Accurate PM2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM2.5 concentration, this paper proposes a hybrid model based on wavelet transform (WT), variational mode decompos...

The nature is composed of infinite process, and each process is surely deterministic (out of mention for the micro process on the level of quantum physics, which is under the uncertainty principle of Heisenberg, 1927), but affected by uncountable number of factors. What we are trying to do with modeling is to find the most dominating factors on a process and to simplify the process with an unde...

This paper compares alternative time-series models to forecast state tax revenues. Forecast accuracy is compared to a benchmark random walk forecast. Quarterly data for California is used to forecast total tax revenue along with its three largest components, sales, income, and corporate tax revenue. For oneand four-quarter-ahead forecasts from 2004 to 2009, Bayesian vector autoregressions gener...

In this study a new type of ensemble forecast assimilation technique is developed in order to improve the forecast skill in the nonlinear dynamical system. The forecast assimilation is an analysis technique in which a true value contained in each ensemble forecast is accumulated into a single assimilated forecast such as a data assimilation. For the experiments, we used a Lorenz model, and a Ka...

An operational model for the forecast of dust storms in Northern Africa, the Middle East and Southwest Asia has been developed for the United States Air Force Weather Agency (AFWA). The dust forecast model uses the 5 generation Penn State Mesoscale Meteorology Model (MM5) and the University of Colorado CARMA dust transport model. In a unique study, AFWA undertook a 60 study to evaluate the effe...

The relative impact of model quality and ensemble deficiencies, on the performance of ensemble based probabilistic forecasts, is investigated from a set of idealized experiments. Data are generated according to a statistical model, the validation of which is achieved by comparing generated data to ECMWF ensemble forecasts and analyses. The performance of probabilistic forecasts is evaluated thr...

Four model selection methods are applied to the problem of predicting business cycle turning points: equally-weighted forecasts, Bayesian model averaged forecasts, and two models produced by the machine learning algorithm boosting. The model selection algorithms condition on different economic indicators at different forecast horizons. Models produced by BMA and boosting outperform equally-weig...

Very short-period quantitative precipitation forecast (QPF) or nowcast schemes provide deterministic output that fails to convey explicit measures of the uncertainty in the forecast. Presented here is a forecast methodology based upon a Bayesian hierarchical model that produces a QPF product for a 1-h period along with an associated estimated forecast error field. The precipitation forecast qua...

All numerical models (e.g., Numerical Weather Prediction models) have certain parameters within model algorithms which effect forecasts to a different degree, depending on the forecast quantity. The specific values of these model parameters are determined either theoretically using fundamental physics laws but incorporating necessary approximations to reduce computational cost, or empirically u...

It is often documented, based on autocorrelation, variance ratio and power spectrum, that exchange rates approximately follow a martingale process. Because autocorrelation, variance ratio and spectrum check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insigniÞcant. In this pa...