Data

The dataset is part of the effort to provide the most detailed climate data for CAPLTER and related projects. This dataset represents average monthly maximum/minimun air temperature derived from spatial interpolation of daily temperature data from a dense network of meteorological sensors for the period of 6 years (2000-2005).

Meteorological data acquisition and preprocessing

Temperature data were obtained from different sources including the Flood Control Disctrict of Maricopa County (ALERT), the National Weather Service (NWS), the Arizona Meteorological Network (AZMET), and the Phoenix Real-time Instrumentation for Surface Meteorological Studies (PRISMS) Network. Instrument locations were first geocoded using the station descriptions from each network. Daily measurements were then aggregated to bi-weekly periods (maxium of all maxima and minimum of all minima during each period) to match the temporal resolution of MODIS (The Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI) images. MODIS is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites designed for global monitoring of key biophysical parameters including vegetation. Tables with aggregated temperature were then joined to the shapefile of meteorological stations.

Spatial interpolation

Temperature was gridded in two steps. First, I converted actual observations into hypothetical air temperature near land surface at 0 m above sea level (ASL) by applying a 6.5 K/km environmental lapse rate, which is the international standard set forth by the International Civil Aviation Organization (ICAO). Second, I used Geostatistical Analyst, the ArcGIS 9.1 extensio and performed ordinary kriging to produce a horizontal temperature distribution at 0 ASL. Standard Error maps were also produced along with temperature surfaces that were all rasterized to a 250 meter resolution grid. Final grids were created by using a digital elevation model (DEM) resampled to 250 meters and inverting the environmental lapse rate to the previously interpolated temperature surface.

Temporal aggregations

Biweekly temperature surfaces were aggregated to monthly time step (again maxium of the corresponding two maxima and minimum of the two minima). For each month I computed the average 6-year temperature.