867 Development of Gridded Daily Ensemble Precipitation and Temperature Datasets for Alaska and the Yukon Territory

Wednesday, 9 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
Andrew Newman, NCAR, Boulder, CO; and M. Clark, A. W. Wood, J. Cherry, and J. R. Arnold

Gridded precipitation and temperature products are inherently uncertain due to myriad factors. These include interpolation from a sparse observation network, measurement representativeness, and measurement errors. Despite this inherent uncertainty, uncertainty is typically not included, or is a specific addition to each dataset without much general applicability across different datasets. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits their utility to support land surface and hydrologic modeling techniques such as data assimilation, probabilistic forecasting and verification. These issues are acutely relevant in Alaska and western Canada, where extreme spatial gradients are present along with sometimes extremely sparse observation networks, and even daily deterministic precipitation and temperature products are nearly nonexistent. To address this critical data gap, a recently developed ensemble methodology is used to produce the first observation-based gridded, daily, ensemble precipitation and temperature estimates for Alaska and the Northwest Territory.

Previously, localized spatial regression of the daily data was used to create daily gridded products including topographic effects. In this case, due to the intense spatial gradients and insufficient station density, localized spatial regression of daily normal ratios for precipitation and temperature anomalies is performed and then monthly climate fields are applied to enforce long-term climatological and topographic spatial patterns, generally known as climatologically aided interpolation (CAI). To generate the monthly long-term climatologies, an open-source locally weighted simple linear regression routine that uses geophysical attributes to encapsulate physical processes into the regression (e.g. orographic enhancement of precipitation, temperature inversions in valleys) is being developed.

The new regression routine and initial results of the new high-resolution product (2 km grid spacing) of daily precipitation mean temperature and diurnal range will be discussed, as well as plans to improve the datasets, focusing on efforts to fuse the sparse station data with high resolution numerical weather prediction model output.

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