Abstract. The potential of weather radar observations for hydrological and meteorological research and applications is undisputed, particularly with increasing world-wide radar coverage. However, several barriers impede the use of weather radar data. These barriers are of both scientific and technical nature. The former refers to inherent measurement errors and artefacts, the latter to aspects such as reading specific data formats, geo-referencing, visualisation. The radar processing library wradlib is intended to lower these barriers by providing a free and open source tool for the most important steps in processing weather radar data for hydro-meteorological and hydrological applications. Moreover, the community-based development approach of wradlib allows scientists to share their knowledge about efficient processing algorithms and to make this knowledge available to the weather radar community in a transparent, structured and well-documented way.
The potential of weather radar observations for hydrological and meteorological research and applications is undisputed, particularly with increasing world-wide radar coverage. However, several barriers impede the use of weather radar data. These barriers are of both scientific and technical nature. The former refers to inherent measurement errors and artefacts, the latter to aspects such as reading specific data formats, geo-referencing, visualisation. The radar processing library wradlib is intended to lower these barriers by providing a free and open source tool for the most important steps in processing weather radar data for hydro-meteorological and hydrological applications. Moreover, the community-based development approach of wradlib allows scientists to share their knowledge about efficient processing algorithms and to make this knowledge available to the weather radar community in a transparent, structured and well-documented way
R e c e n t a d va n ce s in c o m m u n ity -b a s e d s o ftw a r e d e v e lo p m e n t have d e m o n s tr a te d t h a t o p e n -s o u rc e s o ftw a r e can b e a re a l b e n e fit t o t h e ra d a r c o m m u n ity .ince the emergence of weather radar technology in the 1940s, research has sought to tap the full potential of weather radar observations. During the digital age, improvements in radar technology have been closely linked to advancements in com puter science and software engineering. Making use of modern radars is not possible without software.
In a recent BAMS article, it is argued that community-based Open Source Software (OSS) could foster scientific progress in weather radar research, and make weather radar software more affordable, flexible, transparent, sustainable, and interoperable.
Nevertheless, it can be challenging for potential developers and users to realize these benefits: tools are often cumbersome to install; different operating systems may have particular issues, or may not be supported at all; and many tools have steep learning curves.
To overcome some of these barriers, we present an open, community-based virtual machine (VM). This VM can be run on any operating system, and guarantees reproducibility of results across platforms. It contains a suite of independent OSS weather radar tools (BALTRAD, Py-ART, wradlib, RSL, and Radx), and a scientific Python stack. Furthermore, it features a suite of recipes that work out of the box and provide guidance on how to use the different OSS tools alone and together. The code to build the VM from source is hosted on GitHub, which allows the VM to grow with its community.
We argue that the VM presents another step toward Open (Weather Radar) Science. It can be used as a quick way to get started, for teaching, or for benchmarking and combining different tools. It can foster the idea of reproducible research in scientific publishing. Being scalable and extendable, it might even allow for real-time data processing.
We expect the VM to catalyze progress toward interoperability, and to lower the barrier for new users and developers, thus extending the weather radar community and user base.
Snowfall is an important geophysical parameter and the observation of the spatial and temporal distribution of snowfall can provide valuable information for a wide range of applications including climate change studies and atmospheric modelling. This paper investigates the feasibility to estimates the amount of solid precipitation and the cloud liquid water content over the ocean using AMSR-E passive microwave brightness temperature observations. The parameters are retrieved by minimizing the difference between the observed and modeled brightness temperature. The radiative transfer in the atmosphere is solved using the discrete ordinate method (4 streams) and the Henyey-Greenstein phase function. The scattering effect of the snow particles is calculated using Mie theory and the liquidequivalent size of the ice particle. Except for the snowfall and the cloud liquid water content, most parameters, which influence the observation are derived from other data sources. The NewtonRaphson method is used to solve the iteration process using observed brightness temperatures at 89 GHz vertical polarization and 36.5 GHz horizontal polarization. The algorithm was applied using data from the Wakasa Bay Experiment 2003 in Japan and the results are compared to snowfall observation derived using a Z-R relationship and data from the Mikuni Doppler radar. Good agreement was achieved for different atmospheric conditions.
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