Clouds affected by solar eclipses could influence the reflection of sunlight back into space and might change local precipitation patterns. Satellite cloud retrievals have so far not taken into account the lunar shadow, hindering a reliable spaceborne assessment of the eclipse-induced cloud evolution. Here we use satellite cloud measurements during three solar eclipses between 2005 and 2016 that have been corrected for the partial lunar shadow together with large-eddy simulations to analyze the eclipse-induced cloud evolution. Our corrected data reveal that, over cooling land surfaces, shallow cumulus clouds start to disappear at very small solar obscurations (~15%). Our simulations explain that the cloud response was delayed and was initiated at even smaller solar obscurations. We demonstrate that neglecting the disappearance of clouds during a solar eclipse could lead to a considerable overestimation of the eclipse-related reduction of net incoming solar radiation. These findings should spur cloud model simulations of the direct consequences of sunlight-intercepting geoengineering proposals, for which our results serve as a unique benchmark.

Accurate precipitation observations are crucial for hydrological forecasts, notably over rapidly responding urban areas. This study evaluated the accuracy of three gridded spaceborne rainfall products (Integrated Multi-satellitE Retrievals for GPM (IMERG), Meteosat Second Generation Visible (MSG-VIS), and MSG-Infrared (MSG-IR)) and the non-governmental Trans-African Hydro-Meteorological Observatory (TAHMO) gauges across the Odaw catchment (Accra, Ghana) from January 2020-July 2022. IMERG is hardly able to capture the strong spatial variability of rainfall required for flood forecasting, but agrees in annual sums with TAHMO and MSG-IR. MSG-IR has difficulties during the wet season. MSG-VIS, only available during daylight, shows limited accuracy and gives high estimates while other products do not detect rain. TAHMO gauges effectively record high-intensity events and their strong spatial variability, although some (daily) accumulations are doubtful and data gaps exist due to technical issues. These findings assist hydrological modelers in selecting appropriate datasets at suitable spatiotemporal resolutions for their research.

The parameterised description of subgrid-scale processes in the clear and cloudy boundary layer has a strong impact on the performance skill in any numerical weather prediction (NWP) or climate model and is still a prime source of uncertainty. Yet, improvement of this parameterised description is hard because operational models are highly optimised and contain numerous compensating errors. Therefore, improvement of a single parameterised aspect of the boundary layer often results in an overall deterioration of the model as a whole. In this paper, we will describe a comprehensive integral revision of three parameterisation schemes in the High Resolution Local Area Modelling - Aire Limitée Adaptation dynamique Développement InterNational (HIRLAM-ALADIN) Research on Mesoscale Operational NWP In Europe - Applications of Research to Operations at Mesoscale (HARMONIE-AROME) model that together parameterise the boundary layer processes: the cloud scheme, the turbulence scheme, and the shallow cumulus convection scheme. One of the major motivations for this revision is the poor representation of low clouds in the current model cycle. The newly revised parametric descriptions provide an improved prediction not only of low clouds but also of precipitation. Both improvements can be related to a stronger accumulation of moisture under the atmospheric inversion. The three improved parameterisation schemes are included in a recent update of the HARMONIE-AROME configuration, but its description and the insights in the underlying physical processes are of more general interest as the schemes are based on commonly applied frameworks. Moreover, this work offers an interesting look behind the scenes of how parameterisation development requires an integral approach and a delicate balance between physical realism and pragmatism.

Commercial cellular telecommunication networks can be used for rainfall estimation by measuring the attenuation of electromagnetic signals transmitted between antennas from microwave links. However, as the received link signal may also decrease during dry periods, a method to separate wet and dry periods is required. Methods utilizing ground-based radar rainfall intensities or nearby link data cannot always be used. Geostationary satellites can provide a good alternative. A combination of two Meteosat Second Generation satellite precipitation products, Precipitating Clouds and Cloud Physical Properties, is employed to decide whether a 15-min time interval for a given link is rainy or not. A 12-d dataset of link-based rainfall maps for the Netherlands is validated against gauge-adjusted radar rainfall maps. Results clearly improve upon the case when no wet–dry classification is applied and thus the method shows potential for application to large areas of the world where the other methods cannot be applied.

High-resolution inputs of rainfall are important in hydrological sciences, especially for urban hydrology. This is mainly because heavy rainfall-induced events such as flash floods can have a tremendous impact on society given their destructive nature and the short time scales in which they develop. With the development of technologies such as radars, satellites and (commercial) microwave links (CMLs), the spatiotemporal resolutions at which rainfall can be retrieved are becoming higher and higher. For the land surface of The Netherlands, we evaluate here four rainfall products, i.e., link-derived rainfall maps, Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) Final Run (IMERG-Global Precipitation Measurement mission), Meteosat Second Generation Cloud Physical Properties (CPP), and Nighttime Infrared Precipitation Estimation (NIPE). All rainfall products are compared against gauge-adjusted radar data, considered as the ground truth given its high quality, resolution, and availability. The evaluation is done for seven months at 30 min and 24h. Overall, we found that link-derived rainfall maps outperform the satellite products and that IMERG outperforms CPP and NIPE. We also explore the potential of a CML network to validate satellite rainfall products. Usually, satellite derived products are validated against radar or rain gauge networks. If data from CMLs would be available, this would be highly relevant for ground validation in areas with scarce rainfall observations, since link-derived rainfall is truly independent of satellite-derived rainfall. The large worldwide coverage of CMLs potentially offers a more extensive platform for the ground validation of satellite estimates over the land surface of the Earth.