Abstract
The partitioning of available energy into surface sensible and latent heat fluxes impacts the accuracy of simulated near surface temperature and humidity in numerical weather prediction models. This case study evaluates the performance of the Weather Research and Forecasting (WRF) model on the simulation of surface heat fluxes using field observations collected from a surface flux tower in Oregon, USA. Further, WRF-modeled heat flux sensitivities to North American Mesoscale (NAM) and North American Regional Reanalysis (NARR) large-scale input forcing datasets; U.S. Geological Survey (USGS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) land use datasets; Pleim-Xiu (PX) and Noah land surface models (LSM); Yonsei University (YSU) and Mellor-Yamada-Janjic (MYJ) planetary boundary layer (PBL) schemes using the Noah LSM; and Asymmetric Convective Model version 2 (ACM2) PBL scheme using PX LSM are investigated. The errors for simulating 2-m temperature, 2-m humidity, and 10-m wind speed were reduced on average when using NAM compared with NARR. Simulated friction velocity had a positive bias on average, with the YSU PBL scheme producing the largest overestimation in the innermost domain (0.5 km horizontal grid resolution). The simulated surface sensible heat flux had a similar temporal behavior as the observations but with a larger magnitude. The PX LSM produced lower and more reliable sensible heat fluxes compared with the Noah LSM. However, Noah latent heat fluxes were improved with a lower RMSE compared to PX, when NARR forcing data was used. Overall, these results suggest that there is not one WRF configuration that performs best for all the simulated variables (surface heat fluxes and meteorological variables) and situations (day and night).
Highlights
Surface fluxes serve as sinks or sources of energy, moisture, momentum, and atmospheric pollutants and significantly impact the formation and evolution of clouds, precipitation, and air pollution
This type of investigation is important because there are limited datasets available to evaluate the variables that numerical weather prediction (NWP) models have the most uncertainty in, such has surface heat fluxes
The results shown above illuminate discrepancies in model performance, where often times NWP models are able to simulate one property well while experiencing pitfalls in the ability to simulate other variables
Summary
Surface fluxes serve as sinks or sources of energy, moisture, momentum, and atmospheric pollutants and significantly impact the formation and evolution of clouds, precipitation, and air pollution. The significance of land surface interactions with the atmosphere has been increasingly emphasized and investigated during numerical weather prediction (NWP) model (e.g., Weather Research and Forecasting (WRF) model) developments [2,3,4,5]. NWP models are subject to uncertainties in surface interaction parameterization, and parameter choice has significant effects on NWP outputs. Land surface model (LSM) selection was found to have large impacts on WRF simulation results during cold air pool events, since non-realistic representation of low-level jets could lead to errors in simulated temperature and humidity [6].
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