Converting Radiosonde Data and Calculating Derived Physical Quantities

An Introduction to rstool: Converting Radiosonde Data and Calculating Derived Physical Quantities

The field of meteorology relies heavily on radiosonde data to understand weather patterns, atmospheric conditions, and make accurate predictions. Radiosondes are instruments that are launched into the atmosphere and transmit measurements of various meteorological parameters as they ascend and descend through the atmosphere. However, working with radiosonde data and deriving useful insights from it can be a complex task.

Introducing rstool, an open-source command-line program designed specifically for reading and converting native radiosonde data to NetCDF format and calculating derived physical quantities. With rstool, you can seamlessly process radiosonde data and unlock its full potential for analysis.

Supported Instruments

rstool currently supports two main types of radiosondes: InterMet Systems (iMet) radiosondes and Windsond. For iMet radiosondes, rstool can read the data files generated by the iMetOS-II software (.dat files). Similarly, for Windsond radiosondes, rstool can process the data files produced by the Windsond software (.sounding files).

However, the versatility of rstool goes beyond these two instruments. If you have a different type of radiosonde and want to use rstool, you have the option of adding support for your instrument by writing a Python module.

Flexible Conversion and Calculation

rstool offers a range of input and output types to suit your specific needs. For example, it can convert native radiosonde data to NetCDF formats such as raw, points (pts), and profile (prof) datasets. Additionally, rstool can calculate derived profile variables by interpolating the measurement points during the ascent of the radiosonde as a function of height.

The supported input/output type combinations include converting from native instrument format to instrument-dependent raw, points, or profile datasets, as well as converting between different types of datasets.

Installation

rstool is designed to run on Linux, macOS, and Windows operating systems. The installation process varies slightly for each platform.

Linux

On Debian-derived distributions (Ubuntu, Devuan, …), you can install the required system packages by running the following command:

sh
sudo apt install python3 python3-pip pipx

For Fedora, the command to install the required system packages is:

sh
sudo yum install python3 pipx

After installing the system packages, you can install rstool using pipx:

sh
pipx install rstool

Make sure to add $HOME/.local/bin to your PATH environment variable if it’s not already present. You can do this by running pipx ensurepath.

To uninstall rstool, use the following command:

sh
pipx uninstall rstool

macOS

To install rstool on macOS, open the Terminal and run the following command:

sh
python3 -m pip install rstool

Next, add /Users/<user>/Library/Python/<version>/bin to your PATH environment variable. Replace <user> with your system user name and <version> with the Python version. You can find the specific path by running the previous command. To add the path, open the .zprofile file in your home directory, add the following line, and restart the Terminal:

sh
PATH="$PATH:/Users/<user>/Library/Python/<version>/bin"

To uninstall rstool, use the following command:

sh
python3 -m pip uninstall rstool

Windows

For Windows users, start by installing Python 3 from the official Python website and make sure to select the option to add python.exe to the PATH during the installation process.

Next, open the Command Prompt from the Start menu and run the following command to install rstool:

sh
pip install rstool

To uninstall rstool, use the following command:

sh
pip uninstall rstool

Format Descriptions

rstool supports several output NetCDF formats, including points (pts), profile (prof), and raw instrument-dependent formats. These formats store essential meteorological variables and derived quantities in a structured manner.

For points (pts) datasets, rstool includes variables such as relative humidity, air temperature, pressure, wind speed, and altitude. It also provides near-surface variables and station information.

The profile (prof) datasets contain variables interpolated as a function of height. These profiles are calculated by averaging the measurement points on a regular vertical pressure grid. Variables such as temperature, humidity, wind speed, and geopotential height are included in the profile datasets.

In addition to the specific formats above, rstool also supports surface (surf) datasets, which specify near-surface variables obtained from co-located automatic weather stations (AWS). These variables, such as relative humidity, air pressure, and wind speed, are crucial for calculating derived profile variables like the lifting condensation level.

Conclusion

rstool is a powerful tool for processing and analyzing radiosonde data. Its ability to read and convert data from different instruments, calculate derived physical quantities, and support various input and output types makes it a valuable asset for meteorologists and researchers working with radiosonde data.

Whether you are investigating atmospheric conditions, studying weather patterns, or conducting research in the field of meteorology, rstool can streamline your data processing workflow and help you derive valuable insights from radiosonde data.

Unlock the full potential of rstool, dive into its comprehensive documentation, and start exploring the fascinating world of meteorology with the power of data.