1 Brief overview of the Joint Center for Satellite Data Assimilation (JCSDA) & Description of an All-Weather One-Dimensional Variational Assimilation/Retrieval

1

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)

&


Sid Ahmed Boukabara

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



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




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


2




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


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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

58

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

59

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.)

Documents

1 Brief overview of the Joint Center for Satellite Data Assimilation (JCSDA) & Description of an All-Weather One-Dimensional Variational Assimilation/Retrieval