Domain‐size and top‐height dependence in regional predictions for the Northeast Asia in spring

For regional weather forecasts and climate predictions, it is important to determine the optimal domain size, location, and top height. A wide model domain can be chosen to avoid noises from lateral boundaries but this can include the Tibetan Plateau and areas of northern Manchuria to the Kamchatka...

Full description

Bibliographic Details
Published in:Atmospheric Science Letters
Main Authors: Song, In‐Sun, Byun, Ui‐Yong, Hong, Jinkyu, Park, Sang‐Hun
Other Authors: Korea Polar Research Institute, Korea Meteorological Administration
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2018
Subjects:
Online Access:http://dx.doi.org/10.1002/asl.799
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fasl.799
https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/asl.799
Description
Summary:For regional weather forecasts and climate predictions, it is important to determine the optimal domain size, location, and top height. A wide model domain can be chosen to avoid noises from lateral boundaries but this can include the Tibetan Plateau and areas of northern Manchuria to the Kamchatka Peninsula in Northeast Asia. This study shows that topographic regions around the Tibetan Plateau and warm pool areas over the Manchuria in an extended model domain may have harmful effects on the accuracy of short‐ to medium‐range regional predictions on the downwind side in spring. The inaccuracy is related to model errors due to steep terrain regions in the Tibetan Plateau and cold bias in the lower stratosphere north of Manchuria. Well‐designed spectral nudging over the eastern flank of the Tibetan Plateau and the use of a higher model top are found to improve regional predictions for Northeast Asia in spring by effectively eliminating errors associated with steep topography and temperature biases in the upper troposphere and lower stratosphere, respectively. Our findings suggest possible ways to mitigate biases due to steep mountains and upper‐level processes in regional modeling. We discuss the role of our method in terms of uncertainties in regional weather forecasts and climate predictions.