降水量与湿度的关系_温度和湿度与相对湿度表

用降水、比湿和温度计算相对湿度(nc版、python)

有时收集到的气象数据为比湿，但模型中需要的输入为相对湿度，我们可以利用降水、比湿和温度进行换算。
转换公式：https://earthscience.stackexchange.com/questions/2360/how-do-i-convert-specific-humidity-to-relative-humidity

比湿转换为相对湿度

def shum_switch_rhum(temp,shum,pres):
    ''' 利用比湿（specific humidity）计算相对湿度 :param temp: 气温，K :param shum: 比湿 :param pres: 气压，Pa :return: rhum，% '''
    rhum = 0.236 * pres * shum * np.exp((17.67 * (temp - 273.16))/(temp - 29.65)) ** (-1)
    print(rhum)
    return rhum

如果你的数据是nc的，那么你可以尝试用下面的函数进行批量计算

import xarray as xr
import os
import datetime
import numpy as np
import pandas as pd
def Search_File(dirname,suffix):
    ''' This function can search all files with the specified suffix in this dir. :param dirname: string, the path need to be searched :param suffix: string, the specified suffix need to be seached :return: filter_list: list, the path list need to be searched. '''
    filter = [suffix]  # 设置过滤后的文件类型 当然可以设置多个类型
    filter_list = []
    for maindir, subdir, file_name_list in os.walk(dirname):
        #print(maindir) #当前主目录
        for filename in file_name_list:
            apath = os.path.join(maindir, filename)#合并成一个完整路径
            portion = os.path.splitext(apath)
            ext = portion[1]  # 获取文件后缀 [0]获取的是除了文件名以外的内容

            if ext in filter:
                newname = portion[0] + suffix

                filter_list.append((newname,portion[0].split("\\")[-1]))
    # print(filter_list)
    return filter_list
    
def batch_shum_to_rhum(pres_path,shum_path,temp_path,output_path,start_year,end_year):
    ''' 可以利用降水、比湿和温度计算相对湿度，注意数据单位 :param pres_path: :param shum_path: :param temp_path: :param output_path: :return: '''
    # 批量比湿和相对湿度换算
    # 导入数据，压力、比湿以及温度，注意单位
    pres_path_list = Search_File(pres_path,".nc")
    pres_list = [read_nc_data(path[0]) for path in pres_path_list]
    pres = xr.combine_nested(pres_list,concat_dim="time")
    temp_path_list = Search_File(temp_path,".nc")
    temp_list = [read_nc_data(path[0]) for path in temp_path_list]
    temp = xr.combine_nested(temp_list,concat_dim="time")
    shum_path_list = Search_File(shum_path, ".nc")
    shum_list = [read_nc_data(path[0]) for path in shum_path_list]
    shum = xr.combine_nested(shum_list, concat_dim="time")

    acc = xr.merge([shum,temp,pres])
    rhum_data = shum_switch_rhum(acc["temp"].values,acc["shum"].values,acc["pres"].values)
    rhum = xr.Dataset({ 
   "rhum": (["time", "lat", "lon"], rhum_data)}, coords=acc.coords,
               attrs={ 
   "long_name": "relative humidity", "units": "%"})

    years = list(range(start_year, end_year + 1))
    for year in years:
    	# 输入年月数据比较方便！！！
        out = rhum.sel(time=rhum["time.year"] == year)
        out.to_netcdf("{}/rhum_{}.nc".format(output_path,year))
        print(out)

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