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  • 1
    Monograph available for loan
    Monograph available for loan
    Chichester : Wiley
    Call number: AWI A6-08-0012
    Type of Medium: Monograph available for loan
    Pages: X, 280 Seiten , Illustrationen , 1 CD-ROM
    ISBN: 0470861738
    Language: English
    Note: Contents Preface Part I Anatomy of a cyclone 1 Anatomy of a cyclone 1.1 A 'typical' extra-tropical cyclone 1.2 Describing the atmosphere 1.3 Air masses and fronts 1.4 The structure of a typical extra-tropical cyclone Review questions 2 Mathematical methods in fluid dynamics 2.1 Scalars and vectors 2.2 The algebra of vectors 2.3 Scalar and vector fields 2.4 Coordinate systems on the Earth 2.5 Gradients of vectors 2.6 Line and surface integrals 2.7 Eulerian and Lagrangian frames of reference 2.8 Advection Review questions 3 Properties of fluids 3.1 Solids, liquids, and gases 3.2 Thermodynamic properties of air 3.3 Composition of the atmosphere 3.4 Static stability 3.5 The continuum hypothesis 3.6 Practical assumptions 3.7 Continuity equation Review questions 4 Fundamental forces 4.1 Newton's second law: F=ma 4.2 Body, surface, and line forces 4.3 Forces in an inertial reference frame 4.4 Forces in a rotating reference frame 4.5 The Navier-Stokes equations Review questions 5 Scale analysis 5.1 Dimensional homogeneity 5.2 Scales 5.3 Non-dimensional parameters 5.4 Scale analysis 5.5 The geostrophic approximation Review questions 6 Simple steady motion 6.1 Natural coordinate system 6.2 Balanced flow 6.3 The Boussinesq approximation 6.4 The thermal wind 6.5 Departures from balance Review questions 7 Circulation and vorticity 7.1 Circulation 7.2 Vorticity 7.3 Conservation of potential vorticity 7.4 An introduction to the vorticity equation Review questions 8 Simple wave motions 8.1 Properties of waves 8.2 Perturbation analysis 8.3 Planetary waves Review questions 9 Extra-tropical weather systems 9.1 Fronts 9.2 Frontal cyclones 9.3 Baroclinic instability Review questions Part II Atmospheric phenomena 10 Boundary layers 10.1 Turbulence 10.2 Reynolds decomposition 10.3 Generation of turbulence 10.4 Closure assumptions Review questions 11 Clouds and severe weather 11.1 Moist processes in the atmosphere 11.2 Air mass thunderstorms 11.3 Multi-cell thunderstorms 11.4 Supercell thunderstorms and tornadoes 11.5 Mesoscale convective systems Review questions 12 Tropical weather 12.1 Scales of motion 12.2 Atmospheric oscillations 12.3 Tropical cyclones Review questions 13 Mountain weather 13.1 Internal gravity waves 13.2 Flow over mountains 13.3 Downslope windstorms Review questions 14 Polar weather 14.1 Katabatic winds 14.2 Barrier winds 14.3 Polar lows Review questions 15 Epilogue: the general circulation 15.1 Fueled by the Sun 15.2 Radiative-convective equilibrium 15.3 The zonal mean circulation 15.4 The angular momentum budget 15.5 The energy cycle Appendix A - symbols Appendix Β - constants and units Bibliography Index
    Location: AWI Reading room
    Branch Library: AWI Library
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  • 2
    Publication Date: 2024-02-10
    Description: This dataset contains information about the state of the central Arctic lower atmosphere during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Through the merging of MOSAiC radiosonde, 10-m meteorological tower, ceilometer, and radiation station observations, this dataset provides information about the atmospheric boundary layer (depth and stability), temperature features (near-surface temperature and temperature inversion characteristics), wind features (near-surface wind speed and low-level jet characteristics), moisture features (near-surface mixing ratio and cloud characteristics), and surface radiation budget (up- and downwelling longwave and shortwave radiative flux) at the time of each MOSAiC radiosonde launch (approximately 4 times per day between September 2019 and October 2020). The dataset is structured in a NetCDF4 file, which follows the CF-1.10 convention. The objective of this dataset is to provide the user community with a consistent description of general lower atmospheric conditions throughout the MOSAiC year.
    Keywords: ABL; Arctic; Arctic Ocean; Atmosphere; Automatic weather station; AWS; FLUX_TOWER; Flux tower; meteorological data; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; North Greenland Sea; Polarstern; PS122/1; PS122/1_10-103; PS122/1_10-105; PS122/1_10-106; PS122/1_10-107; PS122/1_10-108; PS122/1_10-135; PS122/1_10-21; PS122/1_10-22; PS122/1_10-23; PS122/1_10-24; PS122/1_10-28; PS122/1_10-29; PS122/1_10-3; PS122/1_10-30; PS122/1_10-31; PS122/1_10-4; PS122/1_10-53; PS122/1_10-54; PS122/1_10-56; PS122/1_10-57; PS122/1_10-73; PS122/1_10-74; PS122/1_10-75; PS122/1_10-76; PS122/1_10-94; PS122/1_10-95; PS122/1_10-99; PS122/1_11-10; PS122/1_11-29; PS122/1_11-30; PS122/1_11-31; PS122/1_11-32; PS122/1_11-33; PS122/1_11-43; PS122/1_11-44; PS122/1_11-45; PS122/1_11-46; PS122/1_11-5; PS122/1_11-6; PS122/1_11-7; PS122/1_11-8; PS122/1_11-9; PS122/1_1-299; PS122/1_1-341; PS122/1_1-345; PS122/1_2-100; PS122/1_2-101; PS122/1_2-103; PS122/1_2-104; PS122/1_2-105; PS122/1_2-106; PS122/1_2-107; PS122/1_2-110; PS122/1_2-111; PS122/1_2-112; PS122/1_2-113; PS122/1_2-115; PS122/1_2-116; PS122/1_2-117; PS122/1_2-118; PS122/1_2-119; PS122/1_2-120; PS122/1_2-121; PS122/1_2-122; PS122/1_2-123; PS122/1_2-127; PS122/1_2-135; PS122/1_2-136; PS122/1_2-137; PS122/1_2-139; PS122/1_2-140; PS122/1_2-141; PS122/1_2-143; PS122/1_2-144; PS122/1_2-145; PS122/1_2-146; PS122/1_2-147; PS122/1_2-148; PS122/1_2-149; PS122/1_2-150; PS122/1_2-160; PS122/1_2-161; PS122/1_2-162; PS122/1_2-163; PS122/1_2-171; PS122/1_2-172; PS122/1_2-173; PS122/1_2-174; PS122/1_2-179; PS122/1_2-180; PS122/1_2-181; PS122/1_2-182; PS122/1_2-184; PS122/1_2-185; PS122/1_2-186; PS122/1_2-187; PS122/1_2-188; PS122/1_2-189; PS122/1_2-190; PS122/1_2-191; PS122/1_2-192; PS122/1_2-193; PS122/1_2-51; PS122/1_2-52; PS122/1_2-53; PS122/1_2-54; PS122/1_2-55; PS122/1_2-56; PS122/1_2-59; PS122/1_2-60; PS122/1_2-61; PS122/1_2-62; PS122/1_2-69; PS122/1_2-70; PS122/1_2-71; PS122/1_2-72; PS122/1_2-73; PS122/1_2-74; PS122/1_2-75; PS122/1_2-76; PS122/1_2-77; PS122/1_2-78; PS122/1_2-79; PS122/1_2-80; PS122/1_2-81; PS122/1_2-82; PS122/1_2-83; PS122/1_2-85; PS122/1_2-86; PS122/1_2-87; PS122/1_2-88; PS122/1_2-91; PS122/1_2-92; PS122/1_2-93; PS122/1_2-94; PS122/1_4-19; PS122/1_4-20; PS122/1_4-21; PS122/1_4-22; PS122/1_4-30; PS122/1_4-31; PS122/1_4-32; PS122/1_4-33; PS122/1_4-35; PS122/1_4-36; PS122/1_4-4; PS122/1_4-5; PS122/1_4-6; PS122/1_4-7; PS122/1_4-8; PS122/1_4-9; PS122/1_5-10; PS122/1_5-11; PS122/1_5-12; PS122/1_5-13; PS122/1_5-20; PS122/1_5-21; PS122/1_5-22; PS122/1_5-23; PS122/1_5-31; PS122/1_5-32; PS122/1_5-33; PS122/1_5-34; PS122/1_5-36; PS122/1_5-38; PS122/1_5-39; PS122/1_5-49; PS122/1_5-50; PS122/1_5-51; PS122/1_5-52; PS122/1_5-6; PS122/1_5-7; PS122/1_5-72; PS122/1_5-73; PS122/1_5-74; PS122/1_5-75; PS122/1_5-79; PS122/1_5-80; PS122/1_6-112; PS122/1_6-113; PS122/1_6-114; PS122/1_6-115; PS122/1_6-12; PS122/1_6-125; PS122/1_6-126; PS122/1_6-13; PS122/1_6-14; PS122/1_6-15; PS122/1_6-24; PS122/1_6-25; PS122/1_6-26; PS122/1_6-27; PS122/1_6-3; PS122/1_6-4; PS122/1_6-53; PS122/1_6-54; PS122/1_6-55; PS122/1_6-56; PS122/1_6-71; PS122/1_6-72; PS122/1_6-73; PS122/1_6-74; PS122/1_6-82; PS122/1_6-83; PS122/1_6-84; PS122/1_6-85; PS122/1_7-100; PS122/1_7-101; PS122/1_7-102; PS122/1_7-107; PS122/1_7-108; PS122/1_7-109; PS122/1_7-110; PS122/1_7-113; PS122/1_7-114; PS122/1_7-13; PS122/1_7-14; PS122/1_7-26; PS122/1_7-27; PS122/1_7-28; PS122/1_7-29; PS122/1_7-30; PS122/1_7-43; PS122/1_7-44; PS122/1_7-45; PS122/1_7-46; PS122/1_7-63; PS122/1_7-64; PS122/1_7-65; PS122/1_7-66; PS122/1_7-83; PS122/1_7-84; PS122/1_7-85; PS122/1_7-86; PS122/1_7-99; PS122/1_8-101; PS122/1_8-11; PS122/1_8-115; PS122/1_8-116; PS122/1_8-117; PS122/1_8-118; PS122/1_8-12; PS122/1_8-120; PS122/1_8-121; PS122/1_8-13; PS122/1_8-14; PS122/1_8-39; PS122/1_8-40; PS122/1_8-41; PS122/1_8-42; PS122/1_8-5; PS122/1_8-6; PS122/1_8-63; PS122/1_8-64; PS122/1_8-65; PS122/1_8-66; PS122/1_8-80; PS122/1_8-81; PS122/1_8-82; PS122/1_8-83; PS122/1_8-95; PS122/1_8-96; PS122/1_8-97; PS122/1_9-101; PS122/1_9-102; PS122/1_9-105; PS122/1_9-106; PS122/1_9-13; PS122/1_9-14; PS122/1_9-18; PS122/1_9-19; PS122/1_9-20; PS122/1_9-21; PS122/1_9-41; PS122/1_9-42; PS122/1_9-43; PS122/1_9-44; PS122/1_9-57; PS122/1_9-58; PS122/1_9-59; PS122/1_9-60; PS122/1_9-77; PS122/1_9-78; PS122/1_9-79; PS122/1_9-80; PS122/1_9-88; PS122/1_9-89; PS122/1_9-90; PS122/1_9-91; PS122/1_99-46; PS122/1_99-47; PS122/1_9-99; PS122/2; PS122/2_14-119; PS122/2_14-13; PS122/2_14-14; PS122/2_15-1; PS122/2_15-13; PS122/2_15-2; PS122/2_15-3; PS122/2_15-4; PS122/2_15-5; PS122/2_15-7; PS122/2_16-10; PS122/2_16-11; PS122/2_16-13; PS122/2_16-16; PS122/2_16-17; PS122/2_16-18; PS122/2_16-19; PS122/2_16-2; PS122/2_16-20; PS122/2_16-3; PS122/2_16-30; PS122/2_16-31; PS122/2_16-32; PS122/2_16-33; PS122/2_16-4; PS122/2_16-40; PS122/2_16-41; PS122/2_16-42; PS122/2_16-43; PS122/2_16-5; PS122/2_16-57; PS122/2_16-58; PS122/2_16-59; PS122/2_16-6; PS122/2_16-60; PS122/2_16-67; PS122/2_16-68; PS122/2_16-69; PS122/2_16-7; PS122/2_16-70; PS122/2_16-76; PS122/2_17-10; PS122/2_17-102; PS122/2_17-103; PS122/2_17-104; PS122/2_17-105; PS122/2_17-11; PS122/2_17-110; PS122/2_17-12; PS122/2_17-21; PS122/2_17-22; PS122/2_17-23; PS122/2_17-24; PS122/2_17-35; PS122/2_17-36; PS122/2_17-37; PS122/2_17-38; PS122/2_17-55; PS122/2_17-56; PS122/2_17-57; PS122/2_17-58; PS122/2_17-71; PS122/2_17-72; PS122/2_17-73; PS122/2_17-74; PS122/2_17-92; PS122/2_17-93; PS122/2_17-94; PS122/2_17-95; PS122/2_18-100; PS122/2_18-11; PS122/2_18-12; PS122/2_18-13; PS122/2_18-20; PS122/2_18-21; PS122/2_18-22; PS122/2_18-27; PS122/2_18-29; PS122/2_18-30; PS122/2_18-31; PS122/2_18-48; PS122/2_18-49; PS122/2_18-50; PS122/2_18-51; PS122/2_18-67; PS122/2_18-68; PS122/2_18-69; PS122/2_18-70; PS122/2_18-85; PS122/2_18-86; PS122/2_18-87; PS122/2_18-88; PS122/2_18-94; PS122/2_18-95; PS122/2_18-96; PS122/2_18-97; PS122/2_19-10; PS122/2_19-100; PS122/2_19-11; PS122/2_19-12; PS122/2_19-124; PS122/2_19-125; PS122/2_19-126; PS122/2_19-127; PS122/2_19-143; PS122/2_19-22; PS122/2_19-23; PS122/2_19-24; PS122/2_19-25; PS122/2_19-47; PS122/2_19-48; PS122/2_19-49; PS122/2_19-50; PS122/2_19-71; PS122/2_19-72; PS122/2_19-73; PS122/2_19-74; PS122/2_19-84; PS122/2_19-85; PS122/2_19-86; PS122/2_19-87; PS122/2_19-97; PS122/2_19-98; PS122/2_19-99; PS122/2_20-10; PS122/2_20-103; PS122/2_20-104; PS122/2_20-105; PS122/2_20-106; PS122/2_20-119; PS122/2_20-120; PS122/2_20-121; PS122/2_20-122; PS122/2_20-135; PS122/2_20-19; PS122/2_20-20; PS122/2_20-21; PS122/2_20-22; PS122/2_20-37; PS122/2_20-38; PS122/2_20-39; PS122/2_20-40; PS122/2_20-66; PS122/2_20-67; PS122/2_20-68; PS122/2_20-69; PS122/2_20-8; PS122/2_20-84; PS122/2_20-85; PS122/2_20-86; PS122/2_20-87; PS122/2_20-9; PS122/2_21-106; PS122/2_21-107; PS122/2_21-108; PS122/2_21-109; PS122/2_21-115; PS122/2_21-116; PS122/2_21-117; PS122/2_21-118; PS122/2_21-132; PS122/2_21-133; PS122/2_21-134; PS122/2_21-135; PS122/2_21-136; PS122/2_21-21; PS122/2_21-22; PS122/2_21-23; PS122/2_21-37; PS122/2_21-38; PS122/2_21-39; PS122/2_21-40; PS122/2_21-57; PS122/2_21-58; PS122/2_21-59; PS122/2_21-60; PS122/2_21-79; PS122/2_21-80; PS122/2_21-81; PS122/2_21-82; PS122/2_22-10; PS122/2_22-102; PS122/2_22-103; PS122/2_22-104; PS122/2_22-105; PS122/2_22-11; PS122/2_22-111; PS122/2_22-20; PS122/2_22-21; PS122/2_22-22; PS122/2_22-23; PS122/2_22-38; PS122/2_22-39; PS122/2_22-40; PS122/2_22-41; PS122/2_22-57; PS122/2_22-58; PS122/2_22-59; PS122/2_22-60; PS122/2_22-78; PS122/2_22-79; PS122/2_22-80; PS122/2_22-81; PS122/2_22-87; PS122/2_22-88; PS122/2_22-89; PS122/2_22-9; PS122/2_23-101; PS122/2_23-102; PS122/2_23-103; PS122/2_23-104; PS122/2_23-117; PS122/2_23-118; PS122/2_23-119; PS122/2_23-120; PS122/2_23-129; PS122/2_23-22; PS122/2_23-23; PS122/2_23-24; PS122/2_23-25; PS122/2_23-41; PS122/2_23-42; PS122/2_23-43; PS122/2_23-44; PS122/2_23-54; PS122/2_23-55; PS122/2_23-56; PS122/2_23-57; PS122/2_23-6; PS122/2_23-7; PS122/2_23-8; PS122/2_23-80; PS122/2_23-81; PS122/
    Type: Dataset
    Format: application/x-hdf, 440 kBytes
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  • 3
    Publication Date: 2011-06-08
    Description: Data from the National Centers for Environmental Prediction/National Center for Atmospheric Research and European Center for Medium-Range Weather Forecasts 40 year reanalyses are used to relate large-scale synoptic circulation patterns to local weather at several locations across Alaska. These results are compared to available National Weather Service observations to demonstrate the utility of this method such that it can be applied in future work at locations where local observations are not available. The focus of these comparisons is on surface observations of temperature. The results from the two reanalysis data sets match well to each other and to the observations. Synoptic patterns associated with warm/cold days at five National Weather Service stations representing different climate regions throughout Alaska are identified. In addition, a method to attribute a change in climate to circulation and noncirculation differences is applied to a known climate shift, the Pacific climate shift of 1976, which was associated with an increase in temperatures throughout Alaska. The results from this analysis show that general warming rather than changes in circulation is primarily responsible for the increase in temperatures after 1976.
    Print ISSN: 0148-0227
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 4
  • 5
    Publication Date: 2013-05-25
    Description: There is growing recognition that reductions in Arctic sea ice extent will influence patterns of atmospheric circulation both within and beyond the Arctic. We explore the impact of 2007 ice conditions (the second lowest Arctic sea ice extent in the satellite era) on atmospheric circulation and surface temperatures and fluxes through a series of model experiments with the NCAR Community Atmospheric Model version 3 (CAM3). Two 30-year simulations were performed; one using climatological sea ice extent for the end of the 20th century and other using observed sea ice extent from 2007. Circulation differences over the Northern Hemisphere were most prominent during autumn and winter with lower sea level pressure (SLP) and tropospheric pressure simulated over much of the Arctic for the 2007 sea ice experiment. The atmospheric response to 2007 ice conditions was much weaker during summer, with negative SLP anomalies simulated from Alaska across the Arctic to Greenland. Higher temperatures and larger surface fluxes to the atmosphere in areas of anomalous open water were also simulated. CAM3 experiment results were compared to observed SLP anomalies from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data. The observed SLP anomalies during spring are nearly opposite to those simulated. In summer, large differences were shown between the observed and simulated SLP also, suggesting that the sea ice conditions in the months preceding and during the summer of 2007 were not responsible for creating an atmospheric circulation pattern which favoured the large observed sea ice loss. The simulated and observed atmospheric circulation anomalies during autumn and winter were more similar than spring and summer, with the exception of a strong high pressure system in the Beaufort Sea which was not simulated, suggesting that the forced atmospheric response to reduced sea ice was in part responsible for the observed atmospheric circulation anomalies during autumn and winter.
    Print ISSN: 0899-8418
    Electronic ISSN: 1097-0088
    Topics: Geosciences , Physics
    Published by Wiley
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  • 6
    Publication Date: 2017-08-12
    Description: Data from four reanalyses are analyzed to evaluate the downstream atmospheric response both spatially and temporally to anomalous autumn surface forcing in the Arctic Basin. Running weekly mean skin temperature anomalies were classified using the self-organizing map algorithm. The resulting classes were used to both composite the initial atmospheric state and determine how the atmosphere evolves from this state. The strongest response was to anomalous forcing - positive skin temperature and total surface energy flux anomalies and reduced sea ice concentration - in the Barents and Kara Seas. Analysis of the evolution of the atmospheric state for 12 weeks after the initial forcing showed a persistence in the anomalies in this area which led to a build up of heat in the atmosphere. This resulted in positive 1000-500hPa thickness and high pressure circulation anomalies in this area which were associated with cold air advection and temperatures over much of central and northern Asia. Evaluation of days with the opposite forcing (i.e. negative skin temperature anomalies and increased sea ice concentration in the Barents and Kara Seas) showed a mirrored, opposite downstream atmospheric response. Other patterns with positive skin temperature anomalies in the Arctic Basin did not show the same response most likely because the anomalies were not as strong nor did they persist for as many weeks following the initial forcing.
    Print ISSN: 0148-0227
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 7
    Publication Date: 2016-02-01
    Description: C band backscatter parameters contain information about the upper snowpack/firn in the dry snow zone. The wide incidence angle diversity of the Advanced Scatterometer (ASCAT) gives unprecedented characterisation of backscatter anisotropy, revealing the backscatter response to climatic forcing. The A (isotropic component) and M-2 (bi-sinusoidal azimuth anisotropy) parameters are investigated here, in conjunction with data from atmospheric and snowpack models, to identify the backscatter response to surface forcing parameters (wind speed and persistence, precipitation, surface temperature, density and grain size). The long-term mean A parameter is successfully recreated with a regression using these drivers, indicating strong links between the A parameter and precipitation on long timescales. While the ASCAT time series is too short to determine which factors drive observed trends, factors influencing the seasonal and short timescale variability are revealed. On these timescales, A strongly responds to the propagation of surface temperature cycles/anomalies downward through the firn, via direct modulation of the dielectric constant. The influence of precipitation on A is small at shorter time scales. The M2 parameter is controlled by wind speed and persistence, through modification of monodirectionally-aligned surface roughness. This variability indicates that throughout much of coastal Antarctica, a microwave 'snapshot' is generally not representative of longer-term conditions.
    Print ISSN: 0022-1430
    Electronic ISSN: 1727-5652
    Topics: Geography , Geosciences
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  • 8
    Publication Date: 2017-01-11
    Description: Accurate representation of the stability of the surface layer in numerical weather prediction models is important because of the impact it has on forecasts of surface energy, moisture, and momentum fluxes. It also impacts boundary layer processes such as the generation of turbulence, the creation of near-surface flows, and fog formation. This paper uses observations from a 30-m automatic weather station on the Ross Ice Shelf, Antarctica, to evaluate the near-surface layer in the Antarctic Mesoscale Prediction System (AMPS), a numerical weather prediction system used for forecasting in Antarctica. The method of self-organizing maps (SOM) is used to identify characteristic potential temperature anomaly profiles observed at the 30-m tower. The SOM-identified profiles are then used to evaluate the performance of AMPS as a function of atmospheric stability. The results indicate AMPS underpredicts the frequency of near-neutral profiles and instead overpredicts the frequency of weakly unstable and weak to moderately stable profiles. AMPS does not forecast the strongest statically stable patterns observed by Tall Tower, but in the median, the AMPS forecasts are more statically stable across all wind speeds, indicating a possible mechanical mixing error or a negative radiation bias. The SOM analysis identifies a negative radiation bias under near-neutral to weakly stable conditions, causing an overrepresentation of the static stability in AMPS. AMPS has a positive wind speed bias in moderate to strongly stable conditions, which generates too much mechanical mixing and an underrepresentation of the static stability. Model errors increase with increasing atmospheric stability.
    Print ISSN: 0882-8156
    Electronic ISSN: 1520-0434
    Topics: Geography , Physics
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  • 9
    Publication Date: 2016-12-13
    Description: ABSTRACT The atmospheric state and synoptic situation associated with widespread summer June, July, and August temperature extremes in southern Alaska is explored. Using ERA-Interim data and a self-organizing map framework, the evolution of the atmospheric state leading up to days that are defined as experiencing extreme surface temperature are compared with the evolution for non-extreme days. The variables evaluated include circulation at the surface and aloft and surface radiative fluxes. For warm extremes, blocking evident in the 500 hPa flow combined with anomalously large surface downward shortwave radiation allowed surface temperatures to become extreme. For cold extremes, an upper level trough and cold air advection aloft coupled with a more minor role of anomalously negative surface downward shortwave radiation were important. The self-organizing map framework allowed an investigation of these details beyond a composite analysis of all extremes.
    Print ISSN: 0899-8418
    Electronic ISSN: 1097-0088
    Topics: Geosciences , Physics
    Published by Wiley
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  • 10
    Publication Date: 2016-10-25
    Description: Flight operations in Antarctica rely on accurate weather forecasts aided by the numerical predictions primarily produced by the Antarctic Mesoscale Prediction System (AMPS) that employs the polar version of the Weather Research and Forecasting (Polar WRF) Model. To improve the performance of the model’s Mellor–Yamada–Janjić (MYJ) planetary boundary layer (PBL) scheme, this study examines 1.5 yr of meteorological data provided by the 30-m Alexander Tall Tower! (ATT) automatic weather station on the western Ross Ice Shelf from March 2011 to July 2012. Processed ATT observations at 10-min intervals from the multiple observational levels are compared with the 5-km-resolution AMPS forecasts run daily at 0000 and 1200 UTC. The ATT comparison shows that AMPS has fundamental issues with moisture and handling stability as a function of wind speed. AMPS has a 10-percentage-point (i.e., RH unit) relative humidity dry bias year-round that is highest when katabatic winds from the Byrd and Mulock Glaciers exceed 15 m s−1. This is likely due to nonlocal effects such as errors in the moisture content of the katabatic flow and AMPS not parameterizing the sublimation from blowing snow. AMPS consistently overestimates the wind speed at the ATT by 1–2 m s−1, in agreement with previous studies that attribute the high wind speed bias to the MYJ scheme. This leads to reduced stability in the simulated PBL, thus affecting the model’s ability to properly simulate the transfer of heat and momentum throughout the PBL.
    Print ISSN: 1558-8424
    Electronic ISSN: 1558-8432
    Topics: Geography , Physics
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