Combining GOES Observations with Other Data to Improve Severe Weather Forecasts

1. Combining GOES Observations with Other Data to Improve Severe Weather Forecasts Dan Lindsey, STAR/RAMMB Requirement: Use satellite data to improve severe weather forecasts and nowcasts Science:Use statistical techniques to determine how to best incorporate the Most possible datasets when creating severe weather products Benefit: Increased accuracy and lead times for severe weather watches and warnings Introduction Efforts are currently underway at RAMMB and 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 POES observations, surface data, raobs, and numerical model output. This poster provides some examples of products we’re currently working on which use data from a variety of platforms, in an effort to improve severe weather forecasts. Note the local maximum in the 10.8 – 12.0 µm image, and the lack of clouds at this time By 1245 UTC, cumulus clouds have formed in this same region Probability of severe hail (%) between 23-00 UTC on 10 June 2009 (left), along with observed severe hail reports in this 1-hour time period (black dots), and GOES-12 IR image from 2302 UTC on 10 June 2009 (right) Future versions of this product will use additional GOES information, such as overshooting tops locations, predicted cloud motions based on cloud drift winds and extrapolation, and possibly microphysical information. In addition, we will extend the forecasts to include severe wind and tornadoes. 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. With the current GOES-11, the 10.7 minus 12.0 µm difference product is too noisy to provide useful information about low-level moisture depth, but simulated GOES-R data and 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. Statistical Hail Prediction Product An experimental product is currently being developed which uses 2 years of GOES and RUC 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. The model was developed using a discriminate analysis statistical routine; this identifies the best predictors and weights each one appropriately. Predictors in Version 1.0 of this experimental model include percent of GOES pixels colder than -40 ºC in each grid box, MLCAPE, MLLI, MLCIN, surface dewpoint, total shear, and climatology. The current version of the product can run using current GOES data, and the environmental parameters are extracted from the SPC’s surface mesoanalysis. MSG 10.8 – 12.0 µm (top) and HRV (bottom) from 21 Dec. 2009 at 0830 UTC (left) and 1245 UTC (right) In the example below and left, the 10.35 – 12.3 µm difference is spatially correlated with the low-level water vapor, and local maxima in the difference product can be seen well before clouds form in the model. Above, MSG data from southern Africa shows using real data that this longwave difference really can predict the location of cumulus cloud development. Given the powerful application of these ABI bands to severe storm forecasting, the next step might be to combine this low-level moisture convergence information with other data to generate a convective initiation product. For example, one might use surface data and RUC output to evaluate the strength of the cap, and in areas where the convective inhibition is relatively small and where a maximum in the longwave difference is evident, storm formation may be predicted before a single cumulus cloud has formed. 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 Simulated 10.35 – 12.3 µm ABI product from the 27 June 2005 severe weather simulation (left, units in K), and the 0-3 km AGL precipitable water (mm) from the model (right) at the same time as the image on the left. Probability of severe hail (%) between 22-23 UTC on 10 June 2009 (left), along with observed severe hail reports in this 1-hour time period (black dots), and GOES-12 IR image from 2202 UTC on 10 June 2009 (right)