Comparison of LEO Satellite Moisture Retrieval Derived Total Precipitable Water to GPS Network over Alaska

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Monday, 5 January 2015
Richard Dworak, CIMSS/Univ. of Wisconsin, Madison, WI; and R. Petersen

An insufficient surface based observation network of radiosonde moisture soundings exists over Alaska to provide sufficient information for National Weather Service (NWS) office forecasters and model data assimilation to adequately forecast precipitation events and types. This observation void complicates weather forecasts, not only in longer-range model accuracy but in shorter-term nearcasting situations. Nearcasting is especially important over Alaska during the summer season when convection occurs, potentially igniting forest fires. To fill the gaps, moisture retrievals from Low Earth Orbiting (LEO) satellite sounders can be utilized.

The University of Wisconsin NearCasting System is designed to monitor and predict pre-storm environments in which convection is likely to occur over the next 1-9 hours. The hourly-updated system is exceptionally data-driven and is highly dependent on accurate measurements of atmospheric moisture content. NearCasting System utilizes moisture retrievals provided by satellite soundings.

The main focus of this research is to utilize the GPS Network over Alaska to determine the accuracy of high resolution Satellite sounding retrievals from polar orbiting AIRS, CrIS and IASI derived from standard products available from NASA (AIRS version-6), NOAA (IASI and CrMSS) and EUMETSAT (IASI), also from the University of Wisconsin Dual-Regression technique. The GPS moisture retrieval produces accurate (< 1 mm) total atmospheric column moisture; however detailed sounding structures are not possible, so Total Precipitable Water (TPW) will be the measure to gauge accuracy of polar orbiting satellite moisture retrievals

Results from this study will focus on the Alaskan warm season and will show that the standard products are more accurate than the Dual Regression, with a dry bias common in all retrievals. It is observed that this is related to differences between the surface pressure measured at the GPS sites and that used for the satellite retrievals, with GPS tending to have higher surface pressure observations. Finally, a study of the influences of cloud coverage based on satellite cloud fraction is done to determine the bias corrections in situations where partial cloudiness occurs.