Validation and intercomparison for high resolution gridded product of surface wind vectors over the global ocean
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1 Validation and intercomparison for high resolution gridded product of surface wind vectors over the global ocean Hiroyuki Tomita (Nagoya University) Shin ichirou Kako (Kagoshima University) Tsutomu Hihara (JAMSTEC) Kunio Kutsuwada (Tokai University) Masahisa Kubota (Tokai University) Yu Takahashi (Tokai University)
2 Outline Introduction of our new gridded product Overview Current status J-OFURO 3 Validation of our product including other ones using Quality Control System (QCS) by comparisons with buoy measurements Intercomparisons among gridded products Wind divergence and curl fields Summary
3 J-OFURO3 Japanese Ocean Flux data sets with Use of Remote Sensing Observations J-OFURO1 J-OFURO2 J-OFURO tokai.ac.jp/j ofuro/
4 Available fluxes and parameters Latent heat flux Sensible heat flux Net shortwave radiation flux Net longwave radiation flux Net heat flux Sea surface temperature Zonal and meridional components of momentum flux Scalar wind speed at 10m height Zonal and meridional components of surface wind vectors Surface and air (10m height) specific humidity Sea-minus-air specific humidity Air temperature Sea-minus-air temperature
5 Our Group Hiroyuki Tomita Supervisor, SST, heat flux (Nagoya University) Shin ichirou Kako wind-vector, momentum flux (Kagoshima University) Tsutomu Hihara validation (QCS) (JAMSTEC) Kunio Kutsuwada wind-vector (Tokai University) Masahisa Kubota heat flux (Tokai University) Yu Takahashi momentum flux (Tokai University)
6 Status in our Air-sea Flux Products Products with higher spatial resolution to examine small-scale phenomena in marginal and/or coastal areas Josey et al. (2014) GRL The NWP model products have reliabilities which are spatially inhomogeneous, possibly due to assimilation by buoy measurement data Construction based on satellite measurement data
7 Available period Temporal grid Spatial Grid Sea Surface Temp.SST Humidity QA J OFURO daily mean 1.0 degree grid 0.25degree grid for MGDSST (AVHRR+AMSR E) Schlussel et al with SSMIs J OFURO * , currently daily mean 0.25 degree grid EMSST (8 satellite products) New Algorithm SSMIs, SSMISs, AMSR E, TMI, and AMSR2 Wind Speed W, Wind Vector U, and V SSMIs, ERS 1/2, QuikSCAT, AMSR E, TMI SSMIs, SSMISs, ERS 1/2, QuikSCAT, AMSR E, TMI, WindSAT, Ascat, and AMSR2
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11 Quality Control System (QCS) for Validation of our products including other gridded ones In-situ Data Buoy Locations RAMA KEO JKEO NDBC TAO/TRITON NDBC PIRATA 01 J OFURO3 02 QSCAT (J OFURO2) 03 CCMP 04 NCEP/NCAR (NRA 1) 05 JRA25 06 ERA Interim 07 ASCAT (J OFURO2)
12 Statistics for some products by QCS2 in 2008 Zonal Wind J OFURO3 QSCAT CCMP NCEP JRA25 ERA Interim ASCAT Bias RMSD Correlation Meridional Wind J OFURO3 QSCAT CCMP NCEP JRA25 ERA Interim ASCAT Bias RMSD Correlation The J-OFURO3 and CCMP products have relatively high reliabilities, compared with others.
13 Quality Control System V2(QCS2) J-OFURO3 CCMP There are differences between reliablities in the J-OFURO3 and CCMP products. Low latitudes
14 Intercomparison between J-OFURO3 and CCMP data sets Divergence/Convergence of Wind Field Curl of Wind Field
15 J-OFURO Div_J-OFURO3 Div_CCMP CCMP J-OFURO3 has larger div/conv than CCMP s. Zonally-averages (120 o E-80 o W) of wind-divergence in 2008
16 J-OFURO3 CCMP TAO Buoy locations
17 Zonal section of Wind-divergence along 8 o N in 2008 Zonal section of Wind-divergence along 5 o N in 2008
18 Zonal section of Wind-divergence along 8 o N in 2008 TAO Buoy locations J-OFURO3 CCMP Zonal section of Wind-divergence along 5 o N in 2008 TAO Buoy locations J-OFURO3 CCMP
19 Summary 1 We constructed the new satellite derived global air sea flux data set (J OFURO3) including SST and surface meteorological parameters. The data set is available for users in our web: Spatial resolution : 0.25 x 0.25 deg Spatial coverage : global Time resolution: daily and monthly Time coverage: planned for a period of currently
20 Summary 2 We have made our product with other global wind ones using our designed Quality Control System (QCS), and made intercomparisons among them. The results revealed that the CCMP product (using satellite and NWP data) has highest reliability for buoy comparisons in the tropical Pacific, and also inhomogeneously spatial features in the wind divergence and curl fields, possibly depending on buoy locations. It is suggested that the CCMP product has errors, possibly due to the data assimilation.
21 Please try to visit our product! Thank you for your attention!
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