Climatology of the U.S. Inter-Mountain West

Model. Earth Syst. Environ.

2016

A selective variance reduction (SVR) script is presented that applies linear regression models to the principal components (PCs) of multi-temporal night monthly averaged land surface temperature (LST) imagery, in an attempt to spit the variance associated to elevation, latitude, longitude. The recently released version 6, MODIS LST data (MYD11C2) with spatial resolution 0.05°is used while the method is applied in SW USA. The innovation relies on the use of un-standardized PCs. Thus, the reconstructed LST should express the deviation in degrees Celsius, from the elevation, latitude, longitude predicted LST. The reconstructed data quantifies the temporal and spatial patterns of thermal anomalies. The modeling of the frequency distributions of the reconstructed LST imagery indicate that possibly snow melting and variations in water table depth are associated to the negative thermal anomaly observed during the Spring. The GNU OCTAVE 4.0 SVR software implementation is available at http://selective-variance-reduction.sourceforge. net for testing and evaluation.

Section: Study Areamentioning

confidence: 99%

An unstandardized selective variance reduction script for elevation, latitude and longitude decorrelation stretch of multi-temporal LST imagery

Model. Earth Syst. Environ.

2016

“…1) is bounded by longitudes -124°to -112°(West) and latitudes 32°-44°(North) and includes the states of California, Nevada, Utah and Arizona. NW of the study area (California), the climate is characterized by moderately cold winters with heavy snowfall on the mountains (Sierra Nevada Ranges) and warm, very dry summers with limited rainfall, especially in the south (Wang and Gillies 2012).…”

Section: Study Areamentioning

confidence: 99%

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

Model. Earth Syst. Environ.

2016

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.

Spatial decorrelation stretch of annual (2003–2014) Daymet precipitation summaries on a 1-km grid for California, Nevada, Arizona, and Utah

2016

Environ Monit Assess

A method is presented for elevation (H) and spatial position (X, Y) decorrelation stretch of annual precipitation summaries on a 1-km grid for SW USA for the period 2003 to 2014. Multiple linear regression analysis of the first and second principal component (PC) quantifies the variance in the multi-temporal precipitation imagery that is explained by X, Y, and elevation (h). The multi-temporal dataset is reconstructed from the PC1 and PC2 residual images and the later PCs by taking into account the variance that is not related to X, Y, and h. Clustering of the reconstructed precipitation dataset allowed the definition of positive (for example, in Sierra Nevada, Salt Lake City) and negative (for example, in San Joaquin Valley, Nevada, Colorado Plateau) precipitation anomalies. The temporal and spatial patterns defined from the spatially standardized multi-temporal precipitation imagery provide a tool of comparison for regions in different geographic environments according to the deviation from the precipitation amount that they are expected to receive as function of X, Y, and h. Such a standardization allows the definition of less or more sensitive to climatic change regions and gives an insight in the spatial impact of atmospheric circulation that causes the annual precipitation.