The GOES-R Rainfall Rate, ,Rainfall Potential, and Probability

of Rainfall AlgorithmsBob Kuligowski, NOAA/NESDIS/STAR

Yaping Li Zhihua Zhang Richard Barnhill I M SystemsYaping Li, Zhihua Zhang, Richard Barnhill, I. M. Systems Group

5th International Precipitation Working Group (IPWG) Workshop

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pHamburg, Germany, 12 October 2010

OutlineOutline

Review of GOES-R Status and CapabilitiesGOES R Al ith W ki G GOES-R Algorithm Working Group

Algorithm Descriptions and Examples» Rainfall Rate Algorithm» Rainfall Potential Algorithmg» Probability of Rainfall Algorithm

Algorithm Validation Algorithm Validation Status and Future Work Summary

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Review of GOES-R Status and C bili iCapabilities

Anticipated launch in late 2015 Advanced Baseline Imager (ABI) Increase from 5 to 16 spectral bandsp Improved spatial resolution (42 km IR; 10.5 km

VIS)VIS) Faster scanning (5-min full disk vs. 30 min)

GOES Li ht i M (GLM) GOES Lightning Mapper (GLM) Detects total lightning, not just cloud-to-ground Single-channel, near-IR optical detector Spatial resolution of ~10 km

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Spatial resolution of 10 km

GOES-R Algorithm Working G (A G)Group (AWG)

Algorithm Teams (AT’s) working together to develop a prototype GOES-R ground processing systemprototype GOES R ground processing system

Hydrology AT Products:» Rainfall Rate / QPE (current)» Rainfall Rate / QPE (current)» Probability of Rainfall (next 0-3 h)» Rainfall Potential (next 0 3 h)» Rainfall Potential (next 0-3 h)

Hydrology AT Members: Bob Kuligowski (STAR/SMCD) Chair Bob Kuligowski (STAR/SMCD), Chair Ralph Ferraro (STAR/CORP) Kuo-Lin Hsu (UC-Irvine)Kuo Lin Hsu (UC Irvine) George Huffman (NASA-GSFC/SSAI) Sheldon Kusselson (OSDPD/SSD/SAB)

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( ) Matthew Sapiano (UMCP/ESSIC)

Rainfall Rate Algorithm D i iDescription

IR algorithm calibrated in real time using MW rain rates» IR continuously available but weaker relationship to rain rate» IR continuously available, but weaker relationship to rain rate» MW more strongly related to rain rate, but available ~every 3 h

Calibration by type and region Calibration by type and region» Three cloud types:

“Water cloud”: T <T and T T < 0 3– Water cloud : T7.34<T11.2 and T8.5-T11.2<-0.3– "Ice cloud": T7.34<T11.2 and T8.5-T11.2≥-0.3– "Cold-top convective cloud": T7 34≥T11 2Cold top convective cloud : T7.34≥T11.2

» Four geographic regions: 60-30ºS, 30ºS-EQ, EQ-30ºN, 30-60ºN

Two retrieval steps: Two retrieval steps:» Rain / no rain separation via discriminant analysis» Rain rate via multiple linear regression

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» Rain rate via multiple linear regression

Rainfall Rate Algorithm D i iDescription

8 predictors derived from 5 ABI bandsT T - TT6.19 T8.5 - T7.34

S = 0.568-(Tmin,11.2-217 K) T11.2 - T7.34

T T S T TTavg,11.2 - Tmin,11.2 - S T8.5 - T11.2

T7.34 - T6.19 T11.2 - T12.3

8 additional nonlinear predictors» Regressed against the MW rain rates in log-log space

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Rainfall Rate Algorithm D i iDescription

Initial SCaMPR rain rates strongly underestimate g yheavy rain

Adjust distributionj» For each class and region,

match the CDF of the SCaMPR rain rates against the CDF of the target MW rain ratesrain rates

» Create an interpolated LUT to modify the SCaMPR rainto modify the SCaMPR rain rate distribution

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Rainfall Rate Algorithm i iDescription

Update calibration

Apply most recent

when new MW rain

t

calibration in between

MW rates available

new MW overpasses

Retrieve rain rates from ABI data 8

Rainfall Rate ExamplesRainfall Rate Examples

Radar

Rainfall R tRate

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Rainfall Potential Algorithm D i tiDescription

Identify features (clusters) in Rainfall Rate imagery» Filter rain rate image to reduce noise» Use cost minimization to organize pixels into clusters» Combine smaller clusters into larger ones

Determine motion vectors between features in consecutive images » For each cluster in current image, determine spatial offset that maximizes

match with corresponding cluster in previous imagematch with corresponding cluster in previous image» Objectively analyze the resulting spatial offsets for all clusters to create a

spatially distributed motion field● Apply motion vectors to create rainfall nowcasts

» In 15-minute increments…– Project each pixel forward in time based on motion vectors– Project motion vectors forward in time

» Sum 15-min rain rate fields to get a 3-hour total

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Rainfall Potential ExamplesRainfall Potential Examples

Radar

Rainfall P t ti lPotential

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Probability of Rainfall Al ith D i tiAlgorithm Description

InputsRainfall Potential algorithm output (3 h total)» Rainfall Potential algorithm output (3-h total)

» Intermediate (every 15 min) rainfall nowcasts from the Rainfall Potential algorithmPotential algorithm.

Calibrated using conditional probability tables» Rainfall Potential ≥1 mm: total number of raining 15 min periods» Rainfall Potential ≥1 mm: total number of raining 15-min periods» Rainfall Potential <1 mm: distance to nearest raining pixel

C lib t d i t th R i f ll R t d t Calibrated against the Rainfall Rate product» Eliminate uncertainties associated with Rainfall Rate errors;

All h ti ll id d lib ti ( d t th i» Allow much more spatially widespread calibration (ground truth is generally available over Western Europe only)

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Probability of Rainfall lExamples

Radar

Probability f R i f llof Rainfall

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Validation: Truth DataValidation: Truth Data

Time scales ≤3 h, so must validate against radarg

Validation datasets in SEVIRI region: Tropical Rainfall Measuring MissionTropical Rainfall Measuring Mission

(TRMM) Precipitation Radar for Rainfall Rate

Nimrod radar data from the British Atmospheric Data Centre (BADC) for all 3 algorithms3 algorithms

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Rainfall Rate “Fuzzy” yValidation

Pixel-by-pixel comparisons difficultdifficult» Instantaneous rain rate

varies too much at smallvaries too much at small scales

Neighborhood comparison Neighborhood comparison » Compare to most similar

nearby value (Ebert 2008)nearby value (Ebert 2008) » Better indication of

usefulnessusefulness» Not needed for 3-h Rainfall

Potential / Probability

151515

Potential / Probability

Rainfall Rate ValidationRainfall Rate Validation

Comparison with collocated TRMM PR for 6-9 January, April, July, and

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October 2005 and all of January 2008.

Rainfall Rate ValidationRainfall Rate Validation

CDF of (absolute) errors of RainfallCDF of (absolute) errors of Rainfall CDF of (absolute) errors of Rainfall Rate pixels with rates of 9.5-10.5 mm/h vs. NIMROD radar data for 34 days: 6-9 April July and October 2005

CDF of (absolute) errors of Rainfall Rate pixels with rates of 9.5-10.5 mm/h vs. TRMM PR for 51 days: 6-9 January, April July and October 2005

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9 April, July, and October 2005.April, July, and October 2005.

Rainfall Potential ValidationRainfall Potential Validation

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Comparison with collocated Nimrod radar for 6-9 April, July, and October 2005.

Probability of Rainfall yValidation

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Reliability diagram of Probability of Rainfall vs. Nimrod radar data for 5-9 April, July, and October 2005.

Validation Summary vs. SpecValidation Summary vs. Spec

Validation versus TRMM PR for 51 days of data: 6-9 January, April, July, and October 2005 and all of January 2008:

Rainfall Rate (mm/h)

Requirement vs. TRMM radarAccuracy Precision Accuracy Precision

6 0 9 0 4 9 8 96.0 9.0 4.9 8.9

Rainfall Rate Requirement vs. NIMROD

Validation against

Rainfall Rate (mm/h) Accuracy Precision Accuracy Precision

6.0 9.0 8.6 9.7Validation against Nimrod for 6-9 April, July, and

Rainfall Potential (mm/3h)

Requirement EvaluationAccuracy Precision Accuracy Precision

5 0 5 0 2 4 3 1

Probability of f (%)

Requirement EvaluationAccuracy Precision Accuracy Precision

October 2005:(mm/3h) 5.0 5.0 2.4 3.1

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Rainfall (%) Accuracy Precision Accuracy Precision25 40 25 71

Status and Future WorkStatus and Future Work

Rainfall Rate:» Delivered “final” algorithm to System Prime 30 Sep 2011» Delivered final algorithm to System Prime 30 Sep 2011» Validation against an additional 4 months of data ongoing» Developing real-time and “deep-dive” validation tools for further» Developing real time and deep dive validation tools for further

evaluation and potential improvement» “Maintenance” delivery 30 September 2012 that incorporates y p p

feedback from “deep-dive” validation

Rainfall Potential:» Optimizing parameters; “final” internal delivery May 2011» Final algorithm delivery to System Prime by 30 Sep 2011g y y y p

Probability of Rainfall:» Continuing to recalibrate; “final” internal delivery May 2011

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» Continuing to recalibrate; final internal delivery May 2011» Final algorithm delivery to System Prime by 30 Sep 2011

SummarySummary

Three rainfall-related algorithms for GOES-R:» Rainfall Rate» Rainfall Rate» Rainfall Potential (0-3 h)» Probability of Rainfall (0-3 h)» Probability of Rainfall (0 3 h)

Performance:» Rainfall Rate and Rainfall Potential meet GOES R spec» Rainfall Rate and Rainfall Potential meet GOES-R spec» Probability of Rainfall partially meets spec and is being

recalibratedrecalibrated

Future Work:» Rainfall Rate has been finalized and is in the validation stage» Rainfall Rate has been finalized and is in the validation stage» Rainfall Potential and Probability of Rainfall are still being

modified; final delivery September 2011

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modified; final delivery September 2011

Questions?Questions?

Bob.Kuligowski@noaa.gov

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