A highly modular and scale-consistent Terrestrial Systems Modeling Platform (TerrSysMP) is presented. The modeling platform consists of an atmospheric model (Consortium for Small-Scale Modeling; COSMO), a land surface model (the NCAR Community Land Model, version 3.5; CLM3.5), and a 3D variably saturated groundwater flow model (ParFlow). An external coupler (Ocean Atmosphere Sea Ice Soil, version 3.0; OASIS3) with multiple executable approaches is employed to couple the three independently developed component models, which intrinsically allows for a separation of temporal-spatial modeling scales and the coupling frequencies between the component models.Idealized TerrSysMP simulations are presented, which focus on the interaction of key hydrologic processes, like runoff production (excess rainfall and saturation) at different hydrological modeling scales and the drawdown of the water table through groundwater pumping, with processes in the atmospheric boundary layer. The results show a strong linkage between integrated surface-groundwater dynamics, biogeophysical processes, and boundary layer evolution. The use of the mosaic approach for the hydrological component model (to resolve subgrid-scale topography) impacts simulated runoff production, soil moisture redistribution, and boundary layer evolution, which demonstrates the importance of hydrological modeling scales and thus the advantages of the coupling approach used in this study.Real data simulations were carried out with TerrSysMP over the Rur catchment in Germany. The inclusion of the integrated surface-groundwater flow model results in systematic patterns in the root zone soil moisture, which influence exchange flux distributions and the ensuing atmospheric boundary layer development. In a first comparison to observations, the 3D model compared to the 1D model shows slightly improved predictions of surface fluxes and a strong sensitivity to the initial soil moisture content.
BACKGROUND. State predictions for terrestrial systems are usually performed by means of numerical process models, which consider all compartments. However, it is unclear to what extent system heterogeneity must be considered for a particular set of conditions and for different types of model predictions.Numerical process models of the terrestrial system usually consider three vertically stacked mediarepresenting the subsurface, including ground and surface water; vegetation; and atmosphere-that are typically coded in three separate compartment models. These compartment models interact at their
In 2011, the German Federal Ministry of Transport, Building and Urban Development laid the foundation of the Hans-Ertel Centre for Weather Research [Hans-Ertel-Zentrum für Wetterforschung (HErZ)] in order to better connect fundamental meteorological research and teaching at German universities and atmospheric research centers with the needs of the German national weather service Deutscher Wetterdienst (DWD). The concept for HErZ was developed by DWD and its scientific advisory board with input from the entire German meteorological community. It foresees core research funding of about €2,000,000 yr−1 over a 12-yr period, during which time permanent research groups must be established and DWD subjects strengthened in the university curriculum. Five priority research areas were identified: atmospheric dynamics and predictability, data assimilation, model development, climate monitoring and diagnostics, and the optimal use of information from weather forecasting and climate monitoring for the benefit of society. Following an open call, five groups were selected for funding for the first 4-yr phase by an international review panel. A dual project leadership with one leader employed by the academic institute and the other by DWD ensures that research and teaching in HErZ is attuned to DWD needs and priorities, fosters a close collaboration with DWD, and facilitates the transfer of fundamental research into operations. In this article, we describe the rationale behind HErZ and the road to its establishment, present some scientific highlights from the initial five research groups, and discuss the merits and future development of this new concept to better link academic research with the needs and challenges of a national weather service.
Fog in complex terrain shows large temporal and spatial variations that can only be simulated with a three-dimensional model, but more modifications than simply increasing the resolution are needed. For a better representation of fog, we present a second-moment cloud water scheme with a parametrization of the Köhler theory which is combined with the mixed-phase Ferrier microphysics scheme. The more detailed PAFOG microphysics produce many differences to the first-moment Ferrier scheme and are responsible for the typically low liquid water content of fog. The inclusion of droplet sedimentation in the Ferrier scheme cannot reproduce the results obtained with PAFOG, as there is a large sensitivity to the sedimentation velocity. With explicitly predicted droplet number concentrations, sedimentation of cloud water can be modelled with variable fall speeds, which mainly affects the vertical distribution of cloud water and the end of the fog's life cycle. The complex topography of the Swiss Alps and their surroundings are used for model testing. As the focus is on the model's ability to forecast the spatial distribution of fog, cloud patterns derived from high-resolution MSG satellite data, rather than few point observations from ground stations, are used. In a five-day period of anticyclonic conditions, the satellite-observed fog patterns showed large day-to-day variations from almost no fog to large areas of fog. This variability was very well predicted in the three-dimensional fog forecast. Furthermore, the second-moment cloud water scheme shows a better agreement with the satellite observations than its firstmoment counterpart. For model initialization, the complex topography is actually a simplifying factor, as cold air flow and pooling dominate the more uncertain processes of evapotranspiration or errors in the soil moisture field.
The numerical prediction of fog requires a very high vertical resolution of the atmosphere. Owing to a prohibitive computational effort of high resolution three dimensional models, operational fog forecast is usually done by means of one dimensional fog models. An important condition for a successful fog forecast with one dimensional models consists of the proper integration of observational data into the numerical simulations. The goal of the present study is to introduce new methods for the consideration of these data in the one dimensional radiation fog model PAFOG. First, it will be shown how PAFOG may be initialized with observed visibilities. Second, a nudging scheme will be presented for the inclusion of measured temperature and humidity profiles in the PAFOG simulations. The new features of PAFOG have been tested by comparing the model results with observations of the German Meteorological Service. A case study will be presented that reveals the importance of including local observations in the model calculations. Numerical results obtained with the modified PAFOG model show a distinct improvement of fog forecasts regarding the times of fog formation, dissipation as well as the vertical extent of the investigated fog events. However, model results also reveal that a further improvement of PAFOG might be possible if several empirical model parameters are optimized. This tuning can only be realized by comprehensive comparisons of model simulations with corresponding fog observations.
<p>The field campaign FESSTVaL (Field Experiment on sub-mesoscale spatio-temporal variability in Lindenberg) was carried out by 16 institutions from May to August 2021 in the surroundings of the Meteorological Observatory Lindenberg &#8211; Richard-A&#223;mann-Observatory of the German Meteorological Service (DWD). The project aims at an improved understanding of the initiation and interaction of cold pools and wind gusts in the summertime convective boundary layer. Such weather phenomena can cause great damage, but are, however, difficult to capture by conventional surface networks due to their small-scale nature. Unique to this campaign is the deployment of a high-density near-surface measurement network made of over 100 ground-level stations for measurements of temperature and pressure, complemented by 20 automatic weather stations as well as a dense network of soil moisture measurements. An X-band radar and several energy balance stations were also used. The surface network was augmented by a network of vertical profiling instruments including nine Doppler LiDAR systems for measurements of the wind profile and turbulence variables up to an altitude of several kilometers, four microwave radiometers, and measurement flights with unmanned and remotely-controlled aircraft. As a supplement to these measurements, the project investigates the gain of a citizen science measurement network.</p><p>This presentation will shed light on the 4D structure and evolution of cold pools associated with a strong convective event as viewed by the different sensors. The cold pool observations will be compared to forecasts and to large-eddy simulations conducted for that particular case. Overall, the results of the project will serve to improve the representation of such small-scale processes in numerical weather prediction and to define new measurement strategies. The data products of the campaign are treated under the FAIR principle and are made available via a platform at the Integrated Climate Data Center of the University of Hamburg.&#160;</p>
<p>Measuring submesoscale variability is the core task of the field campaign FESSTVaL (Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg).&#160; 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 &#8211; Richard-A&#223;mann-Observatory (MOL-RAO) of the German Weather Service (DWD) near Berlin and was initiated by the Hans-Ertel-Center for Weather Research (HErZ).</p><p>In order to capture phenomena at the submesoscale (500 m &#8211; 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 &#8220;Internet-of-things&#8221; based technology and low-cost sensors built and maintained by citizens. The measurements are supplemented by high-resolution large-eddy simulations (ICON-LES).</p><p>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. </p>
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