Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR)

1. Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) Bob Kuligowski, Shuang Qiu, Jung-Sun Im NOAA/NESDIS/ORA

2. Theory / Schematic • Algorithm Inputs (IR, MW, NWP): • 6.9, 10.7, and 13.2-µm Tb’s from GOES-12 • SSM/I rain rates from NRL • AMSU rain rates from MSPPS (Ferraro) • All GOES inputs are from GVAR feeds • Algorithm Process (how the inputs are converted to rainfall estimates) • Rain/no rain separation via discriminant analysis • Rain rate estimation via stepwise linear regression • IR Calibration Data Cube • GOES inputs aggregated to SSM/I or AMSU footprints plus SSM/I or AMSU rain rates • Most recent matched data used such that 5,000 raining points are available (nonraining points are included, but the number does not matter)

3. SCaMPR Predictors SCaMPR Predictands GOES T6.9 µm GOES T10.7 µm GOES T13.3 µm SSM/I Rain Rates T10.7-T6.9 T10.7-T13.3 AMSU Rain Rates Derived Quantities S = 0.568 (T10.7,min-217 K) Gt-S = (T10.7,avg-T10.7,min)-S Aggregate to microwave footprint Match predictor and predictand pixels (separate sets for raining and non-raining predictor pixels) Dry pixels Raining pixels Calibrate rain/no rain discrimination via discriminant analysis Calibrate rain rate estimation via multiple regression Apply to independent predictor data for rain rate retrievals

4. Theory / Schematic • Strengths and Weaknesses of Underlying Assumptions • Only as reliable as the SSM/I and AMSU rain rates; SCaMPR will perform poorly where they perform poorly • Differences between SSM/I and AMSU rain rates make adjustments necessary before calibration • Currently calibrated for CONUS as a whole, but geographic differences in Tb-rain rate relationships are inducing time-varying biases in SCaMPR rain rates • Balance of having a long enough training period for statistically significant training, but a short enough training period to capture nonstationarity in relationships between predictors and rain rate; no objective way to determine this

5. Theory / Schematic • Planned Modifications / Improvements • Currently transitioning from all-CONUS to regional calibration • Soon to begin real-time ingest of NAM PW, 1000-700 hPa mean RH, convective EL computed from T, q profile (already shown to have positive impact on case studies) • Soon to begin real-time ingest of cloud-to-ground lightning from National Lightning Detection Network (NLDN)—already shown to have positive impact on case studies • Transition to global production planned for fall

6. Algorithm Output Information • Spatial Resolution: 4 km • Spatial Coverage: CONUS (25–50°N; 125-65°W) • Update Frequency: every 15 min • Data Latency: ??? • Source of Real-Time Data: • Soon to be made available on the Web (graphic images only) as a link from the Flash Flood Web page (http://www.orbit.nesdis.noaa.gov/smcd/emb/ff)

7. Algorithm Output Information • Source of Archive Data: • Limited online archive at http://www.orbit.nesdis.noaa.gov/smcd/emb/ff/validation/validation.html • Offline CONUS archive back to November 2004 • Capability of Producing Retrospective Data (data and resources required / available) • SSM/I and AMSU rain rates can be reproduced from CLASS archive • GOES data available to December 2003 from CLASS archive • Eta data available from ORA tape archive back to 1997