In this study, an extreme rainfall event of 451 mm in 20 hr that occurred in coastal South China on 11 May 2014 during the Southern China Monsoon Rainfall Experiment is investigated using integrated observations from the dual‐Doppler radar pair, polarimetric radar, extensive mesonetwork, and enhanced upper‐air soundings. Results show the generation of the extreme rainfall by two consecutive mesoscale convective systems (MCSs) consisting of multiple meso‐β‐scale rainbands. The two MCSs are maintained by lifting southerly oceanic flows over a quasi‐stationary mesoscale outflow boundary (MOB) along the coastline that are enhanced by convectively generated weak cold pool. Northeastward “echo training” of convective cells, under the influence of environmental southwesterly flows, leads to the formation of the multiple rainbands in each MCS. Their subsequent propagations in a “rainband training” form, together with the echo training, along the coastline account for the production of extreme rainfall. The second MCS is characterized with a leading bowing rainband showing a process of rapid splitting and reestablishment (RSRE), which contributes to the formation of the rainband training. The occurrence of the RSRE process requires ample supply of unstable upstream oceanic air mass, the quasi‐stationary MOB, and a bowing rainband intersecting with the MOB. The second MCS produces more precipitation than the first one as a result of more rainbands, stronger convective intensity, and more moderate‐sized raindrops with larger maximal sizes. The above findings, especially the RSRE process and its associated storm internal circulation, appear to add new Insights into the formation and maintenance of training rainbands and their roles in heavy rainfall production.

Some cloud structure features that can be extracted from infrared images of the sky are suggested for cloud classification. Both the features and the classifier are developed over zenithal images taken by the whole-sky infrared cloud-measuring system (WSIRCMS), which is placed in Nanjing, China. Before feature extraction, the original infrared image was smoothed to suppress noise. Then, the image was enhanced using top-hat transformation and a high-pass filtering. Edges are detected from the enhanced image after adaptive optimization threshold segmentation and morphological edge detection. Several structural features are extracted from the segment image and edge image, such as cloud gray mean value (ME), cloud fraction (ECF), edge sharpness (ES), and cloud mass and gap distribution parameters, including very small-sized cloud mass and gaps (SMG), middle-sized cloud gaps (MG), medium–small-sized cloud gaps (MSG), and main cloud mass (MM). It is found that these features are useful for distinguishing cirriform, cumuliform, and waveform clouds. A simple but efficient supervised classifier called the rectangle method is used to do cloud classification. The performance of the classifier is assessed with an a priori classification carried out by visual inspection of 277 images. The index of agreement is 90.97%.

Cloud properties derived from the whole-sky infrared cloud-measuring system (WSIRCMS) are analyzed in relation to measurements of visual observations and a ceilometer during the period July-August 2010 at the Chinese Meteorological Administration Yangjiang Station, Guangdong Province, China. The comparison focuses on the performance and features of the WSIRCMS as a prototype instrument for automatic cloud observations. Cloud cover derived from the WSIRCMS cloud algorithm compares quite well with cloud cover derived from visual observations. Cloud cover differences between WSIRCMS and visual observations are within ±1 octa in 70.83% and within ±2 octa in 82.44% of the cases. For cloud-base height from WSIRCMS data and Vaisala ceilometer CL51, the comparison shows a generally good correspondence in the lower and midtroposphere up to the height of about 6 km, with some systematic difference due to different detection methods. Differences between the resulting cloud-type classifications derived from the WSIRCMS and from visual observations show that cumulus and cirrus are classified with high accuracy, but that stratocumulus and altocumulus are not. Stratocumulus and altocumulus are suggested to be treated as waveform cloud for classification purposes. In addition, it is considered an intractable problem for automatic cloud-measurement instruments to do cloud classification when the cloud amount is less than 2 octa.

Due to the inadequate understanding of the scattering properties of nonspherical aerosols, considerable uncertainties still exist in the radiative transfer numerical simulation. To this end, a new scattering model for nonspherical aerosols is established based on Multi-Resolution Time-Domain (MRTD) scheme. The model is comprised of three modules: near field calculation module, near-to-far transformation module and scattering parameters computation module, in which, the near electromagnetic field is calculated by MRTD technique, the near-to-far transformation scheme is performed by volume integral method, and the calculation models for extinction and absorption cross section are directly derived from Maxwell's curl equations in the frequency domain. To achieve higher computational efficiency, the model is further parallelized by MPI non-blocking repeated communication technique. The accuracy of the scattering model is validated against Lorenz-Mie, Aden-Kerker and T-matrix theories for spherical particles, particles with inclusions and nonspherical particles. At last, the parallel computational efficiency of the MRTD scattering model is quantitatively discussed as well. The results obtained by parallel MRTD scattering model show an excellent agreement with those of the well-tested scattering theories, where the relative simulation errors of the phase function are less than 5% for most scattering angles. In backward directions, the simulation errors are much larger than that in forward scattering directions due to the stair approximation in particle construction. The computational accuracy of the integral scattering parameters like extinction and absorption efficiencies is higher than phase matrix, where the simulation errors of extinction and absorption efficiencies for the particle with a size parameter of 10 achieve -0.4891% and -1.6933%, respectively.

Abstract. Automatic cloud type recognition of ground-based infrared images is still a challenging task. A novel cloud classification method is proposed to group images into five cloud types based on manifold and texture features. Compared with statistical features in Euclidean space, manifold features extracted on symmetric positive definite (SPD) matrix space can describe the non-Euclidean geometric characteristics of the infrared image more effectively. The proposed method comprises three stages: pre-processing, feature extraction and classification. Cloud classification is performed by a support vector machine (SVM). The datasets are comprised of the zenithal and whole-sky images taken by the Whole-Sky Infrared Cloud-Measuring System (WSIRCMS). Benefiting from the joint features, compared to the recent two models of cloud type recognition, the experimental results illustrate that the proposed method acquires a higher recognition rate with an increase of 2 %–10 % on the ground-based infrared datasets.