AKIndices
alaska climate data

Air temperature data from over 400 communities, reduced to relevant engineering parameters (Additional info)

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Historical (1901-2009) or Projection (2001-2099)
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Results

Community
 		 Years
 		 Tavg
°C		 ATI
°C-days		 AFI
°C-days		 DTI
°C-days		 DFI
°C-days		 Type
 		 Add/
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NOTE: The parameters calculated by AKIndices are based on average monthly temperatures, not average daily temperatures. As well, derived data is provided without any rounding or consideration for significant digits, allowing the user to decide what is appropriate for their analysis.

Info

What
AKIndices provides basic engineering climate parameters that are commonly used for engineering and site-development purposes. These parameters include:
  • Tavg: The average (arithmetic mean) air temperature, based on all of the monthly air temperatures for the specified range of years.
  • ATI: The average (arithmetic mean) annual thawing index. The thawing index is the total number of degree-days above the freezing point. The number displayed by AKIndices is the average of the annual indices for the specified range of years.
  • AFI: The average (arithmetic mean) annual freezing index. The freezing index is the total number of degree-days below the freezing point. The number displayed by AKIndices is the average of the annual indices for the specified range of years.
  • DTI: The design thawing index. The number displayed by AKIndices is the arithmetic mean of the three warmest thawing indices for the specified range of years. If less than three years are displayed, the DTI is listed as 'None.' Typically, the DTI is calculated over a 30-year or 10-year time span.
  • DFI: The design freezing index. The number displayed by AKIndices is the arithmetic mean of the three coolest freezing indices for the specified range of years. If less than three years are displayed, the DFI is listed as 'None.' Typically, the DFI is calculated over a 30-year or 10-year time span.
Why
AKIndices provides quick and simple access to the massive amounts of data released by the SNAP group. It does not aim to replace, modify, or build on SNAP's work, but rather provide an alternative means for users to explore and understand the data.
How
AKIndices is built with python. Check out AKExtract and AKIndices on GitHub for more info on how to install on your own machine, fork the project, or submit bug reports. In a nutshell, AKExtract takes a list of communities and their coordinates, as well as SNAP datasets, and extracts the air temperature data from the data point closest to a community's location. AKIndices is the front-end for interacting with that extracted data.
Who
This project is the work of Matthew Dillon. While this project would not exist without SNAP, AKIndices is not endorsed or supported by SNAP in any way. Before utilizing the derived data from AKIndices make sure to take a look at SNAP's page to learn about the science and the methods behind their products.

This product is provided as-is, with no warranty express or implied. Use at your own risk.

Commercial use disclaimer: It is the sole responsibility of the user to execute any agreements with SNAP regarding commercial use of the SNAP data (potentially including the derived products found on this page).

Question? Comment? Find a problem? Email me or submit a bug report!