https://doi.org/10.1029/2020JD032696

Newman, A. J., Clark, M. P., Wood, A. W., & Arnold, J. R. (2020). Probabilistic Spatial Meteorological Estimates for Alaska and the Yukon. Journal of Geophysical Research: Atmospheres, 125(22). https://doi.org/10.1029/2020JD032696

It is challenging to develop observationally based spatial estimates of meteorology in Alaska and the Yukon. Complex topography, frozen precipitation undercatch, and extremely sparse in situ observations all limit our capability to produce accurate spatial estimates of meteorological conditions. In this Arctic environment, it is necessary to develop probabilistic estimates of precipitation and temperature that explicitly incorporate spatiotemporally varying uncertainty and bias corrections. In this paper we exploit the recently developed ensemble Climatologically Aided Interpolation (eCAI) system to produce daily historical estimates of precipitation and temperature across Alaska and the Yukon Territory at a 2 km grid spacing for the time period 1980–2013. We extend the previous eCAI method to address precipitation gauge undercatch and wetting loss, which is of high importance for this high-latitude region where much of the precipitation falls as snow. Leave-one-out cross-validation shows our ensemble has little bias in daily precipitation and mean temperature at the station locations, with an overestimate in the daily standard deviation of precipitation. The ensemble is statistically reliable compared to climatology and can discriminate precipitation events across different precipitation thresholds. Long-term mean loss adjusted precipitation is up to 36% greater than the unadjusted estimate in windy areas that receive a large fraction of frozen precipitation, primarily due to wind induced undercatch. Comparing the ensemble mean climatology of precipitation and temperature to PRISM and Daymet v3 shows large interproduct differences, particularly in precipitation across the complex terrain of southeast and northern Alaska.

Core Modelling & Forecasting Team

https://doi.org/10.1029/2020JD032696