Synthetic downscaling of tropical cyclones (TCs) is critically important to estimate the long-term hazard of rare high-impact storm events. Existing downscaling approaches rely on statistical or statistical-deterministic models that are capable of generating large samples of synthetic storms with characteristics similar to observed storms. However, these models do not capture the complex two-way interactions between a storm and its environment. In addition, these approaches either necessitate a separate TC size model to simulate storm size or involve post-processing to introduce asymmetries in the simulated surface wind. In this study, we present an innovative data-driven approach for TC synthetic downscaling. Using a machine learning-based high-resolution global weather model (ML-GWM), our approach is able to simulate the full life cycle of a storm with asymmetric surface wind that accounts for the two-way interactions between the storm and its environment. This approach consists of multiple components: a data-driven model for generating synthetic TC seeds, a blending method that seamlessly integrate storm seeds into the surrounding while maintain the seed structure, and a recurrent neural network-based model for correcting the biases in maximum wind speed. Compared to observations and synthetic storms simulated using existing statistical-deterministic and statistical downscaling approaches, our method shows the ability to effectively capture many aspects of TC statistics, including track density, landfall frequency, landfall intensity, and outermost wind extent. Taking advantage of the computational efficiency of ML-GWM, our approach shows substantial potential for TC regional hazard and risk assessment.

In recent years, efforts to assess the evolving risks of coastal compound surge and rainfall-driven flooding from tropical cyclones (TCs) and extratropical cyclones (ETCs) in a warming climate have intensified. While substantial progress has been made, the persistent challenge lies in obtaining actionable insights into the changing magnitude and spatially-varying flood risks in coastal areas. We employ a physics-based numerical hydrodynamic framework to simulate compound flooding from TCs and ETCs in both current and future warming climate conditions, focusing on the western side of Buzzard Bay in Massachusetts. Our approach leverages hydrodynamic models driven by extensive sets of synthetic TCs downscaled from CMIP6 climate models and dynamically downscaled ETC events using the WRF model forced by CMIP5 simulations. Through this methodology, we quantify the extent to which climate change can potentially reshape the risk landscape of compound flooding in the study area. Our findings reveal a significant increase in TC-induced compound flooding risk due to evolving climatology and sea level rise (SLR). Additionally, there is a heightened magnitude of compound flooding from ETCs, in coastal regions, due to SLR. Inland areas exhibit a decline in rainfall-driven flooding from high-frequency ETC events toward the end of the century compared to the current climate. Our methodology is transferable to other vulnerable coastal regions, serving as a valuable decision-making tool for adaptive measures in densely populated areas. It equips decision-makers and stakeholders with the means to effectively mitigate the destructive impacts of compound flooding arising from both current and future TCs and ETCs.

Remote sensing datasets of water vapor isotopic composition are used along with objective measures of convective aggregation to better understand the impact of convective aggregation on the atmospheric hydrologic cycle in the global tropics ($30^{\circ}$N to $30^{\circ}$S) for the period 2015-2020. When convection is unaggregated, vertical velocity profiles are top-heavy, mixing ratios increase and water vapor $\delta D$ decreases as the mean precipitation rate increases, consistent with partial hydrometeor evaporation below anvils into a relatively humid atmospheric column. Aggregated convection is associated with bottom-heavy vertical velocity profiles and a positive correlation between mixing ratio and $\delta D$, a result that is consistent with isotopic enrichment from detrainment of shallow convection near the observation level. Intermediate degrees of aggregation do not display significant variation in $\delta D$ with mixing ratio or precipitation rate. Convective aggregation provides a useful paradigm for understanding the relationships between mixing ratio and isotopic composition across a range of convective settings. The results presented here may have utility for a variety of applications including the interpretation of paleoclimate archives and the evaluation of numerical simulations of convection.

This paper reports a new and significantly enhanced analysis of US flood hazard at 30m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level and inclusion of the impact of local interventions in the flooding system. For the first time we consider pluvial, fluvial and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centred on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high quality local models and the entire catalogue of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69-0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near future time horizon we project clear patterns of changing flood hazard (-3.8 to +16% changes in 100yr inundated area at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defence protection may become compromised by this time.

The science guiding the \EURECA campaign and its measurements are presented. \EURECA comprised roughly five weeks of measurements in the downstream winter trades of the North Atlantic — eastward and south-eastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, \EURECA marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or, or the life-cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso (200 km) and larger (500 km) scales, roughly four hundred hours of flight time by four heavily instrumented research aircraft, four global-ocean class research vessels, an advanced ground-based cloud observatory, a flotilla of autonomous or tethered measurement devices operating in the upper ocean (nearly 10000 profiles), lower atmosphere (continuous profiling), and along the air-sea interface, a network of water stable isotopologue measurements, complemented by special programmes of satellite remote sensing and modeling with a new generation of weather/climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that \EURECA explored — from Brazil Ring Current Eddies to turbulence induced clustering of cloud droplets and its influence on warm-rain formation — are presented along with an overview \EURECA’s outreach activities, environmental impact, and guidelines for scientific practice.