{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "0ac4a6dd", "metadata": {}, "source": [ "# 3. 繪製月平均地圖及輸出計算結果\n", " \n", "首先用最簡單的方式，示範以`xarray`讀取netCDF格式資料、簡易運算和繪製等值地圖的過程。\n", "\n", "**Example 1. 區域2017年12月平均外逸長波輻射 (outgoing longwave radiation, OLR)：** 繪製這個圖會經過幾個步驟\n", "1. 開啟檔案。\n", "2. 選取特定時空範圍。\n", "3. 時間平均。\n", "4. 繪圖。\n", "\n", "```{note}\n", "繪圖經度請先設定在0˚-180˚之內，跨越此範圍的繪圖方法比較複雜，我們會在第9單元說明。\n", "```\n", "\n", "## 讀取檔案並選擇時空範圍" ] }, { "cell_type": "code", "execution_count": 8, "id": "dedb705b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

\n", "

<xarray.DataArray 'olr' (time: 31, lat: 50, lon: 82)>\n",
       "[127100 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2017-12-01 2017-12-02 ... 2017-12-31\n",
       "  * lon      (lon) float32 79.5 80.5 81.5 82.5 83.5 ... 157.5 158.5 159.5 160.5\n",
       "  * lat      (lat) float32 -19.5 -18.5 -17.5 -16.5 -15.5 ... 26.5 27.5 28.5 29.5\n",
       "Attributes:\n",
       "    standard_name:  toa_outgoing_longwave_flux\n",
       "    long_name:      NOAA Climate Data Record of Daily Mean Upward Longwave Fl...\n",
       "    units:          W m-2\n",
       "    cell_methods:   time: mean area: mean

" ], "text/plain": [ "\n", "[127100 values with dtype=float32]\n", "Coordinates:\n", " * time (time) datetime64[ns] 2017-12-01 2017-12-02 ... 2017-12-31\n", " * lon (lon) float32 79.5 80.5 81.5 82.5 83.5 ... 157.5 158.5 159.5 160.5\n", " * lat (lat) float32 -19.5 -18.5 -17.5 -16.5 -15.5 ... 26.5 27.5 28.5 29.5\n", "Attributes:\n", " standard_name: toa_outgoing_longwave_flux\n", " long_name: NOAA Climate Data Record of Daily Mean Upward Longwave Fl...\n", " units: W m-2\n", " cell_methods: time: mean area: mean" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import xarray as xr \n", "\n", "# 選擇資料的時空範圍。\n", "lats = -20\n", "latn = 30\n", "lon1 = 79 \n", "lon2 = 161\n", "time1 = '2017-12-01'\n", "time2 = '2017-12-31'\n", "\n", "# 開啟檔案\n", "olr_ds = xr.open_dataset(\"data/olr.nc\")\n", "olr = (olr_ds.sel(time=slice(time1,time2),\n", " lat=slice(lats,latn),\n", " lon=slice(lon1,lon2)).olr) # 將olr從Dataset中讀出來，成為一個DataArray\n", "\n", "olr" ] }, { "attachments": {}, "cell_type": "markdown", "id": "67fa6c0a", "metadata": {}, "source": [ "一個Dataset會包括座標軸 (時間、空間)，以形成*n*-D的變數場 (如下圖中的立體方塊temperature, precipitation)，這些由座標軸建構出的變數場在xarray中稱為DataArray。一個Dataset可以包含很多個不同變數的DataArray。\n", " \n", "![](http://xarray.pydata.org/en/stable/_images/dataset-diagram.png)\n", " \n", "以上我們還示範了如何利用`slice`選擇資料時空範圍，xarray.DataArray和np.array最大的差別就是，DataArray包含了座標軸，因此選擇範圍的時候可以直接用實際的資料時空範圍來選取，不必去找25˚S和25˚N分別在緯度軸的第幾格才能選取，非常方便。選取資料的方式除了`slice`是選擇實際的時間和經緯度範圍之外 (by label)，還有多種方式，例如也可以選第幾個陣列的格點 (by integer)。[xarray網站](https://xarray.pydata.org/en/stable/user-guide/indexing.html)有表格整理，附如下。\n", "\n", "![](https://i.imgur.com/fko7ZLv.png)\n", "\n", "```{note}\n", "Dataset沒有positional index selection，因為Dataset裡面可以包含很多變數DataArray，這些DataArray可以擁有彼此不同的座標軸，所以positional index selection會讓程式很混淆，沒有選擇的依據。\n", "```\n", "\n", "```{caution}\n", "以上範例在選擇資料時，我們寫 `lat=slice(lats,latn)` ，但`lats`和`latn`的先後順序必須取決於資料檔的緯度軸的排列方式。當資料檔排列緯度的方向是由北到南時，就要寫成 `lat=slice(latn,lats)` ，如果方向相反了緯度軸就會是空的。\n", "```\n", "\n", "## 分析計算\n", "\n", "接下來，我們要對時間軸進行平均，在方法`mean`引數中可以指定要做平均的維度，如以下範例指定`olr`這個DataArray的第0個軸進行平均，也就是time這個軸（也可以寫`dim='time'`）。" ] }, { "cell_type": "code", "execution_count": 2, "id": "67fe0941", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

\n", "

<xarray.DataArray 'olr' (lat: 50, lon: 82)>\n",
       "array([[287.4708 , 287.87192, 286.90723, ..., 271.47592, 269.68332,\n",
       "        266.63043],\n",
       "       [285.6799 , 285.8262 , 286.34995, ..., 268.567  , 266.5395 ,\n",
       "        263.9603 ],\n",
       "       [282.36685, 283.75674, 285.1209 , ..., 267.8831 , 264.08185,\n",
       "        260.59106],\n",
       "       ...,\n",
       "       [262.47873, 264.25   , 263.49432, ..., 269.69827, 271.77063,\n",
       "        273.64374],\n",
       "       [261.1402 , 261.74628, 260.309  , ..., 260.9607 , 262.55548,\n",
       "        263.67596],\n",
       "       [253.06664, 249.58241, 238.8812 , ..., 252.71883, 254.02852,\n",
       "        255.21298]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 79.5 80.5 81.5 82.5 83.5 ... 157.5 158.5 159.5 160.5\n",
       "  * lat      (lat) float32 -19.5 -18.5 -17.5 -16.5 -15.5 ... 26.5 27.5 28.5 29.5

" ], "text/plain": [ "\n", "array([[287.4708 , 287.87192, 286.90723, ..., 271.47592, 269.68332,\n", " 266.63043],\n", " [285.6799 , 285.8262 , 286.34995, ..., 268.567 , 266.5395 ,\n", " 263.9603 ],\n", " [282.36685, 283.75674, 285.1209 , ..., 267.8831 , 264.08185,\n", " 260.59106],\n", " ...,\n", " [262.47873, 264.25 , 263.49432, ..., 269.69827, 271.77063,\n", " 273.64374],\n", " [261.1402 , 261.74628, 260.309 , ..., 260.9607 , 262.55548,\n", " 263.67596],\n", " [253.06664, 249.58241, 238.8812 , ..., 252.71883, 254.02852,\n", " 255.21298]], dtype=float32)\n", "Coordinates:\n", " * lon (lon) float32 79.5 80.5 81.5 82.5 83.5 ... 157.5 158.5 159.5 160.5\n", " * lat (lat) float32 -19.5 -18.5 -17.5 -16.5 -15.5 ... 26.5 27.5 28.5 29.5" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 對時間軸axis = 0 進行平均。 \n", "olrm = olr.mean(axis=0)\n", "olrm" ] }, { "attachments": {}, "cell_type": "markdown", "id": "f2b95fa9", "metadata": {}, "source": [ "如此一來，`olr`就從三維被縮減為二維的陣列了。" ] }, { "attachments": {}, "cell_type": "markdown", "id": "b5130793", "metadata": {}, "source": [ "## 繪圖\n", "\n", "接著就要將平均好的結果`olrm`繪製成等值地圖。\n", "\n", "### `matplotlib`繪圖基本原理\n", "\n", "既然matplotlib是PyAOS的核心套件，我們先來了解一下用Matplotlib繪圖的原理。首先必須開啟一個 **畫布** (習慣上用`fig`來表示)，而畫布上會有許多的 **子圖** (subplots)，這些子圖一般用`ax`來表示。而開啟畫布和子圖有兩種方式，第一種利用 `Matplotlib.figure.figure.add_subplot()`，\n", "\n", "```\n", "add_subplot(nrows, ncols, index, **kwargs)\n", "```\n", "\n", "也就是在指定位置上開啟一個子圖，位置指定的方式就是給定第幾列 (`nrows`)和第幾行(`ncols`)。現在我們只需要開啟一個子圖，因此三個引數都設定為1就可以了。" ] }, { "cell_type": "code", "execution_count": 3, "id": "24e8b8c1", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib as mpl\n", "from matplotlib import pyplot as plt\n", "mpl.rcParams['figure.dpi'] = 100\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(1,1,1)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "b238c9e5", "metadata": {}, "source": [ "第二種方式是利用`Matplotlib.pyplot.subplots()`來新增子圖：\n", "\n", "```\n", "matplotlib.pyplot.subplots(nrows, ncols,...)\n", "```\n", "\n", "這個指令會根據給定的行、列子圖數量 (`nrows`和`ncols`) 來開啟一個已劃分好子圖的畫布。" ] }, { "cell_type": "code", "execution_count": 4, "id": "2a011f99", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax=plt.subplots(1,1)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "319dce99", "metadata": {}, "source": [ "### xarray的繪圖函數\n", "\n", "開好繪圖區後，就可以在子圖`ax`上畫圖了。`xarray.Dataset`和`xarray.DataArray`有內建的繪圖方法`xarray.DataArray.plot.contourf`等，其會將引數設定傳送到`matplotlib`的繪圖函數中，進而畫在`ax`上。其他繪圖方法還有 `xarray.plot.pcolormesh()`、`xarray.plot.contour()`⋯⋯等多種，在第八、九單元我們還會詳細說明。" ] }, { "cell_type": "code", "execution_count": 5, "id": "bb730900", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/waynetsai/micromamba/envs/p3/lib/python3.10/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n", " return lib.intersects(a, b, **kwargs)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from cartopy import crs as ccrs \n", "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", "\n", "# 繪圖：開啟繪圖空間與地圖設定。\n", "proj = ccrs.PlateCarree() # 採用等距地圖投影。\n", "fig,ax = plt.subplots(1,1,subplot_kw={'projection':proj}) # 利用matplotlib開啟畫布以及1 x 1的繪圖區。\n", "\n", "clevs = np.arange(170,350,20)\n", "\n", "# 繪圖 \n", "olrPlot = (olrm.plot.contourf(\"lon\", # 設定x坐標。\n", " \"lat\", # 設定y坐標。\n", " transform=proj, # 指定「資料本身」的網格系統。\n", " ax=ax, # 繪製在繪圖空間ax上。\n", " levels=clevs, # 設定圖例色階間距。\n", " cmap='rainbow', # 設定色階\n", " add_colorbar=True, # 繪製色階。\n", " extend='both', # 色階向兩端延伸。\n", " cbar_kwargs={'orientation': 'horizontal', 'aspect': 30, 'label': ' '}) #設定color bar\n", " )\n", "\n", "ax.set_extent([lon1,lon2,lats,latn],crs=proj)\n", "ax.set_xticks(np.arange(80,180,20), crs=proj)\n", "ax.set_yticks(np.arange(-20,40,10), crs=proj) # 設定x, y座標的範圍，以及多少經緯度繪製刻度。\n", "lon_formatter = LONGITUDE_FORMATTER\n", "lat_formatter = LATITUDE_FORMATTER \n", "ax.xaxis.set_major_formatter(lon_formatter)\n", "ax.yaxis.set_major_formatter(lat_formatter) # 將經緯度以degN, degE的方式表示。\n", "ax.coastlines() # 繪製地圖海岸線。 \n", "ax.set_ylabel(' ') # 設定坐標軸名稱。\n", "ax.set_xlabel(' ')\n", "ax.set_title(' ') # xarray會預設圖片標題，用空白來取代標題以重置\n", "ax.set_title(\"December 2017 mean OLR (W m$^{-2}$)\", loc='left') # 設定圖片標題，並且置於圖的左側。\n", "plt.show()\n", "#plt.savefig(\"olr_mean_201712.png\", dpi=600) # 儲存圖片。" ] }, { "attachments": {}, "cell_type": "markdown", "id": "48a6e8e6", "metadata": {}, "source": [ "補充說明：\n", "\n", "1. Cartopy是繪製地圖、處理投影的函數。在開啟子圖時，需要先用`subplot_kw={'projection':proj}`來指定子圖的投影方式。\n", "\n", "2. cmap可以使用[Matplotlib的色階](https://matplotlib.org/stable/tutorials/colors/colormaps.html)，只要在網站中搜尋到適合的，然後在`plot.contourf`中`cmap`的引數填入色階名稱即可。\n", "\n", "3. 除了Matplotlib的色階之外，也可以使用[NCL Colar Table Gallery](https://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml#matplotlib)的色階，使用上請先`import cmaps`這個套件，而NCL網站上色階的名稱就是`cmaps`套件的attribute。舉例而言，如果想要使用\"GMT_seis\"這個色階，在`plot.contourf`中`cmap`的引數填入`cmap=cmaps.GMT_seis`即可。" ] }, { "attachments": {}, "cell_type": "markdown", "id": "916bbd99", "metadata": {}, "source": [ "## 輸出為netCDF檔\n", "\n", "計算完的結果`olrm` (是一個DataArray) 也可以另外儲存成netCDF檔案。輸出後我們也用ncdump來查詢一下檔案的資訊。" ] }, { "cell_type": "code", "execution_count": 6, "id": "b23e3df0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "netcdf olrm_201712 {\n", "dimensions:\n", "\tlon = 82 ;\n", "\tlat = 50 ;\n", "variables:\n", "\tfloat lon(lon) ;\n", "\t\tlon:_FillValue = NaNf ;\n", "\t\tlon:standard_name = \"longitude\" ;\n", "\t\tlon:long_name = \"longitude\" ;\n", "\t\tlon:units = \"degrees_east\" ;\n", "\t\tlon:axis = \"X\" ;\n", "\t\tlon:bounds = \"lon_bnds\" ;\n", "\tfloat lat(lat) ;\n", "\t\tlat:_FillValue = NaNf ;\n", "\t\tlat:standard_name = \"latitude\" ;\n", "\t\tlat:long_name = \"latitude\" ;\n", "\t\tlat:units = \"degrees_north\" ;\n", "\t\tlat:axis = \"Y\" ;\n", "\t\tlat:bounds = \"lat_bnds\" ;\n", "\tfloat olr(lat, lon) ;\n", "\t\tolr:_FillValue = NaNf ;\n", "}\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "olrｍ.to_netcdf(\"olrm_201712.nc\")\n", "\n", "import os\n", "os.system('ncdump -h olrm_201712.nc')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "835d186e", "metadata": {}, "source": [ "在第二單元中，我們有看到讀進來的OLR資料有\n", "\n", "```\n", "time = UNLIMITED ; // (8760 currently)\n", "```\n", "\n", "時間軸`UNLIMITED`尤其在第11單元，進行Climate Data Operator (CDO) 的操作時，有重要的用途，因此輸出netCDF檔時，建議將時間軸也存成`UNLIMITED`的模式。以輸出`olr`這個變數為例：" ] }, { "cell_type": "code", "execution_count": 7, "id": "21589cd0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "netcdf olr201712_unlim_dims {\n", "dimensions:\n", "\ttime = UNLIMITED ; // (31 currently)\n", "\tlon = 82 ;\n", "\tlat = 50 ;\n", "variables:\n", "\tdouble time(time) ;\n", "\t\ttime:_FillValue = NaN ;\n", "\t\ttime:standard_name = \"time\" ;\n", "\t\ttime:long_name = \"reference time\" ;\n", "\t\ttime:axis = \"T\" ;\n", "\t\ttime:units = \"days since 1979-01-01\" ;\n", "\t\ttime:calendar = \"standard\" ;\n", "\tfloat lon(lon) ;\n", "\t\tlon:_FillValue = NaNf ;\n", "\t\tlon:standard_name = \"longitude\" ;\n", "\t\tlon:long_name = \"longitude\" ;\n", "\t\tlon:units = \"degrees_east\" ;\n", "\t\tlon:axis = \"X\" ;\n", "\t\tlon:bounds = \"lon_bnds\" ;\n", "\tfloat lat(lat) ;\n", "\t\tlat:_FillValue = NaNf ;\n", "\t\tlat:standard_name = \"latitude\" ;\n", "\t\tlat:long_name = \"latitude\" ;\n", "\t\tlat:units = \"degrees_north\" ;\n", "\t\tlat:axis = \"Y\" ;\n", "\t\tlat:bounds = \"lat_bnds\" ;\n", "\tfloat olr(time, lat, lon) ;\n", "\t\tolr:_FillValue = 0.f ;\n", "\t\tolr:standard_name = \"toa_outgoing_longwave_flux\" ;\n", "\t\tolr:long_name = \"NOAA Climate Data Record of Daily Mean Upward Longwave Flux at Top of the Atmosphere\" ;\n", "\t\tolr:units = \"W m-2\" ;\n", "\t\tolr:cell_methods = \"time: mean area: mean\" ;\n", "\t\tolr:missing_value = 0.f ;\n", "}\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "olr.to_netcdf('olr201712_unlim_dims.nc',unlimited_dims='time')\n", "\n", "os.system('ncdump -h olr201712_unlim_dims.nc')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "e1b8aec2", "metadata": {}, "source": [ "## 小結\n", "\n", "以上我們按照開啟檔案、分析資料、視覺化這樣的氣候資料處理最基本的流程，示範了如何將OLR資料進行月平均，並繪製成等值圖。xarray是一個很方便的工具，簡化了許多流程，整體來說讓程式碼變得非常簡潔。" ] }, { "attachments": {}, "cell_type": "markdown", "id": "47c7bb27", "metadata": {}, "source": [ "## Homework 1\n", "\n", "```{admonition} Homework #1\n", ":class: seealso\n", "1. 請以xarray開啟 [CMORPH降雨資料](https://drive.google.com/file/d/1ThleIOB8ThdiywavkYVJbi0qiGxbywdp/view?usp=sharing) ，繪製2023年April 26-30, May 1-5, May 6-10 三候 (pentads) 平均的降雨地圖 (共3張圖)。請調整適當的`cmap` (通常會白色開始，數值越大顏色越深) 和`levels`。\n", "2. 請根據所畫出來的地圖描述五月至今印度洋－東亞地區降雨特徵。\n", "\n", "繪圖區域範圍可以印度洋－東亞地區為主，不需要繪製全球降雨地圖 (可以思考一下怎樣的經緯度範圍呈現的效果最好)。繳交的時候請上傳程式碼 (`.py`或`.ipynb`皆可)、圖 (總共3張圖) 和降雨特色的描述 (可以是.ipynb, Word, PPT, PDF格式)。 \n", "\n", "**Note:** 覺得繪圖步驟很重複嗎？可以考慮用for loop (怎麼用？可以自己查看看！) 另外在第九章有教怎麼一次繪製3個子圖，也可以參考一下。\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" }, "vscode": { "interpreter": { "hash": "8e905df1d4d920326545d879dea538d50859be77412bc9bf54949dad3bde9dd6" } } }, "nbformat": 4, "nbformat_minor": 5 }