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  • 1
    Online Resource
    Online Resource
    Impact of Seasonal Snow‐Cover Change on the Observed and Simulated State of the Atmospheric Boundary Layer in a High‐Altitude Mountain Valley
    American Geophysical Union (AGU) ; 2023
    In:  Journal of Geophysical Research: Atmospheres Vol. 128, No. 12 ( 2023-06-27)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 128, No. 12 ( 2023-06-27)
    Abstract: Temperature profiles retrieved from remotely sensed infrared radiances characterize the valley boundary layer over different snow covers The nocturnal inversion in a high‐altitude mountain valley is mixed out over low snow cover and persists when snow cover is high NOAA's operational weather prediction model struggles to correctly forecast the boundary layer especially when snow cover is high
    Type of Medium: Online Resource
    ISSN: 2169-897X , 2169-8996
    URL: Article
    DOI: 10.1029/2023JD038497
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2023
    detail.hit.zdb_id: 710256-2
    detail.hit.zdb_id: 2016800-7
    detail.hit.zdb_id: 2969341-X
    SSG: 16,13
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    Regime‐Specific Cloud Vertical Overlap Characteristics From Radar and Lidar Observations at the ARM Sites
    American Geophysical Union (AGU) ; 2023
    In:  Journal of Geophysical Research: Atmospheres Vol. 128, No. 6 ( 2023-03-27)
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 128, No. 6 ( 2023-03-27)
    Abstract: Decorrelation length scales varied by cloud regime at the Atmospheric Radiation Measurement Program Southern Great Plains site between 0.04 and 4.58 km Decorrelation length scales varied by site with values ranging from near 1 km in the Arctic to near 3 km in Brazil Decorrelation length scales for the same cloud regime across the sites were similar, although some cloud regimes exhibited differences
    Type of Medium: Online Resource
    ISSN: 2169-897X , 2169-8996
    URL: Article
    DOI: 10.1029/2022JD037772
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2023
    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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  • 3
    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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  • 4
    Online Resource
    Online Resource
    Evaluating convective planetary boundary layer height estimations resolved by both active and passive remote sensing instruments during the CHEESEHEAD19 field campaign
    Copernicus GmbH ; 2022
    In:  Atmospheric Measurement Techniques Vol. 15, No. 8 ( 2022-04-25), p. 2479-2502
    Details
    In: Atmospheric Measurement Techniques, Copernicus GmbH, Vol. 15, No. 8 ( 2022-04-25), p. 2479-2502
    Abstract: Abstract. During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, USA, active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include radar wind profilers, microwave radiometers, atmospheric emitted radiance interferometers, ceilometers, high spectral resolution lidars, Doppler lidars, and collaborative lower-atmospheric mobile profiling systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary layer depth estimations is also investigated. We found that while all instruments are overall able to provide reasonable boundary layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, radar wind profilers perform well during cloud-free conditions, and microwave radiometers and atmospheric emitted radiance interferometers have a very good agreement during all conditions but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from ceilometers and high spectral resolution lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the collaborative lower atmospheric mobile profiling systems can be constricted to a limited height range in low-aerosol conditions.
    Type of Medium: Online Resource
    ISSN: 1867-8548
    URL: Article
    URL: Article
    DOI: 10.5194/amt-15-2479-2022
    DOI: 10.5194/amt-15-2479-2022-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2022
    detail.hit.zdb_id: 2505596-3
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  • 5
    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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  • 6
    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
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