Evaluating Observations of Cloud Base against Nowcasts and Model Forecasts on Whiteface Mountain (1,480 m)

Predicting cloud base throughout the Chemical Processing of Organics within Clouds (CPOC) field campaign at Whiteface Mountain (WFM) in the northern Adirondacks Mountains of New York from 11-25 August 2017 was an important task for the field campaign. The primary goal of CPOC was to investigate aqueous chemical processing within clouds, which is a key process in the formation of secondary organic aerosol mass. To investigate these processes, air composition measurements were taken below cloud on WFM, while within the cloud measurements were taken at the WFM summit. Below cloud measurements were taken at 1250m and within cloud measurements were collected at the WFM summit at 1489m, the threshold between the two locations made predicting the height and evolution of the cloud base challenging.

In this study, we investigated the potential skill at forecasting and nowcasting cloud base over Whiteface by comparing observed cloud base against nowcasts from surface station and forecasts from a mesoscale numerical weather model. Observational estimates of cloud base height were made from a web cam located in Lake Placid, New York, which gives a good view of the mountain and its known features. Nowcasts of cloud base were made by computing the lifted condensation level using the data from the Whiteface Mountain Base New York State Mesonet station located on the northeast side of WFM. Forecasts of cloud base were taken from the NCAR Ensemble, based on the WRF model with 3km grid spacing, run every day at 0z with a 48h forecast. The length of the forecast and the run time of the model allowed for forecasting the cloud base one day out and the morning of a potential intensive operations day. Forecasts and nowcasts are evaluated using season-long correlations against observations on days with weak synoptic forcing. Additionally, individual days and groupings of days based on synoptic scale conditions are considered to help identify sources of error. Results from this study will guide efforts to predict cloud characteristics at WFM for future field projects.