scholarly journals Development of a Global Fire Weather Database

2015 ◽  
Vol 15 (6) ◽  
pp. 1407-1423 ◽  
Author(s):  
R. D. Field ◽  
A. C. Spessa ◽  
N. A. Aziz ◽  
A. Camia ◽  
A. Cantin ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Large Scale ◽  
Prediction Models ◽  
Dry Season ◽  
Weather Data ◽  
Data Sets ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Mato Grosso ◽  
Global Database

Abstract. The Canadian Forest Fire Weather Index (FWI) System is the mostly widely used fire danger rating system in the world. We have developed a global database of daily FWI System calculations, beginning in 1980, called the Global Fire WEather Database (GFWED) gridded to a spatial resolution of 0.5° latitude by 2/3° longitude. Input weather data were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and two different estimates of daily precipitation from rain gauges over land. FWI System Drought Code calculations from the gridded data sets were compared to calculations from individual weather station data for a representative set of 48 stations in North, Central and South America, Europe, Russia, Southeast Asia and Australia. Agreement between gridded calculations and the station-based calculations tended to be most different at low latitudes for strictly MERRA-based calculations. Strong biases could be seen in either direction: MERRA DC over the Mato Grosso in Brazil reached unrealistically high values exceeding DC = 1500 during the dry season but was too low over Southeast Asia during the dry season. These biases are consistent with those previously identified in MERRA's precipitation, and they reinforce the need to consider alternative sources of precipitation data. GFWED can be used for analyzing historical relationships between fire weather and fire activity at continental and global scales, in identifying large-scale atmosphere–ocean controls on fire weather, and calibration of FWI-based fire prediction models.

scholarly journals Development of a global fire weather database for 1980–2012

2014 ◽  
Vol 2 (10) ◽  
pp. 6555-6597 ◽  
Author(s):  
R. D. Field ◽  
A. C. Spessa ◽  
N. A. Aziz ◽  
A. Camia ◽  
A. Cantin ◽  
...  
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Weather Data ◽  
Fire Weather ◽  
Fire Weather Index ◽  
North Central ◽  
Global Database ◽  
Rain Gauges ◽  
The Tropics ◽  
Modern Era

Abstract. The Canadian Fire Weather Index (FWI) System is the mostly widely used fire danger rating system in the world. We have developed a global database of daily, gridded FWI System calculations from 1980–2012. Input weather data were obtained from the NASA Modern Era Retrospective-Analysis for Research, and two different estimates of daily precipitation from rain gauges over land. FWI System Drought Code (DC) calculations from the gridded datasets were compared to calculations from individual weather station data for a representative set of stations in North, Central and South America, Europe, Russia, Southeast Asia and Australia. Agreement between gridded calculations and the station-based calculations tended to be most different over the tropics for strictly MERRA-based calculations. This dataset can be used for analyzing historical relationships between fire weather and fire activity at continental and global scales, in identifying large-scale atmosphere–ocean controls on fire weather, and calibration of FWI-based fire prediction models.

scholarly journals Impact of More Frequent Observations on the Understanding of Tasmanian Fire Danger

2011 ◽  
Vol 50 (8) ◽  
pp. 1617-1626 ◽  
Author(s):  
Paul Fox-Hughes
Keyword(s):  
Fire Danger ◽  
Weather Data ◽  
High Temporal Resolution ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Diurnal Variability ◽  
Weather Events ◽  
Forest Fire Danger ◽  
Synoptic Observations ◽  
Climate Studies

AbstractHalf-hourly airport weather observations have been used to construct high-temporal-resolution datasets of McArthur Mark V forest fire danger index (FFDI) values for three locations in Tasmania, Australia, enabling a more complete understanding of the range and diurnal variability of fire weather. Such an understanding is important for fire management and planning to account for the possibility of weather-related fire flare ups—in particular, early in a day and during rapidly changing situations. In addition, climate studies have hitherto generally been able to access only daily or at best 3-hourly weather data to generate fire-weather index values. Comparison of FFDI values calculated from frequent (subhourly) observations with those derived from 3-hourly synoptic observations suggests that large numbers of significant fire-weather events are missed, even by a synoptic observation schedule, and, in particular, by observations made at 1500 LT only, suggesting that many climate studies may underestimate the frequencies of occurrence of fire-weather events. At Hobart, in southeastern Tasmania, only one-half of diurnal FFDI peaks over a critical warning level occur at 1500 LT, with the remainder occurring across a broad range of times. The study reinforces a perception of pronounced differences in the character of fire weather across Tasmania, with differences in diurnal patterns of variability evident between locations, in addition to well-known differences in the ranges of peak values observed.

scholarly journals Seamless climate change projections and seasonal predictions for bushfires in Australia

2020 ◽  
Vol 70 (1) ◽  
pp. 120
Author(s):  
Andrew J. Dowdy
Keyword(s):  
Climate Change ◽  
Long Range ◽  
Time Scales ◽  
Weather Conditions ◽  
Weather Data ◽  
Future Climate Change ◽  
Data Sets ◽  
Fire Weather ◽  
Climate Change Projections ◽  
Different Time Scales

Spatio-temporal variations in fire weather conditions are presented based on various data sets, with consistent approaches applied to help enable seamless services over different time scales. Recent research on this is shown here, covering climate change projections for future years throughout this century, predictions at multi-week to seasonal lead times and historical climate records based on observations. Climate projections are presented based on extreme metrics with results shown for individual seasons. A seasonal prediction system for fire weather conditions is demonstrated here as a new capability development for Australia. To produce a more seamless set of predictions, the data sets are calibrated based on quantile-quantile matching for consistency with observations-based data sets, including to help provide details around extreme values for the model predictions (demonstrating the quantile matching for extremes method). Factors influencing the predictability of conditions are discussed, including pre-existing fuel moisture, large-scale modes of variability, sudden stratospheric warmings and climate trends. The extreme 2019–2020 summer fire season is discussed, with examples provided on how this suite of calibrated fire weather data sets was used, including long-range predictions several months ahead provided to fire agencies. These fire weather data sets are now available in a consistent form covering historical records back to 1950, long-range predictions out to several months ahead and future climate change projections throughout this century. A seamless service across different time scales is intended to enhance long-range planning capabilities and climate adaptation efforts, leading to enhanced resilience and disaster risk reduction in relation to natural hazards.

Fire weather thresholds and burnt area in Portugal

2021 ◽  
Author(s):  
Tomás Calheiros ◽  
Akli Benali ◽  
João Neves Silva ◽  
Mário Pereira ◽  
João Pedro Nunes
Keyword(s):  
Land Use ◽  
Cluster Analysis ◽  
Fire Suppression ◽  
Cumulative Effect ◽  
Weather Conditions ◽  
Weather Data ◽  
Fire Weather ◽  
Burnt Area ◽  
Fire Weather Index ◽  
Large Fires

<p>Fire strongly depends on the weather, especially in Mediterranean climate regions with rainy winters but dry and hot summers, as in Portugal. Fire weather indices are commonly used to assess the current and/or cumulative effect of weather conditions on fuel moisture and fire behaviour. The Daily Severity Rating (DSR) is a numeric rating of the difficulty of controlling fires, based on the Canadian Fire Weather Index (FWI), developed to accurately assess the expected efforts required for fire suppression. Recently, the 90th percentile of DSR (90pDSR) was identified as a good indicator of extreme fire weather and well related to the burnt area in some regions of the Iberian Peninsula. The purposes of this work were: 1) to verify if this threshold is adequate for all continental Portugal; 2) to identify and characterize local variations of this threshold, at a higher spatial resolution; and, 3) to analyse other variables that can explain this spatial heterogeneity.</p><p>We used fire data from the Portuguese Institute for the Conservation of Nature and Forests and weather data from ERA5, for the 2001 – 2019 study period. We also used the Land Use and Occupation Charter for 2018 (COS2018), provided by the Directorate-General for Territory, to assess land use and cover in Portugal. The meteorological variables to compute the DSR are air temperature, relative humidity, wind speed and daily accumulated precipitation, at 12 UTC. DSR percentiles (DSRp) were computed for summer period (between 15<sup>th</sup> May and 31<sup>st</sup> October) and combined with large (>100 ha) burnt areas (BA), with the purpose to identify which DSRp value is responsible of a large amount of BA (80 or 90%). Cluster analysis was performed using the relation between DSRp and BA, in each municipality of Continental Portugal.</p><p>Results reveal that the 90pDSR is an adequate threshold for the entire territory. However, at the municipalities’ level, some important differences appear between DSRp thresholds that explain 90 and 80% of the total BA. Cluster analysis shows that these differences justified the existence of several statistically significant clusters. Generally, municipalities where large fires take place in high or very high DSRp are located in north and central coastal areas, Serra da Estrela, Serra de Montejunto and Algarve. In contrast, clusters where large fires where registered with low DSRp appear in northern and central hinterland. COS2018 data was assessed to analyse if and how the vegetation cover type influences the clusters’ distribution and affects the relationship between DSRp and total BA. Preliminary results expose a possible vegetation influence, especially between forests and shrublands.</p>

scholarly journals Comparison of methods for spatial interpolation of fire weather in Alberta, Canada

2017 ◽  
Vol 47 (12) ◽  
pp. 1646-1658 ◽  
Author(s):  
P. Jain ◽  
M.D. Flannigan
Keyword(s):  
Spatial Interpolation ◽  
Weather Data ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Interpolation Methods ◽  
Geostatistical Methods ◽  
Geostatistical Interpolation ◽  
Distance Weighting ◽  
Inverse Distance

Spatial interpolation of fire weather variables from station data allow fire danger indices to be mapped continuously across the landscape. This information is crucial to fire management agencies, particularly in areas where weather data are sparse. We compare the performance of several standard interpolation methods (inverse distance weighting, spline, and geostatistical interpolation methods) for estimating output from the Canadian Fire Weather Index (FWI) system at unmonitored locations. We find that geostatistical methods (kriging) generally outperform the other methods, particularly when elevation is used as a covariate. We also find that interpolation of the input meteorological variables and the previous day’s moisture codes to unmonitored locations followed by calculation of the FWI output variables is preferable to first calculating the FWI output variables and then interpolating, in contrast to previous studies. Alternatively, when the previous day’s moisture codes are estimated from interpolated weather, rather than directly interpolated, errors can accumulate and become large. This effect is particularly evident for the duff moisture code and drought moisture code due to their significant autocorrelation.

Impact of climate change on area burned in Alberta's boreal forest

2007 ◽  
Vol 16 (2) ◽  
pp. 153 ◽  
Author(s):  
Cordy Tymstra ◽  
Mike D. Flannigan ◽  
Owen B. Armitage ◽  
Kimberley Logan
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
Climate Model ◽  
Weather Data ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Area Burned ◽  
Northern Alberta

Eight years of fire weather data from sixteen representative weather stations within the Boreal Forest Natural Region of Alberta were used to compile reference weather streams for low, moderate, high, very high and extreme Fire Weather Index (FWI) conditions. These reference weather streams were adjusted to create daily weather streams for input into Prometheus – the Canadian Wildland Fire Growth Model. Similar fire weather analyses were completed using Canadian Regional Climate Model (CRCM) output for northern Alberta (174 grid cells) to generate FWI class datasets (temperature, relative humidity, wind speed, Fine Fuel Moisture Code, Duff Moisture Code and Drought Code) for 1 ×, 2 × and 3 × CO2 scenarios. The relative differences between the CRCM scenario outputs were then used to adjust the reference weather streams for northern Alberta. Area burned was calculated for 21 fires, fire weather classes and climate change scenarios. The area burned estimates were weighted based on the historical frequency of area burned by FWI class, and then normalized to derive relative area burned estimates for each climate change scenario. The 2 × and 3 × CO2 scenarios resulted in a relative increase in area burned of 12.9 and 29.4% from the reference 1 × CO2 scenario.

scholarly journals Large Scale Fault Data Analysis and OSS Reliability Assessment Based on Quantification Method of the First Type

2020 ◽  
Vol 2 (4) ◽  
pp. 436-452
Author(s):  
Yoshinobu Tamura ◽  
Shigeru Yamada
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Reliability Assessment ◽  
Sensor Data ◽  
Weather Data ◽  
Data Sets ◽  
Log Data ◽  
Customer Data ◽  
Server Side ◽  
Quantification Method

Various big data sets are recorded on the server side of computer system. The big data are well defined as a volume, variety, and velocity (3V) model. The 3V model has been proposed by Gartner, Inc. as a first press release. 3V model means the volume, variety, and velocity in terms of data. The big data have 3V in well balance. Then, there are various categories in terms of the big data, e.g., sensor data, log data, customer data, financial data, weather data, picture data, movie data, and so on. In particular, the fault big data are well-known as the characteristic log data in software engineering. In this paper, we analyze the fault big data considering the unique features that arise from big data under the operation of open source software. In addition, we analyze actual data to show numerical examples of reliability assessment based on the results of multiple regression analysis well-known as the quantification method of the first type.

scholarly journals Deciphering Active Wildfires in the Southwestern USA Using Topological Data Analysis

Climate ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 135
Author(s):  
Hannah Kim ◽  
Christian Vogel
Keyword(s):  
Data Analysis ◽  
American Southwest ◽  
Topological Data Analysis ◽  
Weather Data ◽  
Fire Weather ◽  
Weather Variables ◽  
Fire Weather Index ◽  
Active Fire ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Topological Data

The recent droughts in the American Southwest have led to increasing risks of wildfires, which pose multiple threats to the regional and national economy and security. Wildfires cause serious air quality issues during dry seasons and can increase the number of mud and landslides in any subsequent rainy seasons. However, while wildfires are often correlated with warm and dry climates, this relationship is not linear, implying that there may be other factors influencing these fires. The objective of this study was to detect and classify any nonlinear patterns in weather data by applying Topological Data Analysis (TDA) to various weather variables, such as temperature, relative humidity, and precipitation, and the five most and least intense summer fire seasons as determined by the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products. In addition to TDA, persistence diagrams and frequency plots were also used to compare fire seasons and regions in the American Southwest. Active fire seasons were more likely to have a significant correlation between the weather variables and wildfires, the Fire Weather Index (FWI) alone was not an accurate predictor for wildfires in California and Nevada, and fire weather is highly dependent upon the region and season.

Calibration of the Fire Weather Index over Mediterranean Europe based on fire activity retrieved from MSG satellite imagery

2014 ◽  
Vol 23 (7) ◽  
pp. 945 ◽  
Author(s):  
Carlos C. DaCamara ◽  
Teresa J. Calado ◽  
Sofia L. Ermida ◽  
Isabel F. Trigo ◽  
Malik Amraoui ◽  
...  
Keyword(s):  
Land Surface ◽  
Relative Number ◽  
Fire Risk ◽  
Fire Danger ◽  
Weather Data ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Fire Activity ◽  
Mediterranean Europe

Here we present a procedure that allows the operational generation of daily maps of fire danger over Mediterranean Europe. These are based on integrated use of vegetation cover maps, weather data and fire activity as detected by remote sensing from space. The study covers the period of July–August 2007 to 2009. It is demonstrated that statistical models based on two-parameter generalised Pareto (GP) distributions adequately fit the observed samples of fire duration and that these models are significantly improved when the Fire Weather Index (FWI), which rates fire danger, is integrated as a covariate of scale parameters of GP distributions. Probabilities of fire duration exceeding specified thresholds are then used to calibrate FWI leading to the definition of five classes of fire danger. Fire duration is estimated on the basis of 15-min data provided by Meteosat Second Generation (MSG) satellites and corresponds to the total number of hours in which fire activity is detected in a single MSG pixel during one day. Considering all observed fire events with duration above 1h, the relative number of events steeply increases with classes of increasing fire danger and no fire activity was recorded in the class of low danger. Defined classes of fire danger provide useful information for wildfire management and are based on the Fire Risk Mapping product that is being disseminated on a daily basis by the EUMETSAT Satellite Application Facility on Land Surface Analysis.

scholarly journals An assessment of fire occurrence regime and performance of Canadian fire weather index in south central Siberian boreal region

2014 ◽  
Vol 2 (7) ◽  
pp. 4711-4742 ◽  
Author(s):  
T. Chu ◽  
X. Guo
Keyword(s):  
Large Scale ◽  
Weather Conditions ◽  
Reanalysis Data ◽  
Fire Weather ◽  
Fire Occurrence ◽  
Weather Index ◽  
Fire Weather Index ◽  
Central Siberia ◽  
South Central ◽  
Fire Activity

Abstract. Wildfire is the dominant natural disturbance in Eurasian boreal region, which acts as a major driver of the global carbon cycle. An effectiveness of wildfire management requires suitable tools for fire prevention and fire risk assessment. This study aims to investigate fire occurrence patterns in relation to fire weather conditions in the remote south central Siberia region. The Canadian Fire Weather Index derived from large-scale meteorological reanalysis data was evaluated with respects to fire regimes during 14 consecutive fire seasons in south central Siberian environment. All the fire weather codes and indices, including the Fine Fuel Moisture Code (FFMC), the Duff Moisture Code (DMC), the Drought Code (DC), the Buildup Index (BUI), the Initial Spread Index (ISI), and the Fire Weather Index (FWI), were highly reflected inter-annual variation of fire activity in south central Siberia. Even though human-caused fires were major events in Russian boreal forest including south central Siberia, extreme fire years were strongly correlated with ambient weather conditions (e.g. Arctic Oscillation, air temperature, relative humidity and wind), showing by in-phase (or positive linear relationship) and significant wavelet coherence between fire activity and DMC, ISI, BUI, and FWI. Time series observation of 14 fire seasons showed that there was an average of about 3 months lags between the peaks of fire weather conditions and fire activity, which should take into account when using coarse scale fire weather indices in the assessment of fire danger in the study area. The results are expected to contribute to a better reconstruction and prediction of fire activity using large-scale reanalysis data in remote regions in which station data are very few.

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