A13C-0343
Pushing the Regional Arctic System Reanalysis to Higher Resolution: A comparison with the global ERA-Interim Reanalysis for the Arctic and Beyond
Monday, 14 December 2015
Poster Hall (Moscone South)
Aaron Wilson1, David H Bromwich2, Lesheng Bai1, Kent Moore3, Ying-Hwa Kuo4, Zhiquan Liu5, Hui-Chuan Lin5 and Michael J Barlage6, (1)Byrd Polar and Climate Research Center - BPCRC, Columbus, OH, United States, (2)Byrd Polar & Climate Rsrch Ctr, Columbus, OH, United States, (3)University of Toronto, Toronto, ON, Canada, (4)University Corporation for Atmospheric Research, Boulder, CO, United States, (5)National Center for Atmospheric Research, Mesoscale & Microscale Meteorology Division, Boulder, CO, United States, (6)NCAR/RAL, Boulder, CO, United States
Abstract:
The Arctic System Reanalysis, a high-resolution regional assimilation of conventional observations and satellite data across the mid- and high latitudes of the Northern Hemisphere for the period 2000 – 2012, has been refined from 30 km (ASRv1) to 15 km (ASRv2). An interannual comparison with the global European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERAI) and atmospheric observations show the troposphere to be well assimilated in the ASRv2, as monthly and annual near-surface (upper-level) temperature, dew-point (relative humidity), pressure (geopotential height), and wind-speed biases and root-mean-squared errors compared with surface stations and radiosondes are very small. These results are similar to the ERAI, although wind-speed biases are significantly smaller in the ASRv2. The high-resolution land representation in ASRv2 leads to more accurate depictions of topographically forced wind events and atmospheric circulation throughout the Arctic, particularly with respect to tip jets and barrier winds along the southeast coast of Greenland. Overall, these results suggest that despite the ASRv2’s use of a 3D-variational (Var) assimilation compared with the ERAI’s 4D-Var, a regional assimilation laterally forced by a global reanalysis can improve the assimilation of observations and help offset temporal information lost by the 3D-Var compared with the 4D-Var. In addition, forecast fields of clouds, downward surface radiation, and precipitation are validated against ERAI and surface observations.
