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The current study focuses on characterizing the atmospheric details required for dense gas dispersion analysis resulting from release of cryogenic liquids like LNG. The study investigates the effectiveness of coupling the prognostic MM5 mesoscale model with the CALMET diagnostic model for producing meteorological conditions that is characteristic of dry and arid regions like Qatar, with non-neutral boundary conditions. MM5-CALMET wind fields and temperature data are compared with the meteorological field observations from the Doha International Airport (DIA) on a monthly basis, daily basis and hourly basis to study the effect of different averaging periods. The monthly averages replicate the annual patterns of meteorological parameters very well. However difference in observation and model are observed for wind speed and wind direction variable. The daily averages obtained from the model are in good agreement with the observation for wind speed and temperature. For hourly averaging, the model is found capable of mimicking the temperature of a given location, but not wind speed and direction. The prediction of wind direction parameter using MM5-CALMET is moderate for any averaging period. The sub-optimal performance of wind direction variable is attributed to grid resolution of vertical and horizontal layers of MM5-CALMET model. Additionally a case study is performed to illustrate the effect of variation of meteorological parameters on the lower flammability limits (LFL) resulting from flammable dense gas release of LNG. The case study demonstrates the issues that arise in a risk analysis study when "wrong" meteorological data could be used. The overall study indicates that utilizing the coarsest prognostic meteorological model output in a diagnostic model provides an effective option for generating meteorological inputs for dispersion studies. (c) 2015 Elsevier Ltd. All rights reserved.