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

Time

Analysis AnalysisAnalysis

Analysis AnalysisAnalysis

Real Evolving Atmosphere, with imperfect observations. Truth unknown

Climate simulation, with simulated imperfect “observations.” Truth known.

Observing System Simulation Experiment

Data Assimilation of Real Data

Design of an Observation System Simulation ExperimentCapability at the GMAOGoals:

1. 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.

1. A self-consistent and realistic simulation of nature. One such data set has been provided to the community by ECMWF through NCEP.

2. Simulation of all presently-utilized observations, derived from the “nature run” and having simulated instrument plus representativeness errors characteristic of real observations.

3. A validated baseline assimilation of the simulated data that, for various relevant statistics, produces values similar to corresponding ones in a real DAS.

Requirements:

Standard Deviation of theanalysis increment for the u-wind in the formerNCEP/ECMWF OSSE

T170L42 resolutionFeb. 1993 obs network

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.

Immediate Goal

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

New ECMWF Nature Run

1. 13-month “forecast” starting 10 May 20052. Use analyzed SST as lower boundary condition3. Operational model from 20064. T511L91 reduced linear Gaussian grid (approx 35km)5. 3 hourly output

Approximations and Simplifications

1. Partial thinning of radiance obs to reduce computational demand 2. Simple treatment of clouds as elevated black bodies for IR3. No use of surface-affected MW channels over land or ice4. Similar radiative transfer model used to simulate and assimilate5. 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

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)

Experiments

Simulating cloud effects on IR radiances

Probability of significant cloud

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Cloud fraction f

Pro

bab

ility

P(f

)

a b c

Add Random Errors

1. 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.

2. No horizontal correlations of error, but for RAOBs or other “conventional” soundings, errors are vertically correlated.

3. Other implicit “errors” are present due to treatments of clouds or surface emissivity and to interpolations in space and time.

Standard deviationsof analysis incrementsu field, 500 mb

OSSE

Real

mean values of analysis incrementsu field, 500 mb

OSSE

Real

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

January mean of Jo/n

Langland and Baker 2004Gelaro et al 2007, G. and Zhu 2008Errico 2007, Tremolet 2007

Adjoint-Derived Impact Estimates

OSSE Real

NOAA-17 HIRS/3 Brightness Temperatures

OSSE Real

Locations of Brightness Temperature accepted by the Quality-Control for NOAA-17 channel 7 HIRS-3 on 15 Jan 2006 at 0 UTC +/- 3hrs

Real Data

OSSE DataIgnore colors

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

Real Data

OSSE Data

Ignore colors

Locations of Brightness Temperature accepted by the Quality-Control for NOAA-17 channel 1 AMSU-A on 15 Jan 2006 at 0 UTC +/- 3hrs

Real Data

OSSE Data

Ignore colors

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

Real Data

OSSE Data

Ignore colors

What is next?

1. Finish examination of latest experiment2. Work on improving obs simulations a. raob soundings b. MW surface emissivity c. CTW locations d. error correlations3. Look at a wind LIDAR instrument4. Add aerosols to the NR data (Arlindo Da Silva)5. Improving and generalizing the software

Latest version of obs. sim. software available by FTPLatest sim. obs. data available by FTP

End of talk