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Maria Toporov and Ulrich Löhnert

Abstract

Atmospheric stability plays an essential role in the evolution of weather events. While the upper troposphere is sampled by satellite sensors, and in situ sensors measure the atmospheric state close to the surface, only sporadic information from radiosondes or aircraft observations is available in the planetary boundary layer. Ground-based remote sensing offers the possibility to continuously and automatically monitor the atmospheric state in the boundary layer. Microwave radiometers (MWR) provide temporally resolved temperature and humidity profiles in the boundary layer and accurate values of integrated water vapor and liquid water path, and the differential absorption lidar (DIAL) measures humidity profiles with high vertical and temporal resolution up to 3000-m height. Both instruments have the potential to complement satellite observations by additional information from the lowest atmospheric layers, particularly under cloudy conditions. This study presents a neural network retrieval for stability indices, integrated water vapor, and liquid water path from simulated satellite- and ground-based measurements based on the COSMO regional reanalysis (COSMO-REA2). Focusing on the temporal resolution, the satellite-based instruments considered in the study are the currently operational Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the future Infrared Sounder (IRS), both in geostationary orbit. Relative to the retrieval based on satellite observations, the additional ground-based MWR/DIAL measurements provide valuable improvements not only in the presence of clouds, which represent a limiting factor for infrared SEVIRI/IRS, but also under clear-sky conditions. The root-mean-square error for convective available potential energy, for instance, is reduced by 24% if IRS observations are complemented by ground-based MWR measurements.

Open access
Maximilian Maahn and Ulrich Löhnert

Abstract

Retrievals of ice-cloud properties from cloud-radar observations are challenging because the retrieval methods are typically underdetermined. Here, the authors investigate whether additional information can be obtained from higher-order moments and the slopes of the radar Doppler spectrum such as skewness and kurtosis as well as the slopes of the Doppler peak. To estimate quantitatively the additional information content, a generalized Bayesian retrieval framework that is based on optimal estimation is developed. Real and synthetic cloud-radar observations of the Indirect and Semi-Direct Aerosol Campaign (ISDAC) dataset obtained around Barrow, Alaska, are used in this study. The state vector consists of the microphysical (particle-size distribution, mass–size relation, and cross section–area relation) and kinematic (vertical wind and turbulence) quantities required to forward model the moments and slopes of the radar Doppler spectrum. It is found that, for a single radar frequency, more information can be retrieved when including higher-order moments and slopes than when using only reflectivity and mean Doppler velocity but two radar frequencies. When using all moments and slopes with two or even three frequencies, the uncertainties of all state variables, including the mass–size relation, can be considerably reduced with respect to the prior knowledge.

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Ulrich Löhnert, D. D. Turner, and S. Crewell

Abstract

Two independent ground-based passive remote sensing methods are used to retrieve lower-tropospheric temperature and humidity profiles in clear-sky cases. A simulation study for two distinctly different climatic zones is performed to evaluate the accuracies of a standard microwave profiler [humidity and temperature profiler (HATPRO)] and an infrared spectrometer [Atmospheric Emitted Radiance Interferometer (AERI)] by applying a unified optimal estimation scheme to each instrument. Different measurement modes for each instrument are also evaluated in which the retrieval uses different spectral channels and observational view angles. In addition, both instruments have been combined into the same physically consistent retrieval scheme to evaluate the differences between a combined retrieval relative to the single-instrument retrievals. In general, retrievals derived from only infrared measurements yield superior RMS error and bias to retrievals derived only from microwave measurements. The AERI retrievals show high potential, especially for retrieving humidity in the boundary layer, where accuracies are on the order of 0.25–0.5 g m−3 for a central European climate. In the lowest 500 m the retrieval accuracies for temperature from elevation-scanning microwave measurements and spectral infrared measurements are very similar (0.2–0.6 K). Above this level the accuracies of the AERI retrieval are significantly more accurate (<1 K RMSE below 4 km). The inclusion of microwave measurements to the spectral infrared measurements within a unified physical retrieval scheme only results in improvements in the high-humidity tropical climate. However, relative to the HATPRO retrieval, the accuracy of the AERI retrieval is more sensitive to changes in the measurement uncertainty. The discussed results are drawn from a subset of “pristine” clear-sky cases: in the general case in which clouds and aerosols are present, the combined HATPRO–AERI retrieval algorithm is expected to yield much more beneficial results.

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Ulrich Löhnert, Susanne Crewell, and Clemens Simmer

Abstract

A method is presented for deriving physically consistent profiles of temperature, humidity, and cloud liquid water content. This approach combines a ground-based multichannel microwave radiometer, a cloud radar, a lidar-ceilometer, the nearest operational radiosonde measurement, and ground-level measurements of standard meteorological properties with statistics derived from results of a microphysical cloud model. All measurements are integrated within the framework of optimal estimation to guarantee a retrieved profile with maximum information content. The developed integrated profiling technique (IPT) is applied to synthetic cloud model output as a test of accuracy. It is shown that the liquid water content profiles obtained with the IPT are significantly more accurate than common methods that use the microwave-derived liquid water path to scale the radar reflectivity profile. The IPT is also applied to 2 months of the European Cloud Liquid Water Network (CLIWA-NET) Baltic Sea Experiment (BALTEX) BRIDGE main experiment (BBC) campaign data, considering liquid-phase, nonprecipitating clouds only. Error analysis indicates root-mean-square uncertainties of less than 1 K in temperature and less than 1 g m−3 in humidity, where the relative error in liquid water content ranges from 15% to 25%. A comparison of the vertically integrated humidity profile from the IPT with the nearest operational radiosonde shows an acceptable bias error of 0.13 kg m−2 when the Rosenkranz gas absorption model is used. However, if the Liebe gas absorption model is used, this systematic error increases to −1.24 kg m−2, showing that the IPT humidity retrieval is significantly dependent on the chosen gas absorption model.

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Sabrina Schnitt, Ulrich Löhnert, and René Preusker

Abstract

High-resolution boundary layer water vapor profile observations are essential for understanding the interplay between shallow convection, cloudiness, and climate in the trade wind atmosphere. As current observation techniques can be limited by low spatial or temporal resolution, the synergistic benefit of combining ground-based microwave radiometer (MWR) and dual-frequency radar is investigated by analyzing the retrieval information content and uncertainty. Synthetic MWR brightness temperatures, as well as simulated dual-wavelength ratios of two radar frequencies are generated for a combination of Ka and W band (KaW), as well as differential absorption radar (DAR) G-band frequencies (167 and 174.8 GHz, G2). The synergy analysis is based on an optimal estimation scheme by varying the configuration of the observation vector. Combining MWR and KaW only marginally increases the retrieval information content. The synergy of MWR with G2 radar is more beneficial due to increasing degrees of freedom (4.5), decreasing retrieval errors, and a more realistic retrieved profile within the cloud layer. The information and profile below and within the cloud is driven by the radar observations, whereas the synergistic benefit is largest above the cloud layer, where information content is enhanced compared to an MWR-only or DAR-only setup. For full synergistic benefits, however, G-band radar sensitivities need to allow full-cloud profiling; in this case, the results suggest that a combined retrieval of MWR and G-band DAR can help close the observational gap of current techniques.

Open access
Claudia Acquistapace, Ulrich Löhnert, Maximilian Maahn, and Pavlos Kollias

Abstract

Light shallow precipitation in the form of drizzle is one of the mechanisms for liquid water removal, affecting cloud lifetime and boundary layer dynamics and thermodynamics. The early formation of drizzle drops is of particular interest for quantifying aerosol–cloud–precipitation interactions. In models, drizzle initiation is represented by the autoconversion, that is, the conversion of liquid water from a cloud liquid water category (where particle sedimentation is ignored) into a precipitating liquid water category. Various autoconversion parameterizations have been proposed in recent years, but their evaluation is challenging due to the lack of proper observations of drizzle development in the cloud. This work presents a new algorithm for Classification of Drizzle Stages (CLADS). CLADS is based on the skewness of the Ka-band radar Doppler spectrum. Skewness is sensitive to the drizzle growth in the cloud: the observed Gaussian Doppler spectrum has skewness zero when only cloud droplets are present without any significant fall velocity. Defining downward velocities positive, skewness turns positive when embryonic drizzle forms and becomes negative when drizzle starts to dominate the spectrum. CLADS identifies spatially coherent structures of positive, zero, and negative skewness in space and time corresponding to drizzle seeding, drizzle growth/nondrizzle, and drizzle mature, respectively. We test CLADS on case studies from the Jülich Observatory for Cloud Evolution Core Facility (JOYCE-CF) and the Barbados Cloud Observatory (BCO) to quantitatively estimate the benefits of CLADS compared to the standard Cloudnet target categorization algorithm. We suggest that CLADS can provide additional observational constraints for understanding the processes related to drizzle formation better.

Open access
Maximilian Maahn, Ulrich Löhnert, Pavlos Kollias, Robert C. Jackson, and Greg M. McFarquhar

Abstract

Observing ice clouds using zenith pointing millimeter cloud radars is challenging because the transfer functions relating the observables to meteorological quantities are not uniquely defined. Here, the authors use a spectral radar simulator to develop a consistent dataset containing particle mass, area, and size distribution as functions of size. This is an essential prerequisite for radar sensitivity studies and retrieval development. The data are obtained from aircraft in situ and ground-based radar observations during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) campaign in Alaska. The two main results of this study are as follows: 1) An improved method to estimate the particle mass–size relation as a function of temperature is developed and successfully evaluated by combining aircraft in situ and radar observations. The method relies on a functional relation between reflectivity and Doppler velocity. 2) The impact on the Doppler spectrum by replacing measurements of particle area and size distribution by recent analytical expressions is investigated. For this, higher-order moments such as skewness and kurtosis as well as the slopes of the Doppler spectrum are also used as a proxy for the Doppler spectrum. For the area–size relation, it is found that a power law is not sufficient to describe particle area and small deviations from a power law are essential for obtaining consistent higher moments. For particle size distributions, the normalization approach for the gamma distribution of Testud et al., adapted to maximum diameter as size descriptor, is preferred.

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Wiebke Schubotz, Daniel Klocke, Ulrich Löhnert, Andreas Macke, Bjorn Stevens, and Allison Wing
Free access
Ulrich Löhnert, S. Crewell, O. Krasnov, E. O’Connor, and H. Russchenberg

Abstract

This paper describes advances in ground-based thermodynamic profiling of the lower troposphere through sensor synergy. The well-documented integrated profiling technique (IPT), which uses a microwave profiler, a cloud radar, and a ceilometer to simultaneously retrieve vertical profiles of temperature, humidity, and liquid water content (LWC) of nonprecipitating clouds, is further developed toward an enhanced performance in the boundary layer and lower troposphere. For a more accurate temperature profile, this is accomplished by including an elevation scanning measurement modus of the microwave profiler. Height-dependent RMS accuracies of temperature (humidity) ranging from ∼0.3 to 0.9 K (0.5–0.8 g m−3) in the boundary layer are derived from retrieval simulations and confirmed experimentally with measurements at distinct heights taken during the 2005 International Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH) of the German Weather Service. Temperature inversions, especially of the lower boundary layer, are captured in a very satisfactory way by using the elevation scanning mode. To improve the quality of liquid water content measurements in clouds the authors incorporate a sophisticated target classification scheme developed within the European cloud observing network CloudNet. It allows the detailed discrimination between different types of backscatterers detected by cloud radar and ceilometer. Finally, to allow IPT application also to drizzling cases, an LWC profiling method is integrated. This technique classifies the detected hydrometeors into three different size classes using certain thresholds determined by radar reflectivity and/or ceilometer extinction profiles. By inclusion into IPT, the retrieved profiles are made consistent with the measurements of the microwave profiler and an LWC a priori profile. Results of IPT application to 13 days of the LAUNCH campaign are analyzed, and the importance of integrated profiling for model evaluation is underlined.

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Christoph Böhm, Jan H. Schween, Mark Reyers, Benedikt Maier, Ulrich Löhnert, and Susanne Crewell

Abstract

In many hyperarid ecosystems, such as the Atacama Desert, fog is the most important freshwater source. To study biological and geological processes in such water-limited regions, knowledge about the spatiotemporal distribution and variability of fog presence is necessary. In this study, in situ measurements provided by a network of climate stations equipped, inter alia, with leaf wetness sensors are utilized to create a reference fog dataset that enables the validation of satellite-based fog retrieval methods. Further, a new satellite-based fog-detection approach is introduced that uses brightness temperatures measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) as input for a neural network. Such a machine learning technique can exploit all spectral information of the satellite data and represent potential nonlinear relationships. Relative to a second fog-detection approach based on MODIS cloud-top height retrievals, the neural network reaches a higher detection skill (Heidke skill score of 0.56 as compared with 0.49). A suitable representation of temporal variability on subseasonal time scales is provided with correlations mostly greater than 0.7 between fog occurrence time series derived from the neural network and the reference data for individual climate stations, respectively. Furthermore, a suitable spatial representativity of the neural-network approach to expand the application to the whole region is indicated. Three-year averages of fog frequencies reveal similar spatial patterns for the austral winter season for both approaches. However, differences are found for the summer and potential reasons are discussed.

Open access