The World Atlas of Desertification was published by UNEP in 1992 as the result of a cooperative effort between UNEP's Desertification Control Programme Activity Centre (DC/PAC), the Global Environment Monitoring System (GEMS) and the Global Resource Information Database (GRID). GRID compiled and/or derived most of the global and regional databases, produced the maps and carried out the data analyses and tabulations for the Atlas, assisted by a Technical Advisory Group on Desertification Assessment and Mapping composed of various international experts. The Atlas includes information and many maps derived from the Global Assessment of Human-Induced Soil Degradation (GLASOD), as conducted in 1990 by the International Soil Reference and Information Centre (ISRIC) at Wageningen, The Netherlands, on behalf of UNEP.

Aside from GLASOD's data on soil degradation, and in order to capture the multi-dimensional nature of global desertification processes, other data layers relating to global climate and vegetation were compiled by GRID for inclusion in the 1992 World Atlas of Desertification. The NOAA/GVI data set described herein was created by GRID-Nairobi as a unique product for the Desertification Atlas, in order to represent baseline or "normal" conditions of global vegetation, and to be used in combination with the climate, GLASOD and land degradation data sets.

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

Total Annual precipitation was derived from the WorldClim bio-climatic variable: BIO12. Bio-climatic variables are derived from the monthly temperature and rainfall values in order to generate meaningful variables. These are often used in ecological niche modelling (e.g., BIOCLIM, GARP). The bio-climatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environment factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of the three months (1/4 of the year).The WorldClim is a set of global climate layers (climate grids). The data can be used for mapping and spatial modeling in a GIS or with other computer programs.Further Information:Very high resolution interpolated climate surfaces for global land areasDownload data at: WorldClim - Global Climate Data

L’indice normalisé des précipitations (INP) a été généré pour certaines stations climatiques à long terme d’Environnement Canada en Ontario.

L’INP quantifie le déficit et le surplus des précipitations à de multiples échelles de temps, notamment :

1 mois; 3 mois; 6 mois; 9 mois; 12 mois; 24 mois.

Vous pouvez utiliser l’INP pour étudier les répercussions des conditions météorologiques sèches et humides afin de créer des approches de gestion de l’eau complètes.

L’ensemble de données de l’INP est distribué sous forme de base de données géographiques Microsoft Access.

Il s’agit d’un ensemble de données héritées que nous ne maintenons plus ou que nous ne prenons plus en charge.

Les documents mentionnés dans ce dossier peuvent contenir des adresses URL (liens) qui étaient valides au moment de la publication, mais qui sont liées à des sites ou à des pages qui n’existent plus.

Documents supplémentaires

  Indice normalisé des précipitations - Guide d’utilisation [PDF] (document en anglais seulement)

État

Produites: La production des données est terminée

Fréquence de mise à jour des données

Non prévue : La mise à jour des données n’est pas prévue

Personne-resource

Ministère des Richesses naturelles et des Forêts de l'Ontario - Unité de la cartographie de l'Ontario, pmu@ontario.ca

Projected Precipitation Changes by 2050 (Scenario A1B) (Source: CCAFS-Climate)Globally averaged precipitation is projected to increase. At the regional scale, both increases and decreases in precipitation were projected. This layer depicts the A1B scenario of the predicted change in total annual precipitation (in mm) by the end of the year 2050.Further information:Documentation: Downscaling Global Circulation Model Outputs: Delta MethodDownload data:GCM Downscaled (CCAFS-Climate)Downscaling methods based on thin plate spline spatial interpolation of anomalies (deltas) of original GCM outputs were apply. Anomalies are interpolated between GCM cell centroids and are then applied to a baseline climate given by a high resolution surface (WorldClim; Hijmans et al., 2005)

30-year Average precipitation represents the average amount (mm) of precipitation received in a month across a 30 year period (1961-1991, 1971-2000, 1981-2010). These values are calculated across Canada in 10x10 km cells.

In meteorology, precipitation is any form of hydrometeor that falls from the atmosphere and reaches the earth's surface. This phenomenon includes rain, drizzle, snow, sleet, hail, but not virga, mist or dew, which are forms of condensation and not precipitation. This is one of the variables recorded by the meteorological network of the Center for Advanced Studies in Arid Zones (CEAZA). This collection contains the information stored by 31 stations that have recorded, at some point, precipitations since 2004, spaced every hour on those days that some data was recorded. In each hour, the minimum, maximum and average precipitation is indicated. It is important to note that not all stations are currently operational. The data is updated monthly.

For each sensor, the data series are available in npz format for Numpy and mat for Matlab, in addition to being able to be viewed in the Data Series viewer of the Itrend Data Platform.

Last update: Thursday, August 11, 2022

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

Location of rainographs managed by the Department

The Department has several pluviographs distributed throughout its territory to measure precipitation. The rainfall measurements carried out are thus used as part of the management of the departmental sewerage network in real time and in deferred time respectively concerning the management of sanitation works and studies relating to the departmental sanitation scheme.

Special observations

The measurements observed by these rain gauges are available in the dataset “Pluviometry”. The “IDPLUV” field makes it possible to make the link between rain gauges and rainfall measurements.

Related data

Rainfall in the Hauts-de-Seine Access to rainfall measurements of rain gauges

In 2023, the United States saw some 29.5 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 27.53 inches.

The present data consists in a database of isotope (δO18, δD and deuterium excess) data from precipitations, snow and firn/ice cores, gathering the following data:- the isotopic surface snow data from Masson et al. (2008, doi:10.1175/2007JCLI2139.1)- the Antarctica2k database from Stenni et al. (2017, doi:10.5194/cp-13-1609-2017), available on https://www.ncdc.noaa.gov/paleo-search/study/22589- the data from Fernandoy et al. (2012, doi:10.5194/tc-6-313-2012)- the precipitation data from Rozanski et al. (1993, doi:10.1029/GM078p0001) and available on the IAEA/GNIP platform-data personnally communicatedSee below for full references of articles and datasets.The "averages" xls file give necessary informations to retrieve the data a its original temperoral scale, as well as time-averaged, standard deviations and extremum values. They are completed isotope time-averages and standard deviations from the ECHAM5-wiso model forced to the ERA-interim reanalysis and run at the daily scale over the period 1979-2013 (Werner et al., 2011; doi:10.1029/2011JD015681).The "seasonal_snowfall.xls" file give the seasonal cycles of precipitation, temperature, δO18 and deuterium excecss of snowfall data, as used in the associated manuscript.

Weather data collected in the Otemma forefield (Switzerland) from 14 July 2019 to 18 November 2021.
Data were collected by the research teams of Bettina Schaefli² and Stuart N. Lane¹.

¹ Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland

² Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland

For further information, please contact:
tom.muller.1@unil.ch
bettina.schaefli@giub.unibe.ch

Description of data : WeatherData.csv

Time span of data : 14 July 2019 to 18 November 2021
Time step : homogenized 10 minutes-averaged data

Location of data (Coordinate in SWISS LV95 (EPSG:2056)
- Glacier snout Station : 2598615 / 1087375
- Glacier center Station : 2600495 / 1088631
- Floodplain Station : 2598096 / 1087087

STRUCTURE OF DATA : tidy dataframe with following headers :
- date : local date (UTC+01 with daylight saving time)
- variable : parameter of interest, with following classes :
Air_humidity : Air humidity in percent of air saturation [%]
Air_temperature : Air temperature in [°C]
Atm_pressure : Atmospheric pressure in [hPa]
Incoming_radiation : Incoming shortwave radiation in [W/m2]
Precipitation : Liquid precipitation measured [mm]
- name : location of data (see coordinates above)
- dateUTC : date with UTC timezone

Device used for data acquisition :
- Air_humidity/Air_temperature/Atm_pressure : Decagon VP-4
- Incoming_radiation : Apogee Instruments SP-11
- Precipitation : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)

Description of data : RainComposite_Otemma_Arolla.csv

Time span of data : 01 July 2019 to 18 November 2021
Time step : homogenized 10 minutes-averaged data

The dataset compiles the measured rain during summer at the closest weather station (Glacier snout).
For the winter period (and few gaps during the summer), the solid precipitations (snow) from the closest MeteoSwiss weather stations (SwissMetNet) were used.
Gaps where filled with 1) MeteoSwiss Otemma and if data were still missing filled with 2) MeteoSwiss Arolla

Location of data (Coordinate in SWISS LV95 (EPSG:2056)
- Glacier snout Station : 2598615 / 1087375
- Otemma camp Station : 2597508 / 1086653
- MeteoSwiss Station : 2596476 / 1085864
- MeteoSwiss Arolla : 2603507 / 1095832

STRUCTURE OF DATA : tidy dataframe with following headers :
- date : local date (UTC+01 with dst)
- variable : parameter of interest
Precipitation : Liquid and solid precipitation [mm]. Composite dataset composed of melted snow (snow Water Equivalent, in mm, from MeteoSwiss station) and Rain (in mm from Glacier station).
- Location : location of data (see above)
- dateUTC : date in UTC timezone

Device used for data acquisition :
- Glacier snout Station : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)
- Otemma camp Station : Double tipping buckets rain gauge, Spectrum WatchDog 1120 (resolution 0.25 mm)
- MeteoSwiss : see SwissMetNet project

Description of data : Otemma_weather_Plot_alldata.html

An interactive plot generated with python plotly (open in web browser) containing all above described data.

Daily rainfall measured by rainfall in the Hauts-de-Seine territory.

As part of the management of the departmental sanitation network, the Department has several rainfall gauges spread across its territory to measure rainfall. These rainfall measures are used in real-time as part of the management of the sanitation works and in delayed time for studies on the departmental sanitation scheme.

Specific observations

The lack of measurement on a rainbow results in a “empty” or “null” cell value. Indeed, in the case of damage or a technical problem encountered, the measurements may not be raised.

The location of pluviographs is available on the platform in the “pluviograph” dataset. The Identifier of the pluviograph allows the link between rainfall measurements and rainfall.

Related data

The rainbows Location of pluviographs managed by the Hauts-de-Seine Department

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.

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.

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.

This dataset, also known as the Long-term Alpine Precipitation Reconstruction (LAPrec), provides gridded fields of monthly precipitation for the Alpine region (eight countries). The dataset is derived from station observations and is provided in two issues:

LAPrec1871 starts in 1871 and is based on data from 85 input series; LAPrec1901 starts in 1901 and is based on data from 165 input series.

This allows user flexibility in terms of requirements defined by temporal extent or spatial accuracy. LAPrec was constructed to satisfy high climatological standards, such as temporal consistency and the realistic reproduction of spatial patterns in complex terrain. As the dataset covers over one-hundred years in temporal extent, it is a qualified basis for historical climate analysis in a mountain region that is highly affected by climate change. The production of LAPrec combines two data sources:

HISTALP (Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region) offers homogenised station series of monthly precipitation reaching back into the 19th century.

APGD (Alpine Precipitation Grid Dataset) provides daily precipitation gridded data for the period 1971–2008 built from more than 8500 rain gauges.

The adopted reconstruction method, Reduced Space Optimal Interpolation (RSOI), establishes a linear model between station and grid data, calibrated over the period when both are available. RSOI involves a Principal Component Analysis (PCA) of the high-resolution grid data, followed by an Optimal Interpolation (OI) using the long-term station data. The LAPrec dataset is updated on a two-year basis, by no later than the end of February each second year. The latest version of the dataset will extend until the end of the year before its release date. LAPrec has been developed in the framework of the Copernicus Climate Change Service in a collaboration between the national meteorological services of Switzerland (MeteoSwiss, Federal Office of Meteorology and Climatology) and Austria (ZAMG, Zentralanstalt für Meteorologie und Geodynamik). For more information on input data, methodical construction, applicability, versioning and data access, see the product user guide in the Documentation tab. The latest version of the dataset will temporally extend until the end of the year before its release date.

Variables in the dataset/application are: Precipitation

The average for 2020 based on 178 countries was 1168 mm per year. The highest value was in Colombia: 3240 mm per year and the lowest value was in Egypt: 18 mm per year. The indicator is available from 1961 to 2020. Below is a chart for all countries where data are available.

The Cooperative Observer Program (COOP) Hourly Precipitation Data (HPD) consists of quality controlled precipitation amounts, which are measurements of hourly accumulation of precipitation, including rain and snow for approximately 2,000 observing stations around the country, and several U.S. territories in the Caribbean and Pacific from the National Weather Service (NWS) Fischer-Porter Network. This new version of COOP HPD with faster automations due updated stations will result in faster access for the public. The data are from 1940 to present, depending upon when each station was installed. These stations, nearly all of which were part of HPD version 1, also known as DSI-3240, were gradually upgraded from paper punch tape data recording systems to a more modern electronic data logger system from 2004-2013. The 15-min gauge depth time series are processed at NCEI via automated quality control and filtering algorithms to identify and remove spurious observations from noise and malfunctioning equipment, and also those due to natural phenomena such as evaporation and the necessary occasional emptying of the gauge. Hourly precipitation totals are then computed from the 15-min data and are quality controlled by a suite of automated algorithms that combine checks on the daily and hourly time scale. Data and metadata are ingested on a daily basis and combined in a single integrated dataset. As with the legacy punch paper instrumentation, the electronic loggers record rain gauge depth every 15 minutes. Monthly site visits to each station are still performed, but instead of collecting punched paper (that would subsequently need conversion to a digital record via a MITRON reader), data are downloaded from the station's datalogger to a memory stick and centrally collected at the local Weather Forecast Office (WFO) for all stations in the WFO area. The WFO subsequently combines all data into a single tar file and transfers the data to NCEI via ftp upload nominally each month. This updated HPD includes the historical data from the punch paper era and the recent digital era in order to provide the full period of record for each location. These data are formatted consistent with practices for NCEI Global In-situ datasets.