1 km resolution composite data from the Met Office's UK rainfall radars via the Met Office NIMROD system. The NIMROD system is a very short range forecasting system used by the Met Office. Data are available from 2004 until present at UK stations and detail rain-rate observations taken every 5 minutes. Each file has been compressed and then stored within daily tar archive files.

The precipitation rate analysis uses processed radar and satellite data, together with surface reports and Numerical Weather Prediction (NWP) fields. The UK has a network of 15 C-band rainfall radars and data form these are processed by the Met Office NIMROD system.

Please note CEDA are not able to fulfil requests for missing data from this archive. The data may be available at a cost by contacting the Met Office directly with required dates. It is worth contacting the CEDA first to check if the reason for the gap is already identified as being due to the data not existing at all.

CEDA does not support reading software but programs written by the community to do this task in IDL, Matlab, FORTRAN and Python are available in the dataset software directory.

The data files contain integer precipitation rates in unit of (mm/hr)*32. Each value is between 0 and 32767. In practice it is rare to see a value in excess of 4096 i.e. 128 mm/hr.

At 10:00 on 14 June 2005, the 1 km composite data files became larger with 2175 rows by 1725 columns compared to the previous 775 rows by 640 columns. From 14:55 on 30 August 2006, the 1 km composite data files are gzipped files. From 13 Nov 2007, the 1 km composite is derived directly from processed polar (600m x 1 degree) rain rate estimates and there is more detail in the rain structure.

The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2017. 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. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset.

The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.

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.

The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 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. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset.

Copyright Notice & Acknowledgements

All information regarding MET Office copyright policy can be found at: https://www.metoffice.gov.uk/about-us/legal#licences All data was sourced from: https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data

Context

The MET Office has been responsible for monitoring UK Weather since it's inception in 1854. 36 stations in the UK (often located in RAF bases) gather information that is used to predict future weather patterns and issue public advice. More recently, these large datasets have become useful to investigate how the UK climate has changed over the past 150+ years.

Content

Columns: - year: Year in which the measurements were taken - month: Month in which the measurements were taken - tmax: Mean daily maximum temperature (°C) - tmin: Mean daily minimum temperature (°C) - af: Days of air frost recorded that month (days) - rain: Total rainfall (mm) - sun: Total sunshine duration (hours) - station: Station location where measurement was recorded

Data was collected from the MET Office website as separate station csv files and combined to one data frame with a station label assigned. All characters (*,#,---) that denoted things such as the equipment used were removed from the set. Some sections include significant amounts of NA values. Note that a 0 entry does not denote an NA value but a score of 0 in that measured field.

Inspiration

Has the UK climate changed since the Victorian era? How does any climate change impact the UK in terms of weather risks? Are some regions more affected than others?

A good starting point: The monthly mean temperature is calculated from the average of the mean daily maximum and mean daily minimum temperature i.e. (tmax+tmin)/2.

The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2018. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details. 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. 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. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection.

The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2021. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.

This version supersedes the previous version (202107) 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 2021, and additional historical data for Colmonell (Ayrshire, 1924-1960), Camps Reservoir (Lanarkshire, 1934-1960), and Greenock (Renfrewshire, 1910-1960).

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. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection.

This data has been sourced from the Met Office's historical station data available here: https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data

I have included the python script used to generate the dataset.

The data is held at a monthly level and contains: - max_temp: Mean daily maximum temperature - min_temp: Mean daily minimum temperature - air_frost_days: Days of air frost - rain_mm: Total rainfall - sun: Total sunshine duration (hours) - station: the station of the observation - lat: latitude of the station - long: longitude of the station - month_year: month date of the observation

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.

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...

What does the data show?

This data shows annual averages of precipitation (mm/day) for 2050-2079 from the UKCP18 regional climate projections. The data is for the high emissions scenario (RCP8.5).

Limitations of the data

We recommend the use of multiple grid cells or an average of grid cells around a point of interest to help users get a sense of the variability in the area. This will provide a more robust set of values for informing decisions based on the data.

What are the naming conventions and how do I explore the data?

This data contains a field for the average over the period. They are named 'pr' (precipitation), the month, and 'upper' 'median' or 'lower'. E.g. 'pr Median' is the median value.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘pr January Median’ values.

What do the ‘median’, ‘upper’, and ‘lower’ values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the annual averages of precipitation for 2050-2079 were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.

The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

Data source

pr_rcp85_land-rcm_uk_12km_12_ann-30y_200912-207911.nc (median)

pr_rcp85_land-rcm_uk_12km_05_ann-30y_200912-207911.nc (lower)

pr_rcp85_land-rcm_uk_12km_04_ann-30y_200912-207911.nc (upper)

UKCP18 v20190731 (downloaded 04/11/2021)

Useful links

Further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal

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.

This dataset contains synthetic weather data generated for ten different locations, including New York, Los Angeles, Chicago, Houston, Phoenix, Philadelphia, San Antonio, San Diego, Dallas, and San Jose. The data includes information about temperature, humidity, precipitation, and wind speed, with 1 million data points generated for each parameter.

Features:

• Location: The city where the weather data was simulated.
• Date_Time: The date and time when the weather data was recorded.
• Temperature_C: The temperature in Celsius at the given location and time.
• Humidity_pct: The humidity in percentage at the given location and time.
• Precipitation_mm: The precipitation in millimeters at the given location and time.
• Wind_Speed_kmh: The wind speed in kilometers per hour at the given location and time.

Additional Information:

• Variability and Complexity: The dataset incorporates variability and complexity to simulate realistic weather patterns. For example, adjustments have been made to temperature and precipitation based on seasonal variations observed in certain locations. In New York, higher temperatures and precipitation are simulated during the summer months, while in Phoenix, lower temperatures and increased precipitation are simulated during the winter months.
• Data Generation Method: The dataset was generated using Python's Faker library to create synthetic weather data for each location. Random values within realistic ranges were generated for temperature, humidity, precipitation, and wind speed, with adjustments made to reflect seasonal variations.

Potential Use Cases:

• Weather Prediction Models: Researchers and data scientists can use this dataset to develop and train weather prediction models for various locations.
• Climate Studies: The dataset can be used for climate studies and analysis to understand weather patterns and trends in different regions.
• Educational Purposes: Students and educators can use this dataset to learn about data analysis, visualization, and modeling techniques in the context of weather data.

Acknowledgements:

• This dataset was generated using Python's Faker library.
• Special thanks to the Faker library developers for providing tools to create synthetic data for various purposes.

Image Credits :

Image by Mohamed Hassan from Pixabay

Context

1. Develop a predictive classifier to predict whether there will be rain on a particular day or not based on the target variableRainToday.
2. Develop a predictive regressor to predict the amount of rainfall and average temperature of a particular day using the target variable Rainfall , MinTemp, MaxTemp, Temp9am and Temp3pm.

Content

This dataset contains about 10 years of daily weather observations from many locations across Bangladesh. It contains observations of weather metrics for each day from 2013 to 2022.

Data Description

Date - Date of the Observation in DD-MM-YYYY
MinTemp - The Minimum temperature during a particular day. (degree Celsius)
MaxTemp - The maximum temperature during a particular day. (degree Celsius)
Rainfall - Rainfall during a particular day. (millimeters)
Evaporation - Evaporation during a particular day. (millimeters)
Sunshine - Bright sunshine during a particular day. (hours)
WindGusDir - The direction of the strongest gust during a particular day. (16 compass points)
WindGuSpeed - Speed of strongest gust during a particular day. (kilometers per hour)
WindDir9am - The direction of the wind for 10 min prior to 9 am. (compass points)
WindDir3pm - The direction of the wind for 10 min prior to 3 pm. (compass points)
WindSpeed9am - Speed of the wind for 10 min prior to 9 am. (kilometers per hour)
WindSpeed3pm - Speed of the wind for 10 min prior to 3 pm. (kilometers per hour)
Humidity9am - The humidity of the wind at 9 am. (percent)
Humidity3pm - The humidity of the wind at 3 pm. (percent)
Pressure9am - Atmospheric pressure at 9 am. (hectopascals)
Pressure3pm - Atmospheric pressure at 3 pm. (hectopascals)
Cloud9am- Cloud-obscured portions of the sky at 9 am. (eighths)
Cloud3pm - Cloud-obscured portions of the sky at 3 pm. (eighths)
Temp9am - The temperature at 9 am. (degree Celsius)
Temp3pm - The temperature at 3 pm. (degree Celsius)
RainToday - If today is rainy then ‘Yes’. If today is not rainy then ‘No’.

Source & Acknowledgements

Observations were drawn from numerous weather stations. The daily observations are available from http://live.bmd.gov.bd/
Data source - http://data.gov.bd/dataset/live-weather-condition
Copyright 2013-2022 Bangladesh Meteorological Department.

[ NOTE – 2022/05/06: this dataset supersedes the earlier versions https://doi.org/10.15482/USDA.ADC/1482548 and https://doi.org/10.15482/USDA.ADC/1526329 ]. This dataset contains 15-minute mean weather data from the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) for all days in each year. The data are from sensors placed at 2-m height over a level, grass surface mowed to not exceed 12 cm height and irrigated and fertilized to maintain reference conditions as promulgated by Allen et al. (2005, 1998). Irrigation was by surface flood in 1989 through 1994, and by subsurface drip irrigation after 1994. Sensors were replicated and intercompared between replicates and with data from nearby weather stations, which were sometimes used for gap filling. Quality control and assurance methods are described by Evett et al. (2018). Data from a duplicate sensor were used to fill gaps in data from the primary sensor using appropriate regression relationships. Gap filling was also accomplished using sensors deployed at one of the four large weighing lysimeters immediately west of the weather station, or using sensors at other nearby stations when reliable regression relationships could be developed. The primary paper describes details of the sensors used and methods of testing, calibration, inter-comparison, and use. The weather data include air temperature (C) and relative humidity (%), wind speed (m/s), solar irradiance (W m-2), barometric pressure (kPa), and precipitation (rain and snow in mm). Because the large (3 m by 3 m surface area) weighing lysimeters are better rain gages than are tipping bucket gages, the 15-minute precipitation data are derived for each lysimeter from changes in lysimeter mass. The land slope is <0.3% and flat. The mean annual precipitation is ~470 mm, the 20-year pan evaporation record indicates ~2,600 mm Class A pan evaporation per year, and winds are typically from the South and Southwest. The climate is semi-arid with ~70% (350 mm) of the annual precipitation occurring from May to September, during which period the pan evaporation averages ~1520 mm. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by the Agricultural Model Intercomparison and Improvement Project (AgMIP), by OPENET, and by many others for testing, and calibrating models of ET that use satellite and/or weather data. See the README for details of each data resource.

This dataset, provides detailed weather and climate statistics for major cities in Germany from 2015 to 2023.

It includes rainfall amounts, temperatures, humidity levels, and other geographical and climatic details, making it ideal for analyzing weather patterns, climate change, and their impacts across different regions.

1. City: Name of the city.

2. Latitude: City's latitude in degrees.

3. Longitude: City's longitude in degrees.

4. Month: The month number (1-12).

5. Year: The year of the data.

6. Rainfall (mm): Rainfall amount in millimeters.

7. Elevation (m): City’s elevation above sea level in meters.

8. Climate_Type: The climate classification of the city.

9. Temperature (°C): Average temperature for the month in Celsius.

10. Humidity (%): Average humidity level for the month in percentage.

Typical annual rainfall data were summarized from monthly precipitation data and provided in millimeters (mm). The monthly climate data for global land areas were generated from a large network of weather stations by the WorldClim project. Precipitation and temperature data were collected from the weather stations and aggregated across a target temporal range of 1970-2000.

Weather station data (between 9,000 and 60,000 stations) were interpolated using thin-plate splines with covariates including elevation, distance to the coast, and MODIS-derived minimum and maximum land surface temperature. Spatial interpolation was first done in 23 regions of varying size depending on station density, instead of the common approach to use a single model for the entire world. The satellite imagery data were most useful in areas with low station density. The interpolation technique allowed WorldClim to produce high spatial resolution (approximately 1 km2) raster data sets.

CIMMYT has Meteorological Records for each station office in Mexico. Many variables of weather are measured per day (rawdata) from wh​​ich daily, monthly and yearly sumaries are generated.​ These climate variables are measured by meteorological station and some of them are shown daily or hourly. Some of those variables are: Temperature, Rainfall, Solar Radiation, Relative Humidity, Wind Speed, Evapotranspiration. These files include climate data from 1969 to 2017.

Daily Weather Data (2000–2023)

Dataset Summary

This document provides a detailed summary of the country_weather_data.csv dataset, which contains daily weather observations from different countries spanning over two decades. The dataset is ideal for climate analytics, environmental modeling, and time series forecasting.

Dataset Structure

• Rows: Over 8,000 daily records
• Columns: 9
• Each row represents a unique daily weather record for different countries.

Key Columns

• Country: Country name
• Date: Date of observation (DD-MM-YYYY)
• Temp_Max: Maximum temperature (°C)
• Temp_Min: Minimum temperature (°C)
• Temp_Mean: Mean temperature (°C)
• Precipitation_Sum: Total daily precipitation (mm)
• Windspeed_Max: Maximum wind speed (km/h)
• Windgusts_Max: Maximum wind gusts (km/h)
• Sunshine_Duration: Total sunshine duration (seconds)

Types of Data

• Categorical: Country, Date
• Numerical: Temp_Max, Temp_Min, Temp_Mean, Precipitation_Sum, Windspeed_Max, Windgusts_Max, Sunshine_Duration

Potential Use Cases

• Climate Trend Analysis: Study long-term temperature and precipitation patterns.
• Environmental Research: Assess weather impacts on agriculture, biodiversity, and urban planning.
• Time Series Forecasting: Build predictive models for future weather conditions.
• Data Visualization Projects: Create dashboards and visual stories using real-world weather data.
• Educational Use: Teach data science, meteorology, and statistical modeling with practical examples.

Monthly Rainfall and Temperatures (UK)

Met Office Climate District

By data.world's Admin [source]

About this dataset

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For more datasets, click here.

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How to use the dataset

To use this dataset, start by making sure you are familiar with the following fields: OrganisationName, OrganisationCode, PublishedDate, DurationFrom (start date of reported period), DurationTo (end date of reported period), LatestData (indicating if latest available data is provided or not), GeoName (name of geographical area being reported on), ReportingPeriodType (type of reporting period i.e monthly/yearly/seasonal etc.), Year, Rainfallmm(average rainfall in millimeters), Temp(average temperature in centigrade), Dataset Name(name of the dataset provided). These are all important pieces of information that must be known before delving into the other columns.

Research Ideas

• Developing predictive models for drought and flooding with the help of average temperature and rainfall data
• Producing reports to inform farmers on various farming activities that need to be done depending on the climate conditions in the region
• Creating visualizations which can compare historical trends of average temperature and rainfall in different regions

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: average-rainfall-temperature-1.csv | Column name | Description | |:------------------------|:--------------------------------------------------------------------------------| | OrganisationName | Name of the organisation providing the data. (String) | | OrganisationCode | Code associated with the name of the organisation providing the data. (String) | | PublishedDate | Date when that particular set of data was published. (Date) | | DurationFrom | Start date of that respective period. (Date) | | DurationTo | End date of the respective period. (Date) | | LatestData | It specifies whether or not that particular set is available to you. (Boolean) | | GeoName | Place/location where these climatic conditions exists. (String) | | ReportingPeriodType | Specifies whether it is a monthly/yearly report. (String) | | Year | Indicates year for which these statistical values have been obtained. (Integer) | | Rainfallmm | Average rainfall in millimetres during specified period. (Float) | | Temp | Average temperature in centigrade during specified period. (Float) |

File: average-rainfall-temperature-metatdata-2.csv | Column name | Description | |:--------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dataset Name | Name of the dataset. (String) | | Field | Details a certain aspect or parameter amongst numerous parameters present within a resultset. (String) | | Type | Whether its Numerical value or DoT notation. (String) | | Mandatory or Optional requirement (MOR) | This field tells us if we require anything specific while submitting our queries. (String) | | Field Description | A brief overvie...