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EMER-Met is the new meteorological forecasting system for the protection of the population in Switzerland. It provides the meteorological basis for coping with all types of emergencies, especially in case of nuclear and chemical accidents. EMER-Met consists of a dedicated upper air measurement network and a high-resolution numerical weather prediction model. The measurement network is composed of state-of-the-art remote sensing instruments to measure accurate wind and temperature profiles in the boundary layer. At three sites, a radar wind profiler PCL1300, a Doppler lidar Windcube-200s and a microwave radiometer Hatpro-G5 are installed. The data from the measurement network are assimilated into the operational 1-km ensemble numerical weather prediction (NWP) system. In the case of the microwave radiometers, we assimilate the brightness temperatures using an adapted version of the RTTOV observation operator. To ensure best impact on the NWP results, the data quality of the measurements is of high importance and is monitored closely on a daily and monthly basis against radiosondes and the NWP model itself. EMER-Met is operational since 2022 and to our best knowledge, it is the first time that the brightness temperatures measured by surface-based microwave radiometers are assimilated operationally. This presentation will focus on the upper air network performance and its impact on NWP.

An adequate representation of the interaction between the land surface and the atmosphere is critical for both numerical weather prediction and climate models. The surface energy and mass balances are tightly coupled to the terrestrial water cycle, mainly through the state of soil moisture. An inadequate representation of the terrestrial water cycle will deteriorate the state of the land surface model and introduce biases to the atmospheric model. The validation of land surface models is challenging, as there are very few observations and the soil is highly heterogeneous. In this paper, a validation framework for land surface schemes based on catchment mass balances is presented. The main focus of our development lies in the application to kilometer-resolution numerical weather prediction and climate models, although the approach is scalable in both space and time. The methodology combines information from multiple observation-based datasets. Observational uncertainties are estimated by using independent sets of observations. It is shown that the combination of observation-based datasets and river discharge measurements close the water balance fairly well for the chosen catchments. As a showcase application, the framework is then applied to compare and validate four different versions of TERRA ML, the land surface scheme of the COSMO numerical weather prediction and climate model over five mesoscale catchments in Switzerland ranging from 105 to 1713 km2. Despite large observational uncertainties, validation results clearly suggest that errors in terrestrial storage changes are closely linked to errors in runoff generation and emphasize the crucial role of infiltration processes.

Conventional atmospheric measurement systems fail to provide observations of the essential variables characterizing the planetary boundary layer (PBL) with satisfactory spatial and temporal resolutions. Moreover, the available profile observations are very sparsely distributed. Due to this observational gap the thermodynamic structure of the PBL in the initial conditions for NWP is prone to errors, just as the representation of winds in the lower atmosphere. This affects the accuracy of forecasts of high-impact phenomena such as convective storms or winter fog and low stratus, and is relevant for the quality of downstream applications, warnings, and emergency responses.Humidity and wind exhibit a very high variability in space and time and, together with temperature, determines the atmospheric stability. It is therefore of major interest to investigate the potential benefit of assimilating additional profile observations of humidity, temperature, and wind into the NWP system. In this contribution, we give an overview of our efforts to include novel, ground-based remote sensing profiler observations into the 1km mesh-size, LETKF-based, ensemble data assimilation system COSMO/KENDA-1.We present the assimilation of brightness temperatures from three microwave radiometers installed on the Swiss Plateau using the RTTOV-gb forward operator, as well as experiments assimilating water vapor mixing ratio and temperature profiles from a Raman lidar located in Payerne. Additionally, we show an assimilation case study with wind lidars focusing on a local wind system in Basel.The assimilation of Raman lidar observations leads to improved humidity analyses and precipitation forecasts, particularly for high intensities. It is further shown that state-dependent observation errors lead to more skillful results than constant observation errors.

Over the last decade kilometer-scale weather predictions and climate projections have become established. Thereby both the representation of atmospheric processes, as well as land-surface processes need adaptions to the higher-resolution. Soil moisture is a critical variable for determining the exchange of water and energy between the atmosphere and the land surface on hourly to seasonal time scales, and a poor representation of soil processes will eventually feed back on the simulation quality of the atmosphere. Especially the partitioning between infiltration and surface runoff will feed back on the hydrological cycle. Several aspects of the coupled system are affected by a shift to kilometer-scale, convection-permitting models. First of all, the precipitation-intensity distribution changes to more intense events. Second, the time-step of the numerical integration becomes smaller. The aim of this study is to investigate the numerical convergence of the one-dimensional Richards Equation with respect to the soil hydraulic model, vertical layer thickness and time-step during the infiltration process. Both regular and non-regular (unequally spaced) grids typical in land surface modelling are considered, using a conventional semi-implicit vertical discretization. For regular grids, results from a highly idealized experiment on the infiltration process show poor numerical convergence for layer thicknesses larger than approximately 5 cm and for time steps greater than 40 s, irrespective of the soil hydraulic model. The velocity of the wetting front decreases systematically with increasing time step and decreasing vertical resolution. For non-regular grids, a new discretization based on a coordinate transform is introduced. In contrast to simpler vertical discretizations, it is able to represent the solution second-order accurate. The results for non-regular grids are qualitatively similar, as a fast increase in layer thickness with depth is equivalent to a lower vertical resolution. It is argued that the sharp gradients in soil moisture around the propagating wetting front must be resolved properly in order to achieve an acceptable numerical convergence of the Richards Equation. Furthermore, it is shown that the observed poor numerical convergence translates directly into a poor convergence of infiltration-runoff partitioning for precipitation time series characteristic of weather and climate models. As a consequence, soil simulations with low resolution in space and time may produce almost twice the amount of surface runoff within 24 hours than their high-resolution counterparts. Our analysis indicates that the problem is particularly pronounced for kilometer-resolution models.

Land-Atmosphere coupling is a fundamental process of the earth system. From the perspective of atmospheric sciences, a quantitative understanding of the surface energy and mass balances is of vital importance for both numerical weather prediction and climate modeling and needs to be represented adequately in land surface schemes. The partitioning of net radiation into sensible, latent and ground heat fluxes is dependent on the state of the land surface with soil moisture being an important state variable, introducing memory effects of monthly to annual timescales to the coupling. An inadequate representation of the terrestrial water cycle will therefore degrade forecasts and introduce biases in climate simulations. While there exist reasonable estimates for the terrestrial water cycle on subcontinental scales and various observations for evapotranspiration on the point-scale, the validation of land-surface-schemes on the kilometer-scale remains challenging. The fundamental unit on which one can validate all terms of the water balance is the catchment. Here, we present a validation framework based on water balances of mesoscale catchments. The framework incorporates observations of precipitation and streamflow as well as estimates for evapotranspiration from remote sensing data.The methodology is applied to five mesoscale catchments in Switzerland ranging from 105 km2 to 1713 km2 for the years 2010-2012. Observations include MeteoSwiss operational analyses, hourly discharge measurements provided by the federal office for the environment and the MODIS MOD16A2 evapotranspiration product. While relying on observations, these datasets are subject to substantial uncertainties. We aim to quantify the major part of this observational uncertainty by using data from FLUXNET sites in the Alpine region and a rain-gauge based precipitation dataset from MeteoSwiss (RhiresM).As a showcase application, the validation framework is used in order to compare and validate two different parameterizations for soil/ groundwater hydrology in TERRA, the land surface scheme of the COSMO numerical weather prediction and regional climate model. While both versions are based on Richard’s equation, one is implemented with a free drainage boundary condition, while the other one is implemented with a simple parameterization for groundwater. Results from TERRA standalone runs forced with COSMO analysis fields suggest that errors in terrestrial storage change are mostly driven by errors in runoff. In turn, runoff is very sensitive to the parameterization of infiltration and to soil hydraulic parameter. We show that despite large uncertainties in the observations at hand, it is possible to identify respective shortcomings of the two different groundwater formulations and to improve the simulated water balance by introducing a mathematically sound formulation for infiltration and by tuning key parameter associated to ground water discharge.