Predicting rapid intensity changes in tropical cyclones (TCs) is a major challenge due to the influence of multiple competing processes within the vortex and in the TC environment. We present an empirical framework that quantifies the relative importance of the various factors that influence critical transitions in TC intensities. Our analysis of model simulations of recent TCs over the Bay of Bengal identifies the following variables within the vortex as the biggest influence on TC rapid intensity changes: the amplitudes of wave number 1 of the precipitation in the rainband region and the 700‐to 850‐mb horizontal moisture flux convergence and the amplitude of wave number 0 of precipitation within the radius of maximum winds. Likewise, the most important environmental variables identified are the angle between the driest air and the shear vector and the magnitude of vertical wind shear. These findings provide guidance on guidance for future observational efforts and data assimilation into TC forecasting models.

At any given time, a tropical cyclone (TC) vortex has multiple intensity pathways that are possible. We conceptualize this problem as a scenario where each of the TC's intensity pathways is a distinct attractor basin, and a combination of several external and internal factors across multiple scales dictates as to which of the many pathways the TC vortex actually takes. As with any complex system, it is difficult to know the details of the multiscale processes that cause or initiate the tipping of the TC vortex into an attractor basin. A stochastic shock arising from any of the various scales within a TC vortex and the subsequent cross‐scale energy transactions may rapidly increase the probability of the vortex intensifying or weakening. To address this problem and apply our conceptual framework to actual TC case studies, we formulate a novel scale‐specific stochastic model that examines the multiscale energetics at and across individual wave numbers within the TC vortex. The stochastic term is modeled in a realistic manner in that the lower and higher wave numbers are treated differently. High‐resolution Hurricane Weather and Research Forecast model outputs of two Bay of Bengal TCs, Phailin (intensifying) and Lehar (weakening), are used as case studies. An ensemble of intensity pathways is generated, and the nonstationary probability distributions of the intensity transitions at each time are examined. Our approach is another step toward an improved understanding of the stochastic dynamics of multiscale transitions of a TC vortex.

This study presents a proof of concept for a storm Detection, Delineation, and Tracking (DDT) method based on the vertical integral of condensed water and ice content—namely the Ice Water Path (IWP). Traditional DDT methods rely solely on regularly sampled variables, such as brightness temperatures, which do not directly capture storm evolution. In contrast, IWP is a direct indicator of storm presence and intensity, being zero when absent and high in convective cores. A machine learning approach is proposed to forecast IWP during gaps in passive microwave (PMW) observations. It integrates an initial IWP retrieval with the temporal evolution of geostationary IR data. The method was first evaluated using numerical weather model (NWP) simulations of convective episodes. Then, it was applied to 26 cases of near‐coincident (20 min apart) PMW and IR observations of real storms. The prediction of IWP is addressed at three levels: (a) a cloud/no‐cloud classification achieving probabilities of detection (POD) consistently above 0.60 and often exceeding 0.80; (b) a regression model with some challenges on the spatial structure forecast but normally achieving correlations above 0.60 (c) an intensity‐based, three‐class classification that outperformed simple variable persistence and demonstrated strong spatial coherence.

This is the third of three papers that quantify the high added value of frequent satellite microwave observations of the atmosphere (with a “refresh” time on the order of one minute) to capture the dynamics of weather systems. Recent advances in small-satellite and microwave miniaturization, such as the “Temporal Experiment for Storms and Tropical systems” (TempEST) millimeter-wave radiometer developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between passive microwave observations and their change in time and the underlying cloud variables and processes, and to evaluate the sensitivity to the different physical and instrument parameters. In this third part, simulations are used to demonstrate and quantify the direct sensitivity of a time sequence of mm-wave radiances to the vertical structure of the vertical updrafts in convective storms. It is demonstrated that the brightness temperatures from a pair of low-Earth-orbit radiometers maintaining a separation of one or two minutes can indeed be used to retrieve the column-maximum magnitude of the vertical wind as well as the height of the maximum. While such passive retrievals do not provide the vertical detail that a pair of radars would produce, the radiometers have a vast swath and therefore enable the observation of an entire storm. The application is therefore quite different from the case of a convoy of radars: rather than compiling radar statistics over multiple years, the radiometer-convoy measurements can be used to analyze every storm that is observed.

The INvestigation of Convective UpdraftS (INCUS), a NASA Earth Ventures Mission scheduled for launch in early 2027, will use three small satellites to deliver the first estimates of convective mass flux and its evolution within tropical and subtropical clouds. To assist the retrieval algorithm development, the INCUS team is producing a database of updrafts and their environments using high-resolution simulations of convective clouds conducted with the Weather Research and Forecasting Model (WRF) and the Regional Atmospheric Modeling System (RAMS). Specifically, case studies are selected in 15+ regions across the (sub)tropics. Each case study is simulated three times: once with RAMS (2-moment, bin-emulating microphysics) and twice with WRF (Morrison and Thompson aerosol-aware microphysics). Simulations utilize 3 nested grids, with the outermost grids having 1.6 km grid spacing and typically spanning well over 1,000 km in zonal and meridional directions. The innermost grids also have large areas (~230 km by ~230 km), with 100 m horizontal grid spacing, ~100 m vertical grid spacing, and 30 second output. Using this output, the Tracking and Object-Based Analysis of Clouds (tobac) algorithm is run offline to quantify the 3D evolution of storm updrafts and link them to their associated anvils and environments. The tracked updrafts and their properties are directly used in INCUS algorithm development. The model output is also run through the Community Radiative Transfer Model (CRTM) and INCUS Passive Active Microwave Simulator (PAMS) to forward model geostationary IR brightness temperatures and INCUS-observed radar reflectivity, respectively. The simulations are systematically evaluated with available observations to ensure that the output is realistic and to identify gaps in the model database in terms of observed convective environments, updraft profiles, storm morphologies, and reflectivity profiles. The goals of this talk are to: (1) give an overview of the INCUS LES database and its role in the INCUS mission; (2) provide statistics about the global nature of tropical and subtropical convection obtained using high-resolution models; and (3) showcase results from evaluation of the database against different observational datasets. The multi-model, high-resolution INCUS simulation database continues to grow as more simulations are completed and will be a useful resource for the community for understanding tropical and subtropical convective clouds.