606 lines
27 KiB
Plaintext
606 lines
27 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 从亚马逊云计算上检索或读取公开科学数据库\n",
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"\n",
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"#### —— nmc_met_io程序库使用说明\n",
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"\n",
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"国家气象中心天气预报技术研发室 \n",
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"Dec., 2020 \n",
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"Kan Dai"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 亚马逊开放和公用数据\n",
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"------\n",
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"* 亚马逊云计算面向科学研究需要, 支撑较为广泛的公开数据, [Registry of Open Data on AWS](https://registry.opendata.aws/)\n",
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"* 对于地球或大气科学, 包含多种卫星遥感, 天气雷达, 再分析和数值模式预报资料"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 检索ERA5再分析数据\n",
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"------\n",
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"* ERA5是ECMWF第五代全球大气再分析数据.\n",
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"* Intertrust Technologies Corporation公司利用[AWS公开数据库项目](https://aws.amazon.com/opendata/public-datasets/), 提供ERA5部分变量的云存储\n",
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" * 关于该数据库的说明, 详见https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md\n",
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" * 数据通过AWS的公共S3存储桶进行服务, 提供NetCDF和zarr两种格式\n",
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" * 可以使用 `aws s3 ls s3://era5-pds/ --no-sign-request` 命令来查询数据库中的内容\n",
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"* 通用boto3可以访问读取数据文件信息及内容, 具体参考\n",
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" * [Accessing ERA5 Data on S3 Using Boto](https://github.com/planet-os/notebooks/blob/master/aws/era5-s3-via-boto.ipynb)\n",
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" * [Examining the Weather on Your Date of Birth](https://github.com/zflamig/birthday-weather)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# load necessary libraries\n",
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"import numpy as np\n",
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"import xarray as xr\n",
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"import cartopy.crs as ccrs\n",
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"import cartopy.feature as cfeature\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from nmc_met_io.retrieve_aws_server import retrieve_era5"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# set time parameters\n",
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"year = 1987\n",
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"month = 12\n",
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"datestart = '1987-12-02'\n",
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"dateend = '1987-12-02 23:59'\n",
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"\n",
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"# load data from AWS ERA5 S3 buckets\n",
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"varname = 'air_pressure_at_mean_sea_level'\n",
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"mslp = retrieve_era5(varname, year, month, datestart, dateend)\n",
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"\n",
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"varname = 'air_temperature_at_2_metres'\n",
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"airt = retrieve_era5(varname, year, month, datestart, dateend)\n",
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"\n",
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"varname = 'precipitation_amount_1hour_Accumulation'\n",
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"precip = retrieve_era5(varname, year, month, datestart, dateend)"
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]
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},
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [
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{
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"name": "stderr",
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"text": [
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"ERROR:asyncio:Unclosed connection\n",
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"client_connection: Connection<ConnectionKey(host='era5-pds.s3.amazonaws.com', port=443, is_ssl=True, ssl=None, proxy=None, proxy_auth=None, proxy_headers_hash=None)>\n",
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"ERROR:asyncio:Unclosed connection\n",
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"client_connection: Connection<ConnectionKey(host='era5-pds.s3.amazonaws.com', port=443, is_ssl=True, ssl=None, proxy=None, proxy_auth=None, proxy_headers_hash=None)>\n",
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"ERROR:asyncio:Unclosed connection\n",
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"client_connection: Connection<ConnectionKey(host='era5-pds.s3.amazonaws.com', port=443, is_ssl=True, ssl=None, proxy=None, proxy_auth=None, proxy_headers_hash=None)>\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 29.6 s, sys: 4.04 s, total: 33.7 s\n",
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"Wall time: 18min 40s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"# pre-process the data\n",
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"precip_data = precip.precipitation_amount_1hour_Accumulation[12,:,:].values\n",
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"\n",
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"mslp_data = mslp.air_pressure_at_mean_sea_level[12,:,:].values\n",
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"mslp_data = mslp_data * 0.01 # Pa to hPa\n",
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"\n",
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"airt_data = airt.air_temperature_at_2_metres[12,:,:].values\n",
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"airt_data = (airt_data - 273.15) * 9/5 + 32.0 # K to F\n",
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"\n",
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"# get longitude and latitude coordinates\n",
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"lat = mslp.lat.values\n",
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"lon = mslp.lon.values\n",
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"lons, lats = np.meshgrid(lon, lat)"
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]
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"}\n",
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
|
|
"Dimensions: (lat: 721, lon: 1440)\n",
|
|
"Coordinates:\n",
|
|
" * lat (lat) float32 90.0 89.75 ... -89.75 -90.0\n",
|
|
" * lon (lon) float32 0.0 0.25 0.5 ... 359.5 359.8\n",
|
|
" time0 datetime64[ns] 1987-12-31T22:00:00\n",
|
|
"Data variables:\n",
|
|
" air_pressure_at_mean_sea_level (lat, lon) float32 dask.array<chunksize=(150, 150), meta=np.ndarray>\n",
|
|
"Attributes:\n",
|
|
" institution: ECMWF\n",
|
|
" source: Reanalysis\n",
|
|
" tilte: ERA5 forecasts</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9bc8e952-b6c7-420d-9e61-3bda8daa92ec' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9bc8e952-b6c7-420d-9e61-3bda8daa92ec' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1b112ab8-3a9c-4247-b456-2181e5f7cdf5' class='xr-section-summary-in' type='checkbox' checked><label for='section-1b112ab8-3a9c-4247-b456-2181e5f7cdf5' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-1caf0eb1-8e6e-4a8a-a149-4d1589f45230' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1caf0eb1-8e6e-4a8a-a149-4d1589f45230' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0798d282-5bd5-49e3-bd06-e8a9dd720296' class='xr-var-data-in' type='checkbox'><label for='data-0798d282-5bd5-49e3-bd06-e8a9dd720296' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90. , 89.75, 89.5 , ..., -89.5 , -89.75, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-86d2dfb3-af28-410d-9d0f-0cba488fafe3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86d2dfb3-af28-410d-9d0f-0cba488fafe3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d40627a-f66e-429e-bb6f-8fd22a45b8fd' class='xr-var-data-in' type='checkbox'><label for='data-0d40627a-f66e-429e-bb6f-8fd22a45b8fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
|
|
" 3.5975e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1987-12-31T22:00:00</div><input id='attrs-f1d87f43-5d02-4a4d-825f-77ec27ac3790' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1d87f43-5d02-4a4d-825f-77ec27ac3790' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93ebb6cc-4589-4498-868f-462c278df87a' class='xr-var-data-in' type='checkbox'><label for='data-93ebb6cc-4589-4498-868f-462c278df87a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array('1987-12-31T22:00:00.000000000', dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d65156e2-434b-40cf-b62e-d8abc2f28c67' class='xr-section-summary-in' type='checkbox' checked><label for='section-d65156e2-434b-40cf-b62e-d8abc2f28c67' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air_pressure_at_mean_sea_level</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(150, 150), meta=np.ndarray></div><input id='attrs-e80af91d-ac65-404e-a32b-adcad577b380' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e80af91d-ac65-404e-a32b-adcad577b380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7608a1d-ed31-41ba-88f2-b34172c89ada' class='xr-var-data-in' type='checkbox'><label for='data-c7608a1d-ed31-41ba-88f2-b34172c89ada' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>nameCDM :</span></dt><dd>Mean_sea_level_pressure_surface</dd><dt><span>nameECMWF :</span></dt><dd>Mean sea level pressure</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>msl</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
|
|
"<tr>\n",
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"<td>\n",
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"<table>\n",
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" <thead>\n",
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" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr><th> Bytes </th><td> 4.15 MB </td> <td> 90.00 kB </td></tr>\n",
|
|
" <tr><th> Shape </th><td> (721, 1440) </td> <td> (150, 150) </td></tr>\n",
|
|
" <tr><th> Count </th><td> 151 Tasks </td><td> 50 Chunks </td></tr>\n",
|
|
" <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</td>\n",
|
|
"<td>\n",
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" <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"60\" />\n",
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" <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"60\" />\n",
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" <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"60\" />\n",
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" <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"60\" />\n",
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" <!-- Colored Rectangle -->\n",
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" <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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"\n",
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" <!-- Text -->\n",
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" <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
|
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" <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
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"</svg>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e06ee0b-4b44-436c-b1c5-ca7fee3be077' class='xr-section-summary-in' type='checkbox' checked><label for='section-8e06ee0b-4b44-436c-b1c5-ca7fee3be077' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>source :</span></dt><dd>Reanalysis</dd><dt><span>tilte :</span></dt><dd>ERA5 forecasts</dd></dl></div></li></ul></div></div>"
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],
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"text/plain": [
|
|
"<xarray.Dataset>\n",
|
|
"Dimensions: (lat: 721, lon: 1440)\n",
|
|
"Coordinates:\n",
|
|
" * lat (lat) float32 90.0 89.75 ... -89.75 -90.0\n",
|
|
" * lon (lon) float32 0.0 0.25 0.5 ... 359.5 359.8\n",
|
|
" time0 datetime64[ns] 1987-12-31T22:00:00\n",
|
|
"Data variables:\n",
|
|
" air_pressure_at_mean_sea_level (lat, lon) float32 dask.array<chunksize=(150, 150), meta=np.ndarray>\n",
|
|
"Attributes:\n",
|
|
" institution: ECMWF\n",
|
|
" source: Reanalysis\n",
|
|
" tilte: ERA5 forecasts"
|
|
]
|
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"metadata": {},
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|
|
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