Tuesday, 8 January 2013: 12:00 AM
Room 8ABC (Austin Convention Center)
Given the heterogeneous equipment, maintenance and reporting practices, and siting of surface observing stations, subjective decisions that depend on the application tend to be made to use some observations and avoid others. As part of expanded real-time quality control and impact assessment for publicly accessible surface observations archived in MesoWest, we determine routinely every hour high impact surface observations of temperature, dew point, and wind using the adjoint of a two-dimensional variational surface analysis over the contiguous United States. The analyses reflect a weighted blend of ~15,000 observations and 1-h numerical forecast background grids. These analyses are computed using the Python open source programming language. Differences between the background fields and observations for each hour of the day are accumulated over several weeks as a bias metric to help assess observation quality. To reduce representativeness errors in the UU2DVAR analyses, we use a simplified adaptive bias correction scheme.
We routinely accumulate statistics on bias and impact for individual stations and networks as well as broad network categories (e.g., transportation, air quality, fire weather). High impact observations can arise from low observation quality, observation representativeness errors, or accurate observed weather conditions not evident in the background field. We find impact to be tied strongly to weather events and nearby station density, i.e., high impact observations typically are where the background fields do not reflect local weather conditions underway and/or relatively few other observations are available nearby. Hence, the impact metric is one approach to quantify the utility of the heterogeneous mix of surface observations available now.
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