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Title: The Infrastructure, Design and Applications of


1
The Infrastructure, Design and Applications of
Observing System Simulation Experiments
at NASA's Global
Modeling and Assimilation Office By Ronald M.
Errico (GMAO and GEST) Runhua Yang (GMAO and SSAI
) Acknowledgements Meta Sienkiewicz, Emily
Liu, Ricardo Todling,
Ronald Gelaro, Joanna Joiner, Tong Zhu,
Quansheng Liu,
and Michele Rienecker
2
Data Assimilation of Real Data
Real Evolving Atmosphere, with imperfect
observations. Truth unknown
Analysis
Analysis
Analysis
Time
Analysis
Analysis
Analysis
Climate simulation, with simulated imperfect
observations. Truth known.
Observing System Simulation Experiment
3
Design of an Observation System Simulation
Experiment Capability at the GMAO
Goals
  • Be able to estimate the effect of proposed
    instruments on analysis and
  • forecast skill by flying them in a
    simulated environment.
  • 2. Be able to evaluate present and proposed data
    assimilation techniques in a simulation where
    truth is known perfectly.

Requirements
  • A self-consistent and realistic simulation of
    nature. One such data set
  • has been provided to the community by ECMWF
    through NCEP.
  • Simulation of all presently-utilized
    observations, derived from the
  • nature run and having simulated instrument
    plus representativeness
  • errors characteristic of real observations.
  • A validated baseline assimilation of the
    simulated data that, for various
  • relevant statistics, produces values similar
    to corresponding ones in a real DAS.

4
Standard Deviation of the analysis increment for
the u-wind in the former NCEP/ECMWF OSSE
T170L42 resolution Feb. 1993 obs network
5
Immediate Goal
Quickly generate a prototype baseline set of
simulated observations that is significantly
more realistic than the set of baseline
observations used for the previous NCEP/ECMWF
OSSE.
Account for Resources are somewhat limited
The Nature Run may be unrealistic in some
important ways Some issues are not very
important compared to others Some important
issues may still have many unknown aspects
6
New ECMWF Nature Run
  1. 13-month forecast starting 10 May 2005
  2. Use analyzed SST as lower boundary condition
  3. Operational model from 2006
  4. T511L91 reduced linear Gaussian grid (approx
    35km)
  5. 3 hourly output

7
Approximations and Simplifications
  • Partial thinning of radiance obs to reduce
    computational demand
  • Simple treatment of clouds as elevated black
    bodies for IR
  • No use of surface-affected MW channels over land
    or ice
  • Similar radiative transfer model used to simulate
    and assimilate
  • Locations for all conventional obs given by
    corres. real obs
  • a. locations of significant levels not
    based on sim. soundings
  • b. locations of CTW not based on sim. cloud
    cover
  • 6. Un-biased Gaussian noise added to all
    observations
  • 7. No radiance bias correction

8
Experiments
Evaluation for Jan. 2006, Spin-up starts 1 Dec.
2005 Data assimilation system NCEP/GMAO GSI
(3DVAR), 6-hour periods Resolution of DAS 2
deg lat, 2.5 deg lon, 72 levels, top at 1
Pa Conventional Obs include raobs, aircraft,
ships, vad winds, wind profilers,
sfc stations, SSMI
and Qkscat sfc winds, sat winds
(Approx used 1.4
M/day) Radiance Obs include HIRS2, HIRS3,
AMSUA, AMSUB, AIRS, MSU
(Approx used 3 M/day)
9
Simulating cloud effects on IR radiances
10
b
c
a
11
Add Random Errors
  • Explicit random errors are drawn from a normal
    distribution
  • having mean 0 and variance 0.65 R, where R
    is the sum of the
  • instrument plus representativeness errors
    found in the GSI
  • observation error tables.
  • No horizontal correlations of error, but for
    RAOBs or other
  • conventional soundings, errors are
    vertically correlated.
  • Other implicit errors are present due to
    treatments of clouds
  • or surface emissivity and to
    interpolations in space and time.

12
OSSE
Standard deviations of analysis increments u
field, 500 mb
Real
13
OSSE
mean values of analysis increments u field, 500
mb
Real
14
January mean of Jo/n

Real OSSE Surface pressure
0.320 0.252
Temperature
2.45 1.28 Vector wind
1.11
0.79 Specific humidity
1.33 1.26 Surface wind speed
1.18
1.18 Radiance
0.259 .344
15
Langland and Baker 2004 Gelaro et al 2007, G. and
Zhu 2008 Errico 2007, Tremolet 2007
16
(No Transcript)
17
Adjoint-Derived Impact Estimates
OSSE
Real
18
NOAA-17 HIRS/3 Brightness Temperatures
OSSE
Real
19
Locations of Brightness Temperature accepted by
the Quality-Control for NOAA-17 channel 7
HIRS-3 on 15 Jan 2006 at 0 UTC /- 3hrs
Ignore colors
OSSE Data
Real Data
20
Distribution of Innovations (O-F) of Brightness
Temperature accepted by the Quality-Control for
NOAA-17 channel 7 HIRS-3 on 15 Jan 2006 at 0
UTC /- 3hrs
Ignore colors
OSSE Data
Real Data
21
Locations of Brightness Temperature accepted by
the Quality-Control for NOAA-17 channel 1
AMSU-A on 15 Jan 2006 at 0 UTC /- 3hrs
Ignore colors
OSSE Data
Real Data
22
Distribution of Innovations (O-F) of Brightness
Temperature accepted by the Quality-Control for
NOAA-17 channel 1 AMSU-A on 15 Jan 2006 at 0
UTC /- 3hrs
Ignore colors
OSSE Data
Real Data
23
What is next?
  • Finish examination of latest experiment
  • Work on improving obs simulations
  • a. raob soundings
  • b. MW surface emissivity
  • c. CTW locations
  • d. error correlations
  • 3. Look at a wind LIDAR instrument
  • Add aerosols to the NR data (Arlindo Da Silva)
  • Improving and generalizing the software

Latest version of obs. sim. software available by
FTP Latest sim. obs. data available by FTP
24
End of talk
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