Revealing the precipitation dependency of regional in time and in space thermal anomaly peaks in SW USA

Abstract

The selective variance reduction technique that applies linear regression models to the principal components of multi-temporal night monthly averaged land surface temperature (LST) imagery splits the variance associated to elevation, latitude, longitude in SW USA for the year 2007. The reconstructed multi-temporal imagery indicate the positive or negative deviation (thermal anomaly) from the elevation, latitude, longitude predicted LST. The spatial and temporal patterns of thermal anomalies are revealed by K-means clustering. The mean precipitation computed per month per cluster interprets the observed fluctuations in the temporal pattern of thermal anomaly. Thus, it is possible to isolate the regional thermal anomaly component from the residual component induced from local weather phenomena (precipitation and possibly snow melting, surface water flow and water table depth variations). The regional thermal anomaly component is more likely to relate to geology (lithology-thermal inertia of rocks) and geophysics (geothermal fields) in the study area. The influence of precipitation to the temporal pattern of LST could provide a sort of evaluation of the precipitation data sets available.