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  • ddc:551.6  (11)
  • John Wiley & Sons, Ltd  (11)
  • English  (11)
  • Russian
  • 2020-2024  (8)
  • 2020-2023  (3)
  • 1
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    A nonlocal three‐dimensional turbulence parameterization (NLT3D) for numerical weather prediction models (2021)
    John Wiley & Sons, Ltd | Chichester, UK
    Details
    Publication Date: 2022-03-30
    Description: With increasing resolution of numerical weather prediction (NWP) models, classical subgrid‐scale processes become increasingly resolved on the model grid. In particular, turbulence in the planetary boundary layer (PBL) is vertically already partially resolved in contemporary models. For classical local PBL schemes, resulting up‐gradient heat transports cannot be treated correctly. Thus, nonlocal turbulence schemes have been developed in the past. As the horizontal grid sizes of NWP models become smaller than a few kilometers, the large turbulence eddies in the PBL will also start to become partially resolved in the horizontal direction. A very flexible way to formulate nonlocal turbulent exchange is the transilient matrix method, which is used here to develop a new turbulence parameterization. The resulting NLT3D scheme applies transilient mixing matrices to subgrid‐scale transports in all three dimensions. We compare results of WRF real‐case simulations including our scheme, a classical local turbulence scheme (MYNN), and an existing nonlocal one‐dimensional scheme (ACM2) with observations from field campaigns over homogeneous terrain (CASES‐99) and complex terrain (CAPTEX). Over homogeneous terrain, all three schemes similarly well capture the observed surface fluxes and radiosonde profiles, whereas over complex terrain more differences become obvious. During a tracer release experiment (CAPTEX) over the Appalachian mountain region, the mixing and vertical extent of the PBL turn out to be decisive to reproduce the observed advection speed of the tracer‐marked air mass. Deeper mixing not only accelerates surface winds but also enables tracer to travel faster at higher altitudes and then mix back to the ground. As results from a version of NLT3D with only standard horizontal Smagorinsky diffusion (NLT1D) demonstrate, simulating three‐dimensional turbulence can be beneficial already at horizontal grid sizes of a few kilometers.
    Description: Decreasing grid sizes in numerical weather prediction models demand the inclusion of nonlocal effects and horizontal turbulence in turbulence parameterizations. This is the motivation for the development of the nonlocal three‐dimensional turbulence (NLT3D) scheme. Vertical nonlocal mixing accelerates the horizontal transport of near‐surface tracers by fast advection at higher altitudes (see figure), and horizontal turbulence enhances tracer dispersion. As validated by observations, both effects are beneficial to the forecast quality already at grid sizes of a few kilometers.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:551.6
    Language: English
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  • 2
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    Year‐round sub‐seasonal forecast skill for Atlantic–European weather regimes (2021)
    John Wiley & Sons, Ltd | Chichester, UK
    Details
    Publication Date: 2022-03-29
    Description: Weather regime forecasts are a prominent use case of sub‐seasonal prediction in the midlatitudes. A systematic evaluation and understanding of year‐round sub‐seasonal regime forecast performance is still missing, however. Here we evaluate the representation of and forecast skill for seven year‐round Atlantic–European weather regimes in sub‐seasonal reforecasts from the European Centre for Medium‐Range Weather Forecasts. Forecast calibration improves regime frequency biases and forecast skill most strongly in summer, but scarcely in winter, due to considerable large‐scale flow biases in summer. The average regime skill horizon in winter is about 5 days longer than in summer and spring, and 3 days longer than in autumn. The Zonal Regime and Greenland Blocking tend to have the longest year‐round skill horizon, which is driven by their high persistence in winter. The year‐round skill is lowest for the European Blocking, which is common for all seasons but most pronounced in winter and spring. For the related, more northern Scandinavian Blocking, the skill is similarly low in winter and spring but higher in summer and autumn. We further show that the winter average regime skill horizon tends to be enhanced following a strong stratospheric polar vortex (SPV), but reduced following a weak SPV. Likewise, the year‐round average regime skill horizon tends to be enhanced following phases 4 and 7 of the Madden–Julian Oscillation (MJO) but reduced following phase 2, driven by winter but also autumn and spring. Our study thus reveals promising potential for year‐round sub‐seasonal regime predictions. Further model improvements can be achieved by reduction of the considerable large‐scale flow biases in summer, better understanding and modeling of blocking in the European region, and better exploitation of the potential predictability provided by weak SPV states and specific MJO phases in winter and the transition seasons.
    Description: The overall sub‐seasonal forecast performance (biases and skill) for predicting seven year‐round Atlantic–European weather regimes is highest in winter and lowest in summer. The year‐round skill horizon is shortest for the European Blocking and longest for the Zonal Regime and Greenland Blocking (see figure). Furthermore, the winter skill horizon tends to be enhanced following a strong stratospheric polar vortex but reduced following a weak one. Madden–Julian Oscillation phases 4 and 7 tend to increase and phase 2 to decrease the year‐round skill horizon.
    Description: Helmholtz‐Gemeinschaft http://dx.doi.org/10.13039/501100001656
    Keywords: ddc:551.6
    Language: English
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  • 3
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    Ensemble Kalman filter based data assimilation for tropical waves in the MJO skeleton model (2022)
    John Wiley & Sons, Ltd | Chichester, UK
    Details
    Publication Date: 2022-10-06
    Description: The Madden–Julian oscillation (MJO) is the dominant component of tropical intraseasonal variability, with wide‐reaching impacts even on extratropical weather and climate patterns. However, predicting the MJO is challenging. One reason is the suboptimal state estimates obtained with standard data assimilation (DA) approaches. These are typically based on filtering methods with Gaussian approximations and do not take into account physical properties that are important specifically for the MJO. In this article, a constrained ensemble DA method is applied to study the impact of different physical constraints on the state estimation and prediction of the MJO. The quadratic programming ensemble (QPEns) algorithm utilized extends the standard stochastic ensemble Kalman filter (EnKF) with specifiable constraints on the updates of all ensemble members. This allows us to recover physically more consistent states and to respect possible associated non‐Gaussian statistics. The study is based on identical twin experiments with an adopted nonlinear model for tropical intraseasonal variability. This so‐called skeleton model succeeds in reproducing the main large‐scale features of the MJO and closely related tropical waves, while keeping adequate simplicity for fast experiments on intraseasonal time‐scales. Conservation laws and other crucial physical properties from the model are examined as constraints in the QPEns. Our results demonstrate an overall improvement in the filtering and forecast skill when the model's total energy is conserved in the initial conditions. The degree of benefit is found to be dependent on the observational setup and the strength of the model's nonlinear dynamics. It is also shown that, even in cases where the statistical error in some waves remains comparable with the stochastic EnKF during the DA stage, their prediction is improved remarkably when using the initial state resulting from the QPEns.
    Description: Unsatisfactory predictions of the MJO are partly due to DA methods that do not respect non‐Gaussian PDFs and the physical properties of the tropical atmosphere. Therefore the QPEns, an algorithm extending a stochastic EnKF with state constraints, is tested here on a simplified model for the MJO and associated tropical waves. Our series of identical twin experiments shows, in particular, that a constraint on the truth's nonlinear total energy improves forecasts statistically and can, in certain situations, even prevent filter divergence. image
    Description: Deutsche Forschungsgemeinschaft : Heisenberg Award (DFG JA1077/4‐1); Transregional Collaborative Research Center SFB / TRR 165 “Waves to Weather” http://dx.doi.org/10.13039/501100001659
    Description: Office of Naval Research (ONR) http://dx.doi.org/10.13039/100000006
    Keywords: ddc:551.6
    Language: English
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  • 4
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    Parameterizing building effects on airflows within the urban canopy layer for high‐resolution models using a nudging approach (2023)
    John Wiley & Sons, Ltd | Chichester, UK
    Details
    Publication Date: 2023-11-24
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉In this study, a new multilayer urban canopy parameterization for high‐resolution (∼1 km) atmospheric models using the nudging approach to represent the impacts of urban canopies on airflow is presented. In our parameterization, a nudging term is added to the momentum equations and a source term to the turbulent kinetic energy equation to account for building effects. The challenge of this parameterization lies in defining appropriate values for the nudging coefficient and the weighting function used to reflect canopy effects. Values of both are derived and the parameterization developed is implemented and tested for idealized cases in the Mesoscale Transport and Stream model (METRAS). Comparison data are taken from obstacle‐resolving microscale model results. Results show that the parameterization using the nudging approach can simulate aerodynamic effects induced within the canopy by obstacles well, in terms of reduction of wind speeds and production of additional turbulent kinetic energy. Thus, models with existing nudging can use this approach as an efficient and effective method to parameterize dynamic urban canopy effects.〈/p〉
    Description: 〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉In this study, a new multilayer urban canopy parameterization for high‐resolution (∼1 km) atmospheric models using the nudging approach to represent the impacts of urban canopies on airflow is presented. Results show that the parameterization developed can simulate aerodynamic effects induced within the canopy by obstacles well, in terms of reduction of wind speeds and production of additional turbulent kinetic energy. Models with existing nudging can use this approach as an efficient and effective method to parameterize dynamic urban canopy effects. 〈boxed-text position="anchor" id="qj4524-blkfxd-0001" content-type="graphic" xml:lang="en"〉〈graphic position="anchor" id="jats-graphic-1" xlink:href="urn:x-wiley:00359009:media:qj4524:qj4524-toc-0001"〉 〈/graphic〉 〈/boxed-text〉〈/p〉
    Description: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy‐EXC 2037 'CLICCS‐Climate, Climatic Change, and Society'
    Keywords: ddc:551.6 ; canopy parameterization ; evaluation ; nudging ; numerical modelling ; urban boundary layer ; urban canopy parameterization
    Language: English
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  • 5
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    Convergence of forecast distributions in a 100,000‐member idealised convective‐scale ensemble (2023)
    John Wiley & Sons, Ltd | Chichester, UK
    Details
    Publication Date: 2023-07-25
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Many operational weather services use ensembles of forecasts to generate probabilistic predictions. Computational costs generally limit the size of the ensemble to fewer than 100 members, although the large number of degrees of freedom in the forecast model would suggest that a vastly larger ensemble would be required to represent the forecast probability distribution accurately. In this study, we use a computationally efficient idealised model that replicates key properties of the dynamics and statistics of cumulus convection to identify how the sampling uncertainty of statistical quantities converges with ensemble size. Convergence is quantified by computing the width of the 95% confidence interval of the sampling distribution of random variables, using bootstrapping on the ensemble distributions at individual time and grid points. Using ensemble sizes of up to 100,000 members, it was found that for all computed distribution properties, including mean, variance, skew, kurtosis, and several quantiles, the sampling uncertainty scaled as 〈mml:math id="jats-math-1" display="inline" overflow="scroll"〉〈mml:msup〉〈mml:mrow〉〈mml:mi〉n〈/mml:mi〉〈/mml:mrow〉〈mml:mrow〉〈mml:mo form="prefix"〉−〈/mml:mo〉〈mml:mn〉1〈/mml:mn〉〈mml:mo stretchy="false"〉/〈/mml:mo〉〈mml:mn〉2〈/mml:mn〉〈/mml:mrow〉〈/mml:msup〉〈/mml:math〉 for sufficiently large ensemble size 〈mml:math id="jats-math-2" display="inline" overflow="scroll"〉〈mml:mrow〉〈mml:mi〉n〈/mml:mi〉〈/mml:mrow〉〈/mml:math〉. This behaviour is expected from the Central Limit Theorem, which further predicts that the magnitude of the uncertainty depends on the distribution shape, with a large uncertainty for statistics that depend on rare events. This prediction was also confirmed, with the additional observation that such statistics also required larger ensemble sizes before entering the asymptotic regime. By considering two methods for evaluating asymptotic behaviour in small ensembles, we show that the large‐〈mml:math id="jats-math-3" display="inline" overflow="scroll"〉〈mml:mrow〉〈mml:mi〉n〈/mml:mi〉〈/mml:mrow〉〈/mml:math〉 theory can be applied usefully for some forecast quantities even for the ensemble sizes in operational use today.〈/p〉
    Description: 〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉An idealised ensemble that replicates key properties of the dynamics and statistics of cumulus convection is used to identify how sampling uncertainty of statistical quantities converges with ensemble size. A universal asymptotic scaling for this convergence was found, which was dependent on the statistic and the distribution shape, with largest uncertainty for statistics that depend on rare events. This is demonstrated in the figure below for a Gaussian distributed model variable, where the sampling uncertainty (y‐axis) for 5 quantiles (red lines) indicates that after a certain ensemble size, it begins converging asymptotically (grey lines), and the more extreme the quantile, the more members it requires for this to be the case. 〈boxed-text position="anchor" id="qj4410-blkfxd-0001" content-type="graphic" xml:lang="en"〉〈graphic position="anchor" id="jats-graphic-1" xlink:href="urn:x-wiley:00359009:media:qj4410:qj4410-toc-0001"〉
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: Klaus Tschira Stiftung http://dx.doi.org/10.13039/501100007316
    Keywords: ddc:551.6 ; asymptotic convergence ; distributions ; ensembles ; idealised model ; sampling uncertainty ; weather prediction
    Language: English
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  • 6
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    Estimating the benefit of Doppler wind lidars for short‐term low‐level wind ensemble forecasts (2022)
    John Wiley & Sons, Ltd | Chichester, UK
    Details
    Publication Date: 2024-02-12
    Description: This work focuses on the potential of a network of Doppler lidars for the improvement of short‐term forecasts of low‐level wind. For the impact assessment, we developed a new methodology that is based on ensemble sensitivity analysis (ESA). In contrast to preceding network design studies using ESA, we calculate the explicit sensitivity including the inverse of the background covariance B matrix to account directly for the localization scale of the assimilation system. The new method is applied to a pre‐existing convective‐scale 1,000‐member ensemble simulation to mitigate effects of spurious correlations. We evaluate relative changes in the variance of a forecast metric, that is, the low‐level wind components averaged over the Rhein–Ruhr metropolitan area in Germany. This setup allows us to compare the relative variance change associated with the assimilation of hypothetical observations from a Doppler wind lidar with respect to the assimilation of surface‐wind observations only. Furthermore, we assess sensitivities of derived variance changes to a number of settings, namely observation errors, localization length scale, regularization factor, number of instruments in the network, and their location, as well as data availability of the lidar measurements. Our results demonstrate that a network of 20–30 Doppler lidars leads to a considerable variance reduction of the forecast metric chosen. On average, an additional network of 25 Doppler lidars can reduce the 1–3 hr forecast error by a factor of 1.6–3.3 with respect to 10‐m wind observations only. The results provide the basis for designing an operational network of Doppler lidars for the improvement of short‐term low‐level wind forecasts that could be especially valuable for the renewable energy sector.
    Description: This study presents the potential of a Doppler lidar network to improve short‐term low‐level wind forecasts. The approach used in this study does not require real observations and can provide valuable information for designing an operational network. The study is based on a convective‐scale 1,000‐member ensemble simulation over Germany. The results show that Doppler lidars lead to considerable variance reduction and should be considered for future observational networks.
    Description: Hans‐Ertel‐Centre for Weather Research funded by the German Federal Ministry for Transportation and Digital Infrastructure
    Description: https://doi.org/10.5281/zenodo.6331758
    Keywords: ddc:551.6 ; covariance ; data assimilation ; ensemble sensitivity analysis ; localization ; low‐level wind forecasts ; network of Doppler lidars ; observing system
    Language: English
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  • 7
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    Statistical relevance of meteorological ambient conditions and cell attributes for nowcasting the life cycle of convective storms (2023)
    John Wiley & Sons, Ltd | Chichester, UK
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    Publication Date: 2024-03-06
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉The usually short lifetime of convective storms and their rapid development during unstable weather conditions makes forecasting these storms challenging. It is necessary, therefore, to improve the procedures for estimating the storms' expected life cycles, including the storms' lifetime, size, and intensity development. We present an analysis of the life cycles of convective cells in Germany, focusing on the relevance of the prevailing atmospheric conditions. Using data from the radar‐based cell detection and tracking algorithm KONRAD of the German Weather Service, the life cycles of isolated convective storms are analysed for the summer half‐years from 2011 to 2016. In addition, numerous convection‐relevant atmospheric ambient variables (e.g., deep‐layer shear, convective available potential energy, lifted index), which were calculated using high‐resolution COSMO‐EU assimilation analyses (0.0625°), are combined with the life cycles. The statistical analyses of the life cycles reveal that rapid initial area growth supports wider horizontal expansion of a cell in the subsequent development and, indirectly, a longer lifetime. Specifically, the information about the initial horizontal cell area is the most important predictor for the lifetime and expected maximum cell area during the life cycle. However, its predictive skill turns out to be moderate at most, but still considerably higher than the skill of any ambient variable is. Of the latter, measures of midtropospheric mean wind and vertical wind shear are most suitable for distinguishing between convective cells with short lifetime and those with long lifetime. Higher thermal instability is associated with faster initial growth, thus favouring larger and longer living cells. A detailed objective correlation analysis between ambient variables, coupled with analyses discriminating groups of different lifetime and maximum cell area, makes it possible to gain new insights into their statistical connections. The results of this study provide guidance for predictor selection and advancements of nowcasting applications.〈/p〉
    Description: 〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Based on a combination of data of the cell tracking algorithm KONRAD of the German Weather Service and COSMO‐EU model analyses for the summer half‐years from 2011 to 2016, statistical relationships between storm attributes (lifetime and maximum horizontal area), and ambient variables as well as the storms' history are quantified. The initial growth of the cell area is a better indicator of the lifetime and maximum area than ambient variables are. Of the latter, measures of the midtropospheric wind and vertical wind shear, in particular, are most suitable for distinguishing between convective cells with short and long lifetimes, whereas higher convective instability favours larger cells. 〈boxed-text position="anchor" id="qj4505-blkfxd-0001" content-type="graphic" xml:lang="en"〉〈graphic position="anchor" id="jats-graphic-1" xlink:href="urn:x-wiley:00359009:media:qj4505:qj4505-toc-0001"〉 〈/graphic〉 〈/boxed-text〉〈/p〉
    Description: Bundesministerium für Digitales und Verkehr http://dx.doi.org/10.13039/100008383
    Keywords: ddc:551.6 ; convective storms ; life cycle ; multisource data ; nowcasting ; statistics ; weather prediction
    Language: English
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  • 8
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    Multi‐model assessment of sub‐seasonal predictive skill for year‐round Atlantic–European weather regimes (2023)
    John Wiley & Sons, Ltd | Chichester, UK
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    Publication Date: 2024-02-21
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉The prediction skill of sub‐seasonal forecast models is evaluated for seven year‐round weather regimes in the Atlantic–European region. Reforecasts based on models from three prediction centers are considered and verified against weather regimes obtained from ERA‐Interim reanalysis. Results show that predicting weather regimes as a proxy for the large‐scale circulation outperforms the prediction of raw geopotential height. Greenland blocking tends to have the longest year‐round skill horizon for all three models, especially in winter. On the other hand, the skill is lowest for the European blocking regime for all three models, followed by the Scandinavian blocking regime. Furthermore, all models struggle to forecast flow situations that cannot be assigned to a weather regime (so‐called no regime), in comparison with weather regimes. Related to this, variability in the occurrence of no regime, which is most frequent in the transition seasons, partly explains the predictability gap between transition seasons and winter and summer. We also show that models have difficulties in discriminating between related regimes. This can lead to misassignments in the predicted regime during flow situations in which related regimes manifest. Finally, we document the changes in skill between model versions, showing important improvements for the ECMWF and NCEP models. This study is the first multi‐model assessment of year‐round weather regimes in the Atlantic–European domain. It advances our understanding of the predictive skill for weather regimes, reveals strengths and weaknesses of each model, and thus increases our confidence in the forecasts and their usefulness for decision‐making.〈/p〉
    Description: 〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉This study is the first sub‐seasonal multi‐model assessment of seven year‐round weather regimes in the Atlantic–European domain. Greenland blocking tends to have the longest year‐round skill horizon for all models, especially in winter. The skill is lowest for the European blocking regime for all models, followed by Scandinavian blocking. Variability in the occurrence of no regime partly explains the predictability gap between the transition seasons and winter and summer. 〈boxed-text position="anchor" id="qj4512-blkfxd-0001" content-type="graphic" xml:lang="en"〉〈graphic position="anchor" id="jats-graphic-1" xlink:href="urn:x-wiley:00359009:media:qj4512:qj4512-toc-0001"〉 〈/graphic〉 〈/boxed-text〉〈/p〉
    Description: Helmholtz Association http://dx.doi.org/10.13039/501100001656
    Description: AXPO Solutions AGN/A
    Keywords: ddc:551.6 ; blocking ; Europe ; North Atlantic oscillation ; windows of opportunity
    Language: English
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  • 9
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    Impact of Aeolus wind lidar observations on the representation of the West African monsoon circulation in the ECMWF and DWD forecasting systems (2023)
    John Wiley & Sons, Ltd | Chichester, UK
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    Publication Date: 2023-11-17
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉〈italic toggle="no"〉Aeolus〈/italic〉 is the first satellite mission to acquire vertical profiles of horizontal line‐of‐sight winds globally and thus fills an important gap in the Global Observing System, most notably in the Tropics. This study explores the impact of this dataset on analyses and forecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and Deutscher Wetterdienst (DWD), focusing specifically on the West African Monsoon (WAM) circulation during the boreal summers of 2019 and 2020. The WAM is notoriously challenging to forecast and is characterized by prominent and robust large‐scale circulation features such as the African Easterly Jet North (AEJ‐North) and Tropical Easterly Jet (TEJ). Assimilating 〈italic toggle="no"〉Aeolus〈/italic〉 generally improves the prediction of zonal winds in both forecasting systems, especially for lead times above 24 h. These improvements are related to systematic differences in the representation of the two jets, with the AEJ‐North weakened at its southern flank in the western Sahel in the ECMWF analysis, while no obvious systematic differences are seen in the DWD analysis. In addition, the TEJ core is weakened in the ECMWF analysis and strengthened on its southern edge in the DWD analysis. The regions where the influence of 〈italic toggle="no"〉Aeolus〈/italic〉 on the analysis is greatest correspond to the Intertropical Convergence Zone (ITCZ) region for ECMWF and generally the upper troposphere for DWD. In addition, we show the presence of an altitude‐ and orbit‐dependent bias in the Rayleigh‐clear channel, which causes the zonal winds to speed up and slow down diurnally. Applying a temperature‐dependent bias correction to this channel contributes to a more accurate representation of the diurnal cycle and improved prediction of the WAM winds. These improvements are encouraging for future investigations of the influence of 〈italic toggle="no"〉Aeolus〈/italic〉 data on African Easterly Waves and associated Mesoscale Convective Systems.〈/p〉
    Description: 〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Forecasting in tropical Africa is hampered by large model errors and low availability of conventional observations. The assimilation of 〈italic〉Aeolus〈/italic〉 wind data into the operational ECMWF system leads to a consistent root‐mean‐square error (RMSE) reduction of the order of 2% in +48 h zonal wind forecasts over the region during boreal summer 2019, including the African and Tropical Easterly Jets (AEJ, TEJ) and subtropical jets (STJ). 〈boxed-text position="anchor" id="qj4442-blkfxd-0001" content-type="graphic" xml:lang="en"〉〈graphic position="anchor" id="jats-graphic-1" xlink:href="urn:x-wiley:00359009:media:qj4442:qj4442-toc-0001"〉 〈/graphic〉 〈/boxed-text〉〈/p〉
    Description: https://aeolus-ds.eo.esa.int/oads/access/collection
    Keywords: ddc:551.6 ; aeolus satellite ; doppler wind lidar ; data assimilation ; numerical weather prediction impact ; African easterly jet ; tropical easterly jet ; observing system experiments
    Language: English
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  • 10
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    Comparison of temperature and wind observations in the Tropics in a perfect‐model, global EnKF data assimilation system (2023)
    John Wiley & Sons, Ltd | Chichester, UK
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    Publication Date: 2024-03-12
    Description: 〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Flow‐dependent errors in tropical analyses and short‐range forecasts are analysed using global observing‐system simulation experiments assimilating only temperature, only winds, and both data types using the ensemble Kalman filter (EnKF) Data Assimilation Research Testbed (DART) and a perfect model framework. The idealised, homogeneous observation network provides profiles of wind and temperature data from the nature run for January 2018 using the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) forced by the observed sea‐surface temperature. The results show that the assimilation of abundant wind observations in a perfect model makes the temperature data in the Tropics largely uninformative. Furthermore, the assimilation of wind data reduces the background errors in specific humidity twice as much as the assimilation of temperature observations. In all experiments, the largest analysis uncertainties and the largest short‐term forecast errors are found in regions of strong vertical and longitudinal gradients in the background wind, especially in the upper troposphere and lower stratosphere over the Indian Ocean and Maritime Continent. The horizontal error correlation scales are on average short throughout the troposphere, just several hundred km. The correlation scales of the wind variables in precipitating regions are half of those in nonprecipitating regions. In precipitating regions, the correlations are elongated vertically, especially for the wind variables. Strong positive cross‐correlations between temperature and specific humidity in the precipitating regions are explained using the Clausius–Clapeyron equation.〈/p〉
    Description: China Scholarship Council http://dx.doi.org/10.13039/501100004543
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: ddc:551.6 ; ensemble Kalman filter data assimilation ; forecast‐error correlations ; mass and wind observations ; temperature–moisture cross‐correlations ; Tropics
    Language: English
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  • 11
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    Validation of the Aeolus L2B Rayleigh winds and ECMWF short‐range forecasts in the upper troposphere and lower stratosphere using Loon super pressure balloon observations (2022)
    John Wiley & Sons, Ltd | Chichester, UK
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    Publication Date: 2024-04-03
    Description: The novel Aeolus satellite, which carries the first Doppler wind lidar providing profiles of horizontal line‐of‐sight (HLOS) winds, addresses a significant gap in direct wind observations in the global observing system. The gap is particularly critical in the tropical upper troposphere and lower stratosphere (UTLS). This article validates the Aeolus Rayleigh–clear wind product and short‐range forecasts of the European Centre for Medium‐Range Weather Forecasts (ECMWF) with highly accurate winds from the Loon super pressure balloon network at altitudes between 16 and 20 km. Data from 229 individual balloon flights are analysed, applying a collocation criterion of 2 hr and 200 km. The comparison of Aeolus and Loon data shows systematic and random errors of -0.31 and 6.37 m·s〈sup〉-1〈/sup〉, respectively, for the Aeolus Rayleigh–clear winds. The horizontal representativeness error of Aeolus HLOS winds (nearly the zonal wind component) in the UTLS ranges from 0.6–1.1 m·s〈sup〉-1〈/sup〉 depending on the altitude. The comparison of Aeolus and Loon datasets against ECMWF model forecasts suggests that the model systematically underestimates the HLOS winds in the tropical UTLS by about 1 m·s〈sup〉-1〈/sup〉. While Aeolus winds are currently considered as point winds by the ECMWF data assimilation system, the results of the present study demonstrate the need for a more realistic HLOS wind observation operator for assimilating Aeolus winds.
    Keywords: ddc:551.6 ; Aeolus ; data assimilation ; ECMWF forecasts ; HLOS winds ; Loon ; super pressure balloon observations ; systematic and random errors
    Language: English
    Type: doc-type:article
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