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Latent Heating Evaluation

Latent Heating Evaluation. Yukari N. Takayabu CCSR/Univ. of Tokyo with S. Shige , Osaka Prefecture University, W.-K. Tao, C.-L. Shie , GSFC/NASA, and Y. Kodama , Hirosaki University. TRMM: Spectral Latent Heating (SLH) Estimate.

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Latent Heating Evaluation

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  1. Latent Heating Evaluation Yukari N. Takayabu CCSR/Univ. of Tokyo with S. Shige, Osaka Prefecture University, W.-K. Tao, C.-L. Shie, GSFC/NASA, and Y. Kodama, Hirosaki University

  2. TRMM: Spectral Latent Heating (SLH) Estimate • SLH utilizes precipitation-top-heights and precipitation rate at melting level and at surfaceobtained from TRMM PR for the LH estimates. • With the aid of a 2D Cloud Resolving Model (GCEM) simulations forced by field experiment data, SLH constructs a set of precipitation-LH tables for convective rain and stratiform rain, separately.(Shige et al. 2004, JAM)

  3. Spectral Latent Heating (SLH) Algorithm KWAJEX Field Experiments COARE SCSMEX GATE 2D CRM simulation (GCEM) Lookup Tables Conv / Strat Simulated rain PR2a25 Rain Retrieval GV Diagnosed Q1R Simulated Q1R Reconstructed Q1R Estimated Q1R

  4. GV for LH Apparent Heat Source & Apparent Moisture Sink diagnozed from upper-air network observations (Yanai et al. 1973)  Large-scale network observation, such as GATE, COARE, SCSME, KWAJEX…. Q1R=Q1-QR  Large-scale network observation

  5. Field Experiments

  6. Q1R with COARE-TABLE-SLH vs. GCE-simulated (a) GATE 1-8 Sept 1974 GCE GCE SLH SLH SLH SLH GCE GCE GATE: Reconstructed Q1R is higher than simulated (forced) (b) SCSMEX 2-9 Jun 1998 SCSMEX: Reconstructed Q1R is lower than simulated (forced)

  7. For the same storm height, precipitation profiles, therefore Q1R profiles, differ among experiments.

  8. COARE LH look-up tables (b) Shallow Strat (a) Convective (c) Deep Stratiform Precipitation Top Height (km) PTH (km) Rain Rate at Melting Lvl (mm/hr) Threshold of 0.3 mm/h for determination of PTH Anvil table stratified by Pm Pf: Additional usage of precipitation rate 1km above the melting level for the convective rain We also adjusted the melting level of the table to the observed melting level.

  9. Heating from diagnostic calculations (Johnson and Ciesielski 2002) and SLH algorithm for SCSMEX (15 May – 20 June 1998). Budget Q1 SLHQ1R+GCE QR

  10. Possibility of utilizing wind profiler w-wind data for evaluating Q1 Kodama (2005)

  11. GPM ERA Precipitating Phenomena in the Midlatitude with a significant role of LH • Explosive developments of the extratropical cyclones (ex. QEII Storm in 1978) • Mesoscale (meso-alpha, beta) systems in the Baiu front • Winter disturbances over the Japan Sea

  12. GPM ERA Precipitating Phenomena in the Midlatitude with a significant role of LH • Explosive developments of the extratropical cyclones (ex. QEII Storm in 1978) • Mesoscale (meso-alpha, beta) systems in the Baiu front • Winter disturbances over the Japan Sea

  13. GPM ERA Precipitating Phenomena in the Midlatitude with a significant role of LH • Explosive developments of the extratropical cyclones (ex. QEII Storm in 1978) • Mesoscale (meso-alpha, beta) systems in the Baiu front • Winter disturbances over the Japan Sea

  14. The role of the Latent Heating on the explosive development of Extratropical Cyclone (Queen Elizabeth II Storm) QEII 900hPa 900hPa 700hPa 300hPa 500hPa Omega for different LH peaks Difference of Psfc and winds between with and without LH 300hPa 500hPa 700hPa Pressure drop Anthes et al. 1983 Gyakum 1983 Time 

  15. Mesoscale Precipitation Systems on the Baiu Front X-BAIU-99 Moteki et al. 2004 Three-dimensional dynamical structure determines the precipitation characteristics which feed back to the dynamical fields. Baiu front continental moist air oceanic moist air Water vapor front

  16. Winter Disturbances over the Japan Sea (Yoshizaki et al. 2004) WMO-01 GMS 5km-NHM JPCZ is well simulated QuickSCAT Sfc flux contributes largely to <Q1> <Q1-QR-Q2> =S+LE

  17. From Dr. M. Ishihara (JMA)’s presentation www.ecmwf.int

  18. GPM ERA Precipitating Phenomena in the Midlatitude with a significant role of LH • Interaction with dynamical three-dimensional structures of phenomena becomes more important. • Various network observations and other satellite data (winds, clouds) will be available. • Larger computer resources will be available.

  19. Latent Heating Estimates and GV in GPM ERA With these background, • GPM precipitation + More satellites to observe winds, and clouds • Wind Profiler Networks • Radar Networks (NEXRAD, R-AMeDAS) • Non-hydrostatic Numerical Models with larger computer resources • We should collaborate for 4DVar data assimilation • (In collaboration with operational organizations) • Field experiments to verify the analysis data • Especially, validation for model cloud microphysics will be very important

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