The GOES-R Rainfall Rate, Rainfall Potential, and Probability of Rainfall Algorithms
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1 The GOES-R Rainfall Rate, Rainfall Potential, and Probability of Rainfall Algorithms Bob Kuligowski, NOAA/NESDIS/STAR Yaping Li, Zhihua Zhang, Richard Barnhill, I. M. Systems Group 5 th International Precipitation Working Group (IPWG) Workshop Hamburg, Germany, 12 October
2 Outline Review of GOES-R Status and Capabilities GOES-R RAlgorithm Working Group Algorithm Descriptions and Examples» Rainfall Rate Algorithm» Rainfall Potential Algorithm» Probability of Rainfall Algorithm Algorithm Validation Status and Future Work Summary 2
3 Review of GOES-R Status and Capabilities i Anticipated launch in late 2015 Advanced Baseline Imager (ABI) Increase from 5 to 16 spectral bands Improved spatial resolution (4 2 km IR; km VIS) Faster scanning (5-min full disk vs. 30 min) GOES Lightning i Mapper (GLM) Detects total lightning, not just cloud-to-ground Single-channel, near-ir optical detector Spatial resolution of ~10 km 3
4 GOES-R Algorithm Working G (A G) Group (AWG) Algorithm Teams (AT s) working together to develop a prototype GOES-R ground processing system Hydrology AT Products:» Rainfall Rate / QPE (current)» Probability of Rainfall (next 0-3 h)» Rainfall Potential (next 0-3h) Hydrology AT Members: Bob Kuligowski (STAR/SMCD), Chair Ralph Ferraro (STAR/CORP) Kuo-Lin Hsu (UC-Irvine) George Huffman (NASA-GSFC/SSAI) Sheldon Kusselson (OSDPD/SSD/SAB) Matthew Sapiano (UMCP/ESSIC) 4
5 Rainfall Rate Algorithm D i i Description IR algorithm calibrated in real time using MW rain rates» 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» Three cloud types: Water cloud : T 7.34 <T 11.2 and T 8.5 -T 11.2 <-0.3 "Ice cloud": T 7.34 <T 11.2 and T 8.5 -T "Cold-top convective cloud": T T 11.2» Four geographic regions: 60-30ºS, 30ºS-EQ, EQ-30ºN, 30-60ºN Two retrieval steps:» Rain / no rain separation via discriminant analysis» Rain rate via multiple linear regression 5
6 Rainfall Rate Algorithm Description i 8 predictors derived from 5 ABI bands T 6.19 T T 7.34 S = (T min, K) T T 7.34 T avg, T min, S T T 11.2 T T 6.19 T T additional nonlinear predictors» Regressed against the MW rain rates in log-log space 6
7 Rainfall Rate Algorithm Description i Initial SCaMPR rain rates strongly gyunderestimate heavy rain Adjust distribution» For each class and region, match the CDF of the SCaMPR rain rates against the CDF of the target MW rain rates» Create an interpolated LUT to modify the SCaMPR rain rate distribution 7
8 Rainfall Rate Algorithm Description i Apply most recent calibration in between new MW overpasses Update calibration when new MW rain rates available Retrieve rain rates from ABI data 8
9 Rainfall Rate Examples Radar Rainfall Rate 9
10 Rainfall Potential Algorithm Description 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 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 10
11 Rainfall Potential Examples Radar Rainfall Potential ti 11
12 Probability of Rainfall Algorithm Description Inputs» Rainfall Potential algorithm output (3-h total)» Intermediate (every 15 min) rainfall nowcasts from the Rainfall Potential algorithm. Calibrated using conditional probability tables» Rainfall Potential 1 mm: total number of raining 15-min periods» Rainfall Potential <1 mm: distance to nearest raining pixel Calibrated against the Rainfall Rate product» Eliminate uncertainties associated with Rainfall Rate errors;» Allow much more spatially widespread calibration (ground truth is generally available over Western Europe only) 12
13 Probability of Rainfall Examples Radar Probability of frainfall 13
14 Validation: Truth Data Time scales 3 h, so must validate against radar Validation datasets in SEVIRI region: Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar for Rainfall Rate Nimrod radar data from the British Atmospheric Data Centre (BADC) for all 3 algorithms 14
15 Rainfall Rate Fuzzy Validation Pixel-by-pixel comparisons difficult» Instantaneous rain rate varies too much at small scales Neighborhood comparison» Compare to most similar nearby value (Ebert 2008)» Better indication of usefulness» Not needed for 3-h Rainfall Potential / Probability 15 15
16 Rainfall Rate Validation Comparison with collocated TRMM PR for 6-9 January, April, July, and October 2005 and all of January
17 Rainfall Rate Validation CDF of (absolute) errors of Rainfall Rate pixels with rates of mm/h vs. TRMM PR for 51 days: 6-9 January, April, July, and October CDF of (absolute) errors of Rainfall Rate pixels with rates of mm/h vs. NIMROD radar data for 34 days: 6-9 April, July, and October
18 Rainfall Potential Validation Comparison with collocated Nimrod radar for 6-9 April, July, and October
19 Probability of Rainfall Validation Reliability diagram of Probability of Rainfall vs. Nimrod radar data for 5-9 April, July, and October
20 Validation 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 radar Accuracy Precision Accuracy Precision Validation against Nimrod for 6-9 April, July, and October 2005: Rainfall Rate (mm/h) Rainfall Potential (mm/3h) Requirement vs. NIMROD Accuracy Precision Accuracy Precision Requirement Evaluation Accuracy Precision Accuracy Precision (mm/3h) Probability of Rainfall (%) Requirement Evaluation Accuracy Precision Accuracy Precision
21 Status and Future Work Rainfall Rate:» Delivered final algorithm to System Prime 30 Sep 2011» Validation against an additional 4 months of data ongoing» Developing real-time and deep-dive dive validation tools for further evaluation and potential improvement» Maintenance delivery 30 September 2012 that incorporates feedback from deep-dive validation Rainfall Potential:» Optimizing parameters; final internal delivery May 2011» Final algorithm delivery to System Prime by 30 Sep 2011 Probability of Rainfall:» Continuing to recalibrate; final internal delivery May 2011» Final algorithm delivery to System Prime by 30 Sep
22 Summary Three rainfall-related algorithms for GOES-R:» Rainfall Rate» Rainfall Potential (0-3 h)» Probability of Rainfall (0-3 h) Performance:» Rainfall Rate and Rainfall Potential meet GOES-R spec» Probability of Rainfall partially meets spec and is being recalibrated Future Work:» Rainfall Rate has been finalized and is in the validation stage» Rainfall Potential and Probability of Rainfall are still being modified; final delivery September
23 Questions? 23
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