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\"Developed by literacy experts for students in kindergarten through grade three, this book introduces precipitation to young readers through leveled text and related photos\"--Provided by publisher.

Abstract We investigate the global distribution of hourly precipitation and its connections with the El Niño–Southern Oscillation (ENSO) using both satellite precipitation estimates and the global sub-daily rainfall gauge dataset. Despite limited moisture availability over continental surfaces, we find that the highest mean and extreme hourly precipitation intensity (HPI) values are mainly located over continents rather than over oceans, a feature that is not evident in daily or coarser resolution data. After decomposing the total precipitation into the product of the number of wet hours (NWH) and HPI, we find that ENSO modulates total precipitation mainly through the NWH, while its effects on HPI are more limited. The contrasting responses to ENSO in NWH and HPI is particularly apparent at the rising branches of the Pacific and Atlantic Walker Circulations, and is also notable over land-based gauges in Australia, Malaysia, the USA, Japan and Europe across the whole distribution of hourly precipitation (i.e. extreme, moderate and light precipitation). These results provide new insights into the global precipitation distribution and its response to ENSO forcing.

We analyzed spatiotemporal precipitation trends within the Yellow River Basin (YRB) in China and examined the connection between the changes in average and extreme precipitation indices. Data from 423 weather stations recorded from 1961 to 2016 were analyzed using the Mann-Kendall test to explore the linear trends of relationships between various indices, along with a simple linear regression used to detect monotonic positive or negative trends in the annual and seasonal precipitation data. Moreover, we divided the YRB into three distinct topographic regions to better understand the effect of regional geography on precipitation patterns. Our results demonstrated that mean precipitation and extreme precipitation days in different areas of the YRB had different variation trends. Precipitation in the YRB overall showed a negative trend, as did extreme precipitation days in the lower YRB. Mean and extreme precipitation indices were significantly correlated both annually and seasonally. These results may be helpful in preparing for both drought and flood events.

Mountainous precipitation has different characteristics due to differences in climatic or topographical backgrounds. However, the variation characteristics of modern precipitation in the Nanling Mountains, which are located at the front edge of the Asian summer monsoon, remain poorly understood. Based on monthly precipitation data from twenty-two meteorological stations from 1959 to 2019, we analysed the spatiotemporal characteristics of seasonal and annual precipitation and discussed the possible driving mechanisms in the study region. Annual, spring, summer, and winter precipitation increased, while autumn precipitation decreased from 1959 to 2019; these variation trends will continue as inferred from the Hurst index of the R/S analysis. Annual and seasonal precipitation exhibit periods within a scale of 2–8 years, and their mutation times mostly occurred in the 1980s and 2010s. Spatially, the peak centre of precipitation shifted gradually from the south to the north from spring to winter, as it was influenced by variations in the water vapour source. The eastern and northern weather stations with bimodal patterns of monthly precipitation within the year have longer flood seasons than the southern and western stations with unimodal patterns. Due to the loss of water vapour during northwards and upwards migration, significant negative correlations existed between latitude and annual/spring/summer precipitation, as well as between altitude (below 500 m) and annual/spring precipitation. Regional comparisons revealed that mountainous precipitation exhibited significantly decreasing trends with increasing latitude in China. This study further demonstrated the vital roles of the Asian summer monsoon, sunspots, and ENSO forcings in regulating annual precipitation variations in the study region.

\"Next Time You See a Cloud explains the science behind clouds in a way young children can understand. The book also includes activities and additional resources, as well as color photographs\"-- Provided by publisher.

With global warming, extreme weather frequently and severely appears globally. Extreme precipitation is one of the extreme weather events that can cause many natural disasters, such as floods and waterlogging. In this study, Global Precipitation Climatology Project (GPCP) daily precipitation data were used to investigate extreme precipitation and its contribution to annual precipitation in different global climate regions and typical river basins. The climate types included equatorial climates (EC), arid climates (AC), warm temperate climates (WTC), snowy climates (SC) and polar climates (PC). R99p, Rx5day, CWD and R20 was selected as extreme precipitation indices in this study; extreme precipitation days were defined by CWD and R20. The results showed that EC and WTC had higher extreme precipitation level; SC and PC had lower extreme precipitation amounts and days than AC. R99p, Rx5day and CWD monitored higher extreme precipitation contribution degrees in AC; however, R20 monitored higher contribution degrees in EC and WTC. R99p, Rx5day and CWD showed higher extreme precipitation contribution degrees in North Africa, the Middle East, Australia and northwestern China; R20 showed higher contribution degrees in South America, the southeastern United States and South Asia. Based on historical observational data, Heilongjiang Basin (HB), Yellow River Basin (YERB), Yangtze River Basin (YARB), Ganges River Basin (GRB), Danube River Basin (DRB) and Mekong River Basin (MERB) had high-frequency extreme precipitation in summer. The research results are helpful for understanding the characteristics of extreme precipitation and provide a reference for flood control and disaster reduction in different climatic regions and main river basins.

This study conducted evaluation and analysis on various precipitation products over the eastern Tibetan Plateau (ETP), including four sets of satellite precipitation data (i.e., IMERG_Uncal, IMERG_Cal, GSMaP_MVK, GSMaP_Gauge) and one set of model reanalysis data (i.e., ERA5‐land, hereafter ERA5‐L). We evaluated the spatial‐temporal distribution of their quality at an hourly temporal scale and 0.1° spatial scale, with a special focus on capturing different types of precipitation. The results show that: (a) GSMaP_Gauge exhibits the highest correlation with ground‐based gauges, while IMERG_Uncal and IMERG_Cal perform best in the estimation of the amount of precipitation. Satellite products generally perform better during summer while ERA5‐L sometimes outperforms satellite products in spring and autumn. (b) The evaluation results for different precipitation types reveal that all the QPE products face significant challenges in accurately describing convective precipitation. They tend to underestimate convective precipitation and fail to properly capture the intensity and location of heavy precipitation. (c) In heavy convective precipitation cases, the evaluated QPE products show various issues in accurately capturing the intensity and spatiotemporal variation of precipitation. Almost all QPE products underestimate maximum precipitation (both hourly precipitation and accumulated precipitation) and small‐scale (about 50 km or less) spatial variability of precipitation. IMERG_Uncal, IMERG_Cal, and GSMaP_MVK perform better than other products in heavy convective precipitation cases. This study provides new insights into the quality of QPE products in different types of precipitation. The analysis of the quality of these QPE products serves as a valuable indicator of their potential applications, particularly in flash flood simulations, while also underscoring the critical need for improving precipitation product quality. Plain Language Summary The eastern Tibetan Plateau (ETP) has significant research value in the fields of meteorology and hydrology. Therefore, accurate precipitation information is essential in this region. Quantitative precipitation estimation (QPE) traditionally relies on ground‐based rain gauges, but this method has limitations due to the scarcity and uneven distribution of gauges in mountainous regions. Satellite and reanalysis gridded QPE products provide precipitation information for areas without rain gauge observations. However, it is essential to verify the accuracy of the precipitation information provided by comparing it with ground observation data from rain gauges. In meteorology, precipitation is categorized as stratiform precipitation, convective precipitation. Different types of precipitation have varying causes and characteristics, and QPE products may capture and characterize them with varying degrees of difficulty. So far, quantitative studies are still lacking on how competently QPE products describe different types of precipitation. This study includes four sets of satellite precipitation data and one set of reanalysis data. We compared their correlation and bias with rain gauges in different types of precipitation. After understanding that these products performed the worst in convective precipitation, we analyzed their problem of describing intensity, spatiotemporal structure, and location precipitation in strong convective precipitation. Key Points Satellite QPE products show better quality than ERA5‐L in summer Of all precipitation types, convective precipitation is the most challenging for QPE products due to underestimation The QPE products have difficulty showing the small‐scale spatial‐temporal structure of precipitation