888 research outputs found

    Sea ice-atmosphere interaction: Application of multispectral satellite data in polar surface energy flux estimates

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    The application of multi-spectral satellite data to estimate polar surface energy fluxes is addressed. To what accuracy and over which geographic areas large scale energy budgets can be estimated are investigated based upon a combination of available remote sensing and climatological data sets. The general approach was to: (1) formulate parameterization schemes for the appropriate sea ice energy budget terms based upon the remotely sensed and/or in-situ data sets; (2) conduct sensitivity analyses using as input both natural variability (observed data in regional case studies) and theoretical variability based upon energy flux model concepts; (3) assess the applicability of these parameterization schemes to both regional and basin wide energy balance estimates using remote sensing data sets; and (4) assemble multi-spectral, multi-sensor data sets for at least two regions of the Arctic Basin and possibly one region of the Antarctic. The type of data needed for a basin-wide assessment is described and the temporal coverage of these data sets are determined by data availability and need as defined by parameterization scheme. The titles of the subjects are as follows: (1) Heat flux calculations from SSM/I and LANDSAT data in the Bering Sea; (2) Energy flux estimation using passive microwave data; (3) Fetch and stability sensitivity estimates of turbulent heat flux; and (4) Surface temperature algorithm

    Use of satellite-derived heterogeneous surface soil moisture for numerical weather prediction, The

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    Summer 1996.Bibliography: pages [296]-320

    A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS

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    Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.</jats:p

    An evaluation of novel remotely sensed data to improve and verify ocean- atmosphere forecasting.

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    The aim of this study is to evaluate the use of novel remote observations and spatial data analysis to improve the skill of an ocean forecasting system for the central Mediterranean Sea. A high-resolution (0.042 by 0.042ๆ ocean forecasting system was setup consisting of an atmosphere model (NCEP Eta model) that was coupled to an ocean model (Princeton Ocean Model). This coupling consisted of the provision of surface atmospheric fluxes predicted at 3-hourly intervals to drive forward the ocean model. This research study dealt with a variety of aspects to improve this forecasting system using an inter-disciplinary approach. The main aspect of this thesis is an evaluation of novel, remotely- sensed data acquired by an orbiting passive microwave sensor as a tool to assess and improve ocean forecasting. Thus, SST derived by the Tropical Microwave Imager onboard the TRMM satellite was evaluated for its potential to define one of the lower boundary conditions of the Eta model. The impact was positive, and resulted in an average improvement of the skill of the model to predict lower surface marine winds by approximately 10%. TMI-data proved extremely useful to derive instantaneous turbulent heat fluxes and other surface geophysical fields that were needed to diagnose and fine-tune the skill of the Eta model to forecast these fields. The TMI SST product also proved to be a valuable data source for data assimilation by the ocean model. An optimised data assimilation scheme was derived resulting in a bias of just -0.05 С after a 15-day model integration run. This thesis shows how spatial data analysis can provide more detailed information about the high-resolution forecasts and their quality in addition to standard verification tools. Routines that explore the spatial data of the forecasts, observations and their relationship were developed and applied. Geostatistical analysis was used to model the spatial structure of the residual fields of the predictions and observations, and to translate the degree of spatial correlation in numerical and graphical terms

    Satellite and in situ observations for advancing global Earth surface modelling: a review

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    In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

    Thermal remote sensing of sea surface temperature

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    Sea surface temperature has been an important application of remote sensing from space for three decades. This chapter first describes well-established methods that have delivered valuable routine observations of sea surface temperature for meteorology and oceanography. Increasingly demanding requirements, often related to climate science, have highlighted some limitations of these ap-proaches. Practitioners have had to revisit techniques of estimation, of characterising uncertainty, and of validating observations—and even to reconsider the meaning(s) of “sea surface temperature”. The current understanding of these issues is reviewed, drawing attention to ongoing questions. Lastly, the prospect for thermal remote sens-ing of sea surface temperature over coming years is discussed

    Guidelines for the air-sea interaction special study: An element of the NASA climate research program, JPL/SIO workshop report

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    A program in the area of air sea interactions is introduced. A space capability is discussed for global observations of climate parameters which will contribute to the understanding of the processes which influence climate and its predictability. The following recommendations are some of the suggestions made for air sea interaction studies: (1) a major effort needs to be devoted to the preparation of space based climatic data sets; (2) NASA should create a group or center for climatic data analysis due to the substantial long term effort that is needed in research and development; (3) funding for the analyses of existing data sets should be augmented and continued beyond the termination of present programs; (4) NASA should fund studies in universities, research institutions and governments' centers; and (5) the planning for an air sea interaction mission should be an early task

    The Spaceborne Global Climate Observing Center (SGCOC): Executive summary

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    Conceptual planning of the Spaceborne portion of the Global Climate Observing Systems (SGCOS) is reviewed. Fundamentals of the SGCOS are summarized

    Needs, opportunities and strategies for a long-term oceanic sciences satellite program

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    Several areas of the National Oceanic Satellite System are addressed including Satellite-borne communication systems, subsurface remote sensing, data coordination, color scanners, formatting important historical data sets, and sea surface temperature observations
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