A series of modelling exercises, based on field tests conducted in the Czech Republic, were carried out by the ‘Urban’ Working Groups as part of the International Atomic Energy Agency’s Environmental Modelling for Radiation Safety II, Modelling and Data for Radiological Impact Assessment (MODARIA) I and MODARIA II international data compilation and model validation programmes. In the first two of these programmes, data from a series of field tests involving dispersion of a radiotracer, 99mTc, from small-scale, controlled detonations were used in a comparison of model predictions with field measurements of deposition. In the third programme, data from a similar field test, involving dispersion of 140La instead of 99mTc, were used. Use of longer-lived 140La as a radiotracer allowed a greater number of measurements to be made over a greater distance from the dispersion point and in more directions than was possible for the earlier tests involving shorter-lived 99mTc. The modelling exercises included both intercomparison of model predictions from several participants and comparison of model predictions with the measured data. Several models (HotSpot, LASAIR, ADDAM/CSA-ERM, plus some research models) were used in the comparisons, which demonstrated the challenges of modelling dispersion of radionuclides from detonations and the need for appropriate meteorological measurements.
The IAEA’s model testing programmes have included a series of Working Groups concerned with modelling radioactive contamination in urban environments. These have included the Urban Working Group of Validation of Environmental Model Predictions (1988–1994), the Urban Remediation Working Group of Environmental Modelling for Radiation Safety (EMRAS) (2003–2007), the Urban Areas Working Group of EMRAS II (2009–2011), the Urban Environments Working Group of (Modelling and Data for Radiological Impact Assessments) MODARIA I (2013–2015), and most recently, the Urban Exposures Working Group of MODARIA II (2016–2019). The overarching objective of these Working Groups has been to test and improve the capabilities of computer models used to assess radioactive contamination in urban environments, including dispersion and deposition processes, short-term and long-term redistribution of contaminants following deposition events, and the effectiveness of various countermeasures and other protective actions, including remedial actions, in reducing contamination levels, human exposures, and doses to humans. This paper describes the exercises conducted during the MODARIA I and MODARIA II programmes. These exercises have included short-range and mid-range atmospheric dispersion exercises based on data from field tests or tracer studies, hypothetical urban dispersion exercises, and an exercise based on data collected after the Fukushima Daiichi accident. Improvement of model capabilities will lead to improvements in assessing various contamination scenarios (real or hypothetical), and in turn, to improved decision-making and communication with the public following a nuclear or radiological emergency.
The Earth's atmosphere is highly coupled between the vertical layers and the surface. An understanding of circulations in the atmosphere is important for developing models and improving weather forecasting. The latent heat produced in the atmosphere is one of the key driving forces of these circulations. It is therefore very important to estimate the latent heat in the atmosphere accurately, especially in thunderstorm clouds, which have proved to be one of the major sources of gravity waves in tropical regions. The current space-based latent heat retrievals are limited to precipitationbased estimation which cannot define the complete structure of a thunderstorm where precipitation is not the main indicator of the severity. A novel method is proposed in this study which retrieves the latent heat profiles of thunderstorm clouds using CloudSat W-band radar profiles. A realistic database of simulated thunderstorm events developed using the Regional Atmospheric Modelling System -Cloud Resolving Model (RAMS-CRM) is compared with observations using the Bayesian Monte Carlo method to derive an estimate with an uncertainty analysis for each estimate. The method is validated in the southeast Asian region with European Centre for Medium-Range Weather Forecasts driven RAMS-CRM profiles. The algorithm performance on observation data using linear fit, regression and bias analysis is discussed. A case study retrieval is also performed to demonstrate the retrieval on real CloudSat data.
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