Journal
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
Volume 57, Issue 10, Pages 2375-2397Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/JAMC-D-17-0361.1
Keywords
Boundary layer; Mesoscale models
Categories
Funding
- National Science Foundation [AGS-1359698]
- Lockheed Martin Corporation-American Meteorological Society Graduate Fellowship
- National Science Foundation
Ask authors/readers for more resources
Previous studies have shown that the Weather Research and Forecasting (WRF) Model often underpredicts the strength of the Great Plains nocturnal low-level jet (NLLJ), which has implications for weather, climate, aviation, air quality, and wind energy in the region. During the Lower Atmospheric Boundary Layer Experiment (LABLE) conducted in 2012, NLLJs were frequently observed at high temporal resolution, allowing for detailed documentation of their development and evolution throughout the night. Ten LABLE cases with observed NLLJs were chosen to systematically evaluate the WRF Model's ability to reproduce the observed NLLJs. Model runs were performed with 4-, 2-, and 1-km horizontal spacing and with the default stretched vertical grid and a nonstretched 40-m vertically spaced grid to investigate which grid configurations are optimal for NLLJ modeling. These tests were conducted using three common boundary layer parameterization schemes: Mellor-Yamada Nakanishi Niino, Yonsei University, and Quasi-Normal Scale Elimination. It was found that refining horizontal spacing does not necessarily improve the modeled NLLJ wind. Increasing the number of vertical levels on a non-stretched grid provides more information about the structure of the NLLJ with some schemes, but the benefit is limited by computational expense and model stability. Simulations of the NLLJ were found to be less sensitive to boundary layer parameterization than to grid configuration. The Quasi-Normal Scale Elimination scheme was chosen for future NLLJ simulation studies.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available