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.

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

The Severe Weather Data Inventory (SWDI) is an integrated database of severe weather records for the United States. SWDI enables a user to search through a variety of source data sets in the NCDC (now NCEI) archive in order to find records covering a particular time period and geographic region, and then to download the results of the search in a variety of formats. The formats currently supported are Shapefile (for GIS), KMZ (for Google Earth), CSV (comma-separated), and XML. The current data layers in SWDI are: Storm Cells from NEXRAD (Level-III Storm Structure Product); Hail Signatures from NEXRAD (Level-III Hail Product); Mesocyclone Signatures from NEXRAD (Level-III Meso Product); Digital Mesocyclone Detection Algorithm from NEXRAD (Level-III MDA Product); Tornado Signature from NEXRAD (Level-III TVS Product); Preliminary Local Storm Reports from the NOAA National Weather Service; Lightning Strikes from Vaisala NLDN.

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.

Delhi Weather Data
This dataset falls under the category Environmental Data Climate Data.
It contains the following data: This data was taken out from wunderground with the help of their easy to use api. It contains various features such as temperature, pressure, humidity, rain, precipitation,etc.
This dataset was scouted on 2022-02-04 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.kaggle.com/mahirkukreja/delhi-weather-data?select=testset.csv

Information about weather-related conditions in Sweden during the period 1500-1870 has been extracted from various historical documents. The information is presented as cited text, together with the date and geographical region for which the information is relevant.

Since the database essentially consists of excerpts from different historical documentary sources of various kinds (Institutional chronicles, accountings, private weather diaries etc) the language is Swedish, though citations of original texts are occasionally given in other languages whenever relevant and when other languages were originally used.

See the Swedish description for more information.

The database contains a large number of contemporary descriptions for the period 1500–1870 from various types of documents — direct observations in diaries, administrative notes on activities that have been affected by weather conditions, letter collections, newspaper articles, etc. — of weather conditions in Sweden within current borders.

** Database file structure and content:

The database is collected in a spreadsheet (xlsx). The same information is also presented in a semicolon-separated text file (csv) (character set: Western Europe, ISO-8859-15 / EURO). File size: 1.6 MB (xlsx) and 4.1 MB (csv). The number of file rows, including the title row, is 20896.

In addition to the data file itself, the dataset also contains a source list in xlsx format. The file has two pages: "Otryckta källor" (unprinted sources) and "Bibliografi" (bibliography). The same information is also presented in two comma-separated csv files (character set: Western Europe, ISO-8859-15 / EURO).

The main database file contains information in eight columns with the following headings (here also translated to English):

• År (year)
• Månad (month)
• Dag (day)
• Annan tidsangivelse (other time indication)
• Område (area)
• Väder (weather)
• Källa (source)
• Ytterligare hänvisning/information (additional reference / information)

The database main language is Swedish. Quotations of writings in old language are generally preserved as in their original spellings.

For a more detailed description of the database content, please see the Swedish data description.

** Main sources and collection method:

The data collection was performed by systematically reading through available archive material and literature relevant to the subject. Information that was considered to be of value for climate history was entered into the database, either as a quotation or in the form of comments, together with an indication of the source material for each individual item (row) in the database. Each such item refers to a more or less specified geographical location and either a specific date or an approximate time period.

Data have been collected along three main channels: unprinted archive material, printed sources and literature.

Unprinted archive material has been retrieved mainly from the National Archives. For the period 1500–1540, data comes mainly from the database of the "Svenskt diplomatariums huvudkartotek" (Sdhk) (Swedish diplomatarium's main file). For the time thereafter, data from, among others, the "Riksregistraturet" (national registry), a collection of copies of letters issued by the "kungliga kansliet" (royal chancellery), has been used. Several unprinted letters, diaries, accounts and reports have also been searched.

A particularly extensive individual source is Märta Helena Reenstierna's (known as the Årsta lady) diaries from Årsta Gård in Stockholm, written during the period 1793–1839 and kept in the Nordic Museum's archives. These diaries contain a large number of notes on local weather conditions. More than half of all individual entries in the database originate from the Årsta diaries.

Printed sources include editions of source publications such as Gustav Vasa's (King Gustav I of Sweden) letters in 29 volumes. There are also the Royal Swedish Academy of Sciences' Transactions which, among other things, contain meteorological observations.

The category literature contains a number of local historical presentations. There are also early attempts at climate historical overviews and interpretations. Corporate history and military history literature have also been used.

** The roles of primary researchers during the construction of the database

The main part of the work with building up the database was done during the period 2006–2010 by Johan Söderberg, Lotta Leijonhufvud, Dag Retsö and Ulrica Söderlind at the Department of Economic History, Stockholm University, under the leadership of Johan Söderberg. Curation of the database prior to publication in SND was carried out during 2019–2020 by Lotta Leijonhufvud in collaboration with Anders Moberg, Department of Physical Geography, Stockholm University.

Previous, unpublished, versions of the database have been used in the following studies (see list of publications):

• Edvinsson et al. (2009). Väder, skördar och priser i Sverige.
• Leijonhufuvud et al. (2010). Five centuries of Stockholm winter/spring temperatures reconstructed from documentary evidence and instrumental observations.
• Wetter et al. (2014). The year-long unprecedented European heat and drought of 1540 – a worst case.
• Retsö (2015). Documentary evidence of historical floods and extreme rainfall events in Sweden 1400–1800.

U.S. 15 Minute Precipitation Data is digital data set DSI-3260, archived at the National Climatic Data Center (NCDC). This is precipitation data. The primary source of data for this file is approximately 2,000 mostly U.S. weather stations operated or managed by the U.S. National Weather Service. Stations are primary, secondary, or cooperative observer sites that have the capability to measure precipitation at 15 minute intervals. This dataset contains 15-minute precipitation data (reported 4 times per hour, if precip occurs) for U.S. stations along with selected non-U.S. stations in U.S. territories and associated nations. It includes major city locations and many small town locations. Daily total precipitation is also included as part of the data record. NCDC has in archive data from most states as far back as 1970 or 1971, and continuing to the present day. The major parameter is precipitation amounts at 15 minute intervals, when precipitation actually occurs.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

Note: The data for April 10, 2023 is missing.

Source: Water and Atmospheric Resources Monitoring Program. Illinois Climate Network. (2023). Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. http://dx.doi.org/10.13012/J8MW2F2Q. (Updated 11 June 2024).

DATA SOURCE: National Institute for Water and Atmospheric Research (NIWA) [Technical report available at https://www.mfe.govt.nz/publications/environmental-reporting/ministry-environment-atmosphere-and-climate-report-2020-updated]

Adapted by Ministry for the Environment and Statistics New Zealand to provide for environmental reporting transparency

This lowest aggregation dataset, was used to develop three ‘Our Atmosphere and Climate’ indicators. See Statistics New Zealand indicator links for specific methodologies and state/trend datasets (see ‘Shiny App’ downloads). 1) Temperature (https://www.stats.govt.nz/ndicators/temperature) 2) First and last frost days (https://www.stats.govt.nz/ndicators/frost-and-warm-days) 3) Growing degree days (https://www.stats.govt.nz/ndicators/growing-degree-days)

IMPORTANT INFORMATION Due to the size of this dataset (111 MB), a 32-bit version of Microsoft Excel will only display/download ~ 1 million rows. A DBMS, statistical or GIS application is needed to view the entire dataset.

This dataset shows two measures of temperature change in New Zealand: New Zealand’s national temperature from NIWA’s ‘seven-station’ temperature series from 1909 to 2019, and temperature at 30 sites around the country from at least 1972 to 2019. For national temperature, we report daily average, minimum and maximum temperatures. We also present New Zealand national and global temperature anomalies.

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Weather History Download Hanoi
This dataset falls under the category Environmental Data Climate Data.
It contains the following data: Historical and real-time weather. Access historical weather information for Hanoi with history+. Available worldwide and independent from weather stations. Download consistent and gap-free hourly data for Hanoi as CSV
This dataset was scouted on 2022-02-12 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/hanoi_vietnam_1581130See URL for data access and license information.

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.

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 2019. 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. Of particular note, however, is that as well as including data for 2019, historical data recovery has added temperature and weather data for Bude (1937-1958), Teignmouth (1912-1930), and Eskdalemuir (1915-1948). 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.

Using observation data from various agencies in Taiwan, including the Central Weather Bureau, Water Resources Agency, Irrigation Agency and Taiwan Power Company, supplementary, homogenization, and gridization operations were carried out to establish grid data with a resolution of 5 kilometers throughout Taiwan. This data was produced by the "Taiwan Climate Change Projection Information and Adaptation Knowledge Platform Project" of the National Science Council.

Micro - climate sensors collect telemetry at set intervals throughout the day. Sensors are located at various locations in the City of Canning, Western Australia and each sensor has a unique ID. Contact us at opendata@canning.wa.gov.au for a larger data set (The data is supplied is the sensor reading for 30 days). The following lists the locations of each sensor:18zua9muwbb is located at Wharf Street Basin - Pavilion 2hq3byfebne is located at The City’s Civic and Administration Building uu90853psl is located at Wharf Street Basin - Leila Street entrance xd2su7w05m is located at Wharf Street Basin - Nature Play Area

Tornado TracksThis feature layer, utilizing data from the National Oceanic and Atmospheric Administration (NOAA), displays tornadoes in the United States, Puerto Rico and U.S. Virgin Islands between 1950 and 2022. A tornado track shows the route of a tornado. Per NOAA, "A tornado is a narrow, violently rotating column of air that extends from a thunderstorm to the ground. Because wind is invisible, it is hard to see a tornado unless it forms a condensation funnel made up of water droplets, dust and debris. Tornadoes can be among the most violent phenomena of all atmospheric storms we experience. The most destructive tornadoes occur from supercells, which are rotating thunderstorms with a well-defined radar circulation called a mesocyclone. (Supercells can also produce damaging hail, severe non-tornadic winds, frequent lightning, and flash floods.)"EF-5 Tornado Track (May 3, 1999) near Oklahoma City, OklahomaData currency: December 30, 2022Data source: Storm Prediction CenterData modifications: Added fields Calculated Month and DateFor more information: Severe Weather 101 - Tornadoes; NSSL Research: TornadoesSupport documentation: SPC Tornado, Hail, and Wind Database Format SpecificationFor feedback, please contact: ArcGIScomNationalMaps@esri.comNational Oceanic and Atmospheric AdministrationPer NOAA, its mission is "To understand and predict changes in climate, weather, ocean, and coasts, to share that knowledge and information with others, and to conserve and manage coastal and marine ecosystems and resources."

Data from VCR/LTER meteorological stations is listed in comma-separated-value (CSV) files by year. A combined file for all years is also available, sorted by station, date and time, however, unlike the annual files, this combined file is too large for most spreadsheets to handle. A compressed copy of all the .CSV files is included in a single .ZIP file for users who wish to conveniently download a copy of all the data.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

PaperThis dataset is associated with the paper published in Scientific Data, titled "SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array." You can access the paper: https://www.nature.com/articles/s41597-024-03427-5If you find this dataset useful, please consider citing our paper: Scientific Data Paper@article{zhou2024sdwpf, title={SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array}, author={Zhou, Jingbo and Lu, Xinjiang and Xiao, Yixiong and Tang, Jian and Su, Jiantao and Li, Yu, and Liu, Ji and Lyu, Junfu and Ma, Yanjun and Dou, Dejing},journal={Scientific Data},volume={11},number={1},pages={649},year={2024},url = {https://doi.org/10.1038/s41597-024-03427-5},publisher={Nature Publishing Group}}Baidu KDD Cup Paper@article{zhou2022sdwpf,title={SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022}, author={Zhou, Jingbo and Lu, Xinjiang and Xiao, Yixiong and Su, Jiantao and Lyu, Junfu and Ma, Yanjun and Dou, Dejing}, journal={arXiv preprint arXiv:2208.04360},url = {https://arxiv.org/abs/2208.04360}, year={2022}}BackgroundThe SDWPF dataset, collected over two years from a wind farm with 134 turbines, details the spatial layout of the turbines and dynamic context factors for each. This dataset was utilized to launch the ACM KDD Cup 2022, attracting registrations from over 2,400 teams worldwide. To facilitate its use, we have released the dataset in two parts: sdwpf_kddcup and sdwpf_full. The sdwpf_kddcup is the original dataset used for the Baidu KDD Cup 2022, comprising both training and test datasets. The sdwpf_full offers a more comprehensive collection, including additional data not available during the KDD Cup, such as weather conditions, dates, and elevation.sdwpf_kddcupThe sdwpf_kddcup dataset is the original dataset used for Baidu KDD Cup 2022 Challenge. The folder structure of sdwpf_kddcup is:sdwpf_kddcup --- sdwpf_245days_v1.csv --- sdwpf_baidukddcup2022_turb_location.csv --- final_phase_test --- infile --- 0001in.csv --- 0002in.csv --- ... --- outfile --- 0001out.csv --- 0002out.csv --- ...The descriptions of each sub-folder in the sdwpf_kddcup dataset are as follows:sdwpf_245days_v1.csv: This dataset, released for the KDD Cup 2022 challenge, includes data spanning 245 days.sdwpf_baidukddcup2022_turb_location.csv: This file provides the relative positions of all wind turbines within the dataset.final_phase_test: This dataset serves as the test data for the final phase of the Baidu KDD Cup. It allows for a comparison of methodologies against those of the award-winning teams from KDD Cup 2022. It includes an 'infile' folder containing input data for the model, and an 'outfile' folder which holds the ground truth for the corresponding output. In other words, for a model function y = f(x), x represents the files in the 'infile' folder, and the ground truth of y corresponds to files in the 'outfile' folder, such as {001out} = f({001in}).More information about the sdwpf_kddcup used for Baidu KDD Cup 2022 can be found in Baidu KDD Cup Paper: SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022sdwpf_fullThe sdwpf_full dataset offers more information than what was released for the KDD Cup 2022. It includes not only SCADA data but also weather data such as relative humidity, wind speed, and wind direction, sourced from the Fifth Generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate (ERA5). The dataset encompasses data collected over two years from a wind farm with 134 wind turbines, covering the period from January 2020 to December 2021. The folder structure of sdwpf_full is:sdwpf_full--- sdwpf_turb_location_elevation.csv--- sdwpf_2001_2112_full.csv--- sdwpf_2001_2112_full.parquetThe descriptions of each sub-folder in the sdwpf_full dataset are as follows:sdwpf_turb_location_elevation.csv: This file details the relative positions and elevations of all wind turbines within the dataset.sdwpf_2001_2112_full.csv: This dataset includes data collected two years from a wind farm containing 134 wind turbines, spanning from Jan. 2020 to Dec. 2021. It offers comprehensive enhancements over the sdwpf_kddcup/sdwpf_245days_v1.csv, including:Extended time span: It spans two years, from January 2020 to December 2021, whereas sdwpf_245days_v1.csv covers only 245 days.Enriched weather information: This includes additional data such as relative humidity, wind speed, and wind direction, sourced from the Fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate (ERA5).Expanded temporal details: Unlike during the KDD Cup Challenge where timestamp information was withheld to prevent data linkage, this version includes specific timestamps for each data point.sdwpf_2001_2112_full.parquet: This dataset is identical to sdwpf_2001_2112_full.csv, but in a different data format.

Bangladesh Weather Dataset
This dataset falls under the category Environmental Data Climate Data.
It contains the following data: This dataset contains the monthly average value of Bangladesh temperature and rain from 1901 to 2015
This dataset was scouted on 2022-03-01 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.kaggle.com/yakinrubaiat/bangladesh-weather-dataset