As of 2023, the average data consumption per user per month in India was at **** gigabytes. 4G data traffic contributes to ** percent of the overall data traffic while 5G was launched in India in October 2022. Increased online education, remote working for professionals and higher OTT viewership contributed to the data traffic growth.

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. ERA5 MONTHLY provides aggregated values for each month for seven ERA5 climate reanalysis parameters: 2m air temperature, 2m dewpoint temperature, total precipitation, mean sea level pressure, surface pressure, 10m u-component of wind and 10m v-component of wind. Additionally, monthly minimum and maximum air temperature at 2m has been calculated based on the hourly 2m air temperature data. Monthly total precipitation values are given as monthly sums. All other parameters are provided as monthly averages. ERA5 data is available from 1940 to three months from real-time, the version in the EE Data Catalog is available from 1979. More information and more ERA5 atmospheric parameters can be found at the Copernicus Climate Data Store. Provider's Note: Monthly aggregates have been calculated based on the ERA5 hourly values of each parameter.

The gridded CRU TS (time-series) 3.23 data are month-by-month variations in climate over the period 1901-2014, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.

CRU TS 3.23 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period Jan. 1901 - Dec. 2014.

CRU TS 3.23 data were produced using the same methodology as for the 3.21 datasets. In addition to updating the dataset with 2014 data, some new stations have been added for TMP and PRE only. Known issues predating this release remain; the 4.00 release, due soon, will address these.

The 4.00 release will utilise Angular-Distance Weighting (ADW) gridding, promising more accurate results with far greater adjustability and logging. It will cover the same spatial, temporal and variate spaces as version 3.23 (land areas excluding Antarctica at 0.5°x0.5°, monthly from 1901 to 2014 with no missing values, 10 variables).

Versions 3.23 and 4.00 will run concurrently until 2016, after which the new (ADW) approach will be used. This is to allow comparisons between the methods and results to be made by users of the dataset.

The CRU TS 3.23 data are monthly gridded fields based on monthly observational data, which are calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and netcdf data files both contain monthly mean values for the various parameters.

All CRU TS output files are actual values - NOT anomalies.

CRU TS data are available for download to all CEDA users. The CEDA Web Processing Service (WPS) may be used to extract a subset of the data (please see link to WPS below).

Data provided here is used by WDFW’s partners, government entities, schools, private businesses, and the general public. WDFW actively promotes inter-agency data exchange and resource sharing. Every effort is made to provide accurate, complete, and timely information on this site. However, some content may be incomplete or out of date. The content on this site is subject to change without notice. The Washington Department of Fish and Wildlife (WDFW) shall not be liable for any activity involving this data with regard to lost profits or savings or any other consequential damages; or the fitness for use of the data for a particular purpose; or the installation of the data, its use, or the results obtained.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

This dataset replaces the previous Time Bias Corrected Divisional Temperature-Precipitation Drought Index. The new divisional data set (NClimDiv) is based on the Global Historical Climatological Network-Daily (GHCN-D) and makes use of several improvements to the previous data set. For the input data, improvements include additional station networks, quality assurance reviews and temperature bias adjustments. Perhaps the most extensive improvement is to the computational approach, which now employs climatologically aided interpolation. This 5km grid based calculation nCLIMGRID helps to address topographic and network variability. This data set is primarily used by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) to issue State of the Climate Reports on a monthly basis. These reports summarize recent temperature and precipitation conditions and long-term trends at a variety of spatial scales, the smallest being the climate division level. Data at the climate division level are aggregated to compute statewide, regional and national snapshots of climate conditions. For CONUS, the period of record is from 1895-present. Derived quantities such as Standardized precipitation Index (SPI), Palmer Drought Indices (PDSI, PHDI, PMDI, and ZNDX) and degree days are also available for the CONUS sites. In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set. As of November 2018, NClimDiv includes county data and additional inventory files.

The statistic shows estimated mobile internet data traffic per month in the United States from 2018 to 2023. In 2018, total mobile data traffic was estimated to amount to **** exabytes per month.

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.

This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset. Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-jrn&identifier=210380001 Webpage with information and links to data files for download

Mobile data usage in Singapore reached an average of around 96 petabytes (96,000 terabytes) per month during the fourth quarter of 2024. Despite the expansion of public Wireless Local Area Networks (WLANs), mobile data use remained on the rise during the observed period. Data pricing trend in SingaporeSingapore has always had one of the most expensive data prices in Southeast Asia. However, new technologies such as 5G, as well as the competitive telecommunications market, led to price reductions over the past few years. In 2023, one gigabyte of mobile data cost around 0.63 U.S. dollars on average, indicating a decrease of more than two dollars compared to 2019. Despite the foreseeable trend of decreasing data prices, it would still remain relatively high compared to neighboring Indonesia's 0.28 U.S. dollars per gigabyte. Singapore’s mobile data providersThe telecommunications market in Singapore is shared by three main providers, Singtel, Starhub and M1. Among the leading providers in the country, Singtel had the best 5G coverage experience, as of December 2024.

This data represents the number of individuals registered in the Department of Consumer Protection's Medical Marijuana Program by registration type on the last day of the month.

Forecast: Average Monthly Fixed Broadband Data per User in Germany 2022 - 2026 Discover more data with ReportLinker!

We present a continuous land climate reconstruction dataset extending from 60 kyr before present to the pre-industrial period at 0.5deg resolution on a monthly timestep for 0degN to 90degN. It has been generated from 42 discrete snapshot simulations using the HadCM3B-M2.1 coupled general circulation model. We incorporate Dansgaard-Oeschger (DO) and Heinrich events to represent millennial scale variability, based on a temperature reconstruction from Greenland ice-cores, with a spatial fingerprint based on a freshwater hosing simulation with HadCM3B-M2.1. Interannual variability is also added and derived from the initial snapshot simulations. Model output has been downscaled to 0.5deg resolution (using simple bilinear interpolation) and bias corrected using either the University of East Anglia, Climate Research Unit observational data (for temperature, precipitation, windchill, and minimum monthly temperature), or the EWEMBI dataset (for incoming shortwave energy). Here we provide datasets for; surface air temperature, precipitation, incoming shortwave energy, wind-chill, snow depth (as snow water equivalent), number of rainy days per month, minimum monthly temperature, and the land-sea mask and ice fractions used in the simulations. The datasets are in the form of NetCDF files. The variables are represented by a set of 24 files that have been compressed into nine folders: temp, precip, down_sw, wind_chill, snow, rainy_days, tempmonmin, landmask and icefrac. Each file represents 2500 years. The landmask and ice fraction are provided annually, whereas the climate variables are given as monthly files equivalent to 30000 months, between the latitudes 0deg to 90degN at 0.5deg resolution. Each of the climate files therefore have the dimensions 180 (lat) x 720 (lon) x 30000 (month). We also provide an example subset of the temperature dataset, which gives decadal averages for each month for 0-2500 years.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Ouaka, Mbomou, Bangui, Nana-Mambéré, Ouham, Sangha-Mbaéré, Ombella M'Poko, Mambéré-Kadéï, Vakaga, Ouham Pendé, Lobaye, Haute-Kotto, Kémo, Nana-Gribizi, Bamingui-Bangoran, Haut-Mbomou, Market Average

This statistic displays the M2M average monthly cellular data usage from 2016 to 2021. In 2016, on average M2M consumed *** GBs of cellular data per month. That number is projected to reach *** GBs in 2021.

The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date imports using the End-use classification system. The End-use endpoint in the Census data API also provides value, shipping weight, and method of transportation totals at the district level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

This dataset provides Daymet Version 4 R1 monthly climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Monthly averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and monthly totals are provided for the precipitation variable. Each data file is yearly by variable with 12 monthly time steps and covers the same period of record as the Daymet V4 R1 daily data. The monthly climatology files are derived from the larger datasets of daily weather parameters produced on a 1 km x 1 km grid for North America, Hawaii, and Puerto Rico. Separate monthly files are provided for the land areas of continental North America (Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Data are distributed in standardized Climate and Forecast (CF)-compliant netCDF (.nc) and Cloud-Optimized GeoTIFF (.tif) formats. In Version 4 R1 (ver 4.1), all 2020 and 2021 files (60 total) were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.

The Food Assistance Program provides Electronic Benefit Transfer (EBT) cards that can be used to buy groceries at supermarkets, grocery stores and some Farmers Markets. This dataset provides data on the number of households, recipients and cash assistance provided through the Food Assistance Program participation in Iowa by month and county starting in January 2011 and updated monthly. Beginning January 2017, the method used to identify households is based on the following: 1. If one or more individuals receiving Food Assistance also receives FIP, the household is categorized as FA/FIP. 2. If no one receives FIP, but at least one individual also receives Medical Assistance, the household is categorized as FA/Medical Assistance. 3. If no one receives FIP or Medical Assistance, but at least one individual receives Healthy and Well Kids in Iowa or hawk-i benefits, the household is categorized as FA/hawk-i. 4. If no one receives FIP, Medical Assistance or hawk-i , the household is categorized as FA Only. Changes have also been made to reflect more accurate identification of individuals. The same categories from above are used in identifying an individual's circumstances. Previously, the household category was assigned to all individuals of the Food Assistance household, regardless of individual status. This change in how individuals are categorized provides a more accurate count of individual categories. Timing of when the report is run also changed starting January 2017. Reports were previously ran on the 1st, but changed to the 17th to better capture Food Assistance households that received benefits for the prior month. This may give the impression that caseloads have increased when in reality, under the previous approach, cases were missed.

Russia Living Cost: Average per Month: Annual data was reported at 14,375.000 RUB in 2023. This records an increase from the previous number of 12,654.000 RUB for 2022. Russia Living Cost: Average per Month: Annual data is updated yearly, averaging 12,654.000 RUB from Dec 2021 (Median) to 2023, with 3 observations. The data reached an all-time high of 14,375.000 RUB in 2023 and a record low of 11,653.000 RUB in 2021. Russia Living Cost: Average per Month: Annual data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Living Cost: Average per Month: NC: Republic of Kabardino Balkaria data was reported at 11,311.000 RUB in Dec 2020. This records a decrease from the previous number of 11,723.000 RUB for Sep 2020. Living Cost: Average per Month: NC: Republic of Kabardino Balkaria data is updated quarterly, averaging 4,956.000 RUB from Sep 2002 (Median) to Dec 2020, with 74 observations. The data reached an all-time high of 12,576.000 RUB in Jun 2020 and a record low of 1,413.000 RUB in Sep 2002. Living Cost: Average per Month: NC: Republic of Kabardino Balkaria data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.