2022-05-20

Speaker

Dr. Utkarsh Bhautmage

Time

9:30-10:30, 20th May 2022 (Friday)

Location

`Teams (online)

More about the talk

Title

Incorporation of the Urban Thermal and Moisture Components into the PX-Land Surface Model (LSM) within the Mesoscale Weather Research and Forecasting (WRF) Model

Abstract

In the mesoscale Weather Research and Forecasting (WRF) model, few urban modeling options exist such as Urban Canopy Model (UCM), Building Effect Parameterization (BEP), and Building Energy Model (BEM). These models have certain limitations as far as the choice of land surface models (LSM), planetary boundary layer (PBL) schemes, and computational expenses are concerned. The authors Dy et al., (2019) made an attempt to include the urban momentum drag effect for wind speed modeling by developing a new multilayer model with modifications to the non-local Asymmetric Convective Model version 2 (ACM2) PBL scheme. The urban-based ACM2 (UACM) model has shown a significant improvement in wind speed reduction near to the urban ground surface along-with an inflection point in the vertical wind profile at roof level. In this study, urban thermal and moisture components are newly introduced in the PX-LSM combined with the UACM model. The urban street-level surface composition includes the impervious, vegetated, and bare ground fractions. The diurnal variation in street, walls and roof-surface temperatures is modelled using the two-layer force-restore algorithm. Simple radiation treatment is considered to account shadowing within the streets based on solar zenith angle and building morphology. Heat and moisture flux evolution is considered explicitly on all urban surfaces. The advantages of this novel UACM are simple formulation, more efficient execution, and its requirement for only a few fundamental urban morphological parameters. The upgraded model is tested with the both idealized and real case WRF simulations over the Pearl River Delta (PRD) region in Southern China. The evaluation demonstrated greatly improved wind and temperature predictions at the urban measurement sites compared to the base ACM2 model.