Related papers: Partial recovery of meter-scale surface weather
Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are…
We present a probabilistic data-driven weather model capable of providing an ensemble of high spatial resolution realizations of 87 variables at arbitrary forecast length and ensemble size. The model uses a stretched grid, dedicating 2.5 km…
Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly resolve processes larger than 100 km and approximate any…
The increasing frequency of extreme weather events due to global climate change urges accurate weather prediction. Recently, great advances have been made by the \textbf{end-to-end methods}, thanks to deep learning techniques, but they face…
With the recent discoveries of hundreds of extrasolar planets, the search for planets like Earth and life in the universe, is quickly gaining momentum. In the future, large space observatories could directly detect the light scattered from…
Surface solar irradiance varies on scales as small as seconds or meters due to scattering and absorption by the atmosphere. Clouds are the main driver of this variability, but moisture structures in the atmospheric boundary layer and…
Despite the importance and vulnerability of small island nations and their ecosystems, they frequently have insufficient observations to provide baseline or long-term perspectives on climate variability and change, and global model…
Accurate reconstruction of atmospheric wind fields is essential for applications such as weather forecasting, hazard prediction, and wind energy assessment, yet conventional instruments leave spatio-temporal gaps within the lower…
Understanding subsurface ocean dynamics is essential for quantifying oceanic heat and mass transport, but direct observations at depth remain sparse due to logistical and technological constraints. In contrast, satellite missions provide…
Multi-model ensembles provide a pragmatic approach to the representation of model uncertainty in climate prediction. However, such representations are inherently ad hoc, and, as shown, probability distributions of climate variables based on…
Precipitation data collected at sub-hourly resolution represents specific challenges for missing data recovery by being largely stochastic in nature and highly unbalanced in the duration of rain vs non-rain. Here we present a two-step…
Hurricane track forecasting remains a significant challenge due to the complex interactions between the atmosphere, land, and ocean. Although AI-based numerical weather prediction models, such as Google Graphcast operation, have…
The vertical profile of atmospheric depth is an important element in extensive air shower studies. The depth of shower maximum is one of the most important characteristics of the shower. In the fluorescence technique of shower detection,…
The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data…
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…
Land surface temperature (LST) is vital for land-atmosphere interactions and climate processes. Accurate LST retrieval remains challenging under heterogeneous land cover and extreme atmospheric conditions. Traditional split window (SW)…
To forecast wind power generation in the scale of years to decades, outputs from climate models are often used. However, one major limitation of the data projected by these models is their coarse temporal resolution - usually not finer than…
Modern weather and climate models share a common heritage, and often even components, however they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should…
Earth's energy imbalance at the top of the atmosphere is a key climate system metric, but its natural variability is poorly constrained by the short observational record and large uncertainty in coupled climate models. While existing ocean…
To model the structure and dynamics of the heliosphere well enough for high-quality forecasting, it is essential to accurately estimate the global solar magnetic field used as inner boundary condition in solar wind models. However, our…