Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, also called a PRD simulator, is needed to establish the relation between model physics parameters and polarimetric radar variables. Such a forward operator needs to be accurate enough to make quantitative comparisons between radar observations and model output feasible, and to be computationally efficient so that these observations can be easily incorporated into a data assimilation (DA) scheme. To address this concern, a set of parameterized PRD simulators for the horizontal reflectivity, differential reflectivity, specific differential phase, and cross-correlation coefficient were developed. In this study, we have tested the performance of these new operators in a variational DA system. Firstly, the tangent linear and adjoint (TL/AD) models for these PRD simulators have been developed and checked for the validity. Then, both the forward operator and its adjoint model have been built into the three-dimensional variational (3DVAR) system. Finally, some preliminary DA experiments have been performed with an idealized supercell storm. It is found that the assimilation of PRD, including differential reflectivity and specific differential phase, in addition to radar radial velocity and horizontal reflectivity, can enhance the accuracy of both initial conditions for model hydrometer state variables and ensuing model forecasts. The usefulness of the cross-correlation coefficient is very limited in terms of improving convective-scale data analysis and NWP.

The Mesoscale Heavy Rainfall Observing System (MHROS), supported by the Institute of Heavy Rain (IHR), Chinese Meteorology Administration, is one of the major systems to observe mesoscale convective systems (MCSs) over the middle region of the Yangtze River in China. The IHR MHROS consists of mobile C‐POL and X‐POL precipitation radars, millimeter wavelength cloud radar, fixed S‐band precipitation radars, GPS network, microwave radiometers, radio soundings, wind profiler radars, and disdrometers. The atmospheric variables observed or retrieved by these instruments include the profiles of atmospheric temperature, moisture, wind speed and direction, vertical structures of MCS clouds and precipitation, atmospheric water vapor, and cloud liquid water. These quality‐controlled observations and retrievals have been used in mesoscale numerical weather prediction to improve the accuracy of weather forecasting and MCS research since 2007. These long‐term observations have provided the most comprehensive data sets for researchers to investigate the formation‐dissipation processes of MCSs and for modelers to improve their simulations of MCSs. As the first paper of a series, we briefly introduce the IHR MHROS and describe the specifications of its major instruments. Then, we provide an integrative analysis of the IHR MHROS observations for a heavy rain case on 3–5 July 2014 as well as the application of IHR MHROS observations in improving the model simulations. In a series of papers, we will tentatively answer several key scientific questions related to the MCS and Meiyu frontal systems over the middle region of the Yangtze River using the IHR MHROS observations.

An improved approach to derive pseudo water vapor mass mixing ratio and in- cloud potential temperature was developed in this paper to better initialize numerical weather prediction (NWP) and build convective-scale predictions of severe weather events. The process included several steps. The first was to identify areas of deep moist convection, utilizing Vertically Integrated Liquid water (VIL) derived from a mosaicked 3D radar reflectivity field. Then, pseudo- water vapor and pseudo- in- cloud potential temperature observations were derived based on the VIL. For potential temperature, the latent heat initialization for stratiform cloud and moist adiabatic initialization for deep moist convection were used based on a cloud analysis method. The third step was to assimilate the derived pseudo- water vapor and potential temperature observations, together with radar radial velocity and reflectivity into a convective-scale NWP model during data assimilation cycles spanning several hours. Finally, 3-h forecasts were launched each hour during the data assimilation period. The effects of radar data and pseudo- observation assimilation on the prediction of rainfall associated with convective systems surrounding the Meiyu front in 2018 were explored using two real cases. Two sets of experiments, each including several experiments in each real case, were designed to compare the effects of assimilation radar and pseudo- observations on the ensuing forecasts. Relative to the control experiment without data assimilation and radar experiment, the analyses and forecasts of convections were found to be improved for the two Meiyu front cases after pseudo- water vapor and potential temperature information was assimilated.

Aerosol‐cloud interactions remain a major source of uncertainty in our understanding and modeling of the Earth's hydrological cycle. Based upon a diagnostic and modeling analysis utilizing the latest field measurements from the Integrative Monsoon Frontal Rainfall Experiment (IMFRE) 2018, this paper reports the effects of aerosols on the cloud properties along the Mei‐yu front over the Middle Reaches of Yangtze River in China. Numerical experiments with the Weather Research and Forecasting (WRF) model suggest that increasing aerosol number concentration reduces surface precipitation by ~8.8% and delays the onset of rainfall by ~30 min. Furthermore, warm clouds are suppressed but the convective cores are slightly intensified. This corresponds to an overall aerosol effect of “localization and invigoration” of the Mei‐yu rainfall and thus an elevated probability of short‐term heavy rainfall. The signals of “convective invigoration” with a bulk scheme in this study are relatively weak compared to those simulated by bin microphysics. The increased aerosol concentration strengthens Mei‐yu front and changes local morphology of the front, consistent with earlier studies demonstrating positive effects of convective heating on the genesis and maintenance of Mei‐yu front via conditional instability of the second kind (CISK) and diabatic generation of potential vorticity. Also discussed are the uncertainties of bulk microphysics in simulating aerosol‐cloud interactions, which may shed light on the design of future field campaigns to further understand the impact of aerosol‐cloud interactions on weather and climate over China in boreal summer.