SINFONY - die Kombination aus Nowcasting und Numerischer Wettervorhersage auf der konvektiven Skala am DWD

<p>Das neue Seamless INtegrated FOrecastiNg sYstem (SINFONY) des DWD wird innerhalb der nächsten 2-3 Jahre das Licht der Welt erblicken, nach 4 Jahren intensiver Forschungs- und Entwicklungsarbeiten in zwei internen Projekten. Zunächst sollen damit insbesondere die extremen sommerlich-konvektiven Starkregenereignisse auf der Kürzestfristskala von 0 - 12 h addressiert werden, die noch immer ein großes Problem im Vorhersage- und Warndienst darstellen. Ein zusätzliches hochaufgelöstes ICON-Ensemble-Modell mit Assimilation hochaufgelöster Fernerkundungsdaten, angepasster Modellphysik und stündlich neuen Vorhersagen in einem "Rapid Update Cycle" (SINFONY-RUC-EPS) ist eine der Kernkomponenten des Systems.</p> <p>Es gibt verschiedene "optimale" Vorhersagemethoden für verschiedene Vorhersagezeitbereiche und verschiedene Wetterphänomene. Für Niederschlag und konvektive Ereignisse bis zu einigen Stunden, simple advektionsbasierte Extrapolationstechniken wie z.B. Radar-Nowcasting zeigen guten Skill bis zu etwa 2 h (natürlich situationsabhängig), während die Numerische Wettervorhersage (NWV) erst danach besser wird. Ensembles von Nowcasting sowie NWV helfen, die Vorhersageunsicherheiten abzuschätzen. "Optimal" kombinierte Niederschlagsvorhersagen als Funktion der Vorhersagezeit aus Nowcasting (dominiert am Anfang) und NWV (dominiert am Ende) bilden dann bruchfreie ("seamless") Vorhersagen.</p> <p>Verschiedene interdisziplinäre Projektteams arbeiten eng zusammen bei der Entwicklung von<br />a) Radar Nowcasting ensembles für Niederschlag, Radarreflektivität und konvektive Zell-Objekte<br />b) Stündliche SINFONY-RUC-EPS NWP auf der km-Skala mit Assimilation von 3D Radar Volumendaten (Radialwind, Reflektivität, Zell-Objekte),    Meteosat VIS Kanäle und Blitzdaten<br />c) Optimale Kombination aus Nowcasting und NWV Ensemblevorhersagen im Beobachtungsraum (Niederschlag, Reflektivität, Zell-Objekte)<br />d) Systeme zur vergleichenden Verifikation von Nowcasting und NWV in diesem Beobachtungsraum. Insbesondere die Verifikation von Zell-Objekten wird uns ganz neue Einsichten in die Repräsentation von Eigenschaften konvektiver Zellen in unseren NWV-Modellen geben.</p> <p>Für b) ermöglichen neue innovative und effiziente Vorwärtsoperatoren für Radarvolumendaten und sichtbare Satellitenkanäle die direkte Assimilation solcher Daten im Rahmen unseres LETKF-Systems (Localized Ensemble Transform Kalman Filter).  Eine fortschrittliche Modellphysik (2-Momenten Bulk Mikrophysikparametrisierung, stochastische Grenzschichtparametrisierung) trägt zur verbesserten Vorhersage konvektiver Wolken bei.</p> <p>Für c) und d) gibt das SINFONY-RUC-EPS simulierte Radarreflektivitäts-Volumenscan-Ensembles des gesamten DWD Radarverbunds alle 5 min aus den Vorhersageläufen aus. Ensembles von synthetischen Radarkomposits und vorhergesagten Tracks konvektiver Zell-Objekte werden mit derselben Software und denselben Algorithmen produziert, so wie sie auch auf die Beobachtungen angewandt werden.</p> <p>Bruchfrei kombinierte Zell-Objekte aus Nowcasting und NWV werden dabei helfen, den DWD-Warnprozess vor konvektiven Ereignissen hin zu einem flexiblen "Warn-on-objects" weiterzuentwickeln. Kombinierte Ensembles aus Niederschlags- und Reflektivitätskomposits sind auch für die Hochwasservorhersage interessant.</p> <p>Der Vortrag gibt einen Überblick über das Konzept und den aktuellen Stand des SINFONY sowie über die derzeitigen Pläne hin zu einer operationellen Einführung in den nächsten Jahren.</p> <p> </p> <p> </p>

Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction

Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of the non-Gaussianity of forecast error distributions at 1 km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency and observation number. Regional numerical weather prediction experiments are performed with the SCALE (Scalable Computing for Advanced Library and Environment) model and the LETKF (local ensemble transform Kalman filter) assimilating phased array radar observations every 30 s. The results show that non-Gaussianity develops rapidly within convective clouds and is sensitive to the DA frequency and the number of assimilated observations. The non-Gaussianity is reduced by up to 40 % when the assimilation window is shortened from 5 min to 30 s, particularly for vertical velocity and radar reflectivity.

Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction

Parameter estimation plays an important role in reducing model error and thus is of great significance to improve the simulation and prediction capabilities of the model. However, due to filtering divergence, parameter estimation by ensemble-based filters still faces great challenges. Previous studies have shown that a covariance inflation scheme could alleviate the filtering divergence problem by increasing the signal-to-noise ratio of the state-parameter covariance. In this study, we proposed a new inflation scheme based on a local ensemble transform Kalman filter (LETKF). With the new scheme, the Zebiak–Cane (Z-C) model parameters were estimated by assimilating the sea surface temperature anomaly (SSTA) data. The effectiveness of the parameter estimation and its influence on El Niño–Southern Oscillation (ENSO) prediction were evaluated in an observation system simulation experiments (OSSE) framework and real-world scenario, respectively. With the utilization of the OSSE framework, the results showed that the model parameters were successfully estimated. Parameter estimation reduced the model error when compared with only state estimation (onlySE); however, multiple parameter estimation (MPE) further improved the ENSO prediction skill by providing better initial conditions and parameter values than the single parameter estimation (SPE). Parameter estimation could thus alleviate the spring prediction barrier (SPB) phenomenon of ENSO to a certain extent. In real-world experiments, the optimized parameters significantly improved the ENSO forecasting skill, primarily in prediction of warm events. This study provides an effective parameter estimation strategy to improve climate models and further climate predictions in the real world.

Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models

Prediction of spatiotemporal chaotic systems is important in various fields, such as numerical weather prediction (NWP). While data assimilation methods have been applied in NWP, machine learning techniques, such as reservoir computing (RC), have recently been recognized as promising tools to predict spatiotemporal chaotic systems. However, the sensitivity of the skill of the machine-learning-based prediction to the imperfectness of observations is unclear. In this study, we evaluate the skill of RC with noisy and sparsely distributed observations. We intensively compare the performances of RC and local ensemble transform Kalman filter (LETKF) by applying them to the prediction of the Lorenz 96 system. In order to increase the scalability to larger systems, we applied a parallelized RC framework. Although RC can successfully predict the Lorenz 96 system if the system is perfectly observed, we find that RC is vulnerable to observation sparsity compared with LETKF. To overcome this limitation of RC, we propose to combine LETKF and RC. In our proposed method, the system is predicted by RC that learned the analysis time series estimated by LETKF. Our proposed method can successfully predict the Lorenz 96 system using noisy and sparsely distributed observations. Most importantly, our method can predict better than LETKF when the process-based model is imperfect.

Variability of North Atlantic CO₂ fluxes for the 2000–2017 period estimated from atmospheric inverse analyses

We present new estimates of the regional North Atlantic (15–80∘ N) CO2 flux for the 2000–2017 period using atmospheric CO2 measurements from the NOAA long-term surface site network in combination with an atmospheric carbon cycle data assimilation system (GEOS-Chem–LETKF, Local Ensemble Transform Kalman Filter). We assess the sensitivity of flux estimates to alternative ocean CO2 prior flux distributions and to the specification of uncertainties associated with ocean fluxes. We present a new scheme to characterize uncertainty in ocean prior fluxes, derived from a set of eight surface pCO2-based ocean flux products, and which reflects uncertainties associated with measurement density and pCO2-interpolation methods. This scheme provides improved model performance in comparison to fixed prior uncertainty schemes, based on metrics of model–observation differences at the network of surface sites. Long-term average posterior flux estimates for the 2000–2017 period from our GEOS-Chem–LETKF analyses are −0.255 ± 0.037 PgC yr−1 for the subtropical basin (15–50∘ N) and −0.203 ± 0.037 PgC yr−1 for the subpolar region (50–80∘ N, eastern boundary at 20∘ E). Our basin-scale estimates of interannual variability (IAV) are 0.036 ± 0.006 and 0.034 ± 0.009 PgC yr−1 for subtropical and subpolar regions, respectively. We find statistically significant trends in carbon uptake for the subtropical and subpolar North Atlantic of −0.064 ± 0.007 and −0.063 ± 0.008 PgC yr−1 decade−1; these trends are of comparable magnitude to estimates from surface ocean pCO2-based flux products, but they are larger, by a factor of 3–4, than trends estimated from global ocean biogeochemistry models.

Data assimilation of CrIS-NH₃ satellite observations for improving spatiotemporal NH₃ distributions in LOTOS-EUROS

Atmospheric levels of ammonia (NH3) have substantially increased during the last century, posing a hazard to both human health and environmental quality. The atmospheric budget of NH3, however, is still highly uncertain due to an overall lack of observations. Satellite observations of atmospheric NH3 may help us in the current observational and knowledge gaps. Recent observations of the Cross-track Infrared Sounder (CrIS) provide us with daily, global distributions of NH3. In this study, the CrIS-NH3 product is assimilated into the LOTOS-EUROS chemistry transport model using two different methods aimed at improving the modelled spatio-temporal NH3 distributions. In the first method NH3 surface concentrations from CrIS are used to fit spatially varying NH3 emission time factors to redistribute model input NH3 emissions over the year. The second method uses the CrIS-NH3 column data to adjust the NH3 emissions using a Local Ensemble Transform Kalman Filter (LETKF) in a top-down approach. The two methods are tested separately and combined, focusing on a region in western Europe (Germany, Belgium, and the Netherlands). In this region, the mean CrIS-NH3 total columns were up to a factor 2 higher than the simulated NH3 columns between 2014 and 2018, which, after assimilating the CrIS-NH3 columns using the LETKF algorithm, led to an increase of the total NH3 emissions of up to approximately 30%. Our results illustrate that CrIS-NH3 observations can be used successfully to estimate spatially variable NH3 time factors, and improve NH3 emission distributions temporally, especially in spring (March to May). Moreover, the use of the CrIS-based NH3 time factors resulted in an improved comparison with the onset and duration of the NH3 spring peak observed at observation sites at hourly resolution in the Netherlands. Assimilation of the CrIS-NH3 columns with the LETKF algorithm is mainly advantageous for improving the spatial concentration distribution of the modelled NH3 fields. Compared to in-situ observations, a combination of both methods led to the most significant improvements in modelled monthly NH3 surface concentration and NH4+ wet deposition fields, illustrating the usefulness of the CrIS-NH3 products to improve the temporal representativity of the model and better constrain the budget in agricultural areas.

A novel approach to surface reanalysis

<p>Reanalysis is a procedure to merge numerical model integrations and observations to obtain a synergetic representation of the past climatological state of a system, e.g., of the atmosphere. An alternative to running a full reanalysis scheme is a so-called surface reanalysis. Here, an existing reanalysis is used as prior information (for the near-surface state). This first guess is then corrected in a data assimilation step preferrably by applying observations not used in the original assimilation. In such a scheme, an additional downscaling is often performed to enhance the spatial representation of the surface reanalysis.</p><p>We present here the development of a new approach aiming to establish such a data set based on the COSMO-REA6 regional reanalysis of the Hans-Ertel-Centre and Deutscher Wetterdienst (DWD). The data assimilation step is based on the operational Local Ensemble Transform Kalman Filter (LETKF) of DWD. While the data assimilation is often performed univariately in such surface reanalysis schemes, here we apply it to various parameters at once thus conserving the covariances among the parameters and allowing for a consistent multivariate utilization of the data. Further, this reanalysis will not be restricted to the ground level and near-surface parameters. Instead, it will be extended to the lower part of the boundary layer aiming at an improved representation of wind speeds in wind turbine hub heights especially relevant for renewable energy applications. The envisaged resolution is approximately 1km and therefore enables an enhanced representation of spatial variability and heterogeneity on small scales. In addition, the LETKF is an ensemble-based data assimilation scheme which also provides uncertainty estimates through an ensemble of the re-analyzed parameters which can also be used as input for downstream applications.</p>