Using NUCAPS Retrievals to Diagnose Pre-Convective Environments over the United States

Currently, the only observational platform having adequate vertical resolution of temperature and moisture for severe thunderstorm applications is the radiosonde. The vertical resolution is sufficient to detect sharp low-level temperature inversions and vertical gradients in moisture, common during severe weather events. However, the major limitation of radiosonde data is inadequate temporal and spatial resolution. Balloons are launched only at 00 and 12 UTC (and occasionally at 18 UTC), and the launch sites are 300-500 km apart in the central U.S.

Retrievals based on satellite radiances have finer horizontal resolution in comparison to the radiosonde network. In particular, the NOAA Unique CrIS/ATMS Processing System (NUCAPS) generates 30 retrievals for each 2200 km swath of the CrIS instrument, or roughly one retrieval every 75 km. This resolution, combined with an early afternoon ascending node, allows the NUCAPS data to help fill in both the spatial and temporal gaps of the radiosonde network. Satellite retrievals, however, have a rather coarse resolution in the vertical with respect to radiosonde profiles. Specifically, measurements of low-level temperature and moisture are critical components in forecasting the likelihood of deep convection. To take full advantage of the horizontal resolution of the NUCAPS retrievals, it may be necessary to adjust the lower level temperature and moisture profiles using ancillary data from surface observations and/or model output.

This presentation examines the utility of NUCAPS data as an additional source of information for monitoring pre-convective environments. First, the accuracy of the NUCAPS algorithm will be summarized in the form of a statistical comparison to collocated radiosondes. Particular attention will be paid to the temperature and moisture profiles of the boundary layer. Second, an introduction to the process of combining the NUCAPS retrievals with ancillary observations and model data will be presented. Finally, how this fused product can help in the forecasting of deep convection will be discussed.