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Methods to Gap Fill Daily & Hourly Climate Data. Fanfei Gong Supervisor: Richard Fernandes Date: September 5, 2014. Introduction. 2 Methods Parallel Interpolation Algorithm Accuracy and Effectiveness Downscaling Using Flags Algorithm Accuracy and Effectiveness. Linear Interpolation.
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Methods to Gap Fill Daily & Hourly Climate Data Fanfei Gong Supervisor: Richard Fernandes Date: September 5, 2014
Introduction 2 Methods • Parallel Interpolation • Algorithm • Accuracy and Effectiveness • Downscaling Using Flags • Algorithm • Accuracy and Effectiveness
Linear Interpolation • Algorithm • Find first 10 closest stations. • Generate monthly average or sum. • Adjust neighbouring station’s data by taking monthly normal ratio/difference and gap fill the target station. • Keep a record of monthly normals and program performance for each target station.
Linear Interpolation • Accuracy and Effectiveness • Gaps couldn’t be filled for two reasons: • Missing monthly normal for target station. • No data available in any of the closest 10 station files. • Accuracy
Conclusion for Linear Interpolation • Parameters: • Daily precipitation (2520 CDCD stations). • Hourly: dry/dew temperature, short/long wave radiation, snow/rain flag, pressure, specific/relative humidity, wind speed (135 CWEEDS stations). • Time Range: • Daily Precipitation: 1960-2000. • Other parameters: 1960-2001.
Conclusion for Linear Interpolation • Accuracy: • Precipitation • North Bay A, estimated with a station 6.76 km away: 1.56 mm/day. • Temperature • Vancouver Int’l A, estimated with a station 63.8 km away: 0.75 oC/day.
Downscaling Using Flags • Required parameter: • Daily precipitation. • Snow/rain flag. • Algorithm: • Divide daily precipitation amount by number of hours with snow/rain. • Assign the quotient to hours with snow/rain and 0 to other.
