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Potential evapotranspiration (PET) represents the evaporative demand in the atmosphere for the removal of water from the land and is an essential variable for understanding and modelling land–atmosphere interactions. Weather generators are often used to generate stochastic rainfall time series; however, no such model exists for the generation of a stochastically plausible PET time series. Here we develop a stochastic PET generator, stoPET, by leveraging a recently published global dataset of hourly PET at 0.1∘ resolution (hPET). stoPET is designed to simulate realistic time series of PET that capture the diurnal and seasonal variability in hPET and to support the simulation of various scenarios of climate change. The parsimonious model is based on a sine function fitted to the monthly average diurnal cycle of hPET, producing parameters that are then used to generate any number of synthetic series of randomised hourly PET for a specific climate scenario at any point of the global land surface between 55∘ N and 55∘ S. In addition to supporting a stochastic analysis of historical PET, stoPET also incorporates three methods to account for potential future changes in atmospheric evaporative demand to rising global temperature. These include (1) a user-defined percentage increase in annual PET, (2) a step change in PET based on a unit increase in temperature, and (3) the extrapolation of the historical trend in hPET into the future. We evaluated stoPET at a regional scale and at 12 locations spanning arid and humid climatic regions around the globe. stoPET generates PET distributions that are statistically similar to hPET and an independent PET dataset from CRU, thereby capturing their diurnal/seasonal dynamics, indicating that stoPET produces physically plausible diurnal and seasonal PET variability. We provide examples of how stoPET can generate large ensembles of PET for future climate scenario analysis in sectors like agriculture and water resources with minimal computational demand.

Commercial Microwave Links (CMLs) have demonstrated to be a valuable complementary measuring technique with regard to rainfall measuring. Their intrinsic characteristics give them an edge over traditional networks such as meteorological radars, satellites, and rain gauges. For instance, given their high density, especially in urban areas, they offer a higher spatial (and even temporal) resolution against rainfall observations from rain gauges. Moreover, they observe rainfall in a close proximity to the ground surface compared to radar and/or meteorological satellites. As their use in monitoring rainfall is in its “early stage”, there are still some challenges to overcome, e.g., a low accuracy when observing light rainfall.In general, CMLs networks used to operate within the C, X, Ku, K, and Ka bands of the electromagnetic spectrum (i.e., ~4 - 40GHz) over distances varying from hundreds-of-meters to tens-of-kilometres. A big advantage offered by these bands is the linear relationship between rainfall intensity and power attenuation, which actually is the cornerstone of rainfall retrievals from CMLs. Nevertheless, as the continuously increasing demand for a larger throughput in such networks, mobile operators are gradually moving into the 71 - 86 GHz region, i.e., the E band. This fact alone brings more challenges in the retrieval of rainfall as the relationship between rainfall intensity and power attenuation not only starts departing from linearity in this band but also is more sensitive to the drop size distribution of rainfall. On the other hand, over such frequencies/band, it is possible now to reliably monitor rainfall intensities lower than 1 mm/h, which was practically impossible with lower-frequency CMLs.Our work focuses on the performance of ~250 E-Band CMLs over a continuous period of ~7 months in 2020. These CMLs are part of a larger network located in the city of Prague (Czech Republic) and its surroundings. We evaluate their performance against a local network of ~50 rain gauges. We demonstrate the potential of E-band CMLs in retrieving accurate estimates for both light and heavy rainfall. Recently, there has been only few studies focused on E-band links. Our contribution to the field is in performing analyses over a larger spatio-temporal scale.

Precipitation is an important input for hydrological models. Uncertainty in its spatiotemporal variability is a major error source for forecasts generated with distributed hydrological models, because this uncertainty propagates non-linearly into simulated soil moisture patterns, groundwater table depths, discharge and surface energy flux partitioning. Thus, it is imperative to use accurate rainfall datasets that reproduce rainfall's intrinsic highly-spatiotemporal variability to obtain better forecasts from hydrological models.In this study, we present the evaluation of the high-resolution precipitation product RADKLIM against precipitation from the COSMO-DE analysis over the Rur catchment, in western Germany, at a decadal time scale (2007-2015). RADKLIM is the climate version of the quantitative precipitation estimation product RADOLAN developed by the German national weather service (DWD, Deutscher Wetterdienst) by adjusting radar-derived estimates to gauge observations. Its spatiotemporal resolution is ~1x1 km and 5 minutes. The hourly COSMO-DE analysis precipitation data is obtained from the German weather forecast model (also available from DWD) with a spatial resolution of ~2.8x2.8 km. To make a scale-consistent comparison, the RADKLIM product was upscaled to the COSMO-DE resolution.Overall, the COSMO-DE analysis yields over the studied area 50% more of the average precipitation of the RADKLIM product. The highest biases (COSMO-DE over RADKLIM) predominantly occur during afternoon (i.e., 15:00 - 21:00), and in the summer season; whereas the negative biases predominantly occur during autumn, with their highest in the early afternoon (i.e., 12:00 - 18:00).

We analyze the diurnal cycle of maximum rainfall from ∼300 TCs from March 2014 through February 2017, by cross-referencing the path of tropical cyclones (TCs) and high-resolution rainfall estimates from IMERG (Integrated Multi-satellitE Rainfall from GPM - Global Precipitation Measurement mission). IMERG is a gridded satellite product that offers high-resolution rainfall estimates at a spatiotemporal resolution of 0.1° × 0.1° every 30 min, which are particularly suitable for these analyses. Because of the nature of the data, we use circular statistics. Circular statistics allows us to account for the natural periodicity of a random variable such as the time of the day at which maximum rainfall from TCs occurs. We follow the non-parametric approach of Mixtures of Von Mises-Fisher distribution (MvMF), which enables an easy-to-interpret parameter identification of multimodal and anisotropic distributions of the TC-rainfall. We stratify our analysis by storm duration, maturity, and intensity, basin of origin, radial proximity to the center of the storm, and whether the storm is over the ocean or land. In general, and across all scales, we find that there are mainly two cycles of maximum TC-rainfall: one diurnal cycle with peaks at ∼10 and ∼22 h (local time), and one semi-diurnal cycle with peaks at ∼2 and ∼5 h (local time). Although in a smaller proportion, the latter exhibits a weak afternoon alternative, i.e., ∼14 and ∼18 h (local time).