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Monthly maximum air temperature in May (6 year mean) in Central Arizona
Identifier:345_1
Publication date:2007
Author(s):
Alexander Buyantuyev
Abstract:
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).
Keywords:
Phoenix, Arizona, Sonoran Desert, caplter, Central Arizona Phoenix Longterm Ecological Research, urban, metropolitan area, climate, weather, air temperature, temperature, maximum temperature, Climate Ecosystem Interactions, Geosciences, gis, caplter created, Project id 1, Project id 79
Temporal Coverage:
2000-02-01 - 2005-12-31
Geographic Coverage:
Geographic Description:Central Arizona Phoenix
Bounding Coordinates:
Longitude:-112.860134 to -111.487490
Latitude:33.895150 to 33.009888
Contact:
Data Manager,
Global Institute of Sustainability,Arizona State University,POB 875401,TEMPE
caplter.data@asu.edu
Methods used in producing this dataset:
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.
Entities:
Raster: Band_1
download
Attributes:
Attribute:ObjectID
Description:Internal feature number.
Attribute:Value
Description:Monthly maximum or minimum air temperature (Celsius)
Measurement Unit:number
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