Enforcing Temporal Consistency in Physically Constrained Flow Field Reconstruction with FlowFit by Use of Virtual Tracer Particles

Processing techniques for particle based optical flow measurement data such as 3D Particle Tracking Velocimetry (PTV) or the novel dense Lagrangian Particle Tracking method Shake-The-Box (STB) can provide time-series of velocity and acceleration information scattered in space. The following post-processing is key to the quality of space-filling velocity and pressure field reconstruction from the scattered particle data. In this work we describe a straight-forward extension of the recently developed data assimilation scheme FlowFit, which applies physical constraints from the Navier-Stokes equations in order to simultaneously determine velocity and pressure fields as solutions to an inverse problem. We propose the use of additional artificial Lagrangian tracers (virtual particles), which are advected between the flow fields at single time instants to achieve meaningful temporal coupling. This is the most natural way of a temporal constraint in the Lagrangian data framework. Not FlowFit's core method is altered in the current work, but its input in form of Lagrangian tracks. This work shows that the introduction of such particle memory to the reconstruction process significantly improves the resulting flow fields. The method is validated in virtual experiments with two independent DNS test cases. Several contributions are revised to explain the improvements, including correlations of velocity and acceleration errors in the reconstructions and the flow field regularization within the inverse problem

Data quality of Aeolus wind measurements

The European Space Agency (ESA)'s Earth Explorer Aeolus was launched in August 2018 carrying the world's first spaceborne wind lidar, the Atmospheric Laser Doppler Instrument (ALADIN). ALADIN uses a high spectral resolution Doppler wind lidar operating at 355nm to determine profiles of line-of-sight wind components in near-real-time (NRT). ALADIN samples the atmosphere from 30km altitude down to the Earth's surface or to the level where the lidar signal is attenuated by optically thick clouds. The global wind profiles provided by ALADIN help to improve weather forecasting and the understanding of atmospheric dynamics as they fill observational gaps in vertically resolved wind profiles mainly in the tropics, southern hemisphere, and over the northern hemisphere oceans. Since 2020, multiple national and international weather centres (e.g. ECMWF, DWD, Météo France, MetOffice) assimilate Aeolus observations in their operational forecasting. Additionally, the scientific exploitation of the Aeolus dataset has started. A main prerequisite for beneficial impact and scientific exploitation is data of sufficient quality. Such high data quality has been achieved through close collaboration of all involved parties within the Aeolus Data Innovation and Science Cluster (DISC), which was established after launch to study and improve the data quality of Aeolus products. The tasks of the Aeolus DISC include the instrument and platform monitoring, calibration, characterization, retrieval algorithm refinement, processor evolution, quality monitoring, product validation, and impact assessment for NWP. The achievements of the Aeolus DISC for the NRT data quality and the one currently available reprocessed dataset will be presented. The data quality of the Aeolus wind measurements will be described and an outlook on planned improvements of the dataset and processors will be provided

The Aeolus Data Innovation and Science Cluster

The Data Innovation and Science Cluster (DISC) is a core element of ESA's data quality strategy for the Aeolus mission, which was launched in August 2018. Aeolus provides for the first-time global observations of vertical profiles of horizontal wind information by using the first Doppler wind lidar in space. The Aeolus DISC is responsible for monitoring and improving the quality of the Aeolus aerosol and wind products, for the upgrade of the operational processors as well as for impact studies and support of data usage. It has been responsible for multiple significant processor upgrades which reduced the systematic error of the Aeolus observations drastically. Only due to the efforts of the Aeolus DISC team members prior to and after launch, the systematic error of the Aeolus wind products could be reduced to a global average below 1 m/s which was an important pre-requisite for making the data available to the public in May 2020 and for its use in operational weather prediction. In 2020, the reprocessing of earlier acquired Aeolus data, another important task of the Aeolus DISC, also started. In this way, also observations from June to December 2019 with significantly better quality could be made available to the public, and more data will follow this and next year. Without the thorough preparations and close collaboration between ESA and the Aeolus DISC over the past decade, many of these achievements would not have been possible

Contributions from the DISC to accomplish the Aeolus mission objectives

The Aeolus Data Innovation and Science Cluster (DISC) supports the Aeolus mission with a wide range of activities from instrument and product quality monitoring over retrieval algorithm improvements to numerical weather prediction (NWP) impact assessments for wind and aerosols. The Aeolus DISC provides support to ESA, Cal/Val teams, numerical weather prediction (NWP) centers, and scientific users for instrument special operations and calibration, for the re-processing of Aeolus products from the past and through the provision of bi-annual updates of the L1A, L1B, L2A and L2B operational processors. The Aeolus DISC is coordinated by DLR with partners from ECMWF, KNMI, Météo-France, TROPOS, DoRIT, ABB, s&t, serco, OLA, Physics Solutions, IB Reissig and Les Myriades involving more than 40 scientists and engineers. The presentation will highlight the Aeolus DISC activities with a focus for the year 2021 and early 2022 since the last Aeolus workshop in November 2020. This covers the evolution of the instrument performance including investigations of the cause of the on-going signal loss and the achieved improvement via dedicated laser tests in 2021. In addition, refinements of algorithms and correction of the wind bias will be discussed - including a known remaining seasonal bias in October and March as encountered during the re-processing campaigns. Finally, the strategy for the on-going and future re-processing campaigns will be addressed to inform the scientific community about the availability and quality of the re-processed data products. The Aeolus mission has fully achieved its mission objectives including the unprecedented demonstration of direct-detection Doppler wind lidar technology and high-power laser operation in space in the ultraviolet spectral region over its planned full mission lifetime of 3 years and 3 months. Aeolus wind products have clearly demonstrated positive impact on forecasts using several NWP models. Since early 2020, and thus only 1.5 years after launch, the Aeolus wind products are used in operation at various NWP centers worldwide. This was achieved even despite the larger than expected wind random errors due to lower initial atmospheric signal levels and the observed signal losses during the operation of the first and second laser. In addition to this incredible success, first scientific studies demonstrated the use of Aeolus for atmospheric dynamics research in the stratosphere and for the analysis of aerosol transport. These achievements of the Aeolus mission and its success were only possible with the essential and critical contributions from the Aeolus DISC. This demonstrates the need and potential for setting up such scientific consortia covering a wide range of expertise from instrument, processors, and scientific use of products for Earth Explorer type missions. The invaluable experience gained by the Aeolus DISC during the more then 3 years of Aeolus mission in orbit (preceded by a period of 20 years before launch by a similar study team) is a pre-requisite for a successful preparation of an operational follow-on Aeolus-2 mission

Über das Potential von lagrangem Transport in der Strömungsfeldrekonstruktion unter physikalischen Nebenbedingungen (On the Potential of Lagrangian Transport in Physically Constrained Flow Field Reconstructions)

Moderne Strömungsmessverfahren basieren auf Hochgeschwindigkeitskameraaufnahmen von in der Strömung mitbewegten Tracer Partikeln in ausgeleuchteten Ebenen oder Volumina. Die Bildverarbeitung sowie die zum Tracking eingesetzten Algorithmen spielen dabei eine Schlüsselrolle was die Qualität und Genauigkeit der erzeugten Daten betrifft. Die robuste Shake-The-Box Methode [Schanz et al., 2013] ermöglicht akkurates Partikeltracking auch bei hohen Partikeldichten, liefert jedoch räumlich versprenkelte Informationen. Aufwendige Interpolationsalgorithmen wurden entwickelt um unter Zuhilfenahme weiterer physikalischer Nebenbedingungen aus den Navier-Stokes Gleichungen zugleich Geschwindigkeits- und Beschleunigungsfelder als Lösung eines inversen Problems zu rekonstruieren. Damit ist es praktisch möglich, das klassische Nyquistlimit zu unterschreiten. Die volle zeitliche Entwicklung der Felder wurde bisher jedoch entweder nicht berücksichtigt [Gesemann et al., 2016] oder die zeitliche Kopplung durch aufwendige Zeitintegrationsverfahren resultierte in hohem Rechenaufwand [Schneiders and Scarano, 2018]. Diese Arbeit zeigt auf, dass die zeitliche Kopplung einzelner Zeitschritte durch virtuelle Lagrange Tracerpartikel die Rekonstruktionsfehler senken kann. Hierzu wurden künstliche Experimente auf Basis von DNS Rechnungen verwendet. Der Einfluss zusätzlicherFaktoren wie den Fehlerkorrelationen oder der Regularisierung des inversen Problems wird diskutiert. Flow field measurements are nowadays performed with high-speed camera recordings of tracer particles in an illuminated fluid layer or volume. The following postprocessing is key to the quality of the resulting velocity and/or acceleration fields. Dense 3D particle tracking following the Shake-The-Box method [Schanz et al., 2013] yields accurate but scattered data. Sophisticated interpolation schemes were proposed that can make use of further physical constraints from the Navier-Stokesequations in order to simultaneously determine velocity and acceleration fields as solutions to an inverse problem. This allows to resolve structures beyond the classical Nyquist limit for each single field variable. So far, the full temporal domain has either not been considered yet [Gesemann et al., 2016] or the temporal coupling of several time instants via simulation methods resulted in high computational costs [Schneiders and Scarano, 2018]. This work shows that the introduction of memory to the reconstruction process (by temporal coupling) results in improved flow field reconstructions of artificial DNS experiments. For this purpose, additional artificial Lagrangian tracers (virtual particles) were advected between the fields, which is the most natural way to achieve temporal coupling in the framework of such algorithms. Several contributions like reconstruction error correlations and the flow field regularization within the inverse problem were revised to explain the improvements

Uncertainty Reduction of FlowFit Flow Field Estimation by Use of Virtual Particle

From experiments, data is available in the form of particle pictures from which particle tracks can be inferred by tracking techniques such as tomographic PTV or Shake-The-Box. But complete knowledge of the velocity field is sought on the basis of the scattered velocity and acceleration data. For this purpose different spatial interpolation algorithms were proposed, such asFlowFit and VIC+, which take Lagrangian particle track data (position, velocity and acceleration) as input and exploit known physical properties such as continuity and the Navier-Stokes equations for incompressible and uniform-density flows to reconstruct accurate and high resolution velocity, acceleration and pressure fields. The mentioned algorithms reach higher spatial resolutions beyond Nyquist than interpolation schemes that make use of the constraint of solenoidality only, due to the increased amount of data. We aim to develope a method in which virtual particles from previous reconstructions are advected into the following interpolation timestep with an individual weight dependend on (i) the Lagrangian correlation functions known from the track data and (ii) the local velocity gradient tensor as estimated. Usually, the time steps are about the size of the Kolmogorov time scale so the Lagrangian velocities and accelerations at two subsequent time instants are still significantly correlated. Therefore, a straightforward approach to combine the information of multiple reconstructions is to involve additional virtual particles into the reconstruction process that are advected with the estimated velocity and acceleration in order to act as information carrier between the reconstructed fields, thus enforcing consistency in time

Wind‐Wave Attenuation in Arctic Sea Ice: A Discussion of Remote Sensing Capabilities

International audienc

Aeolus L2A Aerosol Optical Properties Product: Standard Correct Algorithm and Mie Correct Algorithm

Abstract. Aeolus carries ALADIN, the first High Spectral Resolution Lidar (HSRL) in space. Although ALADIN was optimized to measure winds, its two measurement channels can also be used to derive optical properties of atmospheric particles, including a direct retrieval of the lidar ratio. This paper presents the two main algorithms of the optical properties product called Level 2A product, as they are implemented in version 3.12 of the processor, corresponding to the data labelled Baseline 12. The theoretical basis is the same as in Flamant et al. (2008). Here, we also show the in orbit performance of these algorithms. We also explain the adaptation of the calibration method, which is needed to cope with unforeseen variations of the instrument radiometric performance due to the in-orbit strain of the primary mirror under varying thermal conditions. Then we discuss the limitations of the algorithms and future improvements. We demonstrate that the L2A product provides valuable information about airborne particles, in particular we demonstrate the capacity to retrieve a useful lidar ratio from Aeolus observations. This is illustrated on a case of Saharan dust emission, observed in June 2020