1. 1 Satellite QPE RFC/HPC Hydromet 02-1 COMET/Boulder, CO 28 November 2001 Bob Kuligowski NOAA/NESDIS/Office of Research and Applications Camp Springs, MD Bob.Kuligowski@noaa.gov (301) 763 -8251 x 192

2. 2 Outline  Why Use Satellite QPE?  Algorithm Description GOES IR-Based QPE Microwave-Based QPE Blended Algorithm  Algorithm Validation  Where to Find the Data

3. 3 Why Use Satellite QPE? Superior spatial coverage –Offshore coverage (tropical systems, Pacific storms) –Coverage outside CONUS –No beam block problems Consistency –Differences in calibration from radar to radar –Radar range effects –Beam overshoot (especially stratiform precip)  Not a replacement, but a companion to radar

4. 4 Comparison of WGRFC Stage III and satellite coverage for the 24h ended 1200 UTC 24 October 2000.

5. 5 GOES-Based QPE: Theory Basis –Assumes that cloud-top temperature  cloud-top height  cloud-top thickness  rainfall rate Strengths –24/7 coverage every 15 minutes throughout North America –High spatial resolution (~4 km) Weaknesses –Relationship between cloud-top properties and rain rate often does not hold, esp. for non-convective precipitation –Cold cirrus can be mistaken for cumulonimbus

6. 6 GOES-Based Algorithms  The Interactive Flash Flood Analyzer (IFFA) and Its Progeny: IFFA Auto-Estimator (AE) Hydro-Estimator (HE)  The GOES Multi-Spectral Rainfall Algorithm (GMSRA)

7. 7 Interactive Flash Flood Analyzer (IFFA) Manually-produced QPE based on features in GOES imagery (e.g. cold cloud tops, temperature changes, cloud mergers, etc.) Produced as needed for areas of significant rainfall Accompanying SPENES text messages

8. 8 IFFA (continued) Adjustment according to moisture availability (PWRH) Equilibrium level adjustment for relatively warm cloud tops Details in Scofield (MWR, 1987)

9. 9 Sample Interactive Flash Flood Analyzer (IFFA) Estimate

10. 10 Accompanying SPENES text bulletin

11. 11 Auto-Estimator (AE) Automated QPE algorithm: –Instantaneous rate estimates every 15 minutes –1-h, 3-h, and 6-h totals updated hourly –24-h totals at 1200 UTC Calibrated against radar for convective rainfall (power law fit to 10.7-  m T b ) Moisture adjustment (PWxRH) Dynamic (growth) adjustment Details in Vicente et al. (BAMS, 1997)

12. 12

13. 13 AE Improvements Equilibrium level correction –Uses Eta/AVN temperature/moisture profiles Radar screen of nonraining pixels –If no precip in radar, no precip in AE Orographic correction –Uses Eta/AVN 850 winds + digital topography

14. 14 AE rainfall estimates for the 72 h ended 1200 UTC 4 November 2000 Original Operational w/o Radar Mask Operational Raingauge Observations

15. 15 Validation for June-August 1999 Cold-Top Convection (2-4 hours) AlgorithmBias (mm)PODFARCC Original AE22.30.930.330.34 Operational AE9.40.920.240.50  Significant reduction in false alarms, and thus in overall wet bias, in the operational AE.

16. 16 Tips on Using the AE (courtesy of Rich Borneman, SAB) Best for convective events of significant duration/intensity Watch for overestimates for very cold tops with significant cirrus debris Most reliable totals are in the 1-6 h range; 24-h totals tend to be too high Location may be off by one or two counties with strong vertical wind shear—check against radar for location Despite EL adjustment, warm tops often underestimated

17. 17 Hydro-Estimator (HE) Cloud texture adjustment to rain rate curve— cloud “peaks” assigned heavier rain, while cloud “valleys” assigned no rain.  Significant improvement in distinguishing cirrus from cumulonimbus—eliminates dependence on radar Split PW and RH –PW used to adjust rain rate curve –RH used for linear subtraction from rain rate  Significant improvement in estimates for low- PW/high-RH regions (e.g. cold-season precip)

18. 18 AE rainfall estimates for the 72 h ended 1200 UTC 4 November 2000 HE w/o Radar Mask HE w/ Radar Mask Operational AE (Current) Raingauge Observations

19. 19 AE rainfall estimates for the 24 h ended 1200 UTC 9 November 2000 HE w/o Split PW HE w/ Split PW Operational (Current) Radar

20. 20 Future AE/HE Work Rain burst factor Correction for shearing cloud tops Cloud model experiments to quantify and calibrate AE/HE corrections

21. 21 GMSRA GOES Multi-Spectral Rainfall Algorithm –Instantaneous rate estimates every 30 minutes –1-h, 3-h, and 6-h totals updated hourly –24-h totals at 1200 UTC Calibrated against radar (fit of 10.7-  m brightness temperature to rain rate) Uses all 5 GOES imager channels: –Visible for cirrus identification (daytime only) –3.9- 10.7-, 12.0-  m for particle size (daytime only) –6.7-  m to distinguish overshooting tops from cirrus –10.7-  m for texture and cloud growth screens

22. 22 GMSRA (continued) Moisture adjustment (PWxRH) Details in Ba and Gruber (JAM, 2001)

23. 23 GMSRA rainfall estimates for the 24 h ended 1200 UTC 18 August 2000

24. 24 GMSRA Continuing Work Experimental nighttime warm-rain screen using 3.9-  m and 10.7-  m differences Improvement of calibration—longer calibration period and more varied meteorological situations

25. 25 Rainfall estimates for the 6 h ended 1100 UTC 8 September 2000 GMSRAGMSRA--Nighttime Warm-Rain Screen Radar

26. 26 Microwave QPE: Theory Basis –Scattering: ice in clouds scatters terrestrial radiation back downward, resulting in cold areas in MW imagery –Emission: water in clouds emits radiation, can be seen against a radiatively cold background (i.e. oceans) Strength –Amount of cloud water/ice much more strongly related to rain rate than cloud-top temperature Weaknesses –Only available on polar-orbiting platforms, limiting availability –Coarser spatial resolution than IR (15-48 km vs. 4 km)

27. 27 Microwave QPE Algorithms  Special Sensor Microwave/Imager (SSM/I)—available since 1987  Advanced Microwave Sounding Unit-A (AMSU-A)—available since 1999  Advanced Microwave Sounding Unit-B (AMSU-B)—available since 2000

28. 28 SSM/I Algorithms Scattering: T B at 19V, 22V, and 85V GHz regressed against radar data separately for land and ocean Emission: T B at 19V, 22V, and 37V GHz over water in regions of weak scattering Maximum rain rate of 35 mm/h Approximately 25-km horizontal resolution Available 6x/day (~0600, 0915, 1100, 1800, 2115, 2300 LST) Details in Appendix A of Ferraro (JGR, 1997)

29. 29 SSM/I Rainfall Estimates for 2115 LST 3 November 2000

30. 30 AMSU-A Algorithms Scattering: T B at 23, 50, and 89 GHz regressed against radar data over land Emission: T B at 23 and 50 GHz over water Maximum rain rate of 30 mm/h Approximately 48-km horizontal resolution Available 4x/day (~0130, 0730, 1330, 1930 LST) Details in Ferraro et al. (GRL, 2000)

31. 31 Comparison of SSM/I and AMSU-A Rain Rates, 8 November 2000

32. 32 AMSU-B Algorithm Scattering: T B and 89 and 150 GHz regressed against radar data over both land and ocean Maximum rain rate of 35 mm/h Approximately 16-km horizontal resolution Available 4x/day (~0130, 0730, 1330, 1930 LST) Details in Ferraro et al. (GRL, 2000)

33. 33 Rainfall estimates at 0730 LST 8 November 2000 AMSU-A Radar (Stage IV) AMSU-B

34. 34 NEXRAD False signature due to snow on ground 120 W 40 N mm/hr 5 10 15 20 25 30 35 40 45 DBZ Comparison of AMSU Algorithms at 0300 UTC 21 February 2000

35. 35 Microwave-IR Blended Algorithm Relationship between 10.7-  m T b and rain rate calibrated using microwave rain rate estimates –“Best of both worlds”—combine robustness of MW estimates with availability of GOES data Calibration updated every few hours for a 5x5- degree region Uses all operational Auto-Estimator adjustments (PWxRH, equilibrium level, etc.) Developed by F. J. Turk of NRL

36. 36 Blended rainfall estimate for the 24 h ended 1200 UTC 18 August 2000

37. 37 Satellite QPE Validation Initiated 1 April 2001 Six algorithms presently evaluated: –Auto-Estimator –Hydro-Estimator (with and without radar) –GMSRA (with and without nighttime cirrus screen) –GOES-Microwave blended algorithm Validation against Stage III (6-h totals) and gauges (24-h totals) Limited validation region at present (West Coast and southern Plains)

38. 38 2001 Validation for Southern Plains Cold-Top Convection (6-h amounts) RMSE (mm)Bias RatioCC SpringSummerSpringSummerSpringSummer AE5.25.81.251.090.630.58 HE4.95.91.021.220.510.52 HE rad 4.65.50.861.020.550.54 GMSRA 1 6.85.91.951.200.420.46 GMSRA 2 7.06.02.051.210.440.46 Blend6.25.91.131.000.500.54

39. 39 Southern Plains Cold-Top Convection (6-h amounts) Spring 2001Summer 2001

40. 40 Apr-May 2001 Validation for West Coast Stratiform Events RMSE (mm)Bias RatioCC 6-hDaily6-hDaily6-hDaily AE2.46.31.441.490.240.22 HE3.49.01.921.890.230.33 HE rad 3.08.31.211.140.210.17 GMSRA 1 1.64.20.180.170.120.14 GMSRA 2 1.64.30.190.200.12 Blend1.54.10.180.170.240.22

41. 41 Validation Summary AE performs slightly better than HE for cold- top convection, but HE does not need radar and does not have systematic wet bias like AE does HE (without radar) is a significant improvement over AE for West-coast stratiform precipitation (though it’s too wet) Blend is an improvement over AE in some situations but not others—need to investigate why GMSRA needs better calibration

42. 42 Summary Satellite QPE represents a companion to radar to compensate for radar limitations: –Covers offshore, non-CONUS, and mountainous regions where beam block presents problems –Satellite estimates are spatially consistent: no calibration differences, range effects, overshoot GOES IR-based QPE provides continuous, high-resolution coverage, but physics a problem Microwave-based QPE more physically robust, but available only intermittently Combination of the two offers promise

43. 43 Where to Find the Data IFFA: http://www.ssd.noaa.gov/SSD/ML/pcpn-ndx.html Auto-Estimator: http://orbit-net.nesdis.noaa.gov/arad/ht/ff/auto.html GMSRA: http://orbit-net.nesdis.noaa.gov/arad/ht/ff/gmsra.html SSM/I: http://orbit-net.nesdis.noaa.gov/arad2/ AMSU: http://orbit-net.nesdis.noaa.gov/arad2/MSPPS/index.html Blended Algorithm: http://orbit-net.nesdis.noaa.gov/arad/ht/ff/blended.html