1. 1 FWC 2006-10-24 IPWG MIT Lincoln Laboratory * This work was sponsored by the National Aeronautics and Space Administration under Contract NNG 04HZ53C, Grant NNG 04HZ51C, and Grant NAG5-13652, and the National Oceanic and Atmospheric Administration under Air Force Contract FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Government. Satellite-based Estimation of Precipitation Using Passive Opaque Microwave Radiometry* Frederick W. Chen, Laura J. Bickmeier, William J. Blackwell, R. Vincent Leslie MIT Lincoln Laboratory (Lexington, MA, USA) David H. Staelin, Chinnawat “Pop” Surussavadee MIT Research Laboratory of Electronics (Cambridge, MA, USA) 3 rd Workshop of the International Precipitation Working Group Melbourne, VIC, Australia 24 October 2006

2. MIT Lincoln Laboratory 2 FWC 2006-10-24 IPWG Outline Physical basis Algorithm development –AMSU (Advanced Microwave Sounding Unit) –ATMS (Advanced Technology Microwave Sounder) Future work Summary

3. MIT Lincoln Laboratory 3 FWC 2006-10-24 IPWG Physical Basis Transparent channels (or window channels) –Warm water vapor signatures over cold ocean –Scattering signatures due to ice particles over land Opaque channels –Varying atmospheric opacity –Sensitive primarily to specific layers of atmosphere OPAQUE BANDS TRANSPARENT BANDS

4. MIT Lincoln Laboratory 4 FWC 2006-10-24 IPWG 54-GHz and 183-GHz Weighting Functions 54-GHz 183-GHz

5. MIT Lincoln Laboratory 5 FWC 2006-10-24 IPWG Estimation of Precipitation Rate with Opaque  W Channels (54-GHz and 183-GHz) Precipitation rate ~  humidity × vertical wind velocity Absolute humidity –54-GHz band reveal temperature profile –54-GHz and 183-GHz bands reveal water vapor profile Vertical wind velocity –Stronger vertical wind → –Stronger vertical winds results in increased backscattering of cold space radiation –Perturbations (cold spots) in 54-GHz data reveal cloud-top altitude –Absolute albedos reveal hydrometeor abundance –Relative albedos (54 vs. 183-GHz) reveal hydrometeor size Greater hydrometeors size Greater hydrometeor abundance Higher cloud-top altitude

6. MIT Lincoln Laboratory 6 FWC 2006-10-24 IPWG Particle Sizes Revealed in NAST-M Data 54 GHz 118 GHz 183 GHz 425 GHz Visible Leslie & Staelin, IEEE TGRS, 10/2004 TBTB

7. MIT Lincoln Laboratory 7 FWC 2006-10-24 IPWG AMSU Radiometry Passive  W sounder AMSU-A –12 channels in opaque 54- GHz O 2 band –Window channels near 23.8, 31.4, and 89.0 GHz AMSU-B –3 channels in opaque 183.31-GHz H 2 O band –Window channels near 89.0 and 150.0 GHz AMSU-A Channel Frequencies (GHz) 23.8 31.4 50.3 52.8 53.596 ± 0.115 54.4 54.94 55.5 57.290344 57.290344 ± 0.217 57.290344 ± 0.3222 ± 0.048 57.290344 ± 0.3222 ± 0.022 57.290344 ± 0.3222 ± 0.010 57.290344 ± 0.3222 ± 0.0045 89.0 AMSU-B Channel Frequencies (GHz) 89.0 150.0 183.31 ± 1 183.31 ± 3 183.31 ± 7

8. MIT Lincoln Laboratory 8 FWC 2006-10-24 IPWG General Structure of AMSU Algorithm (Chen and Staelin, IEEE TGRS, 2/2003) Signal processing –Regional Laplacian interpolation –Image sharpening –Principal component analysis Neural net –2-layer feedforward neural net –1 st layer: tanh transfer function –2 nd layer: linear transfer function

9. MIT Lincoln Laboratory 9 FWC 2006-10-24 IPWG Signal Processing Components Neural-net correction of angle-dependent variations in T B ’s Cloud-clearing via regional Laplacian interpolation –Temperature profile characterization –Cloud-top altitude characterization Principal component analysis for dimensionality reduction –Temperature profile PC’s –Window channel / H 2 O profile PC’s Image sharpening –AMSU-A data sharpened to AMSU-B resolution

10. MIT Lincoln Laboratory 10 FWC 2006-10-24 IPWG The Algorithm: Precipitation Masks & Precipitation-Induced Perturbations PRECIPITATION DETECTION IMAGE SHARPENING CORRUPT DATA DETECTION LIMB-&-SURFACE CORRECTION REGIONAL LAPLACIAN INTERPOLATION

11. MIT Lincoln Laboratory 11 FWC 2006-10-24 IPWG The Algorithm: Neural Net Trained to NEXRAD

12. MIT Lincoln Laboratory 12 FWC 2006-10-24 IPWG Final Output

13. MIT Lincoln Laboratory 13 FWC 2006-10-24 IPWG Example of Global Retrieval

14. MIT Lincoln Laboratory 14 FWC 2006-10-24 IPWG ATMS Similar to AMSU To be launched on NPP (2009) & NPOESS satellites –NPP = NPOESS Preparatory Project Improvements over AMSU –Additional channels in 54-GHz and 183-GHz bands –Better resolution in 54-GHz band –Better sampling Nyquist sampling of 54-GHz data Identical sampling of all channels

15. MIT Lincoln Laboratory 15 FWC 2006-10-24 IPWG Simulating ATMS T B ’s MM5 Atmospheric Circulation Model –Provides temperature profile, water vapor profile, hydrometeor profile, … –Used Goddard hydrometeor model (Tao & Simpson, 1993) Radiative Transfer –TBSCAT due to Rosenkranz (IEEE TGRS, 8/2002) Multi-stream, initial-value –Improved hydrometeor modeling due to Surussavadee & Staelin (IEEE TGRS, 10/2006) Filtering –Accurate matching of T B ’s on MM5 grid to ATMS resolution and geolocation using “satellite geometry” toolbox for MATLAB Computing angular offset of surface locations from boresight Computing satellite zenith angles from scan angle Computing geolocation from scan angle

16. MIT Lincoln Laboratory 16 FWC 2006-10-24 IPWG MM5 Rain Rate: Typhoon Pongsona, 2002/12/8

17. MIT Lincoln Laboratory 17 FWC 2006-10-24 IPWG AMSU vs. ATMS, 183±7 GHz Observed AMSUSimulated ATMS Simulated ATMS 183±7 GHz data shows reasonable agreement with AMSU-B Morphology difference between AMSU observations and MM5 predicted radiances is due to the inaccuracy of the NCEP analyses used to initialize the MM5 model

18. MIT Lincoln Laboratory 18 FWC 2006-10-24 IPWG AMSU vs. ATMS, 50.3 GHz Observed AMSUSimulated ATMS Simulated ATMS 50.3-GHz data with finer resolution and sampling shows finer features than AMSU-A Intense eyewall signature in simulated ATMS 50.3-GHz data due to NCEP initialization & limited 5-hr MM5 spinup time producing excess of large ice particles

19. MIT Lincoln Laboratory 19 FWC 2006-10-24 IPWG Future Developments Adapting Chen-Staelin algorithm (IEEE TGRS, 2/2003) for ATMS Exploiting Nyquist sampling in the 54-GHz band Using methods from window-channel-based algorithms Improving the signal processing of Chen-Staelin algorithm Improving neural net training –Representations of circular data

20. MIT Lincoln Laboratory 20 FWC 2006-10-24 IPWG Recently Launched & Future Instruments Similar to AMSU-A/B –AMSU/MHS on NOAA-18 (2005) –AMSU/MHS on NOAA-N’, METOP-1, METOP-2, METOP-3 ATMS –NPP (2009) –NPOESS  W instruments on geostationary satellites? –< 1 hr revisit times

21. MIT Lincoln Laboratory 21 FWC 2006-10-24 IPWG Summary Physical basis of precipitation estimation using opaque  W channels –Atmospheric sounding capabilities of opaque  W channels –Cloud shape and particle size distribution from NAST-M 54-, 118-, 183-, and 425-GHz data AMSU precipitation algorithm –Relies primarily on 54-GHz and 183-GHz opaque bands –Signal processing: regional Laplacian interpolation, principal component analysis, image sharpening ATMS precipitation algorithm development –Simulation system using MM5/TBSCAT

22. 22 FWC 2006-10-24 IPWG MIT Lincoln Laboratory Backup Slides

23. MIT Lincoln Laboratory 23 FWC 2006-10-24 IPWG NAST-M NAST = NPOESS Aircraft Sounder Testbed –Risk-reduction effort by NPOESS Integrated Program Office –Cooperative effort of NASA, NOAA, & DoD Equipped with 54-, 118-, 183-, and 425-GHz radiometers Flown on high-altitude aircraft –ER-2 (NASA) –Proteus (Scaled Composites) ~2.5-km resolution near nadir

24. MIT Lincoln Laboratory 24 FWC 2006-10-24 IPWG Scattering in the 54-GHz and 183-GHz Bands 0.7 mm2.4 mm

25. MIT Lincoln Laboratory 25 FWC 2006-10-24 IPWG AMSU Geographical Coverage Aboard NOAA-15, NOAA-16, & NOAA-17 Nearly identical AMSU/HSB on Aqua

26. MIT Lincoln Laboratory 26 FWC 2006-10-24 IPWG AMSU-A/B Sampling & Resolution AMSU-A –3 1/3° sampling (~50 km near nadir) –3.3° resolution (~50 km near nadir) AMSU-B –1.1° resolution (~15 km near nadir) –1.1° sampling (~15-km near nadir) AMSU-A AMSU-B

27. MIT Lincoln Laboratory 27 FWC 2006-10-24 IPWG 15-km AMSU vs. NEXRAD Comparison

28. MIT Lincoln Laboratory 28 FWC 2006-10-24 IPWG RMS Discrepancies (mm/h) between AMSU and NEXRAD Range of NEXRAD rain rate 15-km (30-110 km from radar) 15-km (110-230 km from radar) 50-km (30-110 km from radar) 50-km (110-230 km from radar) < 0.5 mm/h1.01.40.5 0.5 – 1 mm/h2.02.60.91.1 1 – 2 mm/h2.32.71.11.5 2 – 4 mm/h2.73.91.82.3 4 – 8 mm/h3.57.43.25.2 8 – 16 mm/h6.98.46.66.5 16 – 32 mm/h19.017.212.914.6 > 32 mm/h42.939.222.121.7

29. MIT Lincoln Laboratory 29 FWC 2006-10-24 IPWG Features of ATMS vs. AMSU Channel set –Similar to AMSU  Additional 51.76-GHz channel  Additional 183.31±4.5-GHz & 183.31±1.8-GHz –165.5-GHz replaces 150-GHz on AMSU-B  No 89.0-GHz 15-km channel (available on AMSU-B) Resolution  54-GHz and 89-GHz: 2.2° vs. 3.33° on AMSU  23.8- and 31.4-GHz: 5.2° vs. 3.33° on AMSU Sampling –165.5-GHz, 183-GHz: Similar to AMSU-B  Other channels: ~3× finer than AMSU-A cross-track & along-track  All channels sampled at the same locations  Nyquist sampling of 54-GHz and 89-GHz Similar sensitivity

30. MIT Lincoln Laboratory 30 FWC 2006-10-24 IPWG ATMS & AMSU Footprints

31. MIT Lincoln Laboratory 31 FWC 2006-10-24 IPWG ATMS & AMSU Footprints (Near Nadir)

32. MIT Lincoln Laboratory 32 FWC 2006-10-24 IPWG ATMS Rain Rate Retrieval Algorithm Completely new algorithm Neural net Inputs –All 22 channels –sec(satellite zenith angle) Training, validation, and testing sets –MM5 data over Typhoon Pongsona –1 time step (1521 data points) each for training, validation, and testing

33. MIT Lincoln Laboratory 33 FWC 2006-10-24 IPWG ATMS vs. MM5, 1.1°

34. MIT Lincoln Laboratory 34 FWC 2006-10-24 IPWG ATMS vs. MM5, 5.2°

35. MIT Lincoln Laboratory 35 FWC 2006-10-24 IPWG Representations of Geolocation Rectangular (2-D) û Discontinuity across 180° E/W (Int’l Date Line) û Topological distortion around 90° N & 90° S (Geo. N & S Poles) Cylindrical (3-D) Continuity across 180° E/W û Topological distortion around 90° N & 90° S Spherical (3-D) Continuity across 180° E/W No topological distortion around 90° N & and 90° S

36. MIT Lincoln Laboratory 36 FWC 2006-10-24 IPWG Geolocation: Comparing the Representations Spherical representation produces the lowest RMS errors RMS error with 10 weights & biases Linear: 0.16 Cylindrical: 0.16 Spherical: 0.01 Weights & biases needed for RMS error < 1.5 × 10 -2 Rectangular: 23 Cylindrical: 18 Spherical: 6 RECTANGULAR CYLINDRICAL SPHERICAL