Simulating the IHOP_2002 Fair-Weather CBL with the WRF-ARW-Noah Modeling System. Part I: Surface Fluxes and CBL Structure and Evolution along the Eastern Track

Fair-weather data from the May–June 2002 International H2O Project (IHOP_2002) 46-km eastern flight track in southeast Kansas are compared to simulations using the advanced research version of the Weather Research and Forecasting model coupled to the Noah land surface model (LSM), to gain insight into how the surface influences convective boundary layer (CBL) fluxes and structure, and to evaluate the success of the modeling system in representing CBL structure and evolution. This offers a unique look at the capability of the model on scales the length of the flight track (46 km) and smaller under relatively uncomplicated meteorological conditions. It is found that the modeled sensible heat flux H is significantly larger than observed, while the latent heat flux (LE) is much closer to observations. The slope of the best-fit line DLE/DH to a plot of LE as a function of H, an indicator of horizontal variation in available energy H 1 LE, for the data along the flight track, was shallower than observed. In a previous study of the IHOP_2002 western track, similar results were explained by too small a value of the parameter C in the Zilitinkevich equation used in the Noah LSM to compute the roughness length for heat and moisture flux from the roughness length for momentum, which is supplied in an input table; evidence is presented that this is true for the eastern track as well. The horizontal variability in modeled fluxes follows the soil moisture pattern rather than vegetation type, as is observed; because the input land use map does not capture the observed variation in vegetation. The observed westward rise in CBL depth is successfully modeled for 3 of the 4 days, but the actual depths are too high, largely because modeled H is too high. The model reproduces the timing of observed cumulus cloudiness for 3 of the 4 days. Modeled clouds lead to departures from the typical clear-sky straight line relating surface H to LE for a given model time, making them easy to detect. With spatial filtering, a straight slope line can be recovered. Similarly, larger filter lengths are needed to produce a stable slope for observed fluxes when there are clouds than for clear skies.