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1. Introduction Numerical weather prediction (NWP) has steadily improved over the last decades, allowing a multitude of socioeconomic benefits to be realized ( Bauer et al. 2015 ; Alley et al. 2019 ). While progress is unmistakable for 500-hPa geopotential heights and mean sea level pressure in the extratropics, improvements in the predictions of many other parameters are more variable ( Navascués et al. 2013 ). For example, forecasts of European cloud cover have hardly improved over the last
1. Introduction Numerical weather prediction (NWP) has steadily improved over the last decades, allowing a multitude of socioeconomic benefits to be realized ( Bauer et al. 2015 ; Alley et al. 2019 ). While progress is unmistakable for 500-hPa geopotential heights and mean sea level pressure in the extratropics, improvements in the predictions of many other parameters are more variable ( Navascués et al. 2013 ). For example, forecasts of European cloud cover have hardly improved over the last
Aerosol Effects on Clouds and Precipitation over Central Europe in Different Weather Regimesprofiles then lead to differences in the stability and relative humidity, both of which are highly relevant to cloud formation and precipitation. The advantage of this method is that the dominating weather regime and the environmental conditions in the planetary boundary layer and at cloud base are not changed. To cover different weather regimes, this technique is applied to days with weak synoptic forcing (airmass convection) and strong synoptic forcing (passage of frontal zones). In each of these
profiles then lead to differences in the stability and relative humidity, both of which are highly relevant to cloud formation and precipitation. The advantage of this method is that the dominating weather regime and the environmental conditions in the planetary boundary layer and at cloud base are not changed. To cover different weather regimes, this technique is applied to days with weak synoptic forcing (airmass convection) and strong synoptic forcing (passage of frontal zones). In each of these
activity drops off rapidly. The few remaining active clusters are less constrained by the large-scale forcing and rely more on internal processes to maintain convection. Supporting evidence for this argument can be found in the ensemble precipitation variability ( Fig. 3 ), which stays approximately constant even as the total precipitation amount rapidly declines. To understand the nature of cloud clustering, it is important to note that the RDF measures the cloud clustering relative to all clouds in
activity drops off rapidly. The few remaining active clusters are less constrained by the large-scale forcing and rely more on internal processes to maintain convection. Supporting evidence for this argument can be found in the ensemble precipitation variability ( Fig. 3 ), which stays approximately constant even as the total precipitation amount rapidly declines. To understand the nature of cloud clustering, it is important to note that the RDF measures the cloud clustering relative to all clouds in
. Res. , 107 , 8306 , doi: 10.1029/2001JD000879 . Wang , H. , G. Feingold , R. Wood , and J. Kazil , 2010 : Modelling microphysical and meteorological controls on precipitation and cloud cellular structures in Southeast Pacific stratocumulus . Atmos. Chem. Phys. , 10 , 6347 – 6362 , doi: 10.5194/acp-10-6347-2010 . 10.5194/acp-10-6347-2010 Yokohata , T. , S. Emori , T. Nozawa , Y. Tsushima , T. Ogura , and M. Kimoto , 2005 : Climate response to volcanic forcing
. Res. , 107 , 8306 , doi: 10.1029/2001JD000879 . Wang , H. , G. Feingold , R. Wood , and J. Kazil , 2010 : Modelling microphysical and meteorological controls on precipitation and cloud cellular structures in Southeast Pacific stratocumulus . Atmos. Chem. Phys. , 10 , 6347 – 6362 , doi: 10.5194/acp-10-6347-2010 . 10.5194/acp-10-6347-2010 Yokohata , T. , S. Emori , T. Nozawa , Y. Tsushima , T. Ogura , and M. Kimoto , 2005 : Climate response to volcanic forcing
2006 ). Multiple integrations of NWP models (ensembles) can be used to provide probabilistic information but can be set up in different ways, depending on the represented sources of uncertainty. Recent studies have shown that in different weather regimes, different sources of uncertainty dominate: in cases of strong large-scale forcing, initial and boundary conditions uncertainty contributes more to the overall uncertainty, whereas in weak large-scale forcing, model error is more important
2006 ). Multiple integrations of NWP models (ensembles) can be used to provide probabilistic information but can be set up in different ways, depending on the represented sources of uncertainty. Recent studies have shown that in different weather regimes, different sources of uncertainty dominate: in cases of strong large-scale forcing, initial and boundary conditions uncertainty contributes more to the overall uncertainty, whereas in weak large-scale forcing, model error is more important
estimate. This approach to studying diabatic processes has significant limitations since these processes tend to be strongest in cloudy and precipitating regions, which are particularly challenging for both observation and modeling systems. The processes associated with diabatic heating are characterized by a high degree of small-scale variability, particularly in the vertical (e.g., sharp vertical gradients of cloud microphysical processes and their interactions with radiative forcing), which are
estimate. This approach to studying diabatic processes has significant limitations since these processes tend to be strongest in cloudy and precipitating regions, which are particularly challenging for both observation and modeling systems. The processes associated with diabatic heating are characterized by a high degree of small-scale variability, particularly in the vertical (e.g., sharp vertical gradients of cloud microphysical processes and their interactions with radiative forcing), which are
hours ( Hohenegger and Schär 2007a , b ; Zhang et al. 2015 , 2016 ). However, there are steady Earth surface features such as orography or transient dynamical forcing patterns such as weather regimes that potentially provide the means to extend those predictability estimates ( Anthes 1986 ). The prevailing synoptic weather regime exerts a decisive influence on the predictability of convective precipitation. In case studies, Hanley et al. (2011 , 2013) and Barrett et al. (2015) showed how the
hours ( Hohenegger and Schär 2007a , b ; Zhang et al. 2015 , 2016 ). However, there are steady Earth surface features such as orography or transient dynamical forcing patterns such as weather regimes that potentially provide the means to extend those predictability estimates ( Anthes 1986 ). The prevailing synoptic weather regime exerts a decisive influence on the predictability of convective precipitation. In case studies, Hanley et al. (2011 , 2013) and Barrett et al. (2015) showed how the
relevant when synoptic forcing is weak and local mechanisms are the main driver for overcoming convection inhibition ( Keil et al. 2014 ). In these situations insufficient convective initiation has been commonly observed for kilometer-scale models (see e.g., Clark et al. 2016 ). In operational NWP systems such biases are often compensated by tuning other parameters, such as the turbulent length scale in the boundary layer parameterization ( Hanley et al. 2015 ). Smaller mixing lengths allow the lowest
relevant when synoptic forcing is weak and local mechanisms are the main driver for overcoming convection inhibition ( Keil et al. 2014 ). In these situations insufficient convective initiation has been commonly observed for kilometer-scale models (see e.g., Clark et al. 2016 ). In operational NWP systems such biases are often compensated by tuning other parameters, such as the turbulent length scale in the boundary layer parameterization ( Hanley et al. 2015 ). Smaller mixing lengths allow the lowest
drainage. However, the change in precipitation between WRF and WRF-Hydro was modest due to strong oceanic and orographic forcing in their study region. Differences between WRF and WRF-Hydro seasonal precipitation were also small in the case of a steep catchment at the foothills of Mount Kenya, East Africa ( Kerandi et al. 2018 ). In West Africa, Arnault et al. (2016) found that the impact of overland flow and runoff–infiltration partitioning on precipitation was scale dependent, that is, much more
drainage. However, the change in precipitation between WRF and WRF-Hydro was modest due to strong oceanic and orographic forcing in their study region. Differences between WRF and WRF-Hydro seasonal precipitation were also small in the case of a steep catchment at the foothills of Mount Kenya, East Africa ( Kerandi et al. 2018 ). In West Africa, Arnault et al. (2016) found that the impact of overland flow and runoff–infiltration partitioning on precipitation was scale dependent, that is, much more
multimodel ensemble forecast for the present and other cases to shed light on the question on how often such a tropical–extratropical interaction enhances convective-scale predictability. For West African winter monsoon rainfall, such an enhancement of predictability has been shown to extend to almost 1 week ( Davis et al. 2013 ). In addition, Söhne et al. (2008) have shown that cloud forecasts during the West African summer monsoon increased when a synoptic forcing from an African easterly wave is
multimodel ensemble forecast for the present and other cases to shed light on the question on how often such a tropical–extratropical interaction enhances convective-scale predictability. For West African winter monsoon rainfall, such an enhancement of predictability has been shown to extend to almost 1 week ( Davis et al. 2013 ). In addition, Söhne et al. (2008) have shown that cloud forecasts during the West African summer monsoon increased when a synoptic forcing from an African easterly wave is
