Revised error propagation of 40Ar/39Ar data, including covariances

Modeling Retrieval Error Covariances for Global Data Assimilation

A posteriori error covariances in variational data assimilation

The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find some unknown parameters of the model. The equation for the error of the optimal solution is derived through the statistical errors of the input data (background, observation, and model errors). A numerical algorithm is developed to construct an a posteriori covariance...

On Analysis Error Covariances in Variational Data Assimilation

Abstract. 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). This equation is used to show that in a nonlinear case the analysis error covariance operator ...

Mining Data by Query-Based Error-Propagation

Neural networks have advantages of the high tolerance to noisy data as well as the ability to classify patterns having not been trained. While being applied in data mining, the time required to induce models from large data sets are one of the most important considerations. In this paper, we introduce a query-based learning scheme to improve neural networks' performance in data mining. Results ...

Including Covariances in Calibration to Obtain Better Measurement Uncertainty Estimates

Decisions are often based on the results of quantitative analysis. To gain confidence in these results, some indication of their quality is needed. Measurement uncertainty, as proposed by the International Organization for Standardization (ISO), is a way to express this quality. Most measurement techniques compare references and therefore need to be calibrated. Usually least squares methods ign...