Deep convection represents a classic example of limited predictability on the convective scale. We investigate the potential impact of assimilating radar reflectivity and velocity observations on the ...predictive skill of precipitation in short‐term forecasts (up to 6 hr) using the operational COSMO‐KENDA ensemble data assimilation and forecasting system in an idealized set‐up. Additionally, the role of a Gaussian‐shaped mountain providing a permanent source of predictability for the location of convective precipitation is examined with and without data assimilation.
Using a hierarchy of quality measures, we found a long‐lasting beneficial impact of radar data assimilation throughout the entire forecast range of 6 hr. The up‐scaled normalized RMS error and the Fractions Skill Score show that precipitation forecasts based on initial conditions including the assimilation of radar data are skilful on scales larger than 40 km at a lead time of 6 hr and thus are better than a reference ensemble without any data assimilation at lead times of less than 1 hr. The presence of orography strongly increases the predictability of precipitation throughout the forecast range, particularly within the immediate area and where no radar data are assimilated.
This remarkable impact of radar data assimilation exceeding 6 hr is larger and longer‐lasting than in many real modelling systems. While this is partly related to the idealized set‐up assuming a perfect forecast model, perfect large‐scale boundary conditions and a perfect radar forward operator, our study demonstrates the potential impact that could be achieved for radar data assimilation if the systematic model and operator deficiencies, as well as boundary condition errors, could be reduced. Furthermore, our results highlight the important role of orography in structuring the precipitation field, especially if no observations are assimilated.
Deep convection represents a classic example of limited predictability on the convective scale. We investigate the potential impact of assimilating radar reflectivity and velocity observations on the predictive skill of precipitation in short‐term forecasts using the operational COSMO‐KENDA ensemble data assimilation and forecasting system in an idealized set‐up. Additionally, the role of a Gaussian‐shaped mountain providing a permanent source of predictability for the location of convective precipitation is examined with and without data assimilation.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Abstract
We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated ...predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.
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The weather-regime-dependent predictability of precipitation in the convection-permitting kilometric-scale AROME-EPS is examined for the entire HyMeX-SOP1 employing the convective adjustment ...timescale. This diagnostic quantifies variations in synoptic forcing on precipitation and is associated with different precipitation characteristics, forecast skill and predictability. During strong synoptic control, which dominates the weather on 80 % of the days in the 2-month period, the domain-integrated precipitation predictability assessed with the normalized ensemble standard deviation is above average, the wet bias is smaller and the forecast quality is generally better. In contrast, the pure spatial forecast quality of the most intense precipitation in the afternoon, as quantified with its 95th percentile, is superior during weakly forced synoptic regimes. The study also considers a prominent heavy-precipitation event that occurred during the NAWDEX field campaign in the same region, and the predictability during this event is compared with the events that occurred during HyMeX. It is shown that the unconditional evaluation of precipitation widely parallels the strongly forced weather type evaluation and obscures forecast model characteristics typical for weak control.
The relative impact of individual and combined uncertainties of cloud condensation nuclei (CCN) concentration and the shape parameter of the cloud
droplet size distribution (CDSD) in the presence of ...initial and boundary condition uncertainty (IBC) on convection forecasts is quantified using the
convection-permitting model ICON-D2 (ICOsahedral Non-hydrostatic). We performed 180-member ensemble simulations for five real case studies representing different synoptic forcing
situations over Germany and inspected the precipitation variability on different spatial and temporal scales. During weak synoptic control, the
relative impact of combined microphysical uncertainty on daily area-averaged precipitation accounts for about one-third of the variability caused by
operational IBC uncertainty. The effect of combined microphysical perturbations exceeds the impact of individual CCN or CDSD perturbations and is
twice as large during weak control. The combination of IBC and microphysical uncertainty affects the extremes of daily spatially averaged rainfall
of individual members by extending the tails of the forecast distribution by 5 % in weakly forced conditions. The responses are relatively
insensitive in strong forcing situations. Visual inspection and objective analysis of the spatial variability in hourly rainfall rates reveal that
IBC and microphysical uncertainties alter the spatial variability in precipitation forecasts differently. Microphysical perturbations slightly shift
convective cells but affect precipitation intensities, while IBC perturbations scramble the location of convection during weak control. Cloud and
rainwater contents are more sensitive to microphysical uncertainty than precipitation and less dependent on synoptic control.
Based on established and proven technology of water/lithium bromide absorption chillers, customized single-stage and double-stage heat pump cycles adapted to specific applications can be designed, ...especially aiming at medium and large heating capacities of 500
kW and above. These heat pumps can either be fossil fired or driven by heat from combined heating and power (CHP) systems or other sources. In terms of primary energy saving, in many cases this is the most suitable technology to utilize the available heat sources. This is demonstrated by three examples of current installations in southern Germany. An analysis of the energetic performance and of the economic situation has been performed.
At a municipal composting plant, waste heat is generated at a temperature level of about 40–50
°C. Previously, this waste heat had to be rejected to the ambient by means of a cooling tower. A direct-fired single-stage absorption heat pump has been installed which lifts the waste heat to a temperature level of 82
°C enabling its utilization in the local heating network of a commercial area.
At a spa with various swimming pools located next to a thermal spring, a CHP engine plant is installed. The reject heat of the gas engine drives a novel two-stage absorption heat pump that utilizes the spring water as renewable heat source to provide heating of the pools and the building.
In Munich, a solar-assisted local district heating system is installed in a new housing development area with about 300 accommodation units. At this site, a seasonal hot water storage for the solar system of about 5700
m
3 is erected. At the beginning of the heating season, it serves the local heating network directly and afterwards – at a lower temperature level – it is utilized as heat source for an absorption heat pump that is driven by the municipal district heating network. By that concept two effects are accomplished: the available temperature change of the hot water storage is increased and the mean temperature of the solar system is decreased. Thus an increase of the annual efficiency of the solar collectors and finally an increase of the annual solar gain is accomplished.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
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 hourly images over a 36-h forecast period and compared with the bias and equitable threat score. The two conventional measures fail to provide any systematic distinction between different ensemble members, while the new measure identifies ensemble members of differing skill levels with a strong degree of temporal consistency. Using the displacement-based error measure, individual ensemble members are found to compare better with observations than either a short-term deterministic forecast or the ensemble mean throughout the convective period.
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A 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 enhances the analysis spread and is able to provide initial conditions that produce more consistent ensemble forecasts. The forecast quality for short forecast lead times up to 3 h is improved, and 21-h forecasts also benefit from the increased spread. Doubling the ensemble size has not only a clear positive impact on the analysis but also on the short-term ensemble forecasts, while a simple representation of model error perturbing parameters of the model physics has only a small impact. Precipitation and surface wind speed ensemble forecasts using the high-resolution KENDA-derived initial conditions are competitive compared to the operationally used downscaled initial conditions.
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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.
A wide variety of problems in global optimization, combinatorial optimization, as well as systems and control theory can be solved by using linear and semidefinite programming. Sometimes, due to the ...use of floating point arithmetic in combination with ill-conditioning and degeneracy, erroneous results may be produced. The purpose of this article is to show how rigorous error bounds for the optimal value can be computed by carefully postprocessing the output of a linear or semidefinite programming solver. It turns out that in many cases the computational costs for postprocessing are small compared to the effort required by the solver. Numerical results are presented including problems from the SDPLIB and the NETLIB LP library; these libraries contain many ill-conditioned and real-life problems.
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BFBNIB, CEKLJ, DOBA, INZLJ, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK, ZRSKP
To study the combined impact of soil moisture and microphysical perturbations on convective clouds and precipitation over Central Europe, an ensemble of five dozen real‐world weather prediction ...forecasts was conducted with the COnsortium for Small‐scale MOdeling (COSMO) model at convection‐permitting resolution for a case with weak large‐scale forcing (6 June 2016). We find a large sensitivity of precipitation, ranging from +10% to −$$ - $$23% in 12‐hr precipitation totals. While the homogeneous soil‐moisture bias of ±$$ \pm $$25% primarily controls the timing of convection initiation and the amount of surface rainfall, the number of cloud condensation nuclei and width of the cloud droplet size distribution mainly control the number, size, and lifetime of convective clouds. In moisture‐limited conditions, mainly positive couplings are acting. Drier soils, cleaner air, and a broader cloud droplet size distribution result in less rainfall. Wetter soils and more polluted conditions lead to fewer, but larger, cloud clusters. Since microphysical process rates depend systematically on the sign of the perturbations, but rainfall does not, there are compensating effects at work that buffer microphysical perturbations directly and impact the cloud condensate amount and the rainfall at the ground.
This study examines the combined impact of soil moisture and microphysical perturbations on convection. While the homogeneous soil moisture bias primarily controls the timing of convection initiation and the amount of precipitation, the number of cloud condensation nuclei and the width of the cloud droplet size distribution mainly control the number, size, and lifetime of convective clouds. Since microphysical process rates depend systematically on the sign of the perturbations, but rainfall does not, there are compensating effects buffering microphysical perturbations directly and impacting the cloud condensate amount and surface precipitation.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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