Satellite Data Assimilation in Cloudy and Precipitation Conditions

Fuzhong WengNOAA/NESDIS/Center for Satellite Applications and Research

and Sr. Scientist, US Joint Center for Satellite Data Assimilation

The 4th International Precipitation Working Group MeetingOctober 13-17, 2008

Content

• JCSDA Program Update

• Satellite Data Utilization in GFS

• Improving Uses of Satellite Data in Cloudy Conditions

• Summary and Conclusions

JCSDA Partners

Pending

In 2001 the Joint Center was established2 by NASA and NOAA and in 2002, the JCSDA expanded its partnerships to include the U.S. Navy and Air Force weather agencies.

2 Joint Center for Satellite Data Assimilation: Luis Uccellini, Franco Einaudi, James F. W. Purdom, David Rogers: April 2000.

JCSDA Science Priorities

I Improve Radiative Transfer Models II Prepare for Advanced Operational Instruments

III Assimilating Observations of Clouds and Precipitation IV Assimilation of Land Surface Observations from Satellites

V Assimilation of Satellite Oceanic Observations

VI Assimilation for air quality forecasts

SATELLITE DATA STATUS in NCEP GFS – May 2008

Jason Altimeter Implemented into NCEP GODAS

AIRS with All Fields of View Implemented – 1 May

MODIS Winds Implemented– 1 May

NOAA-18 AMSU-A Implemented– 1 May

NOAA-18 MHS Implemented– 1 May

NOAA-17 SBUV Total Ozone 4 December 2007

NOAA-17 SBUV Ozone Profile Implemented– ???

SSMI/S Radiances Preliminary forecast assessment completed

GOES 1x1 sounder radiances Implemented 29 May 2007

METOP AMSU-A, MHS, HIRS Implemented 29 May 2007

COSMIC/CHAMP Implemented (COSMIC – 1 May) CHAMP Data in prep.

MODIS Winds v2. Test and Development

METOP IASI/ASCAT Preliminary forecast assessment completed

AMSR/E Radiances Preliminary forecast assessment completed

AIRS/MODIS Sounding Channels Assim. Data in Preparation

JMA high resolution winds Implemented 4 December 2007

GOES Hourly Winds, SW Winds To be Tested

GOES 11 and 12 Clear Sky Rad. Assim(6.7µm) To be Tested

MTSAT 1R Wind Assim. Data in Preparation

AURA OMI Test and Development

TOPEX,ERS-2 ENVISAT ALTIMETER Test and Development (Envisat) ERS-2 (dead) TOPEX implemented in NCEP GODAS

FY – 2C Data in Preparation

Satellite Data Ingested into Models and Future Data Stream

JCSDA Budget

Initiative Summary: Requested increase in JCSDA funding will accelerate uses of satellite measurements under cloudy and precipitation conditions and will improve the skill for forecasts up to 10 days in length, and predict the intensity and track of severe weather forecasts. Currently, temperature and moisture profiles in cloudy regions are poorly understood and difficult to extract from the available satellite data due to a lack of capabilitiyies in assimilating satellite cloudy and rain-affected radiances.

FY08 NOAA Budget: $3.3M and other leveraged resources: $1.5M

FY09 NOAA Budget: +$0.6M

Community Radiative Transfer Model

Support over 100 Sensors

• GOES-R ABI• Metop IASI/HIRS/AVHRR/AMSU/MHS• TIROS-N to NOAA-18 AVHRR• TIROS-N to NOAA-18 HIRS• GOES-8 to 13 Imager channels• GOES-8 to 13 sounder channel 08-13 • Terra/Aqua MODIS Channel 1-10 • METEOSAT-SG1 SEVIRI • Aqua AIRS• Aqua AMSR-E • Aqua AMSU-A• Aqua HSB• NOAA-15 to 18 AMSU-A• NOAA-15 to 17 AMSU-B• NOAA-18 MHS • TIROS-N to NOAA-14 MSU• DMSP F13 to15 SSM/I• DMSP F13,15 SSM/T1• DMSP F14,15 SSM/T2• DMSP F16 SSMIS • NPP ATMS• Coriolis Windsat• TiROS-NOAA-14 SSU

“Technology transfer made possible by CRTM is a shining example for collaboration among the JCSDA Partners and other organizations, and has been instrumental in the JCSDA success in accelerating uses of new satellite data in operations” – Dr. Louis Uccellini, Director of National Centers for Environmental Prediction

Required Improvements for Assimilation of Passive Microwave Satellite Data

• Better bias correction

• Improved surface emissivity model

• Better cloud detection algorithms

• Direct cloudy radiance assimilation

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Variational Bias Correction

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Update the bias inside the assimilation system by finding corrections that minimize the systematic radiance departures while simultaneously improving the fit to other observed data inside the analysis flow.

Major predictors Scan angle or scan position Lapse rate () Lapse rate squared (2) Cloud liquid water

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O – B Histograms for QC Passed Data over (Cloud-free) Oceans

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Cloud Detection Algorithm & Assimilation Impact

• SSM/I and AMSU CLW algorithms (Weng &Grody, 1994, JGR; Weng et al., 2001. TGRS)

• MHS and SSMIS IWP algorithm (Zhao & Weng, 2002, JAM; Sun & Weng, 2008, TGRS)

Atmospheric Transmittance(a) Atmospheric Transmittance at 52.8 GHz (b) Atmospheric Transmittance at 1837 GHz

(c) Atmospheric Transmittance at 1833 GHz (d) Atmospheric Transmittance at 1831 GHz

Impacts of Snow & Sea Ice Emissivity

•SSMIS and MHS include several sounding channels sensitive to variable emissivity especially over snow and sea ice conditions

• Improved snow and sea ice emissivity models result in around 60% of SSMIS and MHS sounding data passing QC

•The impact of the MHS data using the new emissivity model is positive

a positive impact

a positive impact

Assimilation of Cloudy and Rain-Affected Radiances – Current Approaches

• JCSDA Radiances in cloudy areas (rainy pixels rejected) are handled as clear

pixels in forward calculation Radiance biases due to clouds are corrected through bias correction

algorithms

• ECMWF LWP is first retrieved from simple algorithms and used to check if

radiances are clear or cloudy/rainy. Clear pixels directly go to 4dvar For cloudy/rainy pixels, it goes to 1dvar for better refinement in LWP,

TPW, and other parameters. TPW in rainy areas is assimilated in 4dvar Impacts of TPW on other analysis fields (T, Q, and Wind) are done

through cloud and moisture physics in 4dvar system

• Metoffice Atmospheric parameters under cloudy and precipitating conditions are

retrieved from 1dvar and the 1dvar convergence flag is used to control the radiances into 4dvar, also rain and non-rain pixels

4dvar process include several key hydrometeor parameters (no precipitation) and TL/AD from cloud and moisture physics.

Note: Data over thick cloudy area are screened out but those over thin cloudy area have been assimilated without including cloudy radiance computation

QC Issues in Handling Cloudy Radiances

AMSU Cloud-free Data Over Ocean AMSU Data Passed through QC

New Considerations in Cloudy Radiance Assimilation at JCSDA

• Develop forward radiative transfer and Jacobian models including clouds and precipitation

• Use 1dvar quality control of satellite radiances

• Extent the control variables with more hydrometeor parameters

• Incorporate cloud and moisture physics in minimization processes

• Improve bias corrections using more predictors (e.g. LWP and RWP) from observations and/or moisture physics

Direct SSMIS Cloudy Radiance Assimilation

The initial temperature field from control run (left panels) w/o uses of SSMIS rain-affected radiances and test run (right panels) using SSMIS rain-affected radiances

DMSP F-16 SSMIS radiances is at the first time assimilated using NCEP 3Dvar data analysis. The new data assimilation improves the analysis of surface minimum pressure and temperature fields for Hurricane Katrina. Also, Hurricane 48-hour forecast of hurricane minimum pressure and maximum wind speed was significantly improved from WRF model

Significance: Direct assimilation of satellite radiances under all weather conditions is a central task for Joint Center for Satellite Data Assimilation (JCSDA) and other NWP centers. With the newly released JCSDA Community Radiative Transfer Model (CRTM), the JCSDA and their partners will be benefited for assimilating more satellite radiances in global and mesoscale forecasting systems and can improve the severe storm forecasts in the next decade

Control Experiment

Katrina Warm Core Evolution through NCEP GSI Analysis

Uses of 1dvar for QC

MIRS Environmental Data Records

SDR/EDR POES/METOP

AMSU-A/B; MHS

DMSP

SSMIS

NPOESS

ATMS/MIS

Radiances

Temp. profile

Moist. profile

Total precipitable water*

Hydr. profile

Precip rate*

Snow cover*

Snow water equivalent*

Sea ice *

Cloud water*

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Land temp*

Surface emis*

Soil moisture/Wetness Index

*currently from MSPPS only*currently from MSPPS only

1DVAR including All hydrometeors

MIRS LWP ECMWF

MiRS T vs. RAOB (Ocean)

MiRS T vs. RAOB (Land)

MiRS Q vs. RAOB (Ocean)

MiRS Q vs. RAOB (Land)

Minimization Process including Moisture Physics

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Including GFS Cloud and Moisture Physics

FW, TL, and AD models based on Zhao and Carr (1997) microphysics scheme exist in the current GDAS system and will be tested in 1dvar system (off-line test). 1dvar is a simple version of GSI 3dvar (background covariance matrix is derived from NMC method)

Summary and Conclusions

• NOAA is taking a new initiative on assimilation of cloudy and rain-affected radiances through JCSDA program

• Microwave sensor data from POES, DMSP, EOS are vital for global medium range forecasts and have produced largest impacts through better bias correction, snow and sea ice emissivity models

• 1dvar system including cloudy and rain-affected radiances are developed and used in NESDIS operation for sounding products.

• Impacts from uses of rain-affected radiances in variational analysis are encouraging for storm intensity prediction and better moisture field

Acknowledgements

Dr. Banghua Yan, JCSDA/Perot System – Emissivity model and cloud detection, bias correction

Dr. Min-jeong Kim, JCSDA/CIRA – TL/Adjoint moisture physics

Dr. Sid-Boukabara, NESDIS – 1DVAR

Dr. John Derber, JCSDA/NCEP – 3dvar/4dvar and bias corrections

Drs. Yong Han (NESIDS), Paul vanDelst (NCEP), Mar Liu (JCSDA), Yong Chen (JCSDA/CIRA): CRTM team