|
|
|
|
|
Yanqiu Gao
The ensemble Kalman filter is often used in parameter estimation, which plays an essential role in reducing model errors. However, filter divergence is often encountered in an estimation process, resulting in the convergence of parameters to the improper...
ver más
|
|
|
|
|
|
|
|
|
|
|
Qianlong Jin, Yu Tian, Weicong Zhan, Qiming Sang, Jiancheng Yu and Xiaohui Wang
Efficiently predicting high-resolution and accurate flow fields through networked autonomous marine vehicles (AMVs) is crucial for diverse applications. Nonetheless, a research gap exists in the seamless integration of data-driven flow modeling, real-tim...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shaokun Deng, Zheqi Shen, Shengli Chen and Renxi Wang
It is widely recognized that the initial ensemble describes the uncertainty of the variables and, thus, affects the performance of ensemble-based assimilation techniques, which is investigated in this paper with experiments using the Community Earth Syst...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ganchang He, Yaning Chen, Gonghuan Fang and Zhi Li
The stationarity test and systematic prediction of hydrometeorological parameters are becoming increasingly important in water resources management. Based on the Ensemble Kalman Filter (EnKF) and wavelet analysis, this study selects precipitation, evapor...
ver más
|
|
|
|
|
|
|
|
|
|
|
Haksu Lee, Haojing Shen and Dong-Jun Seo
This paper presents a comparative geometric analysis of the conditional bias (CB)-informed Kalman filter (KF) with the Kalman filter (KF) in the Euclidean space. The CB-informed KFs considered include the CB-penalized KF (CBPKF) and its ensemble extensio...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yulia Timoshenkova,Sergey Porshnev,Nikolai Safiullin
Pág. 15 - 23
The article describes the method developed by the authors for integration of formal methods of time series (TS) forecasting (autoregressive integrated moving average (ARIMA), singular spectrum analysis, group method of data handling, artificial recurrent...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jean Bergeron, Robert Leconte, Mélanie Trudel and Sepehr Farhoodi
An important step when using some data assimilation methods, such as the ensemble Kalman filter and its variants, is to calibrate its parameters. Also called hyper-parameters, these include the model and observation errors, which have previously been sho...
ver más
|
|
|
|
|
|
|
|
|
|
|
Navid Jadidoleslam, Ricardo Mantilla and Witold F. Krajewski
The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ang Su, Liang Zhang, Xuefeng Zhang, Shaoqing Zhang, Zhao Liu, Caili Liu and Anmin Zhang
Due to the model and sampling errors of the finite ensemble, the background ensemble spread becomes small and the error covariance is underestimated during filtering for data assimilation. Because of the constraint of computational resources, it is diffi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Hong Liu, Qiulong Yang and Kunde Yang
Geoacoustic inversion is an efficient method to study the physical properties and structure of ocean bottom while sequential geoacoustic inversion is a challenging task due to the complexity and non-linearity of the underwater environment. In this paper,...
ver más
|
|
|
|
|
|