Ensemble-based approximation of observation impact using an observation-based verification metric

Knowledge on the contribution of observations to forecast accuracy is crucial for the refinement of observing and data assimilation systems. Several recent publications highlighted the benefits of efficiently approximating this observation impact using adjoint methods or ensembles. This study proposes a modification of an existing method for computing observation impact in an ensemble-based data assimilation and forecasting system and applies the method to a pre-operational, convective-scale regional modelling environment. Instead of the analysis, the modified approach uses observation-based verification metrics to mitigate the effect of correlation between the forecast and its verification norm. Furthermore, a peculiar property in the distribution of individual observation impact values is used to define a reliability indicator for the accuracy of the impact approximation. Applying this method to a 3-day test period shows that a well-defined observation impact value can be approximated for most observation types and the reliability indicator successfully depicts where results are not significant

Ensemble-based approximation of observation impact using an observation-based verification metric

Knowledge on the contribution of observations to forecast accuracy is crucial for the refinement of observing and data assimilation systems. Several recent publications highlighted the benefits of efficiently approximating this observation impact using adjoint methods or ensembles. This study proposes a modification of an existing method for computing observation impact in an ensemble-based data assimilation and forecasting system and applies the method to a pre-operational, convective-scale regional modelling environment. Instead of the analysis, the modified approach uses observation-based verification metrics to mitigate the effect of correlation between the forecast and its verification norm. Furthermore, a peculiar property in the distribution of individual observation impact values is used to define a reliability indicator for the accuracy of the impact approximation. Applying this method to a 3-day test period shows that a well-defined observation impact value can be approximated for most observation types and the reliability indicator successfully depicts where results are not significant

Impact of Aeolus wind lidar observations on the representation of the West African monsoon circulation in the ECMWF and DWD forecasting systems

Aeolus 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 Aeolus 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 Aeolus 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 Aeolus data on African Easterly Waves and associated Mesoscale Convective Systems

A convective-scale 1,000-member ensemble simulation and potential applications

This study presents the first convective-scale 1,000-member ensemble simulation over central Europe, which provides a unique data set for various applications. A comparison with the operational regional 40-member ensemble of Deutscher Wetterdienst shows that the 1,000-member simulation exhibits realistic spread properties overall. Based on this, we discuss two potential applications. First, we quantify the sampling error of spatial covariances of smaller subsets compared with the 1,000-member simulation. Knowledge about sampling errors and their dependence on ensemble size is crucial for ensemble and hybrid data assimilation and for developing better approaches for localization in this context. Secondly, we present an approach for estimating the relative potential impact of different observable quantities using ensemble sensitivity analysis. This will provide the basis for consecutive studies developing future observation and data assimilation strategies. Sensitivity studies on the ensemble size indicate that about 200 ensemble members are required to estimate the potential impact of observable quantities with respect to precipitation forecasts.Fil: Necker, Tobias. Ludwig Maximilians Universitat; Alemania. Universidad de Viena; AustriaFil: Geiss, Stefan. Ludwig Maximilians Universitat; AlemaniaFil: Weissmann, Martin. Ludwig Maximilians Universitat; AlemaniaFil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Miyoshi, Takemasa. RIKEN Center for Computational Science; JapónFil: Lien, Guo Yuan. RIKEN Center for Computational Science; Japó

Vertical structure of the lower-stratospheric moist bias in the ERA5 reanalysis and its connection to mixing processes

Numerical weather prediction (NWP) models are known to possess a distinct moist bias in the mid�latitude lower stratosphere, which is expected to affect the ability to accurately predict weather and climate. This paper investigates the vertical structure of the moist bias in the European Centre for Medium-Range Weather Forecasts (ECMWF) latest global reanalysis ERA5 using a unique multi-campaign data set of highly resolved water vapour profiles observed with a differential absorption lidar (DIAL) on board the High Altitude and LOng range research aircraft (HALO). In total, 41 flights in the mid-latitudes from six field campaigns provide roughly 33 000 profiles with humidity varying by 4 orders of magnitude. The observations cover different synoptic sit�uations and seasons and thus are suitable to characterize the strong vertical gradients of moisture in the upper troposphere and lower stratosphere (UTLS). The comparison to ERA5 indicates high positive and negative devi�ations in the UT, which on average lead to a slightly positive bias (15 %–20 %). In the LS, the moist bias rapidly increases up to a maximum of 55 % at 1.3 km altitude above the thermal tropopause (tTP) and decreases again to 15 %–20 % at 4 km altitude. Such a vertical structure is frequently observed, although the magnitude varies from flight to flight. The layer depth of increased moist bias is smaller at high tropopause altitudes and larger when the tropopause is low. Our results also suggest a seasonality of the moist bias, with the maximum in summer exceeding autumn by up to a factor of 3. During one field campaign, collocated ozone and water vapour profile observations enable a classification of tropospheric, stratospheric, and mixed air using water vapour–ozone correlations. It is revealed that the moist bias is high in the mixed air while being small in tropospheric and stratospheric air, which highlights that excessive transport of moisture into the LS plays a decisive role for the formation of the moist bias. Our results suggest that a better representation of mixing processes in NWP models could lead to a reduced LS moist bias that, in turn, may lead to more accurate weather and climate forecasts. The lower-stratospheric moist bias should be borne in mind for climatological studies using reanalysis data

Assimilating visible and infrared radiances in idealized simulations of deep convection

International audienceCloud-affected radiances from geostationary satellite sensors provide the first area-wide observable signal of convection with high spatial resolution in the range of kilometers and high temporal resolution in the range of minutes. However, these observations are not yet assimilated in operational convection-resolving weather prediction models as the rapid, nonlinear evolution of clouds makes the assimilation of related observations very challenging. To address these challenges, we investigate the assimilation of satellite radiances from visible and infrared channels in idealized observing system simulation experiments (OSSEs) for a day with summertime deep convection in central Europe. This constitutes the first study assimilating a combination of all-sky observations from infrared and visible satellite channels, and the experiments provide the opportunity to test various assimilation settings in an environment where the observation forward operator and the numerical model exhibit no systematic errors. The experiments provide insights into appropriate settings for the assimilation of cloud-affected satellite radiances in an ensemble data assimilation system and demonstrate the potential of these observations for convective-scale weather prediction. Both infrared and visible radiances individually lead to an overall forecast improvement, but best results are achieved with a combination of both observation types that provide complementary information on atmospheric clouds. This combination strongly improves the forecast of precipitation and other quantities throughout the whole range of 8-h lead time

Vertical structure of the lower stratospheric bias in ERA5 reanalyses and its relation to mixing processes

Current NWP analyses and reanalyses are known to possess a moist bias in the lower stratosphere of the mid-latitudes. An accurate representation of water vapor in the extratropical upper troposphere and lower stratosphere (UTLS), however, is crucial to correctly predict weather but also when climate models are verified against reanalysis products. This presentation uses a unique airborne multi-campaign water vapor profile data set to better characterize the vertical structure of this bias and to investigate its connection to mixing processes. Highly-resolved water vapor profiles have been recorded with the differential absorption lidar (DIAL) WALES onboard the research aircraft HALO on various field campaigns since 2013. The high-resolution humidity profiles along the flight path provide high data availability across the entire UTLS in cloud-free situations. We analyzed mid-latitude data from more than 40 flights over the Northern Atlantic and Europe that cover a broad spectrum of synoptic situations and different seasons. This comprehensive data set is used for a comparison with the European Centre for Medium-Range Weather Forecast’s (ECMWF) ERA5 reanalysis. First, we show an example specific humidity distribution along a cross-section in the surrounding of an extratropical cyclone. The comparison to ERA5 indicates the largest positive and negative deviations in the UT, but on average no systematic differences. In contrast, we find a coherent layer of strongly overestimated humidity above the thermal tropopause (TP) persisting along the whole flight path. Second, the vertical structure of deviations is verified for all flights. On average, deviations in the UT are relatively weak (+15%) and the minimum bias (+10%) is found at the thermal tropopause. Above the TP, within a layer of 1-1.5 km the bias rapidly increases up to a maximum of +52% while it decreases again to 15-20 % by 4 km. Although the shape of the vertical structure is similar for each flight, variations of the moist bias are observed for different seasons. For instance, the overestimation in summer is more than twice as high as for autumn observations. A possible explanation for this systematic moist bias is overestimation of mixing of water vapor into the LS. During one field campaign, WALES additionally observed ozone profiles which allow a classification of the observations into tropospheric, stratospheric and mixed air using H2O-O3 correlations in tracer-tracer space [2]. We demonstrate that the bias is particularly increased in air that was mixed in its history which indicates that mixing processes are not sufficiently well represented by ERA5. References [1]Bland, J., Gray, S., Methven, J. and Forbes, R.: Characterising the extratropical near-tropopause analysis humidity biases and their radiative effects on temperature forecasts, Q.J.R. Met. Soc., 147(741), 3878-3898, https://doi.org/10.1002/qj.4150, 2021. [2]Schäfler, A., Fix, A., and Wirth, M.: Mixing at the extratropical tropopause as characterized by collocated airborne H2O and O3 lidar observations, Atmos. Chem. Phys., 21, 5217–5234, https://doi.org/10.5194/acp-21-5217-2021, 2021

Vertical structure of the lower-stratospheric moist bias in the ERA5 reanalysis and its connection to mixing processes

A comprehensive data set of airborne lidar water vapor profiles is compared with ERA5 reanalyses for a robust characterization of the vertical structure of the mid-latitude lower-stratospheric moist bias. We confirm a moist bias of up to 55 % at 1.3 km altitude above the tropopause and uncover a decreasing bias beyond. Collocated O3 and H2O observations reveal a particularly strong bias in the mixing layer providing indication for insufficiently modelled transport processes fostering the bias

A virtual centre at the interface of basic and applied weather and climate research

The Hans-Ertel Centre for Weather Research is a network of German universities, research institutes and the German Weather Service (Deutscher Wetterdienst, DWD). It has been established to trigger and intensify basic research and education on weather forecasting and climate monitoring. The performed research ranges from nowcasting and short-term weather forecasting to convective-scale data assimilation, the development of parameterizations for numerical weather prediction models, climate monitoring and the communication and use of forecast information. Scientific findings from the network contribute to better understanding of the life-cycle of shallow and deep convection, representation of uncertainty in ensemble systems, effects of unresolved variability, regional climate variability, perception of forecasts and vulnerability of society. Concrete developments within the research network include dual observation-microphysics composites, satellite forward operators, tools to estimate observation impact, cloud and precipitation system tracking algorithms, large-eddy-simulations, a regional reanalysis and a probabilistic forecast test product. Within three years, the network has triggered a number of activities that include the training and education of young scientists besides the centre's core objective of complementing DWD's internal research with relevant basic research at universities and research institutes. The long term goal is to develop a self-sustaining research network that continues the close collaboration with DWD and the national and international research community

Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble

The errors in numerical weather forecasts resulting from limited ensemble size are explored using 1,000-member forecasts of convective weather over Germany at 3-km resolution. A large number of forecast variables at different lead times were examined, and their distributions could be classified into three categories: quasi-normal (e.g., tropospheric temperature), highly skewed (e.g. precipitation), and mixtures (e.g., humidity). Dependence on ensemble size was examined in comparison to the asymptotic convergence law that the sampling error decreases proportional to N−1/2 for large enough ensemble size N, independent of the underlying distribution shape. The asymptotic convergence behavior was observed for the ensemble mean of all forecast variables, even for ensemble sizes less than 10. For the ensemble standard deviation, sizes of up to 100 were required for the convergence law to apply. In contrast, there was no clear sign of convergence for the 95th percentile even with 1,000 members. Methods such as neighborhood statistics or prediction of area-averaged quantities were found to improve accuracy, but only for variables with random small-scale variability, such as convective precipitation.Fil: Craig, George C.. Ludwig Maximilians Universitat; AlemaniaFil: Puh, Matjaž. Ludwig Maximilians Universitat; AlemaniaFil: Keil, Christian. Ludwig Maximilians Universitat; AlemaniaFil: Tempest, Kirsten. Ludwig Maximilians Universitat; AlemaniaFil: Necker, Tobias. Universidad de Viena; AustriaFil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Weissmann, Martin. Universidad de Viena; AustriaFil: Miyoshi, Takemasa. Riken Center For Computational Science; Japó