Forecast of atmospheric boundary-layer height utilised for ETEX real-time dispersion modelling

Sørensen, J.H.; Rasmussen, Alix; Svensmark, Henrik

doi:10.1016/s0079-1946(97)81138-x

Cited by 38 publications

(24 citation statements)

References 9 publications

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“…In order to provide a more detailed wind field near the source, LINCOM is only used out to about 15 km from the source. The mixing height which is one of the important characteristics for UAQ 25 man for different groups of the population (e.g., adult, children, elderly) may be models, is included in the NWP output data or calculated separately by methods suitable for urban conditions (Sørensen et al, 1996;Baklanov, 2002). However, the mixing height calculation may also be realised by the LSMC using different methods.…”

Section: Copenhagen Emergency Decision-support Systemmentioning

confidence: 99%

Integrated systems for forecasting urban meteorology, air pollution and population exposure

Hänninen²,

et al. 2007

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Abstract. Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

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Section: Copenhagen Emergency Decision-support Systemmentioning

confidence: 99%

Integrated systems for forecasting urban meteorology, air pollution and population exposure

Hänninen²,

et al. 2007

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“…According to the literature, threshold for the bulk Richardson number is generally set between 0.10 and 0.40 (Sørensen et al, 1996). For example, the value 0.25 is used to estimate the boundary layer height provided in the ERA-Interim data (von Engeln and Teixeira, 2013).…”

Section: Atmosmentioning

confidence: 99%

Mixing layer height retrievals by multichannel microwave radiometer observations

et al. 2013

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Abstract. The mixing layer height (MLH) is a key parameter for boundary layer studies, including meteorology, air quality, and climate. MLH estimates are inferred from in situ radiosonde measurements or remote sensing observations from instruments like lidar, wind profiling radar, or sodar. Methods used to estimate MLH from radiosonde profiles are also used with atmospheric temperature and humidity profiles retrieved by microwave radiometers (MWR). This paper proposes an alternative approach to estimate MLH from MWR data, based on direct observations (brightness temperatures, Tb) instead of retrieved profiles. To our knowledge, MLH estimates directly from Tb observations have never been attempted before. The method consists of a multivariate linear regression trained with an a priori set of collocated MWR Tb observations (multifrequency and multi-angle) and MLH estimates from a state-of-the-art lidar system. The proposed method was applied to a 7-month data set collected at a typical midlatitude site. Results show that the method is able to follow both the diurnal cycle and the day-to-day variability as suggested by the lidar measurements, and also it can detect low MLH values that are below the full overlap limit (∼ 200 m) of the lidar system used. Statistics of the comparison between MWR-and reference lidar-based MLH retrievals show mean difference within 10 m, root mean square within 340 m, and correlation coefficient higher than 0.77. Monthly mean analysis for daytime MLH from MWR, lidar, and radiosonde shows consistent seasonal variability, peaking at ∼ 1200-1400 m in June and decreasing down to ∼ 600 m in October. Conversely, nighttime monthly mean MLH from all methods are within 300-500 m without any significant seasonal variability. The proposed method provides results that are more consistent with radiosonde estimates than MLH estimates from MWR-retrieved profiles. MLH monthly mean values agree well within 1 standard deviation with the bulk Richardson number method applied at radiosonde profiles at 11:00 and 23:00 UTC. The method described herewith operates continuously and is expected to work with analogous performances for the entire diurnal cycle, except during considerable precipitation, demonstrating new potential for atmospheric observation by ground-based microwave radiometry.

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“…The surface variables can easily be extracted from the COSMO model, but the determination of the PBL height is not so straightforward, as several methods exist to derive this variable from NWP fields (Seibert et al 2000;Szintai and Kaufmann 2008). Based on a comparison with radiosounding profiles (Szintai et al 2009), the bulk Richardson number method (Sørensen et al 1996) was chosen for the coupling interface to determine the PBL height. Above the PBL the turbulence characteristics are kept constant in the "SIM" approach.…”

Section: Similarity Theory Approachmentioning

confidence: 99%

Simulation of Pollutant Transport in Complex Terrain with a Numerical Weather Prediction–Particle Dispersion Model Combination

Szintai

Kaufmann

Rotach

2010

Boundary-Layer Meteorol

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A new scaling approach, based on the convective velocity obtained from the sun-exposed eastern slopes and thus suited for steep and narrow Alpine valleys, is investigated with respect to pollutant dispersion. The capability of the new method is demonstrated with the operational emergency response system of MeteoSwiss, which consists of the COSMO (COnsortium for Small-scale MOdelling) numerical weather prediction model coupled with a Lagrangian particle dispersion model (LPDM). The new scaling approach is introduced to the interface between COSMO and LPDM, and is compared to results of a classical similarity theory approach and to the operational coupling type, which uses the turbulent kinetic energy (TKE) from the COSMO model directly. For the validation of the modelling system, the TRANSALP-89 tracer experiment is used, which was conducted in highly complex terrain in southern Switzerland. The ability of the COSMO model to simulate the valley wind system is assessed with several meteorological surface stations, and the dispersion simulation is evaluated with the measurements from 25 surface samplers. The sensitivity of the modelling system towards the soil moisture, horizontal grid resolution, and boundary-layer height determination is investigated, and it is shown that, if the flow field is correctly reproduced, the new scaling approach improves the tracer concentration simulation when compared to classical coupling methods.

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Forecast of atmospheric boundary-layer height utilised for ETEX real-time dispersion modelling

Cited by 38 publications

References 9 publications

Integrated systems for forecasting urban meteorology, air pollution and population exposure

Integrated systems for forecasting urban meteorology, air pollution and population exposure

Mixing layer height retrievals by multichannel microwave radiometer observations

Simulation of Pollutant Transport in Complex Terrain with a Numerical Weather Prediction–Particle Dispersion Model Combination

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