High-resolution forest fire weather index computations using satellite remote sensing

2003 ◽  
Vol 33 (6) ◽  
pp. 1134-1143 ◽  
Author(s):  
Kyung-Soo Han ◽  
Alain A Viau ◽  
François Anctil
Keyword(s):  
Remote Sensing ◽  
Relative Humidity ◽  
Weather Forecast ◽  
Forecast Model ◽  
Meteorological Station ◽  
Fire Weather ◽  
Model Output ◽  
Weather Index ◽  
Fire Weather Index ◽  
Weather Forecast Model

Wildfires are important in regions dominated by forest, such as found in large parts of Canada. The principal objective of this study was to provide homogeneously distributed indices for the Canadian Fire Weather Index (FWI) System. The FWI was calculated using four sets of input variables: meteorological station measurements (OBS); weather forecast model output (SIM); meteorological station measurements and remote sensing estimations combined (SAT1); and weather forecast model output and remote sensing estimations combined (SAT2). Remote sensing parameterization of air temperature and relative humidity was performed. The air temperature and relative humidity reproduced showed good agreement with ground-based measurements (R2 = 0.77 and SE = 1.48°C; R2 = 0.73 and SE = 5%, respectively). For the FWI regionalized using this requirement, category SAT1 showed the best fit. Category SAT2 produced more precise results (0.09 to 2.19% of the normalized root mean square error) versus SIM.

scholarly journals Fire weather index: the skill provided by ECMWF ensemble prediction system

2020 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Claudia Vitolo ◽  
Blazej Krzeminski ◽  
Jesús San-Miguel
Keyword(s):  
Weather Forecast ◽  
Research Centre ◽  
Ensemble Prediction ◽  
Fire Weather ◽  
Prediction System ◽  
Weather Index ◽  
Fire Weather Index ◽  
Ensemble Prediction System ◽  
Medium Range Weather Forecast ◽  
Medium Range

Abstract. In the framework of the EU Copernicus program, the European Centre for Medium-range Weather Forecast (ECMWF) on behalf of the Joint Research Centre (JRC) is forecasting daily fire weather indices using its medium range ensemble prediction system. The use of weather forecast in place of local observations can extend early warnings up to 1–2 weeks allowing for greater proactive coordination of resource-sharing and mobilization within and across countries. Using one year of pre-operational service in 2017 and the fire weather index (FWI) here we assess the capability of the system globally and analyze in detail three major events in Chile, Portugal and California. The analysis shows that the skill provided by the ensemble forecast system extends to more than 10 days when compared to the use of mean climate making a case of extending the forecast range to the sub-seasonal to seasonal time scale. However accurate FWI prediction does not translate into accuracy in the forecast of fire activity globally. Indeed when all 2017 detected fires are considered, including agricultural and human induced burning, high FWI values only occurs in 50 % of the cases and only in Boreal regions. Nevertheless for very important events mostly driven by weather condition, FWI forecast provides advance warning that could be instrumental in setting up management strategies.

scholarly journals Development of a Fire Weather Index Using Meteorological Observations within the Northeast United States

2016 ◽  
Vol 55 (2) ◽  
pp. 389-402 ◽  
Author(s):  
Michael J. Erickson ◽  
Joseph J. Charney ◽  
Brian A. Colle
Keyword(s):  
Logistic Regression ◽  
Wind Speed ◽  
Relative Humidity ◽  
Logistic Regression Model ◽  
Daily Maximum ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Wildfire Occurrence ◽  
Binomial Logistic Regression

AbstractA fire weather index (FWI) is developed using wildfire occurrence data and Automated Surface Observing System weather observations within a subregion of the northeastern United States (NEUS) from 1999 to 2008. Average values of several meteorological variables, including near-surface temperature, relative humidity, dewpoint, wind speed, and cumulative daily precipitation, are compared on observed wildfire days with their climatological average (“climatology”) using a bootstrap resampling approach. Average daily minimum relative humidity is significantly lower than climatology on wildfire occurrence days, and average daily maximum temperature and average daily maximum wind speed are slightly higher on wildfire occurrence days. Using the potentially important weather variables (relative humidity, temperature, and wind speed) as inputs, different formulations of a binomial logistic regression model are tested to assess the potential of these atmospheric variables for diagnosing the probability of wildfire occurrence. The FWI is defined using probabilistic output from the preferred binomial logistic regression configuration. Relative humidity and temperature are the only significant predictors in the binomial logistic regression. The binomial logistic regression model is reliable and has more probabilistic skill than climatology using an independent verification dataset. Using the binomial logistic regression output probabilities, an FWI is developed ranging from 0 (minimum potential) to 3 (high potential) and is verified independently for two separate subdomains within the NEUS. The climatology of the FWI reproduces observed fire occurrence probabilities between 1999 and 2008 over a subdomain of the NEUS.

scholarly journals Statistical downscaling of MM5 model output to better assess avalanche threats

2010 ◽  
Vol 51 (54) ◽  
pp. 14-18 ◽  
Author(s):  
K. Srinivasan ◽  
Ajay Kumar ◽  
Jyoti Verma ◽  
Ashwagosha Ganju
Keyword(s):  
Statistical Downscaling ◽  
Winter Season ◽  
Threat Assessment ◽  
Weather Forecast ◽  
Western Himalaya ◽  
Forecast Model ◽  
Weather Data ◽  
Model Output ◽  
Mm5 Model ◽  
Weather Forecast Model

AbstractIn this study, we use MM5 weather-forecast model output and observed surface weather data from 11 stations in the western Himalaya to develop a statistical downscaling model (SDM) to better predict precipitation, 10 m wind speed and 2 m temperature. The analysis covers three consecutive winters: 2004/05, 2005/06 and 2006/07. The performance of the SDM was assessed using an independent dataset from the 2007/08 winter season. This assessment shows that the SDM technique substantially improves the forecast over specific station locations, which is important for avalanche-threat assessment.

scholarly journals Sensitivity of fire weather index to different reanalysis products in the Iberian Peninsula

2012 ◽  
Vol 12 (3) ◽  
pp. 699-708 ◽  
Author(s):  
J. Bedia ◽  
S. Herrera ◽  
J. M. Gutiérrez ◽  
G. Zavala ◽  
I. R. Urbieta ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Iberian Peninsula ◽  
Fire Spread ◽  
Early Warning Systems ◽  
Fire Danger ◽  
General Tendency ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Effective Prevention

Abstract. Wildfires are a major concern on the Iberian Peninsula, and the establishment of effective prevention and early warning systems are crucial to reduce impacts and losses. Fire weather indices are daily indicators of fire danger based upon meteorological information. However, their application in many studies is conditioned to the availability of sufficiently large climatological time series over extensive geographical areas and of sufficient quality. Furthermore, wind and relative humidity, important for the calculation of fire spread and fuel flammability parameters, are relatively scarce data. For these reasons, different reanalysis products are often used for the calculation of surrogate fire danger indices, although the agreement with those derived from observations remains as an open question to be addressed. In this study, we analyze this problem focusing on the Canadian Fire Weather Index (FWI) – and the associated Seasonal Severity Rating (SSR) – and considering three different reanalysis products of varying resolutions on the Iberian Peninsula: NCEP, ERA-40 and ERA-Interim. Besides the inter-comparison of the resulting FWI/SSR values, we also study their correspondence with observational data from 7 weather stations in Spain and their sensitivity to the input parameters (precipitation, temperature, relative humidity and wind velocity). As a general result, ERA-Interim reproduces the observed FWI magnitudes with better accuracy than NCEP, with lower/higher correlations in the coast/inland locations. For instance, ERA-Interim summer correlations are above 0.5 in inland locations – where higher FWI magnitudes are attained – whereas the corresponding values for NCEP are below this threshold. Nevertheless, departures from the observed distributions are generally found in all reanalysis, with a general tendency to underestimation, more pronounced in the case of NCEP. In spite of these limitations, ERA-Interim may still be useful for the identification of extreme fire danger events. (e.g. those above the 90th percentile value) and for the definition of danger levels/classes (with level thresholds adapted to the observed/reanalysis distributions).

Medium-range fire weather forecasts

1991 ◽  
Vol 1 (3) ◽  
pp. 159 ◽  
Author(s):  
JO Roads ◽  
K Ueyoshi ◽  
SC Chen ◽  
J Alpert ◽  
F Fujioka
Keyword(s):  
Relative Humidity ◽  
Surface Wind ◽  
Forecast Errors ◽  
Fire Weather ◽  
Weather Variables ◽  
Weather Index ◽  
Fire Weather Index ◽  
Near Surface ◽  
Medium Range Forecast ◽  
Medium Range

The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has a wet bias during the winter and a slight dry bias during the summer, which has noticeable impact on forecasts of the derived fire weather index. The FWI forecasts are also strongly affected by near-surface wind forecast errors. Still, skillful forecasts of the fire weather index as well as the other relevant fire weather variables are made out to about 10 days. These forecasts could be utilized more extensively by fire weather forecasters.

scholarly journals Combining fire radiative power observations with the fire weather index improves the estimation of fire emissions

2017 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Samuel Rémy ◽  
Florian Pappenberger ◽  
Fredrik Wetterhall
Keyword(s):  
Biomass Burning ◽  
Atmospheric Composition ◽  
Composition Analysis ◽  
Fire Weather ◽  
Atmospheric Conditions ◽  
Weather Index ◽  
Fire Weather Index ◽  
Fire Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

Abstract. The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass burning fire emission estimates from the Global Fire Assimilation System (GFAS). GFAS converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence where observed FRP from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an overestimation of fire duration, which in turn translates into an overestimation of emissions from fires. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

Fire-Weather Index and Climate Change

2020 ◽  
pp. 45-63 ◽  
Author(s):  
Zuzana Hubnerova ◽  
Sylvia Esterby ◽  
Steve Taylor
Keyword(s):  
Climate Change ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index
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