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Acknowledgements: Min-Jeong Kim (NOAA/NCEP/JCSDA and CIRA) John Knaff (NOAA/NESDIS/RAMM)

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Acknowledgements: Min-Jeong Kim (NOAA/NCEP/JCSDA and CIRA) John Knaff (NOAA/NESDIS/RAMM)

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  1. Joint Centers for Satellite Data Assimilation 9th Workshop on Satellite Data AssimilationUniversity of Maryland, College Park, May 24-25, 2011Utility of GOES-R Instruments for Data Assimilation and ForecastingMilija Zupanski1, Man Zhang1, and Karina Apodaca11 Cooperative Institute for Research in the AtmosphereColorado State University Fort Collins, Colorado, U. S. A. Acknowledgements: Min-Jeong Kim (NOAA/NCEP/JCSDA and CIRA) John Knaff (NOAA/NESDIS/RAMM) Mark DeMaria (NOAA/NESDIS/RAMM) Louis Grasso (CIRA) Steve Lord (NOAA/NCEP/EMC) John Derber (NOAA/NCEP/EMC) JCSDA # NA10NES4400012 HFIP (NOAA/NCEP/EMC)

  2. Motivation • GOES-R and future GOES satellites will provide large amounts of cloud and microphysics related observations at high spatiotemporal resolution, and at new wavelengths not observed before • It is a scientific challenge to best utilize these data and to eventually impact the analysis and forecast of tropical cyclones and severe weather. • Early assessment of the future GOES-R observations with NOAA operational systems. • GOES-R instruments of special interest in this research: • - Advanced Baseline Imager (ABI) • - Geostationary Lightning Mapper (GLM) • - Advanced infrared (IR) sounder (follow-up missions) • Other instruments of special interest in this research: • - AMSU-A (MW radiances)

  3. GOES-R Advanced Baseline Imager (ABI) Our main interest is in clouds and precipitation: - Channel 8 (6.19 m) - WV - Channel 9 (6.95 m) - WV - Channel 10 (7.34 m) - WV - Channel 13 (10.35 m) - IR Relevant for tropical cyclones and severe weather Ch8 Ch9 Ch10 Ch13 • 16 channels: Two visible and 14 near infrared and infrared • Spatial resolution: 0.5 km in the visible band - 2 km for the infrared • In contrast the current GOES imager has only 5 channels with resolutions 2 & 4 km

  4. GOES-R Geostationary Lightning Mapper (GLM) • An optical transient detector and imager (1372x1300 pixels) • Sensitive to detect 70-90 % of all lightning strikes • Rapid information on intensity, frequency, and location of lightning discharges • Near uniform spatial resolution 8 km nadir, 12 km edge fov • Coverage up to 52 deg lat

  5. Project goals • Demonstrate the utility of the GOES-R observations in a hybrid variational-ensemble data assimilation system (HVEDAS) using NOAA operational systems (collaboration with Jun Li - CIMMS/U. Wisconsin) • Focus on tropical cyclones and severe weather • Combine ABI, GLM, advanced IR sounder, and all-sky MW radiances (AMSU-A) • Use currently available data as proxies: • - MSG SEVIRI (ABI) • - NLDN and WWLLN (GLM) • - AIRS and IASI (future advanced IR sounder) • Utilize NOAA operational codes (HWRF, GSI, CRTM, shell scripts) • Utilize a CSU-developed HVEDAS to assess components of the future NOAA operational HVEDAS (e.g., flow-dependent error covariance, iterative minimization)

  6. Data Assimilation/Forecasting system • NOAA components: • Hurricane WRF (HWRF) • CRTM (forward component) • - clear-sky and all-sky radiances • - GSI (forward component) • - interpolation/transformation from HWRF to observations • - initial quality control, observation errors • - interface with CRTM • - Shell scripts (HWRF, GSI) • Colorado State University HVEDAS: • Maximum Likelihood Ensemble Filter (MLEF) • Nonlinear iterative minimization with advanced Hessian preconditioning: suitable for assimilation of nonlinear cloud-related observations • First guess is a deterministic control forecast

  7. NOAA code/script MLEF-HWRF Flow Chart MLEF code/script INPUT CONTROL VARIABLExat-1; Pat-1 Interface between MLEF and NOAA Interface: MLEF HWRF HWRF NMM model Ensemble + Control Interface: HWRF MLEF FIRST GUESS:xf t; Pf t Interface: MLEF GSI Assimilate new observations and/or cloudy radiance GSI + CRTM (fwd components) Ensemble + Control Interface: GSI MLEF ITERATIVE MINIMIZATION MINIMIZATION AND PRECONDITIONING CONTROL VARIABLE UPDATE:xa t; Pat NEW DA CYCLE

  8. COV QSNOW, QSNOW COV QSNOW,QRAIN COV QSNOW, V-wind Ensemble forecast error covariance Analysis response to a single cloud snow observation at 500 hPa well-defined horizontal response Response in vertical indicates the dynamical/physical character of error covariance

  9. Current status • Regional MLEF-HWRF data assimilation system has been completed and tested on NOAA Vapor • The system includes HWRF, GSI, CRTM, MLEF • Regional all-sky cloudy radiance assimilation using the approach from global assimilation system (e.g., M.-J. Kim) • Assimilation of all-sky MW radiances completed (poster by Man Zhang) • Transformation to MSG SEVIRI BUFR file completed (poster by Karina Apodaca) • Vertical soundings from full spatial resolution AIRS and IASI completed (Jun Li - CIMMS) • Ready for combined assimilation of all-sky radiances and soundings (AMSU-A, SEVIRI, AIRS-IASI)

  10. METEOSAT imagery 18 Jan 2008 12:12 UTC 19:12 UTC Fast-moving storm FG - OBS ANL - OBS OBS (10.80 m) Assimilation of MSG SEVIRI radiances using hybrid variational/ensemble data assimilation • Kyrill: an extratropical wind storm in Europe in January 2007 • Data assimilation cycle is 1 h • Control variable = (T, q, Qcloud, Qrain, Qice, Qsnow, Qgraupel) • WRF model with 15 km horizontal resolution (300x300x40) • Maximum Likelihood Ensemble Filter (MLEF) • Ensemble size is 48 members MSG SEVIRI 10.80 m (W m-2 sr-1 cm) valid 16Z 18 Jan 2007 Reduction of errors due to data assimilation of MSG SEVIRI radiances

  11. All-Sky AMSU-A Radiance Assimilation: Hurricane Danielle (2010) Contours show the difference between all-sky and clear-sky radiance assimilation: xaall-sky - xaclear-sky Q (g kg-1) at 900 hPa Wind vectors (ms-1) at 850 hPa Absolute vorticity (10-4 ms-2) at 700 hPa Increased humidity in the TC eyewall All-sky radiance assimilation improves all wind components (radial and tangential) A dipole pattern indicates the maximum vorticity is located closer to the TC center All-sky radiance assimilation generally produces an increase of the TC intensity

  12. Summary: • A regional hybrid variational-ensemble DA system (MLEF-HWRF) has been interfaced with NOAA operational hurricane WRF model and with GSI and CRTM observation operators • All-sky radiance assimilation experiments are in progress: MW (AMSU-A), WV and IR channels (MSG SEVIRI), full spatial resolution sounder (AIRS, IASI) • Preliminary results are encouraging Future Work: • Assimilation of combined SEVIRI and AMSU-A all-sky radiances • Add new lightning observation operators through GSI • Conduct assimilation of combined GOES-R ABI and GLM proxy observations • Include microphysical control variable(s) • Evaluate the impact of iterative minimization in all-sky radiance assimilation

  13. Further references:Zupanski, D., S. Q. Zhang, M. Zupanski, A. Y. Hou and S. H. Cheung, 2011: A prototype WRF-based ensemble data data assimilation system for dynamically downscaling satellite precipitation observations. J. Hydrometeorology, 12, 118- 134.Zupanski D., M. Zupanski, L. D. Grasso, R. Brummer, I. Jankov, D. Lindsey and M. Sengupta, and M. DeMaria, 2011: Assimilating synthetic GOES-R radiances in cloudy conditions using an ensemble-based method. Int. J. Remote Sensing, (in print).

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