Results 71 to 80 of about 617,695 (198)

Regressions with Berkson errors in covariates - a nonparametric approach [PDF]

The Annals of Statistics, 2013
This paper establishes that so-called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with Berkson-type measurement error in the regressors. An estimator is proposed and proven to be consistent. Its practical performance and feasibility are investigated via Monte Carlo simulations as well as
openaire   +7 more sources

A New Data Assimilation Scheme: The Space-Expanded Ensemble Localization Kalman Filter

Advances in Meteorology, 2013
This study considers a new hybrid three-dimensional variational (3D-Var) and ensemble Kalman filter (EnKF) data assimilation (DA) method in a non-perfect-model framework, named space-expanded ensemble localization Kalman filter (SELKF).
Hongze Leng, Junqiang Song, Fengshun Lu, Xiaoqun Cao   +3 more
doaj   +1 more source

Comparing Background Error Covariance in WRF for Micro-Meteorological Simulations [PDF]

E3S Web of Conferences
Accurately representing background error covariances is crucial for data assimilation in numerical weather prediction models. This study compared the performance of the National Meteorological Center (NMC) and RandomCV methods for estimating background ...
Shu Hailong, Wang Yue, Guo Huichuang, Li Chaoqun, Song Zhen   +4 more
doaj   +1 more source

On Analysis Error Covariances in Variational Data Assimilation [PDF]

SIAM Journal on Scientific Computing, 2008
The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition function (analysis). The equation for the analysis error is derived through the errors of the input data (background and observation errors).
Gejadze, Igor, Le Dimet, François-Xavier, Shutyaev, Victor P.   +2 more
openaire   +2 more sources

Evaluating Machine Learning Weather Models for Data Assimilation: Fundamental Limitations in Tangent Linear and Adjoint Properties

Geophysical Research Letters
Machine learning (ML) weather models like GraphCast and NeuralGCM show forecasting promise but face fundamental limitations for data assimilation (DA) integration.
Xiaoxu Tian, Daniel Holdaway, Daryl Kleist   +2 more
doaj   +1 more source

Efficient Distributed Estimation of Inverse Covariance Matrices

, 2016
In distributed systems, communication is a major concern due to issues such as its vulnerability or efficiency. In this paper, we are interested in estimating sparse inverse covariance matrices when samples are distributed into different machines.
Arroyo, Jesús, Hou, Elizabeth
core   +1 more source

Smoothing Dynamic Systems with State-Dependent Covariance Matrices

, 2014
Kalman filtering and smoothing algorithms are used in many areas, including tracking and navigation, medical applications, and financial trend filtering.
Aravkin, Aleksandr Y., Burke, James V.
core   +1 more source

Block-Circulant Approximation of the Precision Matrix for Assimilating SWOT Altimetry Data

Remote Sensing
The recently deployed Surface Water and Ocean Topography (SWOT) mission for the first time has observed the ocean surface at a spatial resolution of 1 km, thus giving an opportunity to directly monitor submesoscale sea surface height (SSH) variations ...
Max Yaremchuk, Christopher Beattie, Gleb Panteleev, Joseph D’Addezio   +3 more
doaj   +1 more source

Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance [PDF]

Atmospheric Measurement Techniques, 2015
Ionospheric data assimilation is a powerful approach to reconstruct the 3-D distribution of the ionospheric electron density from various types of observations.
C. Y. Lin, T. Matsuo, J. Y. Liu, C. H. Lin, H. F. Tsai, E. A. Araujo-Pradere   +5 more
doaj   +1 more source

Estimation of Kalman filter model parameters from an ensemble of tests [PDF]

, 1980
A methodology for estimating initial mean and covariance parameters in a Kalman filter model from an ensemble of nonidentical tests is presented. In addition, the problem of estimating time constants and process noise levels is addressed.
Gibbs, B. P., Haley, D. R., Levine, W., Porter, D. W., Vahlberg, C. J.   +4 more
core   +1 more source