Context

The dataset was created by keeping in mind the necessity of such historical weather data in the community. The datasets for top 8 Indian cities as per the population.

Content

The dataset was used with the help of the worldweatheronline.com API and the wwo_hist package. The datasets contain hourly weather data from 01-01-2009 to 01-01-2020. The data of each city is for more than 10 years. This data can be used to visualize the change in data due to global warming or can be used to predict the weather for upcoming days, weeks, months, seasons, etc. Note : The data was extracted with the help of worldweatheronline.com API and I can't guarantee about the accuracy of the data.

Acknowledgements

The data is owned by worldweatheronline.com and is extracted with the help of their API.

Inspiration

The main target of this dataset can be used to predict weather for the next day or week with huge amounts of data provided in the dataset. Furthermore, this data can also be used to make visualization which would help to understand the impact of global warming over the various aspects of the weather like precipitation, humidity, temperature, etc.

Saudi Arabia hourly climate integrated surface data with the below data observations, WindSky conditionVisibilityAir temperatureDewSea level pressureNote: The dataset will contain the last 5 years hourly data, however, check the attachments section in this dataset if you need historical data.

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 2022.

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 2022.

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.

Global Historical Climatology Network-hourly (GHCNh) is a multisource collection of weather station (meteorological) observations from the late 18th Century to the present from fixed weather stations over land across the globe. It is replacing the Integrated Surface Dataset (ISD) and will be used to generate the Local Climatological Data and Global Summary of the Day datasets. It is constructed to align with GHCN daily. Version 1 contains approximately 110 separate data sources and will be updated daily using the United States Air Force and NOAA Surface Weather Observations data streams. GHCNh v1 contains the following variables: altimeter; dew_point_temperature; precipitation; pressure_3hr_change; pres_wx_AU1; pres_wx_AU2; pres_wx_AU3; pres_wx_AW1; pres_wx_AW2; pres_wx_AW3; pres_wx_MW1; pres_wx_MW2; pres_wx_MW3; relative_humidity; Remarks; sea_level_pressure; sky_cov_baseht_1; sky_cov_baseht_2; sky_cov_baseht_3; sky_cover_1; sky_cover_2; sky_cover_3; station_level_pressure; dry bulb temperature; visibility; wet_bulb_temperature; wind_direction; wind_gust; wind_speed.

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. The authors observed an anomalous warming signal over the Great Plains in the end-of-century (2085 - 2094) RCP4.5 time slice. This anomaly is absent in the mid-century slice (2045 - 2054) under RCP4.5 and in both the mid- (2045 - 2054) and end-of-century (2085 - 2094) slices under RCP8.5. Consequently, we recommend that users exercise particular caution when using the RCP4.5 2085-2094 data, especially for analyses involving the Great Plains region.

[WARNING: in-depth description for hourly data is missing at the moment. Please refer to the Open-Meteo website(Air Quality and Historical Weather APIs specifically) for descriptions on columns included in hourly data for the time being. In short though, the hourly data info can be obtained from the daily data info, as the horly data is used to construct the daily data; example: if avg_nitrogen_dioxide is the average of the hourly instant(10 meters above ground in μg/m3) nitrogen dioxide values for a particular day, the "nitrogen_dioxide" column will consist of the hourly instant measurements of nitrogen dioxide (10 meters above ground in μg/m3.]

Result of a course project in the context of the Master's Degree in Data Science at Università Degli Studi di Milano-Bicocca. The dataset was built in hopes of finding ways to tackle the bad air quality for which Milan is becoming renown for, and to make the training of ML models possible. The data was collected through Open-Meteo's APIs, who in turn got it from "Reanalyses Models" of Europea initiative, used for weather and air quality forecast. The data used was validated by the owners of the reanalyses datasets from which the data comes from, and through the construction of this specific dataset it's data quality was assessed across accuracy, completeness and consistency dimensions. We aggregated the data from hourly to daily, it is possible to consult the entire Data Management process in the attached pdf.

File descriptions: - weatheraqDataset.csv : contains DAILY data on weather and air quality for the city of Milan in comma separateda values (csv) format. - weatheraqDataset_Report.pdf : report built to illustrate and explicit the process followed in order to build the final dataset starting from the original data sources; it also explains any processing and aggregation/integration operations carried out. - weatheraqHourly.csv : HOURLY data, counterpart to those in daily dataset(daily data is result of aggregation of hourly data). Higher granularity and number of rows can help with achieving better results, for detailed descriptions on how these hourly values are recorded and at what resolutions please visit the OpenMeteo website as stated in the warning at the start of the description.

GitHub repo of the project: https://github.com/edmos7/weather-aqMilan

Column descriptions for DAILY data (weatheraqDataset.csv):

note: both 'date' in DAILY data and 'datetime' in HOURLY data is in local Milan Time(CET&CEST), adjusted with Daylight Savings(DST).

• date: refers to day in calendar year which other values are relative to. YYYY-MM-DD format, in Milan local time.
• avg_nitrogen_dioxide : the average of the hourly instant(10 meters above ground in μg/m3) nitrogen dioxide values for a particular day.
• max_nitrogen_dioxide : the maximum value among the hourly instant(10 meters above ground in μg/m3) nitrogen dioxide values for a particular day
• min_nitrogen_dioxide : the minimum value among the hourly instant (10 meters above ground in μg/m3) nitrogen dioxidevalues for a particular day
• max_time_nitrogen_dioxide : hour at which hourly nitrogen dioxide values reached their maximum, HH:MM:SS
• min_time_nitrogen_dioxide : hour at which hourly nitrogen dioxide values reached their minimum, HH:MM:SS NOTE: all other "pollutant" columns (pm10, pm2_5, sulphur_dioxide, ozone) follow same structure as the above unless specified below.
• pm2_5_avgdRolls : the average of the 24hr rolling averages for particulate matter with diameter below μg/m (pm2.5), in a particular day. Rolling averages are used to compute the European Air Quality Index(EAQI) in a given moment, so in the computation of our Daily EAQI, averages of rolling averages were used. NOTE: the above goes also for the 'pm10_avgdRolls' field.
• eaqi : the computed air quality level according to European Environment Agency thresholds, considering daily averages for ozone, sulphur dioxide and nitrogen dioxide, and average of daily rolling averages for pm10 and pm2.5. The value corresponds to the highest level among single pollutant levels.
• nitrogen_dioxide_eaqi : the air quality level computed through EAQI thresholds for nitrogen dioxide individually, all other [pollutant]_eaqi fields follow same reasoning.
• avg_temperature_2m: average of hourly air temperatures recorded at 2 meters above ground level for the day(°C);
• max_temperature_2m: maximum among hourly air temperatures recorded at 2 meters above ground level for the day(°C);
• min_temperature_2m: minimum among hourly air temperatures recorded at 2 meters above ground level for the day(°C);
• avg_relative_humidity_2m: average of hourly humidity recorded at 2 meters above ground level for the day(%);
• avg_dew_point_2m: average of hourly dew point temperatures recorded at 2 meters above ground for the day(°C);
• avg_apparent_temperature: average of hourly...

Instead of relying on a single dataset, we integrate multiple authoritative sources, validate them, and tailor the outputs to match each client’s unique needs. From detailed weather statistics and event reconstructions to custom analytics for risk, engineering, insurance, or research, our process ensures accuracy, completeness, and context.

Whether you need... - a one-time dataset - ongoing access - specialized analysis ...we provide information in the format you need - from raw data to GIS-ready layers and summary PDF reports and graphics.

This product is built for anyone seeking high-quality historical weather intelligence: insurers quantifying past risks, engineers assessing infrastructure exposure, researchers analyzing climate trends, or businesses making data-driven decisions.

With our CCM-certified expertise and flexible delivery, you get not just data, but clarity and confidence in how weather impacts your world.

Pricing: Custom quotes available based on coverage, data volume, and deliverables. Typical engagements start at $1,000 (one-time) or $500/month for ongoing access or analytics.

Feel free to FORK THIS NOTEBOOK in order to correctly load the data for your project!

Overview: This dataset offers a comprehensive collection of Daily weather readings from major cities around the world. In the first release, it included only capitals, but now it also adds main cities worldwide and hourly data as well, making up to ~1250 cities. Some locations provide historical data tracing back to January 2, 1833, giving users a deep dive into long-term weather patterns and their evolution.

Data License and Updates: This dataset is updated every Sunday using data from Meteostat API, ensuring access to the latest week's data without overburdening the data source.

Cities DataFrame (cities.csv)

This dataframe offers details about individual cities and weather stations. - Columns: - station_id: Unique ID for the weather station. - city_name: Name of the city. - country: The country where the city is located. - state: The state or province within the country. - iso2: The two-letter country code. - iso3: The three-letter country code. - latitude: Latitude coordinate of the city. - longitude: Longitude coordinate of the city.

Countries DataFrame (countires.csv)

This dataframe contains information about different countries, providing insights into their geographic and demographic characteristics. - Columns: - iso3: The three-letter code representing the country. - country: The English name of the country. - native_name: The native name of the country. - iso2: The two-letter code representing the country. - population: The population of the country. - area: The total land area of the country in square kilometers. - capital: The name of the capital city. - capital_lat: The latitude coordinate of the capital city. - capital_lng: The longitude coordinate of the capital city. - region: The specific region within the continent where the country is located. - continent: The continent to which the country belongs. - hemisphere: The hemisphere in which the country is located (e.g., Northern, Southern).

Daily Weather DataFrame (daily_weather.parquet)

This dataframe provides weather data on a daily basis. - Columns: - station_id: Unique ID for the weather station. - city_name: Name of the city where the station is located. - date: Date of the weather record. - season: Season corresponding to the date (e.g., summer, winter). - avg_temp_c: Average temperature in Celsius. - min_temp_c: Minimum temperature in Celsius. - max_temp_c: Maximum temperature in Celsius. - precipitation_mm: Precipitation in millimeters. - snow_depth_mm: Snow depth in millimeters. - avg_wind_dir_deg: Average wind direction in degrees. - avg_wind_speed_kmh: Average wind speed in kilometers per hour. - peak_wind_gust_kmh: Peak wind gust in kilometers per hour. - avg_sea_level_pres_hpa: Average sea-level pressure in hectopascals. - sunshine_total_min: Total sunshine duration in minutes.

These dataframes can be utilized for various analyses such as weather trend prediction, climate studies, geographic analysis, demographic insights, and more.

Dataset Image Source: Photo credits to 越过山丘. View the original image here.

Weather data from two weather stations at Stuttgart Rice Research and Extension center are archived. Current air temperature, relative humidity, wind speed, solar radiation and soil temperature data are provided by station and are displayed and archived either hourly or daily. Historical weather data goes back to 2008. Resources in this dataset:Resource Title: Weather Station Data. File Name: Web Page, url: https://www.ars.usda.gov/southeast-area/stuttgart-ar/dale-bumpers-national-rice-research-center/docs/weather-station-data/

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.

This dataset contains hourly weather data collected from the DWD in Berlin Tempelhof. The time period may vary depending on the data set. For each CSV there is a separate PDF file with an explanation of the data.

Following climate data comes with this dataset: - air temperature - cloud type - cloudiness - dew point - moisture - pressure - soil temperature - sun duration - visibility - wind

The full of archives can be found here. The full list of weather stations can be found here.

Copyright

Source: Deutscher Wetterdienst

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Acknowlegement

Foto von Claudio Schwarz auf Unsplash

This dataset contains historical weather observations from various global locations, with its data provided at two temporal resolutions: daily and hourly. It includes core meteorological variables such as temperature, precipitation, wind, humidity, and atmospheric pressure, along with geospatial and temporal metadata for each observation. The dataset covers diverse geographic regions, including cities all arround the world, and supports both short-term event analysis and long-term climate trend exploration.

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.

The U.S. Hourly Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters for thousands of U.S. stations located across the 50 states, as well as U.S. territories, commonwealths, the Compact of Free Association nations, and one station inCanada. NOAA Climate Normals are a large suite of data products that provide users with many tools to understand typical climate conditions for thousands of locations across the United States. As many NWS stations as possible are used, including those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the Climate Reference Network (CRN). The comprehensive U.S. Climate Normals dataset includes various derived products including daily air temperature normals (including maximum and minimum temperature normal, heating and cooling degree day normal, and others), precipitation normals (including snowfall and snow depth, percentiles, frequencies and other), and hourly normals (all normal derived from hourly data including temperature, dew point, heat index, wind chill, wind, cloudiness, heating and cooling degree hours, pressure normals). Users can access the data either by product or by station. Included in the dataset is extensive documentation to describe station metadata, filename descriptions, and methodology of producing the data. All data utilized in the computation of the 1981-2010 Climate Normals were taken from the ISD Lite (a subset of derived Integrated Surface Data), the Global Historical Climatology Network-Daily dataset, and standardized monthly temperature data (COOP). These source datasets (including intermediate datasets used in the computation of products) are also archived at the NOAA NCDC.

Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

We created and curated a dataset of historical (1980-2019) hourly meteorology, load, wind, and solar data for the Salt River Project (SRP) region. The data was created by PNNL's GODEEEP project. Each row in the dataset is a single hour and each column is a variable. All meteorological variables are spatially-averaged over the SRP service territory. The variables and their units are as follows:

"Time_UTC"; Coordinated Universal Time (UTC); Time of day. "T2"; Fahrenheit; 2-m air temperature. "Q2"; kg/kg; 2-m water vapor mixing ratio. "SWDOWN"; W/m^2; Downwelling shortwave radiative flux at the surface. "GLW"; W/m^2; Downwelling longwave radiative flux at the surface. "WSPD"; m/s; 10-m wind speed. "Scaled_2019_Load"; MWh; Simulated hourly demand for electricity that is scaled to 2019 levels of annual energy. This load estimate does not account for historical changes in population and economics within the SRP service territory. It is included to make it easier to isolate weather impacts on load without having to consider long-term changes. "Load"; MWh; Simulated hourly demand for electricity. "Agua_Fria_Solar_Capacity"; N/A; Solar capacity factor for the SRP Agua Fria project with plant configurations taken from the EIA-860 database. "Phoenix_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Phoenix, AZ. "Flagstaff_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Flagstaff, AZ. "Phoenix_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Phoenix, AZ. "Flagstaff_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Flagstaff, AZ.

Hourly Precipitation Data (HPD) is digital data set DSI-3240, archived at the National Climatic Data Center (NCDC). The primary source of data for this file is approximately 5,500 US National Weather Service (NWS), Federal Aviation Administration (FAA), and cooperative observer stations in the United States of America, Puerto Rico, the US Virgin Islands, and various Pacific Islands. The earliest data dates vary considerably by state and region: Maine, Pennsylvania, and Texas have data since 1900. The western Pacific region that includes Guam, American Samoa, Marshall Islands, Micronesia, and Palau have data since 1978. Other states and regions have earliest dates between those extremes. The latest data in all states and regions is from the present day. The major parameter in DSI-3240 is precipitation amounts, which are measurements of hourly or daily precipitation accumulation. Accumulation was for longer periods of time if for any reason the rain gauge was out of service or no observer was present. DSI 3240_01 contains data grouped by state; DSI 3240_02 contains data grouped by year.

The 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.

The Historical Gridded Meteorological Dataset in Japan (HGMD-Japan) is a grided high-resolution (1km x 1km, approximately) daily (and in some cases hourly or yearly) meteorological datasets, intended for use in agricultural climate change analysis, created from 1978 to the latest year continuously. The daily data were created by overlaying the spatially interpolated differences between observed and climate normal at the meteorological observation stations onto the 1km resolution gridded climate data. In this process, in order to maintain time-series homogeneity in each variable, possible source of time-series heterogeneities unrelated to climate change, such as changes in statistical methods and instrument types, were corrected as much as possible.

The details of this dataset are described as follows.

■ Common items Projection: Geographic Geodetic system: Tokyo Datum

◆ Daily data Directory structure: HGMDJ_NARO(YYYY)daily[file] File name: (YYYY)_d_(element).bin Element name (element): Mean temperatures (tmp) [0.1 °C]
Maximum temperatures (hourly) (tmx) [0.1 °C] Minimum temperatures (hourly) (tmn) [0.1 °C] Precipitation (pre) [0.1 mm] Solar radiation (srd) [0.1 MJ/m2/d] Sunshine duration (sdr) [0.1 hour] Relative humidity (rhu) [0.1 %] Wind speed at 2.5m height (wsd) [0.1 m/s] Downward long wave radiation (lrd) [0.1 MJ/ m2/d] Potential evapotranspiration (pet) [0.1 mm] FAO reference evapotranspiration (eto) [0.1 mm] Paddy water temperature (LAI=0) (tw0) [0.1 °C] Paddy water temperature (LAI=∞) (twi) [0.1 °C] Error value: -999 Record format: Data format: Binary format (little endian) Data size: 278,237,440 bytes Record length: 736 bytes (4+366*2: see below) Number of rows (meshes): 378040 Structure: 1) 3rd mesh code (4 byte long), data (2 byte short) x 366 days 2) 3rd mesh code (4 byte long), data (2 byte short) x 366 days ・・・ 378040) 3rd mesh code (4 byte long), data (2 byte short) x 366 days * Dummy (-999) for the 366th day in no-leap years

◆ Hourly data Directory structure: HGMDJ_NARO(YYYY)hourly[element][file] File name: (YYYYMMDD)_h_(element).bin Element name (element): Rice panicle temperatures (tp) [0.1 °C] Air temperatures (ta) [0.1 °C] Error value: -999 Record format: Data format: Binary format (little endian) Data size: 19,658,080 bytes Record length: 52 bytes (4+24*2: see below) Number of rows (meshes): 378040 Structure: 1) 3rd mesh code (4 byte long), data (2 byte short) x 24 hours 2) 3rd mesh code (4 byte long), data (2 byte short) x 24 hours ・・・ 378040) 3rd mesh code (4 byte long), data (2 byte short) x 24 hours

◆ Yearly data Directory structure: HGMDJ_NARO(YYYY)yearly[file] File name: (YYYY)_y_(element).bin Element name (element): Heat-dose of daily maximum temperature above 35 ℃ (HD_x35) (hdx35) [0.1 °C day] Heat-dose of daily minimum temperature above 25 ℃ (HD_n25) (hdn25) [0.1 °C day] Heat-dose of daily mean temperature above 26 ℃ (HD_m26) (hdm26) [0.1 °C day] Mean air temperature during 20 days after heading date (hed20atm) [0.1 °C] HD_m26 during 20 days after heading date (hed20hdm26) [0.1 °C day] Mean panicle temperature during daytime within 5 days around heading date (ptm5dc) [0.1 °C] Mean panicle temperature during daytime within 7 days around heading date (ptm7dc) [0.1 °C] Error value: -999 Record format: Data format: Binary format (little endian) Data size: 2,268,240 bytes Record length: 6 bytes (4+2: see below) Number of rows (meshes): 378040 Structure: 1) 3rd mesh code (4 byte long), data (2 byte short) 2) 3rd mesh code (4 byte long), data (2 byte short) ・・・ 378040) 3rd mesh code (4 byte long), data (2 byte short)

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 2018. 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. 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.