Automated calibration of ceilometer data and its applicability for quantitative aerosol monitoring

Aerosols are important constituents of the Earth’s atmosphere. Their impact on global climate, but also on air quality and hence human health is huge. A region were pollutants can disperse is the Mixing Layer (ML), the lowermost part of the Earth’s atmosphere. It’s thickness is directly influenced from the Earth’s surface. The physics of the ML is of great interest for the meteorological community as effects on the dynamics, thermodynamics and air quality of the atmosphere are crucial. Recently, networks of automated single-wavelength backscatter lidars (“ceilometers”) have been implemented, primarily by weather services. As a consequence, the potential of ceilometers to quantitatively determine the spatio-temporal distribution of atmospheric aerosols must be investigated. With regard to ceilometer networks, automatic mixing layer height retrievals for air quality studies and a fully automated calibration of ceilometers to derive aerosol optical properties is required. The absolute calibration approach, which is based on the determination of the lidar constant C_L was fully automated and is applicable in a three-step procedure to several ceilometer types. As a result, the particle backscatter coefficient β_p can be determined at virtually any weather condition during day and night, independent of the main ceilometer issue—the limited signal-to-noise ratio. Applied to 5 years of measurement of a Jenoptik CHM15kx, a lidar constant could be determined on 391 days out of 1900 available days. With knowing C_L, β_p-profiles within an accuracy of typically 17% can be derived. To allow investigations of the ML, the automatic ML-height retrieval algorithm COBOLT (Continuous Boundary Layer Tracing) was developed. In contrast to ML-cycles with large jumps or even temporal gaps, determined by already available and frequently used algorithms, COBOLT uses a time-height tracking procedure. On basis of a best-of-all-approach utilizing state-of-the-art layer detection techniques, a traceable parameter is defined and allows to detect complete diurnal ML-cycles without steps by including a multi-member approach. Validation and crosschecks with ML-heights from radiosonde data and two other ML-height retrieval algorithms demonstrated the reliability of COBOLT. A wide range of applications is possible with a calibrated ceilometer and a reliable ML-height retrieval algorithm. Following examples are shown: a β_p-profile statistic and ML-height statistic above Munich; ML-height comparisons between rural and an urban site, as well as a validation of a chemistry transport model and an investigation of ML-height influences on air quality.Aerosolpartikel sind ein wichtiger Bestandteil der Erdatmosphäre. Sie haben Einfluss auf das globale Klima, aber auch auf die Luftqualität und somit letztlich auch auf die menschliche Gesundheit. Schadstoffe breiten sich insbesondere in der Mischungsschicht (ML) aus, dem untersten Teil der Erdatmosphäre. Die ML-Höhe wird direkt von der Erdoberfläche aus beeinflusst. Die Physik der ML ist von großem Interesse für die Meteorologie, da dynamische und thermodynamische Prozesse sowie die Zusammensetzung der Atmosphäre eine große Rolle spielen. In letzter Zeit haben vor allem Wetterdienste Netzwerke mit automatisierten Rückstreulidargeräten mit einer Wellenlänge (Ceilometer) aufgebaut. Folglich muss untersucht werden, welches Potential Ceilometer bei der quantitativen Bestimmung der räumlichen sowie zeitlichen Aerosolverteilung in der Atmosphäre haben. Im Hinblick auf Ceilometernetzwerke ist die Entwicklung einer automatischen Bestimmung der ML-Höhe für Untersuchungen der Luftqualität und eine automatisierte Kalibrierung der Ceilometer nötig. Erst mit einer automatisierten Kalibrierung kann man optische Eigenschaften anhand von Ceilometernetzwerken ableiten. Es ist gelungen die hier entwickelte absolute Kalibrierung, welche auf der Bestimmung der Lidarkonstante C_L basiert, vollständig zu automatisieren. Sie kann in einem dreistufigen Verfahren auf verschiedenste Ceilometer angewendet werden. Dadurch kann man den Partikelrückstreukoeffizienten β_p unabhängig vom Signal-Rausch-Verhältnis, dem größten Problem der Ceilometer, bei fast allen Wetterbedingungen sowohl bei Tag als auch bei Nacht bestimmen. Mit der Anwendung auf eine fünfjährige Messreihe von einem Jenoptik CHM15kx lässt sich eine Lidarkonstante an 391 von 1900 verfügbaren Tagen ermitteln. Unter Nutzung von C_L können β_p-Profile mit einer Genauigkeit von 17% abgeleitet werden. Um Untersuchungen der ML vorzunehmen, wird der automatische Algorithmus COBOLT (Continuous Boundary Layer Tracing) zur Bestimmung der ML-Höhe entwickelt. Im Gegensatz zu ML-Tagesgängen von häufig benutzten Algorithmen, die große Sprünge oder gar Lücken in der von ihnen bestimmten ML-Höhe aufweisen, basiert COBOLT auf einer Zeit-Höhen-Verfolgung. Auf Grundlage eines “best-of-all”-Ansatzes und unter der Verwendung aktueller Methoden zur Schichtbestimmung wird ein Parameter definiert. Dieser Parameter erlaubt unter Verwendung eines “multi-member”-Ansatzes ein Verfolgen der ML-Höhe zur Auswertung von vollständigen ML-Tagesgängen ohne Zwischenschritte. Der Vergleich und die Validierung von ML-Höhen aus Daten von Radiosonden und zwei anderen Algorithmen zur ML-Höhenbestimmung zeigen die Zuverlässigkeit von COBOLT. Die Kalibrierung von Ceilometern und der Algorithmus zur Mischungsschichthöhenbestimmung eröffnen eine Vielzahl von Anwendungsmöglichkeiten. Zu den gezeigten Beispielen gehört eine Statistik des β_p-Profils und eine Statistik der ML-Höhen über München. Dabei werden sowohl die ML-Höhen von ländlichen Gebieten mit urbanen Zentren verglichen, als auch eine Validierung von Chemietransportmodellen und eine Untersuchung des Einflusses der ML-Höhen auf die Luftqualität vorgenommen

Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents

In August 2018, the first Doppler wind lidar, developed by the European Space Agency (ESA), was launched on board the Aeolus satellite into space. Providing atmospheric wind profiles on a global basis, the Earth Explorer mission is expected to demonstrate improvements in the quality of numerical weather prediction (NWP). For the use of Aeolus observations in NWP data assimilation, a detailed characterization of the quality and the minimization of systematic errors is crucial

Characterization of the planetary boundary layer during SAMUM-2 by means of lidar measurements

Measurements with two Raman-depolarization lidars of the Meteorological Institute of the Ludwig-Maximilians- Universit¨at, M¨unchen, Germany, performed during SAMUM-2, were used to characterize the planetary boundary layer (PBL) over Praia, Cape Verde. A novel approach was used to determine the volume fraction of dust υd in the PBL. This approach primarily relies on accurate measurements of the linear depolarization ratio. Comparisons with independent in situ measurements showed the reliability of this approach. Based on our retrievals, two different phases could be distinguished within the measurement period of almost one month. The first (22–31 January 2008) was characterized by high aerosol optical depth (AOD) in the PBL and large υd > 95%. During the second phase, the AOD in the PBL was considerably lower and υd less than ∼40%. These findings were in very good agreement with ground based in situ measurements, when ambient volume fractions are considered that were calculated from the actual measurements of the dry volume fraction. Only in cases when dust was not the dominating aerosol component (second phase), effects due to hygroscopic growth became important

Quality control and error assessment of the Aeolus L2B wind results from the Joint Aeolus Tropical Atlantic Campaign

Since the start of the European Space Agency's Aeolus mission in 2018, various studies were dedicated to the evaluation of its wind data quality and particularly to the determination of the systematic and random errors in the Rayleigh-clear and Mie-cloudy wind results provided in the Aeolus Level-2B (L2B) product. The quality control (QC) schemes applied in the analyses mostly rely on the estimated error (EE), reported in the L2B data, using different and often subjectively chosen thresholds for rejecting data outliers, thus hampering the comparability of different validation studies. This work gives insight into the calculation of the EE for the two receiver channels and reveals its limitations as a measure of the actual wind error due to its spatial and temporal variability. It is demonstrated that a precise error assessment of the Aeolus winds necessitates a careful statistical analysis, including a rigorous screening for gross errors to be compliant with the error definitions formulated in the Aeolus mission requirements. To this end, the modified Z score and normal quantile plots are shown to be useful statistical tools for effectively eliminating gross errors and for evaluating the normality of the wind error distribution in dependence on the applied QC scheme, respectively. The influence of different QC approaches and thresholds on key statistical parameters is discussed in the context of the Joint Aeolus Tropical Atlantic Campaign (JATAC), which was conducted in Cabo Verde in September 2021. Aeolus winds are compared against model background data from the European Centre for Medium-Range Weather Forecasts (ECMWF) before the assimilation of Aeolus winds and against wind data measured with the 2 µm heterodyne detection Doppler wind lidar (DWL) aboard the Falcon aircraft. The two studies make evident that the error distribution of the Mie-cloudy winds is strongly skewed with a preponderance of positively biased wind results distorting the statistics if not filtered out properly. Effective outlier removal is accomplished by applying a two-step QC based on the EE and the modified Z score, thereby ensuring an error distribution with a high degree of normality while retaining a large portion of wind results from the original dataset. After the utilization of the described QC approach, the systematic errors in the L2B Rayleigh-clear and Mie-cloudy winds are determined to be below 0.3 m s−1 with respect to both the ECMWF model background and the 2 µm DWL. Differences in the random errors relative to the two reference datasets (Mie vs. model is 5.3 m s−1, Mie vs. DWL is 4.1 m s−1, Rayleigh vs. model is 7.8 m s−1, and Rayleigh vs. DWL is 8.2 m s−1) are elaborated in the text.</p

Selection of Unlabeled Source Domains for Domain Adaptation in Remote Sensing

In the context of supervised learning techniques, it can be desirable to utilize existing prior knowledge from a source domain to estimate a target variable in a target domain by exploiting the concept of domain adaptation. This is done to alleviate the costly compilation of prior knowledge, i.e., training data. Here, our goal is to select a single source domain for domain adaptation from multiple potentially helpful but unlabeled source domains. The training data is solely obtained for a source domain if it was identified as being relevant for estimating the target variable in the corresponding target domain by a selection mechanism. From a methodological point of view, we propose unsupervised source selection by voting from (an ensemble of) similarity metrics that follow aligned marginal distributions regarding image features of source and target domains. Thereby, we also propose an unsupervised pruning heuristic to solely include robust similarity metrics in an ensemble voting scheme. We provide an evaluation of the methods by learning models from training data sets created with Level-of-Detail-1 building models and regress built-up density and height on Sentinel-2 satellite imagery. To evaluate the domain adaptation capability, we learn and apply models interchangeably for the four largest cities in Germany. Experimental results underline the capability of the methods to obtain more frequently higher accuracy levels with an improvement of up to almost 10 percentage points regarding the most robust selection mechanisms compared to random source-target domain selections

Airborne Wind Lidar Observations for the Validation of ESA's Wind Mission Aeolus

Since the successful launch of ESA's Earth Explorer mission Aeolus in August 2018, atmospheric wind profiles from the ground to the lower stratosphere are being acquired on a global scale, deploying the first-ever satellite-borne wind lidar system ALADIN (Atmospheric LAser Doppler INstrument). ALADIN provides one component of the wind vector along the instrument's line-of-sight (LOS) with a vertical resolution of 0.25 km to 2 km depending on altitude. The wind accuracy is better than 1 m/s, while the random error ranges from 3 to 6 m/s. The near-real-time wind observations contribute to improving the accuracy of numerical weather prediction and advance the understanding of tropical dynamics and processes relevant to climate variability. Already several years before the launch of the Earth Explorer mission, an airborne prototype of the Aeolus payload - the ALADIN Airborne Demonstrator (A2D) - was developed at the DLR (German Aerospace Center). Like the direct detection Doppler wind lidar on-board Aeolus, the A2D is composed of a frequency-stabilized ultra-violet laser, a Cassegrain telescope and a dual-channel receiver to measure LOS wind speeds by analyzing both molecular and particulate backscatter signals. Thanks to the complementary design of the A2D receiver, broad vertical and horizontal coverage across the troposphere is achieved. In addition to the A2D, DLR's research aircraft carries a well-established coherent Doppler wind lidar (2-µm DWL). It is equipped with a double-wedge scanner which allows for the determination of the wind vector with accuracy of better than 0.1 m/s and precision of better than 1 m/s. Hence, both wind lidars represent key instruments for the calibration/validation activities during the Aeolus mission. After the launch of Aeolus, the A2D and 2-µm DWL were deployed during three airborne validation campaigns between November 2018 and September 2019. 20 coordinated flights along the satellite swath were conducted in Central Europe and the North Atlantic region, yielding a large amount of wind data from the troposphere under various atmospheric conditions in terms of cloud cover and dynamics. The high accuracy of the 2-µm DWL allowed to precisely assess the Aeolus systematic and random errors, and thus enabled a comprehensive evaluation of the satellite's wind data product quality. Due to the high degree of commonality of the A2D with the satellite instrument, the comparative wind results delivered valuable information on potential error sources as well as on the optimization of the Aeolus wind retrieval and related quality-control algorithms. Beyond the airborne campaigns, the A2D has been serving as testbed to explore new measurement strategies and algorithm modifications which cannot be readily implemented in the Aeolus operation modes and processors, respectively

Retrieval improvements for the ALADIN Airborne Demonstrator in support of the Aeolus wind product validation

The realization of the European Space Agency’s Aeolus mission was supported by the long-standing development and field deployment of the ALADIN Airborne Demonstrator (A2D) which, since the launch of the Aeolus satellite in 2018, has been serving as a key instrument for the validation of the Atmospheric LAser Doppler INstrument (ALADIN), the first-ever Doppler wind lidar (DWL) in space. However, the validation capabilities of the A2D are compromised by deficiencies of the dual-channel receiver which, like its spaceborne counterpart, consists of a Rayleigh and a complementary Mie spectrometer for sensing the wind speed from both molecular and particulate backscatter signals, respectively. Whereas the accuracy and precision of the Rayleigh channel is limited by the spectrometer’s high alignment sensitivity, especially in the near field of the instrument, large systematic Mie wind errors are caused by aberrations of the interferometer in combination with the temporal overlap of adjacent range gates during signal readout. The two error sources are mitigated by modifications of the A2D wind retrieval algorithm. A novel quality control scheme was implemented which ensures that only backscatter return signals within a small angular range are further processed. Moreover, Mie wind results with large bias of opposing sign in adjacent range bins are vertically averaged. The resulting improvement of the A2D performance was evaluated in the context of two Aeolus airborne validation campaigns that were conducted between May and September 2019. Comparison of the A2D wind data against a high-accuracy, coherent Doppler wind lidar that was deployed in parallel on-board the same aircraft shows that the retrieval refinements considerably decrease the random errors of the A2D line-of-sight (LOS) Rayleigh and Mie winds from about 2.0 m/s to about 1.5 m/s, demonstrating the capability of such a direct detection DWL. Moreover, the measurement range of the Rayleigh channel could be largely extended by up to 2 km in the instrument’s near field close to the aircraft. The Rayleigh and Mie systematic errors are below 0.5 m/s (LOS), hence allowing for an accurate assessment of the Aeolus wind errors during the September campaign. The latter revealed different biases of the L2B Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) for ascending and descending orbits as well as random errors of about 3 m/s (HLOS) for the Mie and close to 6 m/s (HLOS) for the Rayleigh winds, respectively. In addition to the Aeolus error evaluation, the present study discusses the applicability of the developed A2D algorithm modifications to the Aeolus processor, thereby offering prospects for improving the Aeolus wind data quality

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

During the first three years of European Space Agency’s Aeolus mission, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) performed four airborne campaigns deploying two different Doppler wind lidars (DWL) on-board the DLR Falcon aircraft, aiming to validate the quality of the recent Aeolus Level 2B (L2B) wind data product (processor baseline 11 and 12). The first two campaigns, WindVal III (Nov/Dec 2018) and AVATAR-E (Aeolus Validation Through Airborne Lidars in Europe, May/Jun 2019) were conducted in Europe and provided first insights in the data quality at the beginning of the mission phase. The two later campaigns, AVATAR-I (Aeolus Validation Through Airborne Lidars in Iceland) and AVATAR-T (Aeolus Validation Through Airborne Lidars in the Tropics), were performed in regions of particular interest for the Aeolus validation: AVATAR-I was conducted from Keflavik, Iceland between 9 September and 1 October 2019 to sample the high wind speeds in the vicinity of the polar jet stream. AVATAR-T was carried out from Sal, Cape Verde between 6 September and 28 September 2021 to measure winds in the Saharan dust-laden African easterly jet. Altogether, 10 Aeolus underflights were performed during AVATAR-I and 11 underflights during AVATAR-T, covering about 8000 km and 11000 km along the Aeolus measurement track, respectively. Based on these collocated measurements, statistical comparisons of Aeolus data with the reference lidar (2-µm DWL) as well as with in-situ measurements by the Falcon were performed to determine the systematic and random error of Rayleigh-clear and Mie-cloudy winds that are contained in the Aeolus L2B product. It is demonstrated that the systematic error almost fulfills the mission requirement of being below 0.7 m s−1 for both Rayleigh-clear and Mie-cloudy winds. The random error is shown to vary between 5.5 m s−1 and 7.1 m s−1 for Rayleigh-clear winds and is thus larger than specified (2.5 m s−1 ), whereas it is close to the specifications for Mie-cloudy winds (2.7 to 2.9 m s−1). In addition, the dependency of the systematic and random errors on the actual wind speed, the geolocation, the scattering ratio and the time difference between 2-µm DWL observation and satellite overflight is investigated and discussed. Thus, this work contributes to the characterization of the Aeolus data quality in different meteorological situations and allows to investigate wind retrieval algorithm improvements for reprocessed Aeolus data sets