Estimate Missing Climate Data

Estimate Missing Climate Data

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You can access sample input files for download here: DropBox

What is Estimate Missing Climate Data Tool?

The Estimate Missing Climate Data Tool (EMCD) is a comprehensive solution designed to address and fill gaps in climate data. Utilizing advanced techniques, EMCD draws upon neighboring stations and the historical records of the target station. The tool employs a diverse set of methods, including Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), for simulation with other stations. Additionally, Interpolation methods such as Linear and Akima, along with strategies like LOCF (Last Observation Carried Forward) and NOCB (Next Observation Carried Backward), are utilized for filling in missing data based on the historical context of the station.

Utilizing Autocorrelation, You can use the tool to identify the optimal lag with high correlation, enhancing the precision of filled data. Subsequently, the lagged station can be incorporated as a new station for modeling and simulating station data. In the figure below, observe the monthly Tmean modeled alongside raw data employing the NOCB method and lagged data, with a lag number set to 12.

EMCD's functionality ensures a robust approach to data completion, offering flexibility and accuracy in dealing with diverse climate data scenarios. Whether filling gaps using neighboring stations or historical context, EMCD provides a versatile toolkit for climate scientists and researchers, enabling thorough analysis and interpretation of climate data sets.

Please note that these tools offer flexibility for alternative approaches in filling gap data. For instance, you can input multiple climate variables as stations and address missing data in a dependent variable by utilizing other variables (stations) as independent variables.

Fill the Gaps: Explore the Estimate Missing Climate Tool in Action

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