These data include daily precipitation measurements from nine different recording rain gages (RRG) at Coweeta Hydrologic Laboratory in Macon County, North Carolina, USA. These stations are operated by the Southern Research Station, USDA Forest Service. Data include total daily precipitation for the following recording rain gages: RRG05 (1992-2017), RRG06 (1936-2017), RRG12 (1942-2017), RRG13 (1942-2017), RRG20 (1962-2017), RRG31 (1958-2017), RRG41 (1958-2017), RRG55 (1990-2017), and RRG96 (1943-2017).

Accumulated Precipitation represents the amount of total precipitation (solid and/or liquid in mm) which has been recorded over a given period of time. Products are produced for the following timeframes: Agricultural Year, Growing Season and Winter Season as well as rolling products for 7, 14, 30, 60, 90, 180, 270, 365, 730, 1095, 1460 and 1825 days. These values are intended to provide users with a general idea of the amount of precipitation that has been received by a region over the given timeframe. For more information, visit: http://open.canada.ca/data/en/dataset/708992ad-bc24-4d0d-a087-17b7b5fd4d4d / Les précipitations accumulées représentent la hauteur totale de précipitations (solide et/ou liquide en mm) qui a été enregistrée sur une durée donnée. Les produits sont générés pour les périodes suivantes : L'année agricole, la saison de croissance et la saison hivernale ainsi que les produits roulants pour les jours 7, 14, 30, 60, 90, 180, 270, 365, 730, 1095, 1460 et 1825. Ces valeurs visent à donner aux utilisateurs une idée générale de la hauteur de précipitations reçue dans une région sur une période donnée. Pour plus d'information, consulter : http://ouvert.canada.ca/data/fr/dataset/708992ad-bc24-4d0d-a087-17b7b5fd4d4d

Pluviométrie quotidienne mesurée par les pluviographes répartis sur le territoire des Hauts-de-Seine. Dans le cadre de la gestion du réseau départemental d'assainissement, le Département dispose de plusieurs pluviographes répartis sur son territoire afin de mesurer les précipitations. Ces mesures pluviométriques sont exploitées en temps réel dans le cadre de la gestion des ouvrages d'assainissement et en temps différé concernant les études relatives au schéma d'assainissement départemental. Observations particulières L'absence de mesure sur un pluviographe se traduit par une valeur de cellule "vide" ou "nulle". En effet, dans le cas d'une avarie ou d'un problème technique rencontré, il se peut que les mesures ne soient pas remontées. La localisation des pluviographes est disponible sur la plateforme dans le jeu de données "Pluviographes". L'identifiant du pluviographe permet d'effectuer le lien entre les mesures de pluviométrie et les pluviographes. Données connexes Les pluviographes Localisation des pluviographes gérés par le Département des Hauts-de-Seine

Dates of Images:August 1 - 7, 2024Summary:This data shows the accumulated amounts of precipitation, in inches, from all days in the period August 1 - 7, 2024, from GPM IMERG Late Precipitation V07. The storm track and pressure is from August 1 - 7, 2024. Suggested Use:The darkest red colors represent amounts in the range 10 - 30 inches, received for the 7 days of the indicated period. Densely spaced symbols of storm pressure indicate where the storm was moving very slowly. One of the slowest periods was exactly when Debby was making landfall as Category 1 Hurricane over the big bend of Florida.Satellite/Sensor:Global Precipitation Measurements (GPM) IMERGStorm track and storm pressure: Weather UndergroundResolution:10 kmCredits:Huffman, G.J., E.F. Stocker, D. T. Bolvin, E.J. Nelkin, Jackson Tan (2024), GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1 degree V07, Edited by Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: 8/8/2024, 10.5067/GPM/IMERGDL/DAY/07Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags03/services/tropical_cyclone_debby_2024/PrecipitationsTotals_GPMIMERG_August2024/MapServer/WMSServerData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2024/tropical_cyclone_debby_202408/precipitation/

Climatological radar rainfall dataset of 1 hour precipitation depths at a 1 km grid, which have been adjusted employing validated and complete rain gauge data from both KNMI rain gauge networks. This dataset is updated once a month providing data up to a few months ago.

Bias-Corrected Precipitation data over South Siberia (CPSS 1.2) contains monthly precipitation data for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2019. CPSS data were combined from monthly total precipitation data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and precipitation data records from ground weather stations (Il’in et al., 2013). The ERA5 data were scaled according to the derived scale coefficient. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CPSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CPSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results. Data provided in NetCDF (Network Common Data Form) format.

Copernicus Climate Change Service (C3S), 2017. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), Available at https://cds.climate.copernicus.eu/cdsapp#!/home

Il’yin, B.M., Bulygina, O.N., Bogdanova, E.G, Veselov, V.M. and Gavrilova, S.Y., 2013. Dataset of monthly precipitation totals, with the elimination of systematic errors of precipitation gauges. Available at http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov

Surface total cumulative precipitations and 2m temperature (every 15')

Note: Data for 15th of July 2007 on CDOM is not valid.

Note that, by mistake, data from 2007070200 was archived in this record. The valid data for 2007071500 is not available in this data base.

Bulk precipitation collected using up to two methods - secondary tipping buckets and throughfall tipping buckets. NOTE: primary weighing gauge precipitation has moved to DP1.00044.001: "Precipitation - weighing gauge". Secondary and throughfall bulk precipitation is determined at one- and thirty-minute intervals.

The highest amount of precipitations in 2023 was recorded in the Bucegi Mountains, at the meteorological station in Peak Omu, totaling 926.9 millimeters. By contrast, the lowest amount was reported in Buzău.

Precipitation in Burundi decreased to 1277.52 mm in 2023 from 1349.36 mm in 2022. This dataset includes a chart with historical data for Burundi Average Precipitation.

The SBU Pluvio Precipitation Gauge IMPACTS dataset consists of precipitation intensity and precipitation accumulation collected using the OTT Pluvio2 weighing rain gauge during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. NASA’s Earth Venture program funded IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Data files in this dataset are available in ASCII-CSV format from January 7, 2020, through March 2, 2023.

Cet ensemble de données comprend 5 ans de données de précipitations de qualité contrôlée recueillies à 18 stations métrologiques sur les îles Calvert et Hécate, 1 sur la rivière Koeye, 1 sur l'île Ethel à Rivers Inlet, et 1 sur l'île Quadra sur la côte de la Colombie-Britannique. Les méthodes de contrôle et d'assurance de la qualité des données sont décrites dans le document README d'accompagnement.

Les données sont collectées dans le cadre du réseau d'observation climatique et hydrométrique de Hakai, qui est un réseau de surveillance continue qui collecte des données en temps quasi réel fournissant de nombreuses utilisations opérationnelles pour le grand public et pour le trafic maritime et aérien.

Precipitation percentile products are created by comparing the accumulated precipitation amounts (mm) for the time period being processed against all available historical information from the same window of time. This comparison will rank the current amount and assign it a percentile value determined by where it falls against the historic record. Products are produced for the following timeframes: Agricultural Year, Growing Season, and Winter Season as well as rolling products for 30, 60, 90, and 180 days. These values are intended to provide users with a general idea of the how the amount of precipitation that has been received by a region over the given timeframe compares to the amount which has been received in the historical record. For more information, visit: https://open.canada.ca/data/en/dataset/78b65ae0-fe1e-40ac-9d1d-ed4c7aaa0684 / Centile des précipitationssont établies en comparant les hauteurs de précipitations accumulées (mm) pour la période traitée à toutes les données historiques disponibles pour la même période. Cette comparaison classera le montant actuel et lui attribuera une valeur centile déterminée selon le point où elle tombe par rapport au record historique. Les produits sont générés pour les périodes suivantes : Année agricole, saison de croissance et la saison hivernale ainsi que les produits roulants pour les jours 30, 60, 90, et 180. Ces valeurs visent à donner aux utilisateurs une idée générale de la façon dont la hauteur de précipitations reçue dans une région sur une période donnée se compare à la hauteur reçue selon les données historiques. Pour plus d'information, consulter : http://ouvert.canada.ca/data/fr/dataset/78b65ae0-fe1e-40ac-9d1d-ed4c7aaa0684

This dataset provides global estimates of daily accumulated and monthly means of precipitation. The precipitation estimates are based on a merge of passive microwave observations from two different radiometer classes operating on multiple Low Earth Orbit (LEO) satellites. Spaceborne passive microwave (MW) provides the most effective measurements for the remote sensing of precipitation because the MW upwelling radiation is directly responsive to the cloud microphysical structure and, in particular, to the emission and scattering properties of precipitation-size hydrometeors (solid and liquid). However, they are available at low spatial and temporal resolution, due to the limited number of passes per day (depending on latitude and number of platforms) at each location. On the other hand, infrared (IR) sensors, available also on geostationary platforms, provide measurements that mostly respond to upper-level cloud structure, but at much higher temporal and spatial resolution. Since precipitation is not directly sensed in the infrared, these observations are often merged with microwave-based precipitation estimates and rain gauges. A precipitation product merging IR and MW is also available on the Climate Data Store: GPCP precipitation dataset. The two different radiometer classes used in the present Copernicus micrOwave-based gloBal pRecipitAtion (COBRA) dataset are: i) Conically scanning MW imagers; observations obtained by applying methodologies of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) in the Satellite Application Facility on Climate Monitoring (CM SAF). ii) Cross-track scanning MW sounders; observations obtained through the dedicated Passive microwave Neural network Precipitation Retrieval for Climate Applications (PNPR-CLIM) algorithm. This datset is independent of IR imagery and rain-gauge observations. A pure passive MW-based precipitation dataset overcomes the challenges and limitations of precipitation estimates based on IR observations, and the issues related to the inadequacy of the rain gauge networks in some regions and their almost complete absence over the ocean. The main limitations, however, are linked to the varying (in time and space) revisiting time of the LEO satellites and low temporal sampling compared to geostanionary IR observations. This dataset is produced by the Copernicus Climate Change Service (C3S).

Climatological radar rainfall dataset of 5 minute precipitation depths at a 1-km grid, which have been adjusted employing validated and complete rain gauge data from both KNMI rain gauge networks. Same dataset as "RAD_NL25_RAC_MFBS_5min", except that now an Extended Mask (EM) has been applied to this dataset. As a result, data are also available up to tens of kilometers away from the land surface of the Netherlands, i.e. above Belgium, Germany, and above open water. This dataset is updated once a month providing data up to a few months ago.

Context

As a part of the course of Web-Mapping (2021-22) by Prof Hicham HAJJI: we choosed to devellop a Full stack web app to visualize and forecast precipitations in Morocco. We needed some precipitations data for the forecasting part.

Content

Monthly precipitations (rainfall) in Morocco per communes from 2000 to 2018.

Source

WorldClim (https://worldclim.org/data/monthlywth.html)

Precipitation gauge data from TRCA real time and manual monitoring networks. Data represents preceding 5 minute total (accumulated) rainfall in mm.

Each file contains full period of record of published data. Files are updated annually.

Rainfall
This dataset falls under the category Environmental Data Climate Data.
It contains the following data: Precipitations and climatological analisys map, it wasn't possible to download it even though it's available
This dataset was scouted on 2022-02-13 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.inegi.org.mx/app/mapa/espacioydatos/default.aspx?ag=19039See URL for data access and license information.

Abstract. Raindrops interact with water vapour in ambient air while sedimenting from the cloud base to the ground. They constantly exchange water molecules with the environment and, in sub-saturated air, they evaporate partially or entirely. The latter of these below-cloud processes is important for predicting the resulting surface rainfall amount and it influences the boundary layer profiles of temperature and moisture through to evaporative latent cooling and humidity changes. However, despite its importance, it is very difficult to quantify this process from observations. Stable water isotopes provide such information, as they are influenced by both rain evaporation and equilibration. This study elucidates this option by introducing a novel interpretation framework for stable water isotope measurements performed simultaneously at high temporal resolution in both near-surface vapour and rain. We refer to this viewing device as the ∆δ∆d-diagram, which shows the isotopic composition (δ2H, d-excess) of equilibrium vapour from precipitation samples relative to the ambient vapour. It is shown that this diagram facilitates the diagnosis of below-cloud processes and their effects on the isotopic composition of vapour and rain since equilibration and evaporation lead to different pathways in the two-dimensional phase space of the ∆δ∆d-diagram. For a specific cold front in Central Europe, the analysis shows that below-cloud processes lead to distinct and temporally variable imprints on the isotope signal in surface rain. The influence of evaporation on this signal is particularly strong during periods with a weak precipitation rate. After the frontal passage, the near-surface atmospheric layer is characterised by higher relative humidity and a lower melting layer, leading to weaker below-cloud evaporation and equilibration. Measurements from four cold frontal events reveal a surprisingly similar slope of ∆d/∆δ = −0.30 in the phase space, indicating a potentially characteristic signature of below-cloud processes for this type of rain events.