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Wet deposition monitoring is a critical part of the long-term monitoring of acid deposition, which aims to assess the ecological impact of anthropogenic emissions of SO 2 and NO x . In North America, long-term wet deposition has been monitored through two national networks: the Canadian Air and Precipitation Monitoring Network (CAPMoN) and the US National Atmospheric Deposition Program (NADP), for Canada and the USA, respectively. In order to assess the comparability of measurements from the two networks, collocated measurements have been made at two sites, one in each country, since 1986 (Sirois et al., in Environmental Monitoring and Assessment, 62 , 273–303, 2000; Wetherbee et al., in Environmental Monitoring and Assessment , 1995–2004, 2010). In this study, we compared the measurements from NADP and CAPMoN instrumentation at the collocated sites at the Pennsylvania State University (Penn State), USA, from 1989 to 2016, and Frelighsburg, Quebec, Canada, from 2002 to 2019. We also included in the study the collocated daily-vs-weekly measurements by the CAPMoN network during 1999–2001 and 2016–2017 in order to evaluate the differences in wet concentration of ions due to sampling frequency alone. The study serves as an extension to two previous CAPMoN-NADP inter-comparisons by Sirois et al. ( Environmental Monitoring and Assessment, 62 , 273–303, 2000) and Wetherbee et al., in ( Environmental Monitoring and Assessment , 1995–2004, 2010). At the Penn State University site, for 1986–2019, CAPMoN was higher than NADP for all ions, in terms of weekly concentration, precipitation-weighted annual mean concentration, and annual wet deposition. The precipitation-weighted annual mean concentrations were higher for SO 4 2− (2%), NO 3 − (12%), NH 4 + (16%), H + (6%), and base cations and Cl − (11–15%). For annual wet deposition, CAPMoN was higher for SO 4 −2 , NO 3 − , NH 4 + and H + (5–17%), and base cations and Cl − (12–17%) during 1986–2019. At the Frelighsburg site, NADP changed the sample collector in October 2011. For 2002–2011, the relative differences at the Frelighsburg site were positive and similar in magnitude to those at the Penn State site. For 2012–2019, the precipitation-weighted annual mean concentrations were 5–27% lower than NADP, except for H + , which was 23% higher. The change in sample collector by NADP had the largest effect on between-network biases. The comparisons of daily-vs-weekly measurements conducted by the CAPMoN network during 1999–2001 and 2016–2017 show that the weekly measurements were higher than the daily measurements by 1–3% for SO 4 2− , NO 3 − , and NH 4 + ; 3–9% for Ca 2+ , Mg 2+ , Na + , and Cl − ; 10–24% for K + ; and lower for H + by 8–30% in terms of precipitation-weighted mean concentration. Thus, differences in sampling frequencies did not contribute to the systematically higher CAPMoN measurements. Understanding the biases in the data for these networks is important for interpretation of continental scale deposition models and transboundary comparison of wet deposition trends.

Precipitation plays a crucial role in the global hydrological cycle, and its irregular distribution contributes directly to natural hazards such as floods, waterlogging, and droughts. Satellite remote sensing has emerged as an effective tool for global precipitation monitoring. However, accurately estimating hourly precipitation from satellite observations remains a major challenge due to its high spatiotemporal variability. To address this challenge, we propose a novel framework—Geographically constrained multi‐source Fusion Network with cross Attention (GeoFNA)—designed to enhance the accuracy of hourly satellite precipitation estimates. GeoFNA integrates a spatiotemporal convolutional network with cross‐attention mechanisms to effectively capture complex spatiotemporal patterns and nonlinear relationships across multi‐source precipitation data sets and auxiliary variables. To further improve model robustness, geographically associated input constraints and weight constraints are incorporated to account for the skewed distribution and rapid variability of hourly precipitation. Results demonstrated that GeoFNA outperformed three baseline models, achieving significantly higher agreement with in situ measurements. Specifically, GeoFNA increased the Pearson Correlation Coefficient from 0.38 to 0.89 and reduced the Mean Squared Error from 2.39 to 0.50 (mm/h)2 compared to the original satellite precipitation data. Additionally, GeoFNA exhibited strong spatial robustness, underscoring its potential for accurate and reliable quantitative precipitation estimation. These advancements pave the way for improved hydrological modeling and meteorological research. Plain Language Summary Satellite remote sensing is a powerful tool for monitoring precipitation on a large scale, but its high biases limit its applications. While current methods that combine satellite and ground data have improved daily and monthly precipitation estimates, they still struggle with the high uncertainty of hourly data. To address this issue, we propose a Geographically constrained multi‐source Fusion Network with cross Attention (GeoFNA). This method integrates satellite precipitation data, reanalysis data, and in situ rainfall measurements to improve the accuracy of hourly precipitation estimates. GeoFNA outperformed the original satellite product and three other benchmark methods in both numerical accuracy and classification performance. These improvements are crucial for the practical use of hourly satellite precipitation data, especially for operational flood forecasting and drought monitoring. Key Points A novel geographically constrained multi‐source fusion network is proposed for bias correction of hourly satellite precipitation estimates The model outperforms three benchmark models, demonstrating strong spatial robustness within a 0.8° radius from the nearest station Hourly satellite precipitation estimates are significantly improved, achieving higher numerical accuracy and classification performance

Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities. Plain Language Summary Ground radars can provide continuous spatial observations over large areas with high spatiotemporal resolutions, so they form the infrastructure for precipitation monitoring and observation in many countries. Recently, deep learning (DL) techniques have shown great potential for use in polarimetric radar‐based precipitation estimates. Nevertheless, the black‐box and turn‐key characteristics of DL models make it difficult for researchers to understand the model decision‐making process and cast doubt on the reliability of the model results. This study introduces a physically explainable polarization radar‐based quantitative precipitation estimation (QPE) system built on DL technology that can explain the causes of the precipitation estimates provided by deep learning models under different rainfall amounts. An experiment indicates that our model achieves better estimates than the conventional methods. Furthermore, the explainability methodology allows for visualization of the microphysical precipitation information. Being the initial attempt to apply explainability learning in the QPE domain, the explainability results may offer valuable guidance for rainfall estimation. Key Points A polarimetric radar‐based rainfall estimation system is developed using deep neural networks The deep learning‐based rainfall estimates generally outperform products derived from traditional parametric relations The proposed deep learning interpretation method can provide physical and statistical explanations of the model decision‐making process

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3–5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the internet of things as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems.

The intensity and phase of precipitation at the ground surface can have important implications not only for meteorological and hydrological situations but also in terms of hazards and risks. In the field, Thies disdrometers are sometimes used to monitor the quantity and nature of precipitation with high temporal resolution and very low maintenance and thus provide valuable information for the management of meteorological and hydrological risks. Here, we evaluate the Thies disdrometer with respect to precipitation detection, as well as the estimation of precipitation intensity and phase at a pre-alpine site in Switzerland (1060 m a.s.l.), using a weighing precipitation gauge (OTT pluviometer) and a two-dimensional video disdrometer (2DVD) as a reference. We show that the Thies disdrometer is well suited to detect even light precipitation, reaching a hit rate of around 95 %. However, the instrument tends to systematically underestimate rainfall intensities by 16.5 %, which can be related to a systematic underestimation of the number of raindrops with diameters between 0.5 and 3.5 mm. During snowfall episodes, a similar underestimation is observed in the particle size distribution (PSD), which is, however, not reflected in intensity estimates, probably due to a compensation by snow density assumptions. To improve intensity estimates, we test PSD adjustments (to the 2DVD) and direct adjustments of the resulting intensity estimates (to the OTT pluviometer), the latter of which are able to successfully reduce the systematic deviations during rainfall in the validation period. For snowfall, the combination of the 2DVD and the OTT pluviometer seems promising as it allows for improvement of snow density estimates, which poses a challenge to all optical precipitation measurements. Finally, we show that the Thies disdrometer and the 2DVD agree well insofar as the distinction between rain and snowfall is concerned, such that an important prerequisite for the proposed correction methods is fulfilled. Uncertainties mainly persist during mixed-phase precipitation or low precipitation intensities, where the assignment of precipitation phase is technically challenging, but less relevant for practical applications. We conclude that the Thies disdrometer is suitable not only to estimate precipitation intensity but also to distinguish between rain and snowfall. The Thies disdrometer therefore seems promising for the improvement of precipitation monitoring and the nowcasting of discharge in pre-alpine areas, where considerable uncertainties with respect to these quantities are still posing a challenge to decision-making.

East Africa experienced in the 2001–11 time period some of the worst drought events to date, culminating in the high-impact drought of 2010/11. Long-term monitoring of precipitation is thus essential, and satellite-based precipitation products can help in coping with the relatively sparse rain gauge ground networks of this area of the world. However, the complex topography and the marked geographic variability of precipitation in the region make precipitation retrieval from satellites problematic and product validation and intercomparison necessary. Six state-of-the-artmonthly satellite precipitation products over East Africa during the 2001–09 time frame are evaluated. Eight areas (clusters) are identified by investigating the precipitation seasonality through the Global Precipitation Climatology Centre (GPCC) climatological gauge data. Seasonality was fully reproduced by satellite data in each of the GPCC-identified clusters. Not surprisingly, complex terrain (mountain regions in particular) represents a challenge for satellite precipitation estimates, as demonstrated by the standard deviations of the six-product ensemble. A further confirmation comes from the comparison between satellite estimates and rain gauge measurements as a function of terrain elevation. The 3B42 product performs best, although the satellite–gauge comparative analysis was not completely independent since a few of the products include a rain gauge bias correction.

Tropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) rainfall monitoring products have been extended to provide spatially contiguous rainfall estimates across Africa. This has been achieved through a new, climatology-based calibration, which varies in both space and time. As a result, cumulative estimates of rainfall are now issued at the end of each 10-day period (dekad) at 4- km spatial resolution with pan-African coverage. The utility of the products for decision making is improved by the routine provision of validation reports, for which the 10-day (dekadal) TAMSAT rainfall estimates are compared with independent gauge observations. This paper describes the methodology by which the TAMSAT method has been applied to generate the pan-African rainfall monitoring products. It is demonstrated through comparison with gauge measurements that the method provides skillful estimates, although with a systematic dry bias. This study illustrates TAMSAT’s value as a complementary method of estimating rainfall through examples of successful operational application.

Accurate precipitation monitoring is critical for hazard risk mitigation in regions characterized by complex terrain. To address these challenges, this study proposes a two‐stage framework that integrates thermodynamic controls, including cloud top brightness temperature (BT) from FengYun‐4A geostationary Multi‐Infrared observations And Orographic effects, for Precipitation retrieval (MIAO2Precip) over China's Sichuan Basin and its surrounding plateau regions at high spatio‐temporal resolutions (0.04°, 15 min). The main findings of this study are: (a) orographic effects (∼16%) contribute comparably to cloud top BT (∼19%) in precipitation retrieval, (b) accuracy of infrared‐based precipitation estimation is jointly influenced by cloud top BT and underlying topographic conditions, and (c) incorporating topography improves heavy precipitation detection (>10 mm/hr) with reduced root mean squared error, bias, and improved correlation coefficient, compared to benchmark products. The results highlight the proposed framework's enhanced capability in identifying heavy precipitation and its potential to support early warning systems in regions with complex topography.

Sub6 GHz non‐line‐of‐sight signals are a potential opportunistic source of rainfall information that promises to improve the current urgent need regarding near‐surface rainfall detection, but the complex mechanisms in which these signals are impacted by rainfall have hindered further development in this area. In this study, we focus on four types of microwave propagation processes to explore the theoretical basis for Sub6 GHz signal sensitivity to rainfall. We also investigate how these signals change during rainy conditions using a cellphone signal recording experiment. The results demonstrate that the indirect effect of rainfall‐induced changes in the interfacial water film may significantly affect the Sub6 GHz signal, making it an opportunity to reflect rainfall information. Finally, we offer a comprehensive overview of the potential challenges, benefits, and drawbacks of low‐frequency non‐line‐of‐sight links in the context of rainfall inversion. Plain Language Summary This study investigates Sub6 GHz non‐line‐of‐sight signals, which are widely available and cost‐effective tools that have the potential to enhance our ability to detect rainfall patterns near the Earth's surface. This combination of theory and experiments gives us a better understanding about the behavior of low‐frequency non‐line‐of‐sight signals in rainy conditions. This knowledge can guide the development of innovative and improved methods for rainfall monitoring in the future through opportunistic means. Key Points The analysis provides insights into the physical mechanisms underlying this sensitivity of Sub6 GHz signals to rainfall The experimental results support the theoretical analysis and enhance the understanding of Sub6 GHz signal behavior in rainy environments This summary emphasizes the characteristics and challenges of low‐frequency non‐line‐of‐sight links in potential rainfall monitoring

The monitoring of extreme precipitation events is an important task in environmental research, but the ability of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation products to monitor extreme precipitation events remains poorly understood. In this study, three precipitation products for IMERG version 6, early-, late-, and final-run products (IMERG-E, IMERG-L, and IMERG-F, respectively), were used to capture extreme precipitation, and their applicability to monitor extreme precipitation events over Hubei province in China was evaluated. We found that the accuracy of the three IMERG precipitation products is inconsistent in areas of complex and less complex topography. Compared with gauge-based precipitation data, the results reveal the following: (1) All products can accurately capture the spatiotemporal variation patterns in precipitation during extreme precipitation events. (2) The ability of IMERG-F was good in areas of complex topography, followed by IMERG-E and IMERG-L. In areas of less complex topography, IMERG-E and IMERG-L produced outcomes that were consistent with those of IMERG-F. (3) The three IMERG precipitation products can capture the actual hourly precipitation tendencies of extreme precipitation events. (4) In areas of complex topography, the rainfall intensity estimation ability of IMERG-F is better than those of IMERG-E and IMERG-L.