Christian Keil

MAP D-PHASE: Real-Time Demonstration of Weather Forecast Quality in the Alpine Region

Demonstration of probabilistic hydrological and atmospheric simulation of flood events in the Alpine region (D-PHASE) is made by the Forecast Demonstration Project in connection with the Mesoscale Alpine Programme (MAP). Its focus lies in the end-to-end flood forecasting in a mountainous region such as the Alps and surrounding lower ranges. Its scope ranges from radar observations and atmospheric and hydrological modeling to the decision making by the civil protection agents. More than 30 atmospheric high-resolution deterministic and probabilistic models coupled to some seven hydrological models in various combinations provided real-time online information. This information was available for many different catchments across the Alps over a demonstration period of 6 months in summer/ fall 2007. The Web-based exchange platform additionally contained nowcasting information from various operational services and feedback channels for the forecasters and end users. D-PHASE applications include objective model verification and intercomparison, the assessment of (subjective) end user feedback, and evaluation of the overall gain from the coupling of the various components in the end-to-end forecasting system.

A Displacement-Based Error Measure Applied in a Regional Ensemble Forecasting System

Abstract Errors in regional forecasts often take the form of phase errors, where a forecasted weather system is displaced in space or time. For such errors, a direct measure of the displacement is likely to be more valuable than traditional measures. A novel forecast quality measure is proposed that is based on a comparison of observed and forecast satellite imagery from the Meteosat-7 geostationary satellite. The measure combines the magnitude of a displacement vector calculated with a pyramid matching algorithm and the local squared difference of observed and morphed forecast brightness temperature fields. Following the description of the method and its application for a simplified case, the measure is applied to regional ensemble forecasts for an episode of prefrontal summertime convection in Bavaria. It is shown that this new method provides a plausible measure of forecast error, which is consistent with a subjective ranking of ensemble members for a sample forecast. The measure is then applied to hou...

A Displacement and Amplitude Score Employing an Optical Flow Technique

A field verification measure for precipitation forecasts is presented that combines distance and amplitude errors. It is based on an optical flow algorithm that defines a vector field that deforms, or morphs, one image to match another. When the forecast field is morphed to match the observation field, then for any point in the observation field, the magnitude of the displacement vector gives the distance to the corresponding forecast object (if any), while the difference between the observation and the morphed forecast is the amplitude error. Similarly, morphing the observation field onto the forecast field gives displacement and amplitude errors for forecast features. If observed and forecast features are separated by more than a prescribed maximum search distance, they are not matched to each other, but they are considered to be two separate amplitude errors: a missed event and a false alarm. The displacement and amplitude error components are combined to produce a displacement and amplitude score (DAS). The two components are weighted according to the principle that a displacement error equal to the maximum search distance is equivalent to the amplitude error that would be obtained by a forecast and an observed feature that are too far apart to be matched. The new score, DAS, is applied to the idealized and observed test cases of the Spatial Verification Methods Intercomparison Project (ICP) and is found to accurately measure displacement errors and quantify combined displacement and amplitude errors reasonably well, although with some limitations due to the inability of the image matcher to perfectly match complex fields.

A Polarimetric Radar Forward Operator for Model Evaluation

Abstract A polarimetric radar forward operator has been developed as a tool for the systematic evaluation of microphysical parameterization schemes in high-resolution numerical weather prediction (NWP) models. The application of such a forward operator allows a direct comparison of the model simulations to polarimetric radar observations. While the comparison of observed and synthetic reflectivity gives information on the quality of quantitative precipitation forecasts, the information from the polarimetric quantities allows for a direct evaluation of the capacity of the NWP model to realistically describe the processes involved in the formation and interactions of the hydrometeors and, hence, the performance of the microphysical parameterization scheme. This information is expected to be valuable for detecting systematic model errors and hence improve model physics. This paper summarizes the technical characteristics of the synthetic polarimetric radar (SynPolRad). Different polarimetric radar quantities...

Regime-dependent forecast uncertainty of convective precipitation

Forecast uncertainty of convective precipitation is influenced by all scales, but in different ways in different meteorological situations. Forecasts of the high resolution ensemble prediction system COSMO-DE-EPS of Deutscher Wetterdienst (DWD) are used to examine the dominant sources of uncertainty of convective precipitation. A validation with radar data using traditional as well as spatial verification measures highlights differences in precipitation forecast performance in differing weather regimes. When the forecast uncertainty can primarily be associated with local, small-scale processes individual members run with the same variation of the physical parameterisation driven by different global models outperform all other ensemble members. In contrast when the precipitation is governed by the large-scale flow all ensemble members perform similarly. Application of the convective adjustment time scale confirms this separation and shows a regime-dependent forecast uncertainty of convective precipitation.

The Oder flood in July 1997 : Transport routes of precipitable water diagnosed with an operational forecast model

A regional simulation of the severe precipitation episode which gave rise to the floods in the eastern part of Central Europe during July 1997 was performed using the meso-s-scale weather prediction model DM of Deutscher Wetterdienst. It is shown that the model reproduces the mesoscale precipitation distribution reasonably well as verified against available rain gauge observations. It is therefore used to investigate the transport routes of the bulk of moisture which led to the wide spread heavy precipitation. Inspection of the atmospheric water budget highlights the importance of the cyclonic advection of moist mediterranean air for the formation of strong precipitation in Central Europe. Autochthonous influences are comparably small.

Impact of the MAP reanalysis on the numerical simulation of the MAP-IOP2a convective system

Numerical simulations of the convective system observed during the MAP IOP2a have been performed with the Meso-NH mesoscale model, using a threefold nesting technique with horizontal mesh-sizes of 32, 8 and 2 km. The reference experiment initialized from the operational ECMWF analysis of 17 September 1999 12 UTC succeeds reasonably well in initiating the convective line over the Alpine foothills and reproducing its propagation towards the East. The sensitivity of these results to the reanalysis products is investigated. In the ECMWF MAP reanalysis, the lower atmosphere above Northern Italy is found considerably drier than in the operational analysis. As a consequence, convection is almost entirely inhibited in the mesoscale simulations based upon the reanalysis. Different sensitivity experiments further highlight the strong dependence of the mesoscale results upon the initial moisture fields.

Initial Conditions for Convective-Scale Ensemble Forecasting Provided by Ensemble Data Assimilation

AbstractA kilometer-scale ensemble data assimilation system (KENDA) based on a local ensemble transform Kalman filter (LETKF) has been developed for the Consortium for Small-Scale Modeling (COSMO) limited-area model. The data assimilation system provides an analysis ensemble that can be used to initialize ensemble forecasts at a horizontal grid resolution of 2.8 km. Convective-scale ensemble forecasts over Germany using ensemble initial conditions derived by the KENDA system are evaluated and compared to operational forecasts with downscaled initial conditions for a short summer period during June 2012.The choice of the inflation method applied in the LETKF significantly affects the ensemble analysis and forecast. Using a multiplicative background covariance inflation does not produce enough spread in the analysis ensemble leading to a degradation of the ensemble forecasts. Inflating the analysis ensemble instead by either multiplicative analysis covariance inflation or relaxation inflation methods enhanc...

The Plant–Craig Stochastic Convection Scheme in ICON and Its Scale Adaptivity

AbstractThe emergence of numerical weather prediction and climate models with multiple or variable resolutions requires that their parameterizations adapt correctly, with consistent increases in variability as resolution increases. In this study, the stochastic convection scheme of Plant and Craig is tested in the Icosahedral Nonhydrostatic GCM (ICON), which is planned to be used with multiple resolutions. The model is run in an aquaplanet configuration with horizontal resolutions of 160, 80, and 40 km, and frequency histograms of 6-h accumulated precipitation amount are compared. Precipitation variability is found to increase substantially at high resolution, in contrast to results using two reference deterministic schemes in which the distribution is approximately independent of resolution. The consistent scaling of the stochastic scheme with changing resolution is demonstrated by averaging the precipitation fields from the 40- and 80-km runs to the 160-km grid, showing that the variability is then the ...

Best Member Selection for convective-scale ensembles

This paper addresses the question how to identify the best members of a convection-permitting numerical weather prediction ensemble. Two different metrics, a classical quadratic approach using conventional observations and a spatial approach based on radar derived precipitation estimates, are employed to identify the ensemble members closest to the observations and to separate good and bad ensemble members. The characteristics of the best member selections and their performance in different weather regimes are investigated and evaluated in the context of three potential applications. The results show clear differences between the different best member selections and based on their characteristics their use in different applications is suggested. The classical quadratic metric has a higher persistence and is thus well suited for synoptic-scale applications, while the spatial metric shows good correlations with a reference measure for precipitation for short lead times and is thus better suited for very short-range applications.