MIT Lincoln Laboratory MIS IGARSS11-1 RVL 9/15/2010 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 FA C Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Government.

MIS IGARSS11-2 RVL 9/15/2010 MIT Lincoln Laboratory Outline Overview Physics Retrieval Approach –Neural Networks –Radiative Transfer Training Datasets Expected performance Summary

MIS IGARSS11-3 RVL 9/15/2010 MIT Lincoln Laboratory Atmosphere EDR Suite 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/m 2 or mm in a 3-km vertical segment Total Integrated Water Vapor (TIWV) - kg/m 2 or mm (a.k.a., precipitable water) Precipitation Rate/Type (PRT) – mm/hr and types: rain or ice Cloud Liquid Water Content (CLWC) – kg/m 2 or mm Cloud Ice Water Path (CIWP) - kg/m 2 or mm Profile Subset 2-D Field Subset

MIS IGARSS11-4 RVL 9/15/2010 MIT Lincoln Laboratory Algorithm Simulation Methodology

MIS IGARSS11-5 RVL 9/15/2010 MIT Lincoln Laboratory MIS Atmospheric Algorithm Methodology 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 Physical Models + Stochastic Processing

MIS IGARSS11-6 RVL 9/15/2010 MIT Lincoln Laboratory PHYSICS AND PHENOMENOLOGY

MIS IGARSS11-7 RVL 9/15/2010 MIT Lincoln Laboratory Microwave Scattering and Absorption Atmospheric Transmission Hydrometeor Mie Scattering and Absorption Liquid water Ice Frequency [GHz]

MIS IGARSS11-8 RVL 9/15/2010 MIT Lincoln Laboratory Passive Microwave Sensing of Precipitation 35 km 45 km

MIS IGARSS11-9 RVL 9/15/2010 MIT Lincoln Laboratory Overview of SSMIS Channel Set and Spatial Resolutions V = vertical pol. H = horizontal pol. R = right-hand circ. * subset in precipitation algorithm km

MIS IGARSS11-10 RVL 9/15/2010 MIT Lincoln Laboratory SSMIS UAS Channel Characteristics

MIS IGARSS11-11 RVL 9/15/2010 MIT Lincoln Laboratory Temperature and Water Vapor Weighting Functions TemperatureWater Vapor 45° off-nadir angle

MIS IGARSS11-12 RVL 9/15/2010 MIT Lincoln Laboratory Upper Air Temperature Weighting Functions 26 uT 90 deg. (tropical) 65 uT 53 deg. (polar)

MIS IGARSS11-13 RVL 9/15/2010 MIT Lincoln Laboratory MULTILAYER FEEDFORWARD NEURAL NETWORKS

MIS IGARSS11-14 RVL 9/15/2010 MIT Lincoln Laboratory Neural Networks Nonlinear, Parameterized Function Approximators

MIS IGARSS11-15 RVL 9/15/2010 MIT Lincoln Laboratory Example: Temperature Profile Retrieval Advantages Relative to Linear Regression (LLSE)

MIS IGARSS11-16 RVL 9/15/2010 MIT Lincoln Laboratory Advantages Relative to Linear Regression Better 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

MIS IGARSS11-17 RVL 9/15/2010 MIT Lincoln Laboratory RADIATIVE TRANSFER AND SIMULATION METHODOLOGY

MIS IGARSS11-18 RVL 9/15/2010 MIT Lincoln Laboratory Algorithm Simulation Methodology

MIS IGARSS11-19 RVL 9/15/2010 MIT Lincoln Laboratory Radiative Transfer / NWP Interface Issues Each level requires hydrometeor density per drop radius MM5 Pressure [mb] Mass Density [g/m 3 ] Radius [mm] Mass Density [g/m 3 ] graupel snow rain 10 mb Sekhon-Srivastava Marshall-Palmer Image courtesy of Colorado State University SSMIS (NGES)

MIS IGARSS11-20 RVL 9/15/2010 MIT Lincoln Laboratory PERFORMANCE VERIFICATION DATASETS

MIS IGARSS11-21 RVL 9/15/2010 MIT Lincoln Laboratory Geographical locations of the pixels in the MM5 and NOAA88b data sets

MIS IGARSS11-22 RVL 9/15/2010 MIT Lincoln Laboratory Mean and Standard Deviation of NOAA/MM5 Data Sets TemperatureWater Vapor

MIS IGARSS11-23 RVL 9/15/2010 MIT Lincoln Laboratory MM5 Cloudy Data Set

MIS IGARSS11-24 RVL 9/15/2010 MIT Lincoln Laboratory PERFORMANCE VERIFICATION

MIS IGARSS11-25 RVL 9/15/2010 MIT Lincoln Laboratory Precipitation Rate Retrieval Performance

MIS IGARSS11-26 RVL 9/15/2010 MIT Lincoln Laboratory Summary of Cloud Water/Ice Retrieval Performance

MIS IGARSS11-27 RVL 9/15/2010 MIT Lincoln Laboratory AVTP Retrieval Performance Cloudy (40 km) MM5 not valid at these high altitudes

MIS IGARSS11-28 RVL 9/15/2010 MIT Lincoln Laboratory 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 – mb: 8 K

MIS IGARSS11-29 RVL 9/15/2010 MIT Lincoln Laboratory AVMP Retrieval Performance Cloudy (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)

MIS IGARSS11-30 RVL 9/15/2010 MIT Lincoln Laboratory Clear-Air Atmospheric Pressure Profile Performance (40 km) APP derived using AVTP and AVMP retrievals and surface pressure (assumed perfect) Quality-controlled global radiosondes used for ground truth LandOcean

MIS IGARSS11-31 RVL 9/15/2010 MIT Lincoln Laboratory 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

MIS IGARSS11-32 RVL 9/15/2010 MIT Lincoln Laboratory Backup Slides

MIS IGARSS11-33 RVL 9/15/2010 MIT Lincoln Laboratory Simulated SSMIS Pass Over CONUS 50.3-GHz brightness temperature 40-km Spatial resolution 2/3 CONUS HRRR – 3 km CCA antenna pattern

MIS IGARSS11-34 RVL 9/15/2010 MIT Lincoln Laboratory SSMIS and AMSU Precipitation Rate Retrievals 8

MIS IGARSS11-35 RVL 9/15/2010 MIT Lincoln Laboratory Structure of the SSMIS Precipitation Algorithm Pixel Longitude/Latitude Brightness Temperatures Bias correctionInterpolate to fine retrieval grid Surface classification PCA Transform Channel Selection Spatial Perturbations Specialized Neural Network Surface-Classification-Dependent Weighting Retrieved Precipitation Parameters Channel Selection

MIS IGARSS11-36 RVL 9/15/2010 MIT Lincoln Laboratory Radiance Simulation Methodology 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 MM5 grid levels Cloud Resolving Model (CRM) Radiative Transfer Model (RTM) Simulated Radiances SPATIAL FILTERING “Satellite Geometry” Toolbox (MATLAB) * Successive Order of Interaction: Heidinger A. K., et al., J. Appl. Meteor. Climatol., 2006 † TBSCAT: Rosenkranz, P. W., IEEE Trans. Geosci. Remote Sens. 2002

MIS IGARSS11-37 RVL 9/15/2010 MIT Lincoln Laboratory Histogram of Surface Pressures for the Synoptic Radiosonde Data Set

MIS IGARSS11-38 RVL 9/15/2010 MIT Lincoln Laboratory Geographical Locations of the Pixels in the Synoptic Radiosonde Data Sets ~200,000 quality-controlled radiosondes from representing all seasons

MIS IGARSS11-39 RVL 9/15/2010 MIT Lincoln Laboratory Precipitation Rate Performance

MIS IGARSS11-40 RVL 9/15/2010 MIT Lincoln Laboratory Precipitation Type Retrieval

MIS IGARSS11-41 RVL 9/15/2010 MIT Lincoln Laboratory Precipitation Rate Performance Stratified by Precipitation Type

MIS IGARSS11-42 RVL 9/15/2010 MIT Lincoln Laboratory Cloud Water/Ice Retrieval Performance

MIS IGARSS11-43 RVL 9/15/2010 MIT Lincoln Laboratory Total Integrated Water Vapor Performance (25 km)

MIS IGARSS11-44 RVL 9/15/2010 MIT Lincoln Laboratory AVMP Retrieval Performance Clear-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)

MIS IGARSS11-45 RVL 9/15/2010 MIT Lincoln Laboratory Total Water Content Performance AltitudeOceanLandGlobalSpec. (IORDII) surface kg/m 2 5 km kg/m km kg/m 2 10 km kg/m 2 3-km “slabs” 25 km resolution cloudy MM5 dataset

MIS IGARSS11-46 RVL 9/15/2010 MIT Lincoln Laboratory 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