We present the application of interactive threedimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the...

We present a new method of reducing the error in predicted wind speed, thus enabling better management of wind energy facilities. A numerical weather prediction model, COSMO, was used to produce 48 h forecast data every day in 2008 at horizontal resolutions of 10 and 3 km. A new adaptive statistical method was applied to the model output to improve the forecast skill. The method applied correct...

We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space. Several variants of the ensemble Kalman...

Forecast ensembles typically show a spread-skill relationship, but they are also often underdispersive, and therefore uncalibrated. Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites. This paper introduces the Spatial BMA technique, which combines BMA an...

Density forecasts for weather variables are useful for the many industries exposed to weather risk. Weather ensemble predictions are generated from atmospheric models and consist of multiple future scenarios for a weather variable. The distribution of the scenarios can be used as a density forecast, which is needed for pricing weather derivatives. We consider one to 10 dayahead density forecast...

Short term forecasts of meteorological parameters (including precipitation) over the oceans is often difficult because of the lack of observations used to initialize the forecast models in these regions. While sea surface temperature (SST) fields derived from satellite and in situ data currently provided to the models provide some large scale information on surface forcing, the small scale grad...

In the paper methods aimed at handling high-dimensional weather forecasts data used to predict the concentrations of PM10, PM2.5, SO2, NO, CO and O3 are being proposed. The procedure employed to predict pollution normally requires historical data samples for a large number of points in time – particularly weather forecast data, actual weather data and pollution data. Likewise, it typically invo...

A major challenge for operational numerical weather prediction over the Antarctic and Southern Ocean is the lack of traditional meteorological observations. This increases uncertainty in the model initial condition and results in inferior forecast skills as compared with numerical weather prediction in mid-latitudes over the Northern Hemisphere. With the lack of traditional observations over th...

Weather models forecast the future state of the atmosphere from an estimate of the current state of the atmosphere. However, the atmosphere is a chaotic physical system. That is, small differences illithe current state of the atmosphere lead to dramatic differences in weather events later on. Even if weather mgdels were perfect, small errors in the estimate for the current state of the atmosphe...

1. Introduction Many of the recent conflicts where the United States (US) military forces have been deployed are regions that contain complex terrain (i.e. Korea, Kosovo, Afghanistan, and northern Iraq). Accurate weather forecasts are critical to the success of operations in these regions and are typically supplied by numerical weather prediction (NWP) models like the US Navy NOGAPS, CAOMPS, an...