IMPROVEMENTS TO SCaMPR RAINFALL RATE ALGORITHM Yan Hao, I.M. Systems Group at NOAA, College Park, MD Robert J. Kuligowski, NOAA/NESDIS/STAR, College Park, MD Yaping Li, I.M. Systems Group at NOAA, College Park, MD Original Algorithm Description SCaMPR = Self-Calibrating Multivariate Precipitation Retrieval (Kuligowski 2002; Kuligowski et al. 2013). Running in real time at NESDIS/STAR since The calibration dataset is a rolling-value matched MW-IR dataset with 500 pixels with rates ≥2.5 mm/h (Fig. 1) that is updated whenever new MW rain rates from SSM/I or AMSU/MHS become available (Fig. 2): Discriminant analysis selects the best two rain / no rain predictors; Linear regression selects the best two rain rate predictors (including nonlinear transformations of the predictors). Separate calibrations for overlapping 15x15° regions to account for spatial variability in rainfall type and climatology. Motivation Rain rate estimates from microwave (MW)-frequency data are available only every few hours with a latency of up to 3 h, limiting their direct use in operational forecasting. Infrared (IR)-wavelength data is available every 15 min in many areas with a latency of minutes--highly suitable for operational weather forecasting. Calibrating an IR-based algorithm with MW-based rain rates can optimize accuracy, frequency, and timeliness. GOES-R Algorithm Modifications TMI rain rates were added to the MW calibration data by using the CPC MWCOMB dataset (Joyce et al. 2004) instead of just SSM/I and AMSU/MHS. A version of SCaMPR was developed to take advantage of the additional capabilities of the Advanced Baseline Imager(ABI) on GOES-R, and some of the changes were incorporated into the current-GOES version of the algorithm: Modified predictor set: T 6.7, T T 6.7, S, G t -S (+4 in ABI) Two cloud types (3 in ABI), based on value of T T 6.7 Larger calibration regions (30° latitude bands) Adjusting rain rates via PDF matching with MW rain rates The new version was implemented in real time in August 2011 and is being evaluated by several NWS Forecast Offices in collaboration with the NASA Short-Term Prediction Research and Transition Center (SPoRT). In response to SPoRT feedback and internal validation, added a GFS-based model RH correction; other changes in progress. Comparison with Previous Version of SCaMPR Validation against hourly Stage IV / MPE data at 1-h time resolution for all of There is slight conditional wet bias in the original version is largely eliminated in the new version (Fig. 3). Missed rainfall increases slightly, but false alarms (the primary source of error) are reduced more than enough to compensate, so total error is reduced by abut 30% (Fig. 3). Excessive light rain is reduced in new version and moderate to heavy rain is skillfully added—false alarms decrease and HSS increases at entire range of 1-h rainfalls (Fig. 4). Comparison with Current Operational NESDIS Algorithm (Global Hydro-Estimator) SCaMPR has slightly less additive hit bias than the Global Hydro-Estimator (GHE; Fig. 5). SCaMPR misses slightly more rain than the GHE, but has less false alarm rainfall and thus lower total error (Fig. 5). However, SCaMPR does not perform quite as well as the GHE for moderate to heavy rain—it is still too dry (Fig. 6). This is not reflected in the Fig. 5 statistics since they are so heavily weighted toward light rain. As a caveat, the full GOES-R version of the algorithm performs better than the GHE on test ABI data (not shown). Ongoing Algorithm Modifications Developed an Relative Humidity (RH) correction by relating the bias in MWCOMB (vs. Stage IV) to RH to account for sub- cloud evaporation, and found false alarms greatly reduced, but introduced additional missed rainfall (Fig. 7). Calibrating on smaller regions (15°x15° instead of 30°latitude bands), showing improved consistency in rain rates between deep convective and non-deep-convective rainfall (Fig. 8), improved consistency in rain rates from one period to the next (Fig. 9), and improved consistency between GOES-West and GOES-East (Fig. 10). Taking Q3 dual-pol radar as calibration data over the CONUS instead of MWCOMB to test if there is improvements to the accuracy of SCaMPR. Q3 data were aggregated onto GOES grids. Our statistical results (Fig.11) follow the pattern of MRMS analysis (Fig. 12). The initial analysis showed that using Q3 actually degraded performance compared to using MWCOMB to calibrate. We are applying gauge correction to Q3 and limit the data quality to 80 percent good to improve the input calibration data quality. Future Work Complete work in ingesting radar and test the SCaMPR performance with radar calibration. Improve the RH correction by calibrating MWCOMB against Q3 instead of Stage IV (reduces uncertainty from comparing instantaneous MWCOMB with 1-h Stage IV). Re-visit work on an EL correction (initially no impact) using Q3 instead of Stage IV. Include rain rates derived from GOES cloud property information and evaluate impact on warm-cloud light rain which IR and MW typically have difficulty detecting. Continue experiments with orographic rainfall modulation Via GOES-R supported work. Develop an optimal methodology to merge the resulting estimates of SCaMPR with radar and gauge data. Evaluate the impact of these advances on hydrologic forecasts. References Chen, R., et al., 2011: A study of warm rain detection using A-Train satellite data. Geophys. Res. Lett., 38, doi: /2010GL Joyce, R. J. et al., 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, Kuligowski, R. J., 2002: A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3, , Y. Li, and Y. Zhang, 2013: Impact of TRMM data on a low-latency, high-resolution precipitation algorithm for flash flood forecasting. J. Appl. Meteor. Cli., 52, Lee, H., et al., 2014: Utility of SCaMPR satellite versus ground-based quantitative precipitation estimates in operational flood forecasting: the effects of TRMM data ingest. J. Hydrometeor., 15, Zhang, Y., et al., 2013: Comparative strengths of SCaMPR satellite QPEs with and without TRMM ingest versus gridded gauge-only analyses. J. Hydrometeor., 14, Figure 1. Illustration of the rolling-value matched MW-IR data file.Figure 2. SCaMPR data processing diagram. Figure 5. Tukey box plots of SCaMPR and GHE hourly additive hit bias, missed rainfall, false alarms, and total error as a fraction of total Stage IV rain volume for Figure 6. SCaMPR and GHE POD, FAR, area bias, and HSS as a function of hourly Stage IV accumulation for Figure 3. Tukey box plots of SCaMPR hourly additive hit bias, missed rainfall, false alarms, and total error as a fraction of total Stage IV rain volume for Figure 4. SCaMPR POD, FAR, area bias, and HSS as a function of hourly Stage IV accumulation for Figure 8. Example of improved consistency in rain rates between deep convective and non-deep-convective rainfall. Figure 7. Tukey box plots of false alarms, missed rainfall as a fraction of total Stage IV for GHE, SCaMPR and SCaMPR with RH correction. Figure 9. Example of improved consistency in rain rates from one period to the next UTC1515 UTC Original Calibration Regions 1445 UTC1515 UTC 15x15º Calibration Regions Figure 10. Comparison of GOES-W and GOES-E rain rates at 2345 UTC 25 July Figure 11. Fractional mean absolute error of STIV and Q3 (over 50% good quality) with respect to gauges. Figure 12. Fractional mean absolute error of four daily QPEs with respect to the CoCoRaHS gauges. Stage-II radar only MRMS radar only MRMS local gauge bias corrected STIV Ackonowledgments : This work was supported by NOAA and the PMM Science Team and by the GOES-R Program Office. DISCLAIMER: The contents of this poster are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of NOAA or the U.S. Government.