Uses of Satellite Data in Environmental Modeling Program

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Uses of Satellite Data in Environmental Modeling
Program

• Fuzhong WengJoint Center for Satellite Data
  Assimilation
• 14th Annual International TeraScan Conference
• Nanjing University of Information Science
  Technology, China
• June 10, 2004

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Outline

• Background
• JCSDA Missions and Goals
• Science Priorities
• Ongoing Activities/Recent Accomplishments
• Recommendations

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5-Order Magnitude Increase in Satellite Data
Over 10 Years
Daily Upper Air Observation Count
Satellite Instruments by Platform

NPOESS METEOP NOAA Windsat GOES DMSP
2003
Count
2002
Count (Millions)
1990
2010
2000
1990
2010
2010-250ch
Year
Year
Year
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Global Measurements
TRMM
GOES-R
NPOESS
TOPEX
Aqua
NPP
NOAA/POES
Cloudsat
WindSAT
Aura
SSMIS
CALIPSO
COSMIC/GPS
Terra
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JCSDA Partners
Strategy Plan In 2000, NASA/NOAA White Paper on
A NASA and NOAA Plan to Maximize the Utilization
of Satellite Data to Improve Weather Forecasts
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JCSDA Organizational Structure
NASA NOAA DoD
Management Oversight Board of Directors NOAA
NCEP L. Uccellini (Chair) GSFC ESD F.
Einaudi NOAA ORA M. Colton NOAA OAR J.
Gaynor Navy S. Chang, R. McCoy USAF J. Lanici,
M. Farrar
Advisory Panel
Rotating Chair
Technical Liaisons GMAO D. Dee EMC J.
Derber GMAO M. Rienecker OWAQR A.
Gasiewski ORA D. Tarpley OSDPD Vacant Navy
N. Baker USAF M. McATee Army - Vacant
Joint Center for Satellite Data Assimilation
Staff Director John Le Marshall Executive
Directors Fuzhong Weng NESDIS/ORA Stephen
Lord NWS/NCEP/EMC Lars Peter Riishogjaard
GSFC/GMAO Pat Phoebus
DoD/NRL Secretary Ada Armstrong Consultant
George Ohring
Science Steering Committee
URL Site www.jcsda.noaa.gov
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The JCSDA Mission Goals
Mission accelerate and improve the quantitative
use of research and operational satellite data
in weather and climate prediction models

• Goals
• Reduce from two years to one year the average
  time for operational implementation of new
  satellite technology.
• Increase uses of current satellite data in NWP
  models.
• Advance the common NWP models and data
  assimilation infrastructure.
• Assess the impacts of data from advanced
  satellite sensors on weather and climate
  predictions.

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JCSDA Scientific Priorities

• Improve radiative transfer modeling technique
• Early prepare for advanced instruments
• Advance techniques for assimilating cloud and
  precipitation information
• Improve uses of satellite products in land data
  assimilation system
• Improve uses of satellite data in ocean data
  assimilation system
• Assimilate satellite derived aerosol, ozone and
  trace gas products

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Grants Program (FY03-AO)

• Improve radiative transfer model
• UCLA Advanced radiative transfer
• UMBC Including aerosols in OPTRAN
• NOAA/ETL Fast microwave radiance assimilation
  studies
• Prepare for advanced instruments
• U. Wisconsin Polar winds assimilation
• NASA/GSFC AIRS and GPS assimilation
• Advance techniques for assimilating cloud and
  precipitation information
• U. Wisconsin Passive microwave assimilation of
  cloud and precipitation
• Land data assimilation by improving emissivity
  models/satellite products
• Boston U. - Time varying land vegetation
• U. Arizona Satellite observation for snow data
  assimilation
• Colo. State U. Surface emissivity error
  analysis
• NESDIS/ORA Retrievals of real-time vegetation
  properties
• Improve use of satellite data in ocean data
  assimilation
• U. Md Ocean data assimilation bias correction
• Columbia U. Use of altimeter data
• NRL (Monterey) Aerosol contamination in SST
  retrievals

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Grants Program (FY04-AO)

• Improve radiative transfer model
• AER. Inc Development of RT Models Based on the
  Optimal Spectral Sampling (OSS) Method
• Navy/NRL - Assimilation of Passive Microwave
  Radiances over Land Use of the JCSDA Common
  Microwave Emissivity Model (MEM) in Complex
  Terrain Regions
• NOAA/ETL Fully polarmetric surface models and
  Microwave radiative transfer model
• UCLA Vector radiative transfer model
• Prepare for advanced instruments
• Navy/NRL SSMIS Brightness Temperature
  Evaluation in a Data Assimilation
• Land data assimilation by improving emissivity
  models/satellite products
• NASA/GSFC - Assimilation of MODIS and AMSR-E Land
  Products into the Noah LSMU.
• Princeton U. - Development of improved forward
  models for retrieval of snow properties from
  EOS-era satellites
• Boston U. Real time estimation and assimilation
  of remotely sensed data for NWP

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JCSDA Major Successes
RD Projects
Impacts

• Direct assimilation of AMSU/HIRS radiances
• SSM/I and TMI precipitation estimates in physical
  initialization (later use of TRMM data)
• Uses of QUIKSCAT data in global forecast system
• Upgrade NCEP global forecast system data
  assimilation system
• 55 km/64 levels, top at 0.2 mb
• Microwave land emissivity model
• Improved thinning of all current satellite data
  (reads 95 of all data, improved sampling
  algorithm)
• Implement MODIS winds into GFS
• Assess uses of AIRS data through target
  experiment areas

• Improve the forecast for synoptic scale weather
  by 1-2 days
• Improve the precipitation by removing excessive
  rain in the tropics
• Make 3-8 improvement in 10 m winds vs.
  mid-latitude deep ocean buoys at 24-96 h 7-17
  improvement for MSLP
• Uses more satellite data in stratosphere, over
  land, reads in more data
• MODIS winds show large positive impacts over
  polar regions
• Slightly positive to neutral impacts from AIRS
  data

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Direct Assimilation of Satellite Radiances

• Satellite microwave radiation at each sounding
  channel primarily arises from a particular
  altitude, indicated by its weighting function
• The vertical resolution of sounding is dependent
  on the number of independent channel measurements
• Lower tropospheric channels are also affected by
  the surface radiation which is quite variable
  over land

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Advanced Microwave Sounding UnitSounding Channels
183-3 GHz
52.8 GHz
183-1 GHz
53.7 GHz
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Impacts of AMSU on Forecasts

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Impacts of AMSU on Forecasts
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Uses of MODIS Winds
Unlike geostationary satellites at lower
latitudes, it is not be possible to obtain
complete polar coverage at a snapshot in time
with one or two polar-orbiters. Instead, winds
must be derived for areas that are covered by two
or three successive orbits, an example of which
is shown here. The whitish area is the overlap
between three orbits.
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Case Study Water Vapor Winds
Low Level Mid Level High Level
05 March 2001 Daily composite of 6.7 micron
MODIS data over half of the Arctic region. Winds
were derived over a period of 12 hours. There are
about 13,000 vectors in the image. Vector colors
indicate pressure level - yellow below 700 hPa,
cyan 400-700 hPa, purple above 400 hPa.
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Forecast skills verified against their own
analyses
(CIMSS winds) (NESDIS winds)
Forecast scores are the correlation between the
forecast geopotential height anomalies, with and
without the MODIS winds, and their own analyses.
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3D-VAR Assimilation of TRMM TMI and SSM/I rain
rates

• Notes
• use 1 superobd SSM/I and TMI rain rates
• include rain rates over ocean and land
• assimilate transformed observation variable
• log(1 rain_rate)
• observation error, O, is defined as
• O 1.0 a0 a1log(1 rain_rate)
• coefficients a0 and a1 vary for land and ocean
• Scheme works as designed
• increase light rain rates and reduce excessive
  rain rates
• greater impact on reducing excessive rain rates
• larger impact over ocean than land

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Impact of QuikSCAT Data

• Including QUIKSCAT data resulted in
• 3-8 improvement in 10 m winds vs. mid-latitude
  deep ocean buoys at 24-96 hr
• 7-17 improvement for MSLP
• Based on 40 forecasts from 45 days of GDAS
  (T170, L42) experiment.

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Near-Term Challenges
Expected Outcomes
Requirements

• Better forecasts on severe storms as measured by
  improved threat scores, rainfall potential
• Improved prediction of winter storms over high
  latitudes/polar regions
• Improved information on atmospheric motion for
  mesoscale applications
• Better initialization of clouds and precipitation
  processes and validation of cloud prediction
  schemes
• Improved parameterization in boundary layer
  models

• Advance radiative transfer modeling techniques
  (cloud scattering and polarization)
• Improve snow and sea ice emissivity modeling
• Implement four dimensional data variation (4dvar)
  techniques
• Explore uses of active sensors containing
  hydrometeor profile information
• Use satellite derived products over land and
  ocean (e.g. NDVI, SST, SSH, SSW)

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Planning for the Optimal Use of Satellite Data

• Operational access to advanced satellite
  instrument data
• Early access to the data
• Early evaluation of the data and its products
• Increase capability and accuracy of numerical
  forecast models
• More physical processes (e.g. cloud prognostic
  prediction)
• Higher spatial resolution
• Augment data assimilation techniques and
  algorithms to increase information extraction
• Scattering model for cloud and precipitation
• Surface model over sea ice and snow
• Cloud clearing radiances
• Data thinning techniques for advanced instruments
  

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JCSDA Road Map (2002 - 2010)
By 2010, a numerical weather prediction community
will be empowered to effectively assimilate
increasing amounts of advanced satellite
observations
The radiances can be assimilated under all
conditions with the state-of-the science NWP
models
Resources
NPOESS sensors ( CMIS, ATMS) GOES-R
OK
Deficiency
The CRTM includes scattering polarization from
cloud, precip and surface
Advanced JCSDA community-based radiative transfer
model, Advanced data thinning techniques
The radiances from advanced sounders will be
used. Cloudy radiances will be tested under
rain-free atmospheres, and more products (ozone,
water vapor winds) are assimilated
AIRS, ATMS, CrIS, VIIRS, IASI, SSM/IS, AMSR,
more products assimilated
Science Advance
A beta version of JCSDA community-based radiative
transfer model (CRTM) transfer model will be
developed, including non-raining clouds, snow and
sea ice surface conditions
Improved JCSDA data assimilation science
The radiances of satellite sounding channels were
assimilated into EMC global model under only
clear atmospheric conditions. Some satellite
surface products (SST, GVI and snow cover, wind)
were used in EMC models
AMSU, HIRS, SSM/I, Quikscat, AVHRR, TMI, GOES
assimilated
Pre-JCSDA data assimilation science
Radiative transfer model, OPTRAN, ocean microwave
emissivity, microwave land emissivity model, and
GFS data assimilation system were developed
2002
2008
2009
2003
2010
2004
2007
2005