求助,short格式值相同，转成flt值反而不同，这是啥原因

t_imba

将多年的数据合并起来成为一个新文件，原始数据是short格式的，合并之后用short2flt转浮点数，结果除了第一年的数据和原始数据浮点数值相同，其他年份的数据都不一样，验证SHORT格式的值却是相同的，请问这是什么原因？同事用pythonXARRAY包读好像数据也是正确的。是不是除了SHORT2FLT还有什么short格式转浮点的函数，大佬救命

1971年

一大碗年糕

看到这么多金币激发了我学习的兴趣（不是
short2flt在NCL中描述如下：
Description
This function converts short data to float using the "scale" and "offset" attributes (if present), and preserves metadata.
Other attributes similar to "scale" and "offset" will be recognized:
offset:
"add_offset", "OFFSET", "Offset", "_offset", "Intercept", "intercept"
scale:
"SCALE", "Scale", "_scale", "scale_factor", "Scale_factor", "Slope" , "slope"
Note: this function is slightly different than the built-in set of conversion functions (like floattointeger and shorttobyte), because it requires loading the "contributed.ncl" script and it preserves metadata. It has a slightly different naming style than the built-in functions, just to set it apart from these other functions.
即short2float受scale和offset这两个attributes影响
显然当短型short型解压到float型时需要用的y = x * scale_factor + add_offset
所以问题目测是在合并原始数据时改变了变量的scale和add_offset这两个属性
比如ncl压缩转换函数pack_values中就有设置
the scale_factor (opt@scale_factor) and add_offset (opt@add_offset) values to be used by the function.
解决办法：分别看下两个变量属性，把合并之后的变量的scale和offset属性改成原变量的再用short2float即可。
当然考虑或许这个If the names given do not match any attributes, the scale factor will be assigned a value of 1.0 and the offset will be assigned a value of 0.0. 这种情况也不是没有可能
参考关于scale和offset属性在netcdf中的描述：
scale_factor
If present for a variable, the data are to be multiplied by this factor after the data are read by the application that accesses the data.
add_offset
If present for a variable, this number is to be added to the data after it is read by the application that accesses the data. If both scale_factor and add_offset attributes are present, the data are first scaled before the offset is added. The attributes scale_factor and add_offset can be used together to provide simple data compression to store low-resolution floating-point data as small integers in a netCDF file. When scaled data are written, the application should first subtract the offset and then divide by the scale factor.
When scale_factor and add_offset are used for packing, the associated variable (containing the packed data) is typically of type byte or short, whereas the unpacked values are intended to be of type float or double. The attributes scale_factor and add_offset should both be of the type intended for the unpacked data, e.g. float or double.

t_imba

听大佬一席话，胜过我琢磨好久，确实是scale_factor和add_offset值不同，原来都没注意到过这一点。以后校对数据得检验了。

t_imba

一大碗年糕 发表于 2021-11-3 18:02
看到这么多金币激发了我学习的兴趣（不是
short2flt在NCL中描述如下：
Description

听大佬一席话，胜过我琢磨好久，确实是scale_factor和add_offset值不同，原来都没注意到过这一点。以后校对数据得检验了。

t_imba

一大碗年糕 发表于 2021-11-3 18:02
看到这么多金币激发了我学习的兴趣（不是
short2flt在NCL中描述如下：
Description

我先都转成浮点的再合并了。好像没法分段赋值scale_factor 和 add_offset

一大碗年糕

t_imba 发表于 2021-11-3 22:37
听大佬一席话，胜过我琢磨好久，确实是scale_factor和add_offset值不同，原来都没注意到过这一点。以后 ...

不是大佬我也，水帖群众哈哈

SAREbenjamin

舍友也遇到过这个问题 大概是这样的...
他用的是ERA5数据 后期需要补充层次
他先合并数据到同一个数组 再short2flt 这样出来的结果是错的 会存在一个明显的“断层”

你是最美七月天

哇，上面的回复真的赞！

mmyin_nju

我也遇到过，我的做法是先在命令行用cdo把文件的数据类型从short转为float，然后再用ncl统一读数据

kermit

顶一顶