Pure Appl. Geophys. 174 (2017), 491–510 Ó 2016 The Author(s) This article is published with open access at Springerlink.com DOI 10.1007/s00024-015-1227-2 Pure and Applied Geophysics Sensitivity Study of Cloud Cover and Ozone Modeling to Microphysics Parameterization 1 1 2 1,3 1 KINGA WAłASZEK, MACIEJ KRYZA, MARIUSZ SZYMANOWSKI, MAłGORZATA WERNER, and HANNA OJRZYN´ SKA Abstract—Cloud cover is a significant meteorological param- water cycle, but also its energy budget, and therefore eter influencing the amount of solar radiation reaching the ground radiative processes on the surface and atmospheric surface, and therefore affecting the formation of photochemical pollutants, most of all tropospheric ozone (O3). Because cloud chemistry, and also interacts with aerosols in the amount and type in meteorological models are resolved by atmosphere. Cloudiness affects ozone and other sec- microphysics schemes, adjusting this parameterization is a major ondary pollutant formation by limiting incoming factor determining the accuracy of the results. However, verifica- tion of cloud cover simulations based on surface data is difficult radiative fluxes to the surface layer. In meteorological and yields significant errors. Current meteorological satellite pro- and chemical transport models, e.g. WRF-Chem grams provide many high-resolution cloud products, which can be (GRELL et al. 2005;MADRONICH 1987;TIE et al. 2003; used to verify numerical models. In this study, the Weather Research and Forecasting model (WRF) has been applied for the WILD et al. 2000), cloud cover information is passed area of Poland for an episode of June 17th–July 4th, 2008, when on to photolysis schemes, thus influencing nitrogen high ground-level ozone concentrations were observed. Four sim- dioxide (NO2) oxidation rates. ulations were performed, each with a different microphysics Cloud amount and cloud type are one of the most parameterization: Purdue Lin, Eta Ferrier, WRF Single-Moment 6-class, and Morrison Double-Moment scheme. The results were difficult meteorological parameters to predict. Cloud then evaluated based on cloud mask satellite images derived from formation and dynamics depend on a wide variety of SEVIRI data. Meteorological variables and O3 concentrations were factors and processes, which are not accounted for in also evaluated. The results show that the simulation using Morrison Double-Moment microphysics provides the most and Purdue Lin the model explicitly, simply because the atmospheric the least accurate information on cloud cover and surface meteo- system is too complex and the current computational rological variables for the selected high ozone episode. Those two power is insufficient to resolve them. For these rea- configurations were used for WRF-Chem runs, which showed sons, there is a need to apply approximations, which significantly higher O3 concentrations and better model-measure- ments agreement of the latter. increase the uncertainty of cloud cover prediction (JOHNSON et al. 2015;VAN LIER-WALQUI et al. 2012). Key words: Cloud mask, meteorological modeling, ozone, Since cloud microphysics interacts with many other WRF, Poland, model evaluation. elements of the weather system resolved by the model, those uncertainties are replicated and have an adverse effect on the overall forecast quality. In air 1. Introduction quality modeling, it also affects estimation of pollu- tant concentrations, particularly ozone and other photochemical smog compounds, by regulating the Cloud cover plays important role in many atmo- amount of solar energy transferred to the surface. spheric processes. Not only does it regulate Earth’s There are many data types that cloud cover fore- cast verification can be based on (BRETHERTON et al. 1995). The most commonly used and longest data series that can be acquired are cloud fraction reports 1 Department of Climatology and Atmosphere Protection, University of Wrocław, Wrocław, Poland. E-mail: from ground-based weather stations (e.g. QIAN et al. [email protected]; [email protected] 2012). Surface data are easily accessible in real time 2 Department of Geoinformatics and Cartography, University and widely used for verification of many other of Wrocław, Wrocław, Poland. 3 National Pollen and Aerobiology Research Unit, University meteorological parameters, such as temperature, of Worcester, Worcester, UK. pressure or wind speed, but with cloud cover there are 492 K. Wałaszek et al. Pure Appl. Geophys. some setbacks. As the density of stations may be Second Generation), but spatial resolution is much sufficient for other meteorological variables, cloudi- lower than the polar-orbiting satellites. Meteosat ness measuring network is very irregular and stations MSG has 1 and 3 km resolution at sub-satellite point are located predominantly on land, so there is dis- for High Resolution Visible (HRV) and infrared proportion in data density over land and marine areas. channels, respectively, and it decreases toward the There are also manual and automated stations, and edges of the image. The downside is that their cov- the two different methods of gathering cloud fraction erage is limited by the satellite’s field of view, so information may provide different outcomes (WMO polar regions are either invisible or excluded because 2008). Additionally, the number of synoptic stations of large distortions. worldwide has been decreasing (PETERSON and VOSE Satellite imagery can be processed into a variety 1997;VOSE et al. 1992). Another issue is the fre- of products, and therefore enable various approaches quency of the provided data—surface stations usually to meteorological model verification (TUINDER et al. report at synoptic times, whereas regional meteoro- 2004). One of them is comparison of brightness logical models provide data at finer temporal temperatures (ZINGERLE and NURMI 2008;SO¨ HNE et al. resolution (1 h or less). Finally, there is more than 2008). It is usually not a parameter produced directly one definition of cloud fraction and there are diffi- by meteorological models, but requires additional culties in transforming it into a variable that would be post-processing from other model output variables. suitable for model verification. Much more straightforward approach is to use cloud One data source that solves the problem of mask, which can be easily derived from cloud frac- irregular and sparse coverage of surface data are tions at model levels (CROCKER and MITTERMAIER meteorological radars; however, they are designed to 2013). Satellite cloud mask is derived from multiple detect precipitation rather than cloud cover and are spectral channels, usually based on visible light and not commonly used for that purpose. Finally, there supported by infrared wavelengths, through a series are satellite images, which not only have very large of cloud detection tests. These data can then be spatial extent, but also high spatial and temporal compared with the modeled cloud mask to evaluate resolution and data are homogenous across the globe. its results. Satellite data provide images in over a hundred Meteorological model evaluation can also be spectral bands which allow the diagnosis of a variety based on various methods; one of them, referred to as of cloud products, from an unprocessed visible image categorical verification, uses grid-to-grid comparison, to cloud mask, cloud top height, liquid water content, and another, object-based verification method, pre- or brightness temperatures. Although these data are sents the features being verified as objects. In this not always available in real time and go back only a study, we use both approaches to compare and few decades, it may serve a variety of applications quantify the differences between the cloud mask related to model verification. There are two main derived from the Weather Research and Forecasting types of satellites providing data for meteorological (WRF) meteorological model simulation and satellite purposes: geostationary (e.g., the Meteosat series; data. Four different microphysics parameterizations FENSHOLT et al. 2011) and polar-orbiting (e.g. are tested for a selected period, favorable to forma- NASA’s Terra and Aqua; KING et al. 2003). The main tion of tropospheric ozone. Finally, for two advantage of low Earth orbit satellites is their high parameterizations of microphysics, ozone concentra- spatial resolution, which may be even less than 1 km tions are calculated with the WRF-Chem model, and (down to 250 m at sub-satellite point in case of the role of microphysics scheme on modeled O3 is MODIS) and small distortions of the image. How- also described with the example of the episode of ever, their orbit characteristics result in the data being high ozone concentrations observed in central available at irregular times, approximately 3–4 times Europe. a day. Geostationary satellites, on the other hand, There are two main aims of this study. The first which stay above a fixed point on the equator, have aim is to evaluate the WRF model performance for high temporal resolution (15 min for Meteosat cloud cover, using satellite data and objective Vol. 174, (2017) Sensitivity Study of Cloud Cover and Ozone Modeling to Microphysics Parameterization 493 verification approach, and to test the model sensi- for the intermediate, and 5 km 9 5 km for the tivity to various microphysics schemes. The second innermost domain, covering the area of interest. aim is to examine the sensitivity of the WRF-Chem The model has 38 vertical layers with model top at modeled ozone to the selected