Assessmentof PBL Schemes in the Weather Research and Forecasting Model Using Ceilometer Observations in an Urban Region

Accurate simulation of planetary boundary layer height (PBLH) is key to greenhouse gas emission estimation, air quality prediction and weather forecast. A recurrent question among these communities is how well meteorological models perform regarding this essential feature. However, the lack of measurement data limits understanding of PBL dynamics and validation studies, and therefore parameterization development. PBLH measurements are not very common. Their availability is sparse, in space and time, and rely strongly on operational radiosondes that sample the PBL only twice a day. The introduction of new measurement techniques for mixing height, such as those based on ceilometers and particle Lidars, has the potential to be a game changer for model validation due to the greater temporal coverage and resolution that they provide.

Here, observations from two ceilometers are used along with 8 meteorological surface stations (ISD) to analyze the behavior of an ensemble of configurations of the Weather Research and Forecasting (WRF) model over the Washington DC – Baltimore area (at 1 km spatial resolution) for the month of February 2016. The ensemble of models is composed of six WRF configurations; specifically, we used 4 planetary boundary layer parameterizations (YSU, MYNN2, BouLac, QNSE), 2 sources of initial and boundary conditions (NARR and HRRR) and 1 configuration including the building energy parameterization (BEP) urban canopy model.

Results have shown small bias over the whole domain and period for wind speed, wind direction and temperature with no drastic differences between meteorological drivers (HRRR and NARR) or clear configuration winner. However, hourly bias can be large for specific stations or periods. Hourly comparison showed a nocturnal PBLH bias that is typically smaller than during the day; however, in relative terms, they are comparable, or even larger at night, due to the typically low measured nocturnal PBLH values. The BouLac parameterization resulted in too windy and warm predictions, while QNSE provided the largest PBLH values of all the configuration for daytime on average, followed by BouLac. During nights, BouLac provided the lowest PBLH values, on average, followed by YSU. The inclusion of the urban canopy parameterization caused the temperature to be too warm and increased the PBLH values, slightly improving the BouLac scheme performances during night but increasing the high bias during day time. Overall, MYNN provided the best representation of PBLH for the whole period.