You are here:
Validation of a New Model for Ozone Dry Deposition using Harvard Forest Measurements
Citation:
Alapaty, Kiran, B. Cheng, J. Bash, S. arunachalam, AND J. Munger. Validation of a New Model for Ozone Dry Deposition using Harvard Forest Measurements. NCAR's 2ND OZONE DEPOSITION WORKSHOP, NA, Online, March 28 - 30, 2022.
Impact/Purpose:
The findings from the research may help improve the capability of dry deposition schemes for better estimation of dry deposition fluxes and opens doors for the development of a community dry deposition model for use in regional/global air quality models.
Description:
PROBLEM: Turbulence strength is under-represented in almost all dry deposition models and different types of stability functions are used. These result in biases in the estimated dry deposition of ozone (O3) and contributes to differences among models’ simulations. APPROACH: Developed turbulence kinetic energy-based resistance formulations to accurately represent turbulence strength yielding improved estimation of O3 deposition fluxes. These are: new aerodynamic and cuticle resistance formulations, revised stomatal resistance and all other resistances that include improved representation of turbulence strength. Decadal measurements (1991-2000) available from the Harvard Forest site are used to drive a single-point model and to evaluate O3 deposition flux estimation by STAGE as well as the NEW formulations. RESULTS: In Part-1 of this research, we hypothesized and proved that a new turbulence velocity scale can effectively avoid the usage of stability functions, and improved turbulence strength estimation leading to representative simulation of O3 deposition. In this extension work, Part-4, we introduced a new cuticle resistance formulation that includes turbulence effects and revised stomatal resistance by including impacts of dew formation and particle blockage of abaxial stoma. Decadal averaged monthly & hourly variations of simulated O3 fluxes by NEW are much closer to OBS when compared to STAGE. We found that the bias reduction is attributable to improved representation of processes in NEW formulations.