This paper explores the optimal temperature change in normalized difference vegetation index (NDVI) growth in the Huang-Huai-Hai Plain under the background of climate change, aiming to better cope with the impact of global warming on vegetation growth. The temporal and spatial variation characteristics of the NDVI and temperature factors were analyzed by using the NDVI, average temperature (Tavg), maximum temperature (Tmax) and minimum temperature (Tmin) datasets from 1982 to 2020. Through the Buishand U test and sliding slope detection, 1998 was determined to be a year of abrupt climate change. Furthermore, SHAP important feature analysis, a generalized additive model, correlation analysis, and other methods were used to identify the trend in the optimum temperature of vegetation growth before and after climate change. The results showed that (1) from 1982 to 2020, Tavg, Tmax, Tmin, and the NDVI in Huang-Huai-Hai Plain showed a significant upward trend. At the same time, the spatial distribution of these indicators shows the distribution characteristics of high in the south and low in the north. (2) The NDVI was positively correlated with Tavg, Tmax, and Tmin, and the correlation with Tmin was most significant. (3) The most suitable Tavg, Tmax, and Tmin intervals for vegetation growth were 20~30 °C, 25~35 °C, and 16~25 °C, respectively. (4) The optimum temperature range of vegetation growth was expanded after climate change, and the change rates of the Tavg and Tmax lower limits reached 24% and 25%, respectively, under the best condition of vegetation growth. (5) After abrupt climate change, the temperature suitable for vegetation growth increased significantly in the northern part of the plain but decreased slightly in the southern part.

China New Generation Doppler Weather Radar (CINRAD) plans to upgrade its hardware and software to achieve polarimetric function. However, the small-magnitude polarimetric measurements were negatively affected by the scattering characteristics of ground clutter and the filter’s response to the ground clutter. This polarimetric contamination was characterized by decreased differential reflectivity (Z_DR) and cross-correlation coefficient (ρ_hv), as well as an increased standard deviation of the differential phase (Φ_DP), generating a large-area and long-term observational anomaly for eight polarimetric radars in South China. Considering that outliers simultaneously appeared in the radar mainlobe and sidelobe, the variations in the reflectivity before and after clutter mitigation (ΔZ_H) and ρ_hv were used for quantitatively describing the random dispersion caused by mainlobe and sidelobe clutters. The performance of polarimetric algorithms was also reduced by clutter contamination. The deteriorated membership functions in the hydrometeor classification algorithm changed the proportion of classified echoes. The empirical relations of R(Z_H, Z_DR) and R(K_DP) were broken in the quantitative precipitation estimation algorithm and the extra error considerably exceeded the uncertainty caused by the drop-size distribution (DSD) variability of R(Z_H). The above results highlighted the negative impact of clutter contamination on polarimetric applications that need to be further investigated.

The eastern coastal areas of China span multiple climatic zones, and the impacts of climate warming on their ecological environment show regional differences. In this research, the Normalized Difference Vegetation Index (NDVI) was used as the indicator to characterize the ecological environment, and selected Guangdong, Jiangsu, and Liaoning as its typical research areas. The authors selected the NDVI, average temperature, and precipitation data of the yearly growth season, respectively, from 1982 to 2016. This study adopted the copula functions model based on Markov Chain Monte Carlo to carry out the research of bivariate joint distribution so as to calculate the joint probability, the joint exceedance probability, the joint return period and the co-occurrence return period. The results showed that 1) the temperature and precipitation in the three regions were respectively related to the NDVI sequence showing the characteristic that was correlated at the upper tail and asymptotically independent at the lower tail, which demonstrated that the temperature and precipitation had little effect on NDVI when they reached their minimum values, and the temperature and precipitation had obvious effect on NDVI when they reached their maximum values. 2) The shorter the return period was, the wider the ranges of the climate factor and the NDVI were, showing that when the climate factor was constant, the probability of the NDVI having a shorter return period was higher. The greater the climate factor was, the longer the return period was, indicating that the probability of plant growth inhibition was higher when the climate factor exceeded a certain threshold. 3) The suitable temperature and precipitation for vegetation growth in the three regions gradually decreased from south to north. These results provide some theoretical guidance and scientific foundation for the protection of regional ecological environment and enhance the understanding of the impact of climate change on the ecosystem.

Automated and accurate radar dealiasing algorithms are very important for their assimilation into operational numerical weather forecasting models. A radar radial velocity dealiasing algorithm aimed at radar data assimilation is introduced and assessed using from several S-band and C-band radar observations under the severe weather conditions of hurricanes, typhoons, and deep continental convection in this paper. This dealiasing algorithm, named automated dealiasing for data assimilation (ADDA), is a further development of the dealiasing algorithm named the China radar network (CINRAD) improved dealiasing algorithm (CIDA), originally developed for China’s CINRAD (China Next Generation Weather Radar) radar network. The improved scheme contains five modules employed to remove noisy data, select the suitable first radial, preserve the convective regions, execute multipass dealiasing in both azimuthal and radial directions and conduct the final local dealiasing with an error check. This new dealiasing algorithm was applied to two hurricane cases, two typhoon cases, and three intense-convection cases that were observed from the CINRAD of China, Taiwan‘s radar network, and NEXRAD (Next Generation Weather Radar) of the U.S. with a continuous period of more than 12 h for each case. The dealiasing results demonstrated that ADDA performed better than CIDA for all selected cases. This algorithm not only produced a high success rate for the S-band radar, but also a reasonable performance for the C-band radar.

In this dissertation, the author adopted the normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2016 of the typical climate zones in coastal areas of China to analyze the influence of daytime and nighttime warming asymmetric changes in different seasons on vegetation activities during the growing season period according to the copula function theory optimized based on Markov chain Monte Carlo (MCMC). The main conclusions are as follows: (1) The seasonal daytime and nighttime warming trends of Guangdong, Jiangsu and Liaoning over the past 35 years were significant, and the daytime and nighttime warming rates were asymmetric. In spring and summer of Guangdong province, the warming rate in the daytime was higher than that at night, while, in autumn, the opposite law was observed. However, the warming rate in the daytime was lower than that at night in Jiangsu and Liaoning provinces. There were latitude differences in diurnal and nocturnal warming rate. (2) The daytime and nighttime warming influences on vegetation showed significant seasonal differences in these three regions. In Guangdong, the influence of nighttime warming on vegetation growth in spring is greater than that in summer, and the influences of daytime warming on vegetation growth from strong to weak were spring, summer and autumn. In Jiangsu, both the influences of daytime and nighttime warming on vegetation growth in summer were less than that in autumn. In Liaoning, both the influences of daytime and nighttime warming on vegetation growth from strong to weak were autumn, spring and summer. (3) In Guangdong, Jiangsu and Liaoning provinces, their maximum temperature (Tmax) and minimum temperature (Tmin) and the joint probability distribution functions of NDVI, all had little effect on NDVI when Tmax and Tmin respectively reached their minimum values, but their influences on NDVI were obvious when Tmax and Tmin respectively reached their maximum values. (4) The smaller the return period, the larger the range of climate factor and NDVI, which has indicated that when the climate factor is certain, the NDVI is more likely to have a smaller return period, and the frequency of NDVI over a certain period is higher. In addition, the larger the climate factor, the greater the return period is and NDVI is less frequent over a certain period of time. This research can help with deep understanding of the dynamic influence of seasonal daytime and nighttime asymmetric warming on the vegetation in typical coastal temperature zones of China under the background of global climate change.

Precipitation nowcasting is critical for areas such as agriculture, water resource management, urban drainage systems, transport and disaster preparedness. In recent years, methods such as convolutional recurrent neural networks (ConvRNN) in deep learning techniques have been used to solve this task. Despite the effective improvement in forecasting quality, there are still problems with blurred and distorted prediction images, as well as difficulties in effectively forecasting high echo regions. To solve the above problems, this article presents a spatio-temporal long–short-term memory network model in view of multi-scale context fusion and attention mechanisms. This method fully extracts the short-term context information of different scales of radar image through the multi-scale context fusion module. The attention module broadens the time perception domain of the prediction unit so that the model perceives more historical time dynamics. Using the Hong Kong region weather radar data as a sample, the results of the experimental comparative analysis show that the spatio-temporal long and short-term memory network in view of multi-scale context fusion and attention mechanism achieves better prediction performance. Our model is effective in improving both image quality and meteorological assessment metrics with higher accuracy and more details.