The SOPRANE operational forecasting system is based on a quasi-geostrophic (QG) model of the Northeast Atlantic. The assimilation scheme used to constrain the model consists of a Reduced Order Optimal Interpolation (ROOI) using an Extended Kalman Filter (EKF) formulation. Corrections to the model fields at the surface are consistent with satellite along track data and a priori statistical infor...

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. Bureau of Labor Statistics. This paper demonstrates that, in the context of U.S. housing data, rents and ex ante user costs diverge markedly—in both growth rates and levels—for extended periods of time, a seeming failure of arbitrage and a puzzle from the perspective of st...

Demand planners in supply chain organizations are accustomed to using the Mean Absolute Percentage Error (MAPE) as their best answer to measuring forecast accuracy. It is so ubiquitous that it is hardly questioned. I do not even find a consensus on the definition of the underlying forecast error among supply chain practitioners participating in the demand forecasting workshops I conduct worldwide.

The equations of the forecast sensitivity to observations and to the background estimate in a fourdimensional variational data assimilation system (4D-Var DAS) are derived from the first-order optimality condition in unconstrained minimization. Estimation of the impact of uncertainties in the specification of the error statistics is considered by evaluating the sensitivity to the observation an...

Motivated by the promising results of global-scale ensemble forecasting, a number of groups have attempted mesoscale, short-range ensemble forecasting (SREF), focusing mainly over the eastern half of the United States. To evaluate the performance of mesoscale SREF over the Pacific Northwest and to test the value of using different initial analyses as a means of ensemble forecast generation, a f...

In view of the limitations of single forecast model, forecasted results of different models will have some differences, in order to improve the forecast precision and the forecast results reliability, on the basis of determining the single forecast model for total power of China’s agricultural machinery, the nonlinear combined forecast model for total power of agricultural machinery was establi...

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, P . In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of ...

– The growth of the Internet, wireless mobility, and economic variability all contribute to the difficulty of forecasting the demand for which an optical transport network should be planned. It is therefore of interest to know how various architectural options inherently withstand changes in demand pattern relative to a forecast. In this study, we focus on the span restoration (SR) and shared b...

one of the basic assumptions of management accounting illustrate that costschanges has a significance relationship with increasing and decreasing in the levelof activity, recently after being raised of sticky costs issue by anderson and hiscolleagues this assumption was discussed. it means increases in costs by increasingthe more activity level of reduction in costs is exchange for the reductio...

We propose an ultra-short-term dynamic interval predictor (DIP) of solar irradiance. Our DIP relies on experimentally observed correlations between the derivative of the solar irradiance and the forecast error in the next time-step. The main originalities of this DIP are (i) its independence from the method used for the point forecast of solar irradiance, (ii) its independence from the error di...