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Satellite QPE RFC/HPC Hydromet 02-1 COMET/Boulder, CO 28 November 2001 Bob Kuligowski

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. Outline. Why Use Satellite QPE? Algorithm Description GOES IR-Based QPE

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Satellite QPE RFC/HPC Hydromet 02-1 COMET/Boulder, CO 28 November 2001 Bob Kuligowski

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

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

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

  9. Sample Interactive Flash Flood Analyzer (IFFA) Estimate

  10. Accompanying SPENES text bulletin

  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-mm Tb) • Moisture adjustment (PWxRH) • Dynamic (growth) adjustment • Details in Vicente et al. (BAMS, 1997)

  12. 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

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

  14. Validation for June-August 1999 Cold-Top Convection (2-4 hours) • Significant reduction in false alarms, and thus in overall wet bias, in the operational AE.

  15. 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

  16. 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)

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

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

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

  20. 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-mm brightness temperature to rain rate) • Uses all 5 GOES imager channels: • Visible for cirrus identification (daytime only) • 3.9- 10.7-, 12.0-mm for particle size (daytime only) • 6.7- mm to distinguish overshooting tops from cirrus • 10.7-mm for texture and cloud growth screens

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

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

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

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

  25. 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)

  26. 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

  27. SSM/I Algorithms • Scattering: TB at 19V, 22V, and 85V GHz regressed against radar data separately for land and ocean • Emission: TB 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)

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

  29. AMSU-A Algorithms • Scattering: TB at 23, 50, and 89 GHz regressed against radar data over land • Emission: TB 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)

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

  31. AMSU-B Algorithm • Scattering: TB 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)

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

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

  34. Microwave-IR Blended Algorithm • Relationship between 10.7- mm Tb 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

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

  36. 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)

  37. 2001 Validation for Southern Plains Cold-Top Convection (6-h amounts)

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

  39. Apr-May 2001 Validation for West Coast Stratiform Events

  40. 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

  41. 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

  42. 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

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