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Satellite-based Land-Atmosphere Coupled Data Assimilation

Satellite-based Land-Atmosphere Coupled Data Assimilation. Toshio Koike Earth Observation Data Integration & Fusion Research Initiative (EDITORIA) Department Civil Engineering, Engineering School The University of Tokyo. GCMs. Seasonal variation （ May - September). Sensible （ H ） －

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Satellite-based Land-Atmosphere Coupled Data Assimilation

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  1. Satellite-based Land-Atmosphere Coupled Data Assimilation Toshio Koike Earth Observation Data Integration & Fusion Research Initiative (EDITORIA) Department Civil Engineering, Engineering School The University of Tokyo

  2. GCMs Seasonal variation （May - September) Sensible（H） － Latent(LE） － NCEP OBS 1998 (2003 unavailable) JMA UKMO LE daily-mean ( June) Observed Modeled

  3. Soil Moisture Snow Land Surface Scheme Snow Physics Model Microwave Radiometer Precipitation CloudPhysicsModel Aqua Surface Emissivity & Temp. Land-Atmosphere Data Assimilation TRMM

  4. GCM Forcing Minimization Scheme Satellite Data Land Surface Scheme Radiative Transfer Model Cost Function LDAS

  5. LDASUT- GCMs Seasonal variation （May - September) Sensible（H） － Latent(LE） － NCEP OBS 1998 (2003 unavailable) LDASUT JMA UKMO LE daily-mean ( June) Observed Modeled

  6. GCM Physical Down-scaling Regional Model Forcing Minimization Scheme Satellite Data Land Surface Scheme Radiative Transfer Model Cost Function GCM

  7. (Surface perspective) soil moisture Assimilation No Assimilation

  8. (Atmospheric perspective) No Assimilation case Assimilation case GMS IR1-based convective Index Vertical Wind field Vertical Wind field

  9. Radar at BJ With Land Assimilation Without Assimilation 9-15 16-22 23-04

  10. Cloud Physics Scheme Radiative Transfer Model Cost Function GCM Physical Down-scaling Regional Model Minimization Scheme Satellite Data

  11. IF Jmin IMDAS Framework Precipitation Prediction by ARPS ARPS Model Output (Initial Guess) Cloud Parameter Update Model Operator(Lin Ice Scheme) (Assim. Parameter:ICLWC, IWV) No Optimized Initial Condition Yes Observation Operator (RTM) (Tbmod) Tbobs Global Minimization Scheme (Shuffled Complex Evolution) Duan et al, 1992 Cost (J)= (Tbmod - Tbobs )2

  12. Prediction Start of Prediction with Improved Initial Condition 16:30z17:0018:00 19:00 20:00 Assimilation Window: 40 mins 16:30z 30th Jan 2003 29th Jan 2003 ARPS Model Simulation 12z 16z 20z 24z 04z 08z 12z TBobs AMSR-E Initial Guess 16:30z17:10z Assimilation Window: 40 mins

  13. Precipitation Rate(mm/hr) Initial condition with no assimilation Initial condition with assimilation IMDAS dbz=200R**1.60 (Aonashi, 2004) 29th Jan, 17:00z

  14. Precipitation Rate(mm/hr) 3hour prediction with no assimilation 3hour prediction with assimilation IMDAS dbz=200R**1.60 (Aonashi, 2004) 29th Jan, 20:00z

  15. GCM Regional Model Cloud Physics Scheme Radiative Transfer Model Cost Function Minimization Scheme Satellite Data Land Surface Scheme Radiative Transfer Model Cost Function

  16. Coupled Soil Atmosphere RTM By coupling AIEM with atmosphere RTM we get better agreement. For wetter cases AIEM is sufficient.

  17. Atmosphere-Land Coupled Data Assimilation System

  18. Tb Error LDAS only MODIS/IR A-L Coupled DAS Atmospheric effect derived from AMSR-E vs. MODIS Cloud Top Temperature

  19. Integrated Cloud Liquid Water LDAS only MODIS/IR A-L Coupled DAS Atmospheric effect derived from AMSR-E vs. MODIS Cloud Top Temperature

  20. 24 hour Prediction of Rainfall over the Tibetan Plateau Prediction with the A-L Coupled Data Assimilation As an Initial Condition Only Nesting GOES IR

  21. Coupler System

  22. Preliminary Design for Multi-scaleLand Impact Research by of L-A Coupled DAS • Regional-scale approach by L-A DAS without CMDAS • Extent: 40ºE - 160ºE and 0ºN - 60ºN • Grid size: 25 km → nx = 355, ny = 223, nz = 35 • Meso-scale “mobile” approach by L-A DAS with CMDAS • Point-scale by the CEOP Reference Sites Network +

  23. モデルによる統合化

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