scholarly journals Recent Tendency of Mongolian Wildland Fire Incidence: Analysis Using MODIS Hotspot and Weather Data

2009 ◽  
Vol 31 (1) ◽  
pp. 23-33 ◽  
Author(s):  
Murad Ahmed FARUKH ◽  
Hiroshi HAYASAKA ◽  
Odbayar MISHIGDORJ
Keyword(s):  
Wildland Fire ◽  
Weather Data ◽  
Incidence Analysis ◽  
Fire Incidence ◽  
Recent Tendency

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 High-Resolution Rapid Refresh Model Data Analytics Derived on the Open Science Grid to Assist Wildland Fire Weather Assessment

2018 ◽  
Vol 35 (11) ◽  
pp. 2213-2227 ◽  
Author(s):  
Brian K. Blaylock ◽  
John D. Horel ◽  
Chris Galli
Keyword(s):  
High Resolution ◽  
Wildland Fire ◽  
The United States ◽  
Open Science ◽  
Environmental Modeling ◽  
Weather Data ◽  
Large Set ◽  
Near Surface ◽  
Computer Resources ◽  
Open Science Grid

AbstractTerabytes of weather data are generated every day by gridded model simulations and in situ and remotely sensed observations. With this accelerating accumulation of weather data, efficient computational solutions are needed to process, archive, and analyze the massive datasets. The Open Science Grid (OSG) is a consortium of computer resources around the United States that makes idle computer resources available for use by researchers in diverse scientific disciplines. The OSG is appropriate for high-throughput computing, that is, many parallel computational tasks. This work demonstrates how the OSG has been used to compute a large set of empirical cumulative distributions from hourly gridded analyses of the High-Resolution Rapid Refresh (HRRR) model run operationally by the Environmental Modeling Center of the National Centers for Environmental Prediction. These cumulative distributions derived from a 3-yr HRRR archive are computed for seven variables, over 1.9 million grid points, and each hour of the calendar year. The HRRR cumulative distributions are used to evaluate near-surface wind, temperature, and humidity conditions during two wildland fire episodes—the North Bay fires, a wildfire complex in Northern California during October 2017 that was the deadliest and costliest in California history, and the western Oklahoma wildfires during April 2018. The approach used here illustrates ways to discriminate between typical and atypical atmospheric conditions forecasted by the HRRR model. Such information may be useful for model developers and operational forecasters assigned to provide weather support for fire management personnel.

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.

WEB SYSTEM FOR WEATHER DATA PROCESSING TO HVAC DESIGN

2018 ◽  
Author(s):  
Raama Alves ◽  
Thamires Bernardes ◽  
MANOEL ANTONIO FONSECA COSTA
Keyword(s):  
Data Processing ◽  
Weather Data ◽  
Web System
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