Hourly Precipitation Data (HPD) is digital data set DSI-3240, archived at the National Climatic Data Center (NCDC). The primary source of data for this file is approximately 5,500 US National Weather Service (NWS), Federal Aviation Administration (FAA), and cooperative observer stations in the United States of America, Puerto Rico, the US Virgin Islands, and various Pacific Islands. The earliest data dates vary considerably by state and region: Maine, Pennsylvania, and Texas have data since 1900. The western Pacific region that includes Guam, American Samoa, Marshall Islands, Micronesia, and Palau have data since 1978. Other states and regions have earliest dates between those extremes. The latest data in all states and regions is from the present day. The major parameter in DSI-3240 is precipitation amounts, which are measurements of hourly or daily precipitation accumulation. Accumulation was for longer periods of time if for any reason the rain gauge was out of service or no observer was present. DSI 3240_01 contains data grouped by state; DSI 3240_02 contains data grouped by year.

In 2024, Louisiana recorded ***** inches of precipitation. This was the highest precipitation within the 48 contiguous U.S. states that year. On the other hand, Nevada was the driest state, with only **** inches of precipitation recorded. Precipitation across the United States Not only did Louisiana record the largest precipitation volume in 2024, but it also registered the highest precipitation anomaly that year, around 14.36 inches above the 1901-2000 annual average. In fact, over the last decade, rainfall across the United States was generally higher than the average recorded for the 20th century. Meanwhile, the driest states were located in the country's southwestern region, an area which – according to experts – will become even drier and warmer in the future. How does global warming affect precipitation patterns? Rising temperatures on Earth lead to increased evaporation which – ultimately – results in more precipitation. Since 1900, the volume of precipitation in the United States has increased at an average rate of **** inches per decade. Nevertheless, the effects of climate change on precipitation can vary depending on the location. For instance, climate change can alter wind patterns and ocean currents, causing certain areas to experience reduced precipitation. Furthermore, even if precipitation increases, it does not necessarily increase the water availability for human consumption, which might eventually lead to drought conditions.

The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.

In 2024, the United States saw some **** inches of precipitation. The main forms of precipitation include hail, drizzle, rain, sleet, and snow. Since the turn of the century, 2012 was the driest year on record with an annual precipitation of **** inches. Regional disparities in rainfall Louisiana emerged as the wettest state in the U.S. in 2024, recording a staggering ***** inches (*** meters) of precipitation—nearly **** inches (ca. ** centimeters) above its historical average. In stark contrast, Nevada received only **** inches (ca. ** centimeters), underscoring the vast differences in rainfall across the nation. These extremes illustrate the uneven distribution of precipitation, with the southwestern states experiencing increasingly dry conditions that experts predict will worsen in the coming years. Drought concerns persist Drought remains a significant concern in many parts of the country. The Palmer Drought Severity Index (PDSI) for the contiguous United States stood at ***** in December 2024, indicating moderate to severe drought conditions. This reading follows three years of generally negative PDSI values, with the most extreme drought recorded in December 2023 at *****.

Abstract Daily rainfall data from a meteorological network in Southern Brazil is used to assess the performance of two different outliers detection algorithms. Both methods use a statistical and spatial consistency approach based in distance and elevation difference between two rain gauge measurements. A variation of the Multiple Interval Gamma Distribution method of You, Hubbard, Nadarajah e Kunkel (2007) is considered in this study. Neighboring stations data is gathered to get the local average rainfall distribution. The precipitation range of values is partitioned so one makes the assumption that every interval can be modeled by a Gamma distribution. The second method assumes no prior distribution characteristic, and instead uses point spatial and cumulated temporal information from neighboring rain gauge stations to consist daily rainfall data. In order to assess the reliability of the detected outliers, as well the accuracy, seeded errors are introduced in the historical rainfall series. A two dimensional probability model of introduced/detected error (yes-no) is used to compute metrics related to the correct detection and false alarm probabilities made by the algorithm. We verify that the new proposed method overcomes the Multiple Interval Gamma Distribution method.

This map displays projected visible surface smoke across the contiguous United States for the next 48 hours in 1 hour increments. It is updated every 24 hours by NWS. Concentrations are reported in micrograms per cubic meter.Where is the data coming from?The National Digital Guidance Database (NDGD) is a sister to the National Digital Forecast Database (NDFD). Information in NDGD may be used by NWS forecasters as guidance in preparing official NWS forecasts in NDFD. The experimental/guidance NDGD data is not an official NWS forecast product.Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndgd/GT.aq/AR.conus/ds.smokes01.binSource data archive can be found here: https://www.ncei.noaa.gov/products/weather-climate-models/national-digital-guidance-database look for 'LXQ...' files by date. These are the Binary GRIB2 files that can be decoded via DeGRIB tool.Where can I find other NDGD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.RevisionsJuly 11, 2022: Feed updated to leverage forecast model change by NOAA, whereby the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) forecast model system was replaced with the Rapid Refresh (RAP) forecast model system. Key differences: higher accuracy with RAP now concentrated at 0-8 meter detail vs HYSPLIT at 0-100 meter; earlier data delivery by 6 hrs; forecast output extended to 51 hrs.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Abstract:Rain on Snow is a statewide coverage of rain-on-snow zones. Rain-on-snow zones are based on average amounts of snow on the ground in early January, relative to the amount of snow that could reasonably be melted during a model storm event. Five Rain on Snow zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on Snow was digitized from 1:250,000 USGS quads.Purpose:The Rain-on-snow coverage was created as a screening tool to identify forest practice applications that may be in a significant rain-on-snow zone (WAC 222-22-100).Description:Five ROS zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on snow is a process that exhibits spatial and temporal variation under natural conditions, with the effects of vegetation on snow accumulation and melt adding additional complications in prediction. There is no map that shows the magnitude and frequency of water inputs to be expected from rain on snow events, so we have attempted to create an index map based on what we know about the process controls and their effects in the various climatic zones. If we assume that, averaged over many years, the seasonal storm tracks that bring warm, wet cyclonic storms to the Northwest have access to all parts of Washington , then the main factors controlling and/or reflecting the occurrence and magnitude of a R/S event in any particular place are:1) Climatic region: especially the differences between windward and leeward sides of major mountain ranges, which control seasonal climatic patterns;2) Elevation: controls temperature, thus the likelihood and amount of snow on the ground, and affects orographic enhancement of storm precipitation; 3) Latitude: affects temperature, thus snow;4) Aspect: affects insolation and temperature (especially in winter), thus melting of snow; 5) Vegetation: the species composing forest communities can reflect the climate of an area (tolerance of warmth or cold, wet or dry conditions, deep and/or long lived snowpacks); the height and density of vegetation also partly controls the amount of snow on the ground. As natural vegetation integrates the effects of all of these controls, we tried to find or adapt floral indicators of the various zones of water input. We designed the precipitation zones to reflect the amount of snow likely to be on the ground at the beginning of a storm. We assumed that some middle elevation area would experience the greatest water input due to Rain on Snow, because the amount of snow available would be likely to be approximately the amount that could be melted. Higher and lower elevation zones would bear diminished effects, but for opposite reasons (no snow to melt, vs too cold to melt much). These considerations suggested a three or five zone system. We chose to designate five zones because a larger number of classes reduces the importance of the dividing lines, and thus of the inherent uncertainties of those lines. The average snow water equivalents (SWE) for the early January measurements at about 100 snow courses and snow pillows were compiled; snow depths for the first week in January at about 85 weather stations were converted into SWE. For each region (western North Cascades, Blue Mountains, etc.), the snow amounts were sorted by station elevation to derive a rough indicator of the relationship between snow accumulation and elevation. (Sub regional differences in snow accumulation patterns were also recognized.) After trying various combinations of ratios for areas where the snow hydrology is relatively well known, we adopted the following designations: 5. Highlands: >4 5 times ideal SWE; high elevation, with little likelihood of significant water input to the ground during storms (precipitation likely to be snow, and liquid water probably refreezes in a deep snow pack); effects of harvest on snow accumulation are minor; 4. Snow dominated zone: from "1.25 1.5 ideal SWE, up to "4; melt occurs during R/S (especially during early season storms), but effects can be mitigated by the lag time of percolation through the snowpack; 3. Peak rain on snow zone: "0.5 0.75 up to "1.25 ideal SWE; middle elevations: shallow snow packs are common in winter, so likelihood and effects of R/S in heavy rainstorms are greatest; typically more snow accumulation in clearings than in forest; 2. Rain dominated zone: "0.1 0.5 ideal SWE; areas at lower elevations, where rain occasionally falls on small amounts of snow; 1. Lowlands: <0.1 ideal SWE; coastal, low elevation, and rain shadow areas; lower rainfall intensities, and significant snow depths are rare. Precipitation zones were mapped on mylar overlays on 1:250,000 scale topographic maps. Because snow depth is affected by many factors, the correlation between snow and elevation is crude, and it was not possible to simply pick out contour markers for the boundaries. Ranges of elevations were chosen for each region, but allowance was made for the effects of sub regional climates, aspect, vegetative indicators of snow depth, etc. Thus, a particular boundary would be mapped somewhat lower on the north side of a ridge or in a cool valley (e.g. below a glacier), reflecting greater snow accumulations in such places. The same boundary would be mapped higher on the south side of the ridge, where inter-storm sunshine could reduce snow accumulation. Conditions at the weather stations and snow courses were used to check the mapping; but in areas where measurements are scarce, interpolation had to be performed. The boundaries of the precipitation zones were entered in the DNR's GIS. Because of the small scale of the original mapping and the imprecision of the digitizing process, some errors were introduced. It should not be expected that GIS images can be projected to large scales to define knife edge zone boundaries (which don't exist, anyway), but they are good enough to locate areas tens of acres in size. Some apparent anomalies in the map require explanation. Much of western Washington is mapped in the lowland or highland zones. This does not mean that R/S does not occur in those areas; it does, but on average with less frequency and hydrologic significance than in the middle three zones. Most of central and eastern Washington is mapped in the rain dominated zone, despite meager precipitation there; this means only that the amount of snow likely to be on the ground is small, and storm water inputs are composed dominantly of the rain itself, without much contribution from snow melt. Much of northeastern Washington is mapped in the peak Rain Snow zone, despite the fact that such events are less common there than in western Washington. This is due to the fact that there is less increase in snow depth with elevation (i.e. the snow wedge is less steep), so a wider elevation band has appropriate snow amounts; plus, much of that region lies within that elevation band where the 'ideal' amount of snow is liable to be on the ground when a model Rain Snow event occurs. This does not reflect the lower frequency of such storms in that area.

India weekly precipitation, 1979 through 1985, for 34 mainland divisions and an island, have been digitized at Florida State University, from India's "Weekly Weather Report". This is based on the stations that report operationally (probably 500-1000 stations). More stations are available in delayed time. The division precipitation for a week is the average of stations that did report during that week. The weeks are continuous. The division normals for the period 1901-1970 are included in the data.

This map displays the Quantitative Precipitation Forecast (QPF) for the next 72 hours across the contiguous United States. Data are updated hourly from the National Digital Forecast Database produced by the National Weather Service.The dataset includes incremental and cumulative precipitation data in 6-hour intervals. In the ArcGIS Online map viewer you can enable the time animation feature and select either the "Amount by Time" (incremental) layer or the "Accumulation by Time" (cumulative) layer to view a 72-hour animation of forecast precipitation. All times are reported according to your local time zone.Where is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces forecast data of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Source: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.qpf.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This map service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The Baltimore radar rainfall dataset was developed from a multi-sensor analysis combining radar rainfall estimates from the Sterling, VA WSR88D radar (KLWX) with measurements from a collection of ground based rain gages. The archived data have a 15-minute time resolution and a grid resolution of 0.01 degree latitude/longitude (approximately 1 km x 1 km); 15-minute rainfall accumulations for each grid are in mm. The dataset spans 22 years, 2000-2021, and covers an area of approximately 4,900 km^2 (70 by 70 grids, each with approximate area of 1 km^2) surrounding the Baltimore, MD metropolitan area (Figure 1). The rainfall data cover the six months from April to September of each year. This is the period with most intense sub-daily rainfall and the period for which radar measurements are most accurate. Figure 1 illustrates the climatological analyses of mean annual frequency of days with at least 1 hour rainfall exceeding 25 mm. The striking spatial variability of convective rainfall is illustrated in Figure 2 by the April-September climatology of annual lightning strikes.

As with many long-term environmental data sets, sensor technology has changed during the time period of the archive. The Sterling, VA WSR88D radar underwent a hardware upgrade from single polarization to dual polarization in 2012. Prior to the upgrade, rainfall was estimated using a conventional radar-reflectivity algorithm (HydroNEXRAD) which converts reflectivity measurements in polar coordinates from the lowest sweep to rainfall estimates on a 0.01 degree latitude-longitude grid at the surface (see Seo et al. 2010 and Smith et al. 2012 for details on the algorithm). The polarimetric upgrade introduced new measurements into the radar-rainfall algorithm. In addition to reflectivity, the operational rainfall product, Digital Precipitation Rate (DPR), directly uses differential reflectivity and specific differential phase shift measurements to estimate rainfall (https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00708; see also Giangrande and Ryzhkov 2008). Details of the algorithm structure and parameterization for the DPR radar-rainfall estimates have been modified during the 10-year period of the data set.

A storm-based (daily) multiplicative mean field bias has been applied to both datasets. The mean field bias is computed as the ratio of daily rain gage rainfall at a point to daily radar rainfall for the bin that contains the gage. The rain gage dataset is compiled from rain gages in the Baltimore metropolitan region and surrounding areas and includes gages acquired from both Baltimore City and Baltimore County, and the Global Historical Climatology Network daily (GHCNd). Mean field bias improves rainfall estimates and diminishes the impacts of changing measurement procedures.

The dataset has been archived in 2 formats: netCDF gridded rainfall, 1 file for each 15-minute time period, and csv or excel format point rainfall (1 point at the center of each grid) in a timeseries format with 1 file per calendar month covering the entire 70x70 domain. The csv files are in folders organized by calendar year. The first five columns in each file represent year, month, day, hour, and minute and can be combined to generate a unique date-time value for each time step. Each additional column is a complete time series for the month and represents data from one of the 1-km2 grid cells in the original data set.

The latitude and longitude coordinates for each pixel in the grid are provided. The latitude and longitude represent the centroid of the cell, which is square when represented in latitude and longitude coordinates and rectangular when represented in other distance-based coordinate systems such as State Plane or Universal Transverse Mercator. There are 4900 pixels in the domain. In order to visualize the data using GIS or other software, the user needs to associate each column in the annual rainfall file with the latitude and longitude values for that grid cell number.

These data may be subject to modest revision or reformatting in future versions. The current version is version 2.0 and is being offered to users who wish to explore the data. We will revise this document as needed.

One supplemental file, one link to a repository where the complete matlab code can be found to reproduce the study (41 files). Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

United States Maximum 5-day Rainfall: 25-year Return Level data was reported at 9.986 mm in 2050. United States Maximum 5-day Rainfall: 25-year Return Level data is updated yearly, averaging 9.986 mm from Dec 2050 (Median) to 2050, with 1 observations. The data reached an all-time high of 9.986 mm in 2050 and a record low of 9.986 mm in 2050. United States Maximum 5-day Rainfall: 25-year Return Level data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Environmental: Climate Risk. A 25-year return level of the 5-day cumulative precipitation is the maximum precipitation sum over any 5-day period that can be expected once in an average 25-year period.;World Bank, Climate Change Knowledge Portal (https://climateknowledgeportal.worldbank.org);;

Description This dataset provides real-time weather data for all states and districts in India. It includes essential meteorological parameters such as temperature, humidity, wind speed, and general weather conditions, sourced from WeatherAPI.

Designed for weather analysis, climate research, and data-driven decision-making, this dataset is valuable for data scientists, meteorologists, agricultural experts, and developers working on geospatial and climate-based applications.

With comprehensive geographic coverage, this dataset enables users to track weather trends, analyze regional variations, and develop predictive models for different parts of India.

These data are transitioned to a state of permanent preservation. They are available upon request. More advanced datasets have been developed since. One recommended replacement is the GPCP (doi: 10.5067/DBVUO4KQHXTK) product developed under the MEaSUREs project. Futhermore, the NASA Precipitation Measurement Missions Project released newly processed SSM/I datasets as output from the GPROF (Goddard Profiling Algorithm). (doi: 10.5067/GPM/SSMI/F11/GPROFCLIM/2A/05, 10.5067/GPM/SSMI/F11/GPROFCLIM/3A-MONTH/05, 10.5067/GPM/SSMI/F11/GPROFCLIM/3A-DAY/05) The "RAIN_CHANG" SSM/I Derived Oceanic Monthly Rainfall Indices data set was an early Global Precipitation Climatology Project (GPCP) product. Monthly rainfall indices over the oceans were derived from Special Sensor Microwave Imager (SSM/I) data from the Defense Meteorological Satellite Program (DMSP) satellites F8 and F11, on channels 19 and 22 V. The data set covered the period from July 1987 to December 1995. The monthly rainfall indices are on a 5 degree by 5 degree grid extending from 50 N to 50 S. The Wilheit, Chang and Chiu (1991) method used to derive the indices gives valid values only over ocean areas. Land pixels (including island pixels) and erroneous pixels return a -10 flag. The data are stored on a 72 x 20 grid. Grid point (1,1) contains the index for 45-50 N, 0-5 E, grid point (2,1) contains the index for 45-50 N, 5-10 E, ... and grid point (72,20) contains the index for 45-50 S, 175-180 W. In the data set, each month starts with an ASCII header to identify the year and month. The data is in 10F8.1 format. Each value is the average of AM and PM estimates and corrected for beam filling error. The equation used is: (AM PM)/2.0 * 1.8. Land pixels are set to -10.0. Also there are 33 pixels blocked out due to island contamination (-10.0). If the rain retrieval did not converge, a -10.0 is assigned to the pixel. The objective of this data set was to provide a long term monthly rainfall data set to be used in EOS global change and GEWEX related research.

Rainfall: Uttarakahand: Normal data was reported at 15.100 mm in 27 Jul 2018. This records a decrease from the previous number of 16.100 mm for 26 Jul 2018. Rainfall: Uttarakahand: Normal data is updated daily, averaging 9.400 mm from May 2018 (Median) to 27 Jul 2018, with 56 observations. The data reached an all-time high of 17.400 mm in 25 Jul 2018 and a record low of 2.400 mm in 02 Jun 2018. Rainfall: Uttarakahand: Normal data remains active status in CEIC and is reported by India Meteorological Department. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIS004: Rainfall: by States.

Daily Projected Rainfall (CCSM3A1B) for 2014-2020 by State in Peninsular Malaysia. Data has been produced by NAHRIM, downscaled for State in Peninsular Malaysia based on The Forth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC). Disclaimer: Data/information provided must be checked thoroughly prior to its usage. Any usage of this/these data must be credited to NAHRIM. We will not be responsible for any loss/damage due to usage or manipulation of this/these data without our consultation. No. of Views : 225

Information on the spatio-temporal distribution of rainfall is very critical for addressing water related disasters, especially in the arid to semi-arid regions of the Middle East and North Africa region. However, availability of reliable rainfall datasets for the region is limited. In this study we combined observation from satellite-based rainfall data, in situ rain gauge observation and rainfall climatology to create a reliable regional rainfall dataset for Jordan, West Bank and Lebanon. First, we validated three satellite-based rainfall products using rain gauge observations obtained from Jordan (205 stations), Palestine (44 stations) and Lebanon (8 stations). We used the daily 25-km Tropical Rainfall Measuring Mission over 2000 – 2016; daily 10-km Rainfall Estimate for Africa (RFE) rainfall over 2001 – 2016; daily 5-km Climate Hazards Group Infrared Precipitation with Station (CHIRPS) rainfall over 1981-2015; daily 25-km Multi-Source Weighted-Ensemble Precipitation (MSWEP) ov ...

This dataset provides global estimates of precipitation based on satellite observations. Precipitation is the main component of water transport from the atmosphere to the Earth’s surface within the hydrological cycle. It varies strongly, depending on geographical location, season, synopsis, and other meteorological factors. The supply with freshwater through precipitation is vital for many subsystems of the climate and the environment, but there are also hazards related to extensive precipitation or the lack of precipitation. The present dataset allows the investigation and quantification of these aspects of precipitation, possibly in conjunction with other datasets covering components of the hydrological cycle. The data represent the current state-of-the-art for satellite-based precipitation climate data record production in Europe, which is in line with the “Systematic observation requirements for satellite-based products for climate” as defined by GCOS (Global Climate Observing System). Spaceborne passive microwave (MW) imagers and sounders, available on different Low Earth Orbit (LEO) platforms, provide the most effective measurements for the remote sensing of precipitation because of the sensitivity of the MW upwelling radiation to the cloud microphysical properties, especially the emission and scattering of precipitation-size hydrometeors (solid and liquid). However, they are available at low spatial and temporal resolution, due to the limited number of overpasses per day (depending on latitude and number of platforms) at each location. A further ECV Precipitation product only based on MW observations, COBRA, is also available in the CDS. On the other hand, infrared (IR) sensors onboard geostationary (GEO) platforms, provide only information on the upper-level cloud structure, but at much higher temporal and spatial resolution, for example improving the representative sampling of intermittent precipitation. Since precipitation is not directly observed in the infrared, these measurements are often merged with microwave-based precipitation estimates. This precipitation data record and its processing chain are called Global Interpolated RAinFall Estimate (GIRAFE). GIRAFE provides a global 1° gridded daily accumulated precipitation amount together with uncertainty estimates coming from the sampling, and a global 1° gridded monthly mean of daily accumulation. In the above sense, GIRAFE optimizes the sampling of precipitation by merging observations by LEO MW imagers and sounders (Level-2 data) with GEO-Ring IR brightness temperatures (Level-1 data). The daily accumulated precipitation is also aggregated to monthly mean precipitation. This dataset has been produced by the EUMETSAT Satellite Application Facility on Climate Monitoring.

One projection under SRES A1B scenario for the 21st century. MRI MODEL: The AGCM component of MRI-CGCM2.3.2 is based on a version of the operational weather forecasting model of the Japan Meteorological Agency (JMA). It has a spectral dynamical core where vorticity, divergence, temperature, specific humidity and surface pressure represented in the horizontal by a truncated series of spherical harmonics. SCENARIO A1B: SRES A1B seems to be the most plausible scenario, describes a future world rapid economic growth, a global population that peaks in mid-century and declines thereafter, and increase cultural and social interactions. The technological emphasis of this scenario is on a balance across all energy sources, not relying too heavily on any particular energy source. Data-data ini adalah hasil kajian NAHRIM pada tahun 2014 berdasarkan The Forth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC). Penafian: Maklumat ini perlu disemak dengan teliti sebelum menggunakannya. Institut Penyelidikan Hidraulik Kebangsaan Malaysia (NAHRIM) tidak bertanggungjawab terhadap sebarang isu yang timbul dari atau berkaitan kerana menggunakan maklumat yang telah disediakan ini.

Rainfall: Uttar Pradesh: Deviation data was reported at -100.000 % in 02 Dec 2025. This stayed constant from the previous number of -100.000 % for 01 Dec 2025. Rainfall: Uttar Pradesh: Deviation data is updated daily, averaging -100.000 % from May 2018 (Median) to 02 Dec 2025, with 2701 observations. The data reached an all-time high of 136,868.000 % in 31 Oct 2025 and a record low of -100.000 % in 02 Dec 2025. Rainfall: Uttar Pradesh: Deviation data remains active status in CEIC and is reported by India Meteorological Department. The data is categorized under India Premium Database’s Environment – Table IN.EVB: Rainfall: by States.