Torben Mikkelsen

Airborne transmission of foot-and-mouth disease virus from Burnside Farm, Heddon-on-the-Wall, Northumberland, during the 2001 epidemic in the United Kingdom.

The results of a detailed assessment of the atmospheric conditions when foot-and-mouth disease (FMD) virus was released from Burnside Farm, Heddon-on-the-Wall, Northumberland at the start of the 2001 epidemic in the UK are consistent with the hypothesis that the disease was spread to seven of the 12 farms in the immediate vicinity of the source by airborne virus, and airborne infection could not be ruled out for three other premises; the remaining two premises were unlikely to have been infected by airborne virus. The distances involved ranged from less than 1 km up to 9 km. One of the farms which was most probably infected by airborne virus from Burnside Farm was Prestwick Hall Farm, which is believed to have been key to the rapid spread of the disease throughout the country. In contrast, the results of detailed atmospheric modelling, based on a combination of clinical evidence from the field and laboratory experiments have shown that by assuming a relationship between the 24-hour average virus concentrations and subsequent infection, threshold infection levels were seldom reached at the farms close to Burnside Farm. However, significant short-term fluctuations in the concentration of virus can occur, and short-lived high concentrations may have increased the probability of infection and explain this discrepancy.

Improving Yaw Alignment Using Spinner Based LIDAR

When extracting energy from the wind using horizontal axis wind turbines, the ability to align the rotor axis with the mean wind direction is crucial. The focus of this study is on exploiting recent advances in LIDAR wind speed measurement technology for accurate yaw alignment of horizontal axis wind turbines operating in turbulent flow. A method for yaw error estimation based on measurements from a spinner based LIDAR is developed. The method is applied to simulated measurements obtained using three different scan patterns. A two factor factorial simulation study is carried out for identification of parameters which are significant for the accuracy of the yaw error estimates. The significant parameters are studied further through simulations and results show that with the applied method the yaw error can be estimated with a precision of a few degrees, even in highly turbulent flows. The developed method is tested on data from an operating turbine, and an average yaw error of teen degrees during a period of two hours is observed. The estimated yaw error is compared to met-mast observations and the average yaw error suggested by the LIDAR method is confirmed by the met-mast observations.

Theoretical and experimental signal-to-noise ratio assessment in new direction sensing continuous-wave Doppler lidar

A new direction sensing continuous-wave Doppler lidar based on an image-reject homodyne receiver has recently been demonstrated at DTU Wind Energy, Technical University of Denmark. In this contribution we analyse the signal-to-noise ratio resulting from two different data processing methods both leading to the direction sensing capability. It is found that using the auto spectrum of the complex signal to determine the wind speed leads to a signal-to-noise ratio equivalent to that of a standard self-heterodyne receiver. Using the imaginary part of the cross spectrum to estimate the Doppler shift has the benefit of a zero-mean background spectrum, but comes at the expense of a decrease in the signal-to noise ratio by a factor of √2.

An Operational Real-Time Model Chain for Now- and Forecasting of Radioactive Atmospheric Releases on the Local Scale

A comprehensive atmospheric dispersion modelling system, designed for real-time assessment of nuclear accidental releases from local to European scale, has been established by integrating a number of existing preprocessors, wind, turbulence, and dispersion models together with on-line available meteorology. The resulting dispersion system serves the realtime on-line decision support system for nuclear emergencies RODOS (Ehrhardt 1996; Kelly et al., 1996; Ehrhardt et al., 1997) with a system-integrated atmospheric dispersion module. This module is called met-rodos, Mikkelsen et al. (1997).

Test of a New Concentration Fluctuation Model for Decision-Makers

It is well known that changes in meteorological conditions also change air pollution levels. But even under apparently constant meteorological conditions still fast and random concentration fluctuations exist due to the turbulent motion of the atmosphere. Dispersion models of today can estimate reasonable accurate one-hour average concentrations for operational use. Operational models describing concentrations on shorter averaging times are needed for assessing odour problems, nonlinear toxic effects, risks of infections, risks of explosions, and fast atmospheric chemistry.

The VetMet Veterinary Decision Support System for Airborne Animal Diseases

A veterinary meteorological decision-support system, VetMet, is developed in collaboration between Danish meteorological and veterinary research institutes and authorities. The system, which is implemented at the Danish Meteorological Institute, is used by the Danish Veterinary and Food Administration, which has the responsibility for prevention and control of animal diseases in Denmark. By estimating the risk of atmospheric spread of airborne animal diseases, including first of all foot-and-mouth disease, VetMet improves the preparedness and the disease eradication. The Internet-based system will be used for decision support regarding establishment of surveillance and eradication zones. VetMet can describe both local spread of infectious airborne diseases between neighbouring farms and long-range dispersion, including disease spread to or from other European countries.

Validation of a Combination of Two Models for Long-Range Tracer Simulations

The accident at the Chernobyl nuclear power plant on April 25, 1986 initiated great interest in development of warning systems that could be used as a part of the emergency planning in connection with radioactive and toxic releases due to major industrial accidents. Forecasts of the transport and dispersion of the releases by use of long-range transport models are used as an important tool in the emergency planning and it is therefore crucial to test the quality of the models, for example, by comparison of model simulations with full scale atmospheric tracer experiments.

EXTENSION OF THE FAST SPECTRAL LINCOM MODEL TO FLOW OVER COMPLEX TERRAIN WITH THERMAL STRATIFICATION

LINCOM is a fast linearized and spectral wind flow model. It is designed to generate mean wind field predictions rapidly and is in operational use as wind field driver for fast real-time atmospheric dispersion models like RIMPUFF in assessment of emergency response and decision support. The model has successfully been evaluated in situations involving complex terrain and variable surface roughness under neutral conditions. Recently, an extension of the code to handle non-neutral thermal stratification, proposed by Santabarbara et al. (1995), has been implemented. Both the prescribed vertical temperature field and diffusivity of LINCOM-T strongly affect its solutions. Extensive analysis has been to establish a set of operational inputs for the LINCOM-T based on stability, wind speed and terrain conditions.