Aerosol backscatter profiles from ceilometers: validation of water vapor correction in the framework of CeiLinEx2015

With the rapidly growing number of automated single-wavelength backscatter lidars (ceilometers), their potential benefit for aerosol remote sensing received considerable scientific attention. When studying the accuracy of retrieved particle backscatter coefficients, it must be considered that most of the ceilometers are influenced by water vapor absorption in the spectral range around 910 nm. In the literature methodologies have been proposed to correct for this effect;however, a validation was not yet performed. In the framework of the ceilometer intercomparison campaign CeiLinEx2015 in Lindenberg, Germany, hosted by the German Weather Service, it was possible to tackle this open issue. Ceilometers from Lufft (CHM15k and CHM15kx, operating at 1064 nm), from Vaisala (CL51 and CL31) and from Campbell Scientific (CS135), all operating at a wavelength of approximately 910 nm, were deployed together with a multi-wavelength research lidar (RALPH) that served as a reference. In this paper the validation of the water vapor correction is performed by comparing ceilometer backscatter signals with measurements of the reference system extrapolated to the water vapor regime. One inherent problem of the validation is the spectral extrapolation of particle optical properties. For this purpose AERONET measurements and inversions of RALPH signals were used. Another issue is that the vertical range where validation is possible is limited to the upper part of the mixing layer due to incomplete overlap and the generally low signal-to-noise ratio and signal artifacts above that layer. Our intercomparisons show that the water vapor correction leads to quite a good agreement between the extrapolated reference signal and the measurements in the case of CL51 ceilometers at one or more wavelengths in the specified range of the laser diode's emission. This ambiguity is due to the similar effective water vapor transmission at several wavelengths. In the case of CL31 and CS135 ceilometers the validation was not always successful. That suggests that error sources beyond the water vapor absorption might be dominant. For future applications we recommend monitoring the emitted wavelength and providing "dark" measurements on a regular basis

Aerosol backscatter profiles from ceilometers: validation of water vapor correction in the framework of CeiLinEx2015

With the rapidly growing number of automated single-wavelength backscatter lidars (ceilometers), their potential benefit for aerosol remote sensing received considerable scientific attention. When studying the accuracy of retrieved particle backscatter coefficients, it must be considered that most of the ceilometers are influenced by water vapor absorption in the spectral range around 910 nm. In the literature methodologies have been proposed to correct for this effect; however, a validation was not yet performed. In the framework of the ceilometer intercomparison campaign CeiLinEx2015 in Lindenberg, Germany, hosted by the German Weather Service, it was possible to tackle this open issue. Ceilometers from Lufft (CHM15k and CHM15kx, operating at 1064 nm), from Vaisala (CL51 and CL31) and from Campbell Scientific (CS135), all operating at a wavelength of approximately 910 nm, were deployed together with a multi-wavelength research lidar (RALPH) that served as a reference. In this paper the validation of the water vapor correction is performed by comparing ceilometer backscatter signals with measurements of the reference system extrapolated to the water vapor regime. One inherent problem of the validation is the spectral extrapolation of particle optical properties. For this purpose AERONET measurements and inversions of RALPH signals were used. Another issue is that the vertical range where validation is possible is limited to the upper part of the mixing layer due to incomplete overlap and the generally low signal-to-noise ratio and signal artifacts above that layer. Our intercomparisons show that the water vapor correction leads to quite a good agreement between the extrapolated reference signal and the measurements in the case of CL51 ceilometers at one or more wavelengths in the specified range of the laser diode\u27s emission. This ambiguity is due to the similar effective water vapor transmission at several wavelengths. In the case of CL31 and CS135 ceilometers the validation was not always successful. That suggests that error sources beyond the water vapor absorption might be dominant. For future applications we recommend monitoring the emitted wavelength and providing “dark” measurements on a regular basis

Fernerkundung von atmosphärischem Wasserdampf über Landflächen aus MERIS Messungen und Anwendung für die Validierung von Wettervorhersagemodellen

An advanced algorithm for the retrieval of atmospheric integrated water vapor over cloud free land areas is developed. The presented algorithm is for satellite data acquired by the Medium Resolution Imaging Spectrometer (MERIS) on board the polar-orbiting ENVISAT platform. The algorithm is based on the inversion of the radiative transport calculations in the atmosphere by using an artificial neuronal network. The new algorithm accounts for the impact of the spectral variability of the surface reflectance, which is the major improvement compared to the former algorithm (Albert, 2005; Fischer und Bennartz, 1997). The importance of all influencing parameters is demonstrated in sensitivity studies. It is shown that a variability of 5% in the surface reflectance leads to a 30% uncertainty in the water vapor retrieval. The error produced by an aerosol optical thickness of 0.3 is a 5% uncertainty. A 10% change in the atmospheric pressure leads to a 3.3% error in the retrieved water vapor while a change of 30K in the atmospheric temperature profile leads to a 2.6% uncertainty in the retrieved water vapor. The errors produced by unknown aerosol optical thickness, vertical temperature and pressure profile are small in comparison to the unknown spectral change in the surface albedo. The improved atmospheric water vapor product is available within the MERIS Level 2 dataset processed by ESA´s ground processor MEGS 8.0 (from 2010 on). An extensive validation is provided. The new MERIS water vapor product is compared to three different in situ datasets of integrated water vapor measurements: Microwave Radiometers on the ARM-SGP site in Oklahoma / USA; ground based Global Positioning System stations in Germany; and radio soundings over central Europe. The validation is done for a period of three years from January 2003 to December 2005. A high agreement with the data from Microwave Radiometers and the Global Positioning System is found. The root mean square deviation is 1.40mm and the bias is -0.03mm for Microwave Radiometer data. The root mean square deviation is 1.22mm and the bias is 0.97mm for the Global Positioning System. The agreement between MERIS and Radiosonde measurements is good, with a root mean square deviation of 2.28mm and a bias of 1.63mm. The accuracy range of the new retrieval algorithm for water vapor over cloud free land areas is now comparable (in the same magnitude) with the accuracy of retrievals above open oceans. Based on the very high accuracy of the presented water vapor algorithm, it is compared to two coupled regional numerical weather prediction models for the first time. The regional weather prediction models are the COSMO-EU and the COSMO-DE of the Deutscher Wetterdienst. The comparison is done for a period of 4.5 years (January 2005 to July 2009) and three years (July 2006 to July 2009) for COSMO-EU and COSMO-DE, respectively. The accuracy is calculated for all valid cloud free match points. The models show the typical annual cycle of water vapor with high water vapor values in summer and low values in winter. Spatial water vapor patterns caused by the orographic/geographic structures are very well resampled by the models. Differences in the mean water vapor of 1 to 2.5mm are found. The mean variability in the water vapor was for both datasets in the order of 6-7mm. In general, the MERIS water vapor is slightly higher and thus the model dryer than the observations. However, there are certain regions, like the Netherlands and north France and northern parts of Germany, with significant differences in the water vapor of up to 20%. Since the models are dryer in these regions in comparison to the MERIS measurements, the differences could be a result of weaknesses in the evaporation and the convective scheme in the COSMO models. Further investigations of these model schemes are necessary to improve the water cycle within the models and therefore the cloud development as well.In der Arbeit wurde ein Algorithmus für die Fernerkundung von integriertem atmosphärischen Wasserdampf über wolkenfreien Landflächen entwickelt. Der vorgestellte Algorithmus verarbeitet Strahldichtemessungen des Medium Resolution Imaging Spectrometer (MERIS) an Bord des polar-umlaufenden ENVISAT Satelliten. Der Algorithmus für die Fernerkundung von Wasserdampf basiert auf der Umkehrung von Strahlungstransportberechnungen in der Atmosphäre mit Hilfe eines künstlichen Neuronalen Netzes. Der neue Algorithmus berücksichtigt zusätzlich den spektralen Ganges der Bodenalbedo, welches die wesentliche Verbesserung gegenüber den früheren Algorithmus [Albert2005, Fischer1997] darstellt. Der Einfluss des spektralen Ganges der Bodenalbedo, der aerosoloptischen Dicke und des Aerosoltyps sowie des vertikalen Druck- und Temperaturprofiles auf die Genauigkeit des Wasserdampf Algorithmus wurde mit Hilfe von Sensitivitätstudien berechnet. Es wurde gezeigt dass eine Änderung der spektralen Bodenalbedo um 5% zwischen 885nm und 900nm zu einer 30 prozentigen Ungenauigkeit des abgeleiteten Wasserdampfes. Der Einfluss von Aerosolen auf die Genauigkeit des Algorithmus war gering. Eine aerosol optische Dicke von 0,3 führte zu einer 5 prozentigen Unsicherheit des abgeleiteten Wasserdampfes. Ebenso gering war der Einfluss des vertikalen Druck- und Temperaurprofiles auf die Genauigkeit des Algorithmus. Eine 10 prozentige Änderung des atmosphärischen Drucks führte zu einem 3,3 prozentigen Fehler im abgeleiteten Wasserdampf, während eine Änderung der im atmosphärische Temperatur-Profil um 30K zu einer Unsicherheit von 2,6% führte. Der verbesserte Algorithmus zur Ableitung integrierten atmosphärischen Wasserdampfes wird ab 2010 innerhalb des ESA-Boden-Prozessor MEGS 8.0 eingesetzt werden. Der neue Algorithmus wurde umfassend validiert. Dafür wurde das neue MERIS Wasserdampf Produkt mit drei verschiedenen unabhängigen bodengestützten Wasserdampf Datensätze verglichen. Erstens mit Mikrowellenradiometer der ARM-SGP site in Oklahoma / USA. Zweitens mit bodengestützten Global Positioning System-Stationen in Deutschland und mit Radiosondenaufstiegen über Mitteleuropa. Die Validierung wurde für einen Zeitraum von drei Jahren von Januar 2003 bis Dezember 2005 durchgeführt. Es zeigte sich eine hohe Übereinstimmung der MERIS Wasserdampf Daten mit den Daten aus Mikrowellenradiometer und des Global Positioning System. Für den Vergleich von MERIS Wasserdampfdaten und Mikrowellen Radiometer Daten wurde ein rmse von 1.40mm und ein bias von -0.03mm festgestellt. Ein rmse von 1,22 mm und ein bias von 0,97 mm wurde für den Vergleich mit Wasserdampfdaten des Global Positioning System berechnet. Die Übereinstimmung zwischen MERIS und Radiosonden Wasserdampfmessungen ist gut. Es wurde ein rmse von 2.28mm und bias von 1.63mm berechnet. Die Genauigkeit des neuen Algorithmus zur Fernerkundung von integriertem Wasserdampf über wolkenfreien Landflächen ist jetzt vergleichbar mit der Genauigkeit der Wasserdampf Fernerkundung über offenen Ozean. Basierend auf der sehr hohen Genauigkeit des präsentierten Wasserdampf Algorithmus wurden Wasserdampfdaten erstmalig mit denen von zwei gekoppelten regionalen numerischen Wettervorhersagemodellen verglichen. Die regionalen Wettervorhersagemodelle sind das COSMO-EU Modell und das COSMO-DE Modell des Deutschen Wetterdienst. Der Vergleich wurde für einen Zeitraum von 4,5 Jahren (Januar 2005 bis Juli 2009 durchgeführt) für COSMO-EU bzw von drei Jahren (Juli 2006 bis Juli 2009) für COSMO-DE durchgeführt. Die Modelle zeigen den typischen Jahresgang des Wasserdampfes mit hohen Wasserdampfwerten im Sommer und niedrigen Wasserdampfwerten im Winter. Räumliche Muster im Wasserdampffeld verursacht durch orographische / geografischen Gegebenheiten, sind sehr gut in den Modellen wiedergegeben. Unterschiede von 1 bis 3,5 mm (5% bis 20%) zwischen Modellanalyse und MERIS Messungen sind erkennbar. Die Unterschiede liegen in der Größenordnung der Messgenauigkeit des neuen MERIS Wasserdampfproduktes. Im Allgemeinen sind die MERIS Wasserdampfmessungen etwas höher und damit das Modell trockener als die Beobachtungen. Die mittlere Variabilität des Wasserdampfes beträgt für beide Datensätze 6-7mm

Vertical velocity data from vertical stare Doppler lidar, Falkenberg, FESSTVaL campaign 2020/2021

The dataset contains the level 1 and level 2 data of vertical stare mode Doppler lidar during the FESSTVaL campaign 2020/2021. The dataset is retrieved from Halo Photonics Streamline XR Doppler lidar 161 during FESSTVaL 2020 and Doppler lidar 146 for FESSTVaL 2021. The data was measured during the period 1 June - 10 August 2020 and 25 May - 30 August 2021 at Falkenberg. The data is structured in folders as level1 and level2, one file per day. L1 data contains the configuration variable of Doppler lidar and the primary variable such as vertical velocity data, attenuated backscatter and intensity data. The vertical velocity variance and the estimated mixing layer height with 30-minute resolution and vertical velocity in 1-minute resolution are stored in L2 data. Quality: The dataset period is only available during the vertical stare mode period. The unavailable period is due to the change in the scanning configuration

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Fluorescing aerosols and clouds: investigations of co-existence

RAMSES of the Lindenberg Meteorological Observatory, Germany, is the first multipurpose lidar to routinely measure the fluorescence spectra of atmospheric aerosols. Combined with the other measurement parameters (cloud water content and optical properties, moisture and temperature), this capability allows one to study the co-existence of clouds and fluorescing aerosols for the first time. The fluorescence receiver is briefly described, and measurement examples are presented and discussed

Fluorescing aerosols and clouds: investigations of co-existence

RAMSES of the Lindenberg Meteorological Observatory, Germany, is the first multipurpose lidar to routinely measure the fluorescence spectra of atmospheric aerosols. Combined with the other measurement parameters (cloud water content and optical properties, moisture and temperature), this capability allows one to study the co-existence of clouds and fluorescing aerosols for the first time. The fluorescence receiver is briefly described, and measurement examples are presented and discussed

Doppler lidar mean wind profiles from VAD scans during FESSTVAL 2021

Abstract: This data set contains vertical profiles of the mean wind vector derived from Doppler lidar measurements between June 12 and July 19 2021 at the Grenzschichtmessfeld (GM) Falkenberg (sups_rao_dlidvad00) and between May 17 and July 15 2021 at the Meteorological Observatory Lindenberg – Richard-Aßmann-Observatory (fval_tub_dlidvad00) during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL). The GM Falkenberg as part of the Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory are operated by the German national meteorological service (Deutscher Wetterdienst, DWD). The Doppler lidar system operated at the GM site was kindly provided by Technische Universität Berlin, Institute for Ecology, Chair of Climatology. Both, level 1 and level 2 data are provided. TableOfContents: level1: sups_rao_dlidvad00: sensor azimuth angle; attenuated backscatter coefficient; spectral width of detected signal; radial velocity of scatterers away from instrument (doppler velocity); error of doppler velocity; backscatter intensity; range bands; zenith angle fval_tub_dlidvad00: sensor azimuth angle; attenuated backscatter coefficient; radial velocity of scatterers away from instrument (doppler velocity); error of doppler velocity; backscatter intensity; range bands; zenith angle level2: wind speed; wind direction; eastward wind component u; northward wind component v; upward air velocity w; wind speed uncertainty; wind direction uncertainty; eastward wind component uncertainty; northward wind component uncertainty; upward air velocity uncertainty; wind quality flag; eastward wind component quality flag; northward wind component quality flag; upward air velocity quality flag; coefficient of determination; condition number; number of radial velocities; horizontal width of Doppler LIDAR data; height bounds; time bounds Technical Info: level1: dimension: 17280 x 250; temporalExtent_startDate: 2021-05-17 00:00:00; temporalExtent_endDate: 2021-07-20 00:00:00; temporalResolution: 5; temporalResolutionUnit: seconds; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; rangeResolution: 48; rangeResolutionUnit: meters; verticalResolution: 36; verticalResolutionUnit: meters; verticalStart: 0; verticalStartUnit: meters; verticalEnd: 12000; verticalEndUnit: meters; instrumentNames: Stream Line XR S/N 143 (sups_rao_dlidvad00), Stream Line XR S/N 44 (fval_tub_dlidvad00); instrumentType: Doppler LIDAR; instrumentLocation: Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd. level2: dimension01: 144 timesteps x 250 (10-minute mean); dimension02: 48 timesteps x 250 (30-minute mean); temporalExtent_startDate: 2021-05-17 00:00:00; temporalExtent_endDate: 2021-07-19 15:30:00; temporalResolution01: 10; temporalResolutionUnit01: minutes; temporalResolution02: 30; temporalResolutionUnit02: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: 36; verticalResolutionUnit: meters; verticalStart: 0; verticalStartUnit: meters; verticalEnd: 11600; verticalEndUnit: meters; instrumentNames: Stream Line XR S/N 143 (sups_rao_dlidvad00), Stream Line XR S/N 44 (fval_tub_dlidvad00); instrumentType: Doppler LIDAR; instrumentLocation: Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd.. Methods: fval_tub_dlidvad00: Level-1 data represent the instantaneous backscatter intensity and radial velocity profile measurements along each single ray. Level-2 data represent 10- and 30-minutes averages of the mean wind vector, respectively. All data are organized in daily files. They are based on a step-stare Velocity Azimuth Display (VAD) lidar scan pattern with 24 rays per scan circle and 30000 lidar pulses per ray at a 15 degrees zenith angle. The profiles typically cover a height range between about 90 m a.g.l. and (at least) the top of the boundary layer. The actual maximum height is highly variable, it depends on the maximum nominal range of the instrument, on the scan geometry and on the presence of scatterers in the atmosphere (e.g., aerosols, cloud particles). Besides the meteorological variables, an error estimate and a quality flag is given for each variable. Furthermore, quality test information and instrumental parameters are given. Raw data processing followed the sensitive consensus approach identifying the most reliable cluster of radial velocity retrievals along each scan direction from a series of scans during the averaging interval. Quality tests include an assessment of the coefficient of determination of the VAD fit and of the condition number indicating the sectoral coverage of the fit. The retrieval algorithm has been validated through a long-term inter-comparison of lidar-based winds versus radiosonde and radar wind profiler wind retrievals resulting in a typical uncertainty (rmsd) of 0.8 m/s and 8.0 degrees for wind speed and wind direction, respectively. Each measured value is accompanied by a quality flag where 0 = bad, and 1 = good. Note: Data from June 12 to June 23 had to be reprocessed to correct for an azimuth error, these data are labelled as version 01; all other data are labelled as version 00. sups_rao_dlidvad00: Level-1 data represent the instantaneous backscatter intensity and radial velocity profile measurements along each single ray. Level-2 data represent 10- and 30-minutes averages of the mean wind vector, respectively. They are processed by DWD using the consensus approach (see, e.g., Päschke et al. 2015 - https://doi.org/10.5194/amt-8-2251-2015) identifying the most reliable cluster of radial velocity retrievals along each scan direction from a series of scans during the averaging interval. All data are organized in daily files. They are based on a step-stare Velocity Azimuth Display (VAD) lidar scan pattern with 24 rays per scan circle and 30000 lidar pulses per ray at a 15 degrees zenith angle. The profiles typically cover a height range between about 90 m a.g.l. and (at least) the top of the boundary layer. The actual maximum height is highly variable, it depends on the maximum nominal range of the instrument, on the scan geometry and on the presence of scatterers in the atmosphere (e.g., aerosols, cloud particles). Besides the meteorological variables, an error estimate and a quality flag are given for each variable. Furthermore, quality test information and instrumental parameters are given. Quality tests of the DWD standard processing include an assessment of the coefficient of determination of the VAD fit and of the condition number indicating the sectoral coverage of the fit. The retrieval algorithm has been validated through a long-term inter-comparison of lidar-based winds versus radiosonde and radar wind profiler wind retrievals resulting in a typical uncertainty (rmsd) of 0.8 m/s and 8.0 degrees for wind speed and wind direction, respectively. Each measured value is accompanied by a quality flag where 0 = bad, and 1 = good. Units: Units for all variables (see TableOfContents): level1: sups_rao_dlidvad00: degrees; 1/ (m sr); m/s; m/s; m/s; 1; m; degrees fval_tub_dlidvad00: degrees; 1/ (m sr); m/s; m/s; 1; m; degrees level2: m/s; degrees; m/s; m/s; m/s; m/s; degrees; m/s; m/s; m/s; 1, 1, 1, 1, 1, 1, 1, m; m; s geoLocations: BoundingBox: westBoundLongitude: 14.1222 degrees East; eastBoundLongitude: 14.1287 degrees East; southBoundLatidude: 52.1665 degrees North; northBoundLatitude: 52.2098 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Falkenberg: 52.1665 degrees North, 14.1222 degrees East, 73 meters above mean sea level Lindenberg: 52.2098 degrees North, 14.1287 degrees East, 104 meters above mean sea level Size: All data are organized in daily files. Level-1 and level-2 data of the two instruments are each packed separately into one tar archive; the total number of tar archives is hence 2 + 2 = 4. Files sizes of these archives are: Falkenberg level-1: ~1.94 GByte, level-2: 15 MByte, Lindenberg level-1: 3.75 GByte, level-2: 25 MByte; the total amount is ~ 6 GByte. Format: netCDF DataSources: Single site ground-based remote sensing, see "Technical Info" for instruments Contact: ronny.leinweber (at) dwd.de; frank.beyrich (at) dwd.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/fval-dlidvad-wind.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.htm