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Fabian Senf and Hartwig Deneke

Abstract

The growth phase of convective storms and their transition to maturity is investigated based on more than 100 cases selected from the years 2012–14 over central Europe. Dynamical growth properties as well as cloud-top glaciation and microphysical characteristics are derived from the SEVIRI imaging radiometer aboard the geostationary Meteosat satellites. In addition, onset and intensity of surface precipitation are related to growth and glaciation processes using observations from the radar network of the German Weather Service. The majority of analyzed cases shows a distinct maximum in cloud-top cooling rate, which is used here for temporal synchronization. Cloud growth spans a period of approximately half an hour. Glaciation rate indicators suggest that freezing 15 min prior to the maximum cooling plays an important role in invigorating convective updrafts through the release of latent heat. Smaller ice particles are found for larger cloud-top cooling, which provides observational evidence that ice particles form later and have less time to grow in stronger convective updrafts. Furthermore, maximum cloud-top height, anvil expansion rate, maximum precipitation intensity, and core size are found to be positively correlated. With respect to the onset of precipitation, this analysis shows a high probability that significant precipitation already occurs 30 min prior to maximum cloud-top cooling.

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Martin Rempel, Fabian Senf, and Hartwig Deneke

Abstract

Object-based metrics are adapted and applied to geostationary satellite observations with the evaluation of cloud forecasts in convective situations as the goal. Forecasts of the convection-permitting German-focused Consortium for Small-Scale Modeling (COSMO-DE) numerical model are transformed into synthetic observations using the RTTOV radiative transfer model, and contrasted with the corresponding real observations. Threshold-based segmentation techniques are applied to the fields for object identification. The statistical properties of the traditional measures cold cloud cover and average brightness temperature amplitude are contrasted to object-based metrics of spatial aggregation and object structure. Based on 59 case days from the summer half-years between 2012 and 2014, a variance decomposition technique is applied to the time series of the metrics to identify deficits in day-to-day, diurnal, and weather-regime-related variability of cold cloud characteristics in the forecasts. Furthermore, sensitivities of the considered metrics are discussed, which result from uncertainties in the satellite forward operator and from the choice of parameters in the object identification techniques.

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Fabian Senf, Matthias Brueck, and Daniel Klocke

Abstract

Over the tropical oceans, the large-scale, meridional circulation drives the accumulation of moist and warm air, leading to a relatively narrow, convectively active band. Therein, deep moist convection interacts with its heterogeneous environment—the intertropical convergence zone (ITCZ)—and organizes into multiscale structures that strongly impact Earth’s hydrological cycle and radiation budget. Understanding the spatial correlations and interactions among deep convective clouds is important, but challenging. These clouds are investigated in this study with the help of large-domain, storm-resolving simulations over the tropical Atlantic. Based on vertically integrated mass flux fields, deep convective updraft cells are identified with object-based techniques and analyzed with respect to their structural behavior and spatial arrangement. The pair-correlation method, which compares simulated pair numbers as a function of pair distance to an appropriately chosen reference, is applied and extended to allow for spatial statistics in a heterogeneous environment (i.e., the ITCZ). Based on pair-correlation analysis, the average probability is enhanced to find an updraft cell pair within 100 km compared to a random distribution. Additionally, the spatial arrangement of larger or stronger cells deviates more from randomness compared to smaller or weaker cells, which might be related to their stronger dynamical interaction mechanisms. Using simplified equilibrium statistics of interacting cells, several spatial characteristics of the storm-resolving simulations can be reproduced.

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Fabian Senf, Daniel Klocke, and Matthias Brueck

Abstract

Deep moist convection is an inherently multiscale phenomenon with organization processes coupling convective elements to larger-scale structures. A realistic representation of the tropical dynamics demands a simulation framework that is capable of representing physical processes across a wide range of scales. Therefore, storm-resolving numerical simulations at 2.4 km have been performed covering the tropical Atlantic and neighboring parts for 2 months. The simulated cloud fields are combined with infrared geostationary satellite observations, and their realism is assessed with the help of object-based evaluation methods. It is shown that the simulations are able to develop a well-defined intertropical convergence zone. However, marine convective activity measured by the cold cloud coverage is considerably underestimated, especially for the winter season and the western Atlantic. The spatial coupling across the resolved scales leads to simulated cloud number size distributions that follow power laws similar to the observations, with slopes steeper in winter than summer and slopes steeper over ocean than over land. The simulated slopes are, however, too steep, indicating too many small and too few large tropical cloud cells. It is also discussed that the number of larger cells is less influenced by multiday variability of environmental conditions. Despite the identified deficits, the analyzed simulations highlight the great potential of this modeling framework for process-based studies of tropical deep convection.

Open access
Sebastian Bley, Hartwig Deneke, and Fabian Senf

Abstract

The spatiotemporal evolution of warm convective cloud fields over central Europe is investigated on the basis of 30 cases using observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary Meteosat platforms. Cloud fields are tracked in successive satellite images using cloud motion vectors. The time-lagged autocorrelation is calculated for spectral reflectance and cloud property fields using boxes of 16 × 16 pixels and adopting both Lagrangian and Eulerian perspectives. The 0.6-μm reflectance, cloud optical depth, and water path show a similar characteristic Lagrangian decorrelation time of about 30 min. In contrast, significantly lower decorrelation times are observed for the cloud effective radius and droplet density. It is shown that the Eulerian decorrelation time can be decomposed into an advective component and a convective component using the spatial autocorrelation function. In an Eulerian frame cloud fields generally decorrelate faster than in a Lagrangian one. The Eulerian decorrelation time contains contributions from the spatial decorrelation of the cloud field advected by the horizontal wind. A typical spatial decorrelation length of 7 km is observed, which suggests that sampling of SEVIRI observations is better in the temporal domain than in the spatial domain when investigating small-scale convective clouds. An along-track time series of box-averaged cloud liquid water path is derived and compared with the time series that would be measured at a fixed location. Supported by previous results, it is argued that this makes it possible to discriminate between local changes such as condensation and evaporation on the one hand and advective changes on the other hand.

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Fabian Senf, Felix Dietzsch, Anja Hünerbein, and Hartwig Deneke

Abstract

The early development of severe convective storms over central Europe was investigated on the basis of nine cases from 2012. Using data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) imaging radiometer aboard a geostationary Meteosat Second Generation (MSG) satellite, dynamical and microphysical properties of developing storms were monitored and combined. Several satellite-based storm properties, for example, cloud-top temperature, cloud-top cooling rate, and cloud particle effective radius, were investigated following the storm tracks. A framework for quantification of uncertainties of along-track properties resulting from tracking errors was also introduced. The majority of studied storms show a distinct maximum in cloud-top cooling rate; the corresponding time was used for track synchronization. The cloud growth phase was divided into an initial updraft intensification period before the maximum cooling and a continued growth period afterward. The initial updraft intensification period is variable and strongly depends on the convection initiation mechanism and detection conditions. The continued growth period is more confined, lasting between 30 and 45 min. The change in anvil size and the resulting average anvil edge velocity were determined from infrared satellite images. As a consequence of mass transport, the anvil edge velocity shows its highest correlation with the cloud-top vertical velocity approximately 20–30 min after the maximum in the cloud-top cooling rate. Larger effective radii of ice crystals were observed for vertically slower-growing clouds. The largest anticorrelation between cloud-top vertical velocity and effective radius was found at a time lag of 20 min after the maximum in cloud-top cooling.

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Mirjana Sakradzija, Fabian Senf, Leonhard Scheck, Maike Ahlgrimm, and Daniel Klocke

Abstract

The local impact of stochastic shallow convection on clouds and precipitation is tested in a case study over the tropical Atlantic on 20 December 2013 using the Icosahedral Nonhydrostatic Model (ICON). ICON is used at a grid resolution of 2.5 km and is tested in several configurations that differ in their treatment of shallow convection. A stochastic shallow convection scheme is compared to the operational deterministic scheme and a case with no representation of shallow convection. The model is evaluated by comparing synthetically generated irradiance data for both visible and infrared wavelengths against actual satellite observations. The experimental approach is designed to distinguish the local effects of parameterized shallow convection (or lack thereof) within the trades versus the ITCZ. The stochastic cases prove to be superior in reproducing low-level cloud cover, deep convection, and its organization, as well as the distribution of precipitation in the tropical Atlantic ITCZ. In these cases, convective heating in the subcloud layer is substantial, and boundary layer depth is increased as a result of the heating, while evaporation is enhanced at the expense of sensible heat flux at the ocean’s surface. The stochastic case where subgrid shallow convection is deactivated below the resolved deep updrafts indicates that local boundary layer convection is crucial for a better representation of deep convection. Based on these results, our study points to a necessity to further develop parameterizations of shallow convection for use at the convection-permitting resolutions and to assuredly include them in weather and climate models even as their imperfect versions.

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