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A Winter Fog Experiment (WiFEX) was conducted to study the genesis of fog formation between winters 2016–17 and 2017–18 at Indira Gandhi International Airport (IGIA), Delhi, India. To support the WiFEX field campaign, the Weather Research and Forecasting (WRF) Model was used to produce real-time forecasts at 2-km horizontal grid spacing. This paper summarizes the performance of the model forecasts for 43 very dense fog episodes (visibility < 200 m) and preliminary evaluation of the model against the observations. Similarly, near-surface liquid water content (LWC) from models and continuous visibility observations are used as a metric for model evaluation. Results show that the skill score is relatively promising for the hit rate with a value of 0.78, whereas the false alarm rate (0.19) and missing rate (0.32) are quite low. This indicates that the model has reasonable predictive accuracy, and the performance of the real-time forecast is better for both dense fog events and no-fog events. For success cases, the model accurately captured the near-surface meteorological conditions, particularly the low-level moisture, wind fields, and temperature inversion. In contrast, for failed cases, the WRF Model shows large error in near-surface relative humidity and temperature compared to the observations, although it captures temperature inversions reasonably well. Our results also suggest that the model is able to capture the variability in fog onset for consecutive fog events. Errors in near-surface variables during failed cases are found to be affected by the errors in the initial conditions taken from the Indian Institute of Tropical Meteorology Global Forecasting System (IITM-GFS) spectral model forecast. Further evaluation of the operational forecasts for dense fog cases indicates that the error in predicting fog onset stage is relatively large (mean error of 4 h) compared to the dissipation stage. [ABSTRACT FROM AUTHOR]

A stochastic multicloud model (SMCM) convective parameterization, which mimics the interactions at subgrid scales of multiple cloud types, is incorporated into the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), model (CFSsmcm) in lieu of the preexisting simplified Arakawa-Schubert (SAS) cumulus scheme. A detailed analysis of the tropical intraseasonal variability (TISV) and convectively coupled equatorial waves (CCEW) in comparison with the original (control) model and with observations is presented here. The last 10 years of a 15-yr-long climate simulation are analyzed. Significant improvements are seen in the simulation of the Madden-Julian oscillation (MJO) and most of the CCEWs as well as the Indian summer monsoon (ISM) intraseasonal oscillation (MISO). These improvements appear in the form of improved morphology and physical features of these waves. This can be regarded as a validation of the central idea behind the SMCM according to which organized tropical convection is based on three cloud types, namely, the congestus, deep, and stratiform cloud decks, that interact with each other and form a building block for multiscale convective systems. An adequate accounting of the dynamical interactions of this cloud hierarchy thus constitutes an important requirement for cumulus parameterizations to succeed in representing atmospheric tropical variability. SAS fails to fulfill this requirement, which is evident in the unrealistic physical structures of the major intraseasonal modes simulated by CFSv2 as documented here. [ABSTRACT FROM AUTHOR]