Hourly geographically aggregated weather data for Europe. This data package contains radiation and temperature data, at hourly resolution, for Europe, aggregated by Renewables.ninja from the NASA MERRA-2 reanalysis. It covers the European countries using a population-weighted mean across all MERRA-2 grid cells within the given country.

If you're looking for weather datasets, there are several reputable sources where you can access comprehensive weather data for various applications, including research, machine learning, and more. Here are some popular options:

1. National Centers for Environmental Information (NCEI):

   • The NCEI, part of NOAA, offers a wide range of climate and weather data. You can find historical weather data, global climate data, and more.
   • NCEI Weather Data

2. OpenWeatherMap:

   • Provides current weather data, forecasts, and historical data. They offer free and paid plans depending on the level of access and detail needed.
   • OpenWeatherMap API

3. Weather Underground:

   • Offers a rich set of weather data including current conditions, forecasts, and historical weather data.
   • Weather Underground API

4. European Centre for Medium-Range Weather Forecasts (ECMWF):

   • ECMWF provides datasets including ERA-Interim, ERA5, and seasonal forecasts. They focus on global weather and climate data.
   • ECMWF Data

5. The Weather Company (IBM):

   • Offers a range of weather data services, including historical weather data, forecasts, and more through their APIs.
   • The Weather Company API

6. NASA Earth Observing System Data and Information System (EOSDIS):

   • Provides access to a vast array of global climate data, satellite imagery, and other environmental data.
   • NASA EOSDIS

7. Global Surface Summary of the Day (GSOD):

   • A dataset that includes daily weather summaries from global stations, available through the National Centers for Environmental Information.
   • GSOD Data

8. Climate Data Online (CDO):

   • Another resource from NOAA, offering access to a variety of climate data, including daily and monthly summaries, storm data, and more.
   • NOAA CDO

9. Meteostat:

   • Provides free access to historical weather and climate data, focusing on quality-controlled and easy-to-use datasets.
   • Meteostat

subject to appropriate attribution.

This data release (DR) is the update of the U.S. Geological Survey - ScienceBase data release by Bera (2022), with the processed data through September 30, 2022. The primary data for water year 2022 (a water year is the 12-month period, October 1 through September 30, in which it ends) is downloaded from the Argonne National Laboratory (ANL) website (Argonne National Laboratory, 2022) and is processed following the guidelines documented in Over and others (2010). This DR also describes the Watershed Data Management (WDM) database file ARGN22.WDM. The WDM file ARGN22.WDM is an update of ARGN21.WDM (Bera, 2022) with the processed data from October 1, 2021, through September 30, 2022, appended to it. ARGN22.WDM file contains nine data series: air temperature, in degrees Fahrenheit (dsn 400), dewpoint temperature, in degrees Fahrenheit (dsn 500), wind speed, in miles per hour (dsn 300), solar radiation, in Langleys (dsn 600), computed potential evapotranspiration, in thousandths of an inch (dsn 200), and four data-source flag series: for air temperature (dsn 410), dewpoint temperature (dsn 510), wind speed (dsn 310), and solar radiation (dsn 610), respectively, from January 1,1948, to September 30, 2022. Daily potential evapotranspiration (PET) was computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation and disaggregated to hourly PET, in thousandths of an inch, using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup.” The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2023) station at St. Charles, Illinois, was used as "backup" for the hourly air temperature, solar radiation, and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2022) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service from the station at O'Hare International Airport and used as "backup." Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). This DR provides WDM file ARGN22.WDM and the following tab-delimited text files, each with data from January 1, 1948, to September 30, 2022: "Air temperature.txt" contains hourly air temperature data in degrees Fahrenheit and associated data-source flags. "Dewpoint temperature.txt" contains hourly dewpoint temperature data in degrees Fahrenheit and associated data-source flags. "Solar radiation.txt" contains hourly solar radiation data in Langleys and associated data-source flags. "Wind speed.txt" contains hourly wind speed data in miles per hour and associated data source flags. "PET.txt" contains hourly potential evapotranspiration (PET) data, in thousandths of an inch. Tab-delimited text files can be opened with any text editor or Microsoft Excel. To open the WDM file user needs to install any utility listed in the section "Related External Resources" on this page. References Cited: Argonne National Laboratory, 2022, Meteorological data, accessed on December 19, 2022, at https://www.atmos.anl.gov/ANLMET/numeric/. Bera, M., 2022, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9BBP9EJ. Midwestern Regional Climate Center, 2022, Meteorological data, accessed on December 23, 2022, at https://mrcc.purdue.edu/CLIMATE/. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2023. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on January 3, 2023, at http://dx.doi.org/10.13012/J8MW2F2Q.

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. This dataset provides future weather data under two emissions scenarios - RCP4.5 and RCP8.5 - across two 10-year periods (2045-2054 and 2085-2094). It also includes simulated historical weather data for 1995-2004 to serve as the baseline for climate impact assessments. We strongly recommend using this built-in baseline rather than external sources (e.g., TMY3) for two key reasons: (1) it shares the same model grid as the future projections, thereby minimizing geographic-averaging bias, and (2) both historical and future datasets were generated by the same RCM, so their differences yield anomalies largely free of residual model bias. This dataset offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale.

Weather Data collected by CIMIS automatic weather stations. The data is available in CSV format. Station data include measured parameters such as solar radiation, air temperature, soil temperature, relative humidity, precipitation, wind speed and wind direction as well as derived parameters such as vapor pressure, dew point temperature, and grass reference evapotranspiration (ETo).

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.

Meteorological Observations describe datasets that contain information about weather and climate conditions as available on the City-Pages of the Environment Canada WeatherOffice.gc.ca web site. These pages contain information about current weather conditions and past climate including temperature, wind, and humidity measurements, written descriptions of current conditions, rain and snow amounts, average and extreme temperatures, etc. The current conditions are acquired from a variety of observing system operators and are provided in near-real time with limited quality assurance. Current condition information should not be considered as quality-controlled official values. The availability of values for every observation period is not guaranteed as they may be affected by observing system operations.

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

Weather Source offers the full European Centre for Medium-Range Weather Forecasts (ECMWF) suite which is known as the best forecast model in the world. The products include (i) historical data back to 2000; (ii) short/mid-range forecast (i.e., up to 360-hour or 15 days); (iii) sub-seasonal forecast out to 46 days (iv) and a seasonal forecast in monthly format out to 7 months. We also offer historical forecasts in pristine format.

In addition, we also have the raw and statistically analyzed ensembles and we summarize the ensemble members by deciles and quartiles which are incredibly valuable to understand the potential of forecast variance (i.e., are the ensemble members tightly wound around the forecast mean which tells me the skill score of the forecast is very high or do they expose a bi-modal distribution which indicates I should plan for possible variance in the forecast.).

Weather Source offers European Centre for Medium-Range Weather Forecast (ECMWF) including the ECMWF Long-Range forecast back to January 1, 2014.

Weather Source is known worldwide for its industry leading data and novel weather solutions. Our data products and solutions are the most reliable on the market and provide businesses with properly collocated and actionable data enabling them to identify and quantify the impact of weather on any KPI at their locations of interest. Our curated continuum of weather data was built for analytics and machine learning. Weather Source data is delivered via its high-resolution OnPoint grid, which ensures your location of interest is never more than 2.2 miles away from a grid point.

Utilize Weather Source weather and climate data to reveal meaningful observations for a variety of industries. By leveraging hyper-local weather & climate information, businesses are able to create business intelligence and models around sales and footfall traffic forecasts, advertising and marketing, logistics and supply chain, inventory, staffing, management, and more.

We have gathered data on the power generation of seven different PV modules from three demonstration sites in Oslo, Touzer, and Sevilla for a comparitive analysis. This data was sourced from TIGO cloud for the PV modules and Solcast, an open-source platform, for historical weather information. The data set is spanning from May 2021 to November 2023. These datasets are characterized by high-resolution recordings taken every 5 minutes.

Other contributors: Alexander G Ulyashin (SINTEF Industry), Xiang Ma (SINTEF Industry), Alicia Arce (Ayesa Engineering SA), Rebeca Gutierrez (Ayesa Engineering SA) , Hélène Ben Khemis (Higher Institute of Technological Studies in Touzer, T), Zaher Khantouch (Higher Institute of Technological Studies in Touzer, T) and Ahmet Soylu (OsloMet)

Weather Source, a leading provider of weather and climate technologies for business intelligence, is offering complimentary data for those researching the potential connections between weather and COVID-19 viability and transmission. This share includes: Global historical weather data dating back to October 2019 Present data Forecast data out to 15 days The data supports temperature and humidity, both specific and relative, at the daily level. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . This dataset is created and owned by Weather Source and made available for educational and academic research purposes. This dataset has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate.

🇩🇪 독일 English This data set includes historical weather data for the station of the DWD (Station number: 02712) at Silvanerweg 6 in Constance over a longer period of time. On July 25, 2017, an amendment to the German Weather Service Act ("DWD Act") came into force. The DWD is legally mandated to provide its weather and climate information largely free of charge. Currently, many geodata such as model predictions, radar data, current measurement and observation data as well as a large number of climate data are available on the Open Data Server https://opendata.dwd.de . The climate data is provided under https://opendata.dwd.de/climate_environment/CDC. The freely accessible data may continue to be used without restrictions in accordance with the "Ordinance on the Determination of the Terms of Use for the Provision of Federal Geodata (GeoNutzV)" with the addition of a source note (https://gdz.bkg.bund.de). With regard to the design of the source notes, the German Meteorological Service (DWD) (pursuant to § 7 DWD Act, § 3 GeoNutzV) requests the following information: The obligation to include provided source notes applies to the unchanged use of spatial data and other services of the DWD. References must also be included when extracts are created or the data format is changed. An illustration of the DWD logo is sufficient as a source reference within the meaning of the GeoNutzV. For further changes, edits, new designs or other modifications, the DWD expects at least one mention of the DWD in central source directories or in the imprint. Indications of change according to GeoNutzV can be, for example: "Data base: German Weather Service, raster data graphically reproduced", "Data base: German weather service, individual values averaged" or "Data base: German weather service, own elements added". In the case of a use that does not meet the intended purpose of the performance of the DWD, enclosed source notes must be deleted. This applies in particular to weather warnings if it is not ensured that they are made available to all users at all times completely and immediately. Source: German Weather Service (DWD)

The Canadian Meteorological Centre CMC produces Numerical Weather Prediction NWP as one of the key inputs to the Meteorological Service of Canada's overall public weather and environmental prediction and warning process. The raw data from NWP is made available to outside users who may use it for their own purposes. As NWP data is an early input into the overall MSC public forecast process, it may differ from the official forecast.

For each forecast run, Weather Source processes all of the forecast ensemble members (31 for GFS and 51 for ECMWF) in real-time and users have the ability to create business intelligence around the spread of the distributions of the ensembles to better understand the potential variances of each forecast which can be very useful for scenario planning but also understand the confidence level in the forecast skill score.

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Smart Home Weather Stations And Rain Gauge Market Size 2024-2028

The smart home weather stations and rain gauge market size is forecast to increase by USD 100.5 million, at a CAGR of 9.77% between 2023 and 2028.

The market is experiencing significant growth, driven by the continuous product innovation in terms of technology, performance, features, and design. These advancements enable weather stations to offer more accurate and real-time weather forecasting data, integrating seamlessly with smart home systems and IoT devices. Furthermore, the increasing popularity of wireless connecting devices and the Internet of Things (IoT) is fueling market expansion. However, challenges persist, particularly in developing and underdeveloped countries, where product awareness remains low. Companies seeking to capitalize on market opportunities must focus on raising awareness and providing affordable, user-friendly solutions tailored to these regions.
Navigating these challenges effectively will require strategic partnerships, targeted marketing efforts, and a deep understanding of local market dynamics. Overall, the market presents a compelling growth opportunity for businesses that can successfully innovate, adapt, and expand their reach.

What will be the Size of the Smart Home Weather Stations And Rain Gauge Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The smart home weather station market continues to evolve, integrating various technologies to provide real-time meteorological data for diverse applications. IoT integration enables seamless data acquisition and transmission, allowing for weather alerts and barometric pressure sensors to inform users of potential weather changes. The smart home ecosystem incorporates temperature sensors, humidity sensors, and UV sensors to optimize home automation and energy efficiency. Beyond residential use, agricultural applications benefit from professional weather stations, which provide historical data for climate science and solar power optimization. DIY weather station kits cater to the DIY community, offering open source hardware and software for personal weather stations.

These stations can also be used for educational applications and environmental monitoring. Ultrasonic rain gauges and wind speed sensors contribute to data logging and weather forecasting, while cloud-based platforms ensure data security and data visualization. Open API integration allows for mobile app integration, providing users with easy access to weather information. Green technology and climate change initiatives further drive the market, as weather data analysis becomes increasingly important for home security and environmental studies. The ongoing unfolding of market activities reveals a dynamic industry, with continuous innovation and integration of new technologies. Weather station kits cater to various sectors, from home security to scientific research, and offer wireless connectivity for real-time data and user interface customization.

The evolving patterns in this market reflect the growing importance of weather data in our daily lives and the role of technology in providing accurate and timely information.

How is this Smart Home Weather Stations And Rain Gauge Industry segmented?

The smart home weather stations and rain gauge industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Smart weather stations
  Smart rain gauge


Distribution Channel

  Online
  Offline


Geography

  North America

    US


  Europe

    France
    Germany
    UK


  APAC

    China


  Rest of World (ROW)

.

By Product Insights

The smart weather stations segment is estimated to witness significant growth during the forecast period.

Smart weather stations represent advanced home weather monitoring systems, seamlessly integrating with Android and iOS devices. These systems enable users to access comprehensive weather information on their smartphones, surpassing the reliance on television broadcasts. Smart weather stations offer both indoor and outdoor data, including humidity, temperature, and barometric pressure readings. The outdoor sensor facilitates weather-driven planning. The global market for smart weather stations experiences growth due to the increasing popularity of smart homes and connected devices. Moreover, these weather stations cater to diverse applications, such as agriculture, home security, and scientific research. They incorporate features like data visualization, real-time data transmission, and user interfaces.

Open source hardware and software, cloud-based platfor

This data is from the University of Utah's LEMS instruments captured September - November 2012 and April - June 2013 during the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN-X) field campaign. LEMS are solar powered, low-cost Energy-budget Measurement Stations, which have been designed and assembled at the University of Utah EFD lab. LEMS monitor incoming solar radiation (LI-200), 2-m air temperature and relative humidity (Sensirion SHT 15, Humidity +/- 2-5% Temperature +/- 0.5K), air pressure (Bosch BMP085), surface temperature (Zytemp TN9), as well as soil temperature and moisture (Decagon 5TM) at two levels below ground. Data are logged using an Arduino-based open source controller with a data logger that can store up to 2 GB of data. These are the original raw data files and have not undergone quality control procedures. These files are in ASCII CSV format with a header line. Data filenames contain the specific instrument ID. The time stamps are based off of UTC time for all the instruments for the MATERHORN-X data, unless it was stated otherwise. Please see the README file for more information.

WeatherDataAI Single-Point is a simple yet powerful tool for accessing global historical weather data with just a few clicks. Users select the weather variable(s) they need (like temperature, precipitation, etc.), choose the years of interest, and click a location on the interactive map. The platform automatically fetches the data, creates a custom CSV file, and provides a download link while also emailing the file directly to the user. No technical skills required — it’s built for researchers, businesses, and curious minds who need fast, accurate weather insights without coding or complex interfaces.