Presenter Information

Colin BrustFollow

Presentation Type

Poster

Faculty Mentor’s Full Name

Nicky Phear

Faculty Mentor’s Department

Climate Change Studies

Abstract

Gridded datasets are one of the primary ways that scientists gather temperature and precipitation data for their study areas. Gridded data can be thought of as a series of maps that overlay a study area. For every day of every year, scientists can use these gridded data to determine the temperature, precipitation, or a host of other variables at any given location within their study area. These datasets are created by using point data from the ground (e.g. weather station data) to interpolate the values of climatic variables across the dataset’s area of interest. As a result, gridded datasets are powerful tools that can be used to estimate climatic variables when physical measurements are unavailable. Unfortunately, due to differences in calculation methods, interpolation methods and point data, it is unlikely that these datasets will yield the exact same result for a given point within a study area.

To determine the spatial, temporal, and topographic variations between datasets, I am conducting an inter-model comparison of the gridded temperature and precipitation products available in Montana. To do this, I gathered daily gridded temperature and precipitation data from the past 30 years to create monthly, seasonal, and annual climate normals. I then compared each of the normals to one another to determine which areas of Montana saw the largest discrepancy between datasets. Although using this method will not reveal the accuracy of each dataset, it will show where datasets vary the most and yield uncertain results. This information can be used by scientists to determine which dataset best fits their study area and is most likely to produce accurate results.

Category

Physical Sciences

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Apr 27th, 3:00 PM Apr 27th, 4:00 PM

An Inter-Model Comparison of Gridded Temperature and Precipitation Products in Montana

UC South Ballroom

Gridded datasets are one of the primary ways that scientists gather temperature and precipitation data for their study areas. Gridded data can be thought of as a series of maps that overlay a study area. For every day of every year, scientists can use these gridded data to determine the temperature, precipitation, or a host of other variables at any given location within their study area. These datasets are created by using point data from the ground (e.g. weather station data) to interpolate the values of climatic variables across the dataset’s area of interest. As a result, gridded datasets are powerful tools that can be used to estimate climatic variables when physical measurements are unavailable. Unfortunately, due to differences in calculation methods, interpolation methods and point data, it is unlikely that these datasets will yield the exact same result for a given point within a study area.

To determine the spatial, temporal, and topographic variations between datasets, I am conducting an inter-model comparison of the gridded temperature and precipitation products available in Montana. To do this, I gathered daily gridded temperature and precipitation data from the past 30 years to create monthly, seasonal, and annual climate normals. I then compared each of the normals to one another to determine which areas of Montana saw the largest discrepancy between datasets. Although using this method will not reveal the accuracy of each dataset, it will show where datasets vary the most and yield uncertain results. This information can be used by scientists to determine which dataset best fits their study area and is most likely to produce accurate results.