Question

由于我习惯使用.nc，因此我试图将.csv文件转换为.csv文件以在R中进行进一步分析。

基本上我想解决我的问题（下面有更多详细信息），我需要在_FillValue文件中添加一个.nc，但是我尝试过的所有方法都不起作用。

按照http://geog.uoregon.edu/bartlein/courses/geog490/week04-netCDF.html#replace-netcdf-fillvalues-with-r-nas中采取的步骤，我成功地对许多.nc文件执行了此操作，直到第3.4.3节。

但是，我最近获得了对另一个.nc文件的访问权限，并且同一过程无法正常运行。我想我已将其范围缩小到新的_FillValue文件中没有.nc的事实。

从外观上看，_FillValue应该是“ 9.97e + 36”。我尝试使用

将此数字添加为缺少的值

ncin <- nc_open(ncfname, write=T)
dname <- "tas"
Mvalue <- 9.97e+36
ncvar_change_missval(ncin, dname, Mvalue)

这似乎将missing_value:9.97e+36添加到.nc文件中。但是，当我运行：tmp_array <- ncvar_get(ncin,dname)时，tmp_array仍然具有9.97e + 36。

我希望tmp_array可以将9.97e + 36替换为NA，就像它可以工作的文件一样。

是否可以将_FillValue添加到我的文件中，以便用NA替换这些值？

这是无法使用的文件信息：

> print(ncin)
File ./data/UKCP18/Mean_air_temperature_(tas)/.nc_files/tas_hadukgrid_uk_1km_mon_201801-201812.nc (NC_FORMAT_NETCDF4):

     9 variables (excluding dimension variables):
        double tas[projection_x_coordinate,projection_y_coordinate,time]   (Contiguous storage)  
            standard_name: air_temperature
            long_name: Mean air temperature
            units: degC
            description: Mean air temperature
            label_units: C
            level: 1.5m
            plot_label: Mean air temperature at 1.5m (C)
            cell_methods: time: mid_range within days time: mean over days
            grid_mapping: transverse_mercator
            coordinates: latitude longitude month_number season_year
            missing_value: 9.97e+36
        int transverse_mercator[]   (Contiguous storage)  
            grid_mapping_name: transverse_mercator
            longitude_of_prime_meridian: 0
            semi_major_axis: 6377563.396
            semi_minor_axis: 6356256.909
            longitude_of_central_meridian: -2
            latitude_of_projection_origin: 49
            false_easting: 4e+05
            false_northing: -1e+05
            scale_factor_at_central_meridian: 0.9996012717
        double time_bnds[bnds,time]   (Contiguous storage)  
        double projection_y_coordinate_bnds[bnds,projection_y_coordinate]   (Contiguous storage)  
        double projection_x_coordinate_bnds[bnds,projection_x_coordinate]   (Contiguous storage)  
        8 byte int month_number[time]   (Contiguous storage)  
            units: 1
            long_name: month_number
        8 byte int season_year[time]   (Contiguous storage)  
            units: 1
            long_name: season_year
        double latitude[projection_x_coordinate,projection_y_coordinate]   (Contiguous storage)  
            units: degrees_north
            standard_name: latitude
        double longitude[projection_x_coordinate,projection_y_coordinate]   (Contiguous storage)  
            units: degrees_east
            standard_name: longitude

     4 dimensions:
        time  Size:12
            axis: T
            bounds: time_bnds
            units: hours since 1800-01-01 00:00:00
            standard_name: time
            calendar: gregorian
        projection_y_coordinate  Size:1450
            axis: Y
            bounds: projection_y_coordinate_bnds
            units: m
            standard_name: projection_y_coordinate
        projection_x_coordinate  Size:900
            axis: X
            bounds: projection_x_coordinate_bnds
            units: m
            standard_name: projection_x_coordinate
        bnds  Size:2

    11 global attributes:
        _NCProperties: version=1|netcdflibversion=4.6.1|hdf5libversion=1.10.2
        comment: Monthly resolution gridded climate observations
        creation_date: 2019-08-09T20:34:33
        frequency: mon
        institution: Met Office
        references: doi: 10.1002/joc.1161
        short_name: monthly_meantemp
        source: HadUK-Grid_v1.0.1.0
        title: Gridded surface climate observations data for the UK
        version: v20190808
        Conventions: CF-1.5

Answer 1

我找到了灵魂。我以为我会在这里发布，以防有人发现自己也被卡住！

我意识到，也许missing_value不仅是9.97e+36，而且还有更多的小数点。我用它来查找完整的missing_value，然后将其设置为missing_value，这样ncvar_get()就可以正常工作了。

ncin <- nc_open(ncfname, write=T)
print(ncin)

tmp_array <- ncvar_get(ncin,dname) # This produced an array with the missing value inserted - should be replaced with NAs

# What is the missing value up to 100 decimal points?!
sprintf("%.100f", tmp_array[1,1,1]) 

# Set missing value
Mvalue <- 9.969209968386869047442886268468442020e+36

# insert missing_value to .nc file
ncvar_change_missval(ncin, dname, Mvalue)
print(ncin)

# make new array with values replaced with NAs
tmp_array <- ncvar_get(ncin,dname)

然后我继续遵循http://geog.uoregon.edu/bartlein/courses/geog490/week04-netCDF.html#replace-netcdf-fillvalues-with-r-nas中概述的过程，直到3.4.3产生我的.csv

Ph！谢谢大家：）

将Fill_Value插入R中的nc文件

1 个答案: