Design and Data Analysis of a New Type of Antifreezing Cup-Type Wind Velocity Sensor

In most areas of China, affected by the environment of low temperature and high humidity, the wind speed sensor and wind direction sensor are frozen and cannot output data in autumn, winter or the alternation of winter and spring. In order to solve the freezing situation of the wind sensor, this paper designs a new type of antifreeze wind speed sensor. After meteorology performance testing and field observation tests, the correlation coefficient of the observation data is demonstrated, and the data curve is fitted. The result shows the sensor is stable, and has a good antifreeze effect, the data output is reliable.

Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

Diagnostic Analysis of Wave Action Density During Heavy Rainfall Caused by Landfalling Typhoon

Based on prior investigation,this work defined a new thermodynamic shear advection parameter,which combines the vertical component of convective vorticity vector,horizontal divergence,and vertical gradient of generalized potential temperature.The interaction between waves and fundamental states was computed for the heavyrainfall event generated by landfalling typhoon“Morakot”.The analysis data was produced by ADAS[ARPS(Advanced Regional Prediction System)Data Analysis System]combined with the NCEP/NCAR final analysis data(1°×1°,26 vertical pressure levels and 6-hour interval)with the routine observations of surface and sounding.Because it may describe the typical vertical structure of dynamical and thermodynamic fields,the result indicates that the parameter is intimately related to precipitation systems.The parameter’s positive high-value area closely matches the reported 6-hour accumulated surface rainfall.And the statistical analysis reveals a certain correspondence between the thermodynamic shear advection parameter and the observed 6-hour accumulated surface rainfall in the summer of 2009.This implies that the parameter can predict and indicate the rainfall area,as well as the initiation and evolution of precipitation systems.