Monitoring and nowcasting of urban air temperatures are of high interest for prediction of heat stress in cities. Routine observation is so far limited by the complex coupling between atmosphere and land surface in urban areas, which makes estimation more difficult. In this study, we have investigated the capability of multitemporal land surface temperatures (LSTs) from the geostationary Spinning Enhanced Visible Infra-Red Imager instrument for estimation of urban air temperatures. The results are very promising with root-mean-square errors (RMSEs) of 1.5-1.8 K for six stations in Hamburg and explained variances of 97%-98%. Both the annual and diurnal cycles were well represented by the empirical models and the use of multitemporal data substantially increased the model performance. Further, the model was run in a forecast mode without actual LST information. Here, the best predictors reached RMSEs of 1.9-2.4 K and R ² of 95%-97% for a 2-h forecast.

Purpose Urban soils’ variability in the vertical direction presumably affects hydrological parameters at the timescale. Moreover, horizontal soil alterations at small spatial scales are common in urban areas. This spatio-temporal variability and heterogeneity of soil moisture and the possible influencing factors were to be described and quantified, using data of a soil monitoring network in the city of Hamburg, Germany. Materials and methods Soil moisture data from ten observation sites within the project HUSCO was evaluated for two different years. The sites were located within districts with different mean groundwater table depths and characteristic urban soil properties. Soil hydrological simulations with SWAP were calculated for a selected site. Results and discussion The temporal evolution of soil water content and tension for the sites was very distinct, related to soil substrate, organic matter content, and groundwater table depth. Impacts of different vegetation rooting depths, the soil substrates’ type, and to some extent the degree of disturbance on soil water dynamics could be identified. An impact of groundwater table depth on the water content of the topsoil during low-precipitation periods could be assumed. The comparison of the results of soil hydrological simulations with empirical data indicated an overestimation of infiltration and percolation for the given soil substrates. Conclusions While soil properties are mainly determinant for the long-term progression of soil hydrology, local site factors affect the short-term regime. A shallow groundwater table contributes to more constant water dynamics while the relative decrease of water during a dry phase is diminished.

The local climate in cities differs from the one in rural areas, most prominently characterized by increased surface and air temperatures, known as the “(surface) urban heat island”. As climate has changed and continues to change in all areas of the world, the question arises whether the effects that are noticeable in urban areas are “homemade”, or whether some of them originate from global and regional scale climate changes. Identifying the locally induced changes of urban meteorological parameters is especially relevant for the development of adaptation and mitigation measures. This study aims to distinguish global and regional climate change signals from those induced by urban land cover. Therefore, it provides a compilation of observed and projected climate changes, as well as urban influences on important meteorological parameters. It is concluded that evidence for climate change signals is found predominantly in air temperature. The effect of urban land cover on local climate can be detected for several meteorological parameters, which are air and surface temperature, humidity, and wind. The meteorology of urban areas is a mixture of signals in which the influencing parameters cannot be isolated, but can be assessed qualitatively. Blending interactions between local effects and regional changes are likely to occur.

Measuring submesoscale variability is the core task of the field campaign FESSTVaL (Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg).  FESSTVaL focuses on three sources of submesoscale variability: cold pools, wind gusts and boundary layer pattern. It took place in the summer months of 2021 at the Meteorological Observatory Lindenberg – Richard-Aßmann-Observatory (MOL-RAO) of the German Weather Service (DWD) near Berlin and was initiated by the Hans-Ertel-Center for Weather Research (HErZ).In order to capture phenomena at the submesoscale (500 m – 5 km), generally not captured by conventional measurement network, a hierarchical measurement strategy is adopted. This includes wind profiling stations with a coordinated scanning strategy of several Doppler Lidars, two mobile profilers to measure thermodynamic properties and precipitation, more than 100 stations with near-surface measurements of air temperature, pressure and soil moisture, more than 20 automatic weather stations, an X-Band radar, and a number of energy balance stations. This equipment is augmented by the extensive ground-based remote sensing array at the MOL-RAO, operated by DWD and by flights operated by Unmanned Aerial Systems. Complementing to this, the benefit of a citizen-science measurement network is investigated during the campaign with “Internet-of-things” based technology and low-cost sensors built and maintained by citizens. The measurements are supplemented by high-resolution large-eddy simulations (ICON-LES).Originally planned for the summer 2020, FESSTVaL had to be postponed to 2021 and replaced by three local individual campaigns, conducted in Bayern, Lindenberg and Hamburg in 2020. Those three test campaigns demonstrated the ability of the envisionned measurement strategy and planned instruments to capture submesoscale variability and submesoscale weather phenomean. This talk will give a brief overview on the results of these three campaigns, as a foretaste to FESSTVaL, together with some of the very first measurements taken during FESSTVaL.