Sai Prasanth

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.

NASA's upcoming INCUS mission will observe tropical deep convection at unprecedented spatiotemporal resolution (∼3 km horizontal sampling at intervals of 30 s, 90 s, and 120 s). INCUS will observe a wide variety of deep convective environments and tropical storms at different stages of their life cycle, producing many distinct convective outcomes. How do we distinguish these outcomes? Which differences are associated with genuinely different local environmental states, and which aspects vary even when the local environment is effectively the same?We use Kernel Flows, a nonlinear machine learning estimator, to separate aspects of local deep convection that are constrained by their environmental state from those that are not. We first define the environmental state as a vector X formed from variables always present in the troposphere describing background thermodynamic and kinematic conditions, independent of whether convection is active (temperature, pressure, water vapor, horizontal wind fields). We define convective variables as vertical velocity, condensed water mass, and convective mass flux, which arise only where convection is present and correspond to quantities INCUS algorithms are designed to retrieve.The analysis uses a database of high-resolution simulations (100 m horizontal grid spacing) across tropical and subtropical regions in anticipation of INCUS. From these simulations, we extract 25 km × 25 km neighborhoods containing a mixture of deep moist convective updrafts and surrounding non-updrafts. Within each neighborhood, we coarsen the environmental and convective variables to approximately 3 km resolution to match anticipated INCUS radar resolution. We represent the environmental state using principal components and define 19 scalar descriptors (Yi) to characterize various aspects of convection.Using Kernel Flows with no assumptions on Gaussian conditional distributions, we learn the functional relationship between each convective descriptor (Yi) and environmental state (X) independently. We quantify how much variability in each descriptor is associated with environmental state differences by comparing the estimator's error variance to the prior variance of Yi.We find that aggregate convective descriptors exhibit variability almost entirely associated with environmental state differences. Total vertical velocity and total convective mass flux over the neighborhood, along with neighborhood-mean column maxima of these quantities, achieve R² ≥ 0.97 with at least 84% of standard deviation explained by the environmental state. These convective descriptors act as invariants of the local environment: differences in these metrics reflect environmental state (X) differences, rather than natural variability within an equivalent environmental state. Conversely, convective descriptors emphasizing horizontal and vertical organization of updrafts, such as how total updraft area is divided among horizontally contiguous clusters and maximum heights of vertical velocity and convective mass flux, have R² values typically below 0.5–0.55 and retain substantial variability even when X does not vary significantly. For these descriptors, only a modest fraction of variance is associated with environmental state differences, indicating that most observed differences reflect variability not captured by the local environmental state, potentially from smaller-scale dynamics or stochastic processes.Our findings identify which aspects of deep convection are almost entirely tied to their local environmental state at spatiotemporal scales commensurate with INCUS observations.

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.

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.

This corrigendum corrects the time-averaged plots of the kinetic energy transfer terms in our published article. This corrigendum corrects the time-averaged plots of the kinetic energy transfer terms in our published article.

Our collective understanding of azimuthally asymmetric features within the coherent structure of a tropical cyclone (TC) continues to improve with the availability of more detailed observations and high-resolution model outputs. However, a precise understanding of how these asymmetries impact TC intensity changes is lacking. Prior attempts at investigating the asymmetric impacts follow a mean–eddy partitioning that condenses the effect of all the asymmetries into one term and fails to highlight the differences in the role of asymmetries at different scales. In this study, we present a novel energetics-based approach to analyze the asymmetric impacts at multiple length scales during periods of rapid intensity changes. Using model outputs of TCs under low and high shear, we compute the different energy pathways that enhance/suppress the growth of multiscale asymmetries in the wavenumber (WN) domain. We then compare and contrast the energetics of the mean-flow field (WN 0) with that of the persistent, coherent vortex-scale asymmetric structures (WNs 1 and 2) and the more local, transient, sub-vortex-scale asymmetries (WNs ≥ 3). We find in our case studies that the dominant mechanisms of growth/decay of the asymmetries are the baroclinic conversion from available potential to kinetic energy at individual scales of asymmetries and the transactions of kinetic energy between the asymmetries of various length scales, rather than the barotropic mean–eddy transactions as is typically assumed. Our case study analysis further shows that the growth/decay of asymmetries is largely independent of the mean. Certain aspects of eddy energetics can potentially serve as early-warning indicators of TC rapid intensity changes.

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.

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.