2011. Long-term decreasing trend in forest fires in northwestern Canada. Ecosphere 2, art53. https://doi.org/10.1890/ES11-00055.1

Wang, W., Bruyère, C., Duda, M.G., Dudhia, J., Gill, D.O., Hin, H.C., Michalakes, J., Rizvi, S., Zhang, X., Beezley, J.D., Coen, J.L., Mandel, J., Chuang, H.-Y., Mckee, N., Slovacek, T., Wolff, J., 2012. ARW version 3 modeling system user’s guide.

Watt, S.D., Roberts, A.J., Weber, R.O., 1995. Dimensional reduction of a bushfire model. Math. Comput. Model. 21, 79–83. https://doi.org/10.1016/0895-7177(95)00055-7

Weber, R.O., 1991. Modelling fire spread through fuel beds. Prog. Energy Combust. Sci. 17, 67–82. https://doi.org/10.1016/0360-1285(91)90003-6

Weisman, M.L., Klemp, J.B., 1986. Characteristics of Isolated Convective Storms, in: Mesoscale Meteorology and Forecasting. American Meteorological Society, Boston, MA, pp. 331–358. https://doi.org/10.1007/978-1-935704-20-1_15

Weiss, S.J., Pyle, M.E., Janjic, Z., Bright, D.R., Kain, J.S., Dimego, G.J., 2008. Runs At Ncep : Advantages of Multiple Model Runs, in: 24th Conference on Severe Local Storms. pp. 1–11.

Werth, P., Ochoa, R., 1993. The Evaluation of Idaho Wildfire Growth Using the Haines Index. Weather Forecast. 8, 223–234. https://doi.org/10.1175/1520-0434(1993)008<0223:TEOIWG>2.0.CO;2

Whiteman, C.D., 2000. Mountain Meteorology: Fundamentals and Applications. Oxford University Press, New York.

Whitman, E., Sherren, K., Rapaport, E., 2015. Increasing daily wildfire risk in the Acadian Forest Region of Nova Scotia, Canada, under future climate change. Reg. Environ. Chang. 15, 1447–1459. https://doi.org/10.1007/s10113-014-0698-5

Wilson, G.U., 1969. Meteorological aspects of the Tumut fire experiment. Aust. Meteorol. Mag. 17, 25–47.

Wolfram, S., 1983. Statistical mechanics of cellular automata. Rev. Mod. Phys. 55, 601–644. https://doi.org/10.1103/RevModPhys.55.601

Xue, H., Gu, F., Hu, X., 2012a. Data assimilation using sequential monte carlo methods in wildfire spread simulation. ACM Trans. Model. Comput. Simul. 22, 1–25. https://doi.org/10.1145/2379810.2379816

Xue, H., Hu, X., Dahl, N., Xue, M., 2012b. Post-frontal Combustion Heat Modeling in DEVS-fire for Coupled Atmosphere-fire Simulation. Procedia Comput. Sci. 9, 302–311. https://doi.org

/10.1016/j.procs.2012.04.032

Xue, M., 2000. High-order monotonic numerical diffusion and smoothing. Mon. Weather Rev. 128, 2853–2864. https://doi.org/10.1175/1520-0493(2000)128<2853:homnda>2.0.co;2

Xue, M., Droegemeier, K.K., Wong, V., 2000. The Advanced Regional Prediction System (ARPS) - A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification. Meteorol. Atmos. Phys. 75, 161–193. https://doi.org/10.1007/s007030070003

Xue, M., Droegemeier, K.K., Wong, V., Shapiro, A., Brewster, K., Carr, F., Weber, D., Liu, Y., Wang, D., 2001. The Advanced Regional Prediction System (ARPS) - A multi-scale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications. Meteorol. Atmos. Phys. 76, 143–165. https://doi.org/10.1007/s007030170027

Xystrakis, F., Kallimanis, A.S., Dimopoulos, P., Halley, J.M., Koutsias, N., 2014. Precipitation dominates fire occurrence in Greece (1900–2010): its dual role in fuel build-up and dryness. Nat. Hazards Earth Syst. Sci. 14, 21–32. https://doi.org/10.5194/nhess-14-21-2014

Yair, Y., Lynn, B., Price, C., Kotroni, V., Lagouvardos, K., Morin, E., Mugnai, A., Llasat, M. del C., 2010. Predicting the potential for lightning activity in Mediterranean storms based on the

Weather Research and Forecasting (WRF) model dynamic and microphysical fields. J. Geophys. Res. 115, D04205. https://doi.org/10.1029/2008JD010868

Yamaguchi, T., Feingold, G., 2012. Technical note: Large-eddy simulation of cloudy boundary layer with the Advanced Research WRF model. J. Adv. Model. Earth Syst. 4, n/a-n/a. https://doi.org/10.1029/2012MS000164

Young, J.A., 2003. Static Stability, in: North, G.R., Pyle, J.A., Zhang, F. (Eds.), Encyclopedia of Atmospheric Sciences, Vol. 1-6. Elsevier, p. 2998.

Zeldovich, Y.B., 1937. The Asymptotic Laws of Freely-Ascending Convective Flows. Zh. Eksp. Teor. Fiz., Chemical Physics and Hydrodynanics 7, 1463– 1465.


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