Shiawassee County, Michigan has experienced a variety of weather since 1900, impacting people, communities, and geographies. Track monthly data on how Shiawassee County experiences severe weather, including precipitation and temperature.
Data Updated Aug 2
In the most recent month, Invalid Date, the average temperature in Shiawassee County was °F, which is ---- average when compared to all Invalid Dates since 1985.
Temperature difference from 20th century average for January between 1900 and 2021
Temperature difference from 20th century average for every month between 1900 and 2021
In the chart below, each rectangle is a month and each column is a year. The rows correspond to a specific month across all years available.
The 12-month average temperature decreased NaN°F from January 2022 to January 2022. From January 2022 to January 2022, the 12-month average temperature was NaN°F.
In the most recent month, Invalid Date, Shiawassee County had undefined inches of precipitation. That’s ---- average when compared to all Invalid Dates since 1985.
Precipitation difference from 20th century average for January between 1900 and 2021
Precipitation difference from 20th century average for every month between 1900 and 2021
The 12-month total precipitation decreased NaN inches from January 2022 to January 2022. From January 2022 to January 2022, the average 12-month total precipitation was NaN inches.
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.
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.
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:
AM = Monthly Average
A20 = 20th Century Average
n = Number of Months Represented
Source Agency: Monthly (within first week of each month)
USAFacts: Monthly (14th of every month)
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.
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.
Source Agency: Yearly in June
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Keep up with the latest data and most popular content.