Future projections of daily hazy and clear weather conditions over the North China Plain using a Perturbed Parameter Ensemble

We examine past and future changes in both winter haze and clear weather conditions over the North China Plain (NCP) using a Perturbed Parameter Ensemble (PPE) and elucidate the influence of model physical parameterizations on these future projections for the first time. We use a meteorology-based Haze Weather Index (HWI), which was developed to examine the haze conducive weather conditions for Beijing. We find that the HWI can be used as an indicator of winter haze across the entire NCP due to the extended spatial coherence of the local meteorological conditions. The PPE generated using the UK Met Office HadGEM-GC3 model shows that under a high-emission (RCP8.5) scenario, the frequency of haze conducive weather is likely to increase whereas the frequency of clear weather is likely to decrease in future. However, a change of opposite sign with lower magnitude in the frequencies, though less likely, is also possible. In future, the total number of hazy days for a given winter can be as much as ~3.5 times higher than the number of clear days over the NCP. We also examined the changes in the interannual variability of the frequency of hazy and clear days and find no marked changes in the variability for future periods. The future frequencies of winter hazy and clear days in the PPE are largely driven by changes in zonal-mean mid-tropospheric winds and the vertical temperature gradient over the NCP. We do not find any discernible influence of model physical parameterizations on the future projections of trends in the frequency of hazy or clear days. We find a clear impact of anthropogenic climate change on future trends for both hazy and clear days, however, it is only discernible for specific periods due to the large underlying internal variability in the frequencies of hazy and clear days.

Benefit of microwave radiometer and cloud radar observations for data assimilation and fog process studies during the SOFOG3D experiment

<p>Fog forecasts still remain quite inaccurate due to the complexity, non linearities and fine scale of the main physical processes driving the fog lifecycle. Additionally to the complex modelling of fog processes, current numerical weather prediction models are known to suffer from a lack of operational observations in the atmospheric boundary layer and more generally during cloudy-sky conditions. Continuous observations of both thermodynamics and microphysics during the fog lifecycle are thus essential to develop future operational networks with the aim of validating current physical parameterizations and improving the model initial state through data assimilation techniques. In this context, an international network of 8 ground-based microwave radiometers (MWRs) has been deployed at a regional-scale on a 300 x 300 km domain during the SOFOG3D (SOuth FOGs 3D experiment for fog processes study) that has been conducted from October 2019 to April 2020. The MWR network has been extended with ceilometers at all MWR sites and additional microphysical observations from the 95 GHz cloud radar BASTA at two major sites as well as wind measurements from a Doppler lidar deployed at the super-site. After an overview of the SOFOG3D objectives and experimental set-up, preliminary results exploiting mainly the MWR network and cloud radar observations will be presented. Firstly, the capability of MWRs to provide temperature and humidity retrievals within fog and stratus clouds will be evaluated and discussed against radiosoundings launched during intensive observation periods (IOPs). Secondly, first retrievals of liquid water content profiles within fog and stratus clouds derived from the synergy between MWRs and the BASTA cloud radar will be presented. To that end, a one dimensional variational approach (1D-Var) directly assimilating MWR brightness temperatures and cloud-radar reflectivities has been developed. 1D-Var retrievals will be validated through a dataset of simulated observations and real fog cases of the SOFOG3D experiment. The capability of MWR and cloud radar observations to improve the initial state of the AROME model during fog conditions will be discussed with a focus on selected case studies. Finally, the usefulness of ground-based remote sensing networks to improve our understanding of fog processes and to validate physical parameterizations will be illustrated using the operational AROME model and the AROME Ensemble Prediction System</p>

L'évolution opérationnelle du modèle Arpège et de ses paramétrisations physiques

Au cours de ses années à la direction du Groupe de modélisation et d'assimilation pour la prévision (Gmap), Jean-François Geleyn dirigea avec enthousiasme, dynamisme, efficacité et une grande expertise la préparation de toutes les évolutions des systèmes opérationnels de prévision numérique du temps Arpège et Aladin. Plusieurs évolutions majeures furent implémentées en opérationnel au cours de cette période. La contribution de Jean-François Geleyn fut notamment remarquable sur le noyau dynamique, le post-traitement des prévisions, leur évaluation et tout particulièrement les paramétrisations physiques des modèles de prévision. Il coordonna en effet tous les travaux de recherche et développement sur les paramétrisations physiques des modèles Arpège et Aladin et sur les paramétrisations physiques linéarisées développées pour l'analyse 4D-Var dans son groupe et chez les partenaires Aladin. Over the years when Jean-François Geleyn was the head of GMAP he led with great enthusiasm, dynamism, efficiency and expertise the preparation of all the evolutions of operational numerical weather prediction (NWP) systems of Arpège and Aladin. Several major changes were implemented operationally during this period. Jean-François Geleyn's contribution was remarkable in several topics such as the dynamical core, the post-processing of weather predictions, their validation, and especially the physical parameterizations of the numerical weather predictions models. In principle, he coordinated all the research and development activities on physical parameterizations for Arpège and Aladin NWP models and on the linearized physical parameterizations developed for the 4D-Var analysis in his group and among the Aladin partners.

Les débuts de Jean-François Geleyn : Mayence, Paris et son expérience au CEPMMT

La carrière scientifique de Jean-François Geleyn débute à l'université de Mayence, où il acquit une formation complémentaire sur le rayonnement atmosphérique, après sa formation à l'École nationale de la météorologie. À son retour à Paris, il enseigna son premier cours de rayonnement et expérimenta un premier schéma vertical de paramétrisations physiques. Recruté par le CEPMMT, il intégra la première équipe qui développa avec succès les paramétrisations physiques du modèle du Centre. Grâce à sa polyvalence sur tous les aspects de la prévision numérique, à ses qualités d'analyse et de synthèse, il contribua très activement aux développements du modèle du Centre. Jean-François Geleyn's scientific career began at the University of Mainz, where he received further training in atmospheric radiation, after his training at the National Meteorological School. On his return to Paris, he taught his first radiation course and experimented a first vertical scheme of physical parameterizations. Recruited by the ECMWF, he joined the first team who successfully developed the physical parameterizations of the ECMWF model. Thanks to his versatility in all aspects of numerical forecasting, his analytical and synthesis skills, he contributed very actively to the development of the ECMWF model.

The making of the New European Wind Atlas – Part 1: Model sensitivity

This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They confirmed that the most significant differences in annual mean wind speed at 100 m a.g.l. (above ground level) mostly coincide with areas of high surface roughness length and not with the location of the domains or maximum wind speed. Then an ensemble of more than 50 simulations with different setups for a single year was carried out for one domain covering northern Europe for which tall mast observations were available. We varied many different parameters across the simulations, e.g. model version, forcing data, various physical parameterizations, and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes in the wind climate in the boundary layer are mostly due to using different physical parameterizations, especially the planetary boundary layer scheme, the representation of the land surface, and the prescribed surface roughness length. Also, the setup of the simulations, such as the integration length and the domain size, can considerably influence the results. We assessed the degree of similarity between winds simulated by the WRF ensemble members and the observations using a suite of metrics, including the Earth Mover's Distance (EMD), a statistic that measures the distance between two probability distributions. The EMD was used to diagnose the performance of each ensemble member using the full wind speed and direction distribution, which is essential for wind resource assessment. We identified the most realistic ensemble members to determine the most suitable configuration to be used in the final production run, which is fully described and evaluated in the second part of this study (Dörenkämper et al., 2020).