1 / 13

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

gamba
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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).

More Related