Experimental ensemble forecasts of precipitation based on a convection-resolving model

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1 ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 9: (2008) Published online 17 April 2008 in Wiley InterScience ( Experimental ensemble forecasts of precipitation based on a convection-resolving model Christoph Gebhardt, 1 * Susanne Theis, 1 Peter Krahe 2 and Volker Renner 1 1 Deutscher Wetterdienst (DWD), Offenbach, Germany 2 Bundesanstalt für Gewässerkunde (BfG), Koblenz, Germany *Correspondence to: Christoph Gebhardt, Deutscher Wetterdienst, Kaiserleistraße 42, Offenbach am Main, Germany. christoph.gebhardt@dwd.de Received: 15 November 2007 Revised: 21 February 2008 Accepted: 28 February 2008 Abstract We present the idea and the very first results of an Experimental ensemble based on the COSMO-DE (EELMK). The aim is to understand predictability limits of precipitation forecasts on the convective scale. Ensemble techniques with convection-resolving models are a new field of research. We investigate forecast uncertainties due to imperfect model physics and lateral boundary conditions. The initial case studies demonstrate the potential of EELMK to generate variability between ensemble members in terms of spatial homogeneity and intensity of precipitation. Copyright 2008 Royal Meteorological Society Keywords: forecast short-range ensemble prediction; convection-resolving scale; precipitation 1. Introduction Quantitative information about precipitation is an essential input for hydrological predictions. For a lead time of more than several hours, precipitation forecasts are based on atmospheric models. The benefit of such forecasts for hydrological applications depends considerably on the ability of the model to resolve the relevant scales and processes. The Deutscher Wetterdienst (DWD) has developed the new model COSMO (Consortium for Small-Scale Modeling)-DE (Doms and Förstner, 2004), which is in operational mode since April Forecasts are started every 3 h with a forecast lead time of 18 h each. The model domain covers the area of Germany. One of the major aims is the improvement of heavy precipitation forecasts. The very small grid spacing of 2.8 km allows to explicitly simulate small-scale processes such as deep convection. Another important advance is the assimilation of radar observations. Compared to observations by rain gauges and radar estimates, the forecasts of the model COSMO-DE look much more realistic than those of COSMO-EU (domain covers Europe with a grid spacing of 7 km) with respect to the spatial structure of precipitation patterns. The bias in the diurnal cycle of precipitation is reduced, and the assimilation of radar data has a positive impact in the first 3 4 h of forecast (M. Zimmer, University of Mainz, pers. communication, 2007). The COSMO-DE aims at explicitly simulating convective weather events; it is necessary to investigate the limits of predictability and to estimate the forecast uncertainty of the related processes on this very small scale. Therefore, DWD envisages an ensemble approach for COSMO-DE to estimate inherent uncertainties due to imperfect representation of atmospheric processes and initial conditions in the numerical prediction as well as uncertainties introduced by lateral boundaries of the model domain. As a first step, the German Federal Institute of Hydrology (Bundesanstalt für Gewässerkunde, BfG) and the German national weather service (DWD) have recently started a joint project to develop an experimental ensemble based on the COSMO-DE (EELMK). Existing ensemble prediction systems such as the ECMWF EPS (Molteni et al., 1996), the COSMO- LEPS (Montani et al., 2003), MOGREPS (Bowler et al., 2007), the CMC EPS (Lefaivre et al., 1997), and the NCEP EPS (Toth and Kalnay, 1997) mainly focus on coarser scales and longer lead times, so the project EELMK explores a new field of research. There are two major aims of the EELMK. Firstly, the project will set up a basic framework for experimental ensembles with the model COSMO-DE. Secondly, it will estimate benefits of the ensemble to hydrological predictions. This study introduces two simple ensemble setups which isolate different sources of uncertainty. One of them addresses inherent model uncertainties of the COSMO-DE by perturbing parameters of the physics schemes and vegetation-related properties in a nonstochastic approach. The other ensemble setup looks into uncertainties due to the lateral boundary conditions by nesting the COSMO-DE with unperturbed physics into members of coarser-scale ensembles. This ensemble chain transfers uncertainty across spatial scales. It uses a multimodel ensemble of the Instituto Nacional Meteorologia (INM) (Garcia-Moya et al., 2007) as a boundary condition for the COSMO- SREPS of the Meteo-Hydrological Service of Emilia- Romagna (ARPA-SIM) (Marsigli et al., 2006). The INM ensemble has a grid spacing of 25 km and Copyright 2008 Royal Meteorological Society

2 68 C. Gebhardt et al. the COSMO-SREPS ensemble has a grid spacing of 10 km. Previous studies on short-range ensemble forecasts of precipitation have shown that the uncertainties in the model formulation and in the boundary conditions are important contributors to forecast uncertainty. The relative influence of these two types of uncertainty depends on the properties of, for example, the synoptic situation, the boundary layer, the soil moisture, and the orography (Anthes et al., 1989; Stensrud et al., 1999; Bright and Mullen, 2002; Sattler and Feddersen, 2003). However, these studies are restricted to spatial scales that are coarser than the grid spacing of the COSMO- DE. Uncertainties in model formulation and boundary conditions in general are of importance at different spatial scales. However, the quantitative effect cannot be expected to be the same for high- and lowresolution models, because the resolved processes can be different in terms of scale and nonlinearity. Therefore, it is a main target of the EELMK project to investigate the relative impacts of model and boundary uncertainties on simulations by a convectionresolving model. Uncertainty in initial conditions will be included in further studies. We present the preliminary results of two case studies with the experimental COSMO-DE ensemble, which are both relevant in terms of the hydrological response in the River Saar basin. The drainage basin of the River Saar is of 7430 km 2 size and is shared more or less equally by France and Germany. Figure 1 shows the position of the Saar region in central Europe. River Saar flows into River Moselle in the north-western part of the basin near the border between Luxembourg and Germany. The area within the green boundary is that part of the COSMO-DE model domain which is shown in the following figures. It contains model grid points and covers an area of approximately km 2. The model was run on the full domain, but only the results of this subdomain are presented here. Although a case study does not provide a sufficient basis for general conclusions, the presented results, nevertheless, point to the potential of the EELMK to represent uncertainty on scales which are relevant for hydrological applications. 2. Model uncertainties of COSMO-DE Particularly, in short-range mesoscale prediction, model-related uncertainty can be highly relevant, e.g. when large-scale forcing is weak. A significant contribution to model uncertainty arises from parameterizations. They represent small-scale effects that cannot be explicitly resolved by the model grid. Current parameterizations can only sum up the mean effect of the subscale by functions depending on the resolved scale. These functions depend on empirically estimated parameters. Figure 1. River Saar and tributaries (blue lines) and its drainage basin (red line). River Saar flows into River Moselle close to the border between Luxembourg and Germany. The green boundary encloses the area for which the results are presented in this article. In the EELMK, we perturb a subset of these parameters within a range of physically reasonable values. For each member of the ensemble, we define a fixed set of perturbed parameter values which are kept constant during the entire forecast range of 24 h. As a first step, we perturb one parameter value per ensemble member. All in all, the ensemble comprises 1 unperturbed control member and 22 members with perturbed parameters. The parameters relate to cloud microphysics, boundary layer physics, and turbulence as well as to properties of the vegetation (leaf area index, plant cover, root depth). The following results of a 24-h forecast for a hydrologically relevant situation give an impression of the forecast variability generated by the parameter perturbations. Figure 2 shows the forecast 12-h accumulated precipitation field ( UTC forecast start: 17 August 2006, 0000 UTC), the corresponding radar estimates and rain-gauge observations, and the ensemble spread. The radar estimates are a product that transforms the radar reflectivity (corrected for main errors) into precipitation rate taking into account an adjustment to rain-gauge observations. River Saar drainage basin is indicated by a thick black boundary. The dotted line limits the area covered by radar data. The boundary of the plotted area is rectangular in contrast to the area indicated in Figure 1. This is due to the rotated coordinates of the COSMO-DE. There is a hydrological signal in the observed river discharge in the River Saar drainage basin linked to this rain event with a daily discharge above the mean discharges (not shown). Four representative members have been selected from the total of 23 members and are compared with radar estimates. These four members have been selected because they provide a good representation of the overall variability in the ensemble for this case study. In order to estimate the quality of the radar

3 Experimental ensemble forecasts of precipitation 69 Figure 2. Precipitation (mm) in the Saar region accumulated over UTC (starting date: 17 August 2006, 00 UTC). EELMK with perturbed physics, selected members; Upper left: radar estimates (shaded) and rain-gauge observations (dots). Lower left: ensemble spread over all 23 members. Figure 3. Area mean of precipitation (mm) in the River Saar drainage basin accumulated over UTC (starting date: 17 August 2006, 00 UTC) based on the EELMK with perturbed physics (member 1 is unperturbed). estimate, a comparison of 18 rain gauges with the nearest radar estimate has been carried out. There is a bias of 5.1 mm/12 h with a root mean squared difference of 7.4 mm/12 h. Thus, the radar overestimates the accumulated precipitation amount for this period. All members agree qualitatively with the radar estimates concerning the band of heavy rain stretching from southwest to northeast. Apart from this main feature, there are differences between the members in terms of spatial variability and intensity of precipitation on smaller scales and also in terms of features, which appear only in individual members (e.g. the local maximum in the northwest corner in member 4 and the eastern part of the region with hardly any precipitation in member 13). Comparing the radar estimates of all members reveals systematic differences regarding the location, intensity, and spatial extent of the area with heavy precipitation (e.g. >10 mm/12 h). Furthermore, all four members do not provide accurate precipitation forecasts in the southernmost part of the basin. The low ensemble spread in this region indicates that this under-forecasting is a systematic error affecting all members and that this error cannot be accounted for by uncertainty in model physics. This additionally motivates the representation of further sources of uncertainty in the EELMK. Regarding the entire subdomain, the ensemble spread indicates that there is variability in the ensemble and therefore sensitivity with respect to the parameter perturbations. The spread and the total rain amount are related in such a way that higher rain amounts come along with higher spread. Figure 3 shows the area mean over the Saar drainage basin of the 12-h accumulated precipitation for all 23 members and the radar estimates. Additionally, it shows the contributions to the overall mean by grid boxes with precipitation intensity above given thresholds. For example, this figure shows variability in the local rain intensity among the members independent of the overall mean. There is not only variability in total rain amount but also in the intensity above a given threshold. Again, there are obvious systematic differences between the EELMK and the radar estimates.

4 70 C. Gebhardt et al. The results of this case study are confirmed by results of an experiment with a larger number of forecasts. We have run a 24-h forecast for each day of August 2006 for the full COSMO-DE domain. Generally, these forecasts show a similar degree of sensitivity to the applied parameter perturbations. Further investigations show that the members have a similar mean forecast quality with slight but systematic differences. 3. Uncertainty due to lateral boundary conditions In the operational setup at DWD, the COSMO-DE is nested into forecasts of the regional model COSMO- EU (7 km grid spacing), which provides lateral boundary conditions to COSMO-DE. Depending on the large-scale flow, these lateral boundary conditions exert a noticeable influence on the COSMO-DE forecast. They can induce a considerable amount of uncertainty to the COSMO-DE forecast and consequently to the hydrological application. A promising strategy to represent the uncertainty of the boundary conditions is the generation of a sequence of ensembles which leads from a global EPS to a regional EPS and finally to the EELMK. As a first experiment, we have nested the EELMK into ensemble members of COSMO-SREPS (Marsigli et al., 2006). The COSMO-SREPS is a regional EPS on the basis of the COSMO model with a 10 km grid spacing. It is designed to forecast lead times of up to 2 3 days. COSMO-SREPS is under development at ARPA-SIM in Bologna, Italy. COSMO-SREPS itself is nested into a multimodel EPS which is developed at INM in Madrid, Spain (Garcia-Moya et al., 2007). The COSMO-SREPS comprises 16 ensemble members, which can be categorized into four groups of four members. Each group is linked to one of the global models IFS, GME, NCEP, and UKMO. Each of the four members within a group pertains to one of four different physics perturbations in the COSMO model. We have run a first case study with 16 members, each of which is nested in one of the COSMO- SREPS members. The physics of COSMO-DE are not perturbed in this ensemble. The forecast started on 17 September 2006, 0000 UTC, and covers 24 h. The intense precipitation of this period led to daily water discharges significantly above mean flood levels at the gauges of the upper Saar River. Figure 4 shows radar-estimated and forecast 12-h accumulated precipitation field for the Saar region ( UTC, forecast start: 17 September UTC). Each of the selected members is associated with one of the global models used in the INM ensemble. Within the EELMK, the main precipitation feature is similar in all members. Its location and spatial extent, however, clearly depend on the global model. Compared to the radar estimates, all forecasts can be considered as valuable on the broad scale, but there are discrepancies in spatial homogeneity and intensity. The bias between 18 rain-gauge stations and the nearest radar estimates is low with 0.7 mm/12 h and the root mean squared difference is 5.3 mm/12 h. Figure 5 analyzes the forecasts in an analogous way as Figure 3. In general, Figure 5 shows that the area mean precipitation mainly varies with the choice of the driving (via the INM and COSMO-SREPS runs) global model. However, the differences also depend on the selected intensity threshold. For example, this becomes evident when comparing UKMO-members and IFS-members. In the UKMO-members, the overall mean is higher, but the contribution by grid boxes exceeding 70 mm/12 h is clearly lower. Of course, it has to be kept in mind here that we present a single case study. More exhaustive studies for longer periods will be carried out in the future. Figure 4. Precipitation (mm) in the Saar region accumulated over UTC (starting date: 17 September 2006, 00 UTC) based on the EELMK with COSMO-SREPS boundary conditions, selected members. Upper left: radar estimates (shaded) and rain-gauge observations (dots). Lower left: ensemble spread over all 16 members.

5 Experimental ensemble forecasts of precipitation 71 Figure 5. Area mean of precipitation (mm) in the River Saar drainage basin accumulated over UTC (starting date: 17 September 2006, 00 UTC) based on the EELMK with COSMO-SREPS boundary conditions and unperturbed physics. The global model of the corresponding INM ensemble member is indicated. 4. Conclusions and outlook While a single deterministic forecast describes just one possible realization of the future atmospheric state, an ensemble forecast aims at predicting the evolution of statistical properties such as the forecast uncertainty and the ensemble mean or median. In particular, the knowledge of the forecast uncertainty provides an added value compared to a single forecast, because it allows for an interpretation of the forecast in probabilistic terms, i.e. probability of threshold exceedance or forecasts of quantiles. Such probabilistic information is essential in decision-making processes and risk assessment and risk management. We presented two case studies of ensemble forecasts for the River Saar basin. The forecast for such a river basin on a regional scale is an application where an ensemble approach can be highly valuable. The forecast of rain inside or rain outside the basin and of the amount of rain can be very uncertain. It is important to transfer this information on uncertainty into hydrological applications, e.g. water level forecasts, to ensure proper risk management. The two case studies showed that the experimental ensemble with the COSMO-DE has the potential to represent forecast uncertainty due to uncertainties in model formulation and boundary conditions on the regional scale. The members of the ensemble with perturbed physics differ in terms of spatial variability and intensity on small scales. The differences between members in the case study with varying boundary conditions are mainly controlled by the global models of the INM ensemble, which points at variability at larger scales. Thus, combining the two approaches in one ensemble will be a promising way to account simultaneously for uncertainties on different scales and potentially their interactions. In addition, both case studies show variability in rain intensity above given thresholds (Figures 3 and 5), which is useful in predicting probabilities of threshold exceedance. In future studies, the relative importance of the model uncertainties and the uncertainties due to boundary conditions will be investigated by running the two ensembles independently and as a combined ensemble for selected days of the MAP D-PHASE Operations Period from 1 June to 30 November 2007 ( The COSMO-SREPS runs are provided by ARPA-SIM. The EELMK project is directly linked to the COSMO-DE-EPS project at DWD, which was initiated in 2007 in order to set up an operational shortrange ensemble within the next years. Future research will focus on the combination of parameter perturbations within the physics ensemble, the combination of model and boundary uncertainties, and the representation of uncertainties in initial conditions. A hydrological validation of this new type of meteorological ensemble forecasts and a discussion of its features and benefits with regard to operational discharge and water level forecasting are foreseen by the use of the operational water level forecasting system WAVOS-Saar (Krahe et al., 2006). A precipitationrunoff model (HBV) is implemented, and forecasts are calculated for 13 gauging stations. Of special interest is the question how the knowledge of the ensemble spread can be used in flood warning and flood risk management. Acknowledgements We thank two anonymous reviewers for helpful and valuable comments. We are indebted to Chiara Marsigli and Jose Garcia-Moya for providing boundary data and sharing the expertise in ensemble prediction. We also thank Axel Seifert for valuable discussions. This research project is funded by the Bundesanstalt für Gewässerkunde, Koblenz, Germany. References Anthes RA, Kuo YH, Hsie EY, Lownam S, Bettge TW Estimation of skill and uncertainty in regional numerical models.

6 72 C. Gebhardt et al. Quarterly Journal of the Royal Meteorological Society 115: Bowler NE, Arribas A, Mylne KR, Robertson KB The MOGREPS short-range ensemble prediction system Part I: System description. NWP Technical Report 497, Met Office: Exeter; 20pp. Bright DR, Mullen SL Short-range ensemble forecasts of precipitation during southwest monsoon. Weather and Forecasting 17: Doms G, Förstner J Development of a kilometer-scale NWPsystem: LMK. In COSMO Newsletter No. 4, Doms G, Schättler U, Montani A (eds). COSMO Group: Offenbach, Germany Garcia-Moya JA, Callado A, Santos C, Santos-Munoz D, Simarro J Predictability for short-range forecast: a multi-model approach. Monthly Weather Review Submitted. Krahe P, Rachimow C, Hammer M, Schikowski G Ergebnisse des EU-Projektes FloodMan Operationelles Hochwasserwarn- und Managementsystem unter Verwendung von Satellitendaten sowie von hydrologischen und hydraulischen Modellen. In Niederschlag- Abflussmodellierung zur Verlängerung des Vorhersagezeitraumes operationeller Wasserstands- und Abflussvorhersagen, BfG (eds). BfG Veranstaltungen 3: Koblenz. Lefaivre L, Houtekamer PL, Bergeron A, Verret R The CMC ensemble prediction system. InProceedings ECMWF 6th Workshop on Meteorological Operational Systems, Reading, ECMWF, Marsigli C, Montani A, Paccagnella T The COSMO-SREPS project. Newsletter of the 28th EWGLAM and 13th SRNWP meetings, 9 12 October 2006, Zurich, Molteni F, Buizza R, Palmer TN, Petroliagis T The ECMWF ensemble prediction system: methodology and validation. Quarterly Journal of the Royal Meteorological Society 123: Montani A, Capaldo M, Cesari D, Marsigli C, Modigliani U, Nerozzi F, Paccagnella T, Patruno P, Tibaldi S Operational limited-area ensemble forecasts based on the Lokal Modell. ECMWF Newsletter 98: 2 7. Sattler K, Feddersen H Study on limited-area short-range ensemble approaches targeted for heavy rain in Europe. In Research Activities in Atmospheric and Oceanic Modelling. CAS/JSC Working Group on Numerical Experimentation. Report No. 33, Stensrud DJ, Brooks HE, Du J, Tracton MS, Rogers E Using ensembles for short-range forecasting. Monthly Weather Review 127: Toth Z, Kalnay E Ensemble forecasting at NCEP and the breeding method. Monthly Weather Review 125: