大气与海洋物理
El Ni\~no Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific (CP) events are typically…
Advanced weather and climate models use numerical techniques on grided meshes to simulate atmospheric and ocean dynamics, which are computationally expensive. Data-driven approaches are gaining popularity in weather and climate modeling,…
Operational weather forecasting models have advanced for decades on both the explicit numerical solvers and the empirical physical parameterization schemes. However, the involved high computational costs and uncertainties in these existing…
The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of precipitating clouds…
We present a study on the spatio-temporal pattern underlying the climate dynamics in various locations spread over India, including the Himalayan region, coastal region, central and northeastern parts of India. We try to capture the…
The results of an analysis of temperature variations in the mesopause region based on long-term measurements of hydroxyl airglow at the Zvenigorod Scientific Station of the A.M. Obukhov Institute of Atmospheric Physics RAS (ZSS IAP RAS) in…
We perform three-dimensional direct numerical simulations of surface-driven convection near the temperature of maximum density $\tilde T_{md}$. A dynamic surface boundary condition couples heat flux through the surface to the induced…
The equatorial mixed Rossby-gravity wave (MRGW) is an important contributor to tropical variability. Its excitation mechanism capable of explaining the observed MRGW variance peak at synoptic scales remains elusive. This study investigates…
Artificial intelligence (AI), based on deep-learning algorithm using high-quality reanalysis datasets, is showing enormous potential for weather forecasting. In this context, the European Centre for Medium-Range Weather Forecasts (ECMWF) is…
The low-frequency variability of the $\delta^{18}$O recorded in ice cores (FK17 and TIR18) recently drilled at two different locations in Dronning Maud Land (Antarctica), is investigated using multi-taper spectral method and singular…
The hydrologic cycle has wide impacts on the ocean salinity and circulation, carbon and nitrogen cycles, and the ecosystem. Under anthropogenic global warming, previous studies showed that the intensification of the hydrologic cycle is a…
The land-atmosphere coupling strength has been defined as the percentage of precipitation variability explained by the variation of soil moisture in the Global Land-Atmosphere Coupling Experiment (GLACE). While it is useful to identify…
The Boreal Summer Intraseasonal Oscillation (BSISO) is a pronounced mode of tropical variability. Here, we identify two types of BSISO events, one which propagates northward over South Asia (SA) from the equatorial Indian Ocean (EIO), and…
Convection-permitting models (CPMs) enable the representation of meteorological variables at horizontal high resolution spatial scales (higher than 4 km), where convection plays a significant role. Physical schemes need to be evaluated…
Deep learning has gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress…
The advents of Artificial Intelligence (AI)-driven models marks a paradigm shift in risk management strategies for meteorological hazards. This study specifically employs tropical cyclones (TCs) as a focal example. We engineer a…
A combined radar remote sensing and in situ data set is used to track packets of nonlinear internal waves as they propagate and shoal across the inner shelf (40m - 9m). The dataset consists of high space-time resolution (5m, 2min) radar…
Downscaling, or super-resolution, provides decision-makers with detailed, high-resolution information about the potential risks and impacts of climate change, based on climate model output. Machine learning algorithms are proving themselves…
Addressing complex meteorological processes at a fine spatial resolution requires substantial computational resources. To accelerate meteorological simulations, researchers have utilized neural networks to downscale meteorological variables…
Generated under hurricane conditions, a slip layer composed of foam, bubble emulsion, and spray determines the behavior of the surface drag with wind speed. This study enables us to estimate foam's contribution to this behavior. A…