大气与海洋物理
The Madden-Julian oscillation (MJO), a gigantic tropical weather system, is marked by eastward travel of cumulus cloud clusters over the Indo-Pacific region and often causes severe weather and climate events worldwide. The physics and…
Since weather forecasts are fundamentally uncertain, reliable decision making requires information on the likelihoods of future weather scenarios. We explore the sensitivity of machine learning weather prediction (MLWP) using the 24h Pangu…
High Mountain Asia (HMA) holds the highest concentration of frozen water outside the polar regions, serving as a crucial water source for more than 1.9 billion people. Precipitation represents the largest source of uncertainty for future…
Large bias exists in shortwave cloud radiative effect (SWCRE) of general circulation models (GCMs), attributed mainly to the combined effect of cloud fraction and water contents, whose representations in models remain challenging. Here we…
Atmospheric layer structure is a primary factor affecting the precision of single-point satellite positioning. The assumption of electromagnetic wave rectilinear propagation hinders the accurate implementation of ionospheric and…
Hurricanes rank among the most destructive natural hazards. They are complex phenomena that can cause both direct damage along their path and indirect impacts due to heavy rainfall and strong winds, with effects varying according to…
The mild-slope equation and its various modifications aim to model, with varying degrees of success, linear water wave propagation over sloping or undulating seabed topography. However, despite multiple modifications and attempted…
Both Numerical Weather Prediction (NWP) models and Large-Eddy Simulation (LES) models are used to simulate convective systems, such as squall lines, but with different purposes. NWP models aim for the most accurate weather forecasts,…
Air pollution remains a leading global health threat, with fine particulate matter (PM2.5) contributing to millions of premature deaths annually. Chemical transport models (CTMs) are essential tools for evaluating how emission controls…
Precipitation remains one of the most challenging climate variables to observe and predict accurately. Existing datasets face intricate trade-offs: gauge observations are relatively trustworthy but sparse, satellites provide global coverage…
Relativistic Runaway Electron Avalanches (RREA) are central to understanding a spectrum of high-energy atmospheric phenomena, including Terrestrial Gamma-ray Flashes (TGFs), Thunderstorm Ground Enhancements (TGEs), and gamma-ray glows.…
High-resolution multispectral satellite imagery was utilized to quantify shoreline recession at eleven beaches around Lake Michigan during a record-setting water level increase between 2013 and 2020. Shoreline changes during this period…
The spatial and temporal distribution of precipitation has a significant impact on human lives by determining freshwater resources and agricultural yield, but also rainfall-driven hazards like flooding or landslides. While the ERA5…
Accurate acquisition of high-resolution surface meteorological conditions is critical for forecasting and simulating meteorological variables. Directly applying spatial interpolation methods to derive meteorological values at specific…
Humid air is lighter than dry air at the same temperature and pressure because the molecular weight of water vapor is less than that of dry air. This effect is known as vapor buoyancy (VB). In this work we use experiments in an idealized…
Soil moisture-precipitation coupling (SMPC) plays a critical role in Earth's water and energy cycles but remains difficult to quantify due to synoptic-scale variability and the complex interplay of land-atmosphere processes. Here, we apply…
Coastal regions in North America face major threats from storm surges caused by hurricanes and nor'easters. Traditional numerical models, while accurate, are computationally expensive, limiting their practicality for real-time predictions.…
Extratropical storms shape midlatitude weather and vary due to the slowly evolving climate and the rapid changes in synoptic conditions. While the influence of each factor has been studied extensively, their relative importance remains…
Simulating radiative transfer in the atmosphere with Monte Carlo ray tracing provides realistic surface irradiance in cloud-resolving models. However, Monte Carlo methods are computationally expensive because large sampling budgets are…
We assess the impact of a multi-scale loss formulation for training probabilistic machine-learned weather forecasting models. The multi-scale loss is tested in AIFS-CRPS, a machine-learned weather forecasting model developed at the European…