an atmospheric algorithm suite based on neural networks for microwave imager/sounders
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
MIT Lincoln Laboratory
MIS IGARSS11-1RVL 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 UniversityIGARSS 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.
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Outline
• Overview
• Physics
• Retrieval Approach– Neural Networks– Radiative Transfer
• Training Datasets
• Expected performance
• Summary
MIS IGARSS11-3RVL 9/15/2010
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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/m2 or mm in a 3-km vertical segment
• 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
Profile Subset
2-D Field Subset
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Algorithm Simulation Methodology
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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
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PHYSICS AND
PHENOMENOLOGY
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Microwave Scattering and Absorption
Atmospheric Transmission
Hydrometeor Mie Scattering and Absorption
Liquid water
Ice
Frequency [GHz]
Frequency [GHz]
Frequency [GHz]
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Passive Microwave Sensing of Precipitation
35 km
45 k
m
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Overview of SSMIS Channel Setand Spatial Resolutions
V = vertical pol.H = horizontal pol.R = right-hand circ.* subset in precipitation algorithm
km
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SSMIS UAS Channel Characteristics
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Temperature and Water VaporWeighting Functions
Temperature Water Vapor
45° off-nadir angle
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Upper Air TemperatureWeighting Functions
26 uT 90 deg. (tropical)
65 uT 53 deg. (polar)
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MULTILAYER FEEDFORWARD
NEURAL NETWORKS
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Neural NetworksNonlinear, Parameterized Function Approximators
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Example: Temperature Profile RetrievalAdvantages Relative to Linear Regression (LLSE)
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Advantages Relative to Linear RegressionBetter Noise Immunity and Physical Representation
Noise contribution: Component of retrieval error due only to sensor noiseAtmosphere contribution: Retrieval error in the absence of sensor noise
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RADIATIVE TRANSFER AND
SIMULATION METHODOLOGY
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Algorithm Simulation Methodology
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Radiative Transfer / NWP Interface Issues
Each level requires hydrometeor densityper drop radius
MM5
Pre
ssur
e [m
b]
Mas
s D
ensi
ty [g
/m3 ]
Radius [mm]
Mass Density [g/m3]
graupel
snow
rain
10 mb
Sekhon-Srivastava
Marshall-Palmer
Image courtesy of Colorado State University
SSMIS(NGES)
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PERFORMANCE
VERIFICATION DATASETS
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Geographical locations of the pixels in the MM5 and NOAA88b data sets
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Mean and Standard Deviation of NOAA/MM5 Data Sets
Temperature Water Vapor
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MM5 Cloudy Data Set
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PERFORMANCE
VERIFICATION
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Precipitation Rate Retrieval Performance
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Summary of Cloud Water/Ice Retrieval Performance
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AVTP Retrieval PerformanceCloudy (40 km)
MM5 not valid at these high altitudes
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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
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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)
MIS IGARSS11-30RVL 9/15/2010
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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
Land Ocean
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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
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Backup Slides
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Simulated SSMIS Pass Over CONUS
• 50.3-GHz brightness temperature
• 40-km Spatial resolution
• 2/3 CONUS HRRR – 3 km
• CCA antenna pattern
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SSMIS and AMSU Precipitation Rate Retrievals
8
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Structure of the SSMIS Precipitation Algorithm
Pixel Longitude/Latitude Brightness Temperatures
Bias correction Interpolate to fine retrieval grid
Surface classification
PCA Transform
Channel Selection Channel Selection
Spatial Perturbations
Specialized Neural Network
Surface-Classification-Dependent Weighting
Retrieved Precipitation Parameters
Channel Selection
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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
SPA
TIA
L FI
LTE
RIN
G
“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
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Histogram of Surface Pressures for the Synoptic Radiosonde Data Set
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Geographical Locations of the Pixels in the Synoptic Radiosonde Data Sets
~200,000 quality-controlled radiosondes from 2009-2010 representing all seasons
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Precipitation Rate Performance
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Precipitation Type Retrieval
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Precipitation Rate Performance Stratified by Precipitation Type
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Cloud Water/Ice Retrieval Performance
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Total Integrated Water Vapor Performance (25 km)
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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)
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Total Water Content Performance
Altitude Ocean Land Global Spec. (IORDII)surface 1.20 2.00 1.44 2.0 kg/m2
5 km 0.80 1.40 1.10 2.0 kg/m2
7.5 km 0.45 0.48 0.46 2.0 kg/m2
10 km 0.10 0.12 0.11 2.0 kg/m2
• 3-km “slabs”• 25 km resolution• cloudy MM5 dataset
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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