the generation of 5k land surface forcing dataset in china

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