The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative‐convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud‐resolving models (CRMs), large eddy simulations (LES), and global cloud‐resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self‐aggregation in large domains and agree that self‐aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self‐aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.

Most atmospheric motions of different spatial scales and precipitation are closely related to phase transitions in clouds. The continuously increasing resolution of large-scale and mesoscale atmospheric models makes it feasible to treat the evolution of individual clouds. The explicit treatment of clouds requires the simulation of cloud microphysics. Two main approaches describing cloud microphysical properties and processes have been developed in the past four and a half decades: bulk microphysics parameterization and spectral (bin) microphysics (SBM). The development and utilization of both represent an important step forward in cloud modeling. This study presents a detailed survey of the physical basis and the applications of both bulk microphysics parameterization and SBM. The results obtained from simulations of a wide range of atmospheric phenomena, from tropical cyclones through Arctic clouds using these two approaches are compared. Advantages and disadvantages, as well as lines of future development for these methods are discussed.

The objectives of the Winter Fog Experiment (WIFEX) over the Indo-Gangetic Plains of India are to develop better now-casting and forecasting of winter fog on various time-and spatial scales. Maximum fog occurrence over northwest India is about 48 days (visibility <1000 m) per year, and it occurs mostly during the December-February time-period. The physical and chemical characteristics of fog, meteorological factors responsible for its genesis, sustenance, intensity and dissipation are poorly understood. Improved understanding on the above aspects is required to develop reliable forecasting models and observational techniques for accurate prediction of the fog events. Extensive sets of comprehensive groundbased instrumentation were deployed at the Indira Gandhi International Airport, New Delhi. Major in situ sensors were deployed to measure surface micrometeorological conditions, radiation balance, turbulence, thermodynamical structure of the surface layer, fog droplet and aerosol microphysics, aerosol optical properties, and aerosol and fog water chemistry to describe the complete environmental conditions under which fog develops. In addition, Weather Forecasting Model coupled with chemistry is planned for fog prediction at a spatial resolution of 2 km. The present study provides an introductory overview of the winter fog field campaign with its unique instrumentation.

This paper provides a summary of the assessment report of the World Meteorological Organization (WMO) Expert Team on Weather Modification that discusses recent progress on precipitation enhancement research. The progress has been underpinned by advances in our understanding of cloud processes and interactions between clouds and their environment, which, in turn, have been enabled by substantial developments in technical capabilities to both observe and simulate clouds from the microphysical to the mesoscale. We focus on the two cloud types most commonly seeded in the past: winter orographic cloud systems and convective cloud systems. A key issue for cloud seeding is the extension from cloud-scale research to water catchment–scale impacts on precipitation on the ground. Consequently, the requirements for the design, implementation, and evaluation of a catchment-scale precipitation enhancement campaign are discussed. The paper concludes by indicating the most important gaps in our knowledge. Some recommendations regarding the most urgent research topics are given to stimulate further research.

This study reports the concentrations of nitrogen dioxide (NO 2 ) and formaldehyde (HCHO), retrieved using the Multi AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) technique and collocated observations of surface ozone (O 3 ) conducted over the Indo-Gangetic Plain (IGP) during the 2014 monsoon period as part of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX). The average daytime NO 2 mixing ratio was 0.81 ± 0.20 ppbv (parts per billion by volume) (range: 0.08-6.06 ppbv). NO 2 was observed to decrease during the morning between 06:00 and 09:00 local time and then stabilise for the rest of the day. The average daytime HCHO mixing ratio was 1.93 ± 0.60 ppbv (range: 0.32-8.81 ppbv). Unlike NO 2 , HCHO, driven by daytime photochemical formation from hydrocarbon precursors, increased during the early morning. The average O 3 mixing ratio was 30.0 ± 13.0 ppbv (range: 2.7-81.9 ppbv) during the daytime and 22.5 ± 10.2 ppbv (range: 1-63 ppbv) during the nighttime. Analyses of the back trajectories indicatedfound that the NO 2 mixing ratios during CAIPEEX-2014 were affected by long-range transport from thermal power plants situated about 110 km to the south but the HCHO mixing ratios and O 3 production were influenced by local emissions. These observations suggest that in rural IGP, ozone concentrations are affected by local emission rather than by long-range transport.