The use of satellite observations of fire radiative power to estimate the availabilities (activity patterns) of pyrometallurgical smelters

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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Characterising vegetation fuel moisture conditions from microwave satellite observations for fire danger prediction at continental to global scales

10.5194/egusphere-egu2020-10860 ◽

2020 ◽

Author(s):

Matthias Forkel ◽

Niels Andela ◽

Wouter A. Dorigo ◽

Markus Drüke ◽

Sandy P. Harrison ◽

...

Keyword(s):

Satellite Observations ◽

Fire Danger ◽

Fuel Moisture ◽

Fire Occurrence ◽

Fire Dynamics ◽

Radiative Power ◽

Moisture Conditions ◽

Fuel Moisture Content ◽

The Relationship ◽

Fire Radiative Power

Spatial patterns and temporal changes in live fuel moisture content (LFMC) have been intensively estimated from satellite observations in the optical domain of the electromagnetic spectrum. Such estimates are valuable to predict regional to local variations in fire danger (Yebra et al., 2018). However, optical satellite measurements saturate fast in dense canopies and are generally hampered during cloud cover. Microwave satellite observations can penetrate clouds and the canopy (dependent on the wavelength) and hence have been intensively used to derive surface soil moisture (SSM) or vegetation optical depth (VOD), which is a proxy for vegetation water content (Moesinger et al., 2019). However, the relationship of microwave VOD to LFMC and the predictive capabilities of VOD for fire dynamics have not yet been investigated at large scales. Here we aim to assess how VOD reflects changes in LFMC and the sensitivity of VOD to different properties of fire dynamics such as fire occurrence, size, burned area, and fire radiative power.We compared VOD in different microwave bands (Ku-, X-, and C-band) from the VODCA dataset (Moesinger et al., 2019) with LFMC from MODIS retrievals (Yebra et al., 2018). Our results demonstrate that VOD and LFMC are moderately to highly correlated but the strength and shape of the relationship depends on land cover type. In a preliminary analysis, we then predicted the probability of fire occurrence (Andela et al., 2019) and fire radiative power (Kaiser et al., 2012) from VOD, SSM, and climate data using the random forest machine learning approach. The initial results show that VOD is a skilful predictor for continental-scale fire dynamics. Furthermore, our results suggest that the combination of LFMC from optical satellites with microwave SSM and VOD might allow to comprehensively estimate ecosystem fuel moisture conditions. Hence microwave satellite observations will be valuable for the development of integrated fire danger prediction systems.&#160;ReferencesAndela, N., Morton, D.C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., Werf, G.R. van der, Randerson, J.T., 2019. The Global Fire Atlas of individual fire size, duration, speed and direction. Earth Syst. Sci. Data 11, 529&#8211;552. https://doi.org/10.5194/essd-11-529-2019Kaiser, J.W., Heil, A., Andreae, M.O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M.G., Suttie, M., van der Werf, G.R., 2012. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527&#8211;554. https://doi.org/10.5194/bg-9-527-2012Moesinger, L., Dorigo, W., Jeu, R. de, Schalie, R. van der, Scanlon, T., Teubner, I., Forkel, M., 2019. The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA. Earth Syst. Sci. Data Discuss. 1&#8211;26. https://doi.org/10.5194/essd-2019-42Yebra, M., Quan, X., Ria&#241;o, D., Rozas Larraondo, P., van Dijk, A.I.J.M., Cary, G.J., 2018. A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing. Remote Sens. Environ. 212, 260&#8211;272. https://doi.org/10.1016/j.rse.2018.04.053

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A Bi-Spectral Microbolometer Sensor for Wildfire Measurement

Sensors ◽

10.3390/s21113690 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3690

Author(s):

Denis Dufour ◽

Loïc Le Noc ◽

Bruno Tremblay ◽

Mathieu N. Tremblay ◽

Francis Généreux ◽

...

Keyword(s):

Low Resource ◽

Long Wave ◽

Radiative Power ◽

Infrared Radiances ◽

Moderate Scale ◽

Long Wave Infrared ◽

Detector Type ◽

Experimental Burns ◽

Fire Radiative Power

This study describes the development of a prototype bi-spectral microbolometer sensor system designed explicitly for radiometric measurement and characterization of wildfire mid- and long-wave infrared radiances. The system is tested experimentally over moderate-scale experimental burns coincident with FLIR reference imagery. Statistical comparison of the fire radiative power (FRP; W) retrievals suggest that this novel system is highly reliable for use in collecting radiometric measurements of biomass burning. As such, this study provides clear experimental evidence that mid-wave infrared microbolometers are capable of collecting FRP measurements. Furthermore, given the low resource nature of this detector type, it presents a suitable option for monitoring wildfire behaviour from low resource platforms such as unmanned aerial vehicles (UAVs) or nanosats.

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Real-Time Detection of Daytime and Night-Time Fire Hotspots from Geostationary Satellites

Remote Sensing ◽

10.3390/rs13091627 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1627

Author(s):

Chermelle B. Engel ◽

Simon D. Jones ◽

Karin J. Reinke

Keyword(s):

Real Time ◽

Infrared Imaging ◽

Night Time ◽

Clear Link ◽

Radiative Power ◽

Biogeographical Region ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Fire Radiative Power

This paper introduces an enhanced version of the Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) algorithm. The algorithm runs in real-time and operates over 24 h to include both daytime and night-time detections. The algorithm was executed and tested on 12 months of Himawari-8 data from 1 April 2019 to 31 March 2020, for every valid 10-min observation. The resulting hotspots were compared to those from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The modified BRIGHT hotspots matched with fire detections in VIIRS 96% and MODIS 95% of the time. The number of VIIRS and MODIS hotspots with matches in the coincident modified BRIGHT dataset was lower (at 33% and 46%, respectively). This paper demonstrates a clear link between the number of VIIRS and MODIS hotspots with matches and the minimum fire radiative power considered.

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Combining fire radiative power observations with the fire weather index improves the estimation of fire emissions

10.5194/acp-2017-790 ◽

2017 ◽

Cited By ~ 1

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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