The dataset comes from Climate Data Online website and includes the last 30 years (1993-2022) of daily weather measurements in Warsaw, Poland. The dataset can be a good starting point for employing ARIMA and RNN models for weather forecasting. Columns description: - DATE- Measurement date in the following format: YYYY-MM-DD. - STATION- Station ID. - NAME- Station name. - LATITUDE- Station latitude. - LONGITUDE- Station longitude. - ELEVATION- Station elevation. - PRCP- Precipitation. - SNWD- Snow depth. - TAVG- Average temperature. - TMAX- Maximum temperature. - TMIN- Minimum temperature.

Compiled historical daily temperature and precipitation data for selected 210 U.S. cities.

Cities were selected based on lengths of existing climate records, which start at least earlier than 1900. However, cities may miss substantial amounts of data during their periods of record.

Each file provides available historical daily maximum and minimum temperature and daily precipitation data for one U.S. city. File was named by the city's current active weather station ID (GHCN ID).

Each city may include records from one or multiple stations. Listed latitude and longitude for each city are from the city's current active weather station.

Daily maximum and minimum temperature and daily precipitation were acquired from Applied Climate Information System (ACIS), developed by the NOAA Northeast Regional Climate Center (NRCC).

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets.

The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (Owen et al. 2006).

See included README file for more information.

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

Additionally, GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2022 January updated -

1. Temperature and precipitation records for 2021 were added (using values from GHCN-D at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/).

• 2022 September updated -

Note that this dataset will no longer be updated. Future updates may be provided at: YuchuanLai.com.

• 2021 January updated -

1. Temperature and precipitation records for 2020 were added (using values from GHCN-D at: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/).

• 2020 January updated -

1. Temperature and precipitation records for 2019 were added (using values from GHCN-D at: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/).

2. CITY_ID.csv files were already filled the missing values (represented with NA) to make to continuous time series from start of record to the end of 2019. CITY_ID_fill.csv files from the older version were deleted.

• 2019 June updated -

1. Baltimore (USW00093721) data for 2018 was updated (previously 2018 data appeared to be NA). Original files for Baltimore were removed.

2. The GHCN ID for Baltimore was updated to be the ID for Baltimore-Washington International AP. city_info file was updated accordingly.

This dataset contains daily weather data for Boston, sourced from the Meteostat API, spanning a period of 10 years from March 1st, 2013 to March 1st, 2023.

Each row of the dataset represents a single day and provides information such as the average, minimum, and maximum air temperature in Celsius, daily precipitation total in millimeters, wind direction and speed in kilometers per hour, and the average sea-level air pressure in hectopascals.

With a total of over 3,600 rows, this dataset offers a comprehensive look at Boston's climate trends over the past decade.

Find the script used for extraction below: ```python from datetime import datetime from meteostat import Stations, Daily import pandas as pd

Set time period

start = datetime(2013, 3, 1) end = datetime(2023, 3, 1)

Get daily data

data = Daily('72509', start, end) data = data.fetch() data=data.reset_index().iloc[:,[0,1,2,3,4,6,7,9]]

data.to_csv('boston_weather_data.csv',index=False) ```

AWIS Weather Services has delivered weather forecasts from our small business in Auburn, Alabama to companies all over the world for over 25 years. We started with a few citrus growing clients in Florida and have expanded to worldwide offerings in both Historical Weather Data and Localized Human Weather Forecasts.

One backend, data driven option is our human-checked, quality controlled daily forecast files that can be generated for your specific needs and wants. We have a database stacked full of forecast variables that are generated with over 25 year of weather forecasting expertise that we can pull from. You choose the variables you need. You choose the cities you need covered. You choose how far out into the future you need information for. (We usually suggest 7-10 days) You choose the frequency of delivery. We'll handle the forecast and delivery. Most of the time, it's a simple .csv file saved to the Amazon S3 bucket system that only you have access to.

Variables for our Daily Weather Forecasts include: Max Temperature Min Temperature Total Precipitation Average Wind Speed Average Cloud Cover Average Temperature Max Relative Humidity Min Relative Humidity Evapotranspiration Potential Evapotranspiration Total Hours of Sunshine Solar Radiation Veg Wetting Max Soil Temperature Min Soil Temperature Average Soil Temperature

If a variable not listed is needed, contact us, we can likely generate the output from our many ingested inputs stored in our forecast databases.

Pricing for our Daily Weather Forecast data is fully dependent upon your needs. If you need one city, one variable for the next year the price is something close to $100 per month. If you need 100 cities, with all the variables, you're looking at something close to $1000.00 per month, with long term contract discounts available.

Reach out to us for more details and we can provide a targeted proposal within hours.

Daily average temperature values recorded in major cities of the world

Content

This dataset contains historical daily weather data for 163 countries(with provincial data for some) from Jan 1, 2020 up to April 21, 2020. The countries and provinces were chosen based on the Johns Hopkins COVID-19 dataset. It contains various features such as temperature, pressure, humidity, ozone levels, visibility, precipitation, etc.

Acknowledgements

This data was extracted using the Dark Sky API.

Inspiration

The goal is to assess the role of weather elements as covid19 transmission factors and enable the development of prediction models incorporating these elements.

AWIS Weather Services has delivered weather data from our small business in Auburn, Alabama to companies all over the world for over 25 years. We started with a few citrus growing clients in Florida and have expanded to worldwide offerings in both Historical Weather Data and Localized Human Weather Forecasts.

Our Extensive Historical Weather Database is full of 100% quality checked weather data from over 40,000 US zip codes nationwide The data is REAL WEATHER OBSERVATIONS and visually checked by humans each day.

This service is your access to that database as it gets updated.

You choose the variables you need. You choose the cities you need covered. We'll handle the data pulling, updating, and delivery. Most of the time, it's a simple .csv file saved to the Amazon S3 bucket system that only you have access to.

Variables for this Live United States Weather Data Feed available for most locations are

Max Temperature Min Temperature Precipitation Average Wind Speed Average Cloud Cover Max Relative Humidity Min Relative Humidity Evapotranspiration Potential Evapotranspiration Hours of Sunshine Solar Radiation Veg Wetting Max Soil Temperature Min Soil Temperature Avg Soil Temperature

If a variable not listed is needed, contact us, we can likely generate the output from our many ingested inputs stored in our historical databases.

PRICING ESTIMATES: (The number of variables requested could change the price slightly) $1.50 per site, per month if you need less than 1000 zip codes. $1.25 per site, per month if you need 1001-5000 zip codes. $0.75 per site, per month if you need 5001-10000 zip codes. $0.25 per site, per month if you need over 10k zip codes.

Discounts available for long term deals. HISTORICAL DATA available upon request at a reduced rate. Reach out to us for more details and we can provide a targeted proposal within hours.

contains data entries of weather in Hyderabad, India

AWIS Weather Services has delivered weather data from our small business in Auburn, Alabama to companies all over the world for over 25 years. We started with a few citrus growing clients in Florida and have expanded to worldwide offerings in both Historical Weather Data and Localized Human Weather Forecasts.

Our Extensive Historical Weather Database is full of 100% quality checked weather data from over 30,000 observation sites worldwide. The data is REAL WEATHER OBSERVATIONS and visually checked by humans each day. Our databases go back to the early 1900s for some stations and are still updated daily for over 25,000 sites worldwide that still report.

You choose the variables you need. You choose the cities you need covered. You choose how far back you need data for. You choose the frequency of delivery. We'll handle the data pulling, updating, and delivery. Most of the time, it's a simple .csv file saved to the Amazon S3 bucket system that only you have access to.

Variables for DAILY WEATHER DATA available for most locations are Max Temperature Min Temperature Total Precipitation Average Wind Speed Average Cloud Cover Average Temperature Max Relative Humidity Min Relative Humidity Evapotranspiration Potential Evapotranspiration Total Hours of Sunshine Solar Radiation Veg Wetting Max Soil Temperature Min Soil Temperature Average Soil Temperature Snow Fall Snow Depth

If a variable not listed is needed, contact us, we can likely generate the output from our many ingested inputs stored in our historical databases.

Pricing for our Historical Weather Data data is fully dependent upon your needs. If you need one city, one variable for the last 5 years, the price is something close to $150. If you need 100 cities, with all the variables, for the last 5 years, you're looking at something close to $5000.00, with large purchase discounts available. We can also provide discounts for clients that need Historical Weather Data as well as Real-Time, ongoing future weather observations like daily updates and delivery.

Reach out to us for more details and we can provide a targeted proposal within hours.

Austin Hourly and Daily Weather 2016-2017

Temperature, Visibility, Humidity, Wind Speed, and Precipitation

By Ride Austin [source]

About this dataset

This Austin Weather dataset consists of hourly and daily measurements spanning from June 2016 to April 2017. These include various weather features such as visibility, temperature, humidity, wind speed, and precipitation. It includes a unique RIDES_ID key which can be used to join the information with Ride Austin data. All these attributes give you an insightful look into some of the common parameters that define Austin's climate during this year-long period. From visibility to minimum and maximum dry bulb temperature - get detailed records about changes in the weather conditions for each hour and day! Join us in exploring this exciting dataset to better understand Texas' largest capital city's dramatic environmental change throughout this time period!

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How to use the dataset

This dataset contains hourly and daily weather data for the Austin, TX area from June 2016 through April 2017. It is an invaluable source of information for all kinds of research or forecasting projects related to the climate in this part of the country. The dataset includes readings on visibility, temperature, humidity, wind speed, and precipitation. By analyzing this data you will be able to identify trends in weather conditions and their effects on other phenomena. This guide will help you get started with using this dataset to its fullest potential.

The first step is to familiarize yourself with the different columns in the dataset: - Houly Visibility (HOURLYVISIBILITY) - visibility in miles - Hourly Dry Bulb Temperature (HOURLYDRYBULBTEMPC) - temperature in °C - Hourly Wind Speed (HOURLYWindSpeed) - wind speed in mph
- Hourly Wind Direction (HOURLYWindDirection) - wind direction in degrees
- Hourly Precipitation (HOURLYPrecip) - precipitation amounts measured every hour

There are also daily readings included:

• Daily Maximum Dry Bulb Temperature (DAILYMaximumDryBulbTemp )- maximum temperature per day * Daily Minimum Dry Bulb Temperature (DAILYMinimumDryBulbTemp )- minimum temperature per day * Daily Surfature From Normal Average Temp(DAILYDeptFromNormalAverageTemp)- departure from normal average temp per day * Daily Sunrise Time(DAILYSunrise)- time at which sun rises each day * Daily Sunset Time(DAILYSunset)- time at which sun sets each day * Daily Precipitation(DAILYPrecip)- total amount of rain/snowfall measures from 12AM local time * Daioy Average Wind Speed( DAiLYAverageWindSpeed)- average winds velocity preceived form 12AM to 11:59PM local time * Daly Peak Wind Speedn( DAILyPeakWindSpeedh)- highest velocity associated whitin a particular 24 hour period associated whitin a particular 24 hour period measuring form 12AM To 11:59 PM Local Time

Now that you have an understanding of what is contained within this dataset it’s important to know how best make use it. The most effective ways would be through visual representation such as graphs that plot out data points over certain periods such as

Research Ideas

• Analyzing the daily average wind speed to predict future weather patterns for Austin.
• Correlating hourly temperature, humidity, precipitation, and wind speed with RideAustin (a ride-sharing service) user demand to better optimize service availability and areas of coverage.
• Using the sunrise and sunset times to create a model for energy usage in Austin homes as they turn on their lights in the evenings after sundown and turn them off when it's morning again

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Weather_Data.csv | Column name | Description | |:-----------------------------------|:----------------------------------------------------------------------------------------------------------| | HOURLYVISIBILITY | Visibility in miles at each hour during the day. (Numeric) | | HOURLYDRYBULBTEMPC | Temperature in Celsius at each hour during the day. (Numeric) | | HOURLYWindSpeed | Wind speed in miles per hour for each inspection time period. (Numeric) | | HOURLYWindDirection | Direction of wind by degrees clockwise from true north for each hourly observation time period. (Numeric) | | HOURLYPrecip | Precipitation measurements in inches at every inspection interval throughout a day. (Numeric) | | DAILYMaximumDryBulbTemp | Highest temperature reading for that particular day measured in Celsius. (Numeric) | | DAILYMinimumDryBulbTemp | Lowest temperature reading for that particular day measured in Celsius. (Numeric) | | DAILYDeptFromNormalAverageTemp | Daily departure from normal average temperature. (Numeric) | | DAILYSunrise | Sunrise time for each day. (Timestamp) | | DAILYSunset | Sunset time for each day. (Timestamp) | | DAILYPrecip | Daily precipitation measurements. (Numeric) | | DAILYAverageWindSpeed | Average wind speed throughout hours encompassed by any given date. (Numeric) | | DAILYPeakWindSpeed | Peak wind speeds which topped out across those experiences similarly reported. (Numeric) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Ride Austin.

The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2021. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.

This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2021, and additional historical data for Sheffield (South Yorkshire, 1898-1935).

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

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.

This dataset contains weather information captured from multiple sites in the Simpson Desert. This dataset has been collected since 1995.

The research sites are: - Carlo - Cravens Peak - Field River North - Field River South - Kunnamaku Swamp East - Kunnamaku Swamp West - Main Camp - Main Camp North - Plum Pudding - Shitty Site - South Site - Tobermorey East - Tobermorey West

This dataset contains following information for each site: - Date - Daily maximum temperature - Daily Minimum temperature - Daily average temperature - Daily rainfall

This dataset is stored within a Microsoft Access database and is approximately 50MB and growing.

This collection of this data was funded by Australian Research Council

Note that 2013 and 2014 datasets are available for download in the attachment tab below.The journal article describing GHCN-Daily is: Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910, doi:10.1175/JTECH-D-11-00103.1.Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. [indicate subset used following decimal, e.g. Version 3.12]. NOAA National Climatic Data Center. http://doi.org/10.7289/V5D21VHZ

This dataset provides Daymet Version 4 R1 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset covers the period from January 1, 1980, to December 31 (or December 30 in leap years) of the most recent full calendar year for the Continental North America and Hawaii spatial regions. Data for Puerto Rico is available starting in 1950. Each subsequent year is processed individually at the close of a calendar year. Daymet variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Areas of Hawaii and Puerto Rico are available as files separate from the continental North America. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant netCDF file format. In Version 4 R1 (ver 4.4), all 2020 and 2021 files were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.

The backbone of CustomWeather's forecasting arm is our proven, high-resolution model, the CustomWeather 100 or CW100. The CW100 Model is based on physics, not statistics or airport observations. As a result, it can achieve significantly better accuracy than statistical models, especially for non-airport locations. While other forecast models are designed to forecast the entire atmosphere, the CW100 greatly reduces computational requirements by focusing entirely on conditions near the ground. This reduction of computations allows the model to resolve additional physical processes near the ground that are not resolved by other models. It also allows the CW100 to operate at a much higher resolution, typically 100x finer than standard models and other gridded forecasts.

Detailed Forecasts:
Features a detailed 48-hour outlook broken into four segments per day: morning, afternoon, evening, and overnight. Each segment provides condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, 6-hr forecasted precipitation amounts, and miles/kilometers of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Extended Forecasts Days 1-15:
Features condition descriptions, high/low temperatures, wind speed and direction, humidity, comfort level, UV index, expected and probability of precipitation, and miles/kilometers of visibility. Available for over 85,000 forecast points globally. The information is updated four times per day.

Hour-by-Hour Forecasts: Features Hour-by-Hour forecasts. The product is available as 12 hour, 48 hour and 168 hour blocks. Each hourly forecast includes weather descriptions, wind conditions, temperature, dew point, humidity, visibility, rainfall totals, snowfall totals, and precipitation probability. Available for over 85,000 forecast points globally. Updated four times per day.

This Weather Forecast data is part of CustomWeather's comprehensive data offerings, covering the entire life cycle of weather - past, present, and future. The Weather Forecast data is archived back to 2012.

The CustomWeather 100 Weather Forecasts serve the following categories: Global Weather Data, Place Data, Precipitation Data, Rainfall Data, Surface Data, Storm Data, Agricultural Weather Data, Temperature Data, Weather, Weather Forecasts, Mobile App Data, Natural Disasters Data, and Wind Data.

Several different government offices have published the Daily weather maps over its history. The publication has also gone by different names over time. The U.S. Signal Office began publication of the maps as the War Department maps on Jan. 1, 1871. When the government transferred control of the weather service to the newly-created Weather Bureau in 1891 the title changed to the Department of Agriculture weather map. In 1913 the title became simply Daily weather map. Eventually, in 1969, the Weather Bureau began publishing a weekly compilation of the daily maps with the title Daily weather maps (Weekly series). The the principal charts are the Surface Weather Map, the 500 Millibar Height Contours Chart, the Highest and Lowest Temperatures chart and the Precipitation Areas and Amounts chart. This library contains a very small subset of this series: 11Sep1928-31Dec1928, 01Jan1959-30Jun1959, and 06Jan1997-04Jan1998.

KNMI operates automatic weather stations on land (incl. airports). These weather stations measures meteorological parameters such as temperature, precipitation, wind, air pressure and global radiation. On a daily basis all real-time collected observations and measurements (hourly) are validated on correctness and completeness. The validated data is archived in the Klimatologisch Informatie Systeem (KIS) of KNMI. The daily data is composed from hourly data and each day reference evaporation is calculated using the Makkink method. After the data has been processed and archived in KIS, changes are no longer possible. This assures data integrity.

Daily weather data measured in Caldicot, Wales from January 2017 to March 2020. The weather data was collected using a WS-GP1 weather station supplied by Delta-T Devices Ltd, Cambridge, UK. The data were collected to provide potential evaporative drying and rainfall for (i) a wall capillary uptake model and (ii) a soil water balance model. The measurements include daily maximum temperature (°C), minimum temperature (°C), maximum relative humidity (%), minimum relative humidity (%), wind speed (m/s), rainfall (mm) and radiation (kw/m2).