In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of sola...

The data assimilation of stratospheric constituents is reviewed. Several data assimilation methods are introduced, with particular consideration to their application to stratospheric constituent measurements. Differences from meteorological data assimilation are outlined. Historically, two approaches have been used to carry out constituent assimilation. One approach has carried constituent assi...

The first geostationary sensors produced dramatic images of storms on short time scales, permitting their evolution to be monitored as never before. Prediction of weather now benefits from numerical weather prediction models, which require temperature and humidity inputs from soundings. Significant weather is often located in cloudy areas where infrared (IR) soundings are degraded or fail, and ...

Weather forecasting in the short range and long range has improved dramatically over the years (Anderson et al. 1999; Barnston et al. 2005; Lupo and Market, 2002, 2003). Weather forecasts in the short range are routinely issued for as long as seven to ten days. Long range forecasts are routinely issued at least one month to more than a year in advance. Short range forecasting is an initial valu...

Probabilistic weather forecasting consists of finding a joint probability distribution for future weather quantities or events. It is typically done by using a numerical weather prediction model, perturbing the inputs to the model in various ways, often depending on data assimilation, and running the model for each perturbed set of inputs. The result is then viewed as an ensemble of forecasts, ...

Wind direction is an angular variable, as opposed to weather quantities such as temperature, quantitative precipitation, or wind speed, which are linear variables. Consequently, traditional model output statistics and ensemble postprocessing methods become ineffective, or do not apply at all. This paper proposes an effective bias correction technique for wind direction forecasts from numerical ...

Uncertainty in the prediction of future weather is commonly assessed through the use of forecast ensembles that employ a numerical weather prediction model in distinct variants. Statistical postprocessing can correct for biases in the numerical model and improves calibration. We propose a Bayesian version of the standard ensemble model output statistics (EMOS) postprocessing method, in which sp...

Development of neural network (NN) emulations for fast calculations of physical processes in numerical climate and weather prediction models depends significantly on our ability to generate a representative training set. Owing to the high dimensionality of the NN input vector which is of the order of several hundreds or more, it is rather difficult to cover the entire domain, especially its "fa...

not available at time of publication. Adam Larios Department of Mathematics UC Irvine [email protected] MS8 Poisson Solvers in Thin Domains Abstract not available at time of publication.not available at time of publication. Alberto Scotti UNC Chapel Hill [email protected] MS9 Quantifying Uncertainty in Wind Power Predictions for Stochastic Unit Commitment Optimization We discuss uncertainty ...