East Africa relies heavily on satellite-based rainfall estimates due to the lack of in situ data. However, satellite rainfall products often perform poorly in this region. In this study, data from the Trans-African Hydrometeorological Observatory (TAHMO) were used to build a regional rainfall product in East Africa based on the Soil Moisture to Rain (SM2Rain) algorithm. Subsequently, this regional product was merged with a reanalysis product (ERA5) and two microwave (MW)/infrared (IR)-based rainfall products (IMERG and CHIRPS) based on the Statistical Uncertainty Analysis-Based Precipitation Merging (SUPER) framework. Within this framework, merging weights are derived from the error variances of the rainfall products determined from quadruple collocation on a pixel-to-pixel basis. The merged and individual products are evaluated using data from individual TAHMO stations. We tested SUPER with various interproduct dependency assumptions and found that, in the best-performing configuration, IMERG contributed the most to the merged product, followed by CHIRPS, ERA5, and SM2Rain. SM2Rain showed performance comparable to other rainfall products but is more useful for detecting the offset of the rainy season in drier climates and less reliable under wet conditions. The findings indicated that the merged product outperforms the individual products in most performance metrics. Additionally, we demonstrated the importance of comparing satellite and ground-measured precipitation time series, alongside evaluating performance metrics. The ultimate goal of this study is to develop a workflow to enhance the accuracy of rainfall measurements in East Africa by leveraging information from TAHMO data and different existing products, contributing to the improvement of satellite-based rainfall estimates in East Africa.

Hydrology faces critical challenges in reproducibility, accessibility and collaboration, limiting progress and innovation. This article introduces 'moving research down the academic career scale' (MRDTACS): the idea that work should be reproducible by someone at an earlier career stage and in less time than the original work. We advocate for research tools and methods to be accessible to students and early career researchers. By embedding open and findable, accessible, interoperable, reusable (FAIR) principles, modular tool design and user-friendly interfaces, we can lower barriers to reproducibility and foster equitable participation in hydrological research. Herein, we highlight practical strategies to empower researchers at all levels to build on existing work, reducing time spent overcoming technical challenges and enabling a deeper focus on innovation. When existing technologies and tools do not meet hydrology's advancing needs and innovation is needed, we use eWaterCycle to illustrate how we have practically implemented open and FAIR principles to support MRDTACS. This approach advances equity and inclusivity while strengthening collaboration across academic and professional communities. By prioritizing reproducibility and transparency, we can create a more resilient and effective hydrological science field equipped to tackle urgent global challenges. This article is part of the discussion meeting issue 'Hydrology in the 21st century: challenges in science, to policy and practice'.

Anthropogenic macrolitter (>0.5 cm) in rivers is of increasing concern. It has been found to have an adverse effect on riverine ecosystem health, and the livelihoods of the communities depending on and living next to these ecosystems. Yet, little is known on how macrolitter reaches and propagates through these ecosystems. A better understanding of macrolitter transport dynamics is key in developing effective reduction, preventive, and cleanup measures. In this study, we analyzed a novel dataset of citizen science riverbank macrolitter observations in the Dutch Rhine-Meuse delta, spanning two years of observations on over 200 unique locations, with the litter categorized into 111 item categories according to the river-OSPAR protocol. With the use of regression models, we analyzed how much of the variation in the observations can be explained by hydrometeorology, observer bias, and location, and how much can instead be explained by temporal trends and seasonality. The results show that observation bias is very low, with only a few exceptions, in contrast with the total variance in the observations. Additionally, the models show that precipitation, wind speed, and river flow are all important explanatory variables in litter abundance variability. However, the total number of items that can significantly be explained by the regression models is 19% and only six item categories display an R2 above 0.4. This suggests that a very substantial part of the variability in macrolitter abundance is a product of chance, caused by unaccounted (and often fundamentally unknowable) stochastic processes, rather than being driven by the deterministic processes studied in our analyses. The implications of these findings are that for modeling macrolitter movement through rivers effectively, a probabilistic approach and a strong uncertainty analysis are fundamental. In turn, point observations of macrolitter need to be planned to capture short-term variability.

Particle filters are non-Gaussian filters, which means that the assumption that the error distribution of the ensemble should be Gaussian is unnecessary. Like the ensemble Kalman filter, particle filters are based on the Monte Carlo approximation to represent the distribution of model states. It requires a substantial number of particles to approximate the probability density function of states in high-dimensional models, which is prohibitive for real applications. In order to overcome problems with high dimensionality, localization was applied in an Ensemble-type data assimilation system. This study combines the localization in LETKF (Local Ensemble Transformation Kalman Filter) with particle filters and proposes a new local particle filter with the model state space correction using Gamma test theory for high-dimensional models. A series of tests with various parameter settings, including different the numbers of particles, observation intervals, localization scale, inflation factors, and observation operators, were used to evaluate the performance of this new method using a Lorenz model with 40 variables. Besides, the proposed filter was applied in the Lorenz model with 1,000 variables to evaluate its performance in the model with higher dimensions. The results show that this approach can deal with the issue of dimensionality, which otherwise leads to the collapse of the particle filters in high-dimensional systems. The local particle filter is stable and has considerable potential for complex higher-dimensional models.

The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a session on "Weather and Climate Science in the Digital Era"at the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Roughly 80ĝ€¯% of the studies presented in the conference session showed the added value of open data and software. These studies included open datasets from disparate sources in their analyses or developed tools and approaches that were made openly available to the research community. Furthermore, shared software is a prerequisite for the studies which presented systems like a model coupling framework or digital collaboration platform. Although these studies showed that sharing code and data is important, the consensus among the participants was that this is not sufficient to achieve open weather and climate science and that there are important issues to address. At the level of technology, the application of the findable, accessible, interoperable, and reusable (FAIR) principles to many datasets used in weather and climate science remains a challenge. This may be due to scalability (in the case of high-resolution climate model data, for example), legal barriers such as those encountered in using weather forecast data, or issues with heterogeneity (for example, when trying to make use of citizen data). In addition, the complexity of current software platforms often limits collaboration between researchers and the optimal use of open science tools and methods. The main challenges we observed, however, were non-technical and impact the practice of science as a whole. There is a need for new roles and responsibilities in the scientific process. People working at the interface of science and digital technology - e.g., data stewards and research software engineers - should collaborate with domain researchers to ensure the optimal use of open science tools and methods. In order to remove legal boundaries on sharing data, non-academic parties such as meteorological institutes should be allowed to act as trusted agents. Besides the creation of these new roles, novel policies regarding open weather and climate science should be developed in an inclusive way in order to engage all stakeholders. Although there is an ongoing debate on open science in the community, the individual aspects are usually discussed in isolation. Our approach in this paper takes the discourse further by focusing on "open science in weather and climate research"as a whole. We consider all aspects of open science and discuss the challenges and opportunities of recent open science developments in data, software, and hardware. We have compiled these into a list of concrete recommendations that could bring us closer to open weather and climate science. We acknowledge that the development of open weather and climate science requires effort to change, but the benefits are large. We have observed these benefits directly in the studies presented in the conference and believe that it leads to much faster progress in understanding our complex world.