Browsing by Subject "Datenassimilation"

Now showing 1 - 3 of 3
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    Assimilation of ground-based and airborne lidar data into the MM5 4D-Var system
    (2010) Grzeschik, Matthias; Wulfmeyer, Volker
    This work investigates the impact of assimilating water vapor Light Detection and Ranging (lidar) data into mesoscale Numerical Weather Prediction (NWP) models. Two cases from the field campaigns International H20 Project 2002 (IHOP_2002) and International Lindenberg Campaign for Assessment of Humidity- and Cloud-Profiling Systems and its Impact on High-Resolution Modelling 2005 (LAUNCH-2005) are presented. In the first case, airborne water vapor Differential Absorption Lidar (DIAL) data are used for an assimilation for 24 May 2002, where convection occurred along an eastward moving dryline in western Texas and Oklahoma south of a triple point that formed in western Oklahoma. In the second case, a network of three ground based water vapor Raman lidars, operated behind a sharp frontal rain band with a northwesterly flow, are used. The method employed, Four-Dimensional Variational Data Assimilation (4D-Var), is described in relation to other methods and the implementation is given in detail. The data assimilation results in a large modification of the initial fields. The assimilation into the preconvective conditions changed not only the water vapor field but also the location of convergence lines, causing positive modification of Convective Initiation (CI). In the LAUNCH-2005 case a strong correction of the vertical structure and the absolute values of the initial water-vapor field of the order of 1g/kg was found. This occurred mainly upstream of the lidar systems within an area that was comparable with the domain covered by the lidar systems. The correction of the water-vapor field was validated using independent Global Positioning System (GPS) sensors. Much better agreement with GPS zenith wet path delay was achieved with the initial water-vapor field after 4D-Var. Furthermore, the impact of the assimilation and its temporal evolution was investigated with introduced measures. The results demonstrate the high value of accurate vertically resolved mesoscale water vapor observations and advanced data assimilation systems for short-range weather forecasting.
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    Convective-scale data assimilation of thermodynamic lidar data into the weather research and forecasting model
    (2022) Thundathil, Rohith Muraleedharan; Wulfmeyer, Volker
    This thesis studies the impact of assimilating temperature and humidity profiles from ground-based lidar systems and demonstrates its value for future short-range forecast. Thermodynamic profile obtained from the temperature Raman lidar and the water-vapour differential absorption lidar of the University of Hohenheim during the High Definition of Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) project Observation Prototype Experiment (HOPE) are assimilated into the Weather Research and Forecasting model Data Assimilation (WRFDA) system by means of a new forward operator. The impact study assimilating the high-resolution thermodynamic lidar data was conducted using variational and ensemble-based data assimilation methods. The first part of the thesis describes the development of the thermodynamic lidar operator and its implementation through a deterministic DA impact study. The operator facilitates the direct assimilation of water vapour mixing ratio (WVMR), a prognostic variable in the WRF model, without conversion to relative humidity. Undesirable cross sensitivities to temperature are avoided here so that the complete information content of the observation with respect to the water vapour is provided. The assimilation experiments were performed with the three-dimensional variational (3DVAR) DA system with a rapid update cycle (RUC) with hourly frequency over ten hours. The DA experiments with the new operator outperformed the previously used relative humidity operator, and the overall humidity and temperature analyses improved. The simultaneous assimilation of temperature and WVMR resulted in a degradation of the temperature analysis compared to the improvement observed in the sole temperature assimilation experiment. The static background error covariance matrix (B) in the 3DVAR was identified as the reason behind this behaviour. The correlation between the temperature and WVMR variables in the background error covariance matrix of the 3DVAR, which is static and not flow-dependent, limited the improvement in temperature. The second part of the thesis provides a solution for overcoming the static B matrix issue. A hybrid, ensemble-based approach was applied using the Ensemble Transform Kalman Filter (ETKF) and the 3DVAR to add flow dependency to the B matrix. The hybrid experiment resulted in a 50% lower temperature and water vapour root mean square error (RMSE) than the 3DVAR experiment. Comparisons against independent radiosonde observations showed a reduction of RMSE by 26% for water vapour and 38% for temperature. The planetary boundary layer (PBL) height of the analyses also showed an improvement compared to the available ceilometer. The impact of assimilating a single lidar vertical profile spreads over a 100 km radius, which is promising for future assimilation of water vapour and temperature data from operational lidar networks for short-range weather forecasting. A forecast improvement was observed for 7 hours lead time compared with the ceilometer derived planetary boundary layer height observations and 4 hours with Global Navigation Satellite System (GNSS) derived integrated water vapour observations. With the help of sophisticated DA systems and a robust network of lidar systems, the thesis throws light on the future of short-range operational forecasting.
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    Model evaluation and data assimilation impact studies in the framework of COPS
    (2012) Schwitalla, Thomas; Wulfmeyer, Volker
    The goal of this thesis was the study of new approaches for improving and investigating quantitative precipitation forecasting (QPF), e.g., by optimizing model resolution, physics combination, and data assimilation. A forecasting system based on the Mesoscale Model 5 (MM5) was compared against other operational numerical weather prediction models from Meteo France, MeteoSwiss and the German Weather Service primarily with respect to daytime precipitation. First, a notable daytime dry bias was observed. It appears to be the result of a too small high-resolution domain and the switched-off convection parameterization from the second to the innermost domain. Even the application of a 4-dimensional variational data assimilation (4DVAR) with GPS slant total delays (STD) does not solve this problem due to inconsistent model physics between the 4DVAR and the forecasting model. Nevertheless, the MM5 is in good agreement with the shape of the observed diurnal cycle after the spin-up phase. As the development of the MM5 was suspended, a transition to the new Weather Research and Forecasting (WRF) model system was made after the D-PHASE period (end of 2007). This system features state-of-the-art physics packages and also a variational data assimilation system. As a new observing system, GPS Zenith Total Delay (ZTD) data from Central Europe were incorporated into the 3-dimensional variational data assimilation (3DVAR) system to further improve the initial water vapor field. A first study with this system revealed an improvement of the integrated water vapor RMSE of about 15% and a small but positive impact on the spatial and quantitative precipitation forecast. Additionally, the importance of assimilating upper air observations and the necessity to select a large, convection permitting model domain emerged. Finally a rapid update cycle (RUC) approach, comparable to operational forecast centers, has been developed for a convection-permitting configuration of the WRF model. The system is capable to assimilate radar observations from Germany and France, GPS-ZTD data and satellite radiances and can be applied even for near real-time applications. First experiments with this system show promising results in comparison to other operational models.