The DIAMET project aimed to better the understanding and prediction of mesoscale structures in synoptic-scale storms. Such structures include fronts, rain bands, secondary cyclones, sting jets etc, and are important because much of the extreme weather we experience (e.g. strong winds, heavy rain) comes from such regions. Weather forecasting models are able to capture some of this activity correctly, but there is much still to learn. By a combination of measurements and modelling, mainly using the Met Office Unified Model (UM), the project worked to better understand how mesoscale processes in cyclones give rise to severe weather and how they can be better represented in models and better forecast.

This dataset contains minute resolution meteorological measurements by the Met Office Automatic Weather Stations (AWS) during the DIAMET intensive observation campaigns.

Licence information and source

The MET Office copyright policy can be found at: [https://www.metoffice.gov.uk/about-us/legal#licences] Data source from: [https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data]

Cover image: [https://pixabay.com/photos/scarborough-sunrise-seascape-2850597/]

The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2023.

This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.

For details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.

This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data.

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).

The Hadley Centre at the U.K. Met Office has created a global sub-daily dataset of several station-observed climatological variables which is derived from and is a subset of the NCDC's Integrated Surface Database. Stations were selected for inclusion into the dataset based on length of the data reporting period and the frequency with which observations were reported. The data were then passed through a suite of automated quality-control tests to remove bad data. See the HadISD web page [http://www.metoffice.gov.uk/hadobs/hadisd/] for more details and access to previous versions of the dataset.

Data extracted from the MET Office using the Datapoint API and gives the names, ID codes, elevation (m) and locations of Glasgow's weather stations (lat/long WGS 84). Each station will have different capabilities and the MetOffice Datapoint API's can be used to ascertain which services are available at which station. The MET Office Datapoint API can provide forecast data as well as actual weather observations. The API can extract site specific data which can be presented in a number of formats including text, map overlays and charts. The Met Office provides an API Reference - which is a consolidated guide for using the DataPoint API and its products. A portable document file is also available and icluded with this dataset. The Met Office has a large range of freely available products relating to weather and climate but each user must register to receive an API key. The Met Office Datapoint Terms and Conditions can be examined here. Licence: None datapoint-api-reference.pdf - https://dataservices.open.glasgow.gov.uk/Download/Organisation/494256e0-6740-4597-bc7c-3fd1ba3d46ff/Dataset/231871e4-e2ae-4cc1-a147-ec4f28fd289f/File/9ca1a778-128d-49a6-a044-b078cab70004/Version/cf4ffdeb-3392-45ff-89f2-8dd14390b8be

Weather stations measure a large variety of different meteorological parameters, including air temperature; atmospheric pressure; rainfall; wind speed and direction, humidity; cloud height and visibility. This table includes the locations of weather stations within the West Midlands metropolitan area operating for the Met Office. To request access contact the Data Insight Team.

Monthly Historical information for 37 UK Meteorological Stations. Most go back to the early 1900s, but some go back as far as 1853.

Data includes:

• Mean maximum temperature (tmax)
• Mean minimum temperature (tmin)
• Days of air frost (af)
• Total rainfall (rain)
• Total sunshine duration (sun)

Station data files are updated on a rolling monthly basis, around 10 days after the end of the month. Data are indicated as provisional until the full network quality control has been carried out. After this, data are final.

No allowances have been made for small site changes and developments in instrumentation.

Data and statistics for other stations, and associated charges, can be obtained by contacting our Customer Centre.

The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2023. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.

This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.

This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection.

GC-Net Level 1 automated weather station data In Memory of Dr. Konrad (Koni) Steffen Author: B. Vandecrux Contact: bav@geus.dk Last update: 2023-09-01 Citation Steffen, K.; Vandecrux, B.; Houtz, D.; Abdalati, W.; Bayou, N.; Box, J.; Colgan, L.; Espona Pernas, L.; Griessinger, N.; Haas-Artho, D.; Heilig, A.; Hubert, A.; Iosifescu Enescu, I.; Johnson-Amin, N.; Karlsson, N. B.; Kurup Buchholz, R.; McGrath, D.; Cullen, N.J.; Naderpour, R.; Molotch, N.P.; Pederson, A. Ø.; Perren, B.; Philipps, T.; Plattner, G.K.; Proksch, M.; Revheim, M. K.; Særrelse, M.; Schneebli, M.; Sampson, K.; Starkweather, S.; Steffen, S.; Stroeve, J.; Watler, B.; Winton, Ø. A.; Zwally, J.; Ahlstrøm, A., 2023, "GC-Net Level 1 automated weather station data", https://doi.org/10.22008/FK2/VVXGUT, GEUS Dataverse, V3 as described and processed by: Vandecrux, B., Box, J. E., Ahlstrøm, A. P., Andersen, S. B., Bayou, N., Colgan, W. T., Cullen, N. J., Fausto, R. S., Haas-Artho, D., Heilig, A., Houtz, D. A., How, P., Iosifescu Enescu, I., Karlsson, N. B., Kurup Buchholz, R., Mankoff, K. D., McGrath, D., Molotch, N. P., Perren, B., Revheim, M. K., Rutishauser, A., Sampson, K., Schneebeli, M., Starkweather, S., Steffen, S., Weber, J., Wright, P. J., Zwally, H. J., and Steffen, K.: The historical Greenland Climate Network (GC-Net) curated and augmented Level 1 dataset, Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, 2023. Description The Greenland Climate Network (GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen on the Greenland Ice Sheet (GrIS). This first station, "Swiss Camp" or the "ETH-CU" camp, was initiated in 1990 by A. Ohmura et al. (1991, 1992) with K. Steffen taking over the site from 1995 and expending the network from that year to 31 stations at 30 sites in Greenland (Steffen et al., 1996, 2001). The GC-Net was supported by multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado. Provided in this dataset are the 25 two-level stations from 24 sites on the Greenland ice sheet and 3 experimental stations in Antarctica. The remaining 6 Greenland stations have a different design and will be added once quality checked. Although the GC-Net AWS transmitted their data near-real time through satellite communication, the present dataset was made from uncorrupted datalogger files, retrieved every 1-2 years during maintenance. Full dataset description publication will be forthcoming. The Geological Survey of Denmark and Greenland (GEUS) has undertaken the continuation of multiple GC-Net sites through the Programme for Monitoring of the Greenland Ice Sheet (PROMICE.dk). The level 1 data is provided in the newly described csv-compatible NEAD format, which is a csv file with added metadata header. The format is documented at https://doi.org/10.16904/envidat.187 and a python package is available to read and write NEAD files: https://github.com/GEUS-Glaciology-and-Climate/pyNEAD . The GC-Net stations measure: - Air temperature from four sensors at two heights above the surface - Relative humidity at two heights above the surface - Wind speed and direction at two heights above the surface - Air pressure - Surface height from two sonic sounders - Incoming and outgoing shortwave radiation - Net radiation (long- and short-wave)* - Firn or ice temperatures at 10 levels below the surface In the L1 dataset, these measurements are cleaned from sensor, station or logger malfunctions, adjusted and/or filtered when and where possible. Additionally, the L1 dataset contains the following derived variables: - Surface height (corrected from the shifts in sonic sounder height) - Instrument heights (derived from sonic sounder height and station geometry) - Inter- or extrapolated temperature, relative humidity and wind speed at respectively 2, 2, and 10 m above the surface - Estimated depth of the subsurface temperature measurements (adjusted for snow accumulation, ice ablation and instrument replacement) - Interpolated firn or ice temperature at 10 m below the surface - Calculated solar an azimuth angles - Sensible and latent heat fluxes calculated after Steffen and Demaria (1996) Important links: - The level 1 processing scripts and discussion page for Q&A and issue reporting (under "issues" tab) is available at: https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing - The level 0 data (from which the L1 data was built from) is available at: https://www.doi.org/10.16904/envidat.1. - The compilation of handheld GPS coordinates for each site and for multiple years is available here: Vandecrux, B. and Box, J.E.: GC-Net AWS observed and estimated positions (Version v1) [Data set]. Zenodo....

Current METAR weather stations and associated weather conditions based on Meteorological Terminal Aviation Routine Weather Report (METAR) data collected globally from either airports or permanent weather observation stations by NOAA’s NWS Aviation Weather Center (http://www.aviationweather.gov/metar). IGEMS reads this source data and updates the layer every 10 minutes.

This layer is a component of Interior Geospatial Emergency Management System (IGEMS) General Data.

This map presents the geospatial locations and additional information for global tide monitoring stations, and U.S. stream gages, weather stations and DOI managed lands. This map is part of the Interior Geospatial Emergency Management System (IGEMS) and is supported by the DOI Office of Emergency Management. This map contains data from a variety of public data sources, including non-DOI data, and information about each of these data providers, including specific data source and update frequency is available at: http://igems.doi.gov.

© DOI Office of Emergency Management

ClimateForecasts is a database that provides environmental data for 15,504 weather station locations and 49 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. Data were extracted from the same 30 arc-seconds global grid layers that were prepared when making the TreeGOER (Tree Globally Observed Environmental Ranges) database that is available from https://doi.org/10.5281/zenodo.7922927. Details on the preparations of these layers are provided by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914. A similar extraction process was used for the CitiesGOER database that is also available from Zenodo via https://zenodo.org/doi/10.5281/zenodo.8175429.

ClimateForecasts (as the CitiesGOER) was designed to be used together with TreeGOER and possibly also with the GlobalUsefulNativeTrees database (Kindt et al. 2023) to allow users to filter suitable tree species based on environmental conditions of the planting site. One example of combining data from these different sets in the R statistical environment is available from this Rpub: https://rpubs.com/Roeland-KINDT/1114902.

The identities including the geographical coordinates of weather stations were sourced from Meteostat, specifically by downloading (17-FEB-2024) the ‘lite dump’ data set with information for active weather stations only. Two weather stations where the country could not be determined from the ISO 3166-1 code of ‘XA’ were removed. If weather stations had the same name, but occurred in different ISO 3166-2 regions, this region code was added to the name of the weather station between square brackets. Afterwards duplicates (weather stations of the same name and region) were manually removed.

Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s. Similar methods were used to calculate these median values as in the case studies for the TreeGOER manuscript (calculations were partially done via the BiodiversityR::ensemble.envirem.run function and with downscaled bioclimatic and monthly climate 2.5 arc-minutes future grid layers available from WorldClim 2.1).

Maps were added in version 2024.03 where locations of weather stations were shown on a map of the Climatic Moisture Index (CMI). These maps were created by a similar process as in the TreeGOER Global Zones Atlas from the environmental raster layers used to create the TreeGOER via the terra package (Hijmans et al. 2022, version 1.7-46) in the R 4.2.1 environment. Added country boundaries were obtained from Natural Earth as Admin 0 – countries vector layers (version 5.1.1). Also added after obtaining them from Natural Earth were Admin 0 – Breakaway, Disputed areas (version 5.1.0, coloured yellow in the atlas) and Roads (version 5.0.0, coloured red in the atlas). For countries where the GlobalUsefulNativeTrees database included subnational levels, boundaries were added and depicted as dot-dash lines. These subnational levels correspond to level 3 boundaries in the World Geographical Scheme for Recording Plant Distributions. These were obtained from https://github.com/tdwg/wgsrpd. Check Brummit 2001 for details such as the maps shown at the end of this document.

Maps for version 2024.07 modified the dimensions of the sheets to those used in version 2024.06 of the TreeGOER Global Zones Atlas. Another modification was the inclusion of Natural Earth boundaries for Lakes (version 5.0.0, coloured darkblue in the atlas).

Version 2024.10 includes a new data set that documents the location of the city locations in Holdridge Life Zones. Information is given for historical (1901-1920), contemporary (1979-2013) and future (2061-2080; separately for RCP 4.5 and RCP 8.5) that are available for download from DRYAD and were created for the following article: Elsen et al. 2022. Accelerated shifts in terrestrial life zones under rapid climate change. Global Change Biology, 28, 918–935. https://doi.org/10.1111/gcb.15962. Version 2024.10 further includes Holdridge Life Zones for the climates available from the previously included climates, calculating biotemperatures and life zones with similar methods as used by Holdridge (1947; 1967) and Elsen et al. (2022) (for future climates, median values were determined first for monthly maximum and minimum temperatures across GCMs ). The distributions of the 48,129 species documented in TreeGOER across the Holdridge Life Zones are given in this Zenodo archive: https://zenodo.org/records/14020914.

Version 2024.11 includes a new data set that documents the location of the weather stations in Köppen-Geiger climate zones. Information is given for historical (1901-1930, 1931-1960, 1961-1990) and future (2041-2070 and 2071-2099) climates, with for the future climates seven scenarios each (SSP 1-1.9, SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5). This data set was created from raster layers available via: Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724 (2023). https://doi.org/10.1038/s41597-023-02549-6.

Version 2025.03 includes extra columns for the baseline, 2050s and 2090s datasets that partially correspond to climate zones used in the GlobalUsefulNativeTrees database. One of these zones are the Whittaker biome types, available as a polygon from the plotbiomes package (see also here). Whittaker biome types were extracted with similar R scripts as described by Kindt 2025 (these were also used to calculate environmental ranges of TreeGOER species, as archived here).

Version 2025.03 further includes information for the baseline climate on the steady state water table depth, obtained from a 30 arc-seconds raster layer calculated by the GLOBGM v1.0 model (Verkaik et al. 2024).

When using ClimateForecasts in your work, cite this depository and the following:

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086

Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41(2), 291–307. https://doi.org/10.1111/ecog.02880

Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021

Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.

Meteostat (2024) Weather stations: Lite dump with active weather stations. https://github.com/meteostat/weather-stations (accessed 17-FEB-2024)

When using information from the Holdridge Life Zones, also cite:

Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K.-V., Reside, A. E., & Watson, J. E. M. (2022). Accelerated shifts in terrestrial life zones under rapid climate change. Global Change Biology, 28, 918–935. https://doi.org/10.1111/gcb.15962

When using information from Köppen-Geiger climate zones, also cite:

Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N.J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A.I. and Miralles, D.G. 2023. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724. https://doi.org/10.1038/s41597-023-02549-6

When using information on the Whittaker biome types, also cite:

Ricklefs, R. E., Relyea, R. (2018). Ecology: The Economy of Nature. United States: W.H. Freeman.

Whittaker, R. H. (1970). Communities and ecosystems.

Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7145245

When using information on the steady state water table depth, also cite:

Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X., & Bierkens, M. F. (2024). GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development, 17(1), 275-300. https://gmd.copernicus.org/articles/17/275/2024/

The development of ClimateForecasts and its partial integration in version 2024.03 of the GlobalUsefulNativeTrees database was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project and through the Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso, by the Bezos Earth Fund to the Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.

The weather station on the campus of Loughborough University, in the East Midlands of the UK, had fallen into disuse and disrepair by the mid-2000s, but in 2007 the availability of infrastructure funding made it possible to re-establish regular weather observation with new equipment. The meteorological dataset subsequently collected at this facility between 2008 and 2021 is archived here. The dataset comes as fourteen Excel (.xlsx) files of annual data, with explanatory notes in each.Site descriptionThe campus weather station is located at latitude 52.7632°, longitude -1.235° and 68 m a.s.l., in a dedicated paddock on a green space near the centre-east boundary of the campus. A cabin, which houses power and network points, sits 10 m to the northeast of the main meteorological instrument tower. The paddock is otherwise mostly open on an arc from the northwest to the northeast, but on the other sides there are fruit trees (mainly varieties of prunus domestica) at distances of 13–16 m, forming part of the university's "Fruit Routes" biodiversity initiative.Data collectionInstruments were fixed to a 3 m lattice mast which is concreted into the ground in the centre of the paddock described above. Up to late July 2013, the instruments were controlled by a solar-charged, battery-powered Campbell Scientific CR1000 data logger, and periodically manually downloaded. From early November 2013, this logger was replaced with a Campbell Scientific CR3000, run from the mains power supply from the cabin and connected to the campus network by ethernet. At the same time, the station's Young 01503 Wind Monitor was replaced by a Gill WindSonic ultrasonic anemometer. This combination remained in place for the rest of the measurement period described here. Frustratingly, the CS215 temperature/relative humidity sensor failed shortly before the peak of the 2018 heatwave, and had to be replaced with another CS215. Likewise, the ARG100 rain gauge was replaced in 2011 and 2016. The main cause of data gaps is the unreliable power supply from the cabin, particularly in 2013 and 2021 (the latter leading to the complete replacement of the cabin and all other equipment). Furthermore, even though the post-2013 CR3000 logger had a backup battery, it sometimes failed to restart after mains power was lost, yielding data gaps until it was manually restarted. Nevertheless, out of 136 instrument-years deployment, only 36 are less than 90% complete, and 21 less than 75% complete.Data processingData retrieved manually or downloaded remotely were filtered for invalid measurements. The 15-minute data were then processed to daily and monthly values, using the pivot table function in Microsoft Excel. Most variables could be output simply as midnight-to-midnight daily means (e.g. solar and net radiation, wind speed). However, certain variables needed to be referred to the UK and Ireland standard ‘Climatological Day’ (Burt, 2012:272), 0900-0900: namely, air temperature minimum and maximum, plus rainfall total. The procedure for this follows Burt (2012; https://www.measuringtheweather.net/) and requires the insertion of additional date columns into the spreadsheet, to define two further, separate ‘Climate Dates’ for maximum temperature and rainfall total (the 24 hours commencing at 0900 on the date given, ‘ClimateDateMax’), and for minimum temperatures (24 hours ending at 0900 on the date given, ‘ClimateDateMin’). For the archived data, in the spreadsheet tabs labelled ‘Output - Daily 09-09 minima’, the pivot table function derives daily minimum temperatures by the correct 0900-0900 date, given by the ClimateDateMin variable. Similarly, in the tabs labelled ‘Output - Daily 09-09 maxima’, the pivot table function derives daily maximum temperatures and daily rainfall totals by the correct 0900-0900 date, given by the ClimateDateMax variable. Then in the tabs labelled ‘Output - Daily 00-00 means’, variables with midnight-to-midnight means use the unmodified date variable. To take into account the effect of missing data, the tab ‘Completeness’ again uses a pivot table to count the numbers of daily and monthly observations where the 15-minute data are not at least 99.99% complete. Values are only entered into the ‘Daily data’ tab of the archived spreadsheets where 15-minute data are at least 75% complete; values are only entered into ‘Monthly data’ tabs where daily data are at least 75% complete.Wind directions are particularly important in UK meteorology because they indicate the origin of air masses with potentially contrasting characteristics. But wind directions are not averaged in the same way as other variables, as they are measured on a circular scale. Instead, 15-minute wind direction data in degrees are converted to 16 compass points (the formula is included in the spreadsheets), and a pivot table is used to summarise these into wind speed categories, giving the frequency and strength of winds by compass point.In order to evaluate the reliability of the collected dataset, it was compared to equivalent variables from the HadUK-Grid dataset (Hollis et al., 2019). HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations, which have been interpolated from meteorological station data onto a uniform grid to provide coherent coverage across the UK at 1 km x 1 km resolution. Daily and monthly air temperature and rainfall variables from the HadUK-Grid v1.1.0.0 Met Office (2022) were downloaded from the Centre for Environmental Data Analysis (CEDA) archive (https://catalogue.ceda.ac.uk/uuid/bbca3267dc7d4219af484976734c9527/). Then the grid square containing the campus weather station was identified using the Point Subset Tool of the NOAA Weather and Climate Toolkit (https://www.ncdc.noaa.gov/wct/index.php) in order to retrieve data from that specific location. Daily and monthly HadUK-grid data are included in the spreadsheets for convenience.Campus temperatures are slightly, but consistently, higher than those indicated by HadUK-grid, while HadUK-Grid rainfall is on average almost 10% higher than that recorded on the campus. Trend-free statistical relationships between campus and HadUK-grid data implies that there is unlikely to be any significant temporal bias in the campus dataset.ReferencesBurt, S. (2012). The Weather Observer's Handbook. Cambridge University Press, https://doi.org/10.1017/CBO9781139152167.Hollis, D, McCarthy, M, Kendon, M., Legg, T., Simpson, I. (2019). HadUK‐Grid—A new UK dataset of gridded climate observations. Geoscience Data Journal 6, 151–159, https://doi.org/10.1002/gdj3.78.Met Office; Hollis, D.; McCarthy, M.; Kendon, M.; Legg, T. (2022). HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.1.0.0 (1836-2021). NERC EDS Centre for Environmental Data Analysis, https://dx.doi.org/10.5285/bbca3267dc7d4219af484976734c9527.

The text file "Weather Station Data, St. Louis, Missouri.txt" contains hourly data collected by a Campbell Scientific ET107 weather station located in St. Louis, Missouri. Data were collected from November 29, 2019 through September 17, 2020. Weather station data sets include wind speed, in meters per second; wind direction, in degrees; rainfall, in inches; average air temperature, in degrees Celsius; maximum air temperature, in degrees Celsius; minimum air temperature, in degrees Celsius; average relative humidity, in percent; average solar radiation, in Watts per square meter; and computed potential evapotranspiration, in mm.

This dataset include the Dickerson Weather Station Data. The hourly wind and temperature data are periodically used for Air Quality Dispersion Modeling work for the Resource Recovery Facility, as well as ambient monitoring and health risk studies associated with this facility. The air quality modeling results identify the locations of maximum impacts from Resource Recovery Facility stack emissions. The wind data help us investigate residents' reports of odors possibly coming from the Composting or Resource Recovery Facilities. Update Frequency : Daily

Portable Small Automatic Weather Station Market Outlook

The global market size for Portable Small Automatic Weather Stations is projected to reach USD 5.8 billion by 2032, up from USD 2.5 billion in 2023, growing at a compound annual growth rate (CAGR) of 9.8% over the forecast period. This impressive growth is driven by increasing demand for accurate and real-time weather data across various sectors including agriculture, environmental research, and military applications. Enhanced technological advancements and the integration of IoT and AI in weather monitoring systems also act as significant growth catalysts in this market.

One of the primary growth factors for this market is the increasing awareness and need for precise climate data. As climate change continues to be a pressing global issue, various sectors are increasingly relying on accurate weather data for better decision-making. For instance, in agriculture, portable small automatic weather stations are crucial for monitoring soil moisture, predicting rainfall, and planning irrigation schedules. This helps in improving crop yields and reducing the risk of crop failure, thereby driving market growth. Moreover, these weather stations are becoming more affordable, which makes them accessible to small and medium-sized enterprises and individual farmers, further expanding the market.

Technological advancements also play a pivotal role in the market growth of portable small automatic weather stations. The integration of advanced sensors, IoT, and AI has significantly enhanced the accuracy and functionality of these devices. Modern weather stations can now provide real-time data and analytics, which are crucial for various applications ranging from environmental research to disaster management. The miniaturization of components and the development of compact, energy-efficient systems have also contributed to the proliferation of portable weather stations.

The increasing frequency of extreme weather events and natural disasters is another major growth driver for this market. Governments and private organizations are investing heavily in weather monitoring and forecasting systems to mitigate the impact of such events. Portable small automatic weather stations are particularly useful in remote and disaster-prone areas where traditional weather monitoring infrastructure is lacking. These stations can be rapidly deployed and provide critical data that aid in timely and effective response to natural disasters, thus driving market demand.

In addition to portable solutions, Fixed Station Monitors play a crucial role in providing continuous and long-term weather data. These fixed installations are often strategically placed in locations where consistent monitoring is essential, such as airports, research facilities, and urban centers. The data collected from these stations is invaluable for climate studies, weather forecasting, and environmental monitoring. Fixed Station Monitors are equipped with a wide array of sensors that deliver highly accurate and reliable data, which is critical for making informed decisions in various sectors. The integration of advanced technologies in these monitors ensures that they remain a vital component of the broader weather monitoring infrastructure.

Regionally, North America and Europe are expected to dominate the market due to their advanced infrastructure and significant investments in weather monitoring technologies. However, the Asia Pacific region is anticipated to witness the highest growth rate, driven by increasing awareness about climate change, government initiatives, and the adoption of advanced agricultural practices. The growing need for disaster management and environmental research in this region also contributes to the market's expansion. Latin America and the Middle East & Africa are also expected to show considerable growth, albeit at a slower pace compared to the Asia Pacific.

Product Type Analysis

The market for portable small automatic weather stations can be segmented into fixed weather stations and portable weather stations. Fixed weather stations are generally installed in a permanent location and are used for long-term weather monitoring. These stations are often equipped with a wide range of sensors and provide highly accurate and reliable data. They are commonly used in meteorological research, environmental monitoring, and by government agencies. The demand for fixed weather stations is driven by the need for continuous and long-t

Campus Weather Station Market Outlook

In 2023, the global campus weather station market size was valued at approximately USD 350 million and is projected to reach around USD 650 million by 2032, growing at a compounded annual growth rate (CAGR) of 7.5% during the forecast period. The growth of this market is attributed to several factors, including increased climate awareness, advancements in weather prediction technologies, and the rising need for educational and research institutions to monitor and analyze weather patterns accurately.

One of the primary growth factors driving the campus weather station market is the increasing awareness about climate change and its impact on various aspects of life. Governments and educational institutions are becoming more proactive in monitoring weather conditions to better understand climate patterns and their consequences. This heightened awareness has led to a surge in the installation of weather stations in campuses across the globe. Additionally, the integration of advanced technologies such as IoT, AI, and machine learning into weather stations has significantly improved the accuracy and efficiency of weather data collection and analysis, further fueling market growth.

Another significant factor contributing to the market's growth is the growing importance of weather data in various sectors such as agriculture, aviation, and disaster management. Educational institutions and research facilities are increasingly utilizing campus weather stations to gather precise weather data for research purposes and to enhance the educational experience of students studying meteorology and related fields. The data collected from these weather stations is also crucial for government agencies involved in planning and implementing disaster management strategies, making them indispensable tools in mitigating the effects of natural disasters.

The increasing trend of smart campuses is also playing a pivotal role in the market's expansion. Modern campuses are integrating smart technologies to create more efficient and sustainable environments. Campus weather stations are an integral part of these smart infrastructures, providing real-time weather data that can be used to optimize energy consumption, manage water resources, and ensure the safety and comfort of campus occupants. This trend is expected to continue, driving further growth in the market.

From a regional perspective, North America and Europe are currently the leading markets for campus weather stations, primarily due to the presence of numerous educational and research institutions, as well as government support for climate monitoring initiatives. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increasing investments in smart city projects, and a growing focus on climate change mitigation. Latin America and the Middle East & Africa are also anticipated to experience steady growth, supported by government efforts to enhance weather monitoring infrastructure.

Component Analysis

When segmenting the campus weather station market by component, we observe four primary categories: sensors, data loggers, software, and services. Sensors form the backbone of any weather station, capturing critical data such as temperature, humidity, wind speed, and precipitation levels. Advances in sensor technology have led to more accurate and reliable data collection, which is essential for weather forecasting and climate research. Modern sensors are often equipped with IoT capabilities, allowing for real-time data transmission and remote monitoring, significantly enhancing their utility.

Data loggers are another crucial component of campus weather stations. These devices store the vast amounts of data collected by sensors for future analysis. The evolution of data loggers has been marked by improvements in data storage capacity, durability, and connectivity options. Today's data loggers often come with wireless capabilities, enabling seamless data transfer to cloud-based platforms where it can be accessed and analyzed remotely. This is particularly useful for educational and research institutions that require long-term data storage and easy access to historical weather data.

Software solutions play a significant role in the processing, analysis, and visualization of weather data. Advanced software platforms can integrate data from multiple sensors and data loggers, providing comprehensive and actionable insights. These platforms often come with features such

Observations for wind, soil temperature and radiation from Enhanced Synoptic Automatic Weather Station (ESAWS) not necessarily reported in real-time.

This data set contains hourly resolution surface meteorological data from the California Irrigation Management Information System (CIMIS) weather stations. CIMIS is a program in the Office of Water Use Efficiency (OWUE) in the California Department of Water Resources. The network includes over 120 weather stations in the state of California. The data are in comma-delimited ASCII.