Papers by Christoph Schraff
Quarterly Journal of the Royal Meteorological Society, Jul 1, 2008
To improve very‐short‐range forecasts particularly in convective situations, a version of the COS... more To improve very‐short‐range forecasts particularly in convective situations, a version of the COSMO‐Model (formerly known as LM) which simulates deep convection explicitly (horizontal grid length: 2.8 km) has been developed and is now run operationally at DWD. This model uses a prognostic type of precipitation scheme accounting for the horizontal drift of falling hydrometeors. To initialise convective‐scale events, the latent heat nudging (LHN) approach has been adopted for the assimilation of surface precipitation rates derived from radar reflectivity data. It is found that a conventional LHN scheme designed for larger‐scale models with diagnostic treatment of precipitation does not perform well and leads to strong overestimation of precipitation when applied to the convective‐scale model with a prognostic treatment of precipitation. As illustrated here, surface precipitation and vertically integrated latent heating are far less correlated horizontally and temporally in such a model than with diagnostic precipitation, and this implies a violation of the basic assumption of LHN.Several revisions to the LHN scheme have therefore been developed in view of the characteristic model behaviour so as to re‐enhance the validity of the basic assumption and to reduce greatly the overestimation of precipitation during assimilation. With the revised scheme, the model is able to simulate the precipitation patterns in good agreement with radar observations during the assimilation and the first hours of the forecast. The scheme also has a positive impact on screen‐level parameters and on the longer‐term climatology of the model. Extending the temporal impact of the radar observations further into the free forecast will be the focus of future research. Copyright © 2008 Royal Meteorological Society
Meteorology and Atmospheric Physics, 1997
A number of problems related to mesoscale numerical prediction of low stratus in the Alpine regio... more A number of problems related to mesoscale numerical prediction of low stratus in the Alpine region are formulated, and addressed in a series of experiments for two wintertime cases. These problems include modelling aspects and issues of data assimilation which are relevant particularly in relation to the observation nudging technique. A focus is on the influence of orography. A comparison of operational optimum interpolation, and nudging of routine rawinsonde and surface-level data reveals that nudging often yields better analyses and forecasts of low stratus, and notably of the sharp vertical temperature and humidity gradients. However, the humidity advection scheme of the model and, near steep terrain, particularly the horizontal diffusion along the model's c~levels are identified to contribute to spurious vertical smoothing which can result in erroneous cloud dissipation. On occasions, forecasts succeeding a nudging period are more sensitive to this process due to the sharper initial vertical gradients. Specific problems of representiveness arise when low-level rawinsonde information is spread laterally along the sloping c~-levels from low to high terrain. A new concept for or-layer models is introduced by spreading the observational information along isentropic surfaces, and this tends to improve the low stratus prediction over steep and even moderate orography. A partly successful attempt to take advantage of the steep Alpine orography is made by applying this concept to surface-level humidity data from a high-resolution network of Alpine surface stations which are distributed relatively uniformly in the vertical.
Quarterly Journal of the Royal Meteorological Society, Mar 15, 2016
An ensemble data assimilation system for 3D radar reflectivity data is introduced for the convect... more An ensemble data assimilation system for 3D radar reflectivity data is introduced for the convection-permitting numerical weather prediction model of the COnsortium for Smallscale MOdelling (COSMO) based on the Kilometre-scale ENsemble Data Assimilation system (KENDA), developed by Deutscher Wetterdienst and its partners. KENDA provides a state-of-the-art ensemble data assimilation system on the convective scale for operational data assimilation and forecasting based on the Local Ensemble Transform Kalman Filter (LETKF). In this study, the Efficient Modular VOlume RADar Operator is applied for the assimilation of radar reflectivity data to improve short-term predictions of precipitation. Both deterministic and ensemble forecasts have been carried out. A case-study shows that the assimilation of 3D radar reflectivity data clearly improves precipitation location in the analysis and significantly improves forecasts for lead times up to 4 h, as quantified by the Brier Score and the Continuous Ranked Probability Score. The influence of different update rates on the noise in terms of surface pressure tendencies and on the forecast quality in general is investigated. The results suggest that, while high update rates produce better analyses, forecasts with lead times of above 1 h benefit from less frequent updates. For a period of seven consecutive days, assimilation of radar reflectivity based on the LETKF is compared to that of DWD's current operational radar assimilation scheme based on latent heat nudging (LHN). It is found that the LETKF competes with LHN, although it is still in an experimental phase.
<p><span>The study of radar data assimilation (reflectivity, radial w... more <p><span>The study of radar data assimilation (reflectivity, radial wind and cell object) in NWP models and its effect particularly on short-term forecast has been intensified recently at DWD. In particular, the seamless Integrated ForecastiNg sYstem (SINFONY) project, which develops a short-term forecasting system with a focus on convective events from minutes up to 12 hours ahead, shows clearly the benefit of radar data assimilation in improving the short-term forecast. This system integrates Nowcasting techniques for radar data with numerical weather prediction (NWP based on the new ICON-model) in a seamless way with initial focus on severe summertime convective events and associated hazards such as heavy precipitation, hail and wind gusts. </span></p><p><span>Besides, radar data assimilation is being operationally used in the short-range ensemble numerical weather prediction (SRNWP) system (ICON-D2-KENDA LETKF system) at DWD since 2020 (radial wind starting in March 2020 and reflectivity starting in June 2020). This is in addition to the traditional Latent Heat Nudging (LHN) of 2D radar-derived precipitation rates. For both systems, SRNWP and SINFONY, the usage of 3D radar data is not only advantageous but crucial to improve the forecast skills related to convection and precipitation.</span></p><p><span>We will present the latest results of our research in radar assimilation at DWD including the application of radar data assimilation together with a more sophisticated cloud microphysiscs parameterization (a 2-moment bulk scheme) and in combination with the LHN in the SINFONY forecasting system. We also study to assimilate radar information in the alternative form of convective cell objects. Of particular interest are for example the specification of the radar observation error, but also other topics related to the improvement of short-term forecasts.</span></p>
<p>In the framework of the project SINFONY at Deutscher Wetterdienst, we work towar... more <p>In the framework of the project SINFONY at Deutscher Wetterdienst, we work towards seamless prediction at the very-short range blending over from observation-based nowcasting to numerical weather prediction. The key goals that we pursue in this context are:<br>1.    To deliver forecasts earlier to be displayed in the meteorological workstation NinJo of our forecasters, which is realized by hourly forecast initialization in our newly-developed Rapid Update Cycle (RUC) and shorter latency for observation arrival ahead of data assimilation. <br>2.    To provide seamlessly combined products integrating nowcasted and forecasted radar reflectivities as well as precipitation from both forecasting systems.<br>3.    To achieve a better representation of precipitation processes and convective cells in our NWP model to allow for the seamless blending with nowcasts. For this purpose, we use a two-moment microphysics scheme that predicts not only mixing ratios of hydrometeor species, but also their particle size distribution. This is also of great importance for the data assimilation of geostationary all-sky satellite data assimilation, for data assimilation of lightning data and essentially radar reflectivities.</p><p>In this presentation, we explain how data assimilation of cloudy visible satellite data can help to improve the accuracy of clouds and precipitation processes in NWP forecasts to assist a seamless blending of nowcasting and NWP in terms of radar reflectivities mentioned in 2) and 3). </p><p>Visible satellite data are directly sensitive to liquid water path, ice water path and specific humidity  which are integral quantities related to precipitation processes. Moreover, cloud positioning can be improved by deleting false alarm clouds and convective cells and introducing missing ones to the forecast. A key advantage is that visible data are particularly sensitive to water clouds, which allows to constrain convective cells already at their state of initiation in the initial conditions of our RUC forecasts.</p><p>We elaborate on the basic principles of satellite data assimilation in our ICON-D2-KENDA system making use of an ensemble Kalman filter. Case studies will be shown to demonstrate how data assimilation of all-sky satellite data reduces analysis and forecast error of clouds and precipitation. Finally, we show the impact in our rapid update pre-operational system over longer periods of time. </p>
A scheme based on the nudging method (Newtonian relaxation) is developed for direct assimilation ... more A scheme based on the nudging method (Newtonian relaxation) is developed for direct assimilation of rawinsonde and screen-level data into a mesoscale model for the Alpine region. It is applied to routine observations in various meteorological situations to ex¬ amine its potential capabilities and limitations for numerical weather prediction (NWP) purposes. Aspects of primary interest are the impact of different data, the influence of steep orography particularly with low stratus, and the benefit from the nudging for mixed-layer simulation in typical summertime smog conditions. The impact of different types of data is first studied with idealized experiments de¬ signed to relate to basic theoretical considerations. A version of the operational model (mesh width: 14 km) of the Swiss Meteorological Institute is used to assess the scheme's performance. The benefit from different data is then examined with observing system simulation experiments (OSSE's) and real data experiments using the complete set of routinely available observations. The OSSE approach is also deployed for tuning
Frontiers in Earth Science, Mar 12, 2020
Quarterly Journal of the Royal Meteorological Society, Apr 1, 2018
This article reviews developments towards assimilating cloud and precipitation-affected satellite... more This article reviews developments towards assimilating cloud and precipitation-affected satellite radiances at operational forecasting centres. Satellite data assimilation is moving beyond the 'clear-sky' approach that discards any observations affected by cloud. Some centres already assimilate cloud and precipitation-affected radiances operationally and the most popular approach is known as 'all-sky', which assimilates all observations directly as radiances, whether they are clear, cloudy or precipitating, using models (both for radiative transfer and forecasting) that are capable of simulating cloud and precipitation with sufficient accuracy. Other frameworks are being tried including the assimilation of humidity retrieved from cloudy observations using Bayesian techniques. Although the all-sky technique is now proven for assimilation of microwave radiances, it has yet to be demonstrated operationally for infrared radiances, though several centres are getting close. Assimilating frequently-available allsky infrared observations from geostationary satellites could give particular benefit for short-range forecasting. More generally, assimilating cloud and precipitation-affected satellite observations improves forecasts into the medium-range globally, and it can also improve the analysis and shorter-range forecast of otherwise poorly-observed weather phenomena as diverse as tropical cyclones and wintertime low cloud.
<p>In the framework of the project SINFONY at Deutscher Wetterdienst, we work towar... more <p>In the framework of the project SINFONY at Deutscher Wetterdienst, we work towards seamless prediction at the very-short range blending over from observation-based nowcasting to numerical weather prediction. The key goals that we pursue in this context are:<br>1.    To deliver forecasts earlier to be displayed in the meteorological workstation NinJo of our forecasters, which is realized by hourly forecast initialization in our newly-developed Rapid Update Cycle (RUC) and shorter latency for observation arrival ahead of data assimilation. <br>2.    To provide seamlessly combined products integrating nowcasted and forecasted radar reflectivities as well as precipitation from both forecasting systems.<br>3.    To achieve a better representation of precipitation processes and convective cells in our NWP model to allow for the seamless blending with nowcasts. For this purpose, we use a two-moment microphysics scheme that predicts not only mixing ratios of hydrometeor species, but also their particle size distribution. This is also of great importance for the data assimilation of geostationary all-sky satellite data assimilation, for data assimilation of lightning data and essentially radar reflectivities.</p><p>In this presentation, we explain how data assimilation of cloudy visible satellite data can help to improve the accuracy of clouds and precipitation processes in NWP forecasts to assist a seamless blending of nowcasting and NWP in terms of radar reflectivities mentioned in 2) and 3). </p><p>Visible satellite data are directly sensitive to liquid water path, ice water path and specific humidity  which are integral quantities related to precipitation processes. Moreover, cloud positioning can be improved by deleting false alarm clouds and convective cells and introducing missing ones to the forecast. A key advantage is that visible data are particularly sensitive to water clouds, which allows to constrain convective cells already at their state of initiation in the initial conditions of our RUC forecasts.</p><p>We elaborate on the basic principles of satellite data assimilation in our ICON-D2-KENDA system making use of an ensemble Kalman filter. Case studies will be shown to demonstrate how data assimilation of all-sky satellite data reduces analysis and forecast error of clouds and precipitation. Finally, we show the impact in our rapid update pre-operational system over longer periods of time. </p>
<p><span>The study of radar data assimilation (reflectivity, radial w... more <p><span>The study of radar data assimilation (reflectivity, radial wind and cell object) in NWP models and its effect particularly on short-term forecast has been intensified recently at DWD. In particular, the seamless Integrated ForecastiNg sYstem (SINFONY) project, which develops a short-term forecasting system with a focus on convective events from minutes up to 12 hours ahead, shows clearly the benefit of radar data assimilation in improving the short-term forecast. This system integrates Nowcasting techniques for radar data with numerical weather prediction (NWP based on the new ICON-model) in a seamless way with initial focus on severe summertime convective events and associated hazards such as heavy precipitation, hail and wind gusts. </span></p><p><span>Besides, radar data assimilation is being operationally used in the short-range ensemble numerical weather prediction (SRNWP) system (ICON-D2-KENDA LETKF system) at DWD since 2020 (radial wind starting in March 2020 and reflectivity starting in June 2020). This is in addition to the traditional Latent Heat Nudging (LHN) of 2D radar-derived precipitation rates. For both systems, SRNWP and SINFONY, the usage of 3D radar data is not only advantageous but crucial to improve the forecast skills related to convection and precipitation.</span></p><p><span>We will present the latest results of our research in radar assimilation at DWD including the application of radar data assimilation together with a more sophisticated cloud microphysiscs parameterization (a 2-moment bulk scheme) and in combination with the LHN in the SINFONY forecasting system. We also study to assimilate radar information in the alternative form of convective cell objects. Of particular interest are for example the specification of the radar observation error, but also other topics related to the improvement of short-term forecasts.</span></p>
operationally. Its main purposes is the prediction of severe weather events related to deep moist... more operationally. Its main purposes is the prediction of severe weather events related to deep moist convection and to interactions of the flow with small scale topography. To satisfy this goal the initial conditions include small scale precipitation information obtained from
Moisture is very important for many atmospheric processes. A correct description of the moisture ... more Moisture is very important for many atmospheric processes. A correct description of the moisture of NWP models is essential to simulate the hydrological cycle precisely. An appropriate way to get the model a realistic moisture field is to assimilate moisture observations. In all operational configurations of the COSMO model, radio sondes are the only source of observational information on humidity, except for sceen-level data, which are given very limited weight however. Therefore any systematic error in the radio sondes humidity data will likely be detrimental. Recently many investigations revealed that the humidity measurements of radio sondes seems to be biased compared to other humidity observations. In the two operational configurations of Deutscher Wetterdienst (DWD), COSMO-EU, which has a mesh width of 7 km and covers Europe, and COSMO-DE (2.8 km, Germany and environs) 56% resp. 81% of all radio sondes used are of type Vaisala RS 92. Miloshevich et al. (2009) investigated the...
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Papers by Christoph Schraff