PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations

Abstract

Abstract. This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development, especially by students and early-career scientists. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor to state-of-the-art atmospheric radiative transfer codes that excel in speed and/or versatility (e.g., ARTS, Atmospheric Radiative Transfer Simulator; RTTOV, Radiative Transfer for TOVS (Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder)). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built-in modules to access popular open data archives. The paper also presents examples for computing the simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as the observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) an absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario.

An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles

“…Therefore, more studies need to be conducted to compare different radiosonde instruments (models) for monitoring upper air temperature in developing countries to ensure long-term continuity and homogeneity and to aid in climate change studies. In addition, to maintain a reference-quality record of upper air essential climate variables such as temperature, international initiatives such as GRUAN [10] should be supported, and GRUAN-certified sites should be expanded in the Southern Hemisphere. For instance, currently, there is no GRUAN-certified site in the African continent (mainland), and this is a setback for climate change studies.…”

“…For instance, high-quality RS data can be used to understand boundary layer processes such as vertical structure [6], Boundary Layer Height [7] evolution [8], and turbulence [9]. Furthermore, to understand radiative transfer calculations, RS data provide crucial atmospheric parameters such as temperature, humidity, and pressure which are used to validate and calibrate radiative transfer models [10]. For pollution meteorology, RSs are important for detecting temperature inversions, atmospheric stability and mixing, understanding the transport of pollutants, validation, and the improvement in air quality models [11,12].…”

MWRpy: A Python package for processing microwave radiometer data

“…For that, a training data set is derived from a climatology of the atmospheric state (e.g. profiles from radiosondes or model reanalysis) and simulated 𝑇 𝐵 coming from a microwave radiative transfer model like PyRTlib (Larosa et al, 2024). PyRTlib, as a Python library for non-scattering atmospheric microwave radiative transfer calculations, takes various input profiles to compute down-and upwelling 𝑇 𝐵 for microwave sensors from different observational platforms using built-in atmospheric absorption models.…”