Detection of weather and climate extremes using ground-based GNSS atmospheric products

Weather and climate extremes, such as heavy precipitation and drought, are becoming more frequent and intense, which have profound effect on the sustainable development of human society. Therefore, it is crucial to improve our understanding of the inherent features of weather and climate extremes, as well as our ability to detect their onsets and mitigate their detrimental impacts. Regarded as an essential climate variable, water vapor has the potential to determine the intensity, time, and extent of weather and climate extremes, e.g., heavy precipitation and drought. Therefore, having access to accurate and timely water vapor information is essential for improving our capacity for weather forecasting and climate monitoring. The ground-based Global Navigation Satellite Systems (GNSS) atmospheric sounding technique has become an important tool for the retrieval of atmospheric water vapor contents. GNSS atmospheric sounding technique offers several distinctive benefits including high spatiotemporal resolution, high accuracy, free of instrumental bias, and all-weather capability. Since the inception of GNSS meteorology, GNSS atmospheric products, e.g., zenith total delay (ZTD) and precipitable water vapor (PWV), have attracted significant attention in the meteorological and climate communities. Specifically, the ZTD and PWV time series present promising prospects for monitoring the formation and evolution of heavy precipitation and drought. Hence there exists great scientific value in advancing the utilization of the ground-based GNSS atmospheric sounding technique. This advancement will contribute towards improving the detection and monitoring of weather and climate extremes. This research aims to improve the detection of heavy precipitation and drought using state-of-the-art advancement in the ground-based GNSS atmospheric sounding technique. The main contributions of the thesis are listed as follow: (1) It was found that the forecast lead time of using ZTD or PWV for heavy precipitation was about 8 h. The research has demonstrated that ZTD, PWV and other weather variables, e.g., temperature, pressure, and cloud coverage, can be used as predictors for detecting heavy precipitation. (2) PWV, ZTD, and cloud coverage play prominent roles in heavy precipitation forecasts; while ZTD, temperature, and pressure are crucial references in monitoring drought events. (3) Several statistical models, including two extrapolation-based models and an improved NN-based model, were developed using ZTD, PWV, and their respective derivatives for the nowcasting of heavy precipitation. Results indicated that the correct detection rates resulting from these models were all above 94.5%. Their false alarm rates were reduced to less than 22.4% compared with those from the existing models, i.e., 60%–70%. (4) The research has demonstrated the assimilation of ZTD and PWV into an NWP model resulted in improved accuracy of the initial humidity field and final prediction performance. The largest improvement in the accuracy of atmospheric humidity field reached 26.0%. The optimal spatial resolution for assimilating PWV into an NWP model was determined as 46.40 km and 55.10 km under the scenarios with and without heavy precipitation, respectively. (5) An improved higher-order calibration equation was proposed to improve the accuracy of potential evapotranspiration (PET) using ZTD, temperature, and pressure. Results suggested that, after the calibration process, the accuracy of PET estimates was improved by 80%. (6) A new calculation strategy for estimating diurnal-provided PET values was proposed, in which an improved 31-day sliding window was employed. Compared with monthly PET values, its temporal resolution was improved by over 30 times. (7) The research demonstrated the diurnal-provided evaporative demand drought index estimates, which were calculated from well-calibrated PETs, being applied to the monitoring of flash drought. Results showed that the correct detection rate reached 87.1%, and the mean lead time was extended to 37.74 days. The aforementioned innovative practices not only enhance the reliability and accuracy for the detection of heavy precipitation and drought, but also highlight the robust foundation, significant potential, and wide-ranging prospects for utilizing ground-based GNSS products of ZTD and PWV in various weather and climate applications.