# Presentation: 396279

## Observing System Simulation Experiments in the Joint Center for Satellite Data

- Lars Peter Riishojgaard and Michiko Masutani
- JCSDA

## NCEP: 	Michiko Masutani, John S. Woollen, Yucheng Song, Stephen J. Lord,  Zoltan Toth

**NCEP**: 	Michiko Masutani, John S. Woollen, Yucheng Song, Stephen J. Lord,  Zoltan Toth
**ECMWF**:  	Erik Andersson
**KNMI:** 	Ad Stoffelen, Gert-Jan Marseille
**JCSDA**: 	Lars Peter Riishojgaard
**NESDIS**: 	Fuzhong Weng, Tong Zhu, Haibing Sun,
**SWA:** 	G. David Emmitt,  Sidney A. Wood,  Steven Greco
**NASA/GFSC:** Ron Errico, Oreste Reale, Runhua Yang, Emily Liu, Joanna Joiner, Harper Pryor, Alindo Da Silva, Matt McGill,
**NOAA/ESRL:**Tom Schlatter, Yuanfu Xie, Nikki Prive, Dezso Devenyi, Steve Weygandt
**MSU/GRI:** 	Valentine Anantharaj, Chris Hill, Pat Fitzpatrick,
**JMA **  	Takemasa Miyoshi , Munehiko Yamaguchi
**JAMSTEC** 	Takeshi Enomoto
*So far most of the work is done by volunteers.  *

## OSSEs

**Observing System *****Simulation***** Experiment**
- Typically aimed at assessing the impact of a _*hypothetical*_ data type on a forecast system
- Not straightforward; EVERYTHING must be simulated
- Simulated atmosphere (“nature run”)
- Simulated reference observations (corresponding to existing observations)
- Simulate perturbation observations
- (object of study)
- => Costly in terms of computing and manpower

## Data assimilation

**Nature**
**(atmospheric state)**

- Sensors

- Forecast
- model

- Initial conditions

- Observations
- (RAOB, TOVS,
- GEO, surface,
- aircraft, etc.)

- Short range
- product

_**Assessment**_

_**Data assimilation**_

- End products

## OSE, conceptual model

**Nature**
**(atmospheric state)**

- Sensors

- Forecast
- model

- Candidate
- observations
- (e.g. AIRS)

- Initial conditions

- Reference
- observations
- (RAOB, TOVS,
- GEO, surface,
- aircraft, etc.)

- Short range
- product

_**Assessment**_

_**OSE, conceptual model**_

- End products

## OSSE, conceptual model

- Nature **run**
- (output from high
- resolution, high quality
- climate model)

- Simulator

- Forecast
- model

- Candidate
- observations
- (e.g. GEO MW)

- Initial conditions

- Reference
- observations
- (RAOB, TOVS,
- GEO, surface,
- aircraft, etc.)

- Forecast
- products

_**Assessment**_

_**OSSE, conceptual model**_

- End products

## Role of a National OSSE Capability

- Impact assessment of future missions
- Decadal Survey and other science and/or technology demonstration missions (NASA)
- Future operational systems (NOAA)
- Objective way of establishing scientifically sound and technically feasible user requirements for observing systems
- Tool for assessing performance impact of engineering decisions made throughout the development phases of a space program or system
- Preparation/early learning pre-launch tool for assimilation users of data from new sensors

## Why a Joint OSSE capability?

- OSSEs are expensive
- Nature run, entire reference observing system, additional observations must be simulated
- Calibration experiments, perturbation experiments must be assessed according to standard operational practice and using operational metrics and tools
- OSSE-based decisions have many stakeholders
- Decisions on major space systems have important scientific, technical, financial and political ramifications
- Community ownership and oversight of OSSE capability is important for maintaining credibility
- Independent but related data assimilation systems allows us to test robustness of answers

##  Main OSSE components

- Data assimilation system(s)
- NCEP/EMC GFS
- NASA/GMAO GEOS-5
- NCAR WRF-VAR
- Nature run
- ECMWF
- Plans for embedded WRF Regional NR
- Simulated observations
- Reference observations
- Perturbation (“candidate”) observations
- Diagnostics capability
- “Classical” OSE skill metrics
- Adjoint sensitivity studies

## ECMWF Nature Run (Erik Andersson)

- Based on recommendations/requirements from JCSDA, NCEP, GMAO, GLA, SIVO, SWA, NESDIS, ESRL
- “Low Resolution” Nature Run
- Free-running T511  L91 w. 3-hourly dumps
- May 12 2005 through June 1 2006
- Two “High Resolution” periods of 35 days each
- Hurricane season:  Starting at 12z September 27,2005,
- Convective precipitation over CONUS: starting at 12Z April 10, 2006
- T799  L91 levels, one-hourly dump
- Initial condition from T511 NR

## Nature Run validation

- Purpose is to ensure that pertinent aspects of meteorology are represented adequately in NR
- Contributions from Emmitt, Errico, Masutani, Prive, Reale, Terry, Tompkins and many others
- Clouds
- Precipitation
- Extratropical cyclones (tracks, cyclogenesis, cyclolosis)
- Tropical cyclones (tracks, intensity)
- Mean wind fields
- ....

## Study of drift in NR

- Tropics

**Convective precipitation**
**Large Scale precipitation**
**Total precipitation**

- Two to three weeks spinup in tropical precipitation.
- 		- Michiko Masutani (NCEP/EMC)

**Zonal  wind June 2006**
- By Juan Carlos Jusem (NASA/GSFC)

- Nature Run

- NCEP reanalysis

**Initial Nature Run validation**

- Michiko Masutani (NCEP)

## Tropical cyclone NR validation

- Reale O., J. Terry, M. Masutani, E. Andersson, L. P. Riishojgaard, J. C. Jusem (2007), Preliminary evaluation of the European Centre for Medium-Range Weather Forecasts' (ECMWF) Nature Run over the tropical Atlantic and African monsoon region, Geophys. Res. Lett., 34, L22810, doi:10.1029/2007GL031640.

**HL vortices: vertical structure**

**Tropical cyclone NR validation**
- Preliminary findings suggest good degree of realism of Atlantic tropical cyclones in ECMWF NR.

## Extratropical Cyclone StatisticsJoe Terry NASA/GSFC

-  Nature Run
-  One degree operational NCEP analyses (from several surrounding years)
-  NCEP reanalysis for specific years (La Nina, El Nino, FGGE)

- 2)  Produce diagnostics using the cyclone track information

- (comparisons between Nature Run and NCEP analyses for same month)

-  Distribution of cyclone strength across pressure spectrum
-  Cyclone lifespan
-  Cyclone deepening
-  Regions of cyclogenesis and cyclolysis
-  Distributions of cyclone speed and direction

**Extratropical Cyclone Statistics**Joe Terry NASA/GSFC

## Slide 15

## Evaluation of Cloud

- Simpson weather associates

## Case Events Identified from ECMWF HRNR(Plotted from 1x1 data)

- May 2-4:  squall line affecting all points along US Gulf coast

- May 7-8:  decaying squall line over TX		Oct 10-11:  squall line / tropical wave

- MSLP (hPa)			3-h convective precipitation (mm)                   .

- Christopher M. Hill, Patrick J. Fitzpatrick,  Valentine G. Anantharaj
-  Mississippi State University

## Simulation of observations

- Conventional observations (non-radiances)
- “Resample NR at OBS locations and add error”
- Problem areas:
- Atmospheric state affects sampling for RAOBS, Aircraft observations, satellite AMVs, wind lidars, etc.
- Correlated observations errors
- J. Woollen (NCEP), R. Errico (GMAO)
- Radiance observations
- “Forward radiative transfer on NR input profiles”
- Problem areas:
- Treatment of clouds has substantial impact on availability and quality of observations
- Desire to avoid “identical twin” RTMs
- H. Sun (NESDIS), R. Errico (GMAO)

## OBS91L

- OBS91L for all footprints of HIRS, AMSU, GOES are produced for a few weeks of the T799 period in October 2005.
- Thinned footprints for the entire period.
- Thinning of the footprint is based on operational use of radiance data.
- The OBS91L are also available for development of a Radiative Transfer Model (RTM) for development of other forward model.

**OBS91L**
**Jack Woollen (NCEP/EMC)**

## Radiance Simulation System for OSSEGMAO, NESDIS, NCEP Ron Errico, Runhua Yang, Emily Liu, Lars Peter Riishojgaard  (NASA/GSFC/GMAO) Tong Zhu, Haibing Sun, Fuzhong Weng  (NOAA/NESDIS)Jack Woollen(NOAA/NCEP)

**Existing instruments experiments must be simulated for control and calibration and development of DAS and RTM**
**Test GOESR,NPOESS, and other future satellite data**

- Other resources and/or advisors *David Groff* , *Paul Van Delst* (NCEP)
*Yong Han,* Fuzhong Weng,*Walter Wolf, Cris Barnet, Mark Liu *(NESDIS*)*
*Erik Andersson (ECMWF); Roger Saunders (Met Office)*

- NASA/GMAO developing optimized strategies to simulate complete set of footprints.  This includes development of cloud clearing algorithm.
- NESDIS, NCEP working on thinned data. Full resolution data for GOES-R. Initial data set (OBS91L) produced by Jack Woollen at NCEP

## OBS91L

- OBS91L for all foot prints of HIRS, AMSU, GOES are produced for a few weeks of the T799 period in October 2005.
- Thinned foot prints for the entire period.
- Thinning of the foot print is based on operational use of radiance data.
- The OBS91L are also available for development of a Radiative Transfer Model (RTM) for development of other forward model.

**OBS91L**
**Jack Woollen (NCEP/EMC)**

## Radiance Simulation System for OSSEGMAO, NESDIS, NCEPTong Zhu, Haibing Sun, Fuzhong Weng(NOAA/NESDIS)Jack Woollen(NOAA/NCEP)Ron Errico, Runhua Yang, Emily Liu, Lars Peter Riishojgaard  (NASA/GSFC/GMAO)

**Existing instruments experiments must be simulated for control and calibration and development of DAS and RTM**
**Test GOESR,NPOESS, and other future satellite data**

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

- OBS91L is produced by Jack Woollen at NCEP
- NASA/GMAO is developing best strategies to simulate and work on complete foot prints.  This include development of cloud clearing algorithm.
- NESDIS and NCEP are working on thinned data. Full resolution data for GOESR.

## Simulation of GOES-R ABI radiances for OSSE

- Tong Zhu et al. : 5GOESR P1.31 at AMS annual meeting
- http://www.emc.ncep.noaa.gov/research/JointOSSEs/publications/AMS_Jan2008/Poster-88thAMS2008-P1.31-OSSEABI.ppt

- Simulated from T511 NR.  GOES data will be simulated to investigate its data impact

## Current set of “prototype” simulated observations at      

-   the GMAO derived from the ECMWF Nature Run

- HIRS3,  HIRS2,  AMSU-A/B, AIRS,+ Conventional Obs.

- The satellite data is thinned, but less so than used operationally.
- Thinning is based on time of report and defined effect of clouds.
- Clouds are treated as black at their tops, when defined as present.
- The presence of clouds affecting IR is determined by a tunable
-    stochastic function using NR-provided cloud fractions.  This
-    function is intended to account for holes in grid-boxes and
-    allows simple tuning for possible unrealism in the NR cloud distribution.
- The same CRTM is used as in GSI (the only RTM available to us).
- Locations of cloud track winds are independent of NR clouds.
- The list of simulated obs. types will be expanded along with the realism
- of the simulations and their associated errors as resources permit.
- We hope to have a suitably tuned (validated) set of “prototype” simulated
-  obs. available by the end of Sept. 2009.

***Slide from Errico ***

## OSSEs planned

- Evaluation and development of targeted observation
- Z. Toth, Yucheng Song (NCEP) and other THORPEX team

**Regional OSSEs to Evaluate ATMS and CrIS Observations**
- Cris M. Hill, Pat. J. Fitzpatrick,  Val. G. Anantharaj   GRI- Mississippi State University (MSS)
- Lars-Peter Riishojgaard (NASA/GMAO, JCSDA)

**OSSE to evaluate UAS**
**N. Prive(ESRL), Y.  Xie(ESRL)  **
-  possible at NCEP and others

**OSSEs planned**

**OSSEs to investigate data impact of GOES **
**and prepared for GOES-R**
- Tong Zhu, Fuzhon Weng, J. Woollen (NCEP) M.Masutani(NCEP) and more

**OSSE to evaluate DWL**
- M.Masutani(NCEP), L. P Riishojgaard (JCDA), NOAA/ESRL, and others

## Next steps

- Calibration; impact of main classes of observation should mimic what is seen in operational OSEs
- GMAO will calibrate GEOS-5 using adjoint sensitivity tools
- EMC will calibrate GFS OSSE using OSEs
- Goal is to have calibrated systems available for actual OSSEs by late summer 2008
- Funding
- NASA ROSES proposal
- NOAA JCSDA-led budget initiative
- ESA/EUMETSAT encouraged by ADMAG to participate

## 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