1. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Combining GOES Observations with Other Data to Improve Severe Weather Forecasts Requirement: Address short-range rapid changes in mesoscale environments that affect thunderstorms Science: How can we best use and combine multiple datasets (satellite, surface obs, numerical models, etc) to improve severe weather forecasts? Benefit: Increased accuracy and lead times for severe weather watches and warnings, reduced loss of life and property due to severe weather Science Challenges: How to obtain the maximum amount of information from satellite data, and how to combine that with other data Next Steps: Collect more data to improve statistical results; determine what satellite information provides information helpful for severe weather nowcasting and forecasting Transition Path: PSDI, then SPSRB process to make operational products Dan Lindsey 1(GOVERNMENT PRINCIPAL INVESTIGATOR) 1 NESDIS/STAR/CoRP Introduction Efforts are currently underway at the Regional and Mesoscale Meteorology Branch (RAMMB) and Cooperative Institute for Research in the Atmosphere (CIRA) to develop new products that assist with severe weather nowcasts and forecasts. We recognize that satellite observations are limited by what information can be retrieved from various levels of the atmosphere, so our goal is to combine GOES observations with other data, including surface data, raobs, and numerical model output. Statistical Hail Prediction Product An experimental product is currently being developed which uses 2 years of GOES and Rapid Update Cycle (RUC) model data, along with severe hail reports, to create a statistical model that predicts the probability of severe hail. It is meant to be primarily used prior to significant echo formation on radar, since radar is obviously the primary tool for issuing severe storm warnings. Dots represent observed hail reports Colors are the probability forecast Observed GOES 10.7 image used to make the forecast; note that not all cold clouds are associated with large hail probabilities Looking Ahead to GOES-R In addition to creating severe weather products for the current GOES series, RAMMB is also working on products and algorithms designed for the Advanced Baseline Imager (ABI) aboard GOES-R. One such product currently under development makes use of the ABI’s improved radiometrics, spatial, and spectral resolution. Simulated GOES-R data and Meteosat Second Generation (MSG) data have shown that a similar product from the ABI will be able to predict where cumulus clouds will form, often hours in advance 0-3 km Precipitable Water Simulated 10.35-12.3 µm Product Note how the local maxima in 10.35- 12.3 match with the local 0-3 km PW maxima The top panel is a 11-12 µm product from MSG, and the bottom is visible. Notice how a local maximum can be seen in the 11-12 µm image, and no clouds have formed there. But by 1245 UTC, cumulus clouds have formed. This product provided a few hour lead time of cumulus cloud formation by locating areas of low-level moisture convergence. 0830 UTC1245 UTC 11-12 µm predicts where cumulus clouds will form several hours in advance