WMO OSE Workshop, Geneva 05/2008 Observing System Simulation Experiments in the Joint Center for Satellite Data Lars Peter Riishojgaard and Michiko Masutani JCSDA

JCSDA: Joint Center for Satellite Data Assimilation SWA: Simpson Weather Associates ESRL: Earth System Research Laboratory (formerly FSL, CDC, ETL) 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. Joint OSSE Team

WMO OSE Workshop, Geneva 05/2008 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

Analysis Nature (atmospheric state) Sensors Forecast model Initial conditions Observations (RAOB, TOVS, GEO, surface, aircraft, etc.) Short range product Assessment Data assimilation End products

WMO OSE Workshop, Geneva 05/2008 Analysis 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

WMO OSE Workshop, Geneva 05/2008 Analysis 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

WMO OSE Workshop, Geneva 05/2008 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

WMO OSE Workshop, Geneva 05/2008 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

WMO OSE Workshop, Geneva 05/2008 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

WMO OSE Workshop, Geneva 05/2008 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 through June 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

WMO OSE Workshop, Geneva 05/2008 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 ….

Area averaged precipitation 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 Study of drift in NR Michiko Masutani (NCEP)

Vertical structure of a HL vortex shows distinct eye- like feature and prominent warm core; low-level wind speeds exceed 55 m/s 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: /2007GL HL vortices: vertical structure Tropical cyclone NR validation Preliminary findings suggest good degree of realism of Atlantic tropical cyclones in ECMWF NR.

1) Extract cyclone information using Goddards objective cyclone tracker 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

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 TXOct 10-11: squall line / tropical wave MSLP (hPa)3-h convective precipitation (mm) MSLP (hPa)3-h convective precipitation (mm). Christopher M. Hill, Patrick J. Fitzpatrick, Valentine G. Anantharaj Mississippi State University Mississippi State University

WMO OSE Workshop, Geneva 05/2008 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)

For development purposes, 91-level NR variables are processed at NCEP and interpolated to observational locations with all the information need to simulate data (OBS91L). OBS91L for all footprints of HIRS, AMSU, GOES are produced for a few weeks of the T799 period in October 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

For development purposes, 91-level NR variables are processed at NCEP and interpolated to observational locations with all the information need to simulate data (OBS91L). OBS91L for all foot prints of HIRS, AMSU, GOES are produced for a few weeks of the T799 period in October 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 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 Slide from Errico

OSSEs for THORPEX T-PARC 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

WMO OSE Workshop, Geneva 05/2008 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

WMO OSE Workshop, Geneva 05/2008 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