Wednesday, 25 January 2017: 8:30 AM
602 (Washington State Convention Center )
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 Hawai’i, where extreme spatial gradients are present along with sometimes very sparse observation networks, and even daily deterministic precipitation and temperature products are nearly nonexistent. To address this critical data gap, we have modified a recently developed methodology to produce the first observation-based gridded, daily, ensemble precipitation and temperature estimates for Alaska and Hawai’i.
Previously we used localized spatial regression including topography to create a gridded product with topographic effects. In this case, due to the intense spatial gradients and insufficient station density, we use localized spatial regression of daily normal ratios for precipitation and temperature anomalies and apply monthly climate fields to enforce long-term climatological and topographic spatial patterns. We will present initial results on these new high resolution products (2 km for Alaska and 250 m for Hawai’i), and discuss 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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