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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) - PowerPoint PPT Presentation
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The generation of 5k land surface forcing dataset in China
Xiaogu zheng , Xue Wei
Original data
anusplin
5k 3hr data
Data flow
Data preparation
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
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)
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
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
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
relations among variables
p, t , sw, wind
q lw
prcp
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
Wind
Dimensions[ f (x,y,z) + w@(t-3) ] -> splina
Specific Humidity (q)
Dimensions [ f(x,y) + t + p ] -> splina
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
Precipitation
Prcp_new = sqrt (prcp) Dimensions [f(x,y,z) + q + prcp@(t-3) ] -> spli
na Signal/noise = 0.9
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
Thanks
Thanks to Zuoqi Chen for data plotting