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jiheJunepr.m
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jiheJunepr.m
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%this program is for jiheuybao
clear all;
clc;
datadir='./Ensemble/228/'; %指定批量数据所在文件
filelist=dir([datadir,'*.nc']);
a=filelist(1).name;
b=filelist(2).name;
k=length(filelist);
%创建PRLR数据（PRLR(lat.73, lon.144, time.1960-2005, institution.6(E、I、M、U、C), reference.3 02 05 11 )）
%E: ECWMF, I: IMF-GEOM,
%M: Meteo-France, U: UK_Met_Office,
%C: CMCC-Bologna
%创建PRLR_02系列数据
PRLR_02_E=rand(73,144,46);
PRLR_02_I=rand(73,144,46);
PRLR_02_M=rand(73,144,46);
PRLR_02_U=rand(73,144,46);
PRLR_02_C=rand(73,144,46);
PRLR_02=rand(73,144,46);
%创建PRLR_05系列数据
PRLR_05_E=rand(73,144,46);
PRLR_05_I=rand(73,144,46);
PRLR_05_M=rand(73,144,46);
PRLR_05_U=rand(73,144,46);
PRLR_05_C=rand(73,144,46);
PRLR_05=rand(73,144,46);
%{
%创建PRLR_08系列数据
PRLR_08_E=rand(73,144,46);
PRLR_08_I=rand(73,144,46);
PRLR_08_M=rand(73,144,46);
PRLR_08_U=rand(73,144,46);
PRLR_08_C=rand(73,144,46);
PRLR_08=rand(73,144,46);
%}
%创建PRLR_11系列数据
PRLR_11_E=rand(73,144,46);
PRLR_11_I=rand(73,144,46);
PRLR_11_M=rand(73,144,46);
PRLR_11_U=rand(73,144,46);
PRLR_11_C=rand(73,144,46);
PRLR_11=rand(73,144,46);
%读取PRLR_02数据并做成员平均
for s=1:4:k-3
filename=[datadir,filelist(s).name];
filename;
ncid=netcdf.open(filename,'NC_NOWRITE');
LonData=ncread(filename,'longitude');
LatData=ncread(filename,'latitude');
TimeData=ncread(filename,'leadtime');
%读取ECWMF数据并平均
PRLRdata_E=ncread(filename,'prlr',[1 1 1 5],[144 73 9 1]);
%取相应模态的值 t time=June(4)
PRLR_E=reshape(PRLRdata_E,[144 73 9]);
PRLR_E_1=flipud(rot90(mean(PRLR_E,3)));%画图格式[73 144 time]
PRLR_02_E(:,:,(s+3)/4)=PRLR_E_1(:,:);%按照时间填入（应该是+3/4）
%读取IFM-GEOM数据并平均
PRLRdata_I=ncread(filename,'prlr',[1 1 10 5],[144 73 9 1]);
PRLR_I=reshape(PRLRdata_I,[144 73 9]);
PRLR_I_1=flipud(rot90(mean(PRLR_I,3)));
PRLR_02_I(:,:,(s+3)/4)=PRLR_I_1(:,:);
%读取Meteo-France数据并平均
PRLRdata_M=ncread(filename,'prlr',[1 1 19 5],[144 73 9 1]);
PRLR_M=reshape(PRLRdata_M,[144 73 9]);
PRLR_M_1=flipud(rot90(mean(PRLR_M,3)));
PRLR_02_M(:,:,(s+3)/4)=PRLR_M_1(:,:);
%读取UK_Met_Office数据并平均
PRLRdata_U=ncread(filename,'prlr',[1 1 28 5],[144 73 9 1]);
PRLR_U=reshape(PRLRdata_U,[144 73 9]);
PRLR_U_1=flipud(rot90(mean(PRLR_U,3)));
PRLR_02_U(:,:,(s+3)/4)=PRLR_U_1(:,:);
%读取CMCC-Bologna数据并平均
PRLRdata_C=ncread(filename,'prlr',[1 1 37 5],[144 73 9 1]);
PRLR_C=reshape(PRLRdata_C,[144 73 9]);
PRLR_C_1=flipud(rot90(mean(PRLR_C,3)));
PRLR_02_C(:,:,(s+3)/4)=PRLR_C_1(:,:);
%读取所有Ensemble数据
PRLRdata=ncread(filename,'prlr',[1 1 1 5],[144 73 45 1]);
PRLR=reshape(PRLRdata,[144 73 45]);
PRLR_1=flipud(rot90(mean(PRLR,3)));
PRLR_02(:,:,(s+3)/4)=PRLR_1(:,:);
end
%读取PRLR_05数据并做成员平均
for s=2:4:k-2
filename=[datadir,filelist(s).name];
ncid=netcdf.open(filename,'NC_NOWRITE');
LonData=ncread(filename,'longitude');
LatData=ncread(filename,'latitude');
TimeData=ncread(filename,'leadtime');
%读取ECWMF数据并平均
PRLRdata_E=ncread(filename,'prlr',[1 1 1 2 ],[144 73 9 1]);
%取相应模态的值 其中level=850(1), time=June(4)
PRLR_E=reshape(PRLRdata_E,[144 73 9]);
PRLR_E_1=flipud(rot90(mean(PRLR_E,3)));%画图格式[73 144 time]
PRLR_05_E(:,:,(s+2)/4)=PRLR_E_1(:,:);%按照时间填入（应该是+3/4）
%读取IFM-GEOM数据并平均
PRLRdata_I=ncread(filename,'prlr',[1 1 10 2],[144 73 9 1]);
PRLR_I=reshape(PRLRdata_I,[144 73 9]);
PRLR_I_1=flipud(rot90(mean(PRLR_I,3)));
PRLR_05_I(:,:,(s+2)/4)=PRLR_I_1(:,:);
%读取Meteo-France数据并平均
PRLRdata_M=ncread(filename,'prlr',[1 1 19 2],[144 73 9 1]);
PRLR_M=reshape(PRLRdata_M,[144 73 9]);
PRLR_M_1=flipud(rot90(mean(PRLR_M,3)));
PRLR_05_M(:,:,(s+2)/4)=PRLR_M_1(:,:);
%读取UK_Met_Office数据并平均
PRLRdata_U=ncread(filename,'prlr',[1 1 28 2],[144 73 9 1]);
PRLR_U=reshape(PRLRdata_U,[144 73 9]);
PRLR_U_1=flipud(rot90(mean(PRLR_U,3)));
PRLR_05_U(:,:,(s+2)/4)=PRLR_U_1(:,:);
%读取CMCC-Bologna数据并平均
PRLRdata_C=ncread(filename,'prlr',[1 1 37 2],[144 73 9 1]);
PRLR_C=reshape(PRLRdata_C,[144 73 9]);
PRLR_C_1=flipud(rot90(mean(PRLR_C,3)));
PRLR_05_C(:,:,(s+2)/4)=PRLR_C_1(:,:);
%读取所有Ensemble数据
PRLRdata=ncread(filename,'prlr',[1 1 1 2],[144 73 45 1]);
PRLR=reshape(PRLRdata,[144 73 45]);
PRLR_1=flipud(rot90(mean(PRLR,3)));
PRLR_05(:,:,(s+2)/4)=PRLR_1(:,:);
end
%读取PRLR_08数据并做成员平均
%{
for s=3:4:k-1
filename=[datadir,filelist(s).name];
ncid=netcdf.open(filename,'NC_NOWRITE');
LonData=ncread(filename,'longitude');
LatData=ncread(filename,'latitude');
TimeData=ncread(filename,'leadtime');
%读取ECWMF数据并平均
PRLRdata_E=ncread(filename,'prlr',[1 1 1 1],[144 73 9 1]);
%取相应模态的值 其中level=850(1), time=June(4)
PRLR_E=reshape(PRLRdata_E,[144 73 9]);
PRLR_E_1=flipud(rot90(mean(PRLR_E,3)));%画图格式[73 144 time]
PRLR_08_E(:,:,(s+1)/4)=PRLR_E_1(:,:);%按照时间填入（应该是+3/4）
%读取IFM-GEOM数据并平均
PRLRdata_I=ncread(filename,'prlr',[1 1 10 1],[144 73 9 1]);
PRLR_I=reshape(PRLRdata_I,[144 73 9]);
PRLR_I_1=flipud(rot90(mean(PRLR_I,3)));
PRLR_08_I(:,:,(s+1)/4)=PRLR_I_1(:,:);
%读取Meteo-France数据并平均
PRLRdata_M=ncread(filename,'prlr',[1 1 19 1],[144 73 9 1]);
PRLR_M=reshape(PRLRdata_M,[144 73 9]);
PRLR_M_1=flipud(rot90(mean(PRLR_M,3)));
PRLR_08_M(:,:,(s+1)/4)=PRLR_M_1(:,:);
%读取UK_Met_Office数据并平均
PRLRdata_U=ncread(filename,'prlr',[1 1 28 1],[144 73 9 1]);
PRLR_U=reshape(PRLRdata_U,[144 73 9]);
PRLR_U_1=flipud(rot90(mean(PRLR_U,3)));
PRLR_08_U(:,:,(s+1)/4)=PRLR_U_1(:,:);
%读取CMCC-Bologna数据并平均
PRLRdata_C=ncread(filename,'prlr',[1 1 37 1],[144 73 9 1]);
PRLR_C=reshape(PRLRdata_C,[144 73 9]);
PRLR_C_1=flipud(rot90(mean(PRLR_C,3)));
PRLR_08_C(:,:,(s+1)/4)=PRLR_C_1(:,:);
%读取所有Ensemble数据
PRLRdata=ncread(filename,'prlr',[1 1 1 1],[144 73 45 1]);
PRLR=reshape(PRLRdata,[144 73 45]);
PRLR_1=flipud(rot90(mean(PRLR,3)));
PRLR_08(:,:,(s+1)/4)=PRLR_1(:,:);
end
%}
%读取PRLR_11数据并做成员平均
for s=4:4:k
filename=[datadir,filelist(s).name];
ncid=netcdf.open(filename,'NC_NOWRITE');
LonData=ncread(filename,'longitude');
LatData=ncread(filename,'latitude');
TimeData=ncread(filename,'leadtime');
%读取ECWMF数据并平均
PRLRdata_E=ncread(filename,'prlr',[1 1 1 8],[144 73 9 1]);
%取相应模态的值 其中level=850(1), time=June(4)
PRLR_E=reshape(PRLRdata_E,[144 73 9]);
PRLR_E_1=flipud(rot90(mean(PRLR_E,3)));%画图格式[73 144 time]
PRLR_11_E(:,:,(s+0)/4)=PRLR_E_1(:,:);%按照时间填入（应该是+3/4）
%读取IFM-GEOM数据并平均
PRLRdata_I=ncread(filename,'prlr',[1 1 10 8],[144 73 9 1]);
PRLR_I=reshape(PRLRdata_I,[144 73 9]);
PRLR_I_1=flipud(rot90(mean(PRLR_I,3)));
PRLR_11_I(:,:,(s+0)/4)=PRLR_I_1(:,:);
%读取Meteo-France数据并平均
PRLRdata_M=ncread(filename,'prlr',[1 1 19 8],[144 73 9 1]);
PRLR_M=reshape(PRLRdata_M,[144 73 9]);
PRLR_M_1=flipud(rot90(mean(PRLR_M,3)));
PRLR_11_M(:,:,(s+0)/4)=PRLR_M_1(:,:);
%读取UK_Met_Office数据并平均
PRLRdata_U=ncread(filename,'prlr',[1 1 28 8],[144 73 9 1]);
PRLR_U=reshape(PRLRdata_U,[144 73 9]);
PRLR_U_1=flipud(rot90(mean(PRLR_U,3)));
PRLR_11_U(:,:,(s+0)/4)=PRLR_U_1(:,:);
%读取CMCC-Bologna数据并平均
PRLRdata_C=ncread(filename,'prlr',[1 1 37 8],[144 73 9 1]);
PRLR_C=reshape(PRLRdata_C,[144 73 9]);
PRLR_C_1=flipud(rot90(mean(PRLR_C,3)));
PRLR_11_C(:,:,(s+0)/4)=PRLR_C_1(:,:);
%读取所有Ensemble数据
PRLRdata=ncread(filename,'prlr',[1 1 2 8],[144 73 45 1]);
PRLR=reshape(PRLRdata,[144 73 45]);
PRLR_1=flipud(rot90(mean(PRLR,3)));
PRLR_11(:,:,(s+0)/4)=PRLR_1(:,:);
end
PRLR=rand(73,144,46,6,3);
%创建6个成员的集合 Var_PRLR
%填充20数据
PRLR(:,:,:,1,1)=PRLR_02_E;
PRLR(:,:,:,2,1)=PRLR_02_I;
PRLR(:,:,:,3,1)=PRLR_02_M;
PRLR(:,:,:,4,1)=PRLR_02_U;
PRLR(:,:,:,5,1)=PRLR_02_C;
PRLR(:,:,:,6,1)=PRLR_02;
%填充05数据
PRLR(:,:,:,1,2)=PRLR_05_E;
PRLR(:,:,:,2,2)=PRLR_05_I;
PRLR(:,:,:,3,2)=PRLR_05_M;
PRLR(:,:,:,4,2)=PRLR_05_U;
PRLR(:,:,:,5,2)=PRLR_05_C;
PRLR(:,:,:,6,2)=PRLR_05;
%{
%填充08数据
PRLR(:,:,:,1,3)=PRLR_08_E;
PRLR(:,:,:,2,3)=PRLR_08_I;
PRLR(:,:,:,3,3)=PRLR_08_M;
PRLR(:,:,:,4,3)=PRLR_08_U;
PRLR(:,:,:,5,3)=PRLR_08_C;
PRLR(:,:,:,6,3)=PRLR_08;
%}
%填充11数据
PRLR(:,:,:,1,3)=PRLR_11_E;
PRLR(:,:,:,2,3)=PRLR_11_I;
PRLR(:,:,:,3,3)=PRLR_11_M;
PRLR(:,:,:,4,3)=PRLR_11_U;
PRLR(:,:,:,5,3)=PRLR_11_C;
PRLR(:,:,:,6,3)=PRLR_11;
ncid2=netcdf.create('./Ensemble/228EnsembleJune.nc','clobber');
% Create NetCDF file,输入文件名.
lon=netcdf.defDim(ncid2,'longitude',144);%定义维
lat=netcdf.defDim(ncid2,'latitude',73);
time=netcdf.defDim(ncid2,'time',46);
id=netcdf.defDim(ncid2,'institude',6);
ref=netcdf.defDim(ncid2,'reference',3);
PRLR_NC=netcdf.defVar(ncid2,'prlr','double',[lat lon time id ref]);%定义变量
netcdf.endDef(ncid2);%关闭定义
netcdf.putVar(ncid2,PRLR_NC,PRLR);%填值
netcdf.close(ncid2);
clear PRLR_E PRLR_I PRLR_M PRLR_U PRLR_C ;
clear PRLR_E_1 PRLR_I_1 PRLR_M_1 PRLR_U_1 PRLR_C_1 ;
clear PRLRdata_E PRLRdata_I PRLRdata_M PRLRdata_U PRLRdata_C