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Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods, these observations offer the opportunity to address the challenge of estimating precipitation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model's robustness for global applications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model's superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 min. [ABSTRACT FROM AUTHOR]

This paper presents adjustment routines for Geonor totalizing precipitation gauge data collected from the headwaters of the Oldman River, within the southwestern Alberta Canadian Rockies. The gauges are situated at mountain valley and alpine ridge locations with varying degrees of canopy cover. These data are prone to sensor noise and environment-induced measurement errors requiring an ordered set of quality control (QC) corrections using nearby weather station data. Sensor noise at valley sites with single-vibrating wire gauges accounted for the removal of 5% to 8% (49-76 mm) of annual precipitation. This was compensated for by an increase of 6% to 8% (50-76 mm) from under-catch. A three-wire ridge gauge did not experience significant sensor noise; however, the under-catch of snow resulted in 42% to 52% (784-1342 mm) increased precipitation. When all QC corrections were applied, the annual cumulative precipitation at the ridge demonstrated increases of 39% to 49% (731-1269 mm), while the valley gauge adjustments were 􀀀4% to 1% (􀀀39 mm to 13 mm). Public sector totalizing precipitation gauge records often undergo minimal QC. Care must be exercised to check the corrections applied to such records when used to estimate watershed water balance or precipitation orographic enhancement. Systematic errors at open high-elevation sites may exceed nearby valley or forest sites. [ABSTRACT FROM AUTHOR]

The aim of this study is to present the spatial and temporal variability of the frequency of dry and wet days and dry and wet spells against the background of changes in precipitation and atmospheric circulation. The study is based on daily precipitation totals from 46 meteorological stations in Poland from 1966 to 2023. Additionally, seven circulation indices were used, namely GBI, NAO, AO, EA, EA/WR, SCAND, and AMO. Dry days are defined as days without precipitation. Wet days are days with at least 1 mm of precipitation. It was shown that dry spells are much more common than wet spells, are longer, and cover larger areas. Long-term changes in the annual characteristics of dry and wet days and spells are not statistically significant. Only the length of the most extended dry spell in the year increases. However, there are significant changes in their annual cycles. Spring is drier; in summer, precipitation decreases in the south and increases in the north; November and December, symbols of gloomy rainy weather, are increasingly drier; and rainy weather has shifted to January and February. The impact of circulation varies according to the season, with the NAO, AO, SCAND, and GBI indices having the greatest influence. [ABSTRACT FROM AUTHOR]

Global warming substantially intensifies hydrologic cycles, causing increasing frequency and magnitude of catastrophic floods and droughts. Understanding the patterns and mechanisms of precipitation in historical periods is pivotal for regional disaster prevention and mitigation. Here, we analyzed the daily precipitation of six stations at the Shimantan Reservoir from 1952 to 2013 to examine precipitation characteristics at different time scales. The Mann–Kendall test, moving t-test, and Innovative Polygon Trend Analysis (IPTA) were employed to detect the trends and change points in total precipitation amount, frequency, and duration. Influences of atmospheric circulations on precipitation were then explored via cross-wavelet analysis. Our results showed increased average precipitation and decreased precipitation days annually at the Shimantan Reservoir in the past decades. Specifically, increased seasonal precipitation was only detected in summer, while precipitation days were mainly reduced in winter. There was a noticeable increasing to decreasing transition trend in precipitation from July to August, and a transition from decreasing to increasing from June to July in precipitation days. Summer rainfall was predominantly moderate and light, accompanied by shortening and highly fluctuating rainstorm durations. July exhibited the highest precipitation frequency and always experienced rainstorms. The Arctic Oscillation and East Asian summer monsoon showed positive and negative correlations, respectively, with the changes in precipitation at the Shimantan Reservoir. Our analyses provide a fine-scale portrait of precipitation patterns and mechanisms under a changing climate and benefit regional flood control and sustainable development. [ABSTRACT FROM AUTHOR]

The Amazon Basin is the largest rainforest in the world, and studying the rainfall in this region is crucial for understanding the functioning of the entire rainforest ecosystem and its role in regulating the regional and global climate. This work is part of the application of complex networks, which refer to a network modeled by graphs and are characterized by their high versatility, as well as the extraction of key information from the system under study. The main objective of this article is to examine the precipitation system in the Amazon basin during the austral summer. The networks are defined by nodes and connections, where each node represents a precipitation time series, while the connections can be represented by different similarity functions. For this study, three rainfall networks were created, which differ based on the correlation function used (Pearson, Spearman, and Kendall). By comparing these networks, we can identify the most effective method for analyzing the data and gain a better understanding of rainfall's spatial structure, thereby enhancing our knowledge of its impact on different Amazon basin regions. The results reveal the presence of three important regions in the Amazon basin. Two areas were identified in the northeast and northwest, showing incursions of warm and humid winds from the oceans and favoring the occurrence of large mesoscale systems, such as squall lines. Additionally, the eastern part of the central Andes may indicate an outflow region from the basin with winds directed toward subtropical latitudes. The networks showed a high level of activity and participation in the center of the Amazon basin and east of the Andes. Regarding information transmission, the betweenness centrality identified the main pathways within a basin, and some of these are directly related to certain rivers, such as the Amazon, Purus, and Madeira. Indicating the relationship between rainfall and the presence of water bodies. Finally, it suggests that the Spearman and Kendall correlation produced the most promising results. Although they showed similar spatial patterns, the major difference was found in the identification of communities, this is due to the meridional differences in the network's response. Overall, these findings highlight the importance of carefully selecting appropriate techniques and methods when analyzing complex networks. [ABSTRACT FROM AUTHOR]

In Italy, most of the destructive landslides are triggered by rainfall, particularly in central Italy. Therefore, effective monitoring of rainfall is crucial in hazard management and ecosystem assessment. Global precipitation measurement (GPM) is the next-generation satellite mission, which provides the precipitation measurements worldwide. In this research, we employed the available monthly GPM data to estimate the monthly precipitation for the twenty administrative regions of Italy from June 2000 to June 2021. For each region, we applied the non-parametric Mann–Kendall test and its associated Sen's slope to estimate the precipitation trend for each calendar month. In addition, for each region, we estimated a linear trend and the seasonal cycles of precipitation with the antileakage least-squares spectral analysis (ALLSSA) and showed the annual precipitation variations using box plots. Lastly, we compared machine-learning models based on the auto-regressive moving average for monthly precipitation forecasting and showed that ALLSSA outperformed them. The findings of this research provide a significant insight into processing climate data, both in terms of trend-season estimates and forecasting, and can potentially be used in landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

In 2009–2018, the isotopic composition of oxygen and hydrogen in the atmospheric precipitation, groundwater and river and lake water of Lake Onega basin was studied. The weighted annual isotope composition of precipitation at Petrozavodsk was δ18O = −11.7‰ and δ2H = −84‰ and varied from −30.9 to −4.1‰ for δ18O and from −23 to −22‰ for δ2H. The isotopic composition of the water in Lake Onega was relatively uniform from −11.5 to −9.3‰ for δ18O and from −85 to −71‰ for δ2H. In the bays, the isotopic composition of the water varied more substantially than in the central part of the lake due to the river runoff during springtime flooding. In late summer, the concentrations of deuterium and oxygen-18 increased in the lake water, and figurative points on the δ2H vs. δ18O diagram shifted above the meteoric line. The absorption of the isotopically heavy summer precipitation and disequilibrium isotope fractionation during evaporation led to the enrichment of the lake water by heavy isotopes. Experiments were conducted to estimate the evaporation influence on the isotope enrichment of the residual water, and a comparison of the obtained isotope data with the experimental function showed that commonly, about 4% and up to 12% of water was lost during the spring and summer, respectively. In the water of the tributaries, the abundance of the deuterium and oxygen-18 varied in a wider scale than in the lakes, from −14.4 to −9.1‰ for δ18O and from −102 to −73‰ for δ2H. An evaporation loss of up to 35% was found for the rivers in late summer, and this value was proportional to the area of lakes and wetlands in the elementary watershed. The initial isotope composition of the water in the tributaries prior to evaporation was estimated to be δ18O ≈ −14.1‰ and δ2H ≈ −103‰ on average and crossed the approximation and meteoric lines. This estimation was close to the average composition of the groundwater, i.e., δ18O ≈ −13.4‰ and δ2H ≈ −94‰ on the Lake Onega catchment. The slightly increased isotope depletion of the calculated composition in the initial river water in comparison with the groundwater was the result of the contribution of the spring snowmelt water, which had a significant influence on the lake water balance. [ABSTRACT FROM AUTHOR]

Improving the quality of atmospheric precipitation measurements is crucial in the view of minimizing the uncertainty in weather forecasting, climate change impact assessment, water resource assessment and management, and drought and flood prediction. Remote sensing technology has considerably improved the spatio-temporal assessment of precipitation. Despite the advancement in the remote sensing technology, there is a need to investigate the robust approach towards integrating ground-based-measured and satellite-product precipitation to better understand the hydrologic process of any basin. Several data-merging methods have been proposed; however, the application of merged precipitation products for hydrological simulation has rarely been investigated. Thus, in this review, technical characteristics including basic assumptions, along with their procedures, are discussed. Moreover, the limitations of eight commonly used merging approaches, (1) Multiple Linear Regression, (2) Residual Inverse Distance Weighting, (3) Linearized Weighting, (4) Inverse Root-Mean-Square Error Weighting, (5) Optimal Interpolation, (6) Random-Forest-Based Merging Procedure, (7) Bayesian Model Averaging, and (8) the Kriging Method, and their advances with respect to hydrological simulation are discussed. Finally, future research directions towards improving data merging approaches are recommended. [ABSTRACT FROM AUTHOR]