This layer is the total rainfall for the year 2016, summed from interpolated daily rainfall, in mm, not the average.

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.

In 2023, the annual average rainfall in Japan amounted to around **** thousand millimeters. Figures increased compared to about **** thousand millimeters in the previous year. Most of the rain fell during the rainy season, which is the time of year when most of a region's average annual rainfall occurs. Seasonal rainfall In most of Japan, the rainy season lasts from early June to mid-July. In the southernmost prefecture Okinawa, it roughly starts a month earlier, while the northernmost main island Hokkaido is less affected. Heavy rainfall can cause floods, which can lead to landslides and mudflows in mountainous areas. In recent years, flooded houses accounted for the highest number of damage situations in natural disasters. Furthermore, heavy rain and floods are often caused by typhoons, which develop over the Pacific Ocean and regularly approach the archipelago between July and October. Since the number of typhoons has increased in recent years, the amount of damage caused by floods grew as well. Climate change Climate change has affected Japan in recent years, resulting in increased rainfall and an increase of the average annual temperature in Tokyo. These weather changes can intensify natural disasters such as heavy rain and typhoons. In recent years, Japan was among the countries with the most natural disasters. To counter global warming, Japan aims to reduce greenhouse gas emissions by increasing its renewable and nuclear energy share.

Data for the dataset IPHEx-Southern Appalachian Mountains -- Rainfall Data 2008-2014

The Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Network-Climate Data Record (PERSIANN-CDR) is a satellite-based precipitation dataset for hydrological and climate studies, spanning from 1983 to present. It is the longest satellite-based precipitation record available, with daily data at 0.25° resolution for the 60°S–60°N latitude band.PERSIANN rain rate estimates are generated at 0.25° resolution and calibrated to a monthly merged in-situ and satellite product from the Global Precipitation Climatology Project (GPCP). The model uses Gridded Satellite (GridSat-B1) infrared data at 3-hourly time steps, with the raw output (PERSIANN-B1) bias-corrected and accumulated to produce the daily PERSIANN-CDR.The maps show 31 years (1984–2014) of annual and seasonal median and interquartile range (IQR) data. The median represents the 50th percentile of precipitation, and the IQR reflects the range between the 75th and 25th percentiles, showing data variability. Median and IQR are preferred over mean and standard deviation as they are less influenced by extreme values and better represent non-normally distributed data, such as precipitation, which is skewed and zero-limited.Data and Metadata: NCEIThis is a component of the Gulf Data Atlas (V1.0) for the Physical topic area.

Monthly mean gridded land temperature and total precipitation on a 1/2 degree grid from 1900 to 2014 (V4). Sources are from the GHCN2 (Global Historical Climate Network) and, more extensively, from the archive of Legates & Willmott.

The wettest months in the United Kingdom tend to be at the start and end of the year. In the period of consideration, the greatest measurement of rainfall was nearly 217 millimeters, recorded in December 2015. The lowest level of rainfall was recorded in April 2021, at 20.6 millimeters. Rainy days The British Isles are known for their wet weather, and in 2024 there were approximately 164 rain days in the United Kingdom. A rainday is when more than one millimeter of rain falls within a day. Over the past 30 years, the greatest number of rain days was recorded in the year 2000. In that year, the average annual rainfall in the UK amounted to 1,242.1 millimeters. Climate change According to the Met Office, climate change in the United Kingdom has resulted in the weather getting warmer and wetter. In 2022, the annual average temperature in the country reached a new record high, surpassing 10 degrees Celsius for the first time. This represented an increase of nearly two degrees Celsius when compared to the annual average temperature recorded in 1910. In a recent survey conducted amongst UK residents, almost 80 percent of respondents had concerns about climate change.

Rainfall deciles for 1 January to 31 December 2014. Source : Bureau of Meteorology, see http://www.bom.gov.au/jsp/awap/rain/index.jsp Map prepared by the Department of Environment and Energy in …Show full descriptionRainfall deciles for 1 January to 31 December 2014. Source : Bureau of Meteorology, see http://www.bom.gov.au/jsp/awap/rain/index.jsp Map prepared by the Department of Environment and Energy in order to produce Figure WAT4c in the Inland Waters theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au The map service can be viewed at http://soe.terria.io/#share=s-lsVf3pbzenbQej4isMA70tAzoAu Downloadable spatial data is also available below.

These data are records collected from six automated weather stations in operation between January 1, 2014 and December 31, 2015 within the Colorado River Corridor of Grand Canyon National Park and Glen Canyon National Recreation Area. Data collection locations, equipment configurations and methods follow those of the original report (OFR 2014-1247). These files are 4-minute interval data for each of the automated weather stations, tab separated by parameter (wind direction, wind speed, wind gusts, air temperature, relative humidity, barometric pressure, and rainfall). Climatic conditions during the reporting period were warm and dry early in 2014 and transitioned to average to slightly above precipitation from mid-2014 to the end of 2015. The El Niño/Southern Oscillation was in a neutral to weak El Niño state during most of the reporting period but transitioned to a strong El Niño state by October 2015.

East Africa is a drought prone, food and water insecure region with a highly variable climate. This complexity makes rainfall estimation challenging, and this challenge is compounded by low rain gauge densities and inhomogeneous monitoring networks. The dearth of observations is particularly problematic over the past decade, since the number of records in globally accessible archives has fallen precipitously. This lack of data coincides with an increasing scientific and humanitarian need to place recent seasonal and multi-annual East African precipitation extremes in a deep historic context. To serve this need, scientists from the UC Santa Barbara Climate Hazards Group and Florida State University have pooled their station archives and expertise to produce a high quality gridded 'Centennial Trends' precipitation dataset. Additional observations have been acquired from the national meteorological agencies and augmented with data provided by other universities. Extensive quality control of the data was carried out and seasonal anomalies interpolated using kriging. This paper documents the CenTrends methodology and data.

Supplementary data for studies conducted in the Luquillo Experimental Forest (LEF), eastern Puerto Rico include measurements of temperature, relative humidity and cloud immersion at 30-minute resolution. Temperature and relative humidity were measured at five sites; two primary sites have records from April 2014 to June 2019; other sites have shorter records within that period. From these data, derived values of dew point, vapor pressure deficit (VPD), and evaporative fraction were calculated. Daily 13:00 temperature and VPD gradients with elevation along the forested slope were calculated on days with data from at least 3 of the 5 sites (617, 675, 794, 904 and 1006 m), from April 2014 to November 2018. Cloud immersion frequency data are given for sites having the most complete records (675 m and 1006 m), representing the forested mountain slope near minimum cloud base altitude and the highest elevation in eastern Puerto Rico, respectively. Time-lapse camera images at 30-minute intervals were used to produce a binary series of cloud-immersed or clear conditions in the forest from April 2014 - November 2018. Briefly, contrast, luminance and colorfulness of daytime (color) and nighttime (grayscale) images were analyzed to determine cloud presence at the forested sites (methods in Bassiouni et al., 2017). Daily rainfall data and calculated long-term (27-year) semi-monthly mean rainfall amounts are given for Rio Icacos rain gage (USGS National Water Information System, site 50075000, 2019), for use in calculating departure from long-term mean rainfall in related publications. Also given are calculated data on the frequency of lowest-level cloud base observations below 1006 m altitude, from hourly ceilometer records at Roosevelt Roads, PR located at the coast to the east of the study area (NOAA National Centers for Environmental Information, 2020). These data are supplementary to Scholl et al., 2021. References: Bassiouni, M., Scholl, M. A., Torres-Sanchez, A. J., & Murphy, S. F. (2017). A method for quantifying cloud immersion in a tropical mountain forest using time-lapse photography. Agricultural and Forest Meteorology, 243, 100-112. doi:10.1016/j.agrformet.2017.04.010. Scholl, M.A., Bassiouni, M., and Torres-Sanchez, A.J., 2021, Drought stress and hurricane defoliation influence mountain clouds and moisture recycling in a tropical forest, Proceedings of the National Academy of Sciences, https://doi.org/10.1073/pnas.2021646118. National Centers for Environmental Information, N. (2019). Local Climatological Data, Roosevelt Roads station WBAN 11630. Retrieved February 2020 from https://www.ncdc.noaa.gov/cdo-web/datasets/LCD/stations/WBAN:11630/detail. National Water Information System, U.S. Geological Survey, USGS 50075000 RIO ICACOS NR NAGUABO, PR, Summary of Available Data. Retrieved November 2019 from https://waterdata.usgs.gov/nwis/inventory?agency_code=USGS&site_no=50075000.

WMS service corresponding to geographic information layers in raster format that represent the total monthly rainfall (data expressed in mm) in Andalusia in 2014, based on data from automatic weather stations. The information has been prepared by the Ministry of the Environment using, among others, its own information, from the Ministry of Agriculture and Fisheries and the State Meteorological Agency (Ministry of the Environment, and Rural and Marine Affairs). Node of the Andalusian Environmental Information Network. Regional Government of Andalusia. Integrated in the Spatial Data Infrastructure of Andalusia, following the guidelines of the Cartographic System of Andalusia.

Uzbekistan UZ: Average Precipitation in Depth data was reported at 206.000 mm/Year in 2014. This stayed constant from the previous number of 206.000 mm/Year for 2012. Uzbekistan UZ: Average Precipitation in Depth data is updated yearly, averaging 206.000 mm/Year from Dec 1992 (Median) to 2014, with 6 observations. The data reached an all-time high of 206.000 mm/Year in 2014 and a record low of 206.000 mm/Year in 2014. Uzbekistan UZ: Average Precipitation in Depth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uzbekistan – Table UZ.World Bank.WDI: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.; ; Food and Agriculture Organization, electronic files and web site.; ;

The risk of water erosion in mainland China is intensifying due to climate change. A high-precision rainfall erosivity dataset is crucial for revealing the spatiotemporal patterns of rainfall erosivity and identifying key areas of water erosion. However, due to the insufficient spatiotemporal resolution of historical precipitation data, there are certain biases in the estimation of rainfall erosivity in China, especially in regions with complex terrain and climatic conditions. Over the past decade, the China Meteorological Administration has continuously improved its ground-based meteorological observation capabilities, forming a dense network of ground-based observation stations. These high-precision precipitation data provide a solid foundation for quantifying the patterns of rainfall erosivity in China. In this study, we first performed rigorous quality control on the 1-minute ground observation precipitation data from nearly 70,000 stations nationwide from 2014 to 2022, ultimately selecting 60,129 usable stations. Using the precipitation data from these stations, we calculated event rainfall erosivity and generated a national mean annual rainfall erosivity dataset with a spatial resolution of 0.25°. This dataset shows that the mean annual rainfall erosivity in mainland China is approximately 1241 MJ·mm·ha−1·h−1·yr−1, with areas exceeding 4000MJ·mm·ha−1·h−1·yr−1 mainly concentrated in the southern China and southern Tibetan Plateau. Compared to our study, previously released datasets overestimate China's mean annual rainfall erosivity by more than 33%, and there are significant differences in performance across different river basins. In summary, the release of this dataset facilitates a more accurate assessment of the current water erosion intensity in China.

Individual Monthly Rainfall Erosivity over New South Wales for 2014.

These GIS grids were produced from NOAA and NRCC precipitation frequency estimates for North America based on precipitation data collected from 1816 to 2014. The grids provide information for durations from 24-hour and for recurrence periods of 2 year through 500 years. Grid value units are inches * 1000.

This table presents climate data from the Dutch weather station De Bilt (source: KNMI). The average winter and summer temperatures, which started in 1800, are the longest current series shown in the table. The series on the average year temperature and on hours of sunshine per year started in 1900. For the number of days below of above a certain temperature (ice days, summery days) the ranges started between 1940 and 1950. The complete set of climate data is available from 1980 onwards. Data available from: 1800-2014. Status of the figures: All data are definite. Changes as of 19 April 2016: Not. This table has been discontinued. When will new figures be published? Not applicable anymore. Data on the weather and climate in The Netherlands can be found on the website of the Royal Netherlands Meteorological Institute KNMI

Temperature and rainfall data for St. John USVI. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Based on Tsounis and Edmunds (In press), Ecosphere:\u00a0

Physical environmental conditions were characterized using three features that are well-known to affect coral reef community dynamics (described in Glynn 1993, Rogers 1993, Fabricius et al. 2005): seawater temperature, rainfall, and hurricane intensity. Together, these were used to generate seven dependent variables describing physical environmental features. Seawater temperature was recorded at each site every 15-30 min using a variety of logging sensors (see Edmunds 2006 for detailed information on the temperature measurement regime). Seawater temperature was characterized using five dependent variables calculated for each calendar year: mean temperature, maximum temperature, and minimum temperature (all averaged by day and month for each year), as well as the number of days hotter than 29.3 deg C (\u201chot days\u201d), and the number of days with temperatures greater than or equal to 26.0 deg C (\u201ccold days\u201d). The temperature defining "hot days" was determined by the coral bleaching threshold for St. John ("%5C%22http://www.coral.noaa.gov/research/climate-change/coral-%0Ableaching.html%5C%22">http://www.coral.noaa.gov/research/climate-change/coral- bleaching.html), and the temperature defining "cold days" was taken as 26.0 deg C which marks the lower 12th percentile of all daily temperatures between 1989 and 2005 (Edmunds, 2006). The upper temperature limit was defined by the local bleaching threshold, and the lower limit defined the 12th\u00a0percentile of local seawater temperature records (see Edmunds 2006 for details). Rainfall was measured at various locations around St. John (see\u00a0http://www.sercc.com) but often on the north shore (courtesy of R.\u00a0Boulon) (see Edmunds and Gray 2014). To assess the influence of hurricanes, a categorical index of local hurricane impact was employed, with the index based on qualitative estimates of wave impacts in Great Lameshur Bay as a function of wind speed, wind direction, and distance of the nearest approach of each hurricane to the study area (see Gross and Edmunds 2015). Index values of 0 were assigned to years with no hurricanes, 0.5 to hurricanes with low impacts, and 1 for hurricanes with high impacts, and years were characterized by the sum of their hurricane index values. awards_0_award_nid=55191 awards_0_award_number=DEB-0841441 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=0841441&HistoricalAwards=false awards_0_funder_name=National Science Foundation awards_0_funding_acronym=NSF awards_0_funding_source_nid=350 awards_0_program_manager=Saran Twombly awards_0_program_manager_nid=51702 awards_1_award_nid=562085 awards_1_award_number=OCE-1332915 awards_1_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1332915 awards_1_funder_name=NSF Division of Ocean Sciences awards_1_funding_acronym=NSF OCE awards_1_funding_source_nid=355 awards_1_program_manager=David L. Garrison awards_1_program_manager_nid=50534 awards_2_award_nid=562593 awards_2_award_number=DEB-1350146 awards_2_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=1350146 awards_2_funder_name=NSF Division of Environmental Biology awards_2_funding_acronym=NSF DEB awards_2_funding_source_nid=550432 awards_2_program_manager=Betsy Von Holle awards_2_program_manager_nid=701685 cdm_data_type=Other comment=Physical Data G. Tsounis and P. Edmunds, PIs Version 10 November 2016 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.664755 infoUrl=https://www.bco-dmo.org/dataset/664254 institution=BCO-DMO instruments_0_acronym=PrecipGauge instruments_0_dataset_instrument_description=Measured rainfall instruments_0_dataset_instrument_nid=664662 instruments_0_description=measures rain or snow precipitation instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/381/ instruments_0_instrument_name=Precipitation Gauge instruments_0_instrument_nid=671 instruments_0_supplied_name=Precipitation gauge instruments_1_dataset_instrument_description=Measured seawater temperature instruments_1_dataset_instrument_nid=664661 instruments_1_description=Records temperature data over a period of time. instruments_1_instrument_name=Temperature Logger instruments_1_instrument_nid=639396 instruments_1_supplied_name=Temperature logger metadata_source=https://www.bco-dmo.org/api/dataset/664254 param_mapping={'664254': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/664254/parameters people_0_affiliation=California State University Northridge people_0_affiliation_acronym=CSU-Northridge people_0_person_name=Peter J. Edmunds people_0_person_nid=51536 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=California State University Northridge people_1_affiliation_acronym=CSU-Northridge people_1_person_name=Dr Georgios Tsounis people_1_person_nid=565353 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Hannah Ake people_2_person_nid=650173 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=St. John LTREB,VI Octocorals projects_0_acronym=St. John LTREB projects_0_description=Long Term Research in Environmental Biology (LTREB) in US Virgin Islands: From the NSF award abstract: In an era of growing human pressures on natural resources, there is a critical need to understand how major ecosystems will respond, the extent to which resource management can lessen the implications of these responses, and the likely state of these ecosystems in the future. Time-series analyses of community structure provide a vital tool in meeting these needs and promise a profound understanding of community change. This study focuses on coral reef ecosystems; an existing time-series analysis of the coral community structure on the reefs of St. John, US Virgin Islands, will be expanded to 27 years of continuous data in annual increments. Expansion of the core time-series data will be used to address five questions: (1) To what extent is the ecology at a small spatial scale (1-2 km) representative of regional scale events (10's of km)? (2) What are the effects of declining coral cover in modifying the genetic population structure of the coral host and its algal symbionts? (3) What are the roles of pre- versus post-settlement events in determining the population dynamics of small corals? (4) What role do physical forcing agents (other than temperature) play in driving the population dynamics of juvenile corals? and (5) How are populations of other, non-coral invertebrates responding to decadal-scale declines in coral cover? Ecological methods identical to those used over the last two decades will be supplemented by molecular genetic tools to understand the extent to which declining coral cover is affecting the genetic diversity of the corals remaining. An information management program will be implemented to create broad access by the scientific community to the entire data set. The importance of this study lies in the extreme longevity of the data describing coral reefs in a unique ecological context, and the immense potential that these data possess for understanding both the patterns of comprehensive community change (i.e., involving corals, other invertebrates, and genetic diversity), and the processes driving them. Importantly, as this project is closely integrated with resource management within the VI National Park, as well as larger efforts to study coral reefs in the US through the NSF Moorea Coral Reef LTER, it has a strong potential to have scientific and management implications that extend further than the location of the study. The following publications and data resulted from this project: 2015 Edmunds PJ, Tsounis G, Lasker HR (2015) Differential distribution of octocorals and scleractinians around St. John and St. Thomas, US Virgin Islands. Hydrobiologia. doi: 10.1007/s10750-015-2555-zoctocoral - sp. abundance and distributionDownload complete data for this publication (Excel file) 2015 Lenz EA, Bramanti L, Lasker HR, Edmunds PJ. Long-term variation of octocoral populations in St. John, US Virgin Islands. Coral Reefs DOI 10.1007/s00338-015-1315-xoctocoral survey - densitiesoctocoral counts - photoquadrats vs. insitu surveyoctocoral literature reviewDownload complete data for this publication (Excel file) 2015 Privitera-Johnson, K., et al., Density-associated recruitment in octocoral communities in St. John, US Virgin Islands, J.Exp. Mar. Biol. Ecol. DOI 10.1016/j.jembe.2015.08.006octocoral recruitmentDownload complete data for this publication (Excel file) 2014 Edmunds PJ. Landscape-scale variation in coral reef community structure in the United States Virgin Islands. Marine Ecology Progress Series 509: 137–152. DOI 10.3354/meps10891. Data at MCR-VINP. Download complete data for this publication (Excel file) 2014 Edmunds PJ, Nozawa Y, Villanueva RD. Refuges modulate coral recruitment in the Caribbean and Pacific. Journal of Experimental Marine Biology and Ecology 454: 78-84. DOI: 10.1016/j.jembe.2014.02.00 Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Edmunds PJ, Gray SC. The effects of storms, heavy rain, and sedimentation on the shallow coral reefs of St. John, US Virgin Islands. Hydrobiologia 734(1):143-148. Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Levitan, D, Edmunds PJ, Levitan K. What makes a species common? No evidence of density-dependent recruitment or mortality of the sea urchin Diadema antillarum after the 1983-1984 mass mortality. Oecologia. DOI

Saudi Arabia SA: Average Precipitation in Depth data was reported at 59.000 mm/Year in 2014. This stayed constant from the previous number of 59.000 mm/Year for 2012. Saudi Arabia SA: Average Precipitation in Depth data is updated yearly, averaging 59.000 mm/Year from Dec 1962 (Median) to 2014, with 12 observations. The data reached an all-time high of 59.000 mm/Year in 2014 and a record low of 59.000 mm/Year in 2014. Saudi Arabia SA: Average Precipitation in Depth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.; ; Food and Agriculture Organization, electronic files and web site.; ;

Uganda UG: Average Precipitation in Depth data was reported at 1,180.000 mm/Year in 2014. This stayed constant from the previous number of 1,180.000 mm/Year for 2012. Uganda UG: Average Precipitation in Depth data is updated yearly, averaging 1,180.000 mm/Year from Dec 1962 (Median) to 2014, with 12 observations. The data reached an all-time high of 1,180.000 mm/Year in 2014 and a record low of 1,180.000 mm/Year in 2014. Uganda UG: Average Precipitation in Depth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uganda – Table UG.World Bank.WDI: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.; ; Food and Agriculture Organization, electronic files and web site.; ;