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- Author or Editor: Christian Keil x
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Abstract
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.
Abstract
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.
Abstract
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.
Abstract
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.
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 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.
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 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.
Abstract
The forecasting performance of the operational Lokal Modell (LM) of the German Weather Service (DWD) is evaluated for two severe winter storms that crossed central Europe in December 1999. Synthetic satellite images constructed from model output fields are compared with observed imagery from a Meteosat satellite. Also, synthetic radar images constructed from forecast precipitation fields are taken for validation against observed precipitation as represented by the European radar composite of the DWD. Comparisons are performed by visual inspection of satellite and radar imagery and by calculating statistical measures such as frequency histograms of observed and synthetic brightness temperature. Whereas the visual inspection allows detection of even finescale details in both synthetic satellite and radar imagery, for example, the position of fronts and rainbands, the statistical analysis reveals model deficiencies with respect to the representation of upper-level cloudiness. The operational LM did not incorporate a cloud-ice parameterization scheme at the time that these storms occurred. Additional experiments were performed to investigate the impact of the cloud-ice parameterization on brightness temperature. As expected, the longwave radiative impact is seen to be strongly influenced by the presence of cloud ice. Furthermore, changing the value of the ice-to-snow autoconversion threshold in the microphysics scheme by a factor of 2 leads to a significant improvement in synthetic brightness temperature as compared with observations. The results suggest that synthetic satellite and radar images could be used to perform quality control on numerical weather forecasts in real time. Basic ideas are proposed for an automated quality-control system using an image-matching tool developed at the German Aerospace Center (DLR).
Abstract
The forecasting performance of the operational Lokal Modell (LM) of the German Weather Service (DWD) is evaluated for two severe winter storms that crossed central Europe in December 1999. Synthetic satellite images constructed from model output fields are compared with observed imagery from a Meteosat satellite. Also, synthetic radar images constructed from forecast precipitation fields are taken for validation against observed precipitation as represented by the European radar composite of the DWD. Comparisons are performed by visual inspection of satellite and radar imagery and by calculating statistical measures such as frequency histograms of observed and synthetic brightness temperature. Whereas the visual inspection allows detection of even finescale details in both synthetic satellite and radar imagery, for example, the position of fronts and rainbands, the statistical analysis reveals model deficiencies with respect to the representation of upper-level cloudiness. The operational LM did not incorporate a cloud-ice parameterization scheme at the time that these storms occurred. Additional experiments were performed to investigate the impact of the cloud-ice parameterization on brightness temperature. As expected, the longwave radiative impact is seen to be strongly influenced by the presence of cloud ice. Furthermore, changing the value of the ice-to-snow autoconversion threshold in the microphysics scheme by a factor of 2 leads to a significant improvement in synthetic brightness temperature as compared with observations. The results suggest that synthetic satellite and radar images could be used to perform quality control on numerical weather forecasts in real time. Basic ideas are proposed for an automated quality-control system using an image-matching tool developed at the German Aerospace Center (DLR).
Abstract
The 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 same as that obtained from the 160-km model run. It is shown that upscale averaging of the input variables for the convective closure is important for producing consistent variability at high resolution.
Abstract
The 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 same as that obtained from the 160-km model run. It is shown that upscale averaging of the input variables for the convective closure is important for producing consistent variability at high resolution.
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.
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.
Abstract
This study uses the convective adjustment time scale to identify the climatological frequency of equilibrium and nonequilibrium convection in different parts of the contiguous United States (CONUS) as modeled by the operational convection-allowing High-Resolution Rapid Refresh (HRRR) forecast system. We find a qualitatively different climatology in the northern and southern domains separated by the 40°N parallel. The convective adjustment time scale picks up the fact that convection over the northern domains is governed by synoptic flow (leading to equilibrium), while locally forced, nonequilibrium convection dominates over the southern domains. Using a machine learning algorithm, we demonstrate that the convective adjustment time-scale diagnostic provides a sensible classification that agrees with the underlying dynamics of equilibrium and nonequilibrium convection. Furthermore, the convective adjustment time scale can indicate the model quantitative precipitation forecast (QPF) quality, as it correctly reflects the higher QPF skill for precipitation under strong synoptic forcing. This diagnostic based on the strength of forcing for convection will be employed in future studies across different parts of CONUS to objectively distinguish different weather situations and explore the potential connection to warm-season precipitation predictability.
Significance Statement
An objective classification metric that can delineate a wide range of forecasts into distinct scenarios can serve as a valuable tool. This study represents a pioneering effort in utilizing the convective adjustment time scale to identify the climatological frequency of warm-season precipitation under varying levels of synoptic forcing in different parts of the contiguous United States (CONUS). The results demonstrate that the convective adjustment time scale is a robust metric for categorizing precipitation events and establishing a direct link to their predictability. Overall, this study provides a valuable framework for future studies focused on the CONUS domain, offering guidance on how to employ the convective adjustment time scale to classify weather regimes and explore the influence of environmental conditions on predictability of convection.
Abstract
This study uses the convective adjustment time scale to identify the climatological frequency of equilibrium and nonequilibrium convection in different parts of the contiguous United States (CONUS) as modeled by the operational convection-allowing High-Resolution Rapid Refresh (HRRR) forecast system. We find a qualitatively different climatology in the northern and southern domains separated by the 40°N parallel. The convective adjustment time scale picks up the fact that convection over the northern domains is governed by synoptic flow (leading to equilibrium), while locally forced, nonequilibrium convection dominates over the southern domains. Using a machine learning algorithm, we demonstrate that the convective adjustment time-scale diagnostic provides a sensible classification that agrees with the underlying dynamics of equilibrium and nonequilibrium convection. Furthermore, the convective adjustment time scale can indicate the model quantitative precipitation forecast (QPF) quality, as it correctly reflects the higher QPF skill for precipitation under strong synoptic forcing. This diagnostic based on the strength of forcing for convection will be employed in future studies across different parts of CONUS to objectively distinguish different weather situations and explore the potential connection to warm-season precipitation predictability.
Significance Statement
An objective classification metric that can delineate a wide range of forecasts into distinct scenarios can serve as a valuable tool. This study represents a pioneering effort in utilizing the convective adjustment time scale to identify the climatological frequency of warm-season precipitation under varying levels of synoptic forcing in different parts of the contiguous United States (CONUS). The results demonstrate that the convective adjustment time scale is a robust metric for categorizing precipitation events and establishing a direct link to their predictability. Overall, this study provides a valuable framework for future studies focused on the CONUS domain, offering guidance on how to employ the convective adjustment time scale to classify weather regimes and explore the influence of environmental conditions on predictability of convection.
Abstract
Precipitation is affected by soil moisture spatial variability. However, this variability is not well represented in atmospheric models that do not consider soil moisture transport as a three-dimensional process. This study investigates the sensitivity of precipitation to the uncertainty in the representation of terrestrial water flow. The tools used for this investigation are the Weather Research and Forecasting (WRF) Model and its hydrologically enhanced version, WRF-Hydro, applied over central Europe during April–October 2008. The model grid is convection permitting, with a horizontal spacing of 2.8 km. The WRF-Hydro subgrid employs a 280-m resolution to resolve lateral terrestrial water flow. A WRF/WRF-Hydro ensemble is constructed by modifying the parameter controlling the partitioning between surface runoff and infiltration and by varying the planetary boundary layer (PBL) scheme. This ensemble represents terrestrial water flow uncertainty originating from the consideration of resolved lateral flow, terrestrial water flow uncertainty in the vertical direction, and turbulence parameterization uncertainty. The uncertainty of terrestrial water flow noticeably increases the normalized ensemble spread of daily precipitation where topography is moderate, surface flux spatial variability is high, and the weather regime is dominated by local processes. The adjusted continuous ranked probability score shows that the PBL uncertainty improves the skill of an ensemble subset in reproducing daily precipitation from the E-OBS observational product by 16%–20%. In comparison to WRF, WRF-Hydro improves this skill by 0.4%–0.7%. The reproduction of observed daily discharge with Nash–Sutcliffe model efficiency coefficients generally above 0.3 demonstrates the potential of WRF-Hydro in hydrological science.
Abstract
Precipitation is affected by soil moisture spatial variability. However, this variability is not well represented in atmospheric models that do not consider soil moisture transport as a three-dimensional process. This study investigates the sensitivity of precipitation to the uncertainty in the representation of terrestrial water flow. The tools used for this investigation are the Weather Research and Forecasting (WRF) Model and its hydrologically enhanced version, WRF-Hydro, applied over central Europe during April–October 2008. The model grid is convection permitting, with a horizontal spacing of 2.8 km. The WRF-Hydro subgrid employs a 280-m resolution to resolve lateral terrestrial water flow. A WRF/WRF-Hydro ensemble is constructed by modifying the parameter controlling the partitioning between surface runoff and infiltration and by varying the planetary boundary layer (PBL) scheme. This ensemble represents terrestrial water flow uncertainty originating from the consideration of resolved lateral flow, terrestrial water flow uncertainty in the vertical direction, and turbulence parameterization uncertainty. The uncertainty of terrestrial water flow noticeably increases the normalized ensemble spread of daily precipitation where topography is moderate, surface flux spatial variability is high, and the weather regime is dominated by local processes. The adjusted continuous ranked probability score shows that the PBL uncertainty improves the skill of an ensemble subset in reproducing daily precipitation from the E-OBS observational product by 16%–20%. In comparison to WRF, WRF-Hydro improves this skill by 0.4%–0.7%. The reproduction of observed daily discharge with Nash–Sutcliffe model efficiency coefficients generally above 0.3 demonstrates the potential of WRF-Hydro in hydrological science.
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.
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.
Abstract
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