Climate in the United States

The United States has experienced a wide variety of extreme weather over the last 125 years, impacting people, communities, and geographies. Track monthly data on how counties experience severe weather, including precipitation and temperature.

Latest update on Sep 2023
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State
September
2023

Temperatures nationwide over time

September
2023

Where in the US are temperatures hitting monthly extremes?

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Temperature and population over time

How many Americans experience monthly temperature extremes?

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Population by difference from average temperature in September 2023

Population numbers are calculated by grouping county-level populations experiencing similar differences. Population figures correspond to data from the closest year available.

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Which demographics experience temperature extremes?

The charts below show the population of each racial or ethnic group in the US lived in counties with cooler, average or warmer than normal monthly temperatures for September 2023.

Hispanic/Latino
white
American Indian/Alaska Native
Black
Asian
Native Hawaiian and Other Pacific Islander
 

Population numbers are calculated by grouping county-level populations experiencing similar differences excluding the Hispanic/Latino category, all racial groups include non-Hispanic populations only.

 
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Precipitation nationwide over time

September
2023

Where in the US is precipitation hitting monthly extremes?

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Precipitation and population over time

How many Americans experience monthly precipitation extremes?

0
Wetter
2020 Population
0
Drier
2020 Population

No data available

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Population by difference from average precipitation in September 2023

Population numbers are calculated by grouping county-level populations experiencing similar differences. Population figures correspond to data from the closest year available.

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Which demographics experience precipitation extremes?

The charts below show the population of each racial or ethnic group in the US lived in counties with wetter, average or drier than normal monthly precipitation for September 2023.

Hispanic/Latino
white
American Indian/Alaska Native
Black
Asian
Native Hawaiian and Other Pacific Islander
 

Population numbers are calculated by grouping county-level populations experiencing similar differences excluding the Hispanic/Latino category, all racial groups include non-Hispanic populations only.

 
No additional source information
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Where are climates hitting extremes?

Temperature and precipitation are two of the main ways people experience climate. Explore where these weather factors are staying average and when the monthly average hit a 20-year anomaly. Use this map to understand where, county by county, monthly averages are significantly below or above historical average.

September
2023
 

These labels denote extremes based on 20th-century weather data averages. About 90% of 20th-century data falls into the average category.

Hot

 

Cold

Hot/Wet
Hot
Hot/Dry
Wet
Average
Dry
Cold/Wet
Cold
Cold/Dry
 

Wet

 

Dry

How many people experience extreme temperature and precipitation?

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Data Methodology

Temperature and Precipitation Datasets

Dataset Description

The National Centers for Environmental Information (NCEI), is a sub-bureau of the National Oceanic and Atmospheric Administration (NOAA). Its NOAA Monthly US Climate Divisional Database (NClimDiv)1 provides data for temperature, precipitation, drought indices, and heating and cooling degree days for US climate divisions, states, multi-state regions, and the nation from 1895 to the present. We leveraged the county-level temperature and precipitation averages to showcase climatic anomalies in comparison to the 20th century average.

Those data exclude Hawaii because NCEI indicated county-level averages could not be constructed with the limited data and highly variable climate patterns of the Hawaiian Islands. To provide a comprehensive account of climate across the United States, we supplemented the dataset with individual station data for each county in Hawaii. Although presented side-by-side with the county-level averages, the Hawaiian data are station-specific averages and should not be considered representative of county-level climate.

Dataset Justification

The NClimDiv database hosts multiple types of historical averages: 30-year averages starting from 1901, 1895-2010 average, and 20th century average, the latter is being used in this experience. NCEI references these averages as varieties of climate normals, we will reference these values as average. These averages are specific to each county and month. We reconstructed these averages to verify that we were using the proper methodology and then applied that methodology to the county-level monthly average dataset. This provided the average, which was subsequently used to calculate the standard deviation for each county-month pairing. Such methodology was applicable to all counties in the contiguous United States. These averages are consistent with accepted baseline measures that major governmental and scientific sources use as a point of comparison over long time horizons2, 3.

Alaska data was limited to 1925 forward; therefore our “20th century average” for Alaska is based on the known 75-year time span.

For Hawaiian data, data are limited to a single weather station for each of the state’s four largest counties: Hawaii, Maui, Kauai, and Honolulu. Hawaii County is represented by the weather station in Hilo, Maui County by Kahului, Honolulu County by Honolulu, and Kauai County by Lihue. Although data for Honolulu are available from 1890 onward, data for Lihue and Kahului are limited to 1905 forward and Hilo data are limited to 1949 forward, with certain transitory phases during station maintenance also missing data. Like Alaska, such data limitations required us to constrict our “20th century averages” to the years available.

Dataset Transformations

The transformations to these climatic data were done to provide users with an intuitive understanding of whether a given month’s total precipitation or average temperature were similar to or different than the corresponding historical average.

We defined all monthly temperature and precipitation values to be average in comparison to the 20th century average if they fell within two standard deviations of the 20th century average. All values that fell below or above two standard deviations are defined as climatic anomalies; cooler/wetter than or warmer/drier than the historical norm, respectively. This bucket categorization is critical to eliminate data noise as regional geographies experience natural fluctuations in temperature and precipitation from year to year.

Although the threshold for what is considered extreme weather differs across research and government organizations, we used a standardized baseline to classify approximately 95% of 20th century events as average. The use of a two standard deviation cut-off point, which places approximately 95% of observations into the “average” categorization means that months categorized as “warmer,” “cooler,” “wetter,” and “drier” represent rarer than once-in-20-year events.

A standard deviation measures the amount of variability among the numbers in a data set, the typical distance of a data point from the mean of the data and is calculated against the NClimDiv data as:

Standard Deviation Standard deviation formula

Where:

AM = Monthly Average

A20 = 20th Century Average

n = Number of Months Represented

Expected Dataset Update Frequency

Source Agency: Monthly (within first week of each month)

USAFacts: Monthly (14th of every month)

Relevant Dataset Documentation and Links

NClimDiv Source Data

NOAA National Weather Service-Hawaii

US Climate Division – NClimDiv Explainer

NClimDiv County-level README

NClimDiv Normals README

NClimDiv Overview SlideDeck

Population Datasets

Dataset Description

We used the decennial census population counts for 2010 and 2020 and the intercensal estimates, which the US Census Bureau generates yearly, to produce continuous population distributions for each year increment between each decennial census collection. Because the newest year’s estimate is released the following year, the current year’s population numbers may reflect the nearest year we have data for. The program itself uses the data collected in postcensal population estimates and the 10-year census population count, which calculates the difference between the two, and distributes that difference across the intermediary years, providing a yearly population estimate that is then retroactively verified.

Dataset Justification

The Census Bureau has three population estimation programs: Postcensal, Intercensal, and Vintage. The decennial census and intercensal estimates are the recommended metric according to the US Census Bureau because of their mathematically accurate modelling of intercensal years, as they consider differences between the estimate programs and the census count, and their representation of data that is not available to the census.

Dataset Transformations

None

Expected Dataset Update Frequency

Source Agency: Yearly in June

USAFacts: Yearly

Relevant Dataset Documentation and Links

US Census Bureau – Source Data

US Census Bureau – Rules and Regulations

US Census Bureau, Population Division – Variable Names and Descriptions