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A Generalized Approach to Microwave Satellite Data Assimilation Quality Control and Preprocessing
Kevin Garrett1 and Sid Boukabara2
11th JCSDA Science Workshop on Satellite Data AssimilationJune 6, 2013
College Park, MD
1: RTI @ NOAA/NESDIS/STAR, JCSDA 2: NOAA/NESDIS/STAR, JCSDA
211th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
Outline• Introduction to the MIIDAPS• 1DVAR retrieval process• Potential applications to NWP (GSI)• Current progress• Next steps
Goal: To have a flexible, consistent algorithm applicable to a variety of sensors for use as a preprocessor to NWP data assimilation systems which:• provides quality control information about radiance observations• provides dynamic information about scenes (precip, surface conditions)• has consistent error characteristics across all sensors (using retrieved parameters)• is flexible and easily extended to new/future sensors• is mindful of computing resources/overhead/latency• has meaningful, positive impact on analyses and forecasts
311th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
Assimilation/Retrieval All parameters retrieved simultaneously Valid globally over all surface types Valid in all weather conditions Retrieved parameters depend on information content from sensor frequencies
MIIDAPS OverviewMulti-Instrument Inversion and Data Assimilation Preprocessing System
MIIDAPS
S-NPP ATMS
DMSP F16 SSMI/SDMSP F17 SSMI/SDMSP F18 SSMI/S
GPM GMI
MetOp-A AMSU/MHSMetOp-B AMSU/MHS
GCOM-W1 AMSR2
Megha-TropiquesSAPHIR/MADRAS
TRMM TMI
NOAA-18 AMSU/MHSNOAA-19 AMSU/MHS
Inversion Process Consistent algorithm across all sensors Uses CRTM for forward and jacobian operators Use forecast, fast regression or climatology as first guess/background
MIIDAPS 1DVAR is based on the Microwave Integrated Retrieval System (MiRS)
411th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
MIIDAPS Overview
Over All Surfaces
Using All Channels
-90°
90°0°Latitude
170°
170°10
0 La
yers
Temperature
Emissivity Skin Temperature
Core state variables (products) from MIIDAPSExample of MIIDAPS retrieval using S-NPP ATMS with vertical cross sections at 170° longitude.
Water Vapor
Rain & Graupel
Cloud
Post ProcessingTPW Rain Rate
511th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
Obs Error [E]
No
Convergence
1DVAR Retrieval/Assimilation Process
Init
ial S
tate
Vecto
r [X
]
Climatology
Forecast
Retrieval mode
Assimilation mode CRTM
Simulated TBs
Observed TBs
(processed)
Compare
ConvergenceSolution
[X]Reached
ComputeDX
K
Update State Vector
[X]
Iterative Processes
CovarianceMatrix [B]
Bias Correction
2. Retrieval done in reduced (EOF) space
Reduce the dimensionality of the covariance matrix from 400x400 to 22x22 (or less depending on sensor)
Transform [K] and [B] to EOF space for minimization
LBTLΘB 1. Solution is found by minimizing the cost function:
Convergence is determined by non-constrained cost function:
Y(X)YEY(X)Y
2
1XXBXX
2
1J(X) m1Tm
01T
0
1,
2XYmY1E
TXYmY2
3. X is updated through the Levenberg-Marquardt equation:
nΔXnK)nY(XmY1
ETnBKnK
TnBK
1nΔX
611th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
1DVAR Retrieval/Assimilation Process
1st Attempt 2nd Attempt
Temperature Temperature
Water Vapor Water Vapor
Cloud Liquid Water Rain Water Profile
Ice Water Profile
Skin Temperature Skin Temperature
Surface Emissivity Surface Emissivity
State Vector Parameters per Attempt
• MIIDAPS allows a maximum of 2 retrieval attempts per observation– 1st attempt assumes no scattering signal in the TBs– 2nd attempt assumes scattering from rain/ice is present in TBs– Maximum of 7 iterations per attempt
• Tunable parameters: nattempts, niterations, channels used (optimize efficiency without degrading outputs)
Chi-square with out scattering
Chi-square with scattering
711th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
1DVAR Retrieval/Assimilation Process
Surface preclassifier determines which background and covariances to initialize for retrieval (left)
Retrieved 23 GHz Emissivity Retrieved TPW
Over All Surfaces In All Weather
Seamless transition along surface boundaries
Emissivity inclusion in the state vector is vital for retrieval/assimilation
Emissivity sensitivity to rainfall rate for AMSU-A frequencies
Retrieved Rainfall Rate error as a function of retrieved emissivity error
811th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
Applications to NWPPrimary objective for a 1DVAR preprocessor on microwave observations
Chi-square based QC
Cloudy/rainy radiance detection
Emissivity constraint/assimilation
Focus for Global NWP using GSI• Use chi-sq for QC/filtering• Use CLW/RWP/GWP for detecting
cloudy/rainy obs• For filtering or assimilation• Non-precip cloud/precipitating cloud
• Use surface emissivity as boundary condition for forward simulations to increase surface channel observations
Chi-square based QC
Cloudy/rainy radiance detection
Emissivity constraint/assimilation
911th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
Applications to NWP
Retrieved TPW
Emissivity vital for assimilation of surface sensitive channels in all weather
NEXRAD NEXRAD NEXRAD
0.75
0.8
0.85
0.9
0.95
1
0 20 40 60 80 100 120 140 160
Frequency (GHz)
MIR
S E
mis
siv
ity
04-May
0.75
0.8
0.85
0.9
0.95
1
0 20 40 60 80 100 120 140 160
Frequency (GHz)
MIR
S E
mis
siv
ity
08-May
0.75
0.8
0.85
0.9
0.95
1
0 20 40 60 80 100 120 140 160
Frequency (GHz)
MIR
S E
mis
siv
ity
10-May
5/4
5/8
5/10
Average emissivity spectra before/after a 3-day rain event in May 2008.5/4-5/8 shows ~8% change in 23 GHz emissivity.
5/5-5/7
1011th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
Applications to NWPPrimary objective for a 1DVAR preprocessor on microwave observations
Temperature 400 mb
TPW
Rainfall Rate
Focus for Regional NWP using GSI +HWRF• Assimilation of sounding data near tropical
storm cores• Assimilation of TPW• Assimilation of rainfall rate retrievals
Temperature 400 mb
TPW
Rainfall Rate
1111th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
Applications to NWP
BUFRFiles
‘read_sensor’routines
“setuprad”(clw, O-B filtering)
Implementation of the 1DVAR preprocessor
Implementation of 1DVAR preprocessing at the Bufferization stage:• Process all radiance observations during time window• CPU time spent outside of assimilation (minimize effect on latency)• Encode 1DVAR output in BUFR as metadata or in unique BUFR file• Increased control for radiance thinning/selection during GSI read process• Maintain ability to use 1DVAR geophysical outputs on optimized set
Implementation of 1DVAR preprocessing BUFR read stage:• Process all radiance observations during time window• Increased control for radiance thinning/selection during GSI read process• Maintain ability to use 1DVAR geophysical outputs on optimized set• Separate 1DVAR interface for each satellite sensor• Read routines must be parallelized• CPU time added to the analysis (how much can be afforded?)
Implementation of 1DVAR in “setuprad” stage:• Process only on thinned set of observations• Maintain ability to use 1DVAR geophysical outputs on optimized set• CPU time used in analysis (how much can be afforded?)• Code is universal for all satellite datasets (single interface to 1DVAR)
1211th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
Current Status• Testing currently underway with implementation in read_atms
routine– 1DVAR called for additional QC (based on chisq)– No optimized thinning implemented (every 5 FOVs/Scanlines)
• Prelimenary implementation in setuprad routine– Still testing the interface
Current operational With additional 1DVAR filter
1311th JCSDA Workshop on Satellite Data Assimilation - College Park, MD
Future Work
• Continue with implementation both in setuprad and in the read routines for optimized thinning
• Test impact of cloud filters, use of emissivity in number of obs, O-B, O-A, etc.
• Extend to other sensors, starting with infrared• Apply to regional HWRF (product assimilation)• Involve other interested JCSDA partners (NCEP, OAR,
GMAO, Navy, AFWA, NCAR)