Brief Communication: The interaction of clouds with surface latent heat flux variation before the 2011 <i>M</i> = 6.1 Russia earthquake

Abstract

Abstract.Recently, surface latent heat flux (SLHF) data have been widely used to study the anomalies before earthquakes. Most studies use the daily SLHF data. Here we use both the daily SLHF data and the high temporal resolution (four times one day) SLHF data, and compare the SLHF changes with satellite cloud images at the first time. We check the data from 1 September to 30 October 2011, and the result shows that there is really a very high SLHF anomaly (more than 2σ ) in the epicenter area just 5 days before the M = 6.1 Russia earthquake that occurred on 14 October 2011. It should be considered as a preseismic precursor if judged with previously published methods, but our comparison between SLHF change and satellite images shows that the SLHF anomaly is contaminated by a thick cloud. It is difficult to verify that this SLHF anomaly is caused by an earthquake and our analysis shows that it is more related to meteorological reason. This example tells us that scientists must know the data's meaning before they use it; if not, they may draw a wrong conclusion. Based on this example, we suggest that previously published SLHF anomalies before earthquakes should be reanalyzed with our method to exclude the false anomalies.

Time-series analysis of remotely-sensed thermal infra-red emissions : linking anomalies to physical processes

“…Genzano et al [2007] and Blackett et al [2011] specifically do mention the effect of clouds in the results of detection. Such effects should be considered, as in earthquake related literature [Qu et al, 2006, Jie andGuangmeng, 2014] pre-earthquake anomalies are reported to be linked with meteorological effects (temperature inversions, presence of clouds) rather than earthquakes.…”

“…In our results, period affects anomaly density, even though this effect is present regardless of earthquake occurrence. A possible reason why time can affect anomaly density could be localized temporal variations of atmospheric parameters (as found, for example, by Jie and Guangmeng [2014]; Qu et al [2006]). Water vapour, in particular, influences LST retrievals [Freitas et al, 2010].…”