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JP2.23 . OSSEs: Observing Systems Simulation Experiments. Evaluation of GOES-R and NPOESS instrument in Joint OSSEs http://www.emc.ncep.noaa.gov/research/JointOSSEs 5th NPOESS-GOES-R 16 th Sat Met. 2,5,$ Lars Peter Riishojgaard, 3,5 F. Weng, 5,@ T. Zhu, 1,+ J. Woollen, 3,% H. Sun,

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slide1

JP2.23

OSSEs: Observing Systems Simulation Experiments

Evaluation of GOES-R and NPOESS instrument in Joint OSSEs

http://www.emc.ncep.noaa.gov/research/JointOSSEs

5th NPOESS-GOES-R

16th Sat Met

2,5,$Lars Peter Riishojgaard, 3,5F. Weng, 5,@T. Zhu,1,+J. Woollen, 3,%H. Sun,

1,5,#M. Masutani,8C. M. Hill, 8V. Anantharaj, 8P. J. Fitzpatrick,

2,$R. M. Errico, 1S. J. Lord, 3,5Y. Han,

3,5T. J. Kleespies, 1,+,5D. Groff,

1NOAA/NWS/NCEP/EMC, Camp Springs, MD,

2NASA/GSFC, Greenbelt, MD,

3NOAA/NESDIS/STAR, Camp Springs, MD

4European Centre for Medium-Range Weather Forecasts (ECMWF),

5Joint Center for Satellite and Data Assimilation

8Mississippi State University/GRI, MS,

# Wyle Information Systems, McLean, VA,

+Science Applications International Corporation (SAIC), MD

$Goddard Earth Science and Technology Center, University of Maryland, Baltimore, MD,

%QSS Group, Inc., MD

@Cooperative Institute for Research in the Atmosphere (CIRA)/CSU, CO

slide3

Need for OSSEs

Benefit of OSSEs

♦Quantitatively–based decisions on the design and implementation of future observing systems

♦Evaluate possible future instruments without the costs of developing, maintaining & using observing systems.

●OSSEs help in understanding and formulating observational errors

●DA (Data Assimilation) system will be prepared for the new data

●Enable data formatting and handling in advance of “live” instrument

● OSSE results also showed that theoretical explanations will not be satisfactory when designing future observing systems.

If we cannot simulate observations, how could we assimilate observations?

Need for collaboration

Need one good new Nature Run which will be used by many OSSEs, including regional data assimilation.

Share the simulated data to compare the OSSE results from various DA systems to gain confidence in results.

OSSEs require many experts and require a wide range of resources.

Extensive international collaboration within the Meteorological community is essential for timely and reliable OSSEs to influence decisions.

slide4

Archive and Distribution

New Nature Run by ECMWF Based on discussion with JCSDA, NCEP, GMAO, GLA, SIVO, SWA, NESDIS, ESRL, and ECMWF

To be archived in the MARS system on the THORPEX server at ECMWF

Accessed by external users. Currently available internally as expver=etwu

  • Copies for US are available to designated users for research purpose& users known to ECMWF
  • Saved at NCEP, ESRL, and NASA/GSFC
  • Complete data available from portal at NASA/GSFC
  • Conctact:Michiko Masutani (michiko.masutani@noaa.gov), Harper.Pryor@nasa.gov
    • Gradsdods access is available for T511 NR. The data can be down loaded  in grib1, NetCDF, binary. The data can be retrieved globally or selected region.
    • Provide IP number to :Arlindo da Silva (Arlindo.Dasilva@nasa.gov)

Low Resolution Nature Run

Spectral resolution : T511

Vertical levels: L91

3 hourly dump

Initial conditions: 12Z May 1st, 2005

Ends at: 0Z Jun 1,2006

Daily SST and ICE: provided by NCEP

Model: Version cy31r1

Supplemental low resolution regular lat lon data

1degx1deg for T511 NR, 0.5degx0.5deg for T799 NR

Pressure level data:31 levels,

Potential temperature level data: 315,330,350,370,530K

Selected surface data for T511 NR: Convective precip, Large scale precip, MSLP,T2m,TD2m, U10,V10, HCC, LCC, MCC, TCC, Sfc Skin Temp

Complete surface data for T799 NR

T511 verification data is posted from NCAR CISL Research Data Archive. Data set ID ds621.0. Currently NCAR account is required for access.

T799 verification data are available from NASA/GSFC portal (Contact Harper.Pryor@nasa.gov)

(Also available from NCEP hpss, ESRL, NCAR/MMM, NRL/MRY, Univ. of Utah, JMA,Mississippi State Univ.)

Two High Resolution Nature Runs

35 days long

Hurricane season: Starting at 12z September 27,2005,

Convective precipitation over US: starting at 12Z April 10, 2006

T799 resolution, 91 levels, one hourly dump

Get initial conditions from T511 NR

Note: This data must not be used for commercial purposes and re-distribution rights are not given. User lists are maintained by Michiko Masutani and ECMWF

slide5

Evaluation of the Nature run

  • Utilize Goddard’s cyclone tracking software (Terry and Atlas, AMS conf, Aug 1996):
  • Identifies and tracks mostly extratropical cyclones (cutoff at 20 deg N/S latitude)
  • Interfaces with GrADS contouring algorithm
  • Uses SLP field at 4hPa contour interval
  • Finds centroid of inner-most closed isobar
  • Tracks the centers using extrapolation and 500hPa steering

Cloud Cover

Comparison between the ECMWF T511 Nature Run against climatology

20050601-20060531, exp=eskb, cycle=31r1

Adrian Tompkins, ECMWF

NR

MODIS

NR-MODIS

Tropics

Oreste Reale (NASA/GSFC/GLA)

  • Cyclone tracks generated:
  • Nature run at one degree for Jun 2005 to May 2006 (each month and season)
  • NCEP operational analysis at one degree for 2000 to 2006 (each month, 68 of 84 months were available)

Vertical structure of a HL vortex shows, even at the degraded resolution of 1 deg,

a distinct eye-like feature and a very prominent warm core. Structure even more

impressive than the system observed in August. Low-level wind speed exceeds 55 m/s

slide6

Simulation of Observation

GMAO Observation Simulator for Joint OSSE

OBS91L Nature Run Model level profiles for simulating radiance obs

Jack Woollen (EMC)

• Software for generating conventional obs (Observation type included in NCEP .prepbufr file)

The codes are set up for raobs, aircraft, ships, vad winds, wind profilers, surface station data, SSMI and Quick scat surface winds, Cloud Motion Vector (CMV)

• Software for simulating radiances

Code to simulate HIRS2/3, AMSUA/B, AIRS, MSU has been set up. Community Radiative Transfer Model (CRTM) is used for forward model.

• Software for generating random obs. error

Observations are generated without errors but software to simulate error is provided.

For development purposes, 91-level ML variables are processed at NCEP and interpolated to observational locations with all the information need to simulate radiance data (OBS91L).

The OBS91L are also available for development of a Radiative Transfer Model (RTM) for development of other forward model.

Conventional data, AMSUA, AMSUB, GOES datahas been simulated for entire T511 NR period.

Considerations

Data distribution depends on atmospheric conditions

Cloud and Jet location, Surface orography, RAOB drift

Cloud Motion Vectors

SWA, GMAO and NCEP

- Advised by Chris Velden -

The output of the data is saved in BUFR format which can be read by the Gridpoint Statistical Interpolation (GSI). GSI is a DAS used at NCEP, GMAO and ESRL. The codes are flexible and include many tunable parameters.

Contact: GMAO (Ronald Errico: ronald.m.errico@nasa.gov)

Joint OSSE (Michiko Masutani: michiko.masutani@noaa.gov).

Sat wind was included to provide reasonable fields for SH

Radiation data are not included.

Initial data will have no error added and quality control is not necessary.

slide7

Radiance Simulation System for Joint OSSERon Errico, Runhua Yang, Emily Liu, Meta Sienkiewicz,(NASA/GSFC/GMAO)Tong Zhu,Tom Kleespies,Haibing Sun, Fuzhong Weng, (NOAA/NESDIS) Jack Woollen, Michiko Masutani(NOAA/NCEP)Lars Peter Riishojgaard (JCSDA)

Other possible resources and/or advisors David Groff , Paul Van Delst (NCEP) Yong Han,Walter Wolf, Cris Bernet, Mark Liu, M.-J. Kim, (NESDIS), Erik Andersson (ECMWF); Roger Saunders (Met Office)

The GMAO simulation software was successfully installed at NCEP and initial simulation AIRS, HIRS2 and HIRS3 radiance data were completed for the entire period of T511 NR. GMAO software is also versatile to simulate other observing systems.

Initially, CRTM is used for simulation and assimilation.

CRTM: Community Radiative Transfer Model

Algorithm for determining cloud-cleared observation locations used at GMAO

For each grid box where a satellite observation is given, use the

cloud fraction to specify probability that it is a clear spot. Then

use random number to specify whether pixel is clear. Use a

functional relationship between probability and cloud fraction

that we can tune to get a reasonable distribution.

AMSUA, AMSUB GOES data have been simulated using OBS91L at NESDIS and NCEP for entire period of T511NR

Alternative software to simulate radiance data using the Stand-alone AIRS Radiative Transfer Algorithm (SARTA) as well as the CRTM is also being developed at NESDIS. NESDIS software includes results from various research. This will be important to evaluate CRTM in Joint OSSEs.

Calibration for Joint OSSEs

Discussion forum for observational errors

Extensive discussion on simulation of observational error particularly representativeness error.

Published in “Data assimilation: Making sense of observation” (Springer)

Calibration using the adjoint technique has been conducted at GMAO

Calibration using data denial experiments at ESRL, NCEP, and NESDIS

slide8

Calibration for Joint OSSEs at NASA/GMAO

Try 1

Try 1

Try 2

Latest try

Continue working on tuning parameter for cloud clearing

Investigate problem in surface emissivity

Improving simulation of Cloud motion vector. (Need to work with SWA)

REAL OSSE

REAL OSSE

slide9

Simulation GOES Radiance for OSSETong Zhu (CIRA/CSU), Fuzhong Weng (NOAA/NESDIS), Michiko Masutani (NOAA/EMC), Steve Load (NOAA/EMC), Jack Woollen (NOAA/EMC), Thomas J. Kleespies(NOAA/NESDIS), Yong Han(NOAA/NESDIS), Quanhua, Liu (QSS), Sid Boukabara (NOAA/NESDIS)

Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental Satellite (GOES)-R platform. The sensor will provide enhanced spatial, temporal information for atmospheric moisture, wind and many surface properties. A joint Observation System Simulation Experiments (OSSE) project was started recently to study the impacts of GOES-R ABI measurements on numerical weather prediction.

In this poster, we will present some results of the simulation of GOES radiances based on OSSE nature run output and the evaluation against observations. A case study will be performed to analysis ECMWF T511 natural run results. ABI instrument properties and geometry factors are simulated based on current GOES and MSG SEVIRI sensors. The JCSDA Community Radiative Transfer Model (CRTM) is used to simulate ABI radiances with the natural run atmospheric profiles. The simulated radiances are evaluated by comparing with current GOES observations.

Current GOES Imager IR band has 4 km horizontal resolution (FOV), GOES Sounder has 10 km resolution. A full disk scan has total 10,080,910 observation points, and takes about 26 min. GOES-R ABI sensor will has 1km/2 km resolution.

Steps:

1. The OSSE Nature Run data is come from ECMWF T511 13-month simulation. The data set contains 91 vertical levels variables, which are then horizontally interpolated to observation points without vertical interpolation. Selected model level data and all surface data are included.

2. CRTM model is used to simulate GOES-12 Imager, Sounder, MSG SEVIRI, and GOES-R ABI measurements.

3. Perform validations of the simulated radiances with statistical analysis and comparison with real observations.

Radiance Simulation Design

Objective

GOES data is simulated to test impact of GOES in simulation experiments in comparison with impact of real data.

OSSE for GOES will serve as a calibration for GOESR OSSE.

GOES-EAST Observation Locations

Radiance Simulation Flow Chart

A full disk scan of GOES-12 Imager has total 10,080,910 observation points with 4 km resolution. However, Current NCEP/GSI model only take a thinned 1x1 degree GOES-12 dataset. In this study, we extract GOES-12 Imager observation locations (lon/lat) at 20 km and 60 km resolutions. The Nature Run data is interpolated on to these two different resolution locations.

Reduced to ~20 km, 403239 points

Reduced to ~60 km, 40323 points

slide10

Simulation of GOES-12 Imager

Time Series of Mean Tb

Simulation of GOES-12 Sounder

Simulated GOES-12 Imager 4 bands with ECMWF Nature Run output data at 0300 UTC October 1, 2005. It is found that the water vapor band, 6.5 µm, is most accurate band simulated by CRTM model.

Simulated Radiances

Observed vs. simulated GOES-12 sounder for the mean Tb over North Atlantic Ocean region.Black lines are mean Tb from NR simulated, and the red lines are the mean Tb from observation. They should not be the same but similar statistical features are important.

In nature Run, there is hurricane generated on September 27. At 1200 UTC October 1, it is located at about 43 W, 20N. The high moisture air mass associated with the hurricane is shown clearly.

Progress

Time series of hourly, full disk mean brightness temperature of the simulated GOES-12 Imager 4 bands from September 28 to October 10, 2005.Under clear sky conditions, the brightness temperatures are warmer than that of cloudy conditions. The daily change under clear conditions is apparent.

The observed GOES-12 Sounder

Preliminary simulation of GOES from T511NR has completed for entire Nature Run period (13 month)Other preliminary basic data, Conventional data, AIRS, HIRS,AMSUA/B are also simulated for 13 month.

Observed GOES-12 18 bands on 0230 UTC October 01, 2005 for North Atlantic Ocean section.

Future Work

Simulate GOES-R ABI radiances from Nature Run data,Perform NWP model simulations to investigate the impacts of GOES-12 and GOES-R measurements. Conduct impact test using data assimilation system

Black lines are total points mean Tb, red lines are the mean Tb over clear sky condition, and the blue lines are the mean Tb over cloudy condition. Clear sky condition is defined as where total cloud coverage (TCC)< 0.1, and cloudy condition is where TCC > 0.1

Time Series of Mean Tb

Time Series of Mean Tb

Time Series of Mean Tb

Time Series of Mean Tb

slide11

Regional OSSEs to Evaluate ATMS and CrIS Observations

  • M. Hill, P. J. Fitzpatrick, X. Fan, V. Anantharaj,
  • M. Masutani, L. P. Riishojgaard, and Y. Li
  • GRI/Mississippi State Univ (MSU), JCSDA

MSU OSSE plan

  • regional scale nature run (RSNR) using MM5
    • one-way nested grid set, with ICs and BCs from ECMWF NR data
  • sensitivity experiments
    • use second model (WRF) to avoid similarity in model error (fraternal twin problem)
    • initial analyses tuned to model background errors
    • cold start simulation
    • assimilate synthetic surface, rawinsonde observations
    • assimilate data representing ATMS / CrIS
      • use RTM to simulate sensor-observed radiance, assimilate derived Tandqprofiles
  • investigate NR-simulated squall line events at the U.S. coast of the Gulf of Mexico

General Overview

  • component sensors of NPOESS and NPP to include:
    • Advanced Technology Microwave Sounder (ATMS)
      • across track scanning microwave radiometer
      • cross-track resolution: 1.5 km
    • Cross-track Infrared Sounder (CrIS)
      • Fourier transform spectrometer
      • cross-track resolution: 14.0 km
      • vertical resolution: ~ 3.0 km
  • Combination of ATMS / CrIS designed to improve profiles of T and q
  • OSSE with mesoscale models needed to assess the impact of ATMS / CrIS

For more detail and progress, visit Wed 2:30 JP 6.22 (16 SATMET,5GOESR-NPOESS)

slide12

Challenges in Regional OSSE

There is great deal of interest toward regional OSSEs to study data impact on forecast of hurricanes and midlatitude storms. Even if using same global Nature run, regional OSSEs have to deal with handicaps.

• Lateral boundary conditions eventually dominate the forecast inside the regional domain, obscuring any effect of the observation mix on forecast accuracy. This must be considered when evaluating the OSSE:

• The size of the geographic region controls the length of forecasts that can be considered shorter for smaller regions.

• Ideally, the same observation mix should be used in the regional model as in the global model that supplies the boundary conditions.

• One is forced to execute two nature runs and coordinate two data assimilation and prediction systems.

If regional Nature Runs with higher resolution is produced nesting within the global nature run, uncertainty in regional OSSE will become much more serious. Several groups in Joint OSSEs are investigating strategies for credible regional

Summary

  • OSSEs are expensive, but can be a cost-effective way to optimize investment in future observing systems
  • OSSE capability should be multi-agency, community owned to avoid conflict of interest
  • Independent but related data assimilation systems allows us to test robustness of answers
  • Joint OSSE collaboration remains only partially funded but appears to be headed in right direction

Presentation about Joint OSSEs at IOAS-AOLS 13.2 Thursday 1:30