Titlebook: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV); Seon Ki Park,Liang Xu Book 2022 The Editor(s) (if applic

推崇

GNSS-RO Sounding in the Troposphere and Stratosphere, has become a standard practice of many numerical weather prediction (NWP) centers. The introduction of this observation has seen broad positive impact on analyses and forecasts. On longer timescales the impact of the introduction of this data type in re-analyses can be clearly seen. Further, the ob

Ischemia

航海太平洋

陪審團(tuán)

Sensitivity Analysis in Ocean Acoustic Propagation,opagation model. The sensitivity analysis is extended to temperature and salinity, by deriving the adjoint of the sound polynomial function of temperature and salinity. Numerical experiments using a range dependent model are carried out in a deep and complex environment at the frequency of 300?Hz. I

緯度

Difficulty with Sea Surface Height Assimilation When Relying on an Unrepresentative Climatology,ct, with the construction of synthetic temperature (T) and salinity (S) profiles based on observationally-derived climatological covariances between SSHA, T, and S. The other approach is direct via a four-dimensional variational system, but it relies on a mean SSH (here, one constrained by observati

障礙

Theoretical and Practical Aspects of Strongly Coupled Aerosol-Atmosphere Data Assimilation,deling systems. Among various coupling options, strongly coupled data assimilation is the most efficient option for processing the information from observations. At the same time, coupled aerosol-atmosphere modeling is steadily gaining more interest due to its relevance to air quality, aviation, sol

細(xì)微差別

,Improving Near-Surface Weather Forecasts with Strongly Coupled Land–Atmosphere Data Assimilation,eather prediction (NWP) due to difficulties in surface data assimilation and uncertainties in representing complicated land–atmosphere interactions in numerical models. This chapter summarizes recent developments from the author’s research team to understand and develop effective data assimilation m

談判

https://doi.org/10.1007/978-3-030-77722-7Hybrid Data Assimilation; Kalman Filter; Monte Carlo Method; Artificial Intelligence Application; Wiener

–scent

978-3-030-77724-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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