The Estimate Missing Climate Data Tool (EMCD) stands as a comprehensive solution designed to address and fill gaps in climate data, ensuring a robust and accurate dataset for comprehensive analysis. EMCD incorporates advanced techniques to overcome missing values, utilizing neighboring stations and 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.

One notable feature is the use of Autocorrelation, allowing users to identify the optimal lag with high correlation. This enhances the precision of filled data by incorporating lagged station information into the modeling process.

In practice, users can observe the effectiveness of EMCD in the figure below, showcasing the monthly Tmean modeled alongside raw data using the NOCB method and lagged data, with a lag number set to 12. This graphical representation demonstrates how EMCD contributes to filling gaps and ensuring a more comprehensive and accurate climate dataset for analysis.