The new pbl_met: an open-source library for building meteorological processors and advanced data processing tools

Area of Science:

• Atmospheric Science
• Meteorology
• Environmental Science

Background:

• Planetary Boundary Layer (PBL) research requires robust processing of meteorological data.
• Conventional and modern sensors provide diverse data streams for PBL analysis.
• Existing tools may lack flexibility in handling varied sensor inputs and processing needs.

Purpose of the Study:

• Introduce pbl_met, an open-source library for processing PBL meteorological data.
• Provide flexible routines for estimating meteorological quantities and managing data.
• Facilitate the development of next-generation meteorological processors for dispersion modeling.

Main Methods:

• Developed pbl_met, an open-source Python library.
• Included routines for estimating radiation, turbulence, and mixing height.
• Implemented data processing for ultrasonic anemometers, SODAR/RASS, and LIDARs.
• Enabled construction of diverse data processing paths of varying depths.

Main Results:

• pbl_met allows tailored meteorological data processing based on available sensor data.
• Testing revealed significant differences in results, particularly for mixing height, based on processing path depth.
• The library supports direct use of data from modern sensors.

Conclusions:

• pbl_met provides a versatile platform for PBL meteorological data analysis.
• The library's flexibility is crucial for selecting appropriate processing strategies.
• pbl_met aids in building advanced meteorological processors for dispersion modeling applications.