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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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U. Löhnert, S. Crewell, C. Simmer, and A. Macke

Abstract

A method for combining ground-based passive microwave radiometer retrievals of integrated liquid water (LWP), radar reflectivity profiles (Z), and statistics of a cloud model is proposed for deriving cloud liquid water profiles (LWC). A dynamic cloud model is used to determine Z–LWC relations and their errors as functions of height above cloud base. The cloud model is also used to develop an LWP algorithm based on simulations of brightness temperatures of a 20–30-GHz radiometer. For the retrieval of LWC, the radar determined Z profile, the passive microwave retrieved LWP, and a model climatology are combined by an inverse error covariance weighting method. Model studies indicate that LWC retrievals with this method result in rms errors that are about 10%–20% smaller in comparison to a conventional LWC algorithm, which constrains the LWC profile exactly to the measured LWP. According to the new algorithm, errors in the range of 30%–60% are to be anticipated when profiling LWC. The algorithm is applied to a time series measurement of a stratocumulus layer at GKSS in Geesthacht, Germany. The GKSS 95-GHz cloud radar, a 20–30-GHz microwave radiometer, and a laser ceilometer were collocated within a 5-m radius and operated continuously during the measurement period. The laser ceilometer was used to confirm the presence of drizzle-sized drops.

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U. Löhnert, S. Kneifel, A. Battaglia, M. Hagen, L. Hirsch, and S. Crewell

The Towards an Optimal estimation based Snow Characterization Algorithm (TOSCA) project addresses possible novel measurement synergies for deriving snowfall microphysical parameters from the ground by combining the unique information obtained from a suite of ground-based sensors: microwave radiometers (22–150 GHz), 24- and 36-GHz radar, lidar, and in situ optical disdrometer methods. During the winter of 2008/09, such instruments were deployed at the Environmental Research Station Schneefernerhaus (UFS; at 2650 m MSL) at the Zugspitze Mountain in Germany for deriving microphysical properties of snowfall. This contribution gives an overview of the measurements carried out and discusses the potential for the developments of synergetic retrieval algorithms for deriving snow water content within the vertical column. The identification of potentially valuable ground-based instrument synergy for the retrieval of snowfall parameters from the surface will also be of importance for the development of new space-borne observational techniques. Microwave radiometer measurements show that brightness temperature enhancements at 90 and 150 GHz are correlated with the radar-derived snow water path, which is supported by radiative transfer simulations. The synergy of these measurements toward an improved snow mass content, however, needs to be augmented by knowledge on water vapor, supercooled liquid water, particle size distribution, and shape, thus making clear the necessity of synergetic remote sensing and in situ measurements. The radiometric measurements also reveal the very frequent presence of supercooled water within snow clouds and its importance to microphysical diffusion and aggregation growth of snow crystals. Analysis of the disdrometer measurements shows a “secondary aggregation peak” around −12° to −15°C, a temperature range where the Wegener–Bergeron–Findeisen process is most effective and typically dendrite snow crystals forms dominate.

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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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J. Bühl, S. Alexander, S. Crewell, A. Heymsfield, H. Kalesse, A. Khain, M. Maahn, K. Van Tricht, and M. Wendisch

Abstract

State-of-the-art remote sensing techniques applicable to the investigation of ice formation and evolution are described. Ground-based and spaceborne measurements with lidar, radar, and radiometric techniques are discussed together with a global view on past and ongoing remote sensing measurement campaigns concerned with the study of ice formation and evolution. This chapter has the intention of a literature study and should illustrate the major efforts that are currently taken in the field of remote sensing of atmospheric ice. Since other chapters of this monograph mainly focus on aircraft in situ measurements, special emphasis is put on active remote sensing instruments and synergies between aircraft in situ measurements and passive remote sensing methods. The chapter concentrates on homogeneous and heterogeneous ice formation in the troposphere because this is a major topic of this monograph. Furthermore, methods that deliver direct, process-level information about ice formation are elaborated with a special emphasis on active remote sensing methods. Passive remote sensing methods are also dealt with but only in the context of synergy with aircraft in situ measurements.

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M. Haeffelin, S. Crewell, A. J. Illingworth, G. Pappalardo, H. Russchenberg, M. Chiriaco, K. Ebell, R. J. Hogan, and F. Madonna
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A. Rinke, B. Segger, S. Crewell, M. Maturilli, T. Naakka, T. Nygård, T. Vihma, F. Alshawaf, G. Dick, J. Wickert, and J. Keller

Abstract

Arctic trends of integrated water vapor were analyzed based on four reanalyses and radiosonde data over 1979–2016. Averaged over the region north of 70°N, the Arctic experiences a robust moistening trend that is smallest in March (0.07 ± 0.06 mm decade−1) and largest in August (0.33 ± 0.18 mm decade−1), according to the reanalyses’ median and over the 38 years. While the absolute trends are largest in summer, the relative ones are largest in winter. Superimposed on the trend is a pronounced interannual variability. Analyzing overlapping 30-yr subsets of the entire period, the maximum trend has shifted toward autumn (September–October), which is related to an accelerated trend over the Barents and Kara Seas. The spatial trend patterns suggest that the Arctic has become wetter overall, but the trends and their statistical significance vary depending on the region and season, and drying even occurs over a few regions. Although the reanalyses are consistent in their spatiotemporal trend patterns, they substantially disagree on the trend magnitudes. The summer and the Nordic and Barents Seas, the central Arctic Ocean, and north-central Siberia are the season and regions of greatest differences among the reanalyses. We discussed various factors that contribute to the differences, in particular, varying sea level pressure trends, which lead to regional differences in moisture transport, evaporation trends, and differences in data assimilation. The trends from the reanalyses show a close agreement with the radiosonde data in terms of spatiotemporal patterns. However, the scarce and nonuniform distribution of the stations hampers the assessment of central Arctic trends.

Open access
U. Löhnert, J. H. Schween, C. Acquistapace, K. Ebell, M. Maahn, M. Barrera-Verdejo, A. Hirsikko, B. Bohn, A. Knaps, E. O’Connor, C. Simmer, A. Wahner, and S. Crewell

Abstract

The Jülich Observatory for Cloud Evolution (JOYCE), located at Forschungszentrum Jülich in the most western part of Germany, is a recently established platform for cloud research. The main objective of JOYCE is to provide observations, which improve our understanding of the cloudy boundary layer in a midlatitude environment. Continuous and temporally highly resolved measurements that are specifically suited to characterize the diurnal cycle of water vapor, stability, and turbulence in the lower troposphere are performed with a special focus on atmosphere–surface interaction. In addition, instruments are set up to measure the micro- and macrophysical properties of clouds in detail and how they interact with different boundary layer processes and the large-scale synoptic situation. For this, JOYCE is equipped with an array of state-of-the-art active and passive remote sensing and in situ instruments, which are briefly described in this scientific overview. As an example, a 24-h time series of the evolution of a typical cumulus cloud-topped boundary layer is analyzed with respect to stability, turbulence, and cloud properties. Additionally, we present longer-term statistics, which can be used to elucidate the diurnal cycle of water vapor, drizzle formation through autoconversion, and warm versus cold rain precipitation formation. Both case studies and long-term observations are important for improving the representation of clouds in climate and numerical weather prediction models.

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S. Crewell, H. Bloemink, A. Feijt, S. G. García, D. Jolivet, O. A. Krasnov, A. van Lammeren, U. Löhnert, E. van Meijgaard, J. Meywerk, M. Quante, K. Pfeilsticker, S. Schmidt, T. Scholl, C. Simmer, M. Schröder, T. Trautmann, V. Venema, M. Wendisch, and U. Willén

Clouds cause uncertainties in the determination of climate sensitivity to either natural or anthropogenic changes. Furthermore, clouds dominate our perception of the weather, and the relatively poor forecast of cloud and precipitation parameters in numerical weather prediction (NWP) models is striking. In order to improve modeling and forecasting of clouds in climate and NWP models the BALTEX BRIDGE Campaign (BBC) was conducted in the Netherlands in August/September 2001 as a contribution to the main field experiment of the Baltic Sea Experiment (BALTEX) from April 1999 to March 2001 (BRIDGE). The complex cloud processes, which involve spatial scales from less than 1 mm (condensation nuclei) to 1000 km (frontal systems) require an integrated measurement approach. Advanced remote sensing instruments were operated at the central facility in Cabauw, Netherlands, to derive the vertical cloud structure. A regional network of stations was operated within a 100 km × 100 km domain to observe solar radiation, cloud liquid water path, cloud-base temperature, and height. Aircraft and tethered balloon measurements were used to measure cloud microphysical parameters and solar radiation below, in, and above the cloud. Satellite measurements complemented the cloud observations by providing the spatial structure from above. In order to better understand the effect of cloud inhomogeneities on the radiation field, three-dimensional radiative transfer modeling was closely linked to the measurement activities. To evaluate the performance of dynamic atmospheric models for the cloudy atmosphere four operational climate and NWP models were compared to the observations. As a first outcome of BBC we demonstrate that increased vertical resolution can improve the representation of clouds in these models.

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Vinia Mattioli, Christophe Accadia, Catherine Prigent, Susanne Crewell, Alan Geer, Patrick Eriksson, Stuart Fox, Juan R. Pardo, Eli J. Mlawer, Maria Cadeddu, Michael Bremer, Carlos De Breuck, Alain Smette, Domenico Cimini, Emma Turner, Mario Mech, Frank S. Marzano, Pascal Brunel, Jerome Vidot, Ralf Bennartz, Tobias Wehr, Sabatino Di Michele, and Viju O. John
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