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LAPS Update

LAPS Update. Steve Albers, Dan Birkenheuer, Isidora Jankov Paul Schultz, Zoltan Toth, Yuanfu Xie, Linda Wharton FAB Review - April 27, 2010. LAPS DA-Ensemble System. Data. Data Ingest. Intermediate data files. Error Covariance. Trans. LAPS. GSI. STMAS3D. Trans. Post proc1.

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LAPS Update

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  1. LAPS Update Steve Albers, Dan Birkenheuer, Isidora Jankov Paul Schultz, Zoltan Toth, Yuanfu Xie, Linda Wharton FAB Review - April 27, 2010

  2. LAPS DA-Ensemble System Data Data Ingest Intermediate data files Error Covariance Trans LAPS GSI STMAS3D Trans Post proc1 Post proc2 Post proc3 Model prep WRF-ARW MM5 WRF-NMM Probabilistic Post Processing Ensemble Forecast OAR/ESRL/GSD/Forecast Applications Branch 2

  3. LAPS Motivation • High Resolution (500m – 20km), rapid update (10-60min) • Highly portable system – about 150 users world wide • Federal Gov’t – NWS, RSA, PADS, FAA, DHS • State Gov’t – California Dept of Water Resoures • International – Finnish Met. Inst., China Heavy Rain Inst. • Global analysis – used by SOS • Wide variety • of data sources: OAR/ESRL/GSD/Forecast Applications Branch 3

  4. FH FL LAPS HOT START INITIALIZATION Three-Dimensional Cloud Analysis METAR + FIRST GUESS

  5. Satellite use in LAPS (GOES / SEVIRI*) • 11 micron IR • 3.9 micron data • Visible (with terrain albedo database) • CO2-Slicing method (Cloud-top pressure) • *2010 Paper with Italian Collaborators • Cloud-Drift Winds • Retrieved Soundings (T, Td) Cloud Analysis OAR/ESRL/GSD/Forecast Applications Branch 5

  6. Cloud / Reflectivity / Precip Type (1km analysis) DIA

  7. CloudSat Verification of LAPS Cloud Analysis LAPS CloudSat

  8. 6-hr Diabatically (LAPS) initialized WRF-ARW forecast Analysis 13 June 2002 OAR/ESRL/GSD/Forecast Applications Branch 8

  9. Bias & ETS June 13 2002

  10. 850 mb Analyzed and Simulated Reflectivity Analysis 2hr HOT Fcst 2hr NO-HOT Fcst 16 June 2002

  11. Bias & ETS June 16 2002

  12. Future Analysis Development • Higher Resolution Space / Time • 1km – 15min analysis for FAA demo • 20-50 meter analysis for fire weather • Develop forward models for all data sources being used to more fully implement a variational approach • Incorporate clouds into STMAS & GSI • Consider new data sources • Work with MADIS group to improve quality control • More efficient net enabled data access (e.g. NNEW) • LAPS Workshop Oct 25-27, 2010 OAR/ESRL/GSD/Forecast Applications Branch 12

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