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"# Exploring TC PRIMED, Chapter 1b: The Overpass File\n",
"- Creators: Naufal Razin, Chris Slocum, and Kathy Haynes\n",
"- Affiliations: CIRA and NESDIS/STAR\n",
"\n",
"---\n",
"\n",
"## Overview\n",
"TC PRIMED consists of two types of files: the overpass file and the environmental file. The overpass file contains all available satellite products from *one* overpass of a tropical cyclone, while the environmental file contains all available tropical cyclone information, and environmental diagnostics and fields *at synoptic times* (00, 06, 12, 18 UTC) throughout the storm's life. In this notebook, you will learn how to load and plot data from the TC PRIMED overpass file.\n",
"\n",
"## Prerequisites\n",
"To successfully navigate and use this notebook, you should be familiar with:\n",
"- the basics of Python programming such as loading modules, assigning variables, and list/array indexing\n",
"- NetCDF files and NetCDF groups (see Chapter 1a of this Learning Journey)\n",
"- plotting data using matplotlib\n",
"\n",
"## Learning Outcomes\n",
"By working through this notebook, you should be able to:\n",
"- understand the data structure of a TC PRIMED overpass file\n",
"- interact with (e.g., load and plot) data from a TC PRIMED overpass file"
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"## Background\n",
"Data in the TC PRIMED overpass files are stored in up to six different NetCDF groups. They are:\n",
"- `overpass_metadata`, which contains information about the satellite\n",
"- `overpass_storm_metadata`, which contains information about the storm at the overpass time\n",
"- `passive_microwave`, which contains passive microwave observations of the storm\n",
"- `GPROF`, which contains retrieved precipitation variables. Except for a small minority of cases, these are available almost all the time.\n",
"- `radar_radiometer`, which contains combined radar-radiometer products from the TRMM PR and GPM DPR (when available)\n",
"- `infrared`, which contains infrared observations of the storm and derived metrics (when available)\n",
"\n",
"In this notebook, you will learn to load and plot the field variables, such as the passive microwave and infrared brightness temperatures. In order to successfully do so, you must first understand passive microwave observation data.\n",
"\n",
"As passive microwave sensors orbit the earth on their respective satellites, they rotate and record their observations at each pixel. Each rotation is called a scan, while the size of each pixel is called the instantaneous field-of-view (IFOV). Depending on the sensor architecture, passive microwave brightness temperature observations have different observing frequencies, are available on different grids or swaths, and have different IFOV. The figure below illustrates this concept of different swaths and IFOV using passive microwave observations from the Special Sensor Microwave/Imager (SSM/I), Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Global Precipitation Measurement mission (GPM) Microwave Imager (GMI) sensors."
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"\n",
"\n",
"
\n",
"\n",
"
S4 swath.\n",
"
S4
group instance from the passive_microwave
group to identify the variables you would want to load.\n",
"infrared
group instance to identify the names of the variables you want to obtain.\n",
"