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The generation of 5k land surface forcing dataset in China
Xiaogu zheng , Xue Wei
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Original data
anusplin
5k 3hr data
Data flow
Data preparation
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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
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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)
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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
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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
Previous day 20pmbjToday 20pm
gw Previous day 12pm Today 12pm
12 21 9
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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 p
ool– x, y, z, t-3 (regression), other relative forcing variables
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relations among variables
p, t , sw, wind
q lw
prcp
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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
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Wind
Dimensions[ f (x,y,z) + w@(t-3) ] -> splina
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Specific Humidity (q)
Dimensions [ f(x,y) + t + p ] -> splina
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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
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Precipitation
Prcp_new = sqrt (prcp) Dimensions [f(x,y,z) + q + prcp@(t-3) ] -> spli
na Signal/noise = 0.9
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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
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Thanks
Thanks to Zuoqi Chen for data plotting