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The generation of 5k land surface forcing dataset in China

The generation of 5k land surface forcing dataset in China. Xiaogu zheng , Xue Wei. Data flow. Original data. Data preparation. anusplin. 5k 3hr data. Original Datasets. Five global land surface forcing datasets Prin( 1d, 3hr, 50yr) Ncc (1d,6hr, 50yr) Gswp2 (1d,3hr, 10yr)

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The generation of 5k land surface forcing dataset in China

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  1. The generation of 5k land surface forcing dataset in China Xiaogu zheng , Xue Wei

  2. Data flow Original data Data preparation anusplin 5k 3hr data

  3. Original Datasets • Five global land surface forcing datasets • Prin( 1d, 3hr, 50yr) • Ncc (1d,6hr, 50yr) • Gswp2 (1d,3hr, 10yr) • Gold ( T62,6hr, 50yr) • NCEP_qian( T62, 3hr, 50yr) • 700+ meteorological stations • 1000+ hydrological stations

  4. Variables • forcing datasets ( prin, gswp,ncc) • 3hr/6hr T, P,Q,W, PRCP (rate),SW,LW • Instantaneous field: T,P,Q,W • Average field : PRCP, SW, LW • Different treatment for these two fields when temporal downscaling from 6hr to 3hr for NCC data • meteorological stations • Daily values of T,P, RH,PRCP (amount), W • hydrological stations • Daily value of PRCP (amount)

  5. 1 d mean forcing data • Instantaneous fields (t,p,q,w) • If hr=0,6,12,18 • 1d_mean =(prin + gswp + ncc)/3 • If hr = 3,9,15,21 • 1d_mean= (prin + gswp)/2 • Average fields (sw,lw,prcp) • Downscaling 6hr NCC to 3hr first • 1d_mean = (prin + gswp + ncc)/3

  6. Obs Diurnal cycle • Temporal downscaling for daily obs to 3hr • Daily metero Obs (Beijing time 20pm to 20pm) • Forcing data at Greenwich time • Get diurnal range from 1d forcing mean • Interpolate forcing to obs location ( no elevation adjustment) • Adjusted by obs_daily bj Previous day 20pm Today 20pm 12 21 9 gw Today 12pm Previous day 12pm

  7. Splina input format • Dimensions, variable, weight • Give same weight 1 to both obs & forcing • Can’t calculate predicted error if weight !=1 • Dimension • Independent variables (x, y must in km, not degree) • Independent covariates • varies for each forcing variable, chosen from following pool • x, y, z, t-3 (regression), other relative forcing variables

  8. relations among variables • p, t , sw, wind lw q prcp

  9. Downward Short Wave • No obs used, only 1d data as splina input • sw_new = sw/(s0 *cos(sza)) • Set threshold for solar zenith angle (sza) • If cos(sza)< cos(80 degree) cos(sza) = cos(80) • f(x,y) -> splina • Test z, negative slope, not add in

  10. Wind • Dimensions[ f (x,y,z) + w@(t-3) ] -> splina

  11. Specific Humidity (q) • Dimensions [ f(x,y) + t + p ] -> splina

  12. Downward Long Wave • No obs used, only 1d data as splina input • Dimensions [f(x,y) + t + lw@(t-3) ] -> splina • Test q, no obvious contribution

  13. Precipitation • Prcp_new = sqrt (prcp) • Dimensions [f(x,y,z) + q + prcp@(t-3) ] -> splina • Signal/noise = 0.9

  14. Reference • Hutchinson M.F., Anusplin version 4.2 User guide • Xiaogu zheng and Reid Basher, Thin-Plate Smoothing Spline Modeling of spatial climate data and its application to mapping south pacific rainfalls • Reid Basher and Xiaogu zheng, MAPPING RAINFALL FIELDS AND THEIR ENSO VARIATION IN DATA-SPARSE TROPICAL SOUTH-WEST PACIFIC OCEAN REGION

  15. Thanks • Thanks to Zuoqi Chen for data plotting

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