Search arXivSearch

arXiv · 1606.05793

Canonical transfer and multiscale energetics for primitive and quasi-geostrophic atmospheres

Abstract

The past years have seen the success of a novel multiscale energetic formalism in a variety of ocean and engineering fluid applications. In a self-contained way, this study introduces it to the atmospheric dynamical diagnostics, with important theoretical updates. Multiscale energy equations are derived using a new analysis apparatus, namely, multiscale window transform, with respect to both the primitive equation and quasi-geostrophic models. A reconstruction of the "atomic" energy fluxes on the multiple scale windows allows for a natural and unique separation of the in-scale transports and cross-scale transfers from the intertwined nonlinear processes. The resulting energy transfers bear a Lie bracket form, reminiscent of the Poisson bracket in Hamiltonian mechanics, we hence would call them "canonical". A canonical transfer process is a mere redistribution of energy among scale windows, without generating or destroying energy as a whole. By classification, a multiscale energetic cycle comprises of available potential energy (APE) transport, kinetic energy (KE) transport, pressure work, buoyancy conversion, work done by external forcing and friction, and the cross-scale canonical transfers of APE and KE which correspond respectively to the baroclinic and barotropic instabilities, among others, in geophysical fluid dynamics. A buoyancy conversion takes place in an individual window only, bridging the two types of energy namely KE and APE, it does not involve any processes among different scale windows, and is hence basically not related to instabilities. This formalism is exemplified with a preliminary application to the Madden-Julian Oscillation (MJO) study.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

X. San Liang. 2016-06-18. Canonical transfer and multiscale energetics for primitive and quasi-geostrophic atmospheres. https://doi.org/10.1175/jas-d-16-0131.1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting

Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-driven approaches often lack physical interpretability. We present FAST-ML, a hybrid framework that bridges data-driven efficiency with physical constraints. A physically informed dual-stream neural parameterization ingests 3D ERA5 fields to diagnose ventilation controls---environmental wind shear and mid-level entropy deficit. By optimizing these parameters end-to-end through a differentiable FAST intensity model, this architecture establishes a robust new paradigm for observation-driven parameter optimization, ensuring storm evolution remains strictly governed by thermodynamic principles. By better capturing the storm's continuous intensity evolution, FAST-ML improves upon its physical baseline, reducing ensemble CRPS across forecast lead times, with a reduction of approximately 31% at 60 h and nearly halving the RI false alarm ratio without sacrificing detection skill. In a 100-member ensemble configuration, FAST-ML produces intensity forecasts comparable to FNV3 for selected storms under the evaluated input configurations. Furthermore, zero-shot tests on selected Eastern Pacific storms provide encouraging evidence of cross-basin transferability. FAST-ML provides a modular intensity forecasting framework that can be coupled with externally supplied storm tracks and environmental fields. It demonstrates that observation-driven parameter learning within physically constrained dynamics simultaneously enhances accuracy, interpretability, and computational efficiency.

physics.ao-ph

West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation

We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW3E) 2-km regional reanalysis to produce autoregressive 6-hourly forecasts of precipitation and integrated vapor transport (IVT). Forecasts are evaluated over winters 2020-2023 using gridded precipitation observations, rain gauges, and AR Reconnaissance dropsondes and are benchmarked against coarser-resolution AI forecasts and regional and global numerical weather prediction (NWP) systems. West-WRF AI 2-km reproduces observed precipitation-intensity distributions, retains fine-scale spectral variability, and produces sharper narrow coastal precipitation bands and localized, terrain-sensitive extremes. Its broader-scale performance remains comparable to coarser-resolution configurations while preserving large-scale skill despite higher resolution. Dropsonde verification shows lower errors and improved categorical skill at the most extreme IVT threshold. Overall, West-WRF AI 2-km provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.

physics.ao-ph

Hydrological Constraints on Temperature Sensitivities of Precipitation Frequency and Intensity

To improve understanding of how precipitation frequency and intensity change with warming, we combine the coupled land--atmosphere water balance with a stochastic hydrological model that retains explicit dependence on precipitation frequency and event depth. We find a linear relationship between co-variations of rainfall frequency and intensity, where the slope is a hydrological sensitivity determined by the dryness and storage indices and the intercept is a forcing term associated with changes in moisture convergence, potential evapotranspiration, and effective water-storage capacity. While a given relative change in either rainfall frequency or intensity produces an identical relative change in the mean rate, rainfall intensity additionally influences the runoff coefficient by altering terrestrial water storage relative to rainfall depth. This means frequency and intensity have different impacts on the water cycle, allowing the frequency--intensity slope to depart from -1. Analysis of long-term MOPEX records shows that the forcing term is dominated by moisture convergence and that the frequency--intensity relation becomes steeper from wet to dry regimes, consistent with the slope predicted by the theory. The framework provides a physical interpretation of the observed negative covariation between precipitation frequency and intensity and clarifies the roles of hydrological partitioning and atmospheric moisture supply in their responses to warming.

physics.ao-ph