Development and Verification of Daily Gridded Climate Surfaces in the Okanagan Basin of British Columbia.

Neilsen, D., Duke, G., Taylor, B., Byrne, J.M., and Van der Gulik, T.W. (2010). "Development and Verification of Daily Gridded Climate Surfaces in the Okanagan Basin of British Columbia.", Canadian Water Resources Journal, 35(2), pp. 131-154. doi : 10.4296/cwrj3502131  Access to full text

Abstract

Gridded estimates of daily minimum and maximum temperature and precipitation from 1960 to 2005 were prepared for the Okanagan Basin of British Columbia. The procedure utilized available daily climate data from 182 stations and employed a regression based interpolation scheme at 500 metre grid spacing. Spatial distribution of temperature took into account variations in temperature with elevation, an observed north-south temperature gradient, and proximity to several large lakes in the valley bottom. An inverse distance weighting scheme was used for the interpolation, and a constrained lapse rate approach was used to interpolate in areas that lie above the highest climate station. Temperature inversions were handled by fitting a second order polynomial and introducing a two-layer model. The precipitation routine differs from the temperature model in that the regressions were based on monthly precipitation totals, thus producing daily precipitation surfaces that incorporate an orographic component along with latitudinal trends. A "leave one out" cross-validation procedure was applied to 56 stations to test model performance. Cross-validation of the predicted surfaces indicated that, on average, the daily maximum temperature surfaces were more accurate than the daily minimum temperature surfaces. Mean Absolute Error (MAE) averaged 1.0°C for maximum temperature while for minimum temperature MAE was in the range of 1.3°C to 1.8°C depending on the season. Errors in both temperature and precipitation surfaces are largest at higher elevations where station density is low. Over all stations, monthly MAE for precipitation averaged between 10% and 18%. A MAE calculated from differences between observed and predicted annual precipitation over all stations was 27 mm with a percent error of 6.2%. The model shows a positive bias (1.4) in the daily occurrence of precipitation resulting in an over-prediction of days with drizzle. Over 80% of incorrectly predicted wet days had less than 1 mm precipitation. These error statistics compare favourably to those from similar regression-based interpolation models.

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