Despite marked progress over the past several decades, convective storm nowcasting remains a challenge because most nowcasting systems are based on linear extrapolation of radar reflectivity without much consideration for other meteorological fields. The variational Doppler radar analysis system (VDRAS) is an advanced convective‐scale analysis system capable of providing analysis of 3‐D wind, temperature, and humidity by assimilating Doppler radar observations. Although potentially useful, it is still an open question as to how to use these fields to improve nowcasting. In this study, we present results from our first attempt at developing a support vector machine (SVM) box‐based nowcasting (SBOW) method under the machine learning framework using VDRAS analysis data. The key design points of SBOW are as follows: (1) The study domain is divided into many position‐fixed small boxes, and the nowcasting problem is transformed into one question, i.e., will a radar echo > 35 dBZ appear in a box in 30 min? (2) Box‐based temporal and spatial features, which include time trends and surrounding environmental information, are constructed. (3) And the box‐based constructed features are used to first train the SVM classifier, and then the trained classifier is used to make predictions. Compared with complicated and expensive expert systems, the above design of SBOW allows the system to be small, compact, straightforward, and easy to maintain and expand at low cost. The experimental results show that although no complicated tracking algorithm is used, SBOW can predict the storm movement trend and storm growth with reasonable skill.

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Convective storm nowcasting has attracted substantial attention in various fields. Existing methods under a deep learning framework rely primarily on radar data. Although they perform nowcast storm advection well, it is still challenging to nowcast storm initiation and growth, due to the limitations of the radar observations. This paper describes the first attempt to nowcast storm initiation, growth, and advection simultaneously under a deep learning framework using multi-source meteorological data. To this end, we present a multi-channel 3D-cube successive convolution network (3D-SCN). As real-time re-analysis meteorological data can now provide valuable atmospheric boundary layer thermal dynamic information, which is essential to predict storm initiation and growth, both raw 3D radar and re-analysis data are used directly without any handcraft feature engineering. These data are formulated as multi-channel 3D cubes, to be fed into our network, which are convolved by cross-channel 3D convolutions. By stacking successive convolutional layers without pooling, we build an end-to-end trainable model for nowcasting. Experimental results show that deep learning methods achieve better performance than traditional extrapolation methods. The qualitative analyses of 3D-SCN show encouraging results of nowcasting of storm initiation, growth, and advection.

A greenhouse study was conducted to evaluate the efficacy of imazethapyr at 35 and 53 g ai/ha applied preplant incorporated (PPI) or postemergence (POST) under various soil moisture regimes (13, 19, 25, and 50%) on barnyardgrass and red rice. Response of barnyardgrass and red rice to imazethapyr PPI was affected by soil moisture. With imazethapyr PPI, control of barnyardgrass was reduced at 50% soil moisture compared with other soil moisture regimes, and height of barnyardgrass increased as soil moisture increased from 19 to 50% 2 weeks after treatment (WAT). Barnyardgrass control declined and plant dry weight increased with the increase of soil moisture from 19 to 50% at 3 WAT. Imazethapyr PPI activity on red rice was reduced at 50% compared with other soil moisture regimes, as reflected by decreased control ratings as well as increased plant height and dry weight. Imazethapyr activity on barnyardgrass and red rice was increased at 50% soil moisture when applied POST compared with PPI. Imazethapyr POST activity on barnyardgrass and red rice was generally not affected by the soil moisture regimes or application rates. The results suggested that high soil moisture conditions reduced imazethapyr PPI efficacy on barnyardgrass and red rice. Imazethapyr POST activity seems unaffected by soil moisture conditions and thus may be used to control barnyardgrass and red rice if wet soil conditions are a concern.