Abstract
Predicting boundary layer clouds is important for the accurate modeling of pollutant dispersion. Higher resolution mesoscale models would be expected to produce better forecasts of cloud properties that affect dispersion. Using ceilometer observations, we assess the skill of two operational mesoscale models (RAMS and WRF) to forecast cloud base altitude and cloud fraction at the Savannah River Site in the southeastern US during the springtime. Verifications were performed at small spatial and temporal scales necessary for dispersion modeling. Both models were unreliable with a 50% (RAMS) and a 46% (WRF) rate of predicting clouds observed by the ceilometer which led to low cloud fraction predictions. Results indicated that WRF better predicted daytime cloud bases from convection that occurred frequently later in the period and RAMS better predicted nighttime cloud bases. Using root mean squared error (RMSE) to score the forecast periods also highlighted this diurnal dichotomy, with WRF scores better during the day and RAMS scores better at night. Analysis of forecast errors revealed divergent model cloud base biases—WRF low and RAMS high. A hybrid solution which weighs more heavily the RAMS nighttime forecasts and WRF daytime forecasts will likely provide the best prediction of cloud properties for dispersion.
Highlights
IntroductionScavenging and absorption of atmospheric particles and gases by boundary layer clouds affect the dispersion of pollutants, The processes of washout, chemical transformations, and sedimentation upon droplet evaporation of constituents all impact the rate of atmospheric removal or deposition
A hybrid solution which weighs more heavily the RAMS nighttime forecasts and Weather Research and Forecasting (WRF) daytime forecasts will likely provide the best prediction of cloud properties for dispersion
Scavenging and absorption of atmospheric particles and gases by boundary layer clouds affect the dispersion of pollutants, The processes of washout, chemical transformations, and sedimentation upon droplet evaporation of constituents all impact the rate of atmospheric removal or deposition
Summary
Scavenging and absorption of atmospheric particles and gases by boundary layer clouds affect the dispersion of pollutants, The processes of washout, chemical transformations, and sedimentation upon droplet evaporation of constituents all impact the rate of atmospheric removal or deposition. Atmosphere 2020, 11, x FOR PEER REVIEW affecting these constituents [6] These interactions the importance of accurate cloud forecasts. Relative humidity were alsotoused to model verify model cloud base height this study,we we provide assessment model capabilities forecasting cloud macrophysical. InIn this study, provide anan assessment ofof model capabilities inin forecasting cloud macrophysical properties that affect atmospheric constituent dispersion at fine spatial and temporal scales. Weuse use properties that affect atmospheric constituent dispersion at fine spatial and temporal scales. We use a spatial averaging method based on cloud field advection by the model wind speed to compare model forecasts every 15 min to 15-. We use a spatial averaging method based on cloud field advection by the model wind speed to compare model forecasts every 15 min to 15-min averaged ceilometer cloud fraction and cloud base height. The time period from April to June provides a robust evaluation of these two models
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