Search arXivSearch

arXiv · 1910.00351

New equations for sea water density calculation based on measurements of the sound speed

Abstract

Density is one of the most important properties of seawater and is used in various marine research and technology. Traditionally, in the practice of oceanographic research, it is customary to con-sider density as a dependent parameter, which is a function of several other parameters taken as independent. Usually the following three parameters are used as the independent parameters: temperature, hydrostatic pressure and salinity. The issues of temperature and hydrostatic pressure measuring in situ are technologically well developed, while in the salinity measuring there are still unsolved problems. This is due to the fact that salinity is such a property that it is simply impossi-ble to determine directly in situ. To eliminate the problems associated with measurements of sa-linity, the authors developed the special new kind equation. That equation of the new kind ex-press the density of sea water through independent and in situ measured parameters: temperature, hydrostatic pressure and sound speed. The novelty of this approach is that using of the sound speed as the independent parameter makes it possible to exclude measurements of salinity. The authors developed two such new equations for the different cases of using. The first new equa-tion is intended for use in technical applications and reproduces the sea water density in a wide range of the aquatic environment parameters with a root mean square deviation of 0.062 kg/m3. The second more precise new equation is intended for scientific applications and reproduces the sea water density in a narrower oceanographic range of parameters with a root mean square devi-ation of 0.0018 kg/m3.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Aleksandr N Grekov, Nikolay A Grekov, Evgeniy Sychov. 2019-10-01. New equations for sea water density calculation based on measurements of the sound speed. https://doi.org/10.17587/mau.20.143-151

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