1 Preliminary Validation of the GOES-R Rainfall Rate Algorithm(s) over Guam and Hawaii 30 June 2016 Presented By: Bob Kuligowski NOAA/NESDIS/STAR

2 Outline  Satellite QPE Basics  The GOES-R Rainfall Rate Algorithm »Version 5 (initial operational version) »Improvements since Version 5  Initial Validation results over Guam and Hawaii  Next Steps

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) 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 T (K) Cumulonimbus T b =200 K 3

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 (i.e., HI)  Latency of up to 3 h w/o direct readout; refresh only a few times per day 4 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 * * * * * * * * * * *   

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  Satellite QPE has value, but users need to be aware of its limitations to maximize its usefulness 5

6 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

7 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

8 Rainfall Rate: SPoRT Version vs. “Day 1” Ops Version  The version being distributed by SPoRT on AWIPS2 is different from the version that will be used operationally after launch in several important ways: 1.The operational version will have a static calibration instead of one that updates in time 2.The operational version will not be parallax-corrected 3.The operational version will not have a RH correction for subcloud evaporation 4.The operational version has larger calibration regions »Less consistency in rain rates among rainfall types

9 Day 1 Version cont.  We will work with the GOES-R program to get the 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.

10 Validation Results over Guam and Hawaii  Compared 1-h gauge totals from 29 USGS gauges in Guam and Hawaii to 1-h single-pixel totals from the GOES-R rainfall algorithm applied to ABI for 18 July 2015 to 31 Jan 2016

11 Volume Bias vs. Elevation Guam: moderate dry bias, some improvement in science algorithm HI: very strong dry bias; science algorithm is even drier!

12 Hourly Correlation vs. Elevation Guam: pretty good correlation; science algorithm improves HI: correlation not as good as Guam but still pretty good; significant degradation in science algorithm

13 Time Series for Dededo, Guam

14 Time Series for Dededo, Guam (14-16 Aug 2015)

15 Time Series for Dededo, Guam (24-26 Aug 2015)

16 QPE and AHI Band UTC 25 Aug 2015

17 Time Series for Waiahole, Oahu, HI

18 Guam vs. Hawaii

19 Time Series for Waiahole, Oahu, HI (3-7 Sep 2015)

20 QPE and AHI Band UTC 7 Sep 2015

21 Surprise! It’s a leftover parallax issue!

22 Next Steps  More evaluation, including RT validation over the Himawari coverage area (nearly done)  Fix the parallax correction!  Figure out exactly why the science version degrades the results over HI (can’t blame parallax)  Improve the RH correction (removes too much heavy rain)  Continue to evaluate (daytime) cloud property retrievals to »Improve removal of cirrus anvils; »Improve sensitivity to warm-cloud rainfall;  Develop / steal a reasonably robust orographic correction

23 Questions?