scholarly journals Exploiting the power law distribution properties of satellite fire radiative power retrievals: A method to estimate fire radiative energy and biomass burned from sparse satellite observations

2011 ◽  
Vol 116 (D19) ◽  
Author(s):  
S. S. Kumar ◽  
D. P. Roy ◽  
L. Boschetti ◽  
R. Kremens
Keyword(s):  
Power Law ◽  
Satellite Observations ◽  
Radiative Energy ◽  
Power Law Distribution ◽  
Radiative Power ◽  
Fire Radiative Power

scholarly journals Power-law energy distributions of small-scale impulsive events on the active Sun: results from IRIS

2020 ◽  
Vol 499 (1) ◽  
pp. 1385-1394
Author(s):  
Nived Vilangot Nhalil ◽  
Chris J Nelson ◽  
Mihalis Mathioudakis ◽  
J Gerry Doyle ◽  
Gavin Ramsay
Keyword(s):  
Energy Range ◽  
Power Law ◽  
Coronal Heating ◽  
Radiative Energy ◽  
Small Scale ◽  
Data Sets ◽  
Imaging Data ◽  
Power Law Distribution ◽  
Energy Distributions ◽  
Power Law Index

ABSTRACT Numerous studies have analysed inferred power-law distributions between frequency and energy of impulsive events in the outer solar atmosphere in an attempt to understand the predominant energy supply mechanism in the corona. Here, we apply a burst detection algorithm to high-resolution imaging data obtained by the Interface Region Imaging Spectrograph to further investigate the derived power-law index, γ, of bright impulsive events in the transition region. Applying the algorithm with a constant minimum event lifetime (of either 60 s or 110 s) indicated that the target under investigation, such as Plage and Sunspot, has an influence on the observed power-law index. For regions dominated by sunspots, we always find γ < 2; however, for data sets where the target is a plage region, we often find that γ > 2 in the energy range (∼1023, ∼1026) erg. Applying the algorithm with a minimum event lifetime of three time-steps indicated that cadence was another important factor, with the highest cadence data sets returning γ > 2 values. The estimated total radiative power obtained for the observed energy distributions is typically 10–25 per cent of what would be required to sustain the corona indicating that impulsive events in this energy range are not sufficient to solve coronal heating. If we were to extend the power-law distribution down to an energy of 1021 erg, and assume parity between radiative energy release and the deposition of thermal energy, then such bursts could provide 25–50 per cent of the required energy to account for the coronal heating problem.

Remote classification of head and backfire types from MODIS fire radiative power and smoke plume observations

2005 ◽  
Vol 14 (3) ◽  
pp. 249 ◽  
Author(s):  
Alistair M. S. Smith ◽  
Martin J. Wooster
Keyword(s):  
Front Propagation ◽  
Pollutant Emissions ◽  
Radiative Energy ◽  
Burned Area ◽  
Remote Measurement ◽  
Radiative Power ◽  
Flaming Combustion ◽  
Order Of Magnitude ◽  
Fire Radiative Power

The classification of savanna fires into headfire and backfire types can in theory help in assessing pollutant emissions to the atmosphere via relative apportionment of the amounts of smouldering and flaming combustion occurring, and is also important when assessing a fire’s ecological effects. This paper provides a preliminary assessment of whether a combination of visible and thermal satellite remote sensing can be used to classify fires into head and backfire categories. Remote determination of the fire radiative power, alongside assessments of the prevailing direction of the wind (through identification of the fire-related smoke plumes) and the fire front propagation (through its relation to the previously burned area) were used to infer the fire type category and to calculate ‘radiative’ fireline intensity (FLI). The ratio of radiative FLI for the head and backfire categories was found similar to that of in situ fireline intensity measurements, but the magnitudes of the radiative FLI values were around an order of magnitude lower. This agrees with other data suggesting that a fire’s radiative energy is around an order of magnitude lower than the fuel’s theoretical heat yield, and suggests that the remote measurement of radiative FLI and classification of headfire and backfire types is a realistic proposition for large wildfire activity.

scholarly journals High Temporal Resolution Satellite Observations of Fire Radiative Power Reveal Link Between Fire Behavior and Aerosol and Gas Emissions

2020 ◽  
Vol 47 (23) ◽  
Author(s):  
Elizabeth B. Wiggins ◽  
Amber J. Soja ◽  
Emily Gargulinski ◽  
Hannah S. Halliday ◽  
R. Bradley Pierce ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Fire Behavior ◽  
Satellite Observations ◽  
High Temporal Resolution ◽  
Gas Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

Use of ordinary kriging and Gaussian conditional simulation to interpolate airborne fire radiative energy density estimates

2018 ◽  
Vol 27 (4) ◽  
pp. 228 ◽  
Author(s):  
C. Klauberg ◽  
A. T. Hudak ◽  
B. C. Bright ◽  
L. Boschetti ◽  
M. B. Dickinson ◽  
...  
Keyword(s):  
Energy Density ◽  
Air Force ◽  
Ordinary Kriging ◽  
Conditional Simulation ◽  
Radiative Energy ◽  
Density Estimates ◽  
Prescribed Burns ◽  
Radiative Power ◽  
Air Force Base ◽  
Fire Radiative Power

Fire radiative energy density (FRED, J m−2) integrated from fire radiative power density (FRPD, W m−2) observations of landscape-level fires can present an undersampling problem when collected from fixed-wing aircraft. In the present study, the aircraft made multiple passes over the fire at ~3 min intervals, thus failing to observe most of the FRPD emitted as the flame front spread. We integrated the sparse FRPD time series to obtain pixel-level FRED estimates, and subsequently applied ordinary kriging (OK) and Gaussian conditional simulation (GCS) to interpolate across data voids caused by the undersampling. We compared FRED interpolated via OK and GCS with FRED estimated independently from ground measurements of biomass consumed from five prescribed burns at Eglin Air Force Base, Florida, USA. In four of five burns considered where undersampling prevailed, OK and GCS effectively interpolated FRED estimates across the data voids, improving the spatial distribution of FRED across the burning event and its overall mean. In a fifth burn, the burning characteristics were such that undersampling did not present a problem needing to be fixed. We also determined where burning and FRPD sampling characteristics merited applying OK and CGS only to the highest FRED estimates to interpolate more accurate FRED maps.

scholarly journals Forecasting the regional fire radiative power for regularly ignited vegetation fires

2021 ◽  
Author(s):  
Tero M. Partanen ◽  
Mikhail Sofiev
Keyword(s):  
Remote Sensing ◽  
Meteorological Data ◽  
Grid Cell ◽  
Radiative Energy ◽  
Fire Intensity ◽  
High Temporal Resolution ◽  
South Central ◽  
Radiative Power ◽  
Cloud Mask ◽  
Fire Radiative Power

Abstract. This paper presents a phenomenological framework for forecasting the area-integrated fire radiative power from wildfires. In the method, a region of interest is covered with a regular grid, which cells are uniquely and independently parameterized with regard to the fire intensity according to (i) the fire incidence history, (ii) the retrospective meteorological information, and (iii) remotely-sensed high temporal resolution fire radiative power taken together with (iv) consistent cloud mask data. The parameterization is realized by fitting the predetermined functions for diurnal and annual profiles of fire radiative power to the remote-sensing observations. After the parametrization, the input for the fire radiative power forecast is the meteorological data alone, i.e., the weather forecast. The method is tested retrospectively for south-central African savannah areas with grid cell size of 1.5° × 1.5°. The input data included ECMWF ERA5 meteorological reanalysis and SEVIRI/MSG Fire Radiative Power and Cloud Mask. It has been found that in the areas with large numbers of wildfires regularly ignited on a daily basis during dry seasons from year to year, the temporal fire radiative power evolution is quite predictable, whereas the areas with irregular fire behaviour predictability was low. The predictive power of the method is demonstrated by comparing the predicted fire radiative power patterns and fire radiative energy values against the corresponding remote-sensing observations. The current method showed good skills for the considered African regions and was useful in understanding the challenges in predicting the wildfires in a more general case.

scholarly journals High Temporal Resolution Satellite Observations of Fire Radiative Power Reveal Link Between Fire Behavior and Aerosol and Gas Emissions

2020 ◽  
Author(s):  
Elizabeth Brooke Wiggins ◽  
Amber Jeanine Soja ◽  
Emily M. Gargulinski ◽  
Hannah Selene Halliday ◽  
Brad Pierce ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Fire Behavior ◽  
Satellite Observations ◽  
High Temporal Resolution ◽  
Gas Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

scholarly journals Hindcast experiments of tropospheric composition during the summer 2010 fires over western Russia

2012 ◽  
Vol 12 (9) ◽  
pp. 4341-4364 ◽  
Author(s):  
V. Huijnen ◽  
J. Flemming ◽  
J. W. Kaiser ◽  
A. Inness ◽  
J. Leitão ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Forecast Accuracy ◽  
Satellite Observations ◽  
Atmospheric Composition ◽  
Fire Emissions ◽  
Radiative Power ◽  
The Impact ◽  
Assimilation System ◽  
Fire Radiative Power

Abstract. The severe wildfires in western Russia during July–August 2010 coincided with a strong heat wave and led to large emissions of aerosols and trace gases such as carbon monoxide (CO), hydrocarbons and nitrogen oxides into the troposphere. This extreme event is used to evaluate the ability of the global MACC (Monitoring Atmospheric Composition and Climate) atmospheric composition forecasting system to provide analyses of large-scale pollution episodes and to test the respective influence of a priori emission information and data assimilation on the results. Daily 4-day hindcasts were conducted using assimilated aerosol optical depth (AOD), CO, nitrogen dioxide (NO2) and ozone (O3) data from a range of satellite instruments. Daily fire emissions were used from the Global Fire Assimilation System (GFAS) version 1.0, derived from satellite fire radiative power retrievals. The impact of accurate wildfire emissions is dominant on the composition in the boundary layer, whereas the assimilation system influences concentrations throughout the troposphere, reflecting the vertical sensitivity of the satellite instruments. The application of the daily fire emissions reduces the area-average mean bias by 63% (for CO), 60% (O3) and 75% (NO2) during the first 24 h with respect to independent satellite observations, compared to a reference simulation with a multi-annual mean climatology of biomass burning emissions. When initial tracer concentrations are further constrained by data assimilation, biases are reduced by 87, 67 and 90%. The forecast accuracy, quantified by the mean bias up to 96 h lead time, was best for all compounds when using both the GFAS emissions and assimilation. The model simulations suggest an indirect positive impact of O3 and CO assimilation on hindcasts of NO2 via changes in the oxidizing capacity. However, the quality of local hindcasts was strongly dependent on the assumptions made for forecasted fire emissions. This was well visible from a relatively poor forecast accuracy quantified by the root mean square error, as well as the temporal correlation with respect to ground-based CO total column data and AOD. This calls for a more advanced method to forecast fire emissions than the currently adopted persistency approach. The combined analysis of fire radiative power observations, multiple trace gas and aerosol satellite observations, as provided by the MACC system, results in a detailed quantitative description of the impact of major fires on atmospheric composition, and demonstrate the capabilities for the real-time analysis and forecasts of large-scale fire events.

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