{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 从亚马逊云计算上检索或读取公开科学数据库\n", "\n", "#### —— nmc_met_io程序库使用说明\n", "\n", "国家气象中心天气预报技术研发室 \n", "Dec., 2020 \n", "Kan Dai" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 亚马逊开放和公用数据\n", "------\n", "* 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", "* 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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 检索ERA5再分析数据\n", "------\n", "* ERA5是ECMWF第五代全球大气再分析数据.\n", "* 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", " * 关于该数据库的说明, 详见https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md\n", " * 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", " * 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", "* 通用boto3可以访问读取数据文件信息及内容, 具体参考\n", " * [Accessing ERA5 Data on S3 Using Boto](https://github.com/planet-os/notebooks/blob/master/aws/era5-s3-via-boto.ipynb)\n", " * [Examining the Weather on Your Date of Birth](https://github.com/zflamig/birthday-weather)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# load necessary libraries\n", "import numpy as np\n", "import xarray as xr\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "import matplotlib.pyplot as plt\n", "\n", "from nmc_met_io.retrieve_aws_server import retrieve_era5" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# set time parameters\n", "year = 1987\n", "month = 12\n", "datestart = '1987-12-02'\n", "dateend = '1987-12-02 23:59'\n", "\n", "# load data from AWS ERA5 S3 buckets\n", "varname = 'air_pressure_at_mean_sea_level'\n", "mslp = retrieve_era5(varname, year, month, datestart, dateend)\n", "\n", "varname = 'air_temperature_at_2_metres'\n", "airt = retrieve_era5(varname, year, month, datestart, dateend)\n", "\n", "varname = 'precipitation_amount_1hour_Accumulation'\n", "precip = retrieve_era5(varname, year, month, datestart, dateend)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR:asyncio:Unclosed connection\n", "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", "ERROR:asyncio:Unclosed connection\n", "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", "ERROR:asyncio:Unclosed connection\n", "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 29.6 s, sys: 4.04 s, total: 33.7 s\n", "Wall time: 18min 40s\n" ] } ], "source": [ "%%time\n", "# pre-process the data\n", "precip_data = precip.precipitation_amount_1hour_Accumulation[12,:,:].values\n", "\n", "mslp_data = mslp.air_pressure_at_mean_sea_level[12,:,:].values\n", "mslp_data = mslp_data * 0.01 # Pa to hPa\n", "\n", "airt_data = airt.air_temperature_at_2_metres[12,:,:].values\n", "airt_data = (airt_data - 273.15) * 9/5 + 32.0 # K to F\n", "\n", "# get longitude and latitude coordinates\n", "lat = mslp.lat.values\n", "lon = mslp.lon.values\n", "lons, lats = np.meshgrid(lon, lat)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 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"\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2 {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</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", "<td>\n", "<table>\n", " <thead>\n", " <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n", " </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", "<svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" />\n", " <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n", " <line x1=\"0\" y1=\"50\" x2=\"120\" y2=\"50\" />\n", " <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n", " <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"60\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"60\" />\n", " <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"60\" />\n", " <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"60\" />\n", " <line x1=\"62\" y1=\"0\" x2=\"62\" y2=\"60\" />\n", " <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"60\" />\n", " <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"60\" />\n", " <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"60\" />\n", " <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"60\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <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", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n", " <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", "</svg>\n", "</td>\n", "</tr>\n", "</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>" ], "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" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mslp.sel(time0='1987-12-31T22:00:00')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Meteorology with Python", "language": "python", "name": "met" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }