Cold pools are mesoscale features, that are key for understanding the organization of convection, but are insufficiently captured in conventional observations. This study conducts a statistical characterization of cold-pool passages observed at a 280 m high boundary layer mast in Hamburg (Germany) and discusses factors controlling their signal strength. During 14 summer seasons 489 cold-pool events are identified from rapid temperature drops below -2 K associated with rainfall. The cold-pool activity exhibits distinct annual and diurnal cycles peaking in July and mid afternoon, respectively. The median temperature perturbation is -3.3 K at 2-m height and weakens above. Also the increase in hydrostatic air pressure and specific humidity is largest near the surface. Extrapolation of the vertically weakening pressure signal suggests a characteristic cold-pool depth of about 750 m. Disturbances in the horizontal and vertical wind speed components document a lifting-induced circulation of air masses prior to the approaching cold-pool front. According to a correlation analysis, the near-surface temperature perturbation is more strongly controlled by the pre-event saturation deficit (r=-0.71) than by the event-accumulated rainfall amount (r=-0.35). Simulating the observed temperature drops as idealized wet-bulb processes suggests that evaporative cooling alone explains 64 % of the variability in cold-pool strength. This number increases to 92 % for cases that are not affected by advection of mid-tropospheric low-Θe air masses under convective downdrafts.

The variability of the raindrop size distribution (DSD) contributes to large parts of the uncertainty in radar-based quantitative rainfall estimates. The variety of microphysical processes acting on the formation of rainfall generally leads to significantly different relationships between radar reflectivity Z and rain rate R for stratiform and convective rainfall. High-resolution observation data from three Micro Rain Radars in northern Germany are analyzed to quantify the potential of dual Z–R relationships to improve radar rainfall estimates under idealized rainfall type identification and separation. Stratiform and convective rainfall are separated with two methods, establishing thresholds for the rain rate-dependent mean drop size and the α coefficient of the power-law Z–R relationship. The two types of dual Z–R relationships are tested against a standard Marshall–Palmer relationship and a globally adjusted single relationship. The comparison of DSD-based and reflectivity-derived rain rates shows that the use of stratiform and convective Z–R relationships reduces the estimation error of the 6-month accumulated rainfall between 30% and 50% relative to a single Z–R relationship. Consistent results for neighboring locations are obtained at different rainfall intensity classes. The range of estimation errors narrows by between 20% and 40% for 10-s-integrated rain rates, dependent on rainfall intensity and separation method. The presented technique also considerably reduces the occurrence of extreme underestimations of the true rain rate for heavy rainfall, which is particularly relevant for operational applications and flooding predictions.

Abstract. From June to August 2020, an observational network of 103 meteorological ground-based stations covered the greater area (50 km × 35 km) of Hamburg (Germany) as part of the Field Experiment on Sub-mesoscale Spatio-Temporal variability at Hanseatic city of Hamburg (FESST@HH). The purpose of the experiment was to shed light on the sub-mesoscale (𝒪(100) m–𝒪(10) km) structure of convective cold pools that typically remain under-resolved in operational networks. During the experiment, 82 custom-built, low-cost APOLLO (Autonomous cold POoL LOgger) stations sampled air temperature and pressure with fast-response sensors at 1 s resolution to adequately capture the strong and rapid perturbations associated with propagating cold pool fronts. A secondary network of 21 weather stations with commercial sensors provided additional information on relative humidity, wind speed, and precipitation at 10 s resolution. The realization of the experiment during the COVID-19 pandemic was facilitated by a large number of volunteers who provided measurement sites on their premises and supported station maintenance. This article introduces the novel type of autonomously operating instruments, their measurement characteristics, and the FESST@HH data set (https://doi.org/10.25592/UHHFDM.10172; Kirsch et al., 2021b). A case study demonstrates that the network is capable of mapping the horizontal structure of the temperature signal inside a cold pool, and quantifying a cold pool's size and propagation velocity throughout its life cycle. Beyond its primary purpose, the data set offers new insights into the spatial and temporal characteristics of the nocturnal urban heat island and variations of turbulent temperature fluctuations associated with different urban and natural environments.

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<p>Cold pools are areas of cool downdraft air that form through evaporation underneath precipitating clouds and spread on the surface as density currents. Their importance for the development and maintenance of convection is long known. Modern Large-Eddy simulations with a grid spacing of 1 km or less are able to explicitly resolve cold pools, however, they lack reference data for an adequate validation. Available point measurements from operational networks are too coarse and, therefore, miss the horizontal structure and dynamics of cold pools.</p><p>The upcoming measurement campaign FESSTVaL (Field Experiment on Sub-mesocale Spatio-Temporal Variability in Lindenberg) aims to test novel measurement strategies for the observation of previously unresolved sub-mesoscale boundary layer structures and phenomena, such as cold pools. The key component of the experiment during this summer will be a dense network of ground-based measurements within 15 km around the Meteorological Observatory Lindenberg near Berlin. The network of 100 low-cost APOLLO (Autonomous cold POoL LOgger) stations allows to record air pressure and temperature with a spatial and temporal resolution of 100 m and 1 s, respectively. We present first results from a test campaign during last summer that successfully demonstrated the ability of the proposed network stations to observe cold pool dynamics on the sub-mesoscale.</p>