Quantitative Applications of Weather Satellite Data for Nowcasting:Progress and Challenges

Monitoring and predicting highly localized weather events over a very short-term period,typically ranging from minutes to a few hours,are very important for decision makers and public action.Nowcasting these events usually relies on radar observations through monitoring and extrapolation.With advanced high-resolution imaging and sounding observations from weather satellites,nowcasting can be enhanced by combining radar,satellite,and other data,while quantitative applications of those data for nowcasting are advanced through using machine learning techniques.Those applications include monitoring the location,impact area,intensity,water vapor,atmospheric instability,precipitation,physical properties,and optical properties of the severe storm at different stages(pre-convection,initiation,development,and decaying),identification of storm types(wind,snow,hail,etc.),and predicting the occurrence and evolution of the storm.Satellite observations can provide information on the environmental characteristics in the preconvection stage and are very useful for situational awareness and storm warning.This paper provides an overview of recent progress on quantitative applications of satellite data in nowcasting and its challenges,and future perspectives are also addressed and discussed.

Application of quantitative imaging in oncologic management

Medical imaging, such as computed tomography （CT）, magnetic resonance imaging （MRI） and positron emissiontomography （PET）, plays a vital role for the decision-making in oncologic management. In clinical practice, imaging-derivedtumor metrics are routinely applied in oncologic management as an imaging biomarker. For example, the ResponseEvaluation Criteria in Solid Tumors （RECIST） are commonly used for tumor treatment response evaluation based on thedynamic changes in tumor size. However, the current cross-sectional images are interpreted qualitatively for lesioncharacterization, treatment response evaluation and prognostic prediction by highly trained radiologists, which hasincreasingly apparent limitations. Therefore, there is a demanding shift toward more quantitative imaging interpretation.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

Application of High-Resolution Remote Sensing Technology in Quantitative Study on Coseismic Surface Rupture Zones: An Example of the 2008 M_w7.2 Yutian Earthquake

Objective Nowadays, high-resolution remote sensing technology has brought new changes to surveys of earthquakes, and the quantitative study of seismic faults based on this technology has become a trend in the world(Barzegari et al., 2017). An Mw 7.2 earthquake occurred in Yutian of Xinjiang on the western end of the Altyn Tagh fault on March 21 st, 2008. It is difficult to access this depopulated zone because of the high altitude and only 1–2 months of snowmelt. This study utilized high-resolution