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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.

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.

This study inter-compares the spatiotemporal features of precipitation extremes at global and regional scales within six daily precipitation datasets, i.e., gauge-based (Global Precipitation Climatology Center dataset, GPCC), satellite-retrieval (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record, PERSIANN-CDR), three reanalysis datasets (ERA-Interim, ERAI; the National Center for Atmospheric Research Reanalysis 2, NCEP2; and the WATCH-Forcing-Data-ERA-Interim, WFDEI), and products merged from the three type datasets (the Multi-Source Weighted-Ensemble Precipitation, MSWEP). All datasets reproduce similar spatial patterns of both annual and seasonal precipitation extremes over the period from 1979 to 2017. Compared to the reference dataset gauge-based GPCC, the reanalysis WFDEI outperforms among six products with spatial correlation coefficients of 0.89 and 0.80 for the annual extreme indices (i.e., annual total amount of 95th precipitation and maximum 1-day precipitation), respectively. The satellite-based product PERSIANN-CDR performs better than reanalyses and merged datasets in capturing the temporal variability of the intensity and amount of precipitation extremes with similar changing tendencies and magnitudes of about 45 mm day−1 and 230 mm at the global scale, respectively. The reanalyses and merged products underestimate the intensity of precipitation extremes. The selected six datasets behave differently in various regions. For the percentile-based frequency of precipitation extremes, NCEP2 performs well in regions of the Southeast Asian (SEA) and Amazon (AMZ), while WFDEI better matches GPCC over East North America (ENA) and North Australia (NAU) in both spatial patterns and temporal changes with correlation coefficients of 0.84 and 0.90, respectively. For the intensity features of annual precipitation extremes, NCEP2 performs better than other four datasets over regions of SEA, AMZ and West Africa (WAF). ERAI and WFDEI are consistent with GPCC in ENA and NAU with correlations coefficients of the intensity between ERAI (WFDEI) and GPCC are 0.82 (0.77) and 0.78 (0.64) for ENA and NAU, respectively. For the intensity of seasonal precipitation extremes, GPCC shows the highest estimates in regions of SEA, AMZ, ENA and WAF. ERAI and WFDEI perform better in reproducing the spatial patterns of seasonal precipitation extremes in all regions. NCEP2 (ERAI and WFDEI) show(s) consistent temporal variability of seasonal precipitation extremes with GPCC in regions of AMZ and WAF (ENA and NAU). Overall, there are large discrepancies in the absolute values of daily precipitation among datasets, and performances of non-gauged-based precipitation datasets in capturing the spatiotemporal variability of precipitation extremes are dependent on seasons, regions, and time periods.

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

\"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.

The study assesses the performance of 24 model runs from five COordinated Regional climate Downscaling Experiment (CORDEX) regional climate models (RCMs) in simulating East Africa’s spatio-temporal precipitation characteristics using a set of eight descriptors: consecutive dry days (CDD), consecutive wet days (CWD), simple precipitation intensity index (SDII), mean daily annual (pr_ANN), seasonal (pr_MAM and pr_OND) precipitation, and representatives of heavy precipitation (90p) and very intense precipitation (99p) events. Relatively better performing RCM runs are then used to assess projected precipitation changes (for the period 2071–2099 relative to 1977–2005) over the study domain under the representative concentration pathway (RCP) 8.5 scenario. The performance of RCMs is found to be descriptor and scope specific. Overall, RCA4 (r1i1p1) forced by CNRM-CERFACS-CNRM-CM5 and MPI-M-MPI-ESM-LR, REMO2009 (r1i1p1) forced by MPI-M-MPI-ESM-LR, and RCA4 (r2i1p1) forced by MPI-M-MPI-ESM-LR emerge as the top four RCM runs. We show that an ensemble mean of the top four model runs outperforms an ensemble mean of 24 model simulations and ensemble means for all runs in an RCM. Our analysis of projections shows a reduction (increase) in mean daily precipitation for MAM(OND), an increase(decrease) in CDD(CWD) events, and a general increase in SDII and the width of the right tail of the precipitation distribution (99p–90p). An increase in SDII and 99p–90p implies a possibility of occurrence of heavy and extreme precipitation incidences by the end of the twenty-first century. Our findings provide important information to support the region’s climate change adaptation and mitigation efforts.