The Oklahoma Fire Danger Model: An operational tool for mesoscale fire danger rating in Oklahoma

2002 ◽  
Vol 11 (4) ◽  
pp. 183 ◽  
Author(s):  
J. D. Carlson ◽  
Robert E. Burgan ◽  
David M. Engle ◽  
Justin R. Greenfield
Keyword(s):  
Wildland Fire ◽  
Weather Conditions ◽  
Fire Danger ◽  
Rating System ◽  
Weather Data ◽  
Fuel Load ◽  
Usda Forest Service ◽  
University Of Oklahoma ◽  
Oklahoma Mesonet ◽  
Fire Danger Rating System

This paper describes the Oklahoma Fire Danger Model, an operational fire danger rating system for the state of Oklahoma (USA) developed through joint efforts of Oklahoma State University, the University of Oklahoma, and the Fire Sciences Laboratory of the USDA Forest Service in Missoula, Montana. The model is an adaptation of the National Fire Danger Rating System (NFDRS) to Oklahoma, but more importantly, represents the first time anywhere that NFDRS has been implemented operationally using hourly weather data from a spatially dense automated weather station network (the Oklahoma Mesonet). Weekly AVHRR satellite imagery is also utilized for live fuel moisture and fuel load calculations. The result is a near-real-time mesoscale fire danger rating system to 1-km resolution whose output is readily available on the World Wide Web (http://agweather.mesonet.ou.edu/models/fire). Examples of output from 25 February 1998 are presented.The Oklahoma Fire Danger Model, in conjunction with other fire-related operational tools, has proven useful to the wildland fire management community in Oklahoma, for both wildfire anticipation and suppression and for prescribed fire activities. Instead of once-per-day NFDRS information at two to three sites, the fire manager now has statewide fire danger information available at 1-km resolution at up to hourly intervals, enabling a quicker response to changing fire weather conditions across the entire state.

scholarly journals Prototipe Sistem Peringatan Dini Kebakaran Hutan Berbasis Parameter Cuaca

Wahana Fisika ◽  
2017 ◽  
Vol 2 (2) ◽  
pp. 65
Author(s):  
Hapsoro Agung Nugroho ◽  
Chinthya Margaretta S
Keyword(s):  
Wind Speed ◽  
Early Warning ◽  
Forest Fires ◽  
Fire Danger ◽  
World Meteorological Organization ◽  
Rating System ◽  
Weather Data ◽  
Fuel Moisture ◽  
Fire Danger Rating System ◽  
Sd Card

Sistem peringatan dini kebakaran hutan memiliki peranan penting untuk mengendalikan secara dini kerusakan hutan. Badan Meteorologi Klimatologi dan Geofisika mempunyai tugas pokok, salah satunya yaitu memberikan peringatan dini kebakaran hutan menggunakan metode Fire Danger Rating System (FDRS) dimana data parameter cuaca sebagai masukan, masih terbatas pada lokasi tertentu. Penelitian ini merancang dan membangun prototipe yang menghasilkan skala Fine Fuel Moisture Code (FFMC) sebagai tingkat kemudahan terjadinya kebakaran di suatu lokasi. Perancangan prototipe ini menggunakan mikrokontroler ATMega328, sensor suhu dan kelembaban udara DHT22, penakar hujan jenis tipping bucket, sensor arah dan kecepatan angin JL_FS2, dan micro SD Card sebagai penyimpan data. Hasil kalibrasi sensor menunjukkan adanya selisih nilai sensor yang telah memenuhi nilai toleransi dari World Meteorological Organization (WMO). Pengujian setiap sensor menghasilkan nilai standar deviasi kurang dari 2.5 dengan rata- rata selisih pada sensor suhu +0.5oC, kelembaban relatif +6%, dan kecepatan angin +2 m/s. Setiap data yang diolah dapat ditampilkan dan disimpan secara otomatis oleh sistem. Sistem menampilkan secara realtime dan memberikan informasi peringatan dini kebakaran hutan. Kata Kunci   :  Kebakaran Hutan; FDRS; FFMC; Tipping BucketForest fire early warning system has an important role for the control of early damage to the forest. Indonesia Agency of Meteorology Climatology and Geophysics had a duty, one that is giving early warning forest fires using the method of Fire Danger Rating System (FDRS) where weather data as the input parameters, are still limited on site certain. The study design and build a prototype that generates scale Fine Fuel Moisture Code (FFMC) as the level of ease the onset of fire in any given location. This prototype design using the ATMega328 microcontroller, sensor temperature and humidity DHT22, tipping bucket type of rain gauge, direction and wind speed sensor JL_FS2, and micro SD Card as the data storage. The results showed a difference in sensor calibration value of sensor meets the tolerance values of the World Meteorological Organization (WMO). Test each sensor shows a value less than 2.5 standard deviation by the average difference in temperature sensors + 0.5 oC, + 6% relative humidity, and wind speed + 2 m/s. Data can be displayed and stored automatically by the system. The system displays in realtime and provide early warning information forest fires.           Keywords  :  Forest Fire; FDRS; FFMC; Tipping Bucket

Is Arson Associated with Severe Fire Weather in Southern California?

1991 ◽  
Vol 1 (2) ◽  
pp. 97 ◽  
Author(s):  
R Mees
Keyword(s):  
Southern California ◽  
Wildland Fire ◽  
Fire Suppression ◽  
Weather Conditions ◽  
Fire Danger ◽  
Weather Data ◽  
Fire Weather ◽  
The Public ◽  
Severe Fire ◽  
Large Fires

Under severe fire weather conditions arson is believed to be the primary cause of large wildland fires in southern California. Wildland fire suppression personnel and the public use the the expression "This weather brings out the arsonists" to indicate their awareness of the high potential for large arson-caused fires under these conditions. To determine the accuracy of this statement, fire occurrence and weather data were analyzed for four southern California National Forests for a 10-year period (1975–1984). The results showed that the proportion of arson and non-arson person-caused fires remained the same under most fire-danger conditions; however, a much higher percentage of arson fires became large fires when fire danger was severe. Furthermore, the timing of the arsonist contributed to the frequent occurrence of large arson fires. The data presented here refute the idea that most arson fires occur under severe weather conditions and at the same time-validate the utility of maintaining arson prevention programs during most weather conditions.

scholarly journals Development of Google Earth Engine Fire Weather Index Calculator for Indonesian Fire Danger Rating System

2021 ◽  
Vol 936 (1) ◽  
pp. 012040
Author(s):  
J S Matondang ◽  
H Sanjaya ◽  
R Arifandri
Keyword(s):  
Google Earth ◽  
Fire Danger ◽  
Rating System ◽  
Weather Data ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Weather Stations ◽  
Google Earth Engine ◽  
Fire Danger Rating System

Abstract Tropical peatlands make up almost ten percent of the land surface in Indonesia, making peat fires detrimental not only for global atmospheric carbon levels, but also to public health and socioeconomic activities in the region. Indonesian Fire Danger Rating System (FDRS) was developed based on the Canadian Forest Fire Weather Index System (CFFWIS), using three different fuel codes and three indices representing fire behaviour. Daily Fire Weather Index (FWI) calculation is done by the Meteorological Climatological and Geophysical Agency (BMKG) with data from its synoptic weather stations network. Distribution of such weather stations are sparse, therefore this paper reports on the development of Fire Weather Index calculator on Google Earth Engine, using high resolution weather data, provided by weather model and remote-sensing open datasets. The resulting application is capable of generating daily maps of FWI components to be used by the Indonesian Fire Danger Rating System.

Evaluation of fire danger rating indexes using logistic regression and percentile analysis

2003 ◽  
Vol 12 (2) ◽  
pp. 213 ◽  
Author(s):  
Patricia L. Andrews ◽  
Don O. Loftsgaarden ◽  
Larry S. Bradshaw
Keyword(s):  
Logistic Regression ◽  
Fire Danger ◽  
Rating System ◽  
Weather Data ◽  
Fire Season ◽  
Rating Systems ◽  
Fuel Model ◽  
Fuel Models ◽  
Application Potential ◽  
Fire Danger Rating System

Methods are presented for analysing the relationship between fire danger rating indexes and fire activity as a means of evaluating the performance of fire danger rating systems. Percentile analysis is used to examine the data itself; logistic regression provides a means for statistical analysis. Ranking of selected items indicates indexes that deserve further assessment using subjective considerations. Methods rely on generally available data: the fire danger index for every day in the fire season, fire discovery date, and final fire size. For logistic regression analysis, the independent variable is the index, and dependent variables are fire-day, large-fire-day, and multiple-fire-day. Data analysis methods have been incorporated into the FireFamily Plus computer program for easy application. Potential uses of the analysis include choosing the most appropriate fire danger index and fuel model for an area, evaluating proposed changes to a fire danger rating system, and assessing the performance of a system in a location other than that for which it was designed. As a demonstration, this technique was applied to evaluation of several indexes and fuel models of the U.S. National Fire Danger Rating System on the Tonto National Forest in Arizona, USA, using fire and weather data for 1974–2001.

Preliminary study on the validation of ECMWF with the ground-based data for the Indonesia Fire Danger Rating System (Ina-FDRS)

2021 ◽  
Author(s):  
Reni Sulistyowati ◽  
Fanny Meliani ◽  
Marina C. G. Frederik ◽  
Rizki Amaliyah ◽  
Zilda Dona Okta Permata ◽  
...  
Keyword(s):  
Fire Danger ◽  
Rating System ◽  
Preliminary Study ◽  
Fire Danger Rating System

The OFIDIA Fire Danger Rating System

2015 ◽  
Author(s):  
M. Mirto ◽  
A. Mariello ◽  
A. Nuzzo ◽  
M. Mancini ◽  
A. Raolil ◽  
...  
Keyword(s):  
Fire Danger ◽  
Rating System ◽  
Fire Danger Rating System

Australian grassland fire danger using inputs from the GRAZPLAN grassland simulation model

2010 ◽  
Vol 19 (3) ◽  
pp. 338 ◽  
Author(s):  
A. Malcolm Gill ◽  
Karen J. King ◽  
Andrew D. Moore
Keyword(s):  
Perennial Grass ◽  
Fire Danger ◽  
Weather Data ◽  
Fuel Load ◽  
Fire Intensity ◽  
Shoot Biomass ◽  
Biological Variables ◽  
Eastern Australia ◽  
Grass Model ◽  
Grassland Fire

Assessing and broadcasting the Fire Danger Rating each day of the fire season is an important activity in fire-prone nations. For grasslands in Australia, grass curing and biomass are biological variables that are not usually archived yet as inputs, along with weather data, to the calculation of Grassland Fire Danger Index (GFDI) and potential fire intensity. To assess past changes in the index, the biological inputs for GFDI for Canberra in south-eastern Australia were obtained using a pasture simulator, GRAZPLAN. Shoot biomass (including leaf litter) and grass curing were modelled using three contrasting pasture models (exotic annual, exotic perennial and native perennial) in order to calculate two variants of McArthur’s GFDI Mark 4 (the original and a modified version which includes fuel load); values were either capped at 100 as in the original (the ‘worst possible’ condition) or left open-ended. GFDI, and the potential fire intensity for fires burning with the wind each afternoon during a 54-year period were calculated. The native perennial grass model gave contrasting results to those from the exotic perennial grass model, whereas the annual grass model usually was intermediate in behaviour. GRAZPLAN outputs allow not only retrospective examination, but also provide a basis for predicting potential fire danger and behaviour as a result of climate change.

Application of the Nelson model to four timelag fuel classes using Oklahoma field observations: model evaluation and comparison with National Fire Danger Rating System algorithms

2007 ◽  
Vol 16 (2) ◽  
pp. 204 ◽  
Author(s):  
J. D. Carlson ◽  
Larry S. Bradshaw ◽  
Ralph M. Nelson ◽  
Randall R. Bensch ◽  
Rafal Jabrzemski
Keyword(s):  
Fire Danger ◽  
Rating System ◽  
Field Observations ◽  
Fuel Moisture ◽  
Next Generation ◽  
Nelson Model ◽  
Weather Information ◽  
Weather Observations ◽  
Fire Danger Rating System ◽  
Better Than

The application of a next-generation dead-fuel moisture model, the ‘Nelson model’, to four timelag fuel classes using an extensive 21-month dataset of dead-fuel moisture observations is described. Developed by Ralph Nelson in the 1990s, the Nelson model is a dead-fuel moisture model designed to take advantage of frequent automated weather observations. Originally developed for 10-h fuels, the model is adaptable to other fuel size classes through modification of the model’s fuel stick parameters. The algorithms for dead-fuel moisture in the National Fire Danger Rating System (NFDRS), on the other hand, were originally developed in the 1970s, utilise once-a-day weather information, and were designed to estimate dead-fuel moisture for mid-afternoon conditions. Including all field observations over the 21-month period, the Nelson model showed improvement over NFDRS for each size fuel size class, with r2 values ranging from 0.51 (1000-h fuels) to 0.79 (10-h fuels). However, for observed fuel moisture at or below 30%, the NFDRS performed better than the Nelson model for 1-h fuels and was about the same accuracy as the Nelson for 10-h fuels. The Nelson model is targeted for inclusion in the next-generation NFDRS.

A Method to Initialize the Keetch-Byram Drought Index

1993 ◽  
Vol 8 (4) ◽  
pp. 109-115 ◽  
Author(s):  
Robert E. Burgan
Keyword(s):  
Drought Index ◽  
Fire Danger ◽  
Rating System ◽  
Dead Vegetation ◽  
Fire Danger Rating System

Abstract The 1988 National Fire Danger Rating System implements the Keetch-Byram Drought Index. This index is output both as an estimator of drought in its own right and is used to modify fire danger calculations to account for deep drying of dead vegetation and duff. A method initializes this index for those fire danger rating stations that do not run the National Fire Danger Rating System all year long. West J. Appl. For. 8(4)109-115.

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