Improving the accuracy of forecast models for physical systems such as the atmosphere is a crucial ongoing effort. The primary focus of recent research on these highly nonlinear systems has been errors in state estimation, but as that error has been successfully diminished, the role of model error in forecast uncertainty has duly increased. The present study is an investigation of an empirical ...

It is essential to consider the acceptable threshold in the assessment of a hydrological model because of the scarcity of research in the hydrology community and errors do not necessarily cause risk. Two forecast errors, including rainfall forecast error and peak flood forecast error, have been studied based on the reliability theory. The first order second moment (FOSM) and bound methods are u...

Wind power has been developed rapidly as a clean energy in recent years. The forecast error of wind power, however, makes it difficult to use wind power effectively. In some former statistical models, the forecast error was usually assumed to be a Gaussian distribution, which had proven to be unreliable after a statistical analysis. In this paper, a more suitable probability density function fo...

Most existing ensemble forecast verification statistics are influenced by the quality of not only the ensemble generation scheme, but also the forecast model and the analysis scheme. In this study, a new tool called perturbation versus error correlation analysis (PECA) is introduced that lessens the influence of the initial errors that affect the quality of the analysis. PECA evaluates the ense...

The development of the adjoint of the forecast model and of the adjoint of the data assimilation system (adjointDAS) make feasible the evaluation of the derivative-based forecast sensitivity to DAS input parameters in numerical weather prediction (NWP). The adjoint estimation of the forecast sensitivity to the observation error covariance in the DAS is considered as a practical approach to prov...

Executive Summary 5 A method for solar forecasting using cloud motion vectors (CMV) from satellite imagery with the 6 ability to characterize forecast uncertainty has been developed. On average, the root mean square error 7 (RMSE) for CMV forecast increases with increasing forecast horizon and becomes larger than the North 8 American Model (NAM, a numerical weather prediction model) forecast er...

Error measures for the evaluation of forecasts are usually based on the size of the forecast errors. Common measures are e.g. the Mean Squared Error (MSE), the Mean Absolute Deviation (MAD) or the Mean Absolute Percentage Error (MAPE). Alternative measures for the comparison of forecasts are turning points or hits-and-misses, where an indicator loss function is used to decide, if a forecast is ...

While the conditional mean is known to provide the minimum mean square error (MSE) forecast – and hence is optimal under a squared-error loss function – it must often in practice be replaced by a noisy estimate when model parameters are estimated over a small sample. Here two results are obtained, both of which motivate the use of forecasts biased toward zero (shrinkage forecasts) in such setti...