1. FY10-11 GIMPAP Project Proposal Title Page

1. 1. FY10-11 GIMPAP Project Proposal Title Page • Title: Using Quantitative GOES Information to Improve Short-Term Severe Weather Forecasts • Revised on 21 July 2010 • Project Type: GOES utilization and GOES product development • Status: Renewal • Duration: 2 years • Leads: • Michael Pavolonis (NOAA/NESDIS/STAR) • Andrew Heidinger (NOAA/NESDIS/STAR) • Daniel Lindsey (NOAA/NESDIS/STAR) • Other Participants: • Justin Sieglaff (CIMSS), Corey Calvert (CIMSS), and Kathryn Mozer (CIMSS graduate student), Bob Rabin (NSSL/CIMSS), Louie Grasso (CIRA), Jack Dostalek (CIRA), Kevin Micke (CIRA), Chris Siewert (CIMMS), Russ Schneider (SPC)

2. 2. Project Summary • Quantitative cloud properties from GOES have been under-utilized. A goal of this project is to extract information from the cloud properties that can be used to make short-term predictions on convective storm evolution, throughout the storm’s lifecycle. • GOES Imager data will also be used along with fields from the SPC surface mesoanalysis and the NOAA/ESRL RUC model to predict the probability of severe weather in the 0-6 hour time frame • Cloud properties found to be correlated with severe weather can be tested using a statistical technique • The proposed work will build upon the pilot studies conducted with FY08-09 GIMPAP funds. • Expected Result: Create a product which assists with short-term severe storm forecasting, and which generates a probability of severe weather in the GOES-East domain • ***Research at UW-CIMSS is pending for arrival of 2010 GIMPAP resources with delay due to CIMSS re-compete

3. 3. Motivation/Justification • Supports NOAA Mission Goal(s): Weather and Water • Cloud properties from GOES are under-utilized. This project attempts to extract from them information that is meaningful for NESDIS customers (e.g. NWS forecasters). • No current automated methods exist to quantitatively combine GOES data with traditional severe weather forecast parameters • The final product will assist the SPC in their Convective Outlooks, Mesoscale Discussions, and Severe Weather Watches, and will attempt to improve the severe weather warning lead time offered by NEXRAD alone

4. 4. Methodology • Identify numerous case studies that include severe and non-severe convection. • Quantitatively identify differences in the temporal and spatial evolution of cloud properties for severe and non-severe storms using manual analysis and object tracking software. • Investigate the physical cause of cloud top microphysical property patterns observed at the top of mature convective clouds. • Also collect RUC data, SPC surface mesoanalysis data, and severe storm reports from several severe weather seasons • Use a discriminate analysis method to determine what variables best predict severe wind, hail, and tornadoes • Build a statistical model based on results from the discriminate analysis • Apply this model to real-time data to create an experimental real-time product

5. 5. Expected Outcomes • Incorporate knowledge gained by this research into the CIMSS convective weather decision support tool. • Create an experimental real-time product which predicts the probability of severe hail within the GOES-East domain of the CONUS • If successful, a GOES PSDI proposal will be submitted to transfer this experimental product to NESDIS operations

6. 6. Preliminary Results CIRA’s work toward the GOES hail probability forecasts: • GOES data, RUC data, SPC mesoanalysis data, and observed severe reports were collected from the summers of 2006-2009 • The data were statistically analyzed to determine the best predictors for severe hail • Predictors include GOES 10.7 µm, mixed-layer (ML) lifted index, MLCAPE, surface dewpoint, MLCIN, and climatology • An experimental hail prediction algorithm has been running since April 2010 • Real-time data are collected at CIRA, the forecasts made and updated with every new GOES scan, then converted to GEMPAK format and sent to the SPC via LDM and displayed on their NAWIPS system • The experimental GOES hail probability product was evaluated during the 2010 HWT/SPC Spring Experiment • D. Lindsey participated in the Spring Experiment the week of June 14, and collected the example on the next slide

7. 6. Preliminary Results Hail Probability (%) Observed hail reports between 21-00 UTC denoted by ‘a’ GOES Hail Probability forecast from 21-00 UTC on 16 June 2010

8. 6. Preliminary Results Preliminary Results: The effective particle radius, derived separately from NIR and IR measurements, was analyzed for over 500 deep convective cloud objects to determine the extent to which the IR can provide meaningful particle size information for deep convective clouds. On a cloud object basis, the IR and NIR results are reasonably consistent, suggesting that the IR transmission near cloud top can be sufficiently large enough to allow for the retrieval of particle size information from IR measurements. The effective particle radius may be related to updraft strength. The frequency distribution of the minimum and maximum effective particle radius from a near-infrared retrieval, and the minimum effective particle radius from an infrared retrieval are shown for a total of 541 convective cloud objects. 8

9. 7. FY10-11 Milestones • FY10 • Gather a large database of severe and non-severe storms as viewed by the GOES Imagers. (start delayed by 6 months) • Relate the temporal evolution of cloud optical depth and infrared emissivity to NEXRAD radar trends for developing storms. (start delayed by 6 months) • Collect GOES data, RUC data, and severe weather reports for at least 2 severe weather seasons (March to August) (complete) • Determine the best predictors for severe hail (complete) • Build an experimental product which uses real-time inputs to predict the probability of severe hail (complete) • Determine whether including additional information from GOES beyond the straight 10.7 micron channel provides value to the forecast

10. 7. FY11 Milestones • FY11 • Develop cloud property based metrics aimed at predicting the short-term evolution of mature convection. • Determine whether the hail prediction model can be expanded to also provide useful forecasts for severe wind and tornadoes • Test GOES cloud properties’ severe weather predicting capability using the statistical model • Complete an experimental product and prepare to submit a PSDI proposal in the summer of 2011

11. 8. Funding (K)

12. 9. Expected Purchase Items FY10 • FY10 $135,000 Total Project Budget • Grants $ 116,000 • CIMSS $ 60,000 • CIRA $ 56,000 • Transfer to NSSL (Bob Rabin) $10,000 • Federal Travel -$ 9,000 • From CIMSS to CIRA to collaborate (Jul 2010) $ 2,000 • To AMS severe wx conference for Dan Lindsey (Sep 2010) $ 2,000 • To conferences for M. Pavolonis and/or A. Heidinger $ 3,000 • To SPC for Dan Lindsey (May 2010) $ 2,000 • Federal Publication Charges – NONE • Federal Equipment -NONE • Transfers to other agencies – NONE • Other -NONE 12

13. 9. Expected Purchase Items FY11 • FY11 $135,000 Total Project Budget • Grants $ 115,500 • CIMSS $ 62,200 • CIRA $ 53,300 • Transfer to NSSL (Bob Rabin) $10,000 • Federal Travel -$ 9,500 • To EGU Meeting, Vienna, for Dan Lindsey, Apr 2011 $ 4,000 • To EGU Meeting, Vienna, for M. Pavolonis, Apr 2011 $ 4,000 • To SPC for Dan Lindsey May 2011 $ 1,500 • Federal Publication Charges – NONE • Federal Equipment -NONE • Transfers to other agencies – NONE • Other -NONE 13