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  • 1
    Online Resource
    Online Resource
    Wiley ; 2011
    In:  Quarterly Journal of the Royal Meteorological Society Vol. 137, No. S1 ( 2011-01), p. 306-324
    In: Quarterly Journal of the Royal Meteorological Society, Wiley, Vol. 137, No. S1 ( 2011-01), p. 306-324
    Abstract: In 2007, the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) was operated for a nine‐month period in the Murg Valley, Black Forest, Germany, in support of the Convective and Orographically‐induced Precipitation Study (COPS). The synergy of AMF and COPS partner instrumentation was exploited to derive a set of high‐quality thermodynamic and cloud property profiles with 30 s resolution. In total, clouds were present 72% of the time, with multi‐layer mixed phase (28.4%) and single‐layer water clouds (11.3%) occurring most frequently. A comparison with the Cloudnet sites Chilbolton and Lindenberg for the same time period revealed that the Murg Valley exhibits lower liquid water paths (LWPs; median = 37.5 g m −2 ) compared to the two sites located in flat terrain. In order to evaluate the derived thermodynamic and cloud property profiles, a radiative closure study was performed with independent surface radiation measurements. In clear sky, average differences between calculated and observed surface fluxes are less than 2% and 4% for the short wave and long wave part, respectively. In cloudy situations, differences between simulated and observed fluxes, particularly in the short wave part, are much larger, but most of these can be related to broken cloud situations. The daytime cloud radiative effect (CRE), i.e. the difference of cloudy and clear‐sky net fluxes, has been analysed for the whole nine‐month period. For overcast, single‐layer water clouds, sensitivity studies revealed that the CRE uncertainty is likewise determined by uncertainties in liquid water content and effective radius. For low LWP clouds, CRE uncertainty is dominated by LWP uncertainty; therefore refined retrievals, such as using infrared and/or higher microwave frequencies, are needed. Copyright © 2011 Royal Meteorological Society
    Type of Medium: Online Resource
    ISSN: 0035-9009 , 1477-870X
    URL: Issue
    RVK:
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2011
    detail.hit.zdb_id: 3142-2
    detail.hit.zdb_id: 2089168-4
    SSG: 14
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  • 2
    In: Frontiers in Earth Science, Frontiers Media SA, Vol. 11 ( 2023-4-11)
    Abstract: Distinct events of warm and moist air intrusions (WAIs) from mid-latitudes have pronounced impacts on the Arctic climate system. We present a detailed analysis of a record-breaking WAI observed during the MOSAiC expedition in mid-April 2020. By combining Eulerian with Lagrangian frameworks and using simulations across different scales, we investigate aspects of air mass transformations via cloud processes and quantify related surface impacts. The WAI is characterized by two distinct pathways, Siberian and Atlantic. A moist static energy transport across the Arctic Circle above the climatological 90th percentile is found. Observations at research vessel Polarstern show a transition from radiatively clear to cloudy state with significant precipitation and a positive surface energy balance (SEB), i.e., surface warming. WAI air parcels reach Polarstern first near the tropopause, and only 1–2 days later at lower altitudes. In the 5 days prior to the event, latent heat release during cloud formation triggers maximum diabatic heating rates in excess of 20 K d -1 . For some poleward drifting air parcels, this facilitates strong ascent by up to 9 km. Based on model experiments, we explore the role of two key cloud-determining factors. First, we test the role moisture availability by reducing lateral moisture inflow during the WAI by 30%. This does not significantly affect the liquid water path, and therefore the SEB, in the central Arctic. The cause are counteracting mechanisms of cloud formation and precipitation along the trajectory. Second, we test the impact of increasing Cloud Condensation Nuclei concentrations from 10 to 1,000 cm -3 (pristine Arctic to highly polluted), which enhances cloud water content. Resulting stronger longwave cooling at cloud top makes entrainment more efficient and deepens the atmospheric boundary layer. Finally, we show the strongly positive effect of the WAI on the SEB. This is mainly driven by turbulent heat fluxes over the ocean, but radiation over sea ice. The WAI also contributes a large fraction to precipitation in the Arctic, reaching 30% of total precipitation in a 9-day period at the MOSAiC site. However, measured precipitation varies substantially between different platforms. Therefore, estimates of total precipitation are subject to considerable observational uncertainty.
    Type of Medium: Online Resource
    ISSN: 2296-6463
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2741235-0
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  • 3
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 2016
    In:  Journal of Geophysical Research: Atmospheres Vol. 121, No. 24 ( 2016-12-27)
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 121, No. 24 ( 2016-12-27)
    Abstract: Microphysical properties of thin liquid water clouds Improving statistical retrieval accuracy by combining microwave and infrared regime Evaluation of retrieval performance in a radiative closure study with real measurements
    Type of Medium: Online Resource
    ISSN: 2169-897X , 2169-8996
    URL: Issue
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2016
    detail.hit.zdb_id: 710256-2
    detail.hit.zdb_id: 2016800-7
    detail.hit.zdb_id: 2969341-X
    SSG: 16,13
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  • 4
    Online Resource
    Online Resource
    Schweizerbart ; 2008
    In:  Meteorologische Zeitschrift Vol. 17, No. 4 ( 2008-08-25), p. 421-431
    In: Meteorologische Zeitschrift, Schweizerbart, Vol. 17, No. 4 ( 2008-08-25), p. 421-431
    Type of Medium: Online Resource
    ISSN: 0941-2948
    Uniform Title: Sensitivity of summer precipitation simulated by the CLM with respect to initial and boundary conditions
    RVK:
    Language: English , English
    Publisher: Schweizerbart
    Publication Date: 2008
    detail.hit.zdb_id: 511391-X
    detail.hit.zdb_id: 2045168-4
    SSG: 14
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  • 5
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 100, No. 5 ( 2019-05), p. 841-871
    Abstract: Clouds play an important role in Arctic amplification. This term represents the recently observed enhanced warming of the Arctic relative to the global increase of near-surface air temperature. However, there are still important knowledge gaps regarding the interplay between Arctic clouds and aerosol particles, and surface properties, as well as turbulent and radiative fluxes that inhibit accurate model simulations of clouds in the Arctic climate system. In an attempt to resolve this so-called Arctic cloud puzzle, two comprehensive and closely coordinated field studies were conducted: the Arctic Cloud Observations Using Airborne Measurements during Polar Day (ACLOUD) aircraft campaign and the Physical Feedbacks of Arctic Boundary Layer, Sea Ice, Cloud and Aerosol (PASCAL) ice breaker expedition. Both observational studies were performed in the framework of the German Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC)3 project. They took place in the vicinity of Svalbard, Norway, in May and June 2017. ACLOUD and PASCAL explored four pieces of the Arctic cloud puzzle: cloud properties, aerosol impact on clouds, atmospheric radiation, and turbulent dynamical processes. The two instrumented Polar 5 and Polar 6 aircraft; the icebreaker Research Vessel (R/V) Polarstern ; an ice floe camp including an instrumented tethered balloon; and the permanent ground-based measurement station at Ny-Ålesund, Svalbard, were employed to observe Arctic low- and mid-level mixed-phase clouds and to investigate related atmospheric and surface processes. The Polar 5 aircraft served as a remote sensing observatory examining the clouds from above by downward-looking sensors; the Polar 6 aircraft operated as a flying in situ measurement laboratory sampling inside and below the clouds. Most of the collocated Polar 5/6 flights were conducted either above the R/V Polarstern or over the Ny-Ålesund station, both of which monitored the clouds from below using similar but upward-looking remote sensing techniques as the Polar 5 aircraft. Several of the flights were carried out underneath collocated satellite tracks. The paper motivates the scientific objectives of the ACLOUD/PASCAL observations and describes the measured quantities, retrieved parameters, and the applied complementary instrumentation. Furthermore, it discusses selected measurement results and poses critical research questions to be answered in future papers analyzing the data from the two field campaigns.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2019
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 6
    In: Eos, American Geophysical Union (AGU), ( 2017-01-17)
    Abstract: A new German research consortium is investigating why near-surface air temperatures in the Artic are rising more quickly than in the rest of the world.
    Type of Medium: Online Resource
    ISSN: 2324-9250
    Language: Unknown
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2017
    detail.hit.zdb_id: 2118760-5
    detail.hit.zdb_id: 240154-X
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  • 7
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 2020
    In:  Journal of Advances in Modeling Earth Systems Vol. 12, No. 12 ( 2020-12)
    In: Journal of Advances in Modeling Earth Systems, American Geophysical Union (AGU), Vol. 12, No. 12 ( 2020-12)
    Abstract: Large‐eddy simulations including external variability can bridge the gap between ground‐based observations of clouds and large‐scale models For comparison with local observations, it is important to take external variability (e.g., large‐scale forcing and surface) into account ICON‐LEM offers new possibilities to simulate small scales while considering external variability
    Type of Medium: Online Resource
    ISSN: 1942-2466 , 1942-2466
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2020
    detail.hit.zdb_id: 2462132-8
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  • 8
    Online Resource
    Online Resource
    American Meteorological Society ; 2020
    In:  Bulletin of the American Meteorological Society Vol. 101, No. 9 ( 2020-09-01), p. E1512-E1523
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 101, No. 9 ( 2020-09-01), p. E1512-E1523
    Abstract: Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2020
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 9
    In: Elem Sci Anth, University of California Press, Vol. 10, No. 1 ( 2022-02-07)
    Abstract: With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross-cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system scientific research and provide an important foundation for advancing multiscale modeling capabilities in the Arctic.
    Type of Medium: Online Resource
    ISSN: 2325-1026
    Language: English
    Publisher: University of California Press
    Publication Date: 2022
    detail.hit.zdb_id: 2745461-7
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  • 10
    Online Resource
    Online Resource
    Copernicus GmbH ; 2022
    In:  Atmospheric Measurement Techniques Vol. 15, No. 20 ( 2022-10-21), p. 6035-6050
    In: Atmospheric Measurement Techniques, Copernicus GmbH, Vol. 15, No. 20 ( 2022-10-21), p. 6035-6050
    Abstract: Abstract. Remotely-sensed precipitation retrievals are critical for advancing our understanding of global energy and hydrologic cycles in remote regions. Radar reflectivity profiles of the lower atmosphere are commonly linked to precipitation through empirical power laws, but these relationships are tightly coupled to particle microphysical assumptions that do not generalize well to different regional climates. Here, we develop a robust, highly generalized precipitation retrieval algorithm from a deep convolutional neural network (DeepPrecip) to estimate 20 min average surface precipitation accumulation using near-surface radar data inputs. DeepPrecip displays a high retrieval skill and can accurately model total precipitation accumulation, with a mean square error (MSE) 160 % lower, on average, than current methods. DeepPrecip also outperforms a less complex machine learning retrieval algorithm, demonstrating the value of deep learning when applied to precipitation retrievals. Predictor importance analyses suggest that a combination of both near-surface (below 1 km) and higher-altitude (1.5–2 km) radar measurements are the primary features contributing to retrieval accuracy. Further, DeepPrecip closely captures total precipitation accumulation magnitudes and variability across nine distinct locations without requiring any explicit descriptions of particle microphysics or geospatial covariates. This research reveals the important role for deep learning in extracting relevant information about precipitation from atmospheric radar retrievals.
    Type of Medium: Online Resource
    ISSN: 1867-8548
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2022
    detail.hit.zdb_id: 2505596-3
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