Tipping‐bucket rain gauges are crucial for the measurement of precipitation. However, gauge‐measured precipitation is affected by a myriad of errors, with wind‐induced undercatch typically being the most significant of the local errors. Here, we review the rain gauge undercatch phenomenon and methods developed for its reduction and correction. We find that refined gauge design and installation practices have reduced undercatch, and post‐collection correction methodologies for undercatch have been developed. However, we recommend that more work is needed to communicate the importance of this phenomenon, and that existing correction methodologies are still not robust enough to be widely adopted.

Space is one of the UK’s fastest-growing industry sectors of the last decade. Recognising this, in 2021 the UK Government’s first National Space Strategy established a new vision to make the nation one of the most innovative and attractive worldwide space economies. As part of the strategic funding programme, in 2023, the UK Space Agency (UKSA) funded a scoping study to assess the potential of satellite data to address issues that the three North East England Local Resilience Forums face at all stages of implementing the UK’s Integrated Emergency Management Framework (IEMF). Through dedicated workshops convened by two North East England universities, regional stakeholders from the emergency domain and related industries identified three case studies ripe for applying satellite data in support of multi-agency IEMF activities. Master’s students in the UK’s Centre for Doctoral Training in Geospatial Systems then undertook a month-long integrated group project to assess the potential for satellite imagery to be applied in the identified application areas. The research reported in this paper demonstrates how satellite imagery may be adopted to help address challenges posed during power outages, for mitigating illegal waste site fires, and during periods of snow and extreme cold. While the maturity levels of satellite applications vary in the three case studies due to data availability and image resolution, all three cases demonstrate that space data, particularly when augmented with additional geospatial information, help to enhance IEMF analysis. It is anticipated that the findings from the study will help stakeholders involved in IEMF management appreciate the added value of integrating satellite data into their current processes and analyses. By empowering key stakeholders to use satellite applications more effectively, it is predicted that better decisions will be achieved, thereby improving emergency risk management. A prototype dashboard, an output of the research to demonstrate the potential of space data for emergency management, is available online.

Accurate rainfall measurement remains challenging, even for in-situ point observations commonly considered the “ground truth”, owing to precipitation undercatch primarily caused by wind effects and instrument design. These biases limit reliable rainfall estimation, especially at very high and low intensities, and hinder the robust characterisation of precipitation variability. This study first used disdrometer data from multiple sites across the UK to develop a new rainfall classification system based on observed drop size distributions rather than intensity thresholds alone. The proposed classification distinguished periods of rainfall with similar bulk intensities but different microphysical structures, providing a more physically meaningful framework for precipitation characterisation and supporting the development of more targeted undercatch correction strategies. Second, a custom-built rainfall simulator was developed to replicate the identified rainfall types under controlled laboratory conditions. The simulator enables independent control of rainfall rate and drop size distribution, allowing the reproduction of a wide range of precipitation regimes representative of natural UK rainfall. Controlled experiments were used to systematically quantify the response of rain gauges to different drop populations and intensities, providing new insights into the mechanisms driving undercatch and its dependence on rainfall microstructure. By explicitly linking drop-scale processes, controlled experimentation, and population-level rainfall classification, this work contributes to the improved accuracy of precipitation measurements and the representation of rainfall at hydrologically relevant scales, with direct implications for rainfall monitoring, model input uncertainty, and flood risk assessment.