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  • Other Sources  (3)
  • 1995-1999  (3)
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  • 1
    Publication Date: 2019-08-28
    Description: We report measurements of the differential brightness of interstellar dust emission near the Galactic plane and at high Galactic latitudes. The data were obtained as part of a program to measure anisotropy in the cosmic microwave background (CMB). The measurements were made with a 0.5 deg beam size and a 1.3 deg sinusoidal chop, in broad bands (Delta nu/nu approximately 0.3) centered near frequencies of 6, 9, and 12 cm(exp -1). A measurement made toward the Galactic plane, at longitude 1 = 23.7 deg, is compared with the contrast observed in the 100 micrometers IRAS data. Assuming the dust emission has a brightness I(sub nu) proportional to nu(sup n)B(sub nu)(T(sub d)), where B(sub nu) is the Planck function, a best fit yields n = 1.6 +/- 0.4, T(sub d) = 24 +/- 5 K. In a region near the star mu Pegasi (mu PEG l = 91 deg, b = -31 deg), the comparison of our data with the 100 micrometers IRAS data yields n = 1.4 +/- 0.4, and T(sub d) = 18 +/- 3 K. In a second region near the star gamma Ursa Minoris (GUM l = 108 deg, b = 41 deg), an upper limit is placed on contrast in dust emission. This upper limit is consistent with spectrum measured at mu PEG and the IRAS 100 micrometer emission contrast at GUM, which is approximately 8 times lower than mu PEG.
    Keywords: ASTRONOMY
    Type: Astrophysical Journal, Part 1 (ISSN 0004-637X); 444; 1; p. 226-230
    Format: text
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  • 2
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    Elsevier
    In:  Journal of Marine Systems, 6 (1-2). pp. 31-46.
    Publication Date: 2017-06-26
    Description: A data assimilation system has been developed which has been used in conjunction with a primitive equation model of the tropical Pacific. The assimilation system is based on a time weighted successive correction method. The data are inserted continuously by updating the model solution every time step. The inserted data are taken from a time window, centered on the present model time step. Three experiments were performed. In the first assimilation run, SST observations were assimilated. In the second experiment, island based sea level observations were assimilated, while in the third run subsurface temperature data were assimilated. An intercomparison between the three assimilation runs was made and we discuss two questions. First, to which extent can the model fields be improved, and second, how long is the assimilated information retained by the ocean model.
    Type: Article , PeerReviewed
    Format: text
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  • 3
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    Max-Planck-Institut für Meteorologie
    In:  MPI-Report, 170 . Max-Planck-Institut für Meteorologie, Hamburg, Germany, 30 pp.
    Publication Date: 2019-08-07
    Description: The EL Nino-Southern Oscillation phenomenon (ENSO) is an interannual perturbation of the climate system. It is characterized by a weakening of the trade winds and a warming of the sea surface temperature in the equatorial Pacific. ENSO occurs every 4-7 years and its impacts are felt worldwide. Recently, coupled ocean-atmosphere models have been used to describe ENSO and to predict it at lead times up to one to two years. The predictability of Enso is determined by the oceanic part of the coupled system. For forecasts, the accuracy of the initial state from which the prediction is started is crucial. Hence, data assimilation into ocean models should be a powerful tool to improve ENSO forecasts. The two different types of observational data, most relevant for ENSO predictions are temperature measurements down to a depth of a few hundred meters and sea level observations. In general the accuracy of temperature measurements is quite good and they contain the required information to initialize a coupled ocean atmosphere forecast system. However, these data are mainly taken from ships or buoys which may lead to poor spatial and temporal data coverage in certain areas. In contrast, sea level data are available almost continuously in space and time since they can be measured with good accuracy by satellites. In principle they both contain the ENSO signal, and it is an interesting question, whether the impact on ENSO forecasts is comparable for both types of data. (orig.)
    Type: Report , NonPeerReviewed
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