• Better Coverage of Arizona's Weather and Climate: Gridded Datasets of Daily Surface Meteorological Variables

      Weiss, Jeremy; Crimmins, Michael; Univ Arizona, Coll Agr & Life Sci (College of Agriculture, University of Arizona (Tucson, AZ), 2016-08)
      Many areas that use agricultural and environmental science for management and planning – ecosystem conservation, crop and livestock systems, water resources, forestry and wildland fire management, urban horticulture – often need historical records of daily weather for activities that range from modeling forage production to determining the frequency of freezing temperatures or heavy rainfall. In the past, such applications primarily have used station-based observations of meteorological variables like temperature and precipitation. However, weather stations are sparsely and irregularly located throughout Arizona, and due to the highly variable terrain across the state (Figure 1), information recorded at these sites may not represent meteorological conditions at distant, non-instrumented locations or over broad areas. This issue, along with others related to quality, length, and completeness of station records, can hinder the use of weather and climate data for agricultural and natural resources applications. In response to an increasing demand for spatially and temporally complete meteorological data as well as the potential constraints of station-based records, the number of gridded daily surface weather datasets is expanding. This bulletin reviews a current suite of these datasets, particularly those that integrate both atmospheric and topographic information in order to better model temperature and precipitation on relatively fine spatial scales, and is intended for readers with knowledge of weather, climate, and geospatial data. In addition to addressing how these datasets are developed and what their spatial domain and resolution, record length, and variables are, this bulletin also summarizes where and how to access these datasets, as well as the general suitability of these datasets for different uses.