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| 异常时间,异常值,异常数组索引位置 | ||
| 1997-12-31 17:00:00,9999,8872 | ||
| 1997-12-31 18:00:00,9999,8873 | ||
| 1997-12-31 19:00:00,9999,8874 | ||
| 1997-12-31 20:00:00,9999,8875 | ||
| 1997-12-31 21:00:00,9999,8876 | ||
| 1997-12-31 22:00:00,9999,8877 | ||
| 1997-12-31 23:00:00,9999,8878 | ||
| 1998-01-01 00:00:00,9999,8879 |
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| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# 海洋要素计算 编程作业4\n", | ||
| "计算地转流 \n", | ||
| "- 1.利用两月的月平均资料计算北太平洋*6°N-35°N*范围内的地转流，选取**1500db**作为参考零面画出*10db*，*100 db*，*250 db*，*500 db*等四个深度层上的流场和流速\n", | ||
| "- 2.利用上面计算结果，计算北赤道流水体输运（如130°E，8°N-18°N断面），比较讨论两月结果差异，也可进一步比较不同断面的差异 \n", | ||
| " \n", | ||
| "**Developed By [Hanxue Yu](https://github.com/Yuhan-xue) 02\\06\\2023** \n", | ||
| "**Student ID: 20010006082**" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Import Module" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import netCDF4 as nc\n", | ||
| "import numpy as np\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import pandas as pd\n", | ||
| "import gsw" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## NC file Read&Load" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Read" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Key: PRES\n", | ||
| " | Long Name: Pressure\n", | ||
| " | Units: decibar\n", | ||
| " | Shape: (25,)\n", | ||
| "Key: TOI\n", | ||
| " | Long Name: Temperature.(ITS90)\n", | ||
| " | Units: degree_Celsius\n", | ||
| " | Shape: (25, 132, 360)\n", | ||
| "Key: SOI\n", | ||
| " | Long Name: Salinity.(PSS-78)\n", | ||
| " | Units: psu\n", | ||
| " | Shape: (25, 132, 360)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# NC 文件加载与信息提取\n", | ||
| "das1=nc.Dataset('TS_201801_GLB.nc')\n", | ||
| "das2=nc.Dataset('TS_201807_GLB.nc')\n", | ||
| "def getNCinfo(das):\n", | ||
| " for i in list(das.variables.keys()):\n", | ||
| " ignolist=['time','lat','lon','LONGITUDE', 'LATITUDE', 'TIME', 'bnds','err','ERR','error','ERROR']\n", | ||
| " if i in ignolist or i.split('_')[-1] in ignolist:\n", | ||
| " continue\n", | ||
| " print(f'Key: {i}')\n", | ||
| " print(f' | Long Name: {das.variables[i].long_name}')\n", | ||
| " print(f' | Units: {das.variables[i].units}')\n", | ||
| " print(f' | Shape: {das.variables[i].shape}')\n", | ||
| "getNCinfo(das1)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Load" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# 变量加载\n", | ||
| "## 读取\n", | ||
| "DB_01=np.array(das1.variables['PRES'])\n", | ||
| "TOI_01=np.array(das1.variables['TOI'])\n", | ||
| "SOI_01=np.array(das1.variables['TOI'])\n", | ||
| "DB_07=np.array(das2.variables['PRES'])\n", | ||
| "TOI_07=np.array(das2.variables['TOI'])\n", | ||
| "SOI_07=np.array(das2.variables['TOI'])\n", | ||
| "lon=np.array(das1.variables['LONGITUDE'])\n", | ||
| "lat=np.array(das1.variables['LATITUDE'])\n", | ||
| "## 处理\n", | ||
| "lon[lon<0]=lon[lon<0]+360\n", | ||
| "SOI_01[SOI_01>1000]=np.nan\n", | ||
| "TOI_01[TOI_01>1000]=np.nan\n", | ||
| "SOI_07[SOI_07>1000]=np.nan\n", | ||
| "TOI_07[TOI_07>1000]=np.nan\n", | ||
| "DB_01=np.broadcast_to(DB_01[:,np.newaxis,np.newaxis],SOI_01.shape)\n", | ||
| "DB_01=np.broadcast_to(DB_07[:,np.newaxis,np.newaxis],SOI_07.shape)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Calculate Lattice Distance and Coriolis Parameters" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 13, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# 计算格点距离和科氏参数\n", | ||
| "def getdx(lon):\n", | ||
| " dx=np.zeros(len(lon))\n", | ||
| " dx[:-1]=111*(lon[1:]-lon[:-1])\n", | ||
| " return np.abs(dx[:-1])\n", | ||
| "def getdy(lat):\n", | ||
| " dy=np.zeros(len(lat))\n", | ||
| " dy[:-1]=111*(lat[1:]-lat[:-1])*np.cos(lat[:-1]*np.pi/180)\n", | ||
| " return np.abs(dy[:-1])\n", | ||
| "def getCoriolisParm(lat):\n", | ||
| " return 2 * 7.29e-5 * np.sin(np.radians(lat))\n", | ||
| "dx,dy,f=getdx(lon),getdy(lat),getCoriolisParm(lat)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Calculates specific volume anomaly\n", | ||
| "查询[GSW-Python](https://teos-10.github.io/GSW-Python/gsw_flat.html)文档中gsw.specvol_anom_standard描述与用法\n", | ||
| "- 描述\n", | ||
| " - Calculates specific volume anomaly from Absolute Salinity, Conservative Temperature and pressure. It uses the computationally-efficient expression for specific volume as a function of SA, CT and p (Roquet et al., 2015). The reference value to which the anomaly is calculated has an Absolute Salinity of SSO and Conservative Temperature equal to 0 degrees C. \n", | ||
| "- 输入参数 \n", | ||
| " - SA : Absolute Salinity, g/kg \n", | ||
| " - CT : Conservative Temperature (ITS-90), degrees C \n", | ||
| " - p : Sea pressure (absolute pressure minus 10.1325 dbar), dbar " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# 计算比容异常<使用了GSW-Python模块>\n", | ||
| "anomaly = gsw.specvol_anom_standard(SA=SOI_01, CT=TOI_01, p=DB_01)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "RefLevel=1500" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "pynb310", | ||
| "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": "3.10.10" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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