Itinderjot Singh

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.

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.

Quantifying horizontal spatial variability in high‐resolution simulated convective environments is key to improving parameterizations for coarser models, accurately comparing models and observations, and understanding the physical processes driving this variability. Here, we characterize horizontal spatial variability in a large sample of simulated tropical and subtropical convective environments using structure functions. Near the surface, horizontal wind is spatially rougher (smaller decorrelation lengths) than moisture and temperature over land for length scales between 10 and 100 km. At 500 hPa, tropical oceanic convective environments display smoother moisture fields compared to temperature and wind at the same scales. Besides roughness, we also compute absolute magnitudes of variability at different length scales. Across simulations, tropical oceanic environments show the smallest magnitudes of surface temperature and moisture variability. However, at 500 hPa, the tropical oceanic simulations show greater moisture variability magnitudes compared to land cases, suggesting the influence of strong, large‐scale moisture gradients over tropical oceans.

Quantifying horizontal spatial variability in high-resolution simulated convective environments is key to improving parametrizations for coarser models, accurately comparing models and observations, and understanding the physical processes driving this variability. Here, we characterize horizontal spatial variability in a large sample of simulated tropical and subtropical convective environments using structure functions and variograms. Near the surface, the wind field is spatially rougher (smaller correlation lengths) than the moisture and temperature fields for length scales between 10-100 km. However, at 500 hPa, tropical oceanic convective environments display smoother moisture fields compared to temperature and wind fields at the same scales. Across simulations, tropical oceanic environments show the smallest magnitudes of surface temperature and moisture variability. For scales larger than ~300 km, however, tropical oceanic simulations show greater mid-level moisture variability magnitudes than tropical land and subtropical cases, suggesting the influence of large-scale moisture gradients over tropical oceans.

Convective cloud processes are sensitive to environmental conditions that vary on scales smaller than reanalysis data sets (sub‐reanalysis scales). Convective environment variability within areas representative of reanalysis data sets is quantified using large‐domain, high‐resolution (∆x = 100 m) simulations of convective cloud systems throughout the tropics and subtropics. Even after removing locations of resolved clouds and precipitation, convective environment parameters vary significantly on these scales. For example, for half of the simulated data, 500 hPa relative humidity varies by ∼30% within a typical reanalysis area. Surface winds, convective available potential energy, and middle‐tropospheric moisture are the most variable convective environment parameters for both continental and maritime regimes, while above‐surface temperature and winds are the least variable. While high‐resolution, sub‐reanalysis‐scale extrema are well‐correlated with the reanalysis‐area mean values, some of the most extreme convective environments can occur within regions with moderate reanalysis‐area mean values, particularly for continental regions.

Convective cloud processes are sensitive to environmental conditions that vary on scales smaller than reanalysis datasets (sub-reanalysis scales). Convective environment variability within areas representative of reanalysis datasets is quantified using large-domain, high-resolution (∆x = 100 m) simulations of convective cloud systems throughout the tropics and subtropics. Even after removing locations of resolved clouds and precipitation, convective environment parameters vary significantly on these scales. For example, for half of the simulated data, 500 hPa relative humidity varied by ~30% within a typical reanalysis area. Surface winds, convective available potential energy, and middle-tropospheric moisture are the most variable convective environment parameters for both continental and maritime regimes, while above-surface temperature and winds are the least variable. While high-resolution, sub-reanalysis-scale extrema are well-correlated with the reanalysis-area mean values, some of the most extreme convective environments can occur within regions with moderate reanalysis-area mean values, particularly for continental regions.

The semiarid Salt and Verde River Basins in Arizona are susceptible to atmospheric river (AR)‐related flooding. To understand the precipitation‐related impacts of climate change on extreme ARs affecting Arizona, a pseudo‐global warming method was used. High‐resolution control and future simulations of five intense historical AR events that affected the Salt and Verde River Basins in Central Arizona were carried out using the Weather Research and Forecasting regional climate model. The pseudo‐global warming approach for future simulations involved adding a temperature delta at different vertical levels to the historical initial and lateral boundary conditions of the input data while keeping constant relative humidity. The deltas were calculated using projected changes toward end of the 21st century from an ensemble of nine Global Climate Models for the Representative Concentration Pathway (RCP) 8.5. Future simulations showed an overall increase in vertically integrated transport of vapor and upward moisture flux at cloud base over the region for all events. The changes in precipitation at both domain and basin levels were highly spatially heterogeneous. Precipitation increased in all future simulations; but in general, this increase remained less than the increase in column‐integrated water vapor. It was found that in most cases, cloud ice content decreased while cloud water content increased, indicating the increased role of warm‐rain processes in producing precipitation in the future simulations. Freezing levels rose by more than 600 m, and this along with increased temperature and greater role of warm‐rain processes led to a decrease of more than 80% in the amount of frozen precipitation during the events.