@Python（Anaconda平台下使用Spyder）批量下载ECMWF数据教程
#Python（Anaconda平台下使用Spyder）批量下载ECMWF数据保姆级教程
!!!这篇分享只针对Windows用户
我用的是Anaconda平台，我看很多博主也在推荐这个平台，具体下载安装方法网上有很多，不再赘述，这里主要说在这一平台下使用Spyder批量下载数据的过程。
首先注册ECMWF的账号，并登录
1、没有账号的要先注册账号；
注册ECMWF的账号的网址
2、登录（浏览器记住密码会方便一些）
获取API key，并存储
获取API key
点这个链接获取
然后会看到界面如下图
将大括号“{}”中的内容复制并保存为.ecmwfapirc格式
并保存至C：/Users/Administrator 路径下（cmd的初始目录下）
在Anaconda中安装ecmwfapi
下载ECMWF Web api
Web api下载地址
，找到ecmwf-api-client-python.tgz点击下载即可
将压缩包ecmwf-api-client-python.tgz解压后放入Anaconda的安装路径下
打开Anaconda prompt，然后键入pip install ecmwf-api-client，点击回车，不报错就安装成功了。
在Spyder里面可以验证一下是否安装成功 输入from ecmwfapi import ECMWFDataServer运行一下，没报错，就说明ecmwfapi安装好了
根据官网的数据信息写脚本，下载数据
大家应该都在ECMWF的官网上手动下载过数据吧，这里以ERA-interim200hPa高空纬向风1979-2018年月平均数据为例
我们选好了数据后，下拉至网页页面底部，获取数据下载信息
会得到如下图的界面
我们将灰色框中的内容复制到并保存为.py格式，后面我们就只需要将
grid、date、levelist
后面的内容根据自己下载数据的需要修改一下即可
注意：
1、数据范围信息格式”area”：“
minlat/minlon/maxlat/maxlon
”（我是下载了之后才发现它的范围信息要这样给出，比如下载全球数据就应该写为
“area” :”-90/0/90/360″,
）
2、月平均数据需要给出全部的时间，需要的可以直接复制
“date”:“19790101/19790201/19790301/19790401/19790501/19790601/19790701/19790801/19790901/19791001/19791101/19791201/19800101/19800201/19800301/19800401/19800501/19800601/19800701/19800801/19800901/19801001/19801101/19801201/19810101/19810201/19810301/19810401/19810501/19810601/19810701/19810801/19810901/19811001/19811101/19811201/19820101/19820201/19820301/19820401/19820501/19820601/19820701/19820801/19820901/19821001/19821101/19821201/19830101/19830201/19830301/19830401/19830501/19830601/19830701/19830801/19830901/19831001/19831101/19831201/19840101/19840201/19840301/19840401/19840501/19840601/19840701/19840801/19840901/19841001/19841101/19841201/19850101/19850201/19850301/19850401/19850501/19850601/19850701/19850801/19850901/19851001/19851101/19851201/19860101/19860201/19860301/19860401/19860501/19860601/19860701/19860801/19860901/19861001/19861101/19861201/19870101/19870201/19870301/19870401/19870501/19870601/19870701/19870801/19870901/19871001/19871101/19871201/19880101/19880201/19880301/19880401/19880501/19880601/19880701/19880801/19880901/19881001/19881101/19881201/19890101/19890201/19890301/19890401/19890501/19890601/19890701/19890801/19890901/19891001/19891101/19891201/19900101/19900201/19900301/19900401/19900501/19900601/19900701/19900801/19900901/19901001/19901101/19901201/19910101/19910201/19910301/19910401/19910501/19910601/19910701/19910801/19910901/19911001/19911101/19911201/19920101/19920201/19920301/19920401/19920501/19920601/19920701/19920801/19920901/19921001/19921101/19921201/19930101/19930201/19930301/19930401/19930501/19930601/19930701/19930801/19930901/19931001/19931101/19931201/19940101/19940201/19940301/19940401/19940501/19940601/19940701/19940801/19940901/19941001/19941101/19941201/19950101/19950201/19950301/19950401/19950501/19950601/19950701/19950801/19950901/19951001/19951101/19951201/19960101/19960201/19960301/19960401/19960501/19960601/19960701/19960801/19960901/19961001/19961101/19961201/19970101/19970201/19970301/19970401/19970501/19970601/19970701/19970801/19970901/19971001/19971101/19971201/19980101/19980201/19980301/19980401/19980501/19980601/19980701/19980801/19980901/19981001/19981101/19981201/19990101/19990201/19990301/19990401/19990501/19990601/19990701/19990801/19990901/19991001/19991101/19991201/20000101/20000201/20000301/20000401/20000501/20000601/20000701/20000801/20000901/20001001/20001101/20001201/20010101/20010201/20010301/20010401/20010501/20010601/20010701/20010801/20010901/20011001/20011101/20011201/20020101/20020201/20020301/20020401/20020501/20020601/20020701/20020801/20020901/20021001/20021101/20021201/20030101/20030201/20030301/20030401/20030501/20030601/20030701/20030801/20030901/20031001/20031101/20031201/20040101/20040201/20040301/20040401/20040501/20040601/20040701/20040801/20040901/20041001/20041101/20041201/20050101/20050201/20050301/20050401/20050501/20050601/20050701/20050801/20050901/20051001/20051101/20051201/20060101/20060201/20060301/20060401/20060501/20060601/20060701/20060801/20060901/20061001/20061101/20061201/20070101/20070201/20070301/20070401/20070501/20070601/20070701/20070801/20070901/20071001/20071101/20071201/20080101/20080201/20080301/20080401/20080501/20080601/20080701/20080801/20080901/20081001/20081101/20081201/20090101/20090201/20090301/20090401/20090501/20090601/20090701/20090801/20090901/20091001/20091101/20091201/20100101/20100201/20100301/20100401/20100501/20100601/20100701/20100801/20100901/20101001/20101101/20101201/20110101/20110201/20110301/20110401/20110501/20110601/20110701/20110801/20110901/20111001/20111101/20111201/20120101/20120201/20120301/20120401/20120501/20120601/20120701/20120801/20120901/20121001/20121101/20121201/20130101/20130201/20130301/20130401/20130501/20130601/20130701/20130801/20130901/20131001/20131101/20131201/20140101/20140201/20140301/20140401/20140501/20140601/20140701/20140801/20140901/20141001/20141101/20141201/20150101/20150201/20150301/20150401/20150501/20150601/20150701/20150801/20150901/20151001/20151101/20151201/20160101/20160201/20160301/20160401/20160501/20160601/20160701/20160801/20160901/20161001/20161101/20161201/20170101/20170201/20170301/20170401/20170501/20170601/20170701/20170801/20170901/20171001/20171101/20171201/20180101/20180201/20180301/20180401/20180501/20180601/20180701/20180801/20180901/20181001/20181101/20181201”,
3、如果是日资料则可以直接写为”date”=“19790101/to/20181231” 这个数据量非常大，还是分时段下载比较好
4、 “target” :”D:/data/1979_2018.nc”这个是输出文件名和路径，自己根据需要改
紧接着在Spyder里面打开脚本然后运行就可以了。
等上一会儿，数据就躺在你的电脑里了