Comparison of Radiosonde and Remote Sensing Data to Evaluate Convective Forest Fire Risk: The Haines Index

Haines Index (HI) has been associated with convective forest fires risk. Temperatures and humidities in low atmospheric levels are required to compute HI and usually, atmospheric sounding data are used for this purpose. However, spatial and temporal resolutions of these data are coarse and remote sensing data could improve them. Therefore, the aim of this work is to test remote sensing data from the Atmospheric Infrared Sounder (AIRS) instrument on board the EOS Aqua satellite, specifically the Level 2 V6 products (AIRX2RET and AIRS2RET), for this purpose. First, we validated the remote sensing data with radiosonde daytime and nighttime data located in the Iberian Peninsula in 2014. Significant deviations between daytime and nighttime data were not found in the analysis. The correlations between AIRS and radiosonde data are slightly better for top atmospheric layers than for low layers, and the results show good agreement between both measures, a bit better for the AIRS2RET product. Thus, these remote sensing data can improve the lack of global data in the atmospheric lower layers to evaluate convective forest fire risk. Finally, as an example, we mapped the result of the HI computed from AIRS data for a forest fire event.