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.

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.

License: No license information was provided.

The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation, following similar patterns. For instance, in August 2025, the average temperature across the North American country stood at 22.98 degrees Celsius. Rising temperatures Globally, 2016, 2019, 2021 and 2024 were some of the warmest years ever recorded since 1880. Overall, there has been a dramatic increase in the annual temperature since 1895. Within the U.S. annual temperatures show a great deal of variation depending on region. For instance, Florida tends to record the highest maximum temperatures across the North American country, while Wyoming recorded the lowest minimum average temperature in recent years. Carbon dioxide emissions Carbon dioxide is a known driver of climate change, which impacts average temperatures. Global historical carbon dioxide emissions from fossil fuels have been on the rise since the industrial revolution. In recent years, carbon dioxide emissions from fossil fuel combustion and industrial processes reached over 37 billion metric tons. Among all countries globally, China was the largest emitter of carbon dioxide in 2023.

US Average, Maximum, and Minimum Temperatures

Analyzing Daily Temperatures Across the USA

By Matthew Winter [source]

About this dataset

This dataset features the daily temperature summaries from various weather stations across the United States. It includes information such as location, average temperature, maximum temperature, minimum temperature, state name, state code, and zip code. All the data contained in this dataset has been filtered so that any values equaling -999 were removed. With this powerful set of data you to explore how climate conditions changed throughout the year and how they varied across different regions of the country. Dive into your own research today to uncover fascinating climate trends or use it to further narrow your studies specific to a region or city

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How to use the dataset

This dataset offers a detailed look at daily average, minimum, and maximum temperatures across the United States. It contains information from 1120 weather stations throughout the year to provide a comprehensive look at temperature trends for the year.

The data contains a variety of columns including station, station name, location (latitude and longitude), state name zip code and date. The primary focus of this dataset is on the AvgTemp, MaxTemp and MinTemp columns which provide daily average, maximum and minimum temperature records respectively in degrees Fahrenheit.

To use this dataset effectively it is useful to consider multiple views before undertaking any analysis or making conclusions:
- Plot each individual record versus time by creating a line graph with stations as labels on different lines indicating changes over time. Doing so can help identify outliers that may need further examination; much like viewing data on a scatterplot looking for confidence bands or examining variance between points that are otherwise hard to see when all points are plotted on one graph only.
- A comparison of states can be made through creating grouped bar charts where states are grouped together with Avg/Max/Min temperatures included within each chart - thereby showing any variance that may exist between states during a specific period about which it's possible to make observations about themselves (rather than comparing them). For example - you could observe if there was an abnormally high temperature increase in California during July compared with other US states since all measurements would be represented visually providing opportunity for insights quickly compared with having to manually calculate figures from raw data sets only.

With these two initial approaches there will also be further visualizations possible regarding correlations between particular geographical areas versus different climatic conditions or through population analysis such as correlating areas warmer/colder than median observances verses relative population densities etc.. providing additional opportunities for investigation particularly when combined with key metrics collected over multiple years versus one single year's results exclusively allowing wider inferences to be made depending upon what is being requested in terms of outcomes desired from those who may explore this data set further down the line beyond its original compilation starter point here today!

Research Ideas

• Using the Latitude and Longitude values, this dataset can be used to create a map of average temperatures across the USA. This would be useful for seeing which areas were consistently hotter or colder than others throughout the year.
• Using the AvgTemp and StateName columns, predictors could use regression modeling to predict what temperature an area will have in a given month based on it's average temperature.
• By using the Date column and plotting it alongside MaxTemp or MinTemp values, visualization methods such as timelines could be utilized to show how temperatures changed during different times of year across various states in the US

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2015 USA Weather Data FINAL.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Matthew Winter.

The U.S. Climate Normals are a large suite of data products that provide information about typical climate conditions for thousands of locations across the United States. Normals act both as a ruler to compare today’s weather and tomorrow’s forecast, and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables from almost 15,000 U.S. weather stations.

NCEI generates the official U.S. normals every 10 years in keeping with the needs of our user community and the requirements of the World Meteorological Organization (WMO) and National Weather Service (NWS). The 1991–2020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. These data allow travelers to pack the right clothes, farmers to plant the best crop varieties, and utilities to plan for seasonal energy usage. Many other important economic decisions that are made beyond the predictive range of standard weather forecasts are either based on or influenced by climate normals.

Snapshot img

Weather Forecasting Services Market Size 2025-2029

The weather forecasting services market size is valued to increase USD 1.6 billion, at a CAGR of 11.8% from 2024 to 2029. Farmers need weather forecasting services will drive the weather forecasting services market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 40% growth during the forecast period.
By Type - Medium-range segment was valued at USD 555.80 billion in 2023
By Application - Energy and utilities segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 148.85 million
Market Future Opportunities: USD 1595.10 million
CAGR : 11.8%
APAC: Largest market in 2023

Market Summary

The market encompasses a dynamic and essential industry, driven by advancements in core technologies and applications. With the increasing reliance on accurate weather information for various sectors, such as agriculture and renewable energy production, the market's significance continues to grow. For instance, farmers heavily depend on weather forecasting services to optimize crop yields and mitigate potential losses. Moreover, the upsurge in the production of renewable energy necessitates precise weather predictions to ensure efficient energy generation. However, the complexities of weather forecasting models pose significant challenges. These models must account for numerous variables and continually adapt to evolving weather patterns.
One of the major drivers for the market's growth is the increasing adoption of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) to improve forecasting accuracy. As of 2021, AI and ML technologies are estimated to account for over 20% of the market share. Despite these advancements, regulatory frameworks and data privacy concerns pose challenges for market growth. Additionally, regional differences in weather patterns and climatic conditions create diverse market opportunities. As the market continues to evolve, stakeholders must navigate these challenges and capitalize on emerging opportunities to remain competitive.

What will be the Size of the Weather Forecasting Services Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Weather Forecasting Services Market Segmented and what are the key trends of market segmentation?

The weather forecasting services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Medium-range
  Long-range
  Short-range
  Nowcasting


Application

  Energy and utilities
  Aviation
  Media and consumer
  Logistics and transportation
  Others


Method

  Ground-based
  Satellite-based
  Model-based


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The medium-range segment is estimated to witness significant growth during the forecast period.

Weather forecasting services play a crucial role in various sectors, including aviation, agriculture, energy, and finance. Ensemble prediction systems analyze multiple forecasts to enhance accuracy, while aviation weather briefings ensure safe flights. Forecasting model validation ensures reliability, and climate prediction systems help understand long-term trends. Weather risk management mitigates potential losses, and air quality forecasting protects public health. Atmospheric data assimilation combines observations and models, and atmospheric circulation patterns provide context. Weather station networks collect essential data, and severe weather warnings save lives. Weather model ensembles offer probabilistic forecasts, and satellite meteorology provides global coverage. UV index prediction safeguards outdoor activities, and weather prediction accuracy depends on data quality control.

Wind energy forecasting optimizes production, and climate change impacts require adaptation strategies. Marine weather forecasts ensure safe maritime travel, and hydrological forecasting manages water resources. Climate modeling techniques explore future scenarios, high-resolution forecasting enhances precision, and agricultural weather services optimize crop yields. Radar meteorology monitors precipitation, numerical weather prediction models simulate weather, and short-range forecasts provide immediate insights. Geospatial weather data offers location-specific information, and extreme weather events require robust response plans. Model output statistics inform decision-making, and long-range forecasting anticipates trends. Mesoscale modeling focuses on local weather pat

Temperature Over Time by State (Starts: 1895)

State and County Temperature Changes

By Environmental Data [source]

About this dataset

Do you want to know how rising temperatures are changing the contiguous United States? The Washington Post has used National Oceanic and Atmospheric Administration's Climate Divisional Database (nClimDiv) and Gridded 5km GHCN-Daily Temperature and Precipitation Dataset (nClimGrid) data sets to help analyze warming temperatures in all of the Lower 48 states from 1895-2019. To provide this analysis, we calculated annual mean temperature trends in each state and county in the Lower 48 states. Our results can be found within several datasets now available on this repository.

We are offering: Annual average temperatures for counties and states, temperature change estimates for each of the Lower 48-states, temperature change estimates for counties in the contiguous U.S., county temperature change data joined to a shapefile in GeoJSON format, gridded temperature change data for the contiguous U.S. in GeoTiff format - all contained with our dataset! We invite those curious about climate change to explore these data sets based on our analysis over multiple stories published by The Washington Post such as Extreme climate change has arrived in America, Fires, floods and free parking: California’s unending fight against climate change, In fast-warming Minnesota, scientists are trying to plant the forests of the future, This giant climate hot spot is robbing West of its water ,and more!

By accessing our dataset containing columns such as fips code, year range from 1895-2019, three season temperatures (Fall/Spring/Summer/Winter), max warming season temps plus temp recorded total yearly - you can become an active citizen scientist! If publishing a story or graphic work based off this data set please credit The Washington Post with a link back to this repository while sending us an email so that we can track its usage as well - 2cdatawashpost.com.

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How to use the dataset

The main files provided by this dataset are climdiv_state_year, climdiv_county_year, model_state, model_county , climdiv_national_year ,and model county .geojson . Each file contains different information capturing climate change across different geographies of the United States over time spans from 1895.

Research Ideas

• Investigating and mapping the temperatures for all US states over the past 120 years, to observe long-term changes in temperature patterns.
• Examining regional biases in warming trends across different US counties and states to help inform resource allocation decisions for climate change mitigation and adaption initiatives.
• Utilizing the ClimDiv National Dataset to understand continental-level average annual temperature changes, allowing comparison of global average temperatures with US averages over a long period of time

Acknowledgements

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

License

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

Columns

File: climdiv_state_year.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | fips | Federal Information Processing Standard code for each county. (Integer) | | year | Year of the temperature data. (Integer) | | tempc | Temperature change from the previous year. (Float) |

File: climdiv_county_year.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | fips | Federal Information Processing Standard code for each county. (Integer) | | year | Year of the temperature data. (Integer) | | tempc | Temperature change from the previous year. (Float) |

File: model_state.csv | Column name | Description | |:------------------...

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.

According to a survey carried out in 2024, ** percent of the respondents in the U.S. expressed that they experienced worse-than-usual extreme weather events in their communities. More than half of the respondents, on the other hand, responded that they experienced the same things as usual in their communities.

The Storm Events Database is an integrated database of severe weather events across the United States from 1950 to this year, with information about a storm event's location, azimuth, distance, impact, and severity, including the cost of damages to property and crops. It contains data documenting: The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce. Rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the San Diego coastal area. Other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event. Data about a specific event is added to the dataset within 120 days to allow time for damage assessments and other analysis.

Record High Temperatures for US Cities

Clearly Defined Monthly Data

By Gary Hoover [source]

About this dataset

This dataset contains all the record-breaking temperatures for your favorite US cities in 2015. With this information, you can prepare for any unexpected weather that may come your way in the future, or just revel in the beauty of these high heat spells from days past! With record highs spanning from January to December, stay warm (or cool) with these handy historical temperature data points

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How to use the dataset

This dataset contains the record high temperatures for various US cities during the year of 2015. The dataset includes columns for each individual month, along with column for the records highs over the entire year. This data is sourced from www.weatherbase.com and can be used to analyze which cities experienced hot summers, or compare temperature variations between different regions.

Here are some useful tips on how to work with this dataset: - Analyze individual monthly temperatures - this dataset allows you to compare high temperatures across months and locations in order to identify which areas experienced particularly hot summers or colder winters.
- Compare annual versus monthly data - use this data to compare average annual highs against monthly highs in order to understand temperature trends at a given location throughout all four seasons of a single year, or explore how different regions vary based on yearly weather patterns as well as across given months within any one year; - Heatmap analysis - use this data plot temperature information in an interactive heatmap format in order to pinpoint particular regions that experience unique weather conditions or higher-than-average levels of warmth compared against cooler pockets of similar size geographic areas; - Statistically model the relationships between independent variables (temperature variations by month, region/city and more!) and dependent variables (e.g., tourism volumes). Use regression techniques such as linear models (OLS), ARIMA models/nonlinear transformations and other methods through statistical software such as STATA or R programming language;
- Look into climate trends over longer periods - adjust time frames included in analyses beyond 2018 when possible by expanding upon the monthly station observations already present within the study timeframe utilized here; take advantage of digitally available historical temperature readings rather than relying only upon printed reports

With these helpful tips, you can get started analyzing record high temperatures for US cities during 2015 using our 'Record High Temperatures for US Cities' dataset!

Research Ideas

• Create a heat map chart of US cities representing the highest temperature on record for each city from 2015.
• Analyze trends in monthly high temperatures in order to predict future climate shifts and weather patterns across different US cities.
• Track and compare monthly high temperature records for all US cities to identify regional hot spots with higher than average records and potential implications for agriculture and resource management planning

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: Highest temperature on record through 2015 by US City.csv | Column name | Description | |:--------------|:--------------------------------------------------------------| | CITY | Name of the city. (String) | | JAN | Record high temperature for the month of January. (Integer) | | FEB | Record high temperature for the month of February. (Integer) | | MAR | Record high temperature for the month of March. (Integer) | | APR | Record high temperature for the month of April. (Integer) | | MAY | Record high temperature for the month of May. (Integer) | | JUN | Record high temperature for the month of June. (Integer) | | JUL | Record high temperature for the month of July. (Integer) | | AUG | Record high temperature for the month of August. (Integer) | | SEP | Record high temperature for the month of September. (Integer) | | OCT | Record high temperature for the month of October. (Integer) | | ...

This statistic shows cities in the United States with the highest average annual temperatures. Data is based on recordings from 1981 to 2010. In San Antonio, Texas the average temperature is 80.7 degrees Fahrenheit. Some cities that have the hottest maximum summer temperatures will not be included in this list due to their extreme temperature variance.

This dataset includes the monthly and daily data used for the analysis of historical and future trends in precipitation and temperature at five Long-Term Agroecosystem Research (LTAR) sites: Kellogg Biological Station (KBS) in Michigan, Upper Mississippi River Basin (UMRB) in Iowa, Central Mississippi River Basin (CMRB) in Missouri, Southern Plains (SP) in Oklahoma, and Lower Mississippi River Basin (LMRB) in Mississippi. Historical data include the longest available record of daily precipitation, minimum temperature, and maximum temperature at weather stations from KBS, UMRB, CMRB, and LMRB, and the monthly 1895-2020 data from the National Ocean and Atmospheric Administration for the climate divisions that represent the five LTAR sites. Future data include 2020-2100 monthly predictions for the five sites from 26 Earth System Models and two Shared Socio-economic Pathways (SSP): the middle of the road SSP245 (a continuation of current emission rates and geo-political conditions), and the fossil fueled development scenario SSP 585 (intensification of fossil fuel energy sources and corresponding emissions). In addition, the data includes the trends calculated from historical and future data, snippets of R code used to calculate these trends, and README files that detail the content of each file.Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 ℃ decade-1) than maximum (-0.1 - 0.2 ℃ decade-1), more for winter (-0.1 - 0.3 ℃ decade-1) than summer (-0.1 - 0.1 ℃ decade-1), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade-1 or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.

Rasters of air temperature, including both Alaskan and lower 48 datasets, with historical and projected data, and projected change.

The U.S. Annual Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters for thousands of U.S. stations located across the 50 states, as well as U.S. territories, commonwealths, the Compact of Free Association nations, and one station in Canada. NOAA Climate Normals are a large suite of data products that provide users with many tools to understand typical climate conditions for thousands of locations across the United States. As many NWS stations as possible are used, including those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the Climate Reference Network (CRN). The comprehensive U.S. Climate Normals dataset includes various derived products including daily air temperature normals (including maximum and minimum temperature normal, heating and cooling degree day normal, and others), precipitation normals (including snowfall and snow depth, percentiles, frequencies and other), and hourly normals (all normal derived from hourly data including temperature, dew point, heat index, wind chill, wind, cloudiness, heating and cooling degree hours, pressure normals). In addition to the standard set of normals, users also can find "agricultural normals", which are used in many industries including but not limited to construction, architecture, pest control, etc. These supplemental "agricultural normals" include frost-freeze date probabilities, growing degree day normals, probabilities of reaching minimum temperature thresholds, and growing season length normals. Users can access the data either by product or by station. Included in the dataset is extensive documentation to describe station metadata, filename descriptions, and methodology of producing the data. All data utilized in the computation of the 1981-2010 Climate Normals were taken from the ISD Lite (a subset of derived Integrated Surface Data), the Global Historical Climatology Network-Daily dataset, and standardized monthly temperature data (COOP). These source datasets (including intermediate datasets used in the computation of products) are also archived at the NOAA NCDC.

The North American Mesoscale Forecast System (NAM) is one of the major regional weather forecast models run by the National Centers for Environmental Prediction (NCEP) for producing weather forecasts. Dozens of weather parameters are available from the NAM grids, from temperature and precipitation to lightning and turbulent kinetic energy. The NAM generates multiple grids (or domains) of weather forecasts over the North American continent at various horizontal resolutions. High-resolution forecasts are generated within the NAM using additional numerical weather models. These high-resolution forecast windows are generated over fixed regions and are occasionally run to follow significant weather events, like hurricanes. This dataset contains a 12 km horizontal resolution Lambert Conformal grid covering the Continental United States (CONUS) domain. It is run four times daily at 00z, 06z, 12z and 18z out to 84 hours with a 1 hour temporal resolution.

U.S. Storm Events Database

An In-depth Look at Historical Weather Events 1950-Present

By US Open Data Portal, data.gov [source]

About this dataset

The National Weather Service (NWS) provides Storm Data, detailing the statistics of personal injuries and damage estimates resulting from numerous types of severe weather events that have occurred in the United States. Compiling records as early as 1950 to the present, Storm Data allows users to select storms by county or other custom criteria, listing hurricanes, tornadoes, thunderstorms, hail, floods, drought conditions, lightning strikes, blustering winds and snowfall accumulations among many other natural phenomena of varying intensities. All this raw material is organized chronologically by state and used selectively to gain a better understanding of our nation's diverse weather experiences. A maximum 120 day delay may exist in providing up-to-date Storm Data due to periodic updates released by NWS so users are afforded greater accuracy with their research efforts regardless for what purpose it’s being sought – whether for analysis or education. Making an informed decision about safety measures or studying historic trends related to climate change demands reliable data from trusted sources such as NCDC Storm Events Database; empowering us all with unimpeded access towards achieving a higher understanding of our environment

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How to use the dataset

This dataset contains detailed information about various types of storms that have occured in the United States since 1950, including hurricanes, tornadoes, hail storms, and floods. The data is organized by county and can be used to analyze weather patterns or conduct research on severe weather events in particular regions of the country. Here are some steps to help you get started using this dataset:

• Select a geographic area: You can start by selecting a specific state or county from where you would like to explore data on storm events. This will narrow down your search results so you can easily find more relevant data points.

• Filter for desired storm type(s): Next step is to filter for the particular type of storm event that interests you—such as hurricanes, snowstorms, lightning strikes —to further refine your search results based on specific criteria such as date range and damage estimates etc..

• Analyze the resulting data set: Lastly you’ll need to analyze any additional fields available post filtering process like fatalities numbers , damage estimates , cities affected . Run analyses that could result in trends on severity of storms over time or location-based distribution etc.. Alternatively create charts / graphs which could help visualize any insights drawn from your findings better

Research Ideas

• Looking into the correlation between severe weather events and climate change by tracking historical data points from 1950 onwards with the NCDC Storm Events Database.
• Identifying trends in storm damages, such as those caused by hail, high winds, and other weather phenomena that could lead to better preparedness strategies for businesses or individuals who are vulnerable to certain risks in their area.
• Analyzing which states or counties have experienced the most severe weather events over the years and use this information to inform better mitigation planning ahead of potential disasters in those areas

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit US Open Data Portal, data.gov.

The number of billion-dollar disasters stood at *** events in the period between 2010 and 2019. This is double the number in the previous decade. While increased costs due to the disasters are likely due to higher exposure of vulnerable high-value assets, climate change also plays a role in the intensity and frequency of some climatological events.

A large share of U.S. citizens acknowledged that climate change was affecting their local areas. However, concerns varied from region to region, with concerns focusing on various extreme events. In the West Mountain region, around ** percent of the respondents stated that unusually long periods of hot weather as well as droughts and water shortages were the main consequence of climate change in their local areas.

The U.S. Annual Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters that provide users with many tools to understand typical climate conditions for thousands of locations across the United States, as well as U.S. territories, commonwealths, the Compact of Free Association nations, and one station in Canada. As many NWS stations as possible are used, including those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the Climate Reference Network (CRN). THe Annual Climate Normals dataset includes various derived products such as daily air temperature normals (including maximum and minimum temperature normal, heating and cooling degree day normal, and others), precipitation normals (including snowfall and snow depth, percentiles, frequencies and other), and hourly normals (all normal derived from hourly data including temperature, dew point, heat index, wind chill, wind, cloudiness, heating and cooling degree hours, pressure normals). All data utilized in the computation of the 1981-2010 Climate Normals were taken from the ISD Lite (a subset of derived Integrated Surface Data), the Global Historical Climatology Network-Daily dataset, and standardized monthly temperature data (COOP). These source datasets (including intermediate datasets used in the computation of products) are also archived at the NOAA NCDC.