Turbulent kinetic energy during wildfires in the north central and north-eastern US

2010 ◽  
Vol 19 (3) ◽  
pp. 346 ◽  
Author(s):  
Warren E. Heilman ◽  
Xindi Bian
Keyword(s):  
Boundary Layer ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Quantitative Measure ◽  
Fire Weather ◽  
Fire Behaviour ◽  
Weather Index ◽  
Fire Weather Index ◽  
Near Surface ◽  
North Eastern

The suite of operational fire-weather indices available for assessing the atmospheric potential for extreme fire behaviour typically does not include indices that account for atmospheric boundary-layer turbulence or wind gustiness that can increase the erratic behaviour of fires. As a first step in testing the feasibility of using a quantitative measure of turbulence as a stand-alone fire-weather index or as a component of a fire-weather index, simulations of the spatial and temporal patterns of turbulent kinetic energy during major recent wildfire events in the western Great Lakes and north-eastern US regions were performed. Simulation results indicate that the larger wildfires in these regions of the US were associated with episodes of significant boundary-layer ambient turbulence. Case studies of the largest recent wildfires to occur in these regions indicate that the periods of most rapid fire growth were generally coincident with occurrences of the product of the Haines Index and near-surface turbulent kinetic energy exceeding a value of 15 m2 s–2, a threshold indicative of a highly turbulent boundary layer beneath unstable and dry atmospheric layers, which is a condition that can be conducive to erratic fire behaviour.

scholarly journals Climatic Variability of Near-Surface Turbulent Kinetic Energy over the United States: Implications for Fire-Weather Predictions

2013 ◽  
Vol 52 (4) ◽  
pp. 753-772 ◽  
Author(s):  
Warren E. Heilman ◽  
Xindi Bian
Keyword(s):  
United States ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Atmospheric Turbulence ◽  
Climatic Variability ◽  
Fire Behavior ◽  
The United States ◽  
Spatial And Temporal Variability ◽  
Fire Weather ◽  
Near Surface

AbstractRecent research suggests that high levels of ambient near-surface atmospheric turbulence are often associated with rapid and sometimes erratic wildland fire spread that may eventually lead to large burn areas. Previous research has also examined the feasibility of using near-surface atmospheric turbulent kinetic energy (TKEs) alone or in combination with the Haines index (HI) as an additional indicator of anomalous atmospheric conditions conducive to erratic or extreme fire behavior. However, the application of TKEs-based indices for operational fire-weather predictions in the United States on a regional or national basis first requires a climatic assessment of the spatial and temporal patterns of the indices that can then be used for testing their operational effectiveness. This study provides an initial examination of some of the spatial and temporal variability patterns across the United States of TKEs and the product of HI and TKEs (HITKEs) using data from the North American Regional Reanalysis dataset covering the 1979–2008 period. The analyses suggest that there are regional differences in the behavior of these indices and that regionally dependent threshold values for TKEs and HITKEs may be needed for their potential use as operational indicators of anomalous atmospheric turbulence conditions conducive to erratic fire behavior. The analyses also indicate that broad areas within the northeastern, southeastern, and southwestern regions of the United States have experienced statistically significant positive trends in TKEs and HITKEs values over the 1979–2008 period, with the most substantial increases in values occurring over the 1994–2008 period.

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 Boundary-layer turbulent processes and mesoscale variability represented by numerical weather prediction models during the BLLAST campaign

2016 ◽  
Vol 16 (14) ◽  
pp. 8983-9002 ◽  
Author(s):  
Fleur Couvreux ◽  
Eric Bazile ◽  
Guylaine Canut ◽  
Yann Seity ◽  
Marie Lothon ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Weather Prediction ◽  
Sensible Heat Flux ◽  
Sensible Heat ◽  
Mesoscale Variability ◽  
Near Surface ◽  
Order Of Magnitude

Abstract. This study evaluates the ability of three operational models, with resolution varying from 2.5 to 16 km, to predict the boundary-layer turbulent processes and mesoscale variability observed during the Boundary Layer Late-Afternoon and Sunset Turbulence (BLLAST) field campaign. We analyse the representation of the vertical profiles of temperature and humidity and the time evolution of near-surface atmospheric variables and the radiative and turbulent fluxes over a total of 12 intensive observing periods (IOPs), each lasting 24 h. Special attention is paid to the evolution of the turbulent kinetic energy (TKE), which was sampled by a combination of independent instruments. For the first time, this variable, a central one in the turbulence scheme used in AROME and ARPEGE, is evaluated with observations.In general, the 24 h forecasts succeed in reproducing the variability from one day to another in terms of cloud cover, temperature and boundary-layer depth. However, they exhibit some systematic biases, in particular a cold bias within the daytime boundary layer for all models. An overestimation of the sensible heat flux is noted for two points in ARPEGE and is found to be partly related to an inaccurate simplification of surface characteristics. AROME shows a moist bias within the daytime boundary layer, which is consistent with overestimated latent heat fluxes. ECMWF presents a dry bias at 2 m above the surface and also overestimates the sensible heat flux. The high-resolution model AROME resolves the vertical structures better, in particular the strong daytime inversion and the thin evening stable boundary layer. This model is also able to capture some specific observed features, such as the orographically driven subsidence and a well-defined maximum that arises during the evening of the water vapour mixing ratio in the upper part of the residual layer due to fine-scale advection. The model reproduces the order of magnitude of spatial variability observed at mesoscale (a few tens of kilometres). AROME provides a good simulation of the diurnal variability of the turbulent kinetic energy, while ARPEGE shows the right order of magnitude.

An examination of the sensitivity of numerically simulated wildfires to low-level atmospheric stability and moisture, and the consequences for the Haines Index

2002 ◽  
Vol 11 (4) ◽  
pp. 213 ◽  
Author(s):  
Mary Ann Jenkins
Keyword(s):  
High Risk ◽  
Fire Behavior ◽  
Atmospheric Stability ◽  
High Potential ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Near Surface ◽  
Severe Fire ◽  
Moisture Conditions

The Haines Index, an operational fire–weather index introduced in 1988 and based on the observed stability and moisture content of the near-surface atmosphere, has been a useful indicator of the potential for high-risk fires in low wind conditions and flat terrain. The Haines Index is of limited use, however, as a predictor of actual fire behavior. To develop a fire–weather index to predict severe or erratic wildfire behavior, an understanding of how the ambient lower-level atmospheric stability and moisture affects the growth of a wildfire is needed. This study is a first step in this process. This study investigates, through four comparative numerical simulations with a coupled wildfire–atmosphere model, the sensitivity of wildland fires to atmospheric stability and moisture, and in the process explores the correspondence between atmospheric stability and moisture, wildfire behavior, and the Haines Index. In the first three fire simulations, the model atmosphere was initially set to identical moisture but different instability conditions that correspond to Haines Indexes for low, moderate, and high potential for severe fire development. In the fourth fire simulation, the initial atmospheric and moisture conditions were for a high-risk fire Haines Index rating, but different from the initial conditions of dryness and stability of the previous experiments. The study indicates that high-risk fire development is sensitive to near-surface atmospheric stability and moisture, and that there is a range of atmospheric stability and moisture conditions that is important to the development of severe or erratic fire behavior, and that this range is within the atmospheric stability and moisture conditions represented by a Haines Index for high potential for severe fire. The analyses also suggest that there is a substantial latitude of fire behavior for fires rated as this Index, indicating that this Index should be further divided, or refined.

scholarly journals Fire prediction with logistic regression on territory of Bosnia and Herzegovina

2021 ◽  
Vol 1208 (1) ◽  
pp. 012033
Author(s):  
Mursel Musabašić ◽  
Denis Mušić ◽  
Elmir Babović
Keyword(s):  
Logistic Regression ◽  
Index System ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Supervised Machine Learning ◽  
Fire Weather ◽  
Fire Behaviour ◽  
Weather Index ◽  
Fire Weather Index ◽  
User Input

Abstract The Canadian Fire Weather Index system [1] has been used worldwide by many countries as classic approach in fire prediction. It represents system that account for the effects of fuel moisture and weather conditions on fire behaviour. It numerical outputs are based on calculation of four meteorological elements: air temperature, relative humidity, wind speed and precipitation in last 24h. In this paper meteorological data in combination with Canadian Fire Weather Index system (CFWI) components is used as input to predict fire occurrence using logistic regression model. As logistic regression is a supervised machine learning method it’s based on user input in the form of dataset. Dataset is collected using NASA GES DISC Giovanni web-based application in the form of daily area-averaged time series in period of 31.7.2010 to 31.7.2020, it’s analysed and pre-processed before it is used as input for logit model. CFWI components values are not imported but calculated in run-time based on pre-processed meteorological data. As a result of this research windows application was developed to assist fire managers and all those involved in studying the fire behaviour.

scholarly journals Spatiotemporal Dynamics of the Kinetic Energy in the Atmospheric Boundary Layer from Minisodar Measurements

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 421
Author(s):  
Alexander Potekaev ◽  
Liudmila Shamanaeva ◽  
Valentina Kulagina
Keyword(s):  
Boundary Layer ◽  
Kinetic Energy ◽  
Atmospheric Boundary Layer ◽  
Turbulent Kinetic Energy ◽  
Time Of Day ◽  
Linear Increase ◽  
Spatiotemporal Dynamics ◽  
Intermediate Layers ◽  
Subsequent Decrease ◽  
Stable Atmospheric Boundary Layer

Spatiotemporal dynamics of the atmospheric kinetic energy and its components caused by the ordered and turbulent motions of air masses are estimated from minisodar measurements of three velocity vector components and their variances within the lowest 5–200 m layer of the atmosphere, with a particular emphasis on the turbulent kinetic energy. The layered structure of the total atmospheric kinetic energy has been established. From the diurnal hourly dynamics of the altitude profiles of the turbulent kinetic energy (TKE) retrieved from minisodar data, four layers are established by the character of the altitude TKE dependence, namely, the near-ground layer, the surface layer, the layer with a linear TKE increase, and the transitive layer above. In the first layer, the most significant changes of the TKE were observed in the evening hours. In the second layer, no significant changes in the TKE values were observed. A linear increase in the TKE values with altitude was observed in the third layer. In the fourth layer, the TKE slightly increased with altitude and exhibited variations during the entire observation period. The altitudes of the upper boundaries of these layers depended on the time of day. The MKE values were much less than the corresponding TKE values, they did not exceed 50 m2/s2. From two to four MKE layers were distinguished based on the character of its altitude dependence. The two-layer structures were observed in the evening and at night (under conditions of the stable atmospheric boundary layer). In the morning and daytime, the four-layer MKE structures with intermediate layers of linear increase and subsequent decrease in the MKE values were observed. Our estimates demonstrated that the TKE contribution to the total atmospheric kinetic energy considerably (by a factor of 2.5–3) exceeded the corresponding MKE contribution.

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.

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