Impact of boundary layer parameterizations on simulated seasonal meteorology over North-East India

Abstract

This study evaluated the accuracy of six planetary boundary layer (PBL) parameterization schemes in simulating two different seasons of pre-monsoon and monsoon in India's North-East region through the Weather Research and Forecasting (WRF) model. Twelve one-month simulations were conducted with the PBL schemes, six each for April (pre-monsoon) and July (monsoon), and the model outputs were compared against observations. Three non-local schemes, Asymmetric Convective Model (ACM2), Yonsei University (YSU), Shin-Hong (HONG), and three local schemes, Quasi Normal Scale Elimination (QNSE), Mellor Yamada Janjic (MYJ) and Mellor Yamada Nakanishi Nino (MYNN3), were tested. The meteorological variables of temperature, relative humidity (RH), wind speed, wind direction, and rainfall were evaluated, and the performance of each scheme for each meteorological variable is reported. The 2 m temperature (T2) variable was well simulated by ACM2, MYJ in April, and YSU in July, while MYNN3 best simulated the 2 m RH (RH2) during both seasons. 10 m wind speed (WS10) and directions (WD10) were better simulated by MYNN3, HONG and YSU. HONG also best-simulated rainfall in April and MYJ in July. April and July being rainfall periods, an analysis of the schemes’ simulated rainfall frequency was also carried out. Moreover, the PBL schemes were also ranked, considering their combined performance with all the above meteorological parameters. While considering both the seasons and all meteorological variables, the scale-aware scheme, HONG, was the best scheme and can be used to simulate both seasons. Additionally, an in-depth analysis of surface and atmospheric parameters was also carried out to reason the simulated meteorology. QNSE expends the highest amount of its surface energy through surface evaporation, leading to the lowest surface skin temperature and T2 predictions. In contrast, MYNN3 produced the lowest mixing, which caused the moistest boundary layer, highest RH, cloud cover, and highly overestimated rainfall. Besides evaluation, which will help to choose a suitable PBL scheme for weather predictions in this region, this study also identifies the characteristics and deficiencies of PBL and surface layer schemes for improvement.