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Brief overview of the Joint Center for Satellite Data Assimilation (JCSDA)
&
Description of an All-Weather One-Dimensional Variational Assimilation/Retrieval System
Brief overview of the Joint Center for Satellite Data Assimilation (JCSDA)
&
Description of an All-Weather One-Dimensional Variational Assimilation/Retrieval System
Sid Ahmed Boukabara
MSFC/SPoRT Seminar, November 19th 2010
MSFC/SPoRT Seminar, November 19th 2010
The Joint Center for Satellite Data Assimilation (JCSDA)
Sid Ahmed Boukabara, Deputy Director, JCSDAand
Lars Peter Riishojgaard, Director, JCSDA
NASA/Earth Science Division
US Navy/Oceanographer andNavigator of the Navy and NRL
NOAA/NESDIS NOAA/NWS
NOAA/OAR
US Air Force/Director of Weather
Mission:
…to accelerate and improve the quantitative use of research and operational satellite data in weather, ocean, climate and environmental analysis
and prediction models.
Vision:
An interagency partnership working to become a world leader in applying satellite data and research to operational goals in environmental
analysis and prediction
JCSDA Partners
JCSDA Executive TeamDirector (Riishojgaard)
Deputy Director (Boukabara)
Partner Associate Directors
(Lord, Rienecker, Phoebus, Zapotocny)
Management Oversight Board
NOAA / NWS / NCEP (Uccellini)NASA/GSFC/Earth Sciences Division (Lee,
acting)NOAA / NESDIS / STAR (Powell)
NOAA / OAR (Atlas)Department of the Air Force / Air Force
Director of Weather (Zettlemoyer)Department of the Navy / N84 and NRL
(Chang, Curry)
Agency ExecutivesNASA, NOAA, Department of the Navy, and
Department of the Air Force
Advisory Panel
Co-chairs: Jim Purdom, Tom Vonder Haar, CSU
Science Steering Committee
(Chair: Craig Bishop, NRL)
JCSDA Management Structure
New JCSDA short-term goal:(adopted 03/2008)
“Contribute to making the forecast skill of the operational NWP systems of the JCSDA partners internationally competitive by assimilating the largest possible number of satellite observations in the most effective way”
JCSDA Science Priorities
Radiative Transfer Modeling (CRTM) Preparation for assimilation of data from new instruments Clouds and precipitation Assimilation of land surface observations Assimilation of ocean surface observations Atmospheric composition; chemistry and aerosol
Driving the activities of the Joint Center since 2001, approved by the Science Steering Committee
Overarching goal: Help the operational services improve the quality of their prediction products via improved and accelerated use of satellite data and
related research
JCSDA Mode of operation
Directed research Carried out mainly by the partners Mixture of new and leveraged funding JCSDA plays coordinating role Also accessible to external community (CIs)
External research Historically implemented as a NOAA-administered FFO, open to the broader research
community Typically ~$1.5 M/year available => revolving portfolio of ~15 three-year projects Extended to include contracts (administred by NASA)
Visiting Scientists Open to all experts (global reach) Main conditions: Have a host at one of the partners and work on a JCSDA-related
activity
Results and progress from both directed and external work reported at annual JCSDA Science Workshop (most recent held on May 2010)
JCSDA Working Groups
Composed of working level scientists from (in principle) all JCSDA partners, plus additional members where appropriate
Tasked with sharing information and coordinating work where possible
Six WGs formed CRTM IR sounders Microwave sensors Ocean data assimilation Atmospheric composition Land data assimilation
Some of JCSDA Past Accomplishments
Common assimilation infrastructure (between NCEP/EMC, NASA/GMAO) Community radiative transfer model Common NOAA/NASA/AFWA land data assimilation system Interfaces between JCSDA models and external researchers Snow/sea ice emissivity model MODIS polar winds AIRS radiances assimilated COSMIC data assimilation Improved physically based SST analysis Advanced satellite data systems such as DMSP (SSMIS), CHAMP GPS, WindSat
tested for implementation Data denial experiments completed for major data base components in support of
system optimization (performed @ NASA/GSFC/GMAO)
IASI Impact on Standard Verification Scores
N. Hemisphere 500 hPa AC Z 20N - 80N Waves 1-20
1 Aug - 31 Aug 2007
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
0 1 2 3 4 5 6 7
Forecast [days]
An
om
aly
Co
rrel
atio
n '
Control IASI_EUMETSAT
S. Hemisphere 500 hPa AC Z 20S - 80S Waves 1-20
1 Aug - 31 Aug 2007
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
0 1 2 3 4 5 6 7
Forecast [day]
An
om
aly
Co
rrel
atio
n '
Control IASI_EUMETSAT
NH 500 hPa Height Anom. Cor.
1-31 August 2007
SH 500 hPa Height Anom. Cor.
J. Jung
IASIControl
S. Hemisphere 1000 hPa AC Z 20S - 80S Waves 1-20 1 Aug - 31 Aug 2007
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
0 1 2 3 4 5 6 7
Forecast [day]
An
om
aly
Co
rrel
atio
n Control ASCAT
ASCAT Impact Experiments with GFS
COSMIC: recent impact
AC scores (the higher the better) as a function of the forecast day for the 500 mb gph in Southern Hemisphere
40-day experiments: expx (NO COSMIC) cnt (operations - with
COSMIC) exp (updated RO
assimilation code - with COSMIC)• Many more observations• Reduction of high and low
level tropical winds error
L. Cucurull
Challenges
US falling behind internationally in terms of NWP skill
Risk of falling further behind if no remedies and current readiness for upcoming missions is not improved
NOAA/NCEP vs. ECMWF skill over 20+ years
Potential Remedies
Bring resources to adequate levels (Human & IT)Bring science up to standards (4DVAR, etc)
Better leveraging/coordination between partners
Get help from experts (Technology transfer) or better R2O
Potential Strategy for R2O Improvement (underway)
JCSDA IT Infrastructure
NASA
NOAACooperative
Institutes
Research InstitutionsIn general
(Supported by grants, contracts, etc)
Operational Centers (NCEP,FNMOC, etc)
Navy
All benefit from improvements being made in Central Testbed
Tools to be (1) developed, (2) improved, (3) validated, (4) made portable and (5) modularized or
(6) simply made available:-CRTM
-GSI-Calibration tools, BUFR tools,
-OSSE/OSE -Diagnostic Tools
-Etc
AFWA
Summary
JCSDA Recent refocus on NWP skill to address issue of underperforming US forecast skill
Multi-level efforts needed and underway: Operational readiness for GOES-R, NPP/JPSS and other missions Science improvements in Data Assimilation Set Up of an IT infrastructure (O2R, OSSE/OSE, etc) Coordination of efforts between JCSDA partners Potential coordination with other programs? (GOES-R, SpoRT,
HFIP, OSD/PSDI, Testbeds, etc) for a better leveraging of efforts/resources?
Continued need for interaction with outside research community
MiRS: A Physical Algorithm for Rain, Cloud, Ice, Atmospheric Sounding, and Surface Parameters
MiRS: A Physical Algorithm for Rain, Cloud, Ice, Atmospheric Sounding, and Surface Parameters
Sid-Ahmed Boukabara, Kevin Garrett, Wanchun Chen, Flavio Iturbide-Sanchez, Chris Grassotti and Cezar Kongoli
NOAA/NESDISCamp Springs, Maryland, USA
NOAA/NESDISCamp Springs, Maryland, USA
Description of an All-Weather One-Dimensional Variational Assimilation/Retrieval System
MSFC/SPoRT Seminar, November 19th 2010
19
Contents
All-Weather and All-Surface Applicability(or Cloudy/Rainy data assimilation & Surface Handling)
2
Performance Assessment3
General Overview and Mathematical Basis1
Summary & Conclusion4
20
Retrieval Mathematical Basis
Main Goal in ANY Retrieval System is to find a vector X with a maximum probability of being the source
responsible for the measurements vector Ym
Main Goal in ANY Retrieval System is to find a vector X: P(X|Ym) is Max
In plain words:In plain words:
Mathematically:Mathematically:
P(Y)Y)|P(XP(X)X)|P(YY)P(X, Bayes Theorem (of Joint probabilities)Bayes Theorem (of Joint probabilities)
)mP(YP(X)X)|mP(Y)mY|P(X
=1=1
21
Mathematically:Mathematically:
Core Retrieval Mathematical Basis
P(X)X)|mP(Y
Main Goal in ANY Retrieval System is to find a vector X with a maximum probability of being the source
responsible for the measurements vector Ym
Main Goal in ANY Retrieval System is to find a vector X: P(X|Ym) is Max
In plain words:In plain words:
Problem reduces to how to maximize:Problem reduces to how to maximize:
Probability PDF Assumed Gaussian around Background X0 with a
Covariance B
0
XX1BT
0XX
21exp
Mathematically:Mathematically:Probability PDF Assumed Gaussian around Background Y(X) with a
Covariance E
Y(X)mY1E
TY(X)mY
21exp
Y(X)mY1E
TY(X)mY
21exp
0XX1B
T0
XX21exp
Maximizing Maximizing
Y(X)mY1ETY(X
)mY2
1exp0XX1BT
0XX21exp
Is Equivalent to Minimizing Is Equivalent to Minimizing
)mY|P(Xln
Which amounts to Minimizing J(X) –also called COST FUNCTION –Same cost Function used in 1DVAR Data Assimilation System
Which amounts to Minimizing J(X) –also called COST FUNCTION –Same cost Function used in 1DVAR Data Assimilation System
Y(X)YEY(X)Y
21
XXBXX21
J(X) m1Tm0
1T0
)mY|P(X
22
Y(X)YEY(X)Y
21
XXBXX21
J(X) m1Tm0
1T0
Cost Function to Minimize:
To find the optimal solution, solve for:Assuming Linearity This leads to iterative solution:
Cost Function Minimization
0(X)'JX
J(X)
nΔXnK)nY(XmY1ETnK
1nK1ET
nK1B1n
ΔX
nΔXnK)nY(XmY
1ET
nBKnKTnBK
1nΔX
0
xxK)0
y(xy(x)
More efficient(1 inversion) Preferred when nChan << nParams (MW)
Jacobians & Radiance Simulation from Forward Operator: CRTM
23
Assumptions Made in Solution Derivation
The PDF of X is assumed GaussianOperator Y able to simulate measurements-like
radiancesErrors of the model and the instrumental noise
combined are assumed (1) non-biased and (2) Normally distributed.
Forward model assumed locally linear at each iteration.
24
Retrieval in Reduced Space (EOF Decomposition)
Covariance matrix(geophysical space)
Transf. Matrx(computed offline)
Diagonal Matrix(used in reduced space retrieval)
LBTLΘ
All retrieval is done in EOF space, which allows: Retrieval of profiles (T,Q, RR, etc): using a limited number of EOFs More stable inversion: smaller matrix but also quasi-diagonal Time saving: smaller matrix to invert
Mathematical Basis: EOF decomposition (or Eigenvalue Decomposition)
• By projecting back and forth Cov Matrx, Jacobians and X
25
CRTM as the Forward Model
Have a fully-validated, externally maintained forward operator,
Unrivaled leverage (~4 FT working on CRTM at JCSDA plus a number of on-going funded projects with academia, industry to upgrade CRTM ) . Funded by JCSDA
Have access to a model capable of producing not only radiances but also Jacobians
Long-term benefit: stay up to science art by benefiting from advances in CRTM modeling capabilities
26
MiRS General Overview
Radiances
Rapid Algorithms
(Regression)
Advanced Retrieval(1DVAR)
Vertical Integration &
Post-processing
selection
1st Guess
MIRS Products
Vertical Integration and Post-Processing1D
VA
RO
utpu
ts
VerticalIntegration
PostProcessing
(Algorithms)
TPWRWPIWPCLW
Core Products
Temp. Profile
Humidity Profile
Emissivity Spectrum
Skin Temperature
Liq. Amount Prof
Ice. Amount Prof
Rain Amount Prof
-Sea Ice Concentration-Snow Water Equivalent-Snow Pack Properties-Land Moisture/Wetness-Rain Rate-Snow Fall Rate-Wind Speed/Vector-Cloud Top-Cloud Thickness-Cloud phase
27
1D-Variational Retrieval/Assimilation
MiRS Algorithm
Measured Radiances
Init
ial
Sta
te V
ecto
r
Solution Reached
Forward Operator(CRTM)
Simulated RadiancesComparison: Fit
Within Noise Level ?
Update State Vector
New State Vector
Yes
NoJacobians
Geophysical Covariance
Matrix B
Measurement& RTM
UncertaintyMatrix E
Geophysical Mean
Background
Climatology (Retrieval Mode)Forecast Field (1D-Assimilation Mode)
28
Parameters are Retrieved Simultaneously
X is the solution
F(X) Fits Ym within Noise levels
X is a solution
Necessary Condition (but not sufficient)
If X is the set of parameters that impact the radiances Ym, and F the Fwd Operator
If X is the set of parameters that impact the radiances Ym, and F the Fwd Operator
If F(X) Does not Fit Ym within Noise
X is not the solution
All parameters are retrieved simultaneously to fit all radiances together
All parameters are retrieved simultaneously to fit all radiances together
Suggests it is not recommended to use independent algorithms for different parameters, since they don’t guarantee the fit to the radiances
Suggests it is not recommended to use independent algorithms for different parameters, since they don’t guarantee the fit to the radiances
29
Solution-Reaching: Convergence Convergence is reached everywhere: all surfaces, all weather
conditions including precipitating, icy conditions A radiometric solution (whole state vector) is found even when
precip/ice present. With CRTM physical constraints.
Previous version(non convergence when precip/ice present)
Current version
XYmY1E
TXYmY2
MiRS is applied to a number of microwave sensors, each time gaining robustness and improving validationfor Future New Sensors • The exact same executable, forward operator, covariance matrix used for all sensors• Modular design• Cumulative validation and consolidation of MiRS
MiRS is applied to a number of microwave sensors, each time gaining robustness and improving validationfor Future New Sensors • The exact same executable, forward operator, covariance matrix used for all sensors• Modular design• Cumulative validation and consolidation of MiRS
POES N18/N19
DMSPSSMIS
F16/F18
AQUAAMSR-E
NPP/JPSSATMS
: Applied Operationally
: Applied Operationally
: Applied occasionally: Applied occasionally
: Tested in Simulation: Tested in Simulation
Metop-A
TRMM/GPM/M-T
TMI, GMI proxy,SAPHIR/MADRAS
Current & Planned Capabilities
31
Contents
All-Weather and All-Surface Applicability(or Cloudy/Rainy data assimilation & Variational Handling of Surface)
2
Performance Assessment3
General Overview and Mathematical Basis1
Summary & Conclusion4
32
All-Weather and All-Surfaces
Upw
ellin
g R
adia
nce
Dow
nwel
ling
Rad
ianc
e
Surf
ace-
refle
cted
Rad
ianc
e
Clo
ud-o
rigi
natin
g R
adia
nce
Surf
ace-
orig
inat
ing
Rad
ianc
e Scattering Effect
Scattering Effect
Absorption
Surface
sensorMajor Parameters for RT:• Sensing Frequency• Absorption and scattering properties of material• Geometry of material/wavelength interaction• Vertical Distribution • Temperature of absorbing layers• Pressure at which wavelength/absorber interaction occurs• Amount of absorbent(s)• Shape, diameter, phase, mixture of scatterers.
Sounding Retrieval:• Temperature• Moisture
Instead of guessing and then removing the impact of cloud and rain and ice on TBs (very hard), MiRS approach is to account for cloud, rain and ice within its state vector.
It is highly non-linear way of using cloud/rain/ice-impacted radiances.
To account for cloud, rain, ice, we add the following in the state vector:• Cloud (non-precipitating)• Liquid Precipitation • Frozen precipitation
To handle surface-sensitive channels, we add the following in the state vector:• Skin temperature• Surface emissivity (proxy parameter for all surface parameters)
33
Contents
All-Weather and All-Surface Applicability(or Cloudy/Rainy data assimilation & Variational Handling of Surface)
2
Performance Assessment3
General Overview and Mathematical Basis1
Summary & Conclusion4
34
MiRS List of Products
Official ProductsOfficial Products Products being investigatedProducts being investigated
Vertical Integration and Post-Processing
1DV
AR
Out
puts
VerticalIntegration
PostProcessing
(Algorithms)
TPWRWPIWPCLW
Core Products
Temp. Profile
Humidity Profile
Emissivity Spectrum
Skin Temperature
Liq. Amount Prof
Ice. Amount Prof
Rain Amount Prof
-Sea Ice Concentration-Snow Water Equivalent-Snow Pack Properties-Land Moisture/Wetness-Rain Rate-Snow Fall Rate-Wind Speed/Vector-Cloud Top-Cloud Thickness-Cloud phase
1. Temperature profile2. Moisture profile3. TPW (global coverage)4. Land Surface Temperature 5. Emissivity Spectrum 6. Surface Type (sea, land, snow,
sea-ice)
7. Snow Water Equivalent (SWE)
8. Snow Cover Extent (SCE)9. Sea Ice Concentration (SIC)10.Cloud Liquid Water (CLW)11. Ice Water Path (IWP)12.Rain Water Path (RWP)
1. Cloud Profile2. Rain Profile3. Atmospheric Ice Profile4. Snow Temperature (skin) 5. Sea Surface Temperature 6. Effective Snow grain size 7. Multi-Year (MY) Type SIC 8. First-Year (FY) Type SIC9. Wind Speed10.Soil Wetness Index
The following section about performance assessment is a snapshot.
35
Temperature Profile Assessment(against ECMWF)
N18
MIRS
MIRS – ECMWF Diff
Note: Retrieval is done over all surface backgrounds but also in all weather conditions (clear, cloudy, rainy, ice)
ECMWF
MIRS – ECMWF Diff
Angle dependence taken care of very well, without any limb correction
36
Moisture Profile(against ECMWF)
N18
MIRS
Validation of WV done by comparing to:
-GDAS-ECMWF-RAOB
Assessment includes:
- Angle dependence- Statistics profiles- Difference maps
ECMWF
Stdev
Biasland
Sea
When assessing, keep in mind all ground truths (wrt GDAS, ECMWF, RAOB)
37
TPW Global Coverage
Smooth transition over coasts
Very similar features to GDAS
MiRSMiRS GDASGDASMiRS TPW Retrieval (zoom over CONUS)MiRS TPW Retrieval (zoom over CONUS)
38
RainFall Rate Assessment
Significant reduction in Rain false alarm using MiRS, at surface transitions and edges
MiRS Monthly composite (Metop-A)1DVAR
MiRS Monthly composite (Metop-A)1DVAR
MSPPS Monthly composite (Metop-A)Heritage algorithm: based on physical regression
MSPPS Monthly composite (Metop-A)Heritage algorithm: based on physical regression
39
MiRS RR part of IPWG Intercomparison(N. America, S. America and Australia sites)
Image taken from IPWG web site: credit to John Janowiak
This is an independent assessment where comparisons of MiRS RR composites are made against radar and gauges data.
Image taken from IPWG web site: credit to Daniel Villa
No discontinuity at coasts (MiRS applies to both land and ocean)No discontinuity at coasts (MiRS applies to both land and ocean)
Independent Validation (IPWG) 2/2
Monitor a running time series of statistics relative to rain gauges
Intercomparison with other PE algorithms and radar
Caution: algorithms perfs depend on how many sensors are used
Global Variationally-based Inversion of Emissivity: Routine Assessment
41
MiRS inverts emissivities for all channels, including high-frequency (Inversion performed in EOF space)
Emissivity is assessed by comparing it to analytically-inverted emissivity
Surface Emissivity Inter-Comparison12/01/2007 – 02/28/2009
Frequency (GHz)
Es
Es
MiRS N18
GDAS
MiRS N18 minus GDAS
Em
iss
ivit
y d
iffe
ren
ce (
MiR
S-A
nal
yt)
Frequency (GHz)
Ocean __
Sea Ice (Antartic) ___
Sea Ice (Arctic) ___
Sea Ice (First Year) ___
Desert __
Amazon __
Wet Land __
Snow __
Intercomparison between MiRS variational emissivities and analytical ones
Differences within 2%. Larger diffs noticed for snow (~8%) & Arctic sea-ice (3%). Questions: Tskin used in analytical emiss from GDAS accurate enough?
Is assumption of specularity valid for snow and sea-ice?
Case area after rain event
CPC Figures courtesy http://www.cpc.necp.noaa.gov
CPC real-time 24-hour precipitation from 12Z 2010-10-19, 2010-10-20, 2010-10-22 and 2010-10-23 (from left to right)
MiRS N18 retrieved emissivity at 31 GHz ascending node for 2010-10-19, 2010-10-20, 2010-10-22 and 2010-10-23 (from left to right)
Day in October
Es 19.35V channel37.0 V channel
Illustration of High Variability of Emissivity
44
MIRS Emissivity Response to Surface Moisture Variations –Case study-
. A significant storm system recorded for its wide-spread damage in human life and
property These storms hit the Midwest during May 5-7, 2007, as seen from MSPPS (top) and
NEXRAD Radar (bottom) images
05/05/07 05/06/07 05/07/07
MSPPS MSPPS MSPPS
NEXRAD NEXRAD NEXRAD
45
MIRS Emissivity Response to Surface Moisture Variations –MIRS Emissivity response
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
MIRS responds to surface wetness variations before (May 4), right after the storm (May 8) and later (May 10). Note the emissivity depression at 21 GHz and the inverted emissivity spectra on May 8, 2007.
Physically-consistent behavior noticed in the emissivity variation
May 4, 2007 (before the event)
May 8, 2007 (1 day after the event, no rain anymore)May 8, 2007 (1 day after the event, no rain anymore)
May 10, 2007 (3 days after event, emiss back to previous state)May 10, 2007 (3 days after event, emiss back to previous state)
Emisivity at 23 GHzEmisivity at 23 GHz
Emisivity at 89 GHzEmisivity at 89 GHz
Emisivity Spectra (20-160 GHz)
Emisivity Spectra (20-160 GHz)
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
sivi
ty
10-May
46
MiRS/N18 Sea-Ice Concentration AssessmentComparison with AMSR-E
MiRS/N18 AMSR-E
All MiRS surface parameters are obtained from emissivity, not TBs (so the validation of these
products is an indirect validation of emissivity itself)
47
MiRS/F16 SSMIS Snow Cover Extent (SCE)Comparison with IMS & AMSR-E
AMSRE
F16 MIRS F16 NRL
IMS
False alarms
Extensive snow cover
Less Extensive snow cover
2008-11-18
All MiRS surface parameters are obtained from emissivity, not TBs (so the validation of these
products is an indirect validation of emissivity itself)
48
Contents
All-Weather and All-Surface Applicability(or Cloudy/Rainy data assimilation & Variational Handling of Surface)
2
Performance Assessment3
General Overview and Mathematical Basis1
Summary & Conclusion4
49
Summary of Added Values All physical approach & simultaneous retrieval Consistent solution that fit the measurements (satisfying a
necessary but often overlooked requirement!). Applicability to all microwave sensors with same code All-Weather Sounding
Temperature/Moisture sounding in rainy/cloudy conditions using an all-weather RT/Jacobians operator
Emissivity-Based Retrieval of surface paremeters Higher accuracy of surface products by using Emissivity instead of
Radiances (for Wind speed, Soil moisture, Snow, Ice concentration, etc)
Extended retrieval of TPW to land, sea-ice, snow, coasts, sea Physical Retrieval of atmospheric rain, ice over ocean & land System is a retrieval & assimilation system
50
Extension to new sensors: sounders/imagers (ATMS, GPM/GMI, Megha-Tropiques, etc)
Multi-Sensors Synergy Take advantage of wider spectral coverage to fully characterize
surface emissivity and therefore improve surface classification as well as retrieval of other parameters
Take advantage of multi-angle viewing geometries to more accurately sound temperature and moisture
Extension to other spectral Regions (IR). Feasible since CRTM is valid in all spectral regions
Cloud/Rain/Ice Sounding Retrieval of cloud and rain in profile form. Combination of sensors,
could reduce ill-posed nature of the problem. Many by-products could result from the cloud profiling (cloud top, thickness, bottom, multi-layer nature, mixed phase information, etc)
Better geophysical background characterization
Foreseen Scientific Advances
MiRS Extension to TRMM/TMI (GPM project)
Example of retrieved rainfall rate from MiRS on TMI data at ~5 km resolution (left) compared to TRMM 2A12 (right) for 2010-09-19
MiRS has been extended to TRMM/TMI (work still in progress)
Current issues being addressed:-Non-convergence
-Coastal false alarm signal
Extension of MiRS to GPM/GMI (1/2)
52
GPM/GMI proxy data (simulated brightness temperatures) were generated to test MiRS algorithm.
Simulations performed using CRTM forward model and ECMWF geophysical inputs
Simulations over all surfaces TRMM/TMI metadata used (for
scanning geometry, angle, swath, time, etc) and also for emissivity
Simulations performed daily at NOAA.
Goal: Make sure the algorithm is ready on day-1 for GPM/GMI data (switch between proxy data flow and real data stream)
Example: GMI simulated 36.5 GHz H-pol TB
Current issue being addressed: Apparent pixel shift
GMI is similar to TMI with additional high frequency channels (166 and 183 GHz)
We look forward to using L1B data from GPM simulator (Matsui et al)
Extension of MiRS to GPM/GMI (2/2)
53
MiRS has been applied on the GPM/GMI Proxy data. All products are being assessed, including RR, Emissivity, TPW, etc
Draft Results: Work is still in progress to optimize the emissivity covariance for GMI and TMI
GMI Emiss @ 36.5 GHz H-pol
GMI TPW
Current Limitations & Planned Improvements
Sensor Applicability
Current Limitations Planned Improvements
Importance/Difficulty
All sensors Current atmospheric covariance is a single covariance used globally
Current effort aims at developing stratified covariances, by latitude and season
Important (to improve warm season perfs)
All sensors Rain Rate relationship (w 1DVAR hydrometeors) is also a single relationship, used globally
Investigate the stratification of rainrate relationship by season/latitude
Important (to improve warm season perfs)
All sensors Very low false alarm rate but Low detection Rate, especially for light rain, due to compensation of light rain signal by other parameters (such as WV)
Make sure high frequency channels have a stronger weight in the Chi-Square computation
Moderate
TMI/GMI Important coastal False Alarm
Improve emissivity covariance (not mature yet for these sensors)
Low
Future improvement:Stratification of Covariances
Mid-latitudeProfiles
TropicalProfiles
Rain
Rain
Ice
Ice
WRF Model Simulation
WRF Model Simulation
Differences in vertical structures of ice, cloud, rain
Differences in how temperature and moisture correlate to hydrometeors
Differences in how rainfall rate relate to integrated values of rain and ice (IWP, RWP)
Atmospheric Covariance Matrix
New Atmospheric Background Covariance Matrix based on ECMWF 60, and WRF simulations over tropic oceans
performed during SON season
Cloud liquid, Rain and Ice water from WRF
MiRS Current Atmospheric Background Covariance Matrix based on Global ECMWF 60, and tropic-ocean
MM5 simulations
Temperature, Water Vapor and CLW from ECMWF 60
Rain and Ice water from MM5
Temperature and Water Vapor from ECMWF 60
Noticeable Differences noticed in covariances, especially in hydrometeors. Impact assessment on RR performances in progress
Rainy (RPW>0.05mm) Land Surf. Emissivity Correlation Matrix from 5,000 scenes Oct. 2010
Non-Precipitating Land Surface Emissivity Correlation Matrixfrom 53,000 scenes Oct. 2010
Channel Freq. (MHz):1 = 50.3 H2 = 52.8 H3 = 53.6 H4 = 54.4 H5 = 55.5 H
6 = 57.29 RCP7 = 59.4 RCP
8 = 150 H9,10,11 = 183.31 H12,13 = 19.35 H/V
14 = 22.235 V15,16 = 37 H/V
17,18 = 91.655 V/H19 = 63.28 RCP
20-24 = 60.79 RCP
Note: difference in color bar range
SSMI/S Surface Emissivity Correlation Matrix Clear & Rainy Conditions over Land
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Future MiRS Application: 2dVAR Geostationary Application
Using 5 GDAS analyses, a 24-hour time series was simulated using linear time-interpolation
CRTM used to simulate brightness temperatures Regular 1DVAR applied on TBs (independent retrievals) 2DVAR applied (Red)
2DVAR2DVAR
1DVAR1DVAR
Simulated Time-seriesSimulated Time-series
2dVAR2dVAR
1dVAR1dVAR
2dVAR2dVAR
1dVAR1dVAR
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Future Challenges Assessment of Profiling in Active Areas
• Case of July 8th 2005
Zoom in space (over the Hurricane Eye) and Time (within 2 hours)
MHS footprint size at nadir is 15 Kms.
But at this angles range (around 28o), the MHS
footprint is around 30 KmsAll these 4 Dropsondes were
dropped within 45 minutes and are located within 10 kms from
each other
Temperature [K]
Water Vapor [g/Kg]
700 mb
700 mb
DeltaQ=4g/Kg
DeltaT=3K
More Information Publications
S.A. Boukabara, F. Weng and Q. Liu, Passive Microwave Remote Sensing of Extreme Weather Events Using NOAA-18 AMSUA and MHS. IEEE Trans. on Geoscience and Remote Sensing, July 2007. Vol 45, (7), 2228-2246
S.A. Boukabara, F. Weng, Microwave Emissivity Retrieval over Ocean in All-Weather Conditions. Validation Using Airborne GPS-Dropsondes. IEEE Trans Geos Remote Sens, 46, 376-384, 2007
S.-A. Boukabara, K. Garrett, and W. Chen, “Global Coverage of Total Precipitable Water using a Microwave Variational Algorithm,” IEEE TGARS, vol. 48, Sept. 2010
F. Iturbide-Sanchez, S.-A. Boukabara, R. Chen, K. Garrett, C. Grassotti, W. Chen, and F. Weng, “Assessment of a Variational Inversion System for Rainfall Rate over Land and Water Surfaces,” Submitted IEEE TGARS, July 2010.
S.-A. Boukabara et al. “MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System,” Submitted IEEE TGARS, May 2010.
Websitehttp://mirs.nedsis.noaa.gov
For More Information:
MiRS is a community algorithm (available publicly), benefiting from community-driven improvements, suggestions, scrutiny and assessment.
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BACKUP SLIDESBACKUP SLIDES
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Qualitative check of the Cloudy/Rainy radiance handling
MiRS Rain Water Path
TRMM (2A12) Rain Rate
Vertical Cross section
Vertical Cross section
A test case comparison with TRMM rain/ice product was conducted on 2010/02/02-The rain events were not captured exactly at the same time (shift noticed)-A qualitative assessment was done on the vertical cross-section-MiRS produces T(p), Q(p), cloud, rain and ice profile-Purpose is to check if these products behave physically
A test case comparison with TRMM rain/ice product was conducted on 2010/02/02-The rain events were not captured exactly at the same time (shift noticed)-A qualitative assessment was done on the vertical cross-section-MiRS produces T(p), Q(p), cloud, rain and ice profile-Purpose is to check if these products behave physically
MiRS MoistureMiRS Moisture
MiRS TemperatureMiRS Temperature
MiRS Rain/Ice ProfilesMiRS Rain/Ice Profiles
TRMM Rain/Ice ProfilesTRMM Rain/Ice Profiles
Cross-sections of both TRMM and MiRS products at 25 degrees North
Notes:-Generally, consistent features between TRMM and MiRS (except for expected shift)
- Ice is found on top of liquid rain
-Transition between frozen and liquid is delineated by the freezing level determined from the temperature profile.
-Moisture increases in and around the rain event
- Suggests that these products are reasonably constrained within physical inversion
Ice bottomIce bottom
Rain topRain top
Freezing levelFreezing level
Summary MiRS is a variational algorithm (1DVAR) and can be applied to virtually any
microwave sensor MiRS uses CRTM as forward and jacobian operators Retrieves sounding & surface parameters simultaneously, including
hydrometeor profiles, rain rate & surface emissivity Applicable over all surfaces (emissivity is part of the state vector), allowing
a spot-by-spot variability of the surface emissivity. Extensively assessed both internally and independently. Applicability in all-weather conditions (including rainy) Run operationally at NOAA for N18, N19, SSMIS F16, F18 and Metop-A,
and being integrated for NPP/JPSS ATMS MiRS is also currently being extended to support GPM (GMI) and Megha-
Tropiques (MADRAS and SAPHIR) Current enhancements to the algorithm expected to improve performances
of hydrometeor retrievals for all sensors We look forward to using GV data when they become available (plan to
extend CRTM, and therefore MiRS to airborne setups) and GPM simulator. Variational Emissivities from MiRS are available (all surfaces, for all
frequencies) as well as corresponding covariances.MiRS is a community algorithm (available publicly), benefiting from
community-driven improvements, suggestions, scrutiny and assessment.
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TPW Global Coverage
Smooth transition over coasts
Very similar features to GDAS
MiRSMiRS GDASGDASMiRS TPW Retrieval (zoom over CONUS)MiRS TPW Retrieval (zoom over CONUS)
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MiRS Emissivity
Assessment using target areas (ocean, desert, snow, young and old ice, wetland, Amazon) for:-Angle and spectral variations -Seasonal time series and Geographic distribution
Time series: Julian Day
Frequency (GHz)
Angle Dependence
Stable ocean emissivities
Seasonally-varying Sea-Ice emissivities
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Variational vs Analytical Emissivity(Land and Snow)
50.3 GHz
Std Dev
Bias
Land 0.03 0.000
Snow 0.03 0.012
MiRS/N18MiRS/N18 AnalyticalAnalytical
Difference (Varia.-Analy.)Difference (Varia.-Analy.)