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.

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.

Any Data is as good as its Description, so here's a brief explanation:

The following data set contains Temperature data (Minimum, Average, Maximum) in degrees Centigrade and Precipitation data in mm.

This data set contains daily Temperature and Precipitation data from 01/01/1990 to 20/07/2022. Data for the following cities is present : * Delhi * Bangalore * Chennai * Lucknow * Rajasthan * Mumbai * Bhubaneswar * Rourkela

The station Geolocation file will give you the approximate location from where these measurements are taken.

What Can you do with this Data Set ? * Can you Find the hottest/coldest years for each city? * Can you Find precipitation averages and tell when rainfall was abnormally less or abnormally more? * Can you Prove that temperature is increasing and if so at what rate (degree increase/ year)? * Can you create Effective Visualization to convey the same?

Note: This Data set is ideal for Beginners and college students to hone their data science and Visualization skills.

Weather Data for Locations Across the UK

This dataset contains historical weather data from various locations across the UK, spanning from 2009 to 2024. Each entry records the weather conditions for a specific day, providing insights into temperature, rain, humidity, cloud cover, wind speed, and wind direction. The data is useful for analyzing weather patterns and trends over time.

Columns:

• location: The name of the location (e.g., Holywood, Ardkeen).
• date: The date of the weather record (format: YYYY-MM-DD).
• min_temp (°C): The minimum temperature recorded on that day (in degrees Celsius).
• max_temp (°C): The maximum temperature recorded on that day (in degrees Celsius).
• rain (mm): The amount of rainfall recorded (in millimeters).
• humidity (%): The percentage of humidity.
• cloud_cover (%): The percentage of cloud cover.
• wind_speed (km/h): The wind speed recorded (in kilometers per hour).
• wind_direction: The direction of the wind (e.g., N, SSE, WSW).
• wind_direction_numerical: The numerical representation of the wind direction (e.g., 90.0 for east).

Example Data:

Use Cases:

• Weather pattern analysis for specific regions: By analyzing temperature, humidity, and wind patterns across different locations, users can study how weather behaves seasonally and regionally, identifying patterns or anomalies.
• Long-term climate studies: This dataset spans over 15 years, making it useful for examining long-term climate trends such as temperature fluctuations, increased rainfall, or shifts in wind direction.
• Building predictive models for weather forecasting: The data can be used to build machine learning models that predict future weather conditions based on historical patterns. This is helpful for industries such as agriculture, transportation, and event planning.
• Climate change research: Researchers can use this dataset to study the effects of climate change on temperature, precipitation, and wind patterns over time.
• Energy sector applications: The wind speed and temperature data can be used to optimize energy production, especially for renewable energy sources like wind and solar power.
• Tourism and event planning: By analyzing weather trends, businesses in the tourism and event industries can better plan for weather conditions, helping with decisions on the best times to host outdoor events or market destinations.
• Agriculture and crop planning: Farmers and agronomists can use this data to analyze seasonal weather conditions, helping them plan for crop planting, growth, and harvesting by understanding local climate conditions.
• Urban planning and infrastructure design: City planners can use this data to assess how weather conditions impact infrastructure, from drainage and flood risk management to energy usage patterns.

Data Range:

• Start Date: 2009-01-01
• End Date: 2024-11-12

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.

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 National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.

World Weather Records (WWR) is an archived publication and digital data set. WWR is meteorological data from locations around the world. Through most of its history, WWR has been a publication, first published in 1927. Data includes monthly mean values of pressure, temperature, precipitation, and where available, station metadata notes documenting observation practices and station configurations. In recent years, data were supplied by National Meteorological Services of various countries, many of which became members of the World Meteorological Organization (WMO). The First Issue included data from earliest records available at that time up to 1920. Data have been collected for periods 1921-30 (2nd Series), 1931-40 (3rd Series), 1941-50 (4th Series), 1951-60 (5th Series), 1961-70 (6th Series), 1971-80 (7th Series), 1981-90 (8th Series), 1991-2000 (9th Series), and 2001-2011 (10th Series). The most recent Series 11 continues, insofar as possible, the record of monthly mean values of station pressure, sea-level pressure, temperature, and monthly total precipitation for stations listed in previous volumes. In addition to these parameters, mean monthly maximum and minimum temperatures have been collected for many stations and are archived in digital files by NCEI. New stations have also been included. In contrast to previous series, the 11th Series is available for the partial decade, so as to limit waiting period for new records. It begins in 2010 and is updated yearly, extending into the entire decade.

Context

I prepared this dataset for a project on rainfall forecasting. Met Éireann is the Irish Meteorological Service and a scientific organisation that undertakes research in numerous fields such as Numerical Weather Prediction and Climate Modelling. Their short-term predictions work very well. I was curious to see how Machine Learning and Deep Learning models would handle these types of tasks. Care to join me?

Description

This dataset contains: * Folder with Individual CSV files for 24 Met Éireann weather stations in Ireland capable to record hourly weather data (the start date of individual time series depends on when the particular station was opened, the end date is 2022-02-01); * Aggregated hourly weather data from 24 stations in Ireland for the period of time from 2007-12-31 to 2022-02-01 (with station names and locations added); * The list of stations.

Variables measured by stations (available variables may vary depending on the station): * date: Date and Time of observation * ind: Encoded Rainfall Indicators (see KeyHourly.txt for details) * rain: Precipitation Amount, mm * ind.1: Encoded Temperature Indicators (see KeyHourly.txt for details) * temp: Air Temperature, °C * ind.2: Encoded Wet Bulb Indicators (see KeyHourly.txt for details) * wetb: Wet Bulb Air Temperature, °C * dewpt: Dew Point Air Temperature, °C * vappr: Vapour Pressure, hPa * rhum: Relative Humidity, % * msl: Mean Sea Level Pressure, hPa * ind.3: Encoded Wind Speed Indicators (see KeyHourly.txt for details) * wdsp: Mean Hourly Wind Speed, knot * ind.4: Encoded Wind Direction Indicators (see KeyHourly.txt for details) * wddir: Predominant Hourly wind Direction, degree * ww: Synop Code Present Weather (see KeyHourly.txt for details) * w: Synop Code Past Weather (see KeyHourly.txt for details) * sun: Sunshine duration, hours * vis: Visibility, m * clht: Cloud Ceiling Height (if none value is 999), 100s of feet * clamt: Cloud Amount, okta

"Wind direction is usually reported in cardinal (or compass) direction, or in degrees. Consequently, a wind blowing from the north has a wind direction referred to as 0° (360°); a wind blowing from the east has a wind direction referred to as 90°, etc." Wikipedia page for "Wind direction"

Table: Common Cardinal (or compass) direction vs degrees

Information on the stations: * county: County the station is losated in * st_id: Station number * st_name: Station name * st_height: Station Height, m * st_lat: Station Latitude, sexagesimal degrees (degrees, minutes, and seconds - DMS notation) * st_long: Station Longitude, sexagesimal degrees (degrees, minutes, and seconds - DMS notation)

Latitude and longitude are presented in sexagesimal degrees (degrees, minutes, and seconds - DMS notation). To convert them into decimal degrees (DD) which are used in GIS and GPS apply the following formula: DD = D + M/60 + S/3600. More details can be found here.

Acknowledgements

Data were obtained from the Met Éireann website.

Copyright statement: Copyright Met Éireann Source: www.met.ie Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). Disclaimer: Met Éireann does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.

Hourly weather data for 24 stations were downloaded and aggregated into one dataframe. Station names and locations were added.

Photo by Nils Nedel on Unsplash was used as a Banner image.

Inspiration

For EDA and Data Visualization: 1. What are the most prominent seasonal weather patterns in Ireland? 2. How does the weather conditions affect city life? * Pedestrian footfall * Bikeshare sevices * Road accidents * Taxi

For ML and Neural Networks modelling: 1. Can you predict the probability of rain using weather data obtained from a single station in the previous 24, 36 or 48 hours? 2. How does the addition of data recorded by neighbouring stations affect the accuracy of the model?

Articles for ideas: 1. Streamflow and rainfall forecasting by two long short-term memory-based models 2. [Short-Term Rainfall Forecasting Using Multi-Layer Perceptron](https://ieeexplore.ieee.org/document/8468...

The dataset provides official temperature data measured from 513 weather stations in Germany from 1990 to 2021.

The original data are provided by the German Meteorological Service (DWD, Deutscher Wetterdienst) via the OpenData area of the Climate Data Center (CDC). These data are provided in 1611 files, resulting in > 500 million rows of measurement information (or missing values), a format that is poorly suited for further analysis.

Therefore, the data are converted from "long format" to "wide format". The result is a time series with 10 minute frequency containing one column per weather station. The exact columns in the file are: - MESS_DATUM: the datetime values of the time series, representing the index of the time series - list of weather station ids: one column per weather station, represented by the weather station id

From the five numerical measurement values of the original data, only "air temperature at 2m height in °C" was kept.

In addition to the extracted temperature data, a notebook is provided which can be used to extract the other four types of measurements in the same format.

The following files are provided in this dataset: - german_temperature_data_1990_2021.csv, containing the extracted original data (download and transformation, see this notebook). - german_temperature_data_1996_2021_from_selected_weather_stations.csv, containing a selection of the original data from 55 weather stations that have continuously provided a high amount of measurements from 1996-2021 (and thus no change in distribution over time). For the selection process, see this notebook. - zehn_min_tu_Beschreibung_Stationen.txt, additional information about the weather stations. - DESCRIPTION_obsgermany_climate_10min_tu_historical_en.pdf, the official data set description.

The terms of use are described by https://opendata.dwd.de/climate_environment/CDC/Nutzungsbedingungen_German.pdf and https://gdz.bkg.bund.de.

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.

Weather History Download New Delhi
This dataset falls under the category Environmental Data Climate Data.
It contains the following data: Climate datasets - historical datasets
This dataset was scouted on 2022-02-05 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.meteoblue.com/en/weather/archive/export/new-delhi_india_1261481

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 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.

The Sri Lanka Weather Dataset is a comprehensive collection of weather data for 30 prominent cities in Sri Lanka, covering the period from January 1, 2010, to January 1, 2023. The dataset offers a wide range of meteorological parameters, enabling detailed analysis and insights into the climate patterns of different regions in Sri Lanka.

The dataset includes information such as: - Time: The timestamp of each weather observation. - Weather Code: A numerical code representing the weather conditions at the given time. - Temperature: Maximum, minimum, and mean values of 2-meter temperature. - Apparent Temperature: Maximum, minimum, and mean values of apparent temperature, which takes into account factors like wind chill or heat index. - Sunrise and Sunset: The times of sunrise and sunset for each day. - Shortwave Radiation: Sum of shortwave radiation received during the observation period. - Precipitation: Total sum of precipitation, including rainfall and snowfall. - Precipitation Hours: The duration of time with measurable precipitation. - Wind Speed and Gusts: Maximum values of wind speed and wind gusts at 10 meters above ground level. - Wind Direction: Dominant wind direction at 10 meters above ground level. - Evapotranspiration: Reference evapotranspiration (ET0) based on the FAO Penman-Monteith equation. - Latitude, Longitude, and Elevation: Geographic coordinates and elevation of each city. - Country and City: Names of the country and city corresponding to each weather observation.

This dataset was sourced from Open-Meteo and simplemaps, and the data was collected using a basic Python script. The collected data was pre-processed to ensure cleanliness and readability before being stored in CSV format.

This product consists of meteorological data from 105 Arctic weather stations and 137 Antarctic stations, extracted from the National Climatic Data Center (NCDC)'s Integrated Surface Hourly (ISH) database. Variables include wind direction, wind speed, visibility, air temperature, dew point temperature, and sea level pressure. Temporal coverage varies by station, with the earliest record in 1913 and the latest in 2002. Data are in tab-delimited ASCII text format, with one file per station and year. Graphs of meteorological variables throughout the time series accompany the ASCII data.

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

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.

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

This dataset provides daily summaries of weather conditions at Norfolk International Airport, sourced from the National Oceanic and Atmospheric Administration (NOAA). NOAA publishes this data as part of their Global Historical Climatology Network – Daily dataset. It includes essential metrics such as maximum temperature, minimum temperature, average temperature, precipitation, snowfall, and average wind speed. The dataset is updated daily.