Atmospheric and Oceanic Physics Papers of the Week

The land carbon sink explains most of the variability of the atmospheric CO2 growth rate, but the Global Carbon Budget estimates it with dynamic global vegetation models (DGVMs) run once a year. Emulators could provide it sooner if they reproduce each model outside its training period.

Here we present NeuralPOM, a hybrid ocean forecast model that couples differentiable explicit mode-split and semi-implicit free-surface implementations of the Princeton Ocean Model (POM) to a conservation-constrained neural corrector.

This study explores the value of blending the ensemble version of the Artificial Intelligence Forecasting System (AIFS-CRPS) with the Met Office's convection permitting physics-based ensemble, MOGREPS-UK.

We present RadarFill, an observation-constrained conditional diffusion framework for gap filling in three-dimensional radar reflectivity mosaics.

We propose a multidimensional framework comprising overall pointwise accuracy, physical consistency, and distributional consistency.

We introduce SATFRONT, a machine learning framework for detecting cold, warm, and occluded fronts over Europe from geostationary satellite observations, with reanalysis data as an optional additional input.

We investigate machine learning (ML) methods for detecting weather fronts and quantify the contribution of reanalysis data to performance.

We propose, by using 70 years (1954-2023) of ERA5 reanalysis, that the trends in extremes are associated with the shifts of Rossby wave regimes, via the westerly-induced changes in Rossby wave group velocity (Cgx), which are two to three times larger than those in Cpx.

Using high-frequency sonic anemometer data from site-testing campaigns at the Giant Magellan Telescope (GMT) and Thirty Meter Telescope (TMT) sites in Chile, we apply detrended fluctuation analysis (DFA)---a technique from fractal time-series analysis---to identify the inertial sub-range and evaluate statistical isotropy in the ABL, comparing our approach with the Fourier-based algorithm for robust identification of the inertial subrange (ARIIS).

This work presents a lightweight dataset of tropical cyclone (TC)-following meteorological variables to facilitate the development and application of machine learning (ML) methods in TC research.

This paper presents an application of machine learning to the problem of the computational expense of atmospheric radiative transfer calculations from physics-based models.

Because w is extremely difficult to measure directly and is normally obtained only diagnostically from the continuity equation, predicting it from the horizontal velocity field is a problem of both practical and physical interest. We address this problem with two data-driven methods, a physically-motivated linear baseline and a convolutional neural network conditioned on depth through Feature-wise Linear Modulation (FiLM).

This note summarises established ensemble-size adjustment methods and characterises the behaviour of $r_{\infty}$, a correlation estimator that is constructed from an unbiased and ensemble-size-independent estimate of the signal variance.

Two independent methods of NLC altitude determination based on the same photometric data are considered and compared.

In this study, we present a laboratory investigation on six natural mineral dust samples with contrasting compositions and originated from major dust source regions worldwide, including Arizona, Atacama, China, Niger, Saudi Arabia, and Tunisia.

To investigate the interaction between convection and polar vortices, this study develops a polar $\gamma$-plane shallow-water model coupled with a quasi-stochastic convective parameterization.

Using warm-rain collision-coalescence as a test case, we evaluate agents' ability to synthesize scientific literature, reimplement published bulk parameterizations, and propose new functional forms as differentiable JAX code.