Methods to Gap Fill Daily & Hourly Climate Data

1. Methods to Gap Fill Daily & Hourly Climate Data Fanfei Gong Supervisor: Richard Fernandes Date: September 5, 2014

2. Introduction 2 Methods • Parallel Interpolation • Algorithm • Accuracy and Effectiveness • Downscaling Using Flags • Algorithm • Accuracy and Effectiveness

3. 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.

4. 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

5. Accuracy of Linear Interpolation

6. Accuracy of Linear Interpolation

7. Accuracy of Linear Interpolation

8. 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.

9. 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.

10. 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.

11. Downscaling Using Flags

12. Downscaling Using Flags