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.

The backbone of CustomWeather's forecasting arm is our proven, high-resolution model, the 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 it 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 precip amounts, and miles 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 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.

Historical Longer Term Forecasts: Includes historical hourly and/or daily forecast data from 2009 until present date. Data will include condition descriptions, high/low temperatures, wind speed and direction, dew point, humidity, comfort level, UV index, probability of precipitation, rainfall and snowfall amounts. Available for over 85,000 forecast points globally. The information is updated four times per day.

Below are available time periods per each type of forecast from the GFS model and from CustomWeather's proprietary CW100 model:

GFS: 7-day hourly forecasts from August 2nd 2009; 48-hour to 5-day detailed forecasts from August 4th 2009; 15-day forecasts from October 9th 2008.

CW100: 7-day hourly forecasts from November 27, 2012; 48-hour detailed forecasts from November 12, 2011; 7-day forecasts from December 6, 2010, 15-day forecasts from August 6, 2012. CW100 is CustomWeather's proprietary model.

MOS: (Model Output Statistics) for any global location using archive of model and observation data. 0.25 degree resolution. 15-day hourly forecasts from January 1, 2017; 15-day forecasts from April 19, 2017.

Year: year MeanLayDate: mean Julian date when first egg of the each clutch was laid ENSOWinter: mean ENSO score from December to March. Hurricanes: total number of hurricanes in the North Atlantic basin DaysBelow18_max: number of days with maximum daily temperature below 18.5 degrees Celsius or with rain during the 28 days after the mean fledging date. A crude measurement of weather conditions post fledging. TimePeriod: population trajectory at the time (growing, declining, post-decline)

The backbone of CustomWeather's forecasting arm is our proven, high-resolution model, the 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 it 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 precip amounts, and miles 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 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.

Historical Longer Term Forecasts: Includes historical hourly and/or daily forecast data from 2009 until present date. Data will include condition descriptions, high/low temperatures, wind speed and direction, dew point, humidity, comfort level, UV index, probability of precipitation, rainfall and snowfall amounts. Available for over 85,000 forecast points globally. The information is updated four times per day.

Below are available time periods per each type of forecast from the GFS model and from CustomWeather's proprietary CW100 model:

GFS: 7-day hourly forecasts from August 2nd 2009; 48-hour to 5-day detailed forecasts from August 4th 2009; 15-day forecasts from October 9th 2008.

CW100: 7-day hourly forecasts from November 27, 2012; 48-hour detailed forecasts from November 12, 2011; 7-day forecasts from December 6, 2010, 15-day forecasts from August 6, 2012. CW100 is CustomWeather's proprietary model.

MOS: (Model Output Statistics) for any global location using archive of model and observation data. 0.25 degree resolution. 15-day hourly forecasts from January 1, 2017; 15-day forecasts from April 19, 2017.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Historical. The data include parameters of historical with a geographic location of . The time period coverage is from Unavailable begin date to Unavailable end date in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Diaries and Journals containing weather information in a non-tabular format. Records date from 1735 through the early 20th century. Much of the weather and climate data recorded by the founding fathers of this country (Washington, Jefferson and Franklin to name a few) were archived in original manuscripts, then microfilmed and stored at the National Archive and Records Administration (NARA). Those records available from NARA on microfilm have been imaged and placed on the EV2 system. To date, there are more than 42 million of those images on-line. These colonial diaries and data are a treasure trove to the climatologist seeking data on climate of the 19th century.

The data provided data are historical weather measurement and forecast at 48 locations in France and its boundary.

Measurements are inside files named MES_YYYY.csv with YYYY is the id code of the station.

The file "Station_list.csv" contains the list of the 45 locations with the id code, the name and then the latitude and longitude.

Forecasts are inside files named XXX_YYYY.csv with YYYY the id code corresponding of the location of the grid ouput close to the associated observation location.
XXX is the id of the numerical forecast:
"GFS0.25-Complet" for GFS file at 0.25° resolution
"LEXIS" for WRF produced by EVEREST project using the LEXIS chain
"WRF3KM-Complet" for WRF at 3km resolution produced by NUMTECH
"WRF12KM-Complet" for WRF at 12km resolution produced by NUMTECH

Description of MES-YYYY files:
- One line per measurement with hourly resolution
- columns are: Date(TU),Temperature2m_degC,WindSpeed10m_m/s,WindDirection10m_m/s
Date = date of measurement in TU and format DD/MM/YYYY HH:MM
Temperature2m_degC = air temperature at 2m height in °Celsius
WindSpeed10m_m/s = wind speed at 10m height in m/s
WindDirection10m_deg = wind direction at 10m height in deg. (0 or 360 = wind from north to south, 45°=wind from east to east, ....)
If measurement is not available for a specific hour for one parameter, the value "-999" is used.

The observation data gocfrom 28/01/2024 00HTU to 17/03/2024 23HTU

Description of XXX_YYYY forecast files:
- One line per forecast with hourly resolution
- columns are: First date run (TU),Forecast date,Temperature2m_degC,WindSpeed10m_m/s,WindDirection10m_m/s
First date run (TU) = date of start of the forecast in TU and format DD/MM/YYYY HH:MM. HH could be 00 and 12 according to the cycle of forecast start.
Forecast date = date of the forecast in TU and format DD/MM/YYYY HH:MM. HH go from 00 to 23.
Temperature2m_degC = air temperature at 2m height in °Celsius
WindSpeed10m_m/s = wind speed at 10m height in m/s
WindDirection10m_deg = wind direction at 10m height in deg. (0 or 360 = wind from north to south, 45°=wind from east to east, ....)
If forecast is not available for a specific hour for one parameter, the value "-999" is used.

The forecast data go from 28/01/2024 00HTU to 17/03/2024 23HTU

This dataset contains Raleigh Durham International Airport weather data pulled from the NOAA web service described at Climate Data Online: Web Services Documentation. We have pulled this data and converted it to commonly used units. This dataset is an archive - it is not being updated.

Introduction

This study has exploited the daily weather records of Seungjeongwon Ilgi from the NIKH database. Seungjeongwon Ilgi (http://sjw.history.go.kr/main.do) is a daily record of the Seungjeongwon, the Royal Secretariat of the Joseon Dynasty of Korea. These diaries span from 1623 to 1910 and generally involve daily weather records in the entry header. Their observational site would be located in Seoul (N37°35′, E126°59′). We have exploited the weather records from the NIKH database and classified the daily weather using text mining method. We have also converted the report dates from the traditional lunisolar calendar to the Gregorian calendar, to better contextualise our data into the contemporary daily measurements.

Data

We provide different formats (csv, xlsx, json) to facilitate the usage of data. The main contents of data are listed as below.

• ID: The unique identifier of a specific record in the metadata, which can also serve as the identifier to merge with external data in the NIKH digital database.
• Traditional calendar: The original lunar dates in the NIKH digital database, which are listed in data format "YYYY-MM-DD". More specifically, "L0" implies the leap year and "L1" implies the common year.
• Leap: The identifier of a leap year.
• Gregorian calendar: The Gregorian calendar date that converted by the traditional calendar date.
• Weather Text: The text that describe the weather conditions. Specifically, multiple weather descriptions of the same day have been put together.
• Flag: The computed value that indicates different combinations of weather conditions.
• Volume: The volume of text in the original record.
• Herbal Volume: The volume of text in the herbal record.
• Sunny: A dummy variable that represents whether the weather description contains the expression of sunny.
• Cloudy: A dummy variable that represents whether the weather description contains the expression of cloudy.
• Rainy: A dummy variable that represents whether the weather description contains the expression of rainy.
• Snow: A dummy variable that represents whether the weather description contains the expression of snow.
• Wind: A dummy variable that represents whether the weather description contains the expression of wind.

Import Data

# Python
# CSV file
import pandas as pd
data=pd.read_csv('~/SJWilgi_Seoul_Weather_YR1623_1910.csv',encoding="utf-8") 
# JSON file
data=pd.read_json('~/SJWilgi_Seoul_Weather_YR1623_1910.json',encoding="utf-8")
# Excel file
data=pd.read_excel('~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx') # Excel file

# R
# CSV file
library(readr)
data<- read_csv("~/SJWilgi_Seoul_Weather_YR1623_1910.csv")
# Excel file
library(readxl)
data <- read_excel("~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx")

Context

Who among us doesn't talk a little about the weather now and then? Will it rain tomorrow and get so cold to shake your chin or will it make that cracking sun? Does global warming exist?

With this dataset, you can apply machine learning tools to predict the average temperature of Detroit city based on historical data collected over 5 years.

Content

The given data set was produced from the Historical Hourly Weather Data [https://www.kaggle.com/selfishgene/historical-hourly-weather-data], which consists of about 5 years of hourly measurements of various weather attributes (eg. temperature, humidity, air pressure) from 30 US and Canadian cities.

From this rich database, a cutout was made by selecting only the city of Detroit (USA), highlighting only the temperature, converting it to Celsius degrees and keeping only one value for each date (corresponding to the average daytime temperature - from 9am to 5pm).

In addition, temperature values ​​were artificially and gradually increased by a few Celsius degrees over the available period. This will simulate a small global warming (or is it local?)...

In summary, the available dataset contains the average daily temperatures (collected during the day), artificially increased by a certain value, for the city of Detroit from October 2012 to November 2017.

The purpose of this dataset is to apply forecasting models in order to predict the value of the artificially warmed average daily temperature of Detroit.

See graph in the following image: black dots refer to the actual data and the blue line represents the predictive model (including a confidence area).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3089313%2Faf9614514242dfb6164a08c013bf6e35%2Fplot-ts2.png?generation=1567827710930876&alt=media" alt="">

Acknowledgements

This dataset wouldn't be possible without the previous work in Historical Hourly Weather Data.

Inspiration

What are the best forecasting models to address this particular problem? TBATS, ARIMA, Prophet? You tell me!

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

Records of past climate and environment from historical references and documentary evidence such as church records, harvest dates, and diaries. Parameter keywords describe what was measured in this data set. Additional summary information can be found in the abstracts of papers listed in the data set citations. For details please see: http://www.ncdc.noaa.gov/paleo/historical.html

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

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

These data are published and intended for use in the map Historic date of first snow.These data show the historic date by which there’s a 50% chance at least 0.1” of snow will have accumulated, based on each location’s snowfall history from 1981-2010, based on an analysis of the U.S. Climate Normals by Mike Squires, National Centers for Environmental Information. For a more detailed assessment of the historic date of first snow, please see this Climate.gov blog post by Deke Arndt, NOAA NCEI scientist. For a broad overview of NOAA's 1981–2010 Climate Normals, see NOAA's 1981-2010 U.S. Climate Normals: An Overview published in the Bulletin of the American Meteorological Society, or for a detailed description of snow Normals, seeNOAA's 1981-2010 U.S. Climate Normals: Monthly Precipitation, Snowfall, and Snow Depth published in the Journal of Applied Meteorology and Climatology.

The US Global Change Research Program sponsors the semi-annual National Climate Assessment, which is the authoritative analysis of climate change and its potential impacts in the United States. The 4th National Climate Assessment (NCA4), issued in 2018, used high resolution, downscaled LOCA climate data for many of its national and regional analyses. The LOCA downscaling was applied to multi-model mean weighted averages, using the following 32 CMIP5 model ensemble:ACCESS1-0, ACCESS1-3, bcc-csm1-1, bcc-csm1-1-m, CanESM2, CCSM4, CESM1-BGC, CESM1-CAM5, CMCC-CM, CMCC-CMS, CNRM-CM5, CSIRO-Mk3-6-0, EC EARTH, FGOALS-g2, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H-p1, GISS-E2-R-p1, HadGEM2-AO, HadGEM2-CC, HadGEM2-ES, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM-CHEM, MIROC-ESM, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, NorESM1-M.All of the LOCA variables used in NCA4 are presented here. Many are thresholded to provide 47 actionable statistics, like days with precipitation greater than 3", length of the growing season, or days above 90 degrees F. Time RangesStatistics for each variables were calculated over a 30-year period. Four different time ranges are provided:Historical: 1976-2005Early-Century: 2016-2045Mid-Century: 2036-2065Late-Century: 2070-2099Climate ScenariosClimate models use estimates of greenhouse gas concentrations to predict overall change. These difference scenarios are called the Relative Concentration Pathways. Two different RCPs are presented here: RCP 4.5 and RCP 8.5. The number indicates the amount of radiative forcing(watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but RCP 4.5 aligns with the international targets of the COP-26 agreement, while RCP 8.5 is aligns with a more "business as usual" approach. Detailed documentation and the original data from USGCRP, processed by NOAA's National Climate Assessment Technical Support Unit at the North Carolina Institute for Climate Studies, can be accessed from the NCA Atlas. Variable DefinitionsCooling Degree Days: Cooling degree days (annual cumulative number of degrees by which the daily average temperature is greater than 65°F) [degree days (degF)]Consecutive Dry Days: Annual maximum number of consecutive dry days (days with total precipitation less than 0.01 inches)Consecutive Dry Days Jan Jul Aug: Summer maximum number of consecutive dry days (days with total precipitation less than 0.01 inches in June, July, and August)Consecutive Wet Days: Annual maximum number of consecutive wet days (days with total precipitation greater than or equal to 0.01 inches)First Freeze Day: Date of the first fall freeze (annual first occurrence of a minimum temperature at or below 32degF in the fall)Growing Degree Days: Growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F) [degree days (degF)]Growing Degree Days Modified: Modified growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F; before calculating the daily average temperatures, daily maximum temperatures above 86°F and daily minimum temperatures below 50°F are set to those values) [degree days (degF)]growing-season: Length of the growing (frost-free) season (the number of days between the last occurrence of a minimum temperature at or below 32degF in the spring and the first occurrence of a minimum temperature at or below 32degF in the fall)Growing Season 28F: Length of the growing season, 28°F threshold (the number of days between the last occurrence of a minimum temperature at or below 28°F in the spring and the first occurrence of a minimum temperature at or below 28°F in the fall)Growing Season 41F: Length of the growing season, 41°F threshold (the number of days between the last occurrence of a minimum temperature at or below 41°F in the spring and the first occurrence of a minimum temperature at or below 41°F in the fall)Heating Degree Days: Heating degree days (annual cumulative number of degrees by which the daily average temperature is less than 65°F) [degree days (degF)]Last Freeze Day: Date of the last spring freeze (annual last occurrence of a minimum temperature at or below 32degF in the spring)Precip Above 99th pctl: Annual total precipitation for all days exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]Precip Annual Total: Annual total precipitation [inches]Precip Days Above 99th pctl: Annual number of days with precipitation exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]Precip 1in: Annual number of days with total precipitation greater than 1 inchPrecip 2in: Annual number of days with total precipitation greater than 2 inchesPrecip 3in: Annual number of days with total precipitation greater than 3 inchesPrecip 4in: Annual number of days with total precipitation greater than 4 inchesPrecip Max 1 Day: Annual highest precipitation total for a single day [inches]Precip Max 5 Day: Annual highest precipitation total over a 5-day period [inches]Daily Avg Temperature: Daily average temperature [degF]Daily Max Temperature: Daily maximum temperature [degF]Temp Max Days Above 99th pctl: Annual number of days with maximum temperature greater than the 99th percentile, calculated with reference to 1976-2005Temp Max Days Below 1st pctl: Annual number of days with maximum temperature lower than the 1st percentile, calculated with reference to 1976-2005Days Above 100F: Annual number of days with a maximum temperature greater than 100degFDays Above 105F: Annual number of days with a maximum temperature greater than 105degFDays Above 110F: Annual number of days with a maximum temperature greater than 110degFDays Above 115F: Annual number of days with a maximum temperature greater than 115degFTemp Max 1 Day: Annual single highest maximum temperature [degF]Days Above 32F: Annual number of icing days (days with a maximum temperature less than 32degF)Temp Max 5 Day: Annual highest maximum temperature averaged over a 5-day period [degF]Days Above 86F: Annual number of days with a maximum temperature greater than 86degFDays Above 90F: Annual number of days with a maximum temperature greater than 90degFDays Above 95F: Annual number of days with a maximum temperature greater than 95degFTemp Min: Daily minimum temperature [degF]Temp Min Days Above 75F: Annual number of days with a minimum temperature greater than 75degFTemp Min Days Above 80F: Annual number of days with a minimum temperature greater than 80degFTemp Min Days Above 85F: Annual number of days with a minimum temperature greater than 85degFTemp Min Days Above 90F: Annual number of days with a minimum temperature greater than 90degFTemp Min Days Above 99th pctl: Annual number of days with minimum temperature greater than the 99th percentile, calculated with reference to 1976-2005Temp Min Days Below 1st pctl: Annual number of days with minimum temperature lower than the 1st percentile, calculated with reference to 1976-2005Temp Min Days Below 28F: Annual number of days with a minimum temperature less than 28degFTemp Min Max 5 Day: Annual highest minimum temperature averaged over a 5-day period [degF]Temp Min 1 Day: Annual single lowest minimum temperature [degF]Temp Min 32F: Annual number of frost days (days with a minimum temperature less than 32degF)Temp Min 5 Day: Annual lowest minimum temperature averaged over a 5-day period [degF]For For freeze-related variables:The first fall freeze is defined as the date of the first occurrence of 32degF or lower in the nine months starting midnight August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 32degF or lower are excluded from the analysis.No freeze occurrence, value = 999The last spring freeze is defined as the date of the last occurrence of 32degF or lower in the nine months prior to midnight August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 32degF or lower are excluded from the analysis.No freeze occurrence, value = 999The growing season is defined as the number of days between the last occurrence of 28degF/32degF/41degF or lower in the nine months prior to midnight August 1 and the first occurrence of 28degF/32degF/41degF or lower in the nine months starting August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 28degF/32degF/41degF or lower are excluded from the analysis.No freeze occurrence, value = 999

The average temperature in December 2024 was 38.25 degrees Fahrenheit in the United States, the fourth-largest country in the world. The country has extremely diverse climates across its expansive landmass. Temperatures in the United States On the continental U.S., the southern regions face warm to extremely hot temperatures all year round, the Pacific Northwest tends to deal with rainy weather, the Mid-Atlantic sees all four seasons, and New England experiences the coldest winters in the country. The North American country has experienced an increase in the daily minimum temperatures since 1970. Consequently, the average annual temperature in the United States has seen a spike in recent years. Climate Change The entire world has seen changes in its average temperature as a result of climate change. Climate change occurs due to increased levels of greenhouse gases which act to trap heat in the atmosphere, preventing it from leaving the Earth. Greenhouse gases are emitted from various sectors but most prominently from burning fossil fuels. Climate change has significantly affected the average temperature across countries worldwide. In the United States, an increasing number of people have stated that they have personally experienced the effects of climate change. Not only are there environmental consequences due to climate change, but also economic ones. In 2022, for instance, extreme temperatures in the United States caused over 5.5 million U.S. dollars in economic damage. These economic ramifications occur for several reasons, which include higher temperatures, changes in regional precipitation, and rising sea levels.

Data and Problem Description

More accurate forecasts of building energy consumption mean better planning and more efficient energy use. So The objective is to forecast energy consumption from following data: (For each data set, several test periods over which a forecast is required will be specified.)

Historical Consumption

A selected time series of consumption data for over 260 buildings.

-obs_id - An arbitrary ID for the observationaa -SiteId - An arbitrary ID number for the building, matches across datasets -ForecastId - An ID for a timeseries that is part of a forecast (can be matched with the submission file) -Timestamp - The time of the measurement -Value - A measure of consumption for that building

Building Metadata

Additional information about the included buildings.

-SiteId - An arbitrary ID number for the building, matches across datasets -Surface - The surface area of the building -Sampling - The number of minutes between each observation for this site. The timestep size for each ForecastId can be found in the separate "Submission Forecast Period" file on the data download page. -BaseTemperature - The base temperature for the building -IsDayOff - True if DAY_OF_WEEK is not a work day

Historical Weather Data

This dataset contains temperature data from several stations near each site. For each site several temperature measurements were retrieved from stations in a radius of 30 km if available. Note: Not all sites will have available weather data.

Note: Weather data is available for test periods under the assumption that reasonably accurate forecasts will be available to algorithms that the time that we are attempting to make predictions about the future.

-SiteId - An arbitrary ID number for the building, matches across datasets -Timestamp - The time of the measurement -Temperature - The temperature as measured at the weather station -Distance - The distance in km from the weather station to the building in km

Public Holidays

Public holidays at the sites included in the dataset, which may be helpful for identifying days where consumption may be lower than expected.Note: Not all sites will have available public holiday data.

-SiteId - An arbitrary ID number for the building, matches across datasets -Date - The date of the holiday -Holiday - The name of the holiday

Acknowledgements

Forecasting energy consumption data published by Schneider Electric.

Inspiration

Three time horizons and time steps are distinguished for more than 260 building sites are provided. The goal is either:

• To forecast the consumption for each quarter for the length of time specified by the submission format.
• To forecast the consumption for each hour for the length of time specified by the submission format.
• To forecast the consumption for each day for the length of time specified by the submission format.

Historical data are given at the granularity that is required for the consumption forecast. So, when historical data are given by steps of 15 minutes, forecasts are required by steps of 15 minutes. When historical data are given by steps of 1 hour, forecasts are required by steps of 1 hour. When historical data are given by steps of 1 day, forecasts are required by steps of 1 day.

#

*If you find the data useful your upvote is an explicit feedback for future works, Have fun exploring data!*

Context

This dataset is meant to complement the Austin Bikesharing Dataset.

Content

Contains the:
Date (YYYY-MM-DD)
TempHighF (High temperature, in Fahrenheit)
TempAvgF (Average temperature, in Fahrenheit)
TempLowF (Low temperature, in Fahrenheit)
DewPointHighF (High dew point, in Fahrenheit)
DewPointAvgF (Average dew point, in Fahrenheit)
DewPointLowF (Low dew point, in Fahrenheit)
HumidityHighPercent (High humidity, as a percentage)
HumidityAvgPercent (Average humidity, as a percentage)
HumidityLowPercent (Low humidity, as a percentage)
SeaLevelPressureHighInches (High sea level pressure, in inches)
SeaLevelPressureAvgInches (Average sea level pressure, in inches)
SeaLevelPressureLowInches (Low sea level pressure, in inches)
VisibilityHighMiles (High visibility, in miles)
VisibilityAvgMiles (Average visibility, in miles)
VisibilityLowMiles (Low visibility, in miles)
WindHighMPH (High wind speed, in miles per hour)
WindAvgMPH (Average wind speed, in miles per hour)
WindGustMPH (Highest wind speed gust, in miles per hour)
PrecipitationSumInches (Total precipitation, in inches) ('T' if Trace)
Events (Adverse weather events. ' ' if None)

This dataset contains data for every date from 2013-12-21 to 2017-07-31.

Acknowledgements

This dataset was obtained from WeatherUnderground.com, at the Austin KATT station.

Inspiration

Can we use this dataset to explain some of the variation in the Austin Bikesharing Dataset?

Q: Where is severe weather likely at this time of year? A: Shading on each map reflects how often severe weather occurred within 25 miles during a 30-year base period. The darker the shading, the higher the number of severe weather reports near that date. For this map, severe weather encompasses tornadoes, thunderstorm winds over 58 miles per hour, and hail larger than three-quarters of an inch in diameter. Q: How were these maps produced? A: For each day of the year, scientists plotted reports of severe weather from 1982 to 2011 on a gridded map. To reveal the long-term patterns of these events, they applied mathematical filters to smooth the counts in time and space. Keep in mind that severe weather is possible at any location on any day of the year. Q: What do the colors mean? A: Shaded areas show the historical probability of severe weather occurring within 25 miles. Meteorologists estimated these probabilities from severe weather reports submitted from 1982-2011. For each day of the year, scientists plotted reports of severe events onto a map marked with grid cells 50 miles on a side. For each grid cell, they counted the number of years with at least one report, and divided by the total number of years. To reveal the long-term patterns suggested by this relatively small dataset, they used statistical methods to smooth the data. For instance, to smooth clusters of events in time, a mathematical filter replaced the value in every grid cell with a 15-day average. Another filter extended report locations over a 25-mile-wide circle to indicate the probability that the event could have occurred at other points within that area. Q: Why do these data matter? A: Knowing when and where severe weather tends to occur through the year promotes preparedness. Residents who are alert to the possibility of severe weather are better able to respond in ways that keep them safe. These data can also help emergency response personnel plan for when and where their services may be necessary. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's National Weather Service Storm Prediction Center produced the Severe Weather Climatology files. To produce our images, we obtained the climatology data as a numpy array, and ran a set of scripts to display the mapped areas on our base maps with a custom color bar. Additional information Data for these images represents an update and extension of work first put forth by Dr. Harold Brooks of the National Severe Storms Laboratory. References Brooks, H. E., C. A. Doswell, III, and M. P. Kay, (2003) Climatological estimates of local daily tornado probability, Wea. Forecasting, 18, 626-640.Source: https://www.climate.gov/maps-data/data-snapshots/data-source/historic-probability-severe-weather This upload includes two additional files:* Historic Probability of Severe Weather _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/historic-probability-severe-weather )* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.