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Moving toward Multispectral, Multiplatform Operational Satellite Precipitation Estimates at NESDIS

Moving toward Multispectral, Multiplatform Operational Satellite Precipitation Estimates at NESDIS. Robert J. Kuligowski Roderick A. Scofield NOAA/NESDIS Office of Research and Applications. Outline. Brief History of Precipitation Work at ORA Future Directions Multi-Satellite Blending

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Moving toward Multispectral, Multiplatform Operational Satellite Precipitation Estimates at NESDIS

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  1. Moving toward Multispectral, Multiplatform Operational Satellite Precipitation Estimates at NESDIS Robert J. Kuligowski Roderick A. Scofield NOAA/NESDIS Office of Research and Applications

  2. Outline • Brief History of Precipitation Work at ORA • Future Directions • Multi-Satellite Blending • Lightning • Multiple-Channel Algorithms • Nowcasting

  3. History: GOES Algorithms • Emphasis on operational forecast support (Satellite Analysis Branch) • Progression from manual techniques (Interactive Flash Flood Analyzer—IFFA) to automated (Auto-Estimator/Hydro-Estimator) • Exploration of multi-channel techniques (GOES Multi-Spectral Rainfall Algorithm—GMSRA)

  4. History: Microwave Algorithms • Emphasis on climate applications • Progression from statistical algorithms to physical algorithms (Goddard PROFiling algorithm—GPROF) • Development of some forecasting applications (TRopical Rainfall Potential—TRaP)

  5. History: Blended Algorithms • Resolution and latency favor GOES IR estimates; accuracy favors polar-orbiter MW estimates. • Efforts by many researchers to obtain the accuracy of MW with the resolution of IR. • Some ORA collaboration with F. Joseph Turk on Naval Research Lab algorithm. • Development at ORA of Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR).

  6. History: SCaMPR • Flexible framework for automatically-calibrated precipitation estimation: • Calibrates against SSM/I and AMSU • Discriminant analysis selects and calibrates best rain/no rain predictors • Stepwise forward regression selects and calibrates the best rain rate predictors • Predictors AND calibration updated regularly

  7. SCaMPR continued: • SCaMPR is being transitioned into real-time applications • Initial version uses basic predictors: T6.9, T10.7, T13.2, temperature differences, T10.7 texture information • Eta model PW, RH will be added soon • SCaMPR can use ANY gridded field as a predictor

  8. Preliminary SCaMPR Performance • LIMITED sample; comparisons of 6-h estimates to Stage IV during the Oct. 7-15 test period. • Fewer false alarms than H-E, but also fewer correct detections, especially for lighter precipitation. • Less bias than the H-E, but bias increases with amount; GMSRA is least biased of the three. • Overall, SCaMPR performs slightly worse than H-E and GMSRA for low amounts (<10 mm/6h) but slightly better for high amounts (>20 mm/6h).

  9. Blended Algorithms and GPM • Blended algorithms are not intended to compete with GPM • No IR algorithm is a perfect substitute for MW! • Enhanced timeliness and latency in GPM era will enhance combination IR/MW algorithms • Ultimate solution is Geo MW, but that remains at least a decade away

  10. SCaMPR and Lightning • Receiving National Lightning Detection Network (NLDN) data in real time • Working to design and test lightning-based SCaMPR predictors • Wider applications anticipated with increase in number of spaceborne lightning platforms

  11. Multiple-Channel Algorithms GMSRA laid the groundwork, incorporating a number of research techniques into a real-time algorithm: • Visible: daytime thin cloud identification • 3.9 µm: retrieving cloud particle size during the daytime (after Rosenfeld and Gutman 1994) • 6.9 µm-10.7 µm: identifying overshooting cloud tops (after Tjemkes et al. 1997) • 10.7 µm – 12.0 µm: identifying thin clouds during day or night (after Inoue 1987)

  12. Multiple-Channel Algorithms • Increased channel selection on current and planned geostationary imagers (e.g., 12 on SEVIRI, 16 on ABI) • Research needs to be transitioned into operations as the data become available, including: • Cloud phase using 8.5, 11, 12 µm (Ackerman et al.) • Vertical profiles of cloud water/ice particle size (Chang and Li) • Research is being conducted at ORA using MODIS data for rain/no rain discrimination

  13. The Hydro-Nowcaster • Nowcasts enhance the utility of satellite precipitation estimates by increasing the lead time of precipitation information. • The H-N produces 0-3 hour nowcasts of rainfall (based on estimates by the Hydro-Estimator) and updates every 15 min. • Two components: • Extrapolation: identifies cloud clusters, tracks and extrapolates motions out to 3 hours • Growth/decay: changes in cluster size and temperature are used to determine time change of rain intensity during the nowcast period

  14. Example: Hurricane Isabel on 18-19 September 2003 1 h nowcast: 2100 UTC – 2200 UTC 3-h nowcast: 2100 UTC – 0000 UTC (19)

  15. Hurricane Isabel on 18-19 September 2003 Statistics for 2100 UTC 18 September to 0000 UTC 19 September 2003

  16. Summary • Many opportunities for progress in precipitation estimation/nowcasting: • Blending of IR/MW data • New instruments and channels • Space-based lightning (and someday MW?) sensors • International cooperation—development, data sharing, and education—are essential for maximum impact

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