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  • 1
    Online Resource
    Online Resource
    Implications of Limited Liquid Water Path on Static Mixing within Arctic Low-Level Clouds
    American Meteorological Society ; 2014
    In:  Journal of Applied Meteorology and Climatology Vol. 53, No. 12 ( 2014-12), p. 2775-2789
    Details
    In: Journal of Applied Meteorology and Climatology, American Meteorological Society, Vol. 53, No. 12 ( 2014-12), p. 2775-2789
    Abstract: Observations of cloud properties and thermodynamics from two Arctic locations, Barrow, Alaska, and Surface Heat Budget of the Arctic (SHEBA), are examined. A comparison of in-cloud thermodynamic mixing characteristics for low-level, single-layer clouds from nearly a decade of data at Barrow and one full annual cycle over the sea ice at SHEBA is performed. These cloud types occur relatively frequently, evident in 27%–30% of all cloudy cases. To understand the role of liquid water path (LWP), or lack thereof, on static in-cloud mixing, cloud layers are separated into optically thin and optically thick LWP subclasses. Clouds with larger LWPs tend to have a deeper in-cloud mixed layer relative to optically thinner clouds. However, both cloud LWP subclasses are frequently characterized by an in-cloud stable layer above the mixed layer top. The depth of the stable layer generally correlates with an increased temperature gradient across the layer. This layer often contains a specific humidity inversion, but it is more frequently present when cloud LWP is optically thinner (LWP 〈 50 g m −2 ). It is suggested that horizontal thermodynamic advection plays a key role modifying the vertical extent of in-cloud mixing and likewise the depth of in-cloud stable layers. Furthermore, longwave atmospheric opacity above the cloud top is generally enhanced during cases with optically thinner clouds. Thermodynamic advection, cloud condensate distribution within the stable layer, and enhanced atmospheric radiation above the cloud are found to introduce a thermodynamic–radiative feedback that potentially modifies the extent of LWP and subsequent in-cloud mixing.
    Type of Medium: Online Resource
    ISSN: 1558-8424 , 1558-8432
    URL: Article
    DOI: 10.1175/JAMC-D-14-0065.1
    RVK:
    UA 4547
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2014
    detail.hit.zdb_id: 2227779-1
    detail.hit.zdb_id: 2227759-6
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  • 2
    Online Resource
    Online Resource
    Connecting Land–Atmosphere Interactions to Surface Heterogeneity in CHEESEHEAD19
    American Meteorological Society ; 2021
    In:  Bulletin of the American Meteorological Society Vol. 102, No. 2 ( 2021-02), p. E421-E445
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 102, No. 2 ( 2021-02), p. E421-E445
    Abstract: The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-19-0346.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 3
    Online Resource
    Online Resource
    Measurements and Model Improvement: Insight into NWP Model Error Using Doppler Lidar and Other WFIP2 Measurement Systems
    American Meteorological Society ; 2023
    In:  Monthly Weather Review Vol. 151, No. 12 ( 2023-12), p. 3063-3087
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 151, No. 12 ( 2023-12), p. 3063-3087
    Abstract: Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s −1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement. Significance Statement Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    URL: Article
    DOI: 10.1175/MWR-D-23-0069.1
    DOI: 10.1175/MWR-D-23-0069.s1
    RVK:
    RA 1000
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2023
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 4
    Online Resource
    Online Resource
    Supporting Advancement in Weather and Water Prediction in the Upper Colorado River Basin: The SPLASH Campaign
    American Meteorological Society ; 2023
    In:  Bulletin of the American Meteorological Society ( 2023-08-09)
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, ( 2023-08-09)
    Abstract: Water is a critical resource that causes significant challenges to inhabitants of the western United States. These challenges are likely to intensify as the result of expanding population and climate-related changes that act to reduce runoff in areas of complex terrain. To better understand the physical processes that drive the transition of mountain precipitation to streamflow, the National Oceanic and Atmospheric Administration has deployed suites of environmental sensors throughout the East River Watershed of Colorado as part of the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH). This includes surface-based sensors over a network of five different observing sites, airborne platforms, and sophisticated remote sensors to provide detailed information on spatiotemporal variability of key parameters. With a two-year deployment, these sensors offer detailed insight into precipitation, the lower atmosphere, and surface, and support the development of datasets targeting improved prediction of weather and water. Initial datasets have been published and are laying a foundation for improved characterization of physical processes and their interactions driving mountain hydrology, evaluation and improvement of numerical prediction tools, and educational activities. SPLASH observations contain a depth and breadth of information that enables a variety of atmospheric and hydrological science analyses over the coming years that leverage collaborations between national laboratories, academia, and stakeholders, including industry.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-22-0147.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2023
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 5
    Online Resource
    Online Resource
    The vertical distribution of thin features over the Arctic analysed from CALIPSO observations: Part I: Optically thin clouds
    Stockholm University Press ; 2011
    In:  Tellus B: Chemical and Physical Meteorology Vol. 63, No. 1 ( 2011-01-01), p. 77-
    Details
    In: Tellus B: Chemical and Physical Meteorology, Stockholm University Press, Vol. 63, No. 1 ( 2011-01-01), p. 77-
    Type of Medium: Online Resource
    ISSN: 1600-0889 , 0280-6509
    URL: Article
    DOI: 10.1111/j.1600-0889.2010.00516.x
    RVK:
    RA 1000
    RVK:
    UA 8382.2
    Language: Unknown
    Publisher: Stockholm University Press
    Publication Date: 2011
    detail.hit.zdb_id: 2026992-4
    detail.hit.zdb_id: 246061-0
    SSG: 16,13
    Location Call Number Limitation Availability
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  • 6
    Online Resource
    Online Resource
    A Process-Based Climatological Evaluation of AIRS Level 3 Tropospheric Thermodynamics over the High-Latitude Arctic
    American Meteorological Society ; 2019
    In:  Journal of Applied Meteorology and Climatology Vol. 58, No. 8 ( 2019-08), p. 1867-1886
    Details
    In: Journal of Applied Meteorology and Climatology, American Meteorological Society, Vol. 58, No. 8 ( 2019-08), p. 1867-1886
    Abstract: Measurements from spaceborne sensors have the unique capacity to fill spatial and temporal gaps in ground-based atmospheric observing systems, especially over the Arctic, where long-term observing stations are limited to pan-Arctic landmasses and infrequent field campaigns. The AIRS level 3 (L3) daily averaged thermodynamic profile product is widely used for process understanding across the sparsely observed Arctic atmosphere. However, detailed investigations into the accuracy of the AIRS L3 thermodynamic profiles product using in situ observations over the high-latitude Arctic are lacking. To address this void, we compiled a wealth of radiosounding profiles from long-term Arctic land stations and included soundings from intensive icebreaker-based field campaigns. These are used to evaluate daily mean thermodynamic profiles from the AIRS L3 product so that the community can understand to what extent such data records can be applied in scientific studies. Results indicate that, while the mid- to upper-troposphere temperature and specific humidity are captured relatively well by AIRS, the lower troposphere is susceptible to specific seasonal, and even monthly, biases. These differences have a critical influence on the lower-tropospheric stability structure. The relatively coarse vertical resolution of the AIRS L3 product, together with infrared radiation through persistent low Arctic cloud layers, leads to artificial thermodynamic structures that fail to accurately represent the lower Arctic atmosphere. These thermodynamic errors are likely to introduce artificial errors in the boundary layer structure and analysis of associated physical processes.
    Type of Medium: Online Resource
    ISSN: 1558-8424 , 1558-8432
    URL: Article
    URL: Article
    DOI: 10.1175/JAMC-D-18-0306.1
    DOI: 10.1175/jamc-d-18-0306.s1
    RVK:
    UA 4547
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2019
    detail.hit.zdb_id: 2227779-1
    detail.hit.zdb_id: 2227759-6
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  • 7
    Online Resource
    Online Resource
    Development of a Random-Forest Cloud-Regime Classification Model Based on Surface Radiation and Cloud Products
    American Meteorological Society ; 2021
    In:  Journal of Applied Meteorology and Climatology Vol. 60, No. 4 ( 2021-04), p. 477-491
    Details
    In: Journal of Applied Meteorology and Climatology, American Meteorological Society, Vol. 60, No. 4 ( 2021-04), p. 477-491
    Abstract: Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.
    Type of Medium: Online Resource
    ISSN: 1558-8424 , 1558-8432
    URL: Article
    DOI: 10.1175/JAMC-D-20-0153.1
    RVK:
    UA 4547
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2227779-1
    detail.hit.zdb_id: 2227759-6
    Location Call Number Limitation Availability
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    Online Resource
  • 8
    Online Resource
    Online Resource
    The vertical distribution of thin features over the Arctic analysed from CALIPSO observations. Part I: Optically thin clouds
    Stockholm University Press ; 2011
    In:  Tellus B Vol. 63, No. 1 ( 2011-2-1)
    Details
    In: Tellus B, Stockholm University Press, Vol. 63, No. 1 ( 2011-2-1)
    Type of Medium: Online Resource
    ISSN: 1600-0889 , 0280-6509
    URL: Article
    DOI: 10.3402/tellusb.v63i1.16189
    RVK:
    RA 1000
    RVK:
    UA 8382.2
    Language: Unknown
    Publisher: Stockholm University Press
    Publication Date: 2011
    detail.hit.zdb_id: 2026992-4
    detail.hit.zdb_id: 246061-0
    SSG: 16,13
    Location Call Number Limitation Availability
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