1. 23-27 October 20063rd IPWG Melbourne, Australia The Status of the NOAA/NESDIS Operational AMSU/MHS Precipitation Algorithm Ralph Ferraro NOAA/NESDIS College Park, MD USA Wanchun Chen, Cezar Kongoli, Huan Meng, Paul Pellegrino, Daniel Vila, Nai-Yu Wang, Fuzhong Weng, Limin Zhao

2. 23-27 October 20063rd IPWG Melbourne, Australia Outline Review of AMSU and operational algorithm Recent changes –NOAA-18 –Coastlines Upcoming Improvements –Ocean/emission rainfall –LZA bias removal –Snowfall rates Future –METOP –NPP –MIRS

3. 23-27 October 20063rd IPWG Melbourne, Australia NOAA AMSU Sensor AMSU is a cross-track scanning radiometer (unlike SSM/I, AMSR-E, TMI) AMSU-A (45 km nadir FOV) 15 channels 23, 31, 50 – 57 (13), 89 GHz AMSU-B (15 km nadir FOV) 89, 150, 183+1,3,7 GHz MHS replaces AMSU-B on N-18 89, 157, 183+1,3, 190.3 GHz SatelliteLaunch DateLTAN NOAA-1513 May 19981735 NOAA-1621 September 20001529 NOAA-1724 June 20022218 NOAA-1820 May 20051342 METOP-119 October 20062130

4. 23-27 October 20063rd IPWG Melbourne, Australia NOAA Produces Operational Products from AMSU

5. 23-27 October 20063rd IPWG Melbourne, Australia Characteristics of the NOAA AMSU Rain Rate Algorithm Physical retrieval of IWP and D e – 89 & 150 GHz –Use of other window and sounding channels Derive needed parameters for retrieval Filters for possible ambiguous surfaces –Use of ancillary data & other AMSU derived products IWP to RR based on limited CRM data and RTM – RR = A 0 + A 1 *IWP + A 2 *IWP 2 183 GHz bands used to identify deep convection –Use another set of ‘A’ coefficients 50 GHz bands used to identify snowfall over land

6. 23-27 October 20063rd IPWG Melbourne, Australia Example of Global Real Time Data

7. 23-27 October 20063rd IPWG Melbourne, Australia Example of Regional Retrievals

8. 23-27 October 20063rd IPWG Melbourne, Australia Example of Monthly Data

9. 23-27 October 20063rd IPWG Melbourne, Australia Continuous monitoring of algorithm performance via IPWG validation sites

10. 23-27 October 20063rd IPWG Melbourne, Australia Performance vs. GPCC (8/03 – 12/05) Mean Bias RR SSMIAMSUGPCCSSMIAMSUSSMIAMSU 60S-60N60.966.575.60.800.880.640.69 40S-40N72.076.480.50.890.950.700.74 20S-20N102.1119.3129.30.720.940.81

11. 23-27 October 20063rd IPWG Melbourne, Australia AMSU Summary/Limitations Land –In general, performs well Too high in convective situations Regional biases (of course!), esp. too high in drier regimes –Better sensitivity to lighter rain rates –Falling snow detection (but not rates) Ocean –Restricted to convective precipitation Overall, low due to missing precipitation without ice (generally lighter rain intensities) Rain coverage less than other sensors –Conditional rain rates too high LZA bias in IWP Coastlines –Not adequately handled View angle dependencies –Larger FOV on scan edges results in varying rain rate distributions –Unrealistic PDF’s

12. 23-27 October 20063rd IPWG Melbourne, Australia NOAA-18: MHS replaces AMSU-B NOAA, DMSP and METOP will operate POES constellation Changes include –Microwave Humidity Sounder (MHS) instead of AMSU-B 157 GHz vs. 150; 190.3 GHz vs. 183+7 –MHS will fly on NOAA-N (18), -N’ and METOP (Successful launch 10/19) Synthetic NOAA-N 150 and 183+7 GHz based on coincident measurements with NOAA-16 Operational 29 Sep 2005

13. 23-27 October 20063rd IPWG Melbourne, Australia Coastline Precipitation Most passive MW algorithms fail miserably in coastal regions –Emissivity contrast between land and ocean –Different physics package Imager approaches –“Extend” land algorithm to coasts Bring in scattering rain types –“Correct” TB’s based on % of FOV filled with land Computer expensive; used in regional approaches Sounders –Utilize channels that are mainly insensitive to surface

14. 23-27 October 20063rd IPWG Melbourne, Australia Improved AMSU Coastline Algorithm Utilizes AMSU 53.6, 150, 183+1, 3 and 7 GHz to identify potential rain and a synthetic IWP along coastlines –Compute rain rate in same manner via IWP Also updated land/sea/coast tag Implemented into operations 7/31/06 Substantially better retrievals with minimal false alarms

15. 23-27 October 20063rd IPWG Melbourne, Australia OLDNEW RADAR

16. 23-27 October 20063rd IPWG Melbourne, Australia Improvement of Oceanic Rain Rates and Removal of Angular Biases Daniel Vila (details at his poster) High oceanic bias attributed to unrealistic PDF’s –Function of LZA, problem traced to IWP retrieval Corrected via adjustment with SSM/I PDF’s Oceanic rain coverage low –Non-convergence of IWP/De algorithm in mostly light rain rates Corrected by adding in emission component Low bias at edge of scan due to large FOV

17. 23-27 October 20063rd IPWG Melbourne, Australia Results – April 2005 Current AMSU Corrected AMSU SSM/I GPROF V6 Warm/shallow rain Reduced ocean rainfall Slight reduction over land

18. 23-27 October 20063rd IPWG Melbourne, Australia Zonal Mean Rain Frequency Little change AMSU>SSMI due to 150 GHz Significant improvement due to CLW

19. 23-27 October 20063rd IPWG Melbourne, Australia Zonal Mean Rain Amount Reduction in rainfall amounts, mostly in convective zones Corrected AMSU much closer to SSM/I

20. 23-27 October 20063rd IPWG Melbourne, Australia Snowfall Rate Algorithm Development Huan Meng Use RTM to retrieve IWP under snow condition –1 layer & two-streams Derive empirical equation connecting IWP with NEXRAD reflectivity Adopt an existing reflectivity-snowfall rate equation Derive snowfall rate from IWP

21. 23-27 October 20063rd IPWG Melbourne, Australia Matching AMSU-B with Radar Data WSR-88D radar reflectivity (Z) Z is Quality controlled by QCNN (Quality Control Neural Network) algorithm Use radar data from the lowest elevation (0.4º) and within 100 km. Assume AMSU-B spatial sensitivity follows Gaussian function within an FOV when matching with radar data

22. 23-27 October 20063rd IPWG Melbourne, Australia Retrieved AMSU IWP Corresponding NEXRAD Reflectivity

23. 23-27 October 20063rd IPWG Melbourne, Australia Regression between IWP and Z Data from 10 snow cases with 227 matching points 3 rd order fit between IWP and Z  = 0.48

24. 23-27 October 20063rd IPWG Melbourne, Australia Choice of Z-R Relationship?

25. 23-27 October 20063rd IPWG Melbourne, Australia Choice of Z-R Relationship (2) Sekhon & Srivastava (1970) has the smallest RMS with the 1-hr-lag observation: Z = 398 R 2.21 C&MVASOHTIMAPUHB&WS&SFUJ 0.51400.40840.37220.37350.35320.35220.35140.3872

26. 23-27 October 20063rd IPWG Melbourne, Australia Example of Snow Rate Retrieval Pro: Catch basic snowfall patterns Con: Miss snow or underestimate high snowfall rate

27. 23-27 October 20063rd IPWG Melbourne, Australia Next Steps… Use more realistic snow characteristics in the RTM Explore approaches to classify snowfall type and utilize in snow rate retrieval. Add more snow cases to improve the accuracy of IWP-Z regression. Implement experimental retrievals for CONUS winter 2006-07

28. 23-27 October 20063rd IPWG Melbourne, Australia Future for AMSU/MHS Implement near term algorithm improvements –FOV biases –CLW component for ocean rain –Experimental snowfall rates over CONUS Longer term –Improved AMSU physics package “MIRS” – Microwave Integrated Retrieval System –Temperature and moisture sounding –Bayesian precipitation retrieval using O 2 and H 2 O channels –Reprocessing of entire AMSU time series –Snowfall rates Transition to operations –METOP-1 (Oct07) Jan/Feb 2007? Pipeline processing Prepare for NPP/ATMS

29. 23-27 October 20063rd IPWG Melbourne, Australia Backup Slides

30. 23-27 October 20063rd IPWG Melbourne, Australia http://www.orbit.nesdis.noaa.gov/corp/scsb/mspps/

31. 23-27 October 20063rd IPWG Melbourne, Australia AMSU Climate Products shown in Nature Michael Evans, April 2006

32. 23-27 October 20063rd IPWG Melbourne, Australia Falling Snow over Land from AMSU Use of AMSU-B 183 GHz bands along with AMSU- A 53.6 GHz allows for expansion of current algorithm to over cold and snow covered surfaces: –AMSU-B channels allow for detection of scattering associated with precipitation, but surface blind when “sufficient moisture” exists –AMSU-A channel 5 allows for discrimination between “rain” and “snow” Feature added in 11/03, snowfall detection only (assigned arbitrary rate of 0.1 mm/hr) Validation over CONUS winter 2003-04

33. 23-27 October 20063rd IPWG Melbourne, Australia Summary/Limitations Algorithm Performance –Can detect snowfall associated with synoptic scale systems –Low false alarms –Can increase region of application by lowering TB 54L threshold up to 5 K Some increase in false alarms Limitations –Relative moist atmospheres - -5 to 0 C –Southern extent of snow pack/temperate latitudes –Precip layer needs to extend to ~4-5 km or higher –No signal in extreme cold climate regimes and shallow snow

34. 23-27 October 20063rd IPWG Melbourne, Australia Retrieving Ice Water Path Using Radiative Transfer Model One-layer two-stream RTM (Yan & Weng, 2006, to be submitted to JGR) I : ice water path V: total precipitable water D e : cloud particle effective diameter T s : surface temperature A: derivatives of T Bi over I, V, D e, & T s E: error matrix T Bi : brightness temperatures at 23.8, 31.4, 89, 150, and 180±7 GHz Use iteration scheme. Iteration stops if ΔT Bi ≤ δ i (i = 1, 5). Adopt fast one-layer RTM to meet near real-time operational requirement.

35. 23-27 October 20063rd IPWG Melbourne, Australia Choice of Z-R Empirical Eq – Cont’d Observation Data: hourly snowfall observations from 12 weather stations Highest correlation coefficient between retrieved R and snowfall observations with 1-hr lag: 0-hr late0.5-hr late1-hr late2-hr late3-hr late4-hr late 0.12810.23700.32390.28980.21220.1921 Retrieved R represents snow in the atmosphere because channel 183±7 GHz is sensitive to 600 – 700 mb. The time lag represents the time it takes the snow to fall to the ground.

36. 23-27 October 20063rd IPWG Melbourne, Australia Application to AMSU Measurements Radar Reflectivity AMSU Precipitation Rates 2004 Nov 24

37. 23-27 October 20063rd IPWG Melbourne, Australia 0-10 mm 10-30mm Unrealistic peak locationInability to retrieve high rain rates for large LZA The position of the peak in the histograms (systematic bias), is corrected performing a Gaussian PDF with µ (peak histogram position) and б (standard deviation of observed distribution) depending on LZA and latitude. For high rain rates, a linear correction scheme with the slope depending on SSM/I and AMSU-B footprint ratio is proposed to normalize AMSU-B derived rain rates.

38. 23-27 October 20063rd IPWG Melbourne, Australia Adding Emission Component to Oceanic Rain Mean AMSU-B derived rain rate for different CLW values. CI is the convective index computed by using the three 183 GHz channels. Satellite: NOAA 15 - 60N-60S: Year: 2005. RR vs. CLW: Proposed correction scheme

39. 23-27 October 20063rd IPWG Melbourne, Australia Results – April 2005 Mean rain rate of AMSU-B NOAA-15 operational derived rain rates (mm/day) (upper panel), corrected values (middle panel) and mean derived rain rates (mm/day) for SSM/I F-13 GPROF v6.0 for April 2005. Monthly mean absolute bias (upper panel) and RMSE (bottom panel) for UNCORR and CORR algorithm compared with SSM/I GPROF V6.0 estimates for 2005.