Disdrometers and other non-catching precipitation measurement instruments have the potential to address some of the limitations of conventional catching precipitation gauges. For example, disdrometers have a greater sensitivity to detect trace amounts of precipitation compared to conventional catching-type gauges. They provide additional information on precipitation types (for which many of these instruments were originally developed). And the lack of a catching device reduces the required maintenance in the field. Despite being in use for some decades already, non-catching instruments have not been standardized for measuring precipitation amounts and related quantities in a similar manner to conventional catching gauges. As a result, uncertainties and limitations are difficult to assess systematically.  

One of the main challenges is the use of proprietary software that varies between instrument types, which hampers data comparability. Researchers who choose to work with raw data face additional challenges, including the need to develop algorithms, handle large volumes of data, and manage metadata effectively. This often results in a lack of consistency and limits collaboration between researchers. Furthermore, many users adopt these instruments as "ground truth" for other precipitation research without critically evaluating their unique measurement methods.  

This paper introduces disdrodb, a novel toolset that provides a standardized approach for processing, sharing, and analyzing raw data from disdrometers.   

The authors present a well-organized and thorough discussion of disdrometer technology, its challenges, and the solutions offered by their disdrodb software package. They deliver an in-depth description of the tool, including its infrastructure, metadata archive, decentralized data sharing capabilities, and software for generating standardized data products. They provide examples that illustrate how the software processes and organizes data and also show selected use cases especially for radar applications. An appendix with methodological explanations gives further details on the methodology.   

This paper addresses a critical gap in the field of precipitation measurement by providing a practical and standardized solution for processing and sharing disdrometer data. Although the paper is lengthy, the comprehensive detail it provides enhances its utility for a broad audience. Instead of requiring readers to consult multiple sources, the paper consolidates essential information, making it a practical, one-stop resource. I appreciate that the paper not only introduces a technical solution but also raises awareness of the implications and challenges of using these instruments. This is especially beneficial for users who may rely on disdrometers without fully understanding their measurement principles or limitations.  

By fostering standardization and encouraging data sharing, disdrodb has the potential to significantly advance the utility and credibility of disdrometers in precipitation research. I strongly support its publication. 

I only have very few specific comments:

Please use the citations as suggested by WMO for the WMO-Guide nr. 8 to meteorological instruments and measurements of observation: World Meteorological Organization (WMO). Guide to Instruments and Methods of Observation (WMO-No. 8), Volume I. Geneva, 2024. see https://library.wmo.int/records/item/68695-guide-to-instruments-and-methods-of-observation

I assume that you have produced Figure 4 yourself, but in the text you are mentioning that the partioning is based on Friedrich et al. (2013). I suggest to add this information also in the figure caption.