An Atmospheric Algorithm Suite Based on Neural Networks for Microwave Imager/Sounders

1. An Atmospheric Algorithm Suite Based on Neural Networks for Microwave Imager/Sounders W. J. Blackwell, R. Czerwinski, R. V. Leslie, M. Pieper, P. Rosenkranz*, J. Samra, D. H. Staelin*, C. Surrussavadee ‡, K. Wallenstein, & D. Zhang MIT Lincoln Laboratory * Research Laboratory of Electronics at MIT ‡ Prince of Songkla University IGARSS 2011: Vancouver, Canada 28 July 2011 This work was sponsored by the National Oceanic and Atmospheric Administration under 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.

2. Outline • Overview • Physics • Retrieval Approach • Neural Networks • Radiative Transfer • Training Datasets • Expected performance • Summary

3. Atmosphere EDR Suite Profile Subset • Atmospheric Vertical Temperature Profile (AVTP) – Kelvin • Lower Atmospheric Sounding (Surface to 10 mb) • Upper Atmospheric Sounding (10 mb to ~0.01 mb) • Atmospheric Vertical Moisture Profile (AVMP) – MMR g/kg • Atmospheric Pressure Profile (APP) – millibar • Total Water Content (TWC) - kg/m2 or mm in a 3-km vertical segment 2-D Field Subset • Total Integrated Water Vapor (TIWV) - kg/m2 or mm (a.k.a., precipitable water) • Precipitation Rate/Type (PRT) – mm/hr and types: rain or ice • Cloud Liquid Water Content (CLWC) – kg/m2 or mm • Cloud Ice Water Path (CIWP) - kg/m2 or mm

4. Algorithm Simulation Methodology

5. MIS Atmospheric Algorithm Methodology Physical Models + Stochastic Processing • Cloud/precipitation products derived from cloud-resolving NWP models combined with multi-stream scattering models • Global NWP runs over ~5M pixels • Multi-phase microphysical modeling • Profile products derived from global high-resolution analysis fields • Performance validated over many years (millions of pixels) for similar AMSU/AIRS algorithm • Framework allows for optimization of product spatial resolution • Neural network estimators offer accuracy/robustness/speed • Very easy to code (large infrastructure currently available) • Very easy to upgrade (simply replace coefficient file) • Very low computational burden – can run on mobile terminals

6. Physics and Phenomenology

7. Microwave Scattering and Absorption Hydrometeor Mie Scattering and Absorption Atmospheric Transmission Liquid water Frequency [GHz] Ice Frequency [GHz] Frequency [GHz]

8. Passive Microwave Sensing of Precipitation 45 km 35 km

9. Overview of SSMIS Channel Setand Spatial Resolutions km V = vertical pol. H = horizontal pol. R = right-hand circ. * subset in precipitation algorithm

10. SSMIS UAS Channel Characteristics

11. Temperature and Water VaporWeighting Functions Temperature Water Vapor 45° off-nadir angle

12. Upper Air TemperatureWeighting Functions 26 uT 90 deg. (tropical) 65 uT 53 deg. (polar)

13. Multilayer FeedForward Neural Networks

14. Neural NetworksNonlinear, Parameterized Function Approximators

15. Example: Temperature Profile RetrievalAdvantages Relative to Linear Regression (LLSE)

16. Advantages Relative to Linear RegressionBetter Noise Immunity and Physical Representation Noise contribution: Component of retrieval error due only to sensor noise Atmosphere contribution: Retrieval error in the absence of sensor noise

17. Radiative Transfer and Simulation Methodology

18. Algorithm Simulation Methodology

19. Radiative Transfer / NWP Interface Issues SSMIS (NGES) Marshall-Palmer Mass Density [g/m3] Sekhon-Srivastava Image courtesy of Colorado State University 10 mb Radius [mm] MM5 snow Pressure [mb] graupel Each level requires hydrometeor density per drop radius rain Mass Density [g/m3]

20. Performance Verification datasets

21. Geographical locations of the pixels in the MM5 and NOAA88b data sets

22. Mean and Standard Deviation of NOAA/MM5 Data Sets Temperature Water Vapor

23. MM5 Cloudy Data Set

24. Performance Verification

25. Precipitation Rate Retrieval Performance

26. Summary of Cloud Water/Ice Retrieval Performance

27. AVTP Retrieval PerformanceCloudy (40 km) MM5 not valid at these high altitudes

28. Upper Air Sounding Performance • SSMIS UAS channels (CH20-24) • No Doppler effects • IGRF-11 geomagnetic model • Multi-layer Feedforward Neural Network • NOAA88b dataset • SSMIS Spec: • 7-1 mb: 5 K • 0.4 mb: 5.5 K • 0.2-0.03 mb: 8 K

29. AVMP Retrieval PerformanceCloudy (40 km) • SSMIS: Greater of 1.5 g/kg or 20% • IORDII: • 10% objective • Greater of 0.2 g/kg or 20% (surf. to 600 mb)

30. Clear-Air Atmospheric Pressure Profile Performance (40 km) Land Ocean APP derived using AVTP and AVMP retrievals and surface pressure (assumed perfect) Quality-controlled global radiosondes used for ground truth

31. Summary • Comprehensive, end-to-end performance assessment capability in place for all products in the Atmosphere EDR Suite • Minimal retrieval optimization performed at this point • Clear path to requirement compliance for all products • Flexible, modular algorithm architecture easily accommodates changes to sensor characteristics and performance

32. Backup Slides

33. Simulated SSMIS Pass Over CONUS • 50.3-GHz brightness temperature • 40-km Spatial resolution • 2/3 CONUS HRRR – 3 km • CCA antenna pattern

34. SSMIS and AMSU Precipitation Rate Retrievals 8

35. Structure of the SSMIS Precipitation Algorithm Brightness Temperatures Pixel Longitude/Latitude Bias correction Interpolate to fine retrieval grid Channel Selection Channel Selection Surface classification PCA Transform Spatial Perturbations Channel Selection Specialized Neural Network Surface-Classification-Dependent Weighting Retrieved Precipitation Parameters

36. Radiance Simulation Methodology MM5 grid levels Cloud Resolving Model (CRM) Radiative Transfer Model (RTM) • CRM = MM5 1-km saved every 15 min • RTM = multiple-stream radiative transfer solution (TBSCAT† or TBSOI*) • Simulated NAST-M radiances • Developed and adapted MIT software to LLGrid parallel computing facility Simulated Radiances SPATIAL FILTERING “Satellite Geometry” Toolbox (MATLAB) † TBSCAT: Rosenkranz, P. W., IEEE Trans. Geosci. Remote Sens. 2002 * Successive Order of Interaction: Heidinger A. K., et al., J. Appl. Meteor. Climatol., 2006

37. Histogram of Surface Pressures for the Synoptic Radiosonde Data Set

38. Geographical Locations of the Pixels in the Synoptic Radiosonde Data Sets ~200,000 quality-controlled radiosondes from 2009-2010 representing all seasons

39. Precipitation Rate Performance

40. Precipitation Type Retrieval

41. Precipitation Rate Performance Stratified by Precipitation Type

42. Cloud Water/Ice Retrieval Performance

43. Total Integrated Water Vapor Performance (25 km)

44. AVMP Retrieval PerformanceClear-air (40 km) • Black = Ocean • Green = Land • Blue = Global • SSMIS: Greater of 1.5 g/kg or 20% • IORDII: • 10% objective • Greater of 0.2 g/kg or 20% (surf. to 600 mb)

45. Total Water Content Performance • 3-km “slabs” • 25 km resolution • cloudy MM5 dataset

46. Limitations and Degradation • Precipitation • Effects all atmos. EDRs except PRT • Nominally, atmos. EDRs will be retrieved under 1 mm/hr • Difficult to quantify 1 mm/hr, will use status flags to classify the precipitation (e.g., “no precip.”, “stratiform”, “light convective”) • Status flags must determine if a CFOV has even one precipitation-impacted EFOV • Land emissivity • Properly classifying land conditions (e.g., flooded or snow-covered) will make stratifications (i.e., a condition specific NN) more difficult to implement • Difficult to obtain a statistically-adequate sample set • Land elevation • Difficult to obtain a statistically significant sample set to train on • Must evaluate whether training many altitude stratifications is worth the effort and cost