Landfalling tropical cyclone (TC) induced precipitation poses a great risk to the rising coastal population globally. However, the impacts of tropical cyclone precipitation (TCP) are still difficult to predict due to rapid structural changes during landfall. This study applies a shape metric methodology to quantify the spatiotemporal evolution of TCP in the North Indian (NI), Western Pacific (WP), and North Atlantic (NA) basins. The International Best Track Archive for Climate Stewardship (IBTrACS) data and the Global Precipitation Mission (GPM)'s advanced Integrated Multisatellite Retrievals for GPM (IMERG) dataset is employed to study the 2014-2020 landfalling TCP at three analysis times: pre-landfall, landfall, and post-landfall. We examine three thresholds (2, 5, and 10 mm hr-1) and use six spatial metrics (area, closure, solidity, fragmentation, dispersion, and elongation) to quantify the shape of the precipitation pattern. To identify precipitation changes among the three analysis times and three basins, the Kruskal-Wallis test is applied. The three basins show important differences in size evolution. The greatest structural changes occur during post-landfall when the rainfall extent shrinks. The WP has the largest area of TCP and generates the highest maximum TCP of all basins. NA is the only basin where the precipitation area expands after landfall. NA also has the lowest closure for the three precipitation thresholds. NI precipitation has the lowest dispersion and maximum closure. Shape metrics such as closure and dispersion show a consistent inverse correlation. The maximum precipitation direction within the TCs is also examined in each basin. These results can inform guidelines that contribute to improved TCP forecasting and disaster mitigation strategies for vulnerable coastal populations globally. Future studies can apply shape metrics to the sub-basins in NI and WP to examine regional variability as there has been no such study in these basins. Future work can also investigate if the location of heavy rainfall within the TC structure affects flooding or other water hazards. / Master of Science / Landfalling tropical cyclones (TC) pose a significant threat to coastal populations worldwide, primarily due to the heavy rainfall. Predicting the rainfall during landfall is challenging as they undergo rapid changes. This study uses shape metrics to measure how this rainfall changes over time and space in three ocean basins: North Indian (NI), Western Pacific (WP), and North Atlantic (NA). The study uses a comprehensive collection of global TC best-track data i.e., International Best Track Archive for Climate Stewardship (IBTrACS). The rainfall measurement is derived from the satellite data i.e., the Global Precipitation Mission (GPM)'s advanced Integrated Multisatellite Retrievals for GPM (IMERG) to study landfalling rainfall between 2014 to 2020. Six spatial metrics (area, closure, solidity, fragmentation, dispersion, and elongation) were applied to quantify the shape and size of the precipitation pattern at three landfall times: pre-landfall, landfall, and post-landfall. The values of the shape metrics are compared between the ocean basins and landfall times using a statistical test. The results show that the most significant changes occur after landfall when the rainfall area decreases. WP has the largest area of rainfall and generates the highest maximum rainfall of all basins. NA is the only basin where the rainfall area expands after landfall. Shape metrics such as closure and dispersion share a consistent negative relationship. The maximum precipitation direction within the TCs is also examined in each basin. These results can contribute to improved tropical cyclone rainfall forecasting and disaster mitigation strategies for vulnerable coastal populations globally. Future studies can apply shape metrics to the sub-basins in NI and WP to examine regional variability as there has been no such study in these basins.

San Salvador Island is a small Bahamian island located in the subtropics just north of the Tropic of Cancer. Due to its subtropical location, the island is influenced by both mid-latitude and tropical weather patterns. These weather patterns vary in scale from localized convective uplift to synoptic-scale systems. This study compares satellite-derived estimates of precipitation and rain gauge observations from June 2019 through September 2021 to evaluate the relationship between the two datasets. This study then uses the satellite-derived estimates of precipitation over a 20-year period to quantify annual and seasonal variability in precipitation on San Salvador. Corroborating past research, the island exhibits a bimodal pattern of precipitation during the year, but rainfall is highly variable across seasons and between years. Atmospheric fields from a reanalysis dataset indicate the North Atlantic subtropical high influences summertime rainfall, but a relationship between upper-level wind patterns and rainfall is less clear.

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

Located in the subtropical central-eastern Bahamas, San Salvador Island is impacted by both synoptic-scale weather systems as well as local features and the North Atlantic Subtropical High. This study explores rainfall variability via one year of daily rain gauge observations in relation to daily weather patterns identified from 18 UTC surface analyses. Satellite-derived rainfall estimates are then compared to gauge observations to look at days when gauge data was missing. Though non-synoptic classifications comprised 61.1% of the days and synoptic classifications comprised 38.9% of the days, more rainfall was produced by synoptic days. Unlike other studies done on San Salvador, this study uses multiple observations—in situ, surface analyses, and satellite—to further our understanding of San Salvador’s rainfall. This study also establishes methods to explore synoptic and non-synoptic impacts on the island’s rainfall using additional years as more rain gauge data become available.