Case Study: March 1, 2007 The WxIDS approach to predicting areas of high probability for severe weather incorporates various meteorological variables (e.g. wind shear, precipitation, wind velocity, Convective Available Potential Energy) from ensemble model forecasts and radar verification data into a Bayesian Neural Network (BNN) processing architecture. The BNN is used to develop an optimal combination of variables for predicting severe weather up to 24 hours in advance of storms. The ensemble model data is provided by the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Short Range Ensemble Forecast (SREF) system. NOAA Next-generation Radar (NEXRAD) reflectivity data is used as verification for algorithm training. Figure 3 illustrates the two step process utilizing the BNN approach (algorithm training and implementation). Selected References Mecikalski, J. R., and K. Bedka, Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery, AMS Monthly Weather Review, 134, 49-78, Future Work 1.Continue acquisition of SREF numerical weather prediction model output data, GOES satellite data, and radar verification data to improve statistics and capture various seasons and locations for severe weather. 2.Incorporate satellite data into WxIDS BNN processing. 3.Implement Graphical User Interface (GUI) for WxIDS system. Abstract Future geostationary and low earth orbit environmental observational systems will substantially increase the volume and timeliness of data provided to user communities. A multitude of new and improved products will be available as a result of increases in temporal, spatial, and spectral coverage and resolution of future remote sensing systems. Combined with the evolution of numerical weather prediction (NWP) models and the resulting increase in model data, users will face unprecedented challenges to utilize all relevant data to make timely and appropriate decisions relating to severe weather events (thunderstorms, tornadoes, floods, etc.). Automation of the identification, synthesis, integration, and analysis of observations and numerical forecasts within decision support processes is critical to maximizing the societal benefits of future observational systems. The Weather Information and Decision System (WxIDS) is a concept for implementing an automated decision support system that synthesizes observations and probabilistic forecasts with user operational experience and risk criteria designed to meet user requirements. The goal in developing WxIDS is to improve the connectivity between the observations, analyses, predictions, and the decision support systems in order to maximize the effectiveness of decisions and the return on investment from observational systems. There are four primary components to the WxIDS system (illustrated and described below). Severe Convective Weather Scenario The WxIDS concept is demonstrated with an initial scenario focusing on predicting severe convective weather events over a range of lead times using probabilistic forecasts, satellite observations, and radar data (Figure 2). Probabilistic forecasts are used to identify areas with high probability of severe convection 24 hours in advance relying primarily on computer ensemble forecasts. Convective precursor parameters measured from satellite observations are used to narrow the high probability regions of convection 30 minutes to 2 hours prior to convection. Severe convective weather events are verified using radar reflectivity data. Figure 4 shows initial results of the WxIDS severe weather prediction approach (24 hours in advance) for March 1, 2007 at 21Z for different probabilities of occurrence (50%, 70%, and 90%) and NEXRAD composite radar data mapped to the WxIDS grid (lower right). Initial results are in good agreement with NEXRAD verification data particularly for the southwestern United States where severe weather is predicted and verified at 21Z. Satellite data from the NOAA Geostationary Operational Environmental Satellite-12 (GOES-12) was collected for March 1, 2007 for 17Z through 22Z. Figure 5 shows the formation, growth, and movement of three convective cloud formations over the regions identified as likely for severe weather by the WxIDS severe weather prediction approach (above). A satellite derived Convective Initiation (CI) index was developed using GOES data to identify areas of severe convection. Figure 6 shows the resultant CI index (left) and 10.7 micron imagery (right) for the 19Z convective cloud over Louisiana. Figure 7 shows the CI index and NEXRAD radar verification data at three different times over a one hour period for the Louisiana convective cloud. While it is difficult to use surface-based radar data to verify space-borne satellite data (satellite parallax errors, surface drag, etc.), the results indicate the potential for the approach. CI index indicates convection ~40 minutes prior to radar reflectivity > 35dBZ which is associated with heavy precipitation. Figure 8 shows NOAA Storm Prediction Center (SPC) severe weather reports mapped over GOES micron data for the case study. The WxIDS CI index indicated convection ~1.5 hours prior to severe weather reports issued for the Louisiana convective cloud. Weather Information and Decision Systems (WxIDS): Looking Into the Future of Data Processing and Decision Support Systems This poster does not reflect the views or policy of the GOES-R Program Office Figure 3. Neural network processing for predicting severe weather. Currently, the model data (SREF) and radar data (NEXRAD) have been incorporated into the WxIDS system. Future work will incorporate satellite data. Figure 1. WxIDS system components (described below). Dylan Powell 1, John Dutton 2, Jeremy Ross 2, Jeff Sroga 1, Chung-Fu Chang 1, Rod Pickens 1, Kyle Leesman 1, Shanna Pitter 3, George Young 4, Paul Knight 4, Nelson Seaman 4, Jon Nese 4, Glenn Haselfeld 1, Robert Wessels 1, Mike Dhondt 1 1 Lockheed Martin, 2 Weather Ventures Ltd., 3 Itri Corporation, 4 Penn State University Department of Meteorology WxIDS Strategy and Components Local Data Manager: manages weather information acquisition and storage Forecast Generation System: identifies critical situations; combines NWP model predictions and observations into comprehensive probabilistic forecast aimed at user requirements; Bayesian Neural Network processing techniques applied to training sets to determine optimal combination of variables Decision Support System: combines probabilistic forecasts with user operational experience and risk criteria for decision advice and recommendations User Interface: provides interactive capabilities to users or their risk management decision system Figure 2. WxIDS scenario for predicting and identifying severe convective weather events Z1940 Z2002 Z CI Index Radar CI Index indicates convective initiation (radar has values of dBZ) Convection continues as weather intensifies (radar shows values of dBZ) Convection continues and spreads as cloud expands and moves NE (little change in radar) X Hail X Tornado X High Winds CI Index Detected Cloud at 1902 UTC Severe Weather Reports Were Issued for the Detected Cloud At 2040, 2105, 2158,… Satellite Observation of CI ~1.5 Hours Prior To Severe Weather Reports 70% Probability 50% Probability 70% Probability Figure 4. WxIDS severe weather probabilities for March 1, 2007 at 21Z and NEXRAD composite radar data mapped to the WxIDS grid. Figure 5. NOAA GOES micron brightness temperatures for March 1, The top image shows a convective cloud form at 19Z and continue to grow and move north- east at 20Z (lower) while two more convective clouds form behind the initial cloud. Figure 6. CI index (left) and GOES micron data (right) for March 1, 2007 at 19Z. The CI index is computed by combining the time rate of change of selected satellite data (0.65μm, 10.7 μ m, 6.5 μ m-10.7 μ m, and 13.3 μ m-10.7 μ m) averaged over 20 km boxes. Figure7. WxIDS CI index from GOES-12 data (top) and NEXRAD radar data (bottom) for the Louisiana convective cloud formed on March 1, 2007 over a one hour period (1902 through 2002 Z). Figure 8. NOAA SPC severe weather reports mapped over GOES micron imagery for March 1, 2007 for times > 20Z. Contact: Dylan Powell,