Evaluation of Surface Fluxes in the WRF Model: Case Study for Farmland in Rolling Terrain

Abstract

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).

LiDAR‐based characterization of mid‐altitude wind conditions for airborne wind energy systems

“…Mesoscale numerical weather prediction models such as the Weather Research and Forecasting (WRF) model provide detailed data sets at higher resolution compared with reanalysis data. We make use of the sign of the WRF simulated SHF for statistical analyses and assume the sign of the SHF to be better simulated than sign and magnitude . However, temporal difference of times associated with positive and negative SHF between model and measurement will lead to occasional mismatch of transition times as well as random errors.…”

“…We make use of the sign of the WRF simulated SHF for statistical analyses and assume the sign of the SHF to be better simulated than sign and magnitude. 11,12 However, temporal difference of times associated with positive and negative SHF between model and measurement will lead to occasional mismatch of transition times 13 as well as random errors. We believe that these errors are statistically insignificant for the overall evaluation but are aware of the resulting inaccuracy.…”

Analyzing the Synoptic‐, Meso‐ and Local‐ Scale Involved in Sea Breeze Formation and Frontal Characteristics

“…Moreover, this strong overestimation of the SH flux was also observed in the simulations carried out in the area of Cabauw from the WRF model in Steeneveld et al (), although the temperature profiles were adequately reproduced. Sun et al () found an overestimation of the SH flux of similar magnitude using the YSU ABL scheme in their simulations from the WRF model, but in this case producing a positive bias in the 2‐m air temperature. On the other hand, it must also be taken into account that measurements tend to underestimate the turbulent fluxes as reported for instance in Foken ().…”

Cooling Effect of Paddy on Land Surface Temperature in Cold China Based on MODIS Data: A Case Study in Northern Sanjiang Plain

“…The influencing factors involved in the process of surface energy exchange are complex, and surface temperature, wind speed, terrain, and other factors should be taken into account in follow-up research [54][55][56]. In subsequent research on surface energy exchange, attention should be paid to the comprehensive processes of various factors, including LST, as well as to advanced mathematical models [57,58]. Furthermore, the factors affecting crop yield in the study area are relatively complex.…”