1. 1 Recent Improvements to the GOES-R Rainfall Rate Algorithm 1 July 2015 Presented By: Bob Kuligowski NOAA/NESDIS/STAR Yaping Li, Yan Hao I. M. Systems Group

2. 2 Outline  Background »Motivation »Satellite QPE Basics  The Basic GOES-R Rainfall Rate Algorithm  Recent Improvements  Next Steps

3. 3 Motivation  Radar is highly valuable, but provides incomplete coverage due to »Beam block »Beam overshoot »Radar unit placement  This is particularly challenging in regions with complex terrain and when rainfall is coming from offshore Radar Apparent edge of rainMe holding umbrella

4. Satellite QPE Basics: IR  Motivation: low latency and frequent refresh of IR is ideal for rainfall monitoring  IR-based algorithms retrieve rain rates based on cloud- top brightness temperatures (properties)  Works well for convective rainfall but not for stratiform rainfall and warm-top rainfall (i.e., HI) 200 250 290 T (K) T b =230 K T b =224 K T b =212 K T b =200 K Cirrus T b =210 K Nimbostratus T b =240 K 200250290 T (K) Cumulonimbus T b =200 K 4

5. Satellite QPE Basics: MW  Motivation: better physics than IR since clouds are semi- transparent at MW frequencies: »Enhanced emission at low frequencies by cloud water »Enhanced backscattering of upwelling radiation by cloud ice  Emission over land only, so significant detection problems for low-ice clouds over land  Latency of up to 3 h w/o direct readout; refresh only a few times per day 5 Ocean (Emission) Lower T b above clear air Higher T b above cloud Low ε High ε               Land (Scattering) Lower T b above cloud Higher T b above clear air * * * * * * * * * * *   

6. Other Satellite QPE Issues  Primary interest is in rainfall rates at ground level; satellites detect cloud-top (IR) or cloud-level (MW) characteristics.  Thus, no direct accounting for: »Orographic effects »Subcloud evaporation of hydrometeors »Subcloud phase changes (e.g., snow to rain / sleet)  Some of these issues can be addressed with the help of NWP model data 6

7. Implications for Satellite QPE Users  Satellite rain rate estimates are best for convective rain— as good as and sometimes even better than (single-pol) radar without gauge correction (Gourley et al. 2010)  Satellite rain rate estimates still perform poorly for stratiform precipitation—in fact, NWP model forecasts are often more skillful than satellite QPE in higher latitudes during the cool season  Satellite QPE has value, but users need to be aware of its limitations to maximize its usefulness 7

8. 8 Outline  Background »Motivation »Satellite QPE Basics  The Basic GOES-R Rainfall Rate Algorithm  Recent Improvements  Next Steps

9. 9 Rainfall Rate Overview  Estimates of instantaneous rainfall rate… »…every 15 minutes »…at the full ABI pixel resolution (2 km at nadir) »…with a latency of less than 5 minutes »…over the entire full disk  MW-derived rain rates are used to calibrate an algorithm that uses parallax-corrected IR data as inputs: »Only IR provides rapid refresh and low latency »Objective: optimal calibration for a particular geographic area, cloud type, and season. »Calibration is updated whenever new MW data become available

10. 10 Rainfall Rate Calibration  Calibration dataset: »A rolling-value dataset, updated when new MW rain rates become available »Separate datasets for different geographic regions and cloud types  Calibration process: »Discriminant analysis selects up to 2 rain / no rain predictors and calibrates »Linear regression selects up to 2 rain rate predictors and calibrates »Nonlinear transformations (vs. rain rate) added to data set »Histogram matching removes leftward skew (dry bias) in rain rates No adjustment Interpolated adjustment Data- based adjust- ment

11. 11 Outline  Background »Motivation »Satellite QPE Basics  The Basic GOES-R Rainfall Rate Algorithm  Recent Improvements  Next Steps

12. Current-GOES Test Version  A test version of the Rainfall Rate algorithm has been running on current GOES since Aug 2011 and distributed by SPoRT to selected WFOs and RFCs »Coverage: both GOES, 165ºE – 15ºW and 60ºS – 70ºN »Temporal resolution: same as routine GOES scan schedule. Instantaneous rates every 15 min; 1, 3, 6-h totals on the hour; 1, 3, and 7-dy totals at 12Z daily  Significantly simplified (1 WV band, no 8.5 or 12.0µm bands; only 2 cloud types—no water/ice discrimination)  Still has value for evaluation and testing improvements; forecaster feedback has been very helpful

13. 13 Improvement: RH Correction  Many false alarms with new version (often due to lack of extra WV and IR bands); developed an RH correction based on RH-dependent biases in MWCOMB  Impact: reduced false alarms, particularly over the western US and AK No RH correctionWith RH correctionStage IV

14. 14 RH Correction for Tropical Storm Ana, 15-19 Oct 2014 Total Rainfall vs. Gauges No RH CorrectionWith RH Correction

15. 15 Improvement: Smaller Calibration Regions  Users (especially MFR) noticed non-physical changes in rain rates with time; traced back to the size of the calibration regions, which exacerbated limb effects.  Users also noticed “holes” where the convective and non- convective rain rates were inconsistent with each other.  Solution: shrank the calibration regions from 30ºx120º lat/lon bands to 15ºx15º.  Impact: much more stable calibration (though not perfect); holes dealt with.

16. 16 Tropical Storm Ana, 15-19 Oct 2014 OriginalSmaller Calibration Regions

17. 17 Outline  Background »Motivation »Satellite QPE Basics  The Basic GOES-R Rainfall Rate Algorithm  Recent Improvements  Next Steps

18. 18 Current Work: Radar Calibration  GOES-R Risk Reduction-supported work to investigate including dual-pol radar in the calibration data set to improve accuracy, especially where the MW rain rates have less skill (e.g., cool season, shallow rainfall).  Initial results have shown little positive impact over the CONUS (at least in the summer), but extension of the study to AK and HI may yield better results.

19. 19 Near-Term: Himawari AHI  STAR has begun receiving AHI data in real time.  Planning to begin producing AHI rain rates in real time this summer and to make them available through SPoRT

20. 20 Future Work  Use of gauge-adjusted dual-pol radar as an improved ground validation dataset to develop and test: »An improved RH correction; »An improved EL adjustment (first one had neutral impact);  Use of (daytime) cloud property retrievals to: »Improve removal of cirrus anvils; »Improve sensitivity to warm-cloud rainfall;  Orographic adjustment: efforts to develop an adjustment have not yielded good results, so will begin evaluating currently existing adjustments for suitability.

21. 21 An Important Caveat  The ground system for GOES-R was prototyped by STAR, but the final system was coded up by a contractor.  The operational code reflects the version given to them in September 2010, so improvements since then will NOT be in the initial operational version of the algorithm!  Furthermore, because of perceived risk this initial version of the algorithm will use a fixed calibration.  We work with the GOES-R program to get changes implemented into the ground system; it’s unclear at this time how long this will take.  I will run the most recent version of the algorithm in real time at STAR (albeit with 8x5 support) and can make the output available through SPoRT if there is interest.

22. 22 Questions?