This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2021, Africa recorded a temperature departure of 1.30 degrees Celsius above the 1910-2000 average. The temperature anomaly made 2021 the third-warmest year on the continent. According to the source, Africa's annual temperature has grown at an average rate of 0.13 degrees Celsius per decade since 1910.

La temperatura in Sudafrica è aumentata a 18,99 gradi Celsius nel 2024 rispetto ai 18,58 gradi Celsius del 2023. Questa pagina include un grafico con dati storici per la temperatura media del Sud Africa.

Captured mean annual temperature for the years 1901-2021

Temperature in Central African Republic remained unchanged at 25.42 celsius in 2024 from 25.42 celsius in 2023. This dataset includes a chart with historical data for Central African Republic Average Temperature.

The mean surface temperature in North Africa increased by *** degrees Celsius in 2021, the largest variation in Africa. Western Africa followed, warming **** degrees Celsius. Among countries, Tunisia and Algeria recorded the largest increase in mean surface temperature on the African continent that year.

Tunisia recorded the largest increase in surface temperature in Africa as of 2021, with warming above *** degrees Celsius. Algeria followed: The country's land temperature expanded by *** degrees Celsius in the mentioned year. In general, Northern and Western African nations registered the greatest changes in the mean temperature, warming over *** degrees Celsius. On the other hand, Botswana and Zimbabwe did not experience temperature increases in 2021.

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2021, Africa registered its fourth-warmest year on record, with a temperature departure of **** degrees Celsius above the ********* average. The land temperature anomaly was slightly lower than that from 2019. Since 1910, 2016 has been the hottest year on the continent, with a deviation temperature of **** degrees Celsius. Additionally, Africa's ten-warmest years have occurred since 2005.

Predicted mean monthly air temperature (annual). Predictions based on estimates by the Center for resource and Enviornmental Studies (CRES) which can be interpreted as estimates of standard means for the period of 1920 to 1980. The AIRTMP_MN grid data layer is comprised of 1450x1380 derivative raster air temperature features derived based on 0.05 degrees resolution data originally from CRES / FAO. The layer provides nominal analytical/mapping at 1:220 000 000. Madagascar not included. Annual Total Air Temperature, Average Monthly Air Temperature (Annual) and the Monthly Air Temperature from January to December are also available for download. Acronyms and Abbreviations: CRES - Centre for Resource and Environmental Studies, The Australian National University (ANU); FAO - Food and Agriculture Organization of the United Nations.

Site-specific watershed analysis requires site-specific meteorological data. Due to high spatial and temporal variability nature, collecting in-situ weather data is essential for modeling biophysical processes and understanding the biophysical condition of watersheds. In addition, site-specific weather information by itself is useful for improving agricultural practices in selected Africa RISING sites. This data study contains weather-related data which was generated to support various analysis in Africa RISING sites. The data included all the weather elements i.e. precipitation, soil moisture (volumetric water content, soil temperature, cation exchange capacity (CEC)), wind (direction, speed, and gusts), air temperature, solar radiation, and relative humidity. These data were collected at two sites (kebeles) for each Africa RISING sites. They were collected at 15 minutes interval since September 2014. The data collection is still ongoing.

Average daily minimum temperature. The baseline is calculated for 2001–2020, with projections for 2021–2040 and 2041–2060 under two climate scenarios: RCP 4.5 (moderate emissions) and RCP 8.5 (high emissions).

Forecast maps of sea surface temperature (SST) are generated by the ECOWAS Coastal and Marine Resources Management Centre from data obtained from the Operational Mercator global Ocean analysis system via the Copernicus programme at a resolution of 1/12 (0.08) degree. Forecasting of ocean weather is done for a 7-day period including a nowcast product for the present day. The forecast products are generated daily with a geographical area coverage of latitude 10 degrees south to 30 degrees north and longitude 35 degrees west to 15 degrees east. This covers the coastal and oceanic waters of western Africa. Forecast products are updated daily for the seventh day (number of files per day = 1). Further information about this forecast data can be obtained from www.marine.copernicus.eu

This map features the GLDAS total monthly precipitation modeled globally by NASA. The map shows the monthly precipitation for the period of May 2016 to May 2018, focused on Africa. You can click the Play button on the time slider to see precipitation over time.Great parts of Northern Africa and Southern Africa, as well as the whole Horn of Africa, mainly have a hot desert climate, or a hot semi-arid climate for the wetter locations. The equatorial region near the Intertropical Convergence Zone is the wettest portion of the continent. Annually, the rain belt across the country marches northward into Sub-Saharan Africa by August, then moves back southward into south-central Africa by March.Precipitation is water released from clouds in the form of rain, sleet, snow, or hail. It is the primary source of recharge to the planet's fresh water supplies. This map contains a historical record showing the volume of precipitation that fell during each month from March 2000 to the present. Snow and hail are reported in terms of snow water equivalent - the amount of water that will be produced when they melt. Dataset SummaryThe GLDAS Precipitation layer is a time-enabled image service that shows average monthly precipitation from 2000 to the present, measured in millimeters. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. A complete list of the model inputs can be seen here, and the output data (in GRIB format) is available here.Phenomenon Mapped: PrecipitationUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean, instead of total evapotranspiration. Mean evapotranspiration for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is eight years.Variables: This layer has two variables: rainfall and snowfall. By default the two are summed, but you can view either by itself using the multidimensional filter, or by applying the relevant raster function. You must disable time animation on the layer before using its multidimensional filter.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

Gridded values of mean monthly daily minimum and maximum air temperatures. Data obtained from the Centre for Resource and Environmental Studies (CRES) at the Australian National University. Mean annual wind velocity was obtained from UNEP/DEIA/GRID-Geneva. Resolution 5 km x 5 km.

The daily average temperature in the United Kingdom (UK) has remained relatively stable since 2001, with temperatures rarely straying below 10 degrees Celsius. In 2024, the UK had an average daily temperature of 11.9 degrees Celsius. This was the highest average daily temperature recorded since the turn of the century. British summertime Britain is not known for its blisteringly hot summer months, with the average temperatures in this season varying greatly since 1990. In 1993, the average summer temperature was as low as 13.39 degrees Celsius, whilst 2018 saw a peak of 15.8 degrees Celsius. In that same year, the highest mean temperature occurred in July at 17.2 degrees Celsius. Variable weather Due to its location and the fact that it is an island, the United Kingdom experiences a diverse range of weather, sometimes in the same day. It is in an area where five air masses meet, creating a weather front. Each brings different weather conditions, such as hot, dry air from North Africa and wet and cold air from the Arctic. Temperatures across the UK tend to be warmest in England.

Additional file 5. Annual incidence of malaria (per 1,000 population at risk) with data from World Health Organization (WHO) (available at: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/malaria-incidence-(per-1-000-population-at-risk ) and average annual mean temperature with data from Climate Change Knowledge Portal (CCKP) (available at: https://climateknowledgeportal.worldbank.org/ ) for selected countries in study sample: Nigeria, Ethiopia, South Africa, Kenya, Uganda, Ghana, Mozambique, Zambia and Zimbabwe.

This statistic shows a ranking of the estimated average temperature in 2020 in the Middle East and North Africa (MENA), differentiated by country. The figure refers to the projected annual average temperature for the period 2020-2039 as modelled by the GISS-E2-R model in the RCP 4.5 scenario (Medium-low emission).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

CLImate GENerator (CLIGEN) is a stochastic weather generator that produces daily and sub-daily timeseries of weather variables. The resulting timeseries are statistically similar to observed timeseries considering various temporal scales and climate factors. This dataset consisting of CLIGEN inputs may be used to generate timeseries at any point in a 0.25 arc degree resolution grid covering South American and African continents. Estimated parameter values at each grid point are based on 20-year records taken from global climate datasets. Precipitation parameters are statistically downscaled from grid-scale to point-scale based on observations from globally distributed ground networks representing >10,000 stations. This dataset is intended for use in climate-related research in ungauged areas where observed climate records are unavailable. The data are formatted as CLIGEN .par files, which are the only required input for CLIGEN. The files are separated into Africa and South America folders containing n=40936 and n=24588 files, respectively. The files are labeled according to grid point lat/lon coordinates (WGS84) in decimal degrees. The labeling convention uses 'N' and 'E' (north, east) to represent coordinates with a positive sign and 'S' and 'W' (south, west) to represent coordinates with a negative sign. Resources in this dataset:Resource Title: Grid Files. File Name: Grid Files.zipResource Description: CLIGEN input files (.par) for the South America and Africa grid.Resource Title: Summary Table. File Name: Summary Table.docxResource Description: Summary table that lists CLIGEN parameters and basic dataset characteristics of the gridded parameterization.

Climate change, that is a threat to ecosystems and the livelihoods of those that depend on them, is increasingly manifesting as an increased frequency and intensity of severe weather events such as droughts and floods (Déqué et al., 2017). Climate change has created an urgent need for early warning aids or models to enhance the sub-Saharan African health systems ability to prepare for, and cope with escalations in treatment needs of climate sensitive diseases (Nhamo & Muchuru, 2019). This dataset was created from the health and weather data of nine purposively selected study districts in Uganda, whose health and weather data were available for the development of an early warning health model (https://github.com/CHAIUGA/chasa-model) and an accompanying prediction web app (https://github.com/CHAIUGA/chasa-webapp). The districts were selected based on the following criteria: (a) were experiencing climate change and variability, (b) represented different climatologic, and agro-ecological zones, (c) availability of climate information and health information from a health facility within a 40 kilometres radius of a functional weather station. Historical weather data was retrieved from the Uganda National Meteorological Association databases, as monthly averages. The weather variables in this data included: atmospheric pressure, rainfall, solar radiation, humidity, temperature (maximum, minimum and mean), and wind (gusts and average wind speed). The monthly health aggregated data for the period starting September 2018 to December 2019, was retrieved from the National Health Repository (DHIS2) for referral hospitals within the selected districts. Only data for a selection of climate-sensitive disease aggregates was obtained. The dataset contains 436 complete matched disease and weather records. Ethical issues: Both the de-identified aggregate monthly disease diagnosis count data and weather data in this dataset are from national data available to the public on request.