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The performance of 4‐km Weather Research and Forecasting model in predicting reflectivity structure in China over three summer months is investigated using three‐dimensional (3D) reflectivity observations. Three verification domains, namely Southern China (SC), Central and Eastern China , and North China, that correspond to the three major rainfall centers of mainland China are selected. Results show that the forecasts reproduce the distribution and diurnal variation of precipitation well, but significant differences exist in the vertical distributions of the predicted and observed reflectivities. In observations, the highest frequency (of reflectivity ≥35 dBZ) occurs between 3 and 6 km, whereas it is at the surface in the forecast. The forecasts tend to over‐predict reflectivity intensity at the lower levels, especially in SC. Further evaluation using object‐based verification methods show that the forecasts greatly underestimate the afternoon peak frequency of precipitation clouds with reflectivity >30 dBZ. The forecasts fail to reproduce the diurnal variation of 35 dBZ mean and maximum height of the objects, producing less variation than observations. Analyses show that the failure in properly reproducing small‐scale reflectivity objects (with diameter <100 km) is primarily responsible for the underestimation of the mean and maximum object heights. Evaluation using additional 3D information show that the forecasts tend to produce a greater proportion of faster‐moving small‐scale objects. This study reveals that the simulation of the 3D structure of precipitation clouds in terms of reflectivity remains a great challenge, especially for smaller convective cells. Plain Language Summary: The simulation of precipitation clouds is associated carries the largest uncertainty in numerical weather prediction and climate models. With continued advancement in observations and numerical models, forecasts of horizontal distribution, intensity, diurnal variation and propagation of precipitation have been greatly improved. However, faithful representation of three‐dimensional (3D) cloud structure remains a great challenge, with the lack of systematic evaluations being one of the key causes. In this study, 3D gridded radar reflectivity observations are used to evaluate the precipitation clouds structure (with reflectivity ≥30 dBZ) predicted at a convection‐permitting‐resolution (CPR) over three summer months of 2016 in China. The evaluation reveals differences between the simulated and observed reflectivity structures, including the reflectivity intensity profile, the 35 dBZ mean and maximum height, the fraction of fast‐moving objects. CPR models still have difficulties in simulating 3D reflectivity structures especially for relatively small‐scale objects (with diameter <100 km). Key Points: Reflectivity structure predicted by the 4‐km Weather Research and Forecasting model are evaluated using 3D radar observationsThe model overpredicts the intensity of reflectivity at lower levels and fails to reproduce the diurnal variation of object heightThe failure to correctly reproduce the structure of smaller reflectivity objects is primarily responsible for the deviations [ABSTRACT FROM AUTHOR]

Dual‐polarization radars can provide rich three‐dimensional (3D) information on cloud precipitation structure. To utilize the existing polarimetric radar network in operational data assimilation systems, a new polarimetric radar‐based cloud analysis method is introduced. New features include the employment of fuzzy‐logic hydrometeor classification, improved estimation of liquid and ice regions, and newly‐added number concentration estimation. The new scheme is evaluated with a typical squall line case. Results show that the 3D cloud precipitation structure and short‐term precipitation forecast is consistently improved. With extra information from the polarimetric observations, the new scheme is able to produce reasonable polarimetric signatures for analysis. More supercooled water results in more latent heat release which enhances the updraft, leading to stronger convection in the subsequent forecast. This study emphasizes the importance of correct initial liquid and ice particle condition for the prediction of deep convection. Plain Language Summary: Severe convective storms have always been one of the most the disastrous types of weather. However, due to their relatively small scale and rapid evolution, such storms are difficult to predict. Polarimetric radar networks have been established for nearly a decade and have been widely used in the monitoring and short‐term forecast of severe weather. Yet, this extra polarimetric information has not been used in the operational data assimilation system, which is key to a successful forecast. A new dual‐polarization (dual‐pol) radar‐based cloud analysis aiming to improve short‐term forecasts is therefore proposed. Results show that the new scheme is able to capture the 3D in‐cloud signature of deep convection. Consistent improvements are found for the prediction of 3D structure of deep convection and as well as for the quantitative precipitation forecast. Key Points: A modified cloud analysis model is proposed for polarimetric radar data assimilationA fuzzy‐logic method combined with ZDP is used to classify hydrometeor particles, followed by a partially double‐moment retrieval schemeExperiments with polarimetric‐data‐based cloud analysis show consistent improvement on analysis and forecast [ABSTRACT FROM AUTHOR]

The impact of assimilating China's operational X‐band Phased‐Array radar's (X‐PAR) data on the analysis and warning forecast of the vortex structure and intensity of the June 8, 2018 Foshan, Guangdong province, tornadic storm was investigated for the first time using an Ensemble Kalman Filter (EnKF) data assimilation system. Both radar radial velocity (Vr) and reflectivity (Z) from two S‐band operational radars and one X‐PAR were assimilated. Deterministic forecasts were launched every 6 min from 05:42 UTC (20 min before the tornado touched down) to 06:00 UTC from the EnKF mean analysis field. Five experiments were conducted to examine the added capability of Z assimilation of the EnKF system, and to investigate the impact of assimilating X‐PAR data on the analysis and prediction of the tornadic storm. Compared to the experiment without Z assimilation, the assimilation of Z reduced the analysis error and greatly reduced the forecast error of Z. The assimilation of X‐PAR data greatly improved the vortex structure of the tornadic storm at low levels, and improved the intensity of the rear inflow of the tornadic storm, especially with a higher assimilation frequency. Compared to the experiments without X‐PAR data assimilation, assimilating X‐PAR data improved the predictability of tornadic storm. Plain Language Summary: Tornadoes are difficult to predict worldwide. However, the scan speed of current operational S‐band (10 cm wavelength) radar is too slow to capture the evolution of tornadoes. The newly built X‐band (3 cm wavelength) Phase‐Array radar (X‐PAR) network in Guangdong Province, China, are hopeful to fill in the gap with their higher scan frequency and higher spatial resolution, which can capture the fine‐scale structure and rapid evolution of small scale systems such as tornadic storms. This study focuses on a case where a tornado embedded within the typhoon Ewiniar was well captured by the X‐PAR network. It demonstrated for the first time the use of X‐PAR data on the prediction of a tornadic storm in China. We used ensemble‐based data assimilation techniques named Ensemble Kalman Filter to utilize the information provided by the X‐PAR data. With the improved information, the analyzed tornadic storm vortex structure at low levels was greatly improved, especially with a higher assimilation frequency. Compared to the experiments without X‐PAR data, the experiment using X‐PAR data successfully predicted the tornadic vortex a few minutes in advance. Key Points: The impact of assimilating China's X‐band Phased‐Array radar (X‐PAR) radar data on the analysis and forecast of a tornadic storm was first investigated using an Ensemble Kalman Filter systemThe assimilation of X‐PAR data helps to produce a more realistic‐looking tornadic storm structure, especially at low levelsIncreasing assimilation frequency of X‐PAR data reduces the analysis error and subsequently improves the 8‐min tornadic storm forecast [ABSTRACT FROM AUTHOR]

The impact of assimilating differential reflectivity ZDR in addition to reflectivity (ZH) and radial velocity (Vr) from a polarimetric radar on the analysis of a tornadic supercell storm using an ensemble Kalman filter (EnKF) is studied in an observing system simulation experiment (OSSE) framework assuming a perfect forecast model. A double‐moment microphysics scheme is used to allow for proper simulation of polarimetric signatures. Root‐mean‐square errors of analysed state variables are calculated and the structure and intensity of analysed fields and derived quantities are examined. Compared to the baseline experiment assimilating radial velocity and reflectivity only, the assimilation of additional ZDR further reduces the errors of all state variables. The analysed hydrometeor fields are improved in both pattern and intensity distributions. Polarimetric signatures including ZDR and KDP columns, and ZDR arc in the supercell, are much better reproduced. Sensitivity experiments are performed that exclude the updating of hydrometeor number concentrations by ZDR or of state variables not directly linked to ZDR via observation operators. The results show that if number concentrations are not updated together with the mixing ratios, most of the benefit of assimilating ZDR is lost. Among other state variables, the updating of water vapour mixing ratio qv has the largest positive impact while the impact of updating vertical wind w comes in second. The updating of horizontal wind components or temperature has a much smaller but still noticeable impact. Reliable flow‐dependent cross‐covariances among the state variables and observation prior as derived from the forecast ensemble and used in EnKF are clearly very beneficial. [ABSTRACT FROM AUTHOR]

The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias. When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification. [ABSTRACT FROM AUTHOR]