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
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2 Outline Background Motivation Satellite QPE Basics The Basic
GOES-R Rainfall Rate Algorithm Recent Improvements Next Steps
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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
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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
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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 High Land (Scattering) Lower T b above cloud Higher T b above
clear air * * * * * * * * * * *
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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
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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 precipitationin 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
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8 Outline Background Motivation Satellite QPE Basics The Basic
GOES-R Rainfall Rate Algorithm Recent Improvements Next Steps
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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
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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
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11 Outline Background Motivation Satellite QPE Basics The Basic
GOES-R Rainfall Rate Algorithm Recent Improvements Next Steps
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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,
165E 15W and 60S 70N 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 12.0m bands; only 2 cloud typesno water/ice
discrimination) Still has value for evaluation and testing
improvements; forecaster feedback has been very helpful
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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
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14 RH Correction for Tropical Storm Ana, 15-19 Oct 2014 Total
Rainfall vs. Gauges No RH CorrectionWith RH Correction
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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 30x120 lat/lon bands to 15x15.
Impact: much more stable calibration (though not perfect); holes
dealt with.
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16 Tropical Storm Ana, 15-19 Oct 2014 OriginalSmaller
Calibration Regions
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17 Outline Background Motivation Satellite QPE Basics The Basic
GOES-R Rainfall Rate Algorithm Recent Improvements Next Steps
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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.
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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
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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.
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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; its 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.
