Numerical Weather Prediction Models (numerical + weather_prediction_models)

Distribution by Scientific Domains

Selected Abstracts

A self-consistent scattering model for cirrus.

I: The solar region
Abstract In this paper a self-consistent scattering model for cirrus is presented. The model consists of an ensemble of ice crystals where the smallest ice crystal is represented by a single hexagonal ice column. As the overall ice crystal size increases, the ice crystals become progressively more complex by arbitrarily attaching other hexagonal elements until a chain-like ice crystal is formed, this representing the largest ice crystal in the ensemble. The ensemble consists of six ice crystal members whose aspect ratios (ratios of the major-to-minor axes of the circumscribed ellipse) are allowed to vary between unity and 1.84 for the smallest and largest ice crystal, respectively. The ensemble model's prediction of parameters fundamental to solar radiative transfer through cirrus such as ice water content and the volume extinction coefficient is tested using in situ based data obtained from the midlatitudes and Tropics. It is found that the ensemble model is able to generally predict the ice water content and extinction measurements within a factor of two. Moreover, the ensemble model's prediction of cirrus spherical albedo and polarized reflection are tested against a space-based instrument using one day of global measurements. The space-based instrument is able to sample the scattering phase function between the scattering angles of approximately 60 and 180 , and a total of 37 581 satellite pixels were used in the present analysis covering latitude bands between 43.75S and 76.58N. It is found that the ensemble model phase function is well able to minimize significantly differences between satellite-based measurements of spherical albedo and the ensemble model's prediction of spherical albedo. The satellite-based measurements of polarized reflection are found to be reasonably described by more simple members of the ensemble. The ensemble model presented in this paper should find wide applicability to the remote sensing of cirrus as well as more fundamental solar radiative transfer calculations through cirrus, and improved solar optical properties for climate and Numerical Weather Prediction models. Copyright 2007 Royal Meteorological Society [source]

Modeling and predicting complex space,time structures and patterns of coastal wind fields

Montserrat Fuentes
Abstract A statistical technique is developed for wind field mapping that can be used to improve either the assimilation of surface wind observations into a model initial field or the accuracy of post-processing algorithms run on meteorological model output. The observed wind field at any particular location is treated as a function of the true (but unknown) wind and measurement error. The wind field from numerical weather prediction models is treated as a function of a linear and multiplicative bias and a term which represents random deviations with respect to the true wind process. A Bayesian approach is taken to provide information about the true underlying wind field, which is modeled as a stochastic process with a non-stationary and non-separable covariance. The method is applied to forecast wind fields from a widely used mesoscale numerical weather prediction (NWP) model (MM5). The statistical model tests are carried out for the wind speed over the Chesapeake Bay and the surrounding region for 21 July 2002. Coastal wind observations that have not been used in the MM5 initial conditions or forecasts are used in conjunction with the MM5 forecast wind field (valid at the same time that the observations were available) in a post-processing technique that combined these two sources of information to predict the true wind field. Based on the mean square error, this procedure provides a substantial correction to the MM5 wind field forecast over the Chesapeake Bay region. Copyright 2005 John Wiley & Sons, Ltd. [source]

Severe Deep Moist Convective Storms: Forecasting and Mitigation

David L. Arnold
Small-scale (2,20 km) circulations, termed ,severe deep moist convective storms', account for a disproportionate share of the world's insured weather-related losses. Spatial frequency maximums of severe convective events occur in South Africa, India, Mexico, the Caucasus, and Great Plains/Prairies region of North America, where the maximum tornado frequency occurs east of the Rocky Mountains. Interest in forecasting severe deep moist convective systems, especially those that produce tornadoes, dates to 1884 when tornado alerts were first provided in the central United States. Modern thunderstorm and tornado forecasting relies on technology and theory, but in the post-World War II era interest in forecasting has also been driven by public pressure. The forecasting process begins with a diagnostic analysis, in which the forecaster considers the potential of the atmospheric environment to produce severe convective storms (which requires knowledge of the evolving kinematic and thermodynamic fields, and the character of the land surface over which the storms will pass), and the likely character of the storms that may develop. Improvements in forecasting will likely depend on technological advancements, such as the development of phased-array radar systems and finer resolution numerical weather prediction models. Once initiated, the evolution of deep convective storms is monitored by satellite and radar. Mitigation of the hazards posed by severe deep moist convective storms is a three-step process, involving preparedness, response, and recovery. Preparedness implies that risks have been identified and organizations and individuals are familiar with a response plan. Response necessitates that potential events are identified before they occur and the developing threat is communicated to the public. Recovery is a function of the awareness of local, regional, and even national governments to the character and magnitude of potential events in specific locations, and whether or not long-term operational plans are in place at the time of disasters. [source]

Estimating observation impact without adjoint model in an ensemble Kalman filter

Junjie Liu
Abstract We propose an ensemble sensitivity method to calculate observation impacts similar to Langland and Baker (2004) but without the need for an adjoint model, which is not always available for numerical weather prediction models. The formulation is tested on the Lorenz 40-variable model, and the results show that the observation impact estimated from the ensemble sensitivity method is similar to that from the adjoint method. Like the adjoint method, the ensemble sensitivity method is able to detect observations that have large random errors or biases. This sensitivity could be routinely calculated in an ensemble Kalman filter, thus providing a powerful tool to monitor the quality of observations and give quantitative estimations of observation impact on the forecasts. Copyright 2008 Royal Meteorological Society [source]

Aircraft type-specific errors in AMDAR weather reports from commercial aircraft

C. Dre
Abstract AMDAR (Aircraft Meteorological DAta Relay) automated weather reports from commercial aircraft provide an increasing amount of input data for numerical weather prediction models. Previous studies have investigated the quality of AMDAR data. Few of these studies, however, have revealed indications of systematic errors dependent upon the aircraft type. Since different airlines use different algorithms to generate AMDAR reports, it has remained unclear whether a dependency on the aircraft type is caused by physical properties of the aircraft or by different data processing algorithms. In the present study, a special AMDAR dataset was used to investigate the physical type-dependent errors of AMDAR reports. This dataset consists of AMDAR measurements by Lufthansa aircraft performing over 300 landings overall at Frankfurt Rhein/Main (EDDF/FRA) on 22 days in 2004. All of this data has been processed by the same software, implying that influences from different processing algorithms should not be expected. From the comparison of single descents to hourly averaged vertical profiles, it is shown that temperature measurements by different aircraft types can have systematic differences of up to 1 K. In contrast, random temperature errors of most types are estimated to be less than 0.3 K. It is demonstrated that systematic deviations in AMDAR wind measurements can be regarded as an error vector, which is fixed to the aircraft reference system. The largest systematic deviations in wind measurements from different aircraft types (more than 0.5 m s,1) were found to exist in the longitudinal direction (i.e. parallel to the flight direction). Copyright 2008 Royal Meteorological Society [source]

Representing energy drain in numerical weather prediction models induced by boundary layer sub-grid scale processes

C. G. Collier
Abstract The representation of sub-grid scale boundary layer processes is central to understanding the errors in numerical weather prediction model forecasts. Of particular importance are statistics associated with convective turbulence, notably the temporal and spatial variations of kinetic energy dissipation rate. In this paper we outline how a 1.5-micron Doppler lidar system may be used in this context, and propose an operational network of such systems for use in numerical forecasting. Copyright 2009 Royal Meteorological Society [source]


Edward Cripps
Summary This paper develops a space-time statistical model for local forecasting of surface-level wind fields in a coastal region with complex topography. The statistical model makes use of output from deterministic numerical weather prediction models which are able to produce forecasts of surface wind fields on a spatial grid. When predicting surface winds at observing stations, errors can arise due to sub-grid scale processes not adequately captured by the numerical weather prediction model, and the statistical model attempts to correct for these influences. In particular, it uses information from observing stations within the study region as well as topographic information to account for local bias. Bayesian methods for inference are used in the model, with computations carried out using Markov chain Monte Carlo algorithms. Empirical performance of the model is described, illustrating that a structured Bayesian approach to complicated space-time models of the type considered in this paper can be readily implemented and can lead to improvements in forecasting over traditional methods. [source]