Fire-growth modelling using meteorological data with random and systematic perturbations

2007 ◽  
Vol 16 (2) ◽  
pp. 174 ◽  
Author(s):  
Kerry Anderson ◽  
Gerhard Reuter ◽  
Mike D. Flannigan
Keyword(s):  
Wind Speed ◽  
Growth Model ◽  
Forest Fires ◽  
Meteorological Data ◽  
Random Perturbation ◽  
Weather Forecast ◽  
Fire Spread ◽  
Fire Growth ◽  
Hourly Data ◽  
Systematic Perturbation

The focus of this investigation is to quantify the effects of perturbations in the meteorological data used in a fire-growth model. Observed variations of temperature, humidity, wind speed, and wind direction are applied as perturbations to hourly values within a simulated weather forecast to produce several forecasts. In turn, these are used by a deterministic eight-point fire-growth model to produce an ensemble of possible final fire perimeters. Two studies were conducted to assess the value of applying perturbations. In the first study, fire growth using detailed, one-minute data was compared to growth based on the more commonly used hourly data. Results showed that the detailed weather produced fire growth larger and wider than the hourly based data. By applying perturbations, variations in the flank and back-fire spread were captured by the random-perturbation model while the forward spread fell within the 20 to 30% probability prediction. A sensitivity analysis based on the observed variations showed that wind speed accounted for a 44% difference in area burned, while temperature accounted for only a 16% difference. In the second study, case studies were conducted on four observed forest fires in Wood Buffalo National Park. Results showed that daily fire-growth predictions using simulated weather forecasts over-predicted fire growth using actual hourly weather observations by 27%. Systematic-perturbation models best compensated for this with most fire growth falling within the predicted range of the models (52 out of 63 days).

A climatologically based long-range fire growth model

2010 ◽  
Vol 19 (7) ◽  
pp. 879 ◽  
Author(s):  
Kerry Anderson
Keyword(s):  
Long Range ◽  
Growth Model ◽  
Deterministic Model ◽  
Probabilistic Approach ◽  
Fire Spread ◽  
Weather Data ◽  
Fire Season ◽  
Fire Growth ◽  
Probability Of Survival ◽  
Wood Buffalo National Park

A long-range fire growth model was developed to predict the potential size or probable extent of a wildfire if it was allowed to grow unimpeded for the course of the fire season. The model combines the probabilities of fire spread and of survival to produce a probable fire extent map. Probability of spread is determined from exponential distributions of potential rates of spread for various fuel types and eight compass directions, based on 30 years of fire weather data and elliptical fire growth. Probability of survival is determined from the probable evolution of the duff moisture code over the fire season predicted with Markov chains and a duff moisture code of extinction. A comparative study was conducted within Wood Buffalo National Park. The long-term model was compared with a distribution of fire perimeters predicted by repeated simulations using an hourly based, deterministic fire growth model. The study was conducted in stages, starting with a homogeneous fuel type and weather from one station. Later, fuels and weather were introduced to determine their effects on the model. The study showed a close agreement between the long-range model and the deterministic model, supporting the probabilistic approach used by the long-range model.

scholarly journals Forest fire scenario and challenges of mitigation during fire season in North East India

2014 ◽  
Vol XL-8 ◽  
pp. 27-33
Author(s):  
K. Chakraborty ◽  
P. P Mondal ◽  
M. Chabukdhara ◽  
S. Sudhakar
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Meteorological Data ◽  
Fire Spread ◽  
Wildlife Habitat ◽  
Dew Point ◽  
Fire Season ◽  
Slope Aspect ◽  
North East ◽  
Fire Location

Forest fires are a major environmental problem in North East Region (NER) with large tracts of forest areas being affected in every season. Forest fires have become a major threat to the forest ecosystems in the region, leading to loss of timber, biodiversity, wildlife habitat and loss to other natural resources. Studies on forest fire have reported that about 50% of forest fire in the country takes place in NE region. The forest fire in NER is anthropogenic in nature. The forest fire hazard map generated based on appropriate weightage given to the factors affecting fire behavior like topography, fuel characteristic and proximity to roads, settlements and also historical fire locations helped to demarcate the fire prone zones. Whereas, during fire season the weather pattern also governs the fire spread in the given area. Therefore, various data on fuel characteristics (land use/land cover, forest type map, forest density map), topography (DEM, slope, aspect) proximity to settlement, road, waterbodies, meteorological data from AWS on wind speed, wind direction, dew point have been used for each fire point to rank its possible hazard level. Near real time fire location data obtained from MODIS/FIRMSwere used to generate the fire alerts. This work demonstrates dissemination of information in the form of maps and tables containing information of latitude and longitude of fire location, fire occurrence date, state and district name, LULC, road connectivity, slope and aspect, settlements/water bodies and meteorological data and the corresponding rating of possibility of fire spread to the respective fire control authorities during fire season.

Fire Growth in Grassland Fuels

1995 ◽  
Vol 5 (4) ◽  
pp. 237 ◽  
Author(s):  
NP Cheney ◽  
JS Gould
Keyword(s):  
Wind Speed ◽  
Fire Spread ◽  
Small Scale ◽  
Fire Growth ◽  
Spread Rate ◽  
Wind Speeds ◽  
Rate Of Spread ◽  
Steady Rate ◽  
Scale Field ◽  
Wildfire Spread

The development of grass fires originating from both point and line ignitions and burning in both open grasslands and woodlands with a grassy understorey was studied using 487 periods of fire spread and associated fuel, weather and fire-shape observations. The largest fires travelled more than 1000 m from the origin and the fastest 2-minute spread rate was over 2 m s-1. Given continuous fuel of uniform moisture content, the rate of forward spread was related to both the wind speed and the width of the head fire measured normal to the direction of fire travel. The head fire width required to achieve the potential quasi-steady rate of forward spread for the prevailing conditions increased with increasing wind speeds. These findings have important implications for relating small-scale field or laboratory measurements of fire spread to predictions of wildfire spread. The time taken to reach the potential quasi-steady rate of spread at any wind speed was highly variable. This time was strongly influenced by the frequency of changes in wind direction and the rate of development of a wide head fire.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

scholarly journals Empirical Research on Climate Warming Risks for Forest Fires: A Case Study of Grade I Forest Fire Danger Zone, Sichuan Province, China

2021 ◽  
Vol 13 (14) ◽  
pp. 7773
Author(s):  
San Wang ◽  
Hongli Li ◽  
Shukui Niu
Keyword(s):  
Wind Speed ◽  
Forest Fire ◽  
Climate Warming ◽  
Forest Fires ◽  
Meteorological Factors ◽  
Sichuan Province ◽  
Danger Zone ◽  
Climatic Regions ◽  
Area Burned ◽  
Forest Fire Danger

The Sichuan province is a key area for forest and grassland fire prevention in China. Forest resources contribute significantly not only to the biological gene pool in the mid latitudes but also in reducing the concentration of greenhouse gases and slowing down global warming. To study and forecast forest fire change trends in a grade I forest fire danger zone in the Sichuan province under climate change, the dynamic impacts of meteorological factors on forest fires in different climatic regions were explored and a model between them was established by using an integral regression in this study. The results showed that the dominant factor behind the area burned was wind speed in three climatic regions, particularly in Ganzi and A’ba with plateau climates. In Ganzi and A’ba, precipitation was mainly responsible for controlling the number of forest fires while it was mainly affected by temperature in Panzhihua and Liangshan with semi-humid subtropical mountain climates. Moreover, the synergistic effect of temperature, precipitation and wind speed was responsible in basin mid-subtropical humid climates with Chengdu as the center and the influence of temperature was slightly higher. The differential forest fire response to meteorological factors was observed in different climatic regions but there was some regularity. The influence of monthly precipitation in the autumn on the area burned in each climatic region was more significant than in other seasons, which verified the hypothesis of a precipitation lag effect. Climate warming and the combined impact of warming effects may lead to more frequent and severe fires.

Simulation of the wind fields over complex terrain with coupling of CFD and WRF

2021 ◽  
pp. 1-12
Author(s):  
Xiaoyu Luo ◽  
Yiwen Cao
Keyword(s):  
Wind Speed ◽  
Wind Field ◽  
Complex Terrain ◽  
Civil Engineering ◽  
Meteorological Data ◽  
Mass Conservation ◽  
Coupling Method ◽  
Wind Fields ◽  
Civil Structures ◽  
Civil Engineers

In the field of civil engineering, the meteorological data available usually do not have the detailed information of the wind near a certain site. However, the detailed information of the wind field during typhoon is important for the wind-resistant design of civil structures. Furthermore, the resolution of the meteorological data available by the civil engineers is too coarse to be applicable. Therefore it is meaningful to obtain the detailed information of the wind fields based on the meteorological data provided by the meteorological department. Therefore, in the present study, a one-way coupling method between WRF and CFD is adopted and a method to keep the mass conservation during the simulation in CFD is proposed. It is found that using the proposed one-way coupling method, the predicted wind speed is closer to the measurement. And the curvature of the wind streamline during typhoon is successfully reproduced.

scholarly journals Analysis of Ozone Pollution Characteristics and Influencing Factors in Northeast Economic Cooperation Region, China

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 843
Author(s):  
Jiaqi Tian ◽  
Chunsheng Fang ◽  
Jiaxin Qiu ◽  
Ju Wang
Keyword(s):  
Wind Speed ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Economic Cooperation ◽  
Ozone Pollution ◽  
Temporal And Spatial Variation ◽  
Temporal And Spatial ◽  
Pm2.5 And Pm10 ◽  
Peak Pattern ◽  
Pm2.5 Concentration

The increase in tropospheric ozone (O3) concentration has become one of the factors restricting urban development. This paper selected the important economic cooperation areas in Northeast China as the research object and collected the hourly monitoring data of pollutants and meteorological data in 11 cities from 1 January 2015 to 31 December 2019. The temporal and spatial variation trend of O3 concentration and the effects of meteorological factors and other pollutants, including CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and PM2.5 and PM10 (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) on ozone concentration were analyzed. At the same time, the variation period of O3 concentration was further analyzed by Morlet wavelet analysis. The results showed that the O3 pollution in the study area had a significant spatial correlation. The spatial distribution showed that the O3 concentration was relatively high in the south and low in the northeast. Seasonally, the O3 concentration was the highest in spring, followed by summer, and the lowest in winter. The diurnal variation of O3 concentration presented a “single peak” pattern. O3 concentration had a significant positive correlation with temperature, sunshine duration, and wind speed and a significant anticorrelation with CO, NO2, SO2, and PM2.5 concentration. Under the time scale of a = 9, 23, O3 had significant periodic fluctuation, which was similar to those of wind speed and temperature.

Statistical Analysis of Weather Effects on PM2.5

2012 ◽  
Vol 610-613 ◽  
pp. 1033-1040
Author(s):  
Wei Dai ◽  
Jia Qi Gao ◽  
Bo Wang ◽  
Feng Ouyang
Keyword(s):  
Statistical Analysis ◽  
Wind Speed ◽  
Relative Humidity ◽  
Statistical Methods ◽  
Predictive Analytics ◽  
Hypothesis Test ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Statistical Distribution ◽  
Weather Effects

Effects of weather conditions including temperature, relative humidity, wind speed, wind and direction on PM2.5 were studied using statistical methods. PM2.5 samples were collected during the summer and the winter in a suburb of Shenzhen. Then, correlations, hypothesis test and statistical distribution of PM2.5 and meteorological data were analyzed with IBM SPSS predictive analytics software. Seasonal and daily variations of PM2.5 have been found and these mainly resulted from the weather effects.

scholarly journals Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 484 ◽  
Author(s):  
Ana Firanj Sremac ◽  
Branislava Lalić ◽  
Milena Marčić ◽  
Ljiljana Dekić
Keyword(s):  
Downy Mildew ◽  
Fire Blight ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Atmospheric Model ◽  
Plasmopara Viticola ◽  
Forecasting System ◽  
Nwp Model ◽  
The Impact

The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on the occurrence of diseases in fruits and vines (BAHUS) because it contains both diseases with well-known and tested algorithms. Several comparisons were made: (1) forecasted variables for the fifth day are compared against measurements from the agrometeorological network at seven locations for three months (March, April, and May) in the period 2012–2018 to determine forecast efficacy; (2) BAHUS runs driven with observed and forecast meteorology were compared to test the impact of forecasted meteorological data; and (3) BAHUS runs were compared with field disease observations to estimate system efficacy in plant disease forecasts. The BAHUS runs with forecasted and observed meteorology were in good agreement. The results obtained encourage further development, with the goal of fully utilizing this weather-based forecasting system.

scholarly journals Performance of different methods for reference evapotranspiration estimation in Jaíba, Brazil

2018 ◽  
Vol 22 (2) ◽  
pp. 83-89 ◽  
Author(s):  
Gustavo H. da Silva ◽  
Santos H. B. Dias ◽  
Lucas B. Ferreira ◽  
Jannaylton É. O. Santos ◽  
Fernando F. da Cunha
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Standard Method ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Air Humidity ◽  
Method Performance ◽  
Relative Air Humidity ◽  
Evapotranspiration Estimation ◽  
Missing Variables

ABSTRACT FAO Penman-Monteith (FO-PM) is considered the standard method for the estimation of reference evapotranspiration (ET0) but requires various meteorological data, which are often not available. The objective of this work was to evaluate the performance of the FAO-PM method with limited meteorological data and other methods as alternatives to estimate ET0 in Jaíba-MG. The study used daily meteorological data from 2007 to 2016 of the National Institute of Meteorology’s station. Daily ET0 values were randomized, and 70% of these were used to determine the calibration parameters of the ET0 for the equations of each method under study. The remaining data were used to test the calibration against the standard method. Performance evaluation was based on Willmott’s index of agreement, confidence coefficient and root-mean-square error. When one meteorological variable was missing, either solar radiation, relative air humidity or wind speed, or in the simultaneous absence of wind speed and relative air humidity, the FAO-PM method showed the best performances and, therefore, was recommended for Jaíba. The FAO-PM method with two missing variables, one of them being solar radiation, showed intermediate performance. Methods that used only air temperature data are not recommended for the region.

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