Surface Observations and Their Use in RTMA/URMA: Challenges and Coordination

The Real Time Mesoscale Analysis (RTMA) and UnRestricted Mesoscale Analysis (URMA) systems at National Centers for Environmental Prediction (NCEP) generate surface analyses from a background (short-term model forecast) and all available surface observations and low-level satellite observations.  These analyses are used by National Weather Service (NWS) forecasters for situational awareness, verification, bias correction & calibration for the National Blend of Models project and to initialize local forecast grids.  Observations come from many sources including METAR sites, buoys, low-level satellite-derived winds and a variety of mesonets made available through the Meteorological Assimilation Data Ingest System (MADIS).  While the observation network for most variables is generally quite dense, the disparate sources, qualities, and densities of information can present significant challenges when running the RTMA & URMA systems in operations.  

The selection, quality control and weight given to observations can have a major impact on the resulting analysis.  Because RTMA/URMA are operational products that must support NWS activities, this process involves significant coordination with NWS local, regional and national offices.  Even with a high level of coordination, gridded analysis values rarely match observations in the grid, even those of perceived high quality (ie isolated METAR observations).  Work is underway to determine how closely RTMA/URMA analyses should match a given set of observations as well.  Examples of this coordination, different needs of offices, examples of a particular observations impact (or lack thereof) and observation errors will be presented.  Potential solutions will also be explored.