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Thursday, May 7, 2020 | History

2 edition of SSM/I sea ice concentrations using the bootstrap algorithm found in the catalog.

SSM/I sea ice concentrations using the bootstrap algorithm

Josefino C. Comiso

SSM/I sea ice concentrations using the bootstrap algorithm

by Josefino C. Comiso

  • 193 Want to read
  • 29 Currently reading

Published by National Aeronautics and Space Administration, Goddard Space Flight Center in Greenbelt, Md .
Written in English

    Subjects:
  • Sea ice -- Polar regions -- Remote sensing.,
  • Sea ice -- Polar regions -- Measurement.,
  • Microwave remote sensing.

  • Edition Notes

    Includes bibliographical references (p. 19-21).

    StatementJosefino C. Comiso.
    SeriesNASA reference publication -- 1380
    ContributionsGoddard Space Flight Center.
    The Physical Object
    Paginationiii, 49 p. :
    Number of Pages49
    ID Numbers
    Open LibraryOL19572365M

      A temporally more consistent time series of sea ice concentrations is provided, offering improved accuracy over the ice concentration maps created from the original Bootstrap algorithm. More interesting to me was the table provided which shows the strenghts and weaknesses of each process. Sea ice Observations, Modelling and Data Assimilation Sea Ice Analysis and Forecasting (Book) Towards an Increased Reliance on Automated Prediction Systems Edited by Tom Carrieres, Mark Buehner, Jean-Franҫois Lemieux, Leif Toudal Pedersen This book provides an advanced introduction to the science behind automatedFile Size: 7MB.

    The basic algorithm for the geometric correction uses an analytic technique as follows. Firstly, the satellite’s position and attitude are precisely determined by ground control points (GCPs). Then, by applying the relationship between the position / attitude of the satellite and the earth, the relationship between the ground position and the. SMMR, DMSP/SSM/I: Other input: ECMWF re-analysis and forecast for atmospheric correction: Frequency: 1 per day: Central time: Spatial coverage: Global: Spatial sampling: 10km and km: Characteristics & methods: Daily averaged fractional ice cover in percentage.

    Sea ice is frozen seawater floating on the surface of the ocean. Some sea ice is semi-permanent, persisting from year to year, and some is seasonal, melting and refreezing from season to season. The sea ice cover reaches its minimum extent at the end of each summer and the remaining ice is called the perennial ice cover. This visualization shows Artic sea ice from July . The two are the NASA Team algorithm (NT1) and the Bootstrap Algorithms with NT1 subsequently improved using a different technique called NT2. 39 A comparison of ice concentrations and trends from the Bootstrap and NT2 shows good consistency in derived concentrations and trends. 40 In this report, we use results from the Bootstrap algorithm Cited by:


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SSM/I sea ice concentrations using the bootstrap algorithm by Josefino C. Comiso Download PDF EPUB FB2

The Bootstrap algorithm takes advantage of the multichannel capability of the SMMR and SSM/I sensors to obtain the appropriate reference brightness temperature for each data element.

To gain insight into this capability, scatter plots of SSM/I winter data (a weekly average from March 9 to Ma ) in 3-D using (a) 19V vs. 37V vs. A detailed description of the Bootstrap algorithm for sea ice, as discussed previously in earlier publications, is presented.

The algorithm was developed initially for the Nimbus-7 SMMR data and has been enhanced to make it more suitable for the SSM/I (Special Sensor Microwave Imager) data. Passive Microwave Algorithms for Sea Ice Concentrations: A Comparison of Two Techniques.

Remote Sensing of the Environment 60(3) Comiso, J. C., and R. Kwok. The Summer Arctic Sea Ice Cover from Satellite Observations.

Journal of Geophysical Research (C2), Comiso, J. SSM/I Concentrations Using the Bootstrap. English, Book, Illustrated, Government publication edition: SSM/I sea ice concentrations using the bootstrap algorithm / Josefino C.

Comiso. Comiso, Josefino C. Get this edition. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors. The data are generated using the NASA Team algorithm developed by the Oceans and Ice Branch, Laboratory for Hydrospheric Processes at NASA Goddard Space Flight Center (GSFC).

compared SIC for central and edges of the Arctic sea ice from SAR images with SSM/I SIC calculated from the NT and NT2 algorithms, and they showed that NT2 is more correct than NT in the Arctic summer.

Heinrichs et al. [21] have evaluated the AMSR-E NT2 algorithm at the ice edge in the Bering Sea by using RADARSAT-1 SAR and Moderate Resolution. SSM/I Sea Ice Remote Sensing for Mesoscale Ocean-Atmosphere Interaction Analysis.

Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I GHz data proposed by Svendsen et al.

() with an. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors. The data are generated using the NASA Team algorithm developed by the Oceans and Ice Branch, Laboratory for Hydrospheric Processes at NASA Goddard S pace Flight Center (GSFC).File Size: KB.

[1] We use two algorithms to process Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) data in order to determine algorithm dependence, if any, on the estimates of sea ice. No tie-points are used in the algorithm.

All the calculation expressions are derived from theoretical modeling. The design of the algorithm minimizes the impact of atmospheric variability on sea ice concentration retrieval. Beside estimating sea ice concentration, the algorithm makes it possible to indicate ice areas with melting snow and melt Cited by: 6.

Near Real-Time SSM/I EASE-Grid Daily Global Ice Concentration and Snow Extent Credits Images by National Snow and Ice Data Center (NSIDC) in Boulder, Colorado, using satellite passive microwave data from the Special Sensor Microwave/Imager (SSM/I).

While large pancake ice fields are abundant along the ice edge in the oceans surrounding Antarctica, the large predominantly frazil and pancake ice cover in the Odden area of the Greenland Sea is a less typical ice situation in the Arctic (Wadhams et al., ).The Odden forms nearly every year as an ice peninsula appended to the multi- and first-year ice drifting in Cited by:   In this paper, a new algorithm for determining the concentration of the ice cover in Polar Regions by data of satellite microwave radiometry is considered.

The technique of its construction is Cited by: 5. We present a new algorithm for retrieving sea ice concentration from the AMSR-E data, the dual-polarized ratio (DPR) algorithm. The DPR algorithm is developed using vertically and horizontally polarized brightness temperatures at the same channel of GHz.

It depends on the ratio of dual-polarized emissivity, α, which is determined empirically at about by Cited by: 7. September Arctic sea ice extent (solid lines) and area (dashed lines),for the NASA Team (red) and Bootstrap (black) algorithms. Source: NSIDC Data Set Stroeve, J., and W.

Meier.updated Sea Ice Trends and Climatologies from SMMR and SSM/I-SSMIS, Boulder, Colorado USA: NSIDC. Digital media. COlllparison of SSM/I ice-concentration algorithllls for the Weddell Sea BARBARA A.

BURNS Institute of Remote Sensing, University of Bremen, Bre Germany ABSTRACT. Four different algorithms for retrieving ice concentration from passive microwave imagery are applied to SSM/I data collected over the Weddell Sea in September Special Sensor Microwave/Imager (SSM/I; –present) provide bidaily and daily, respectively, composites of sea-ice concentration.

Using data from toZwally and others () determined that the total Antarctic sea-ice extent (for concentrations above 15%) has increased by ( )%decade–1, and that the total sea-ice area has.

optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experi-ment. The skills of 30 sea ice algorithms were evaluated sys-tematically over low and high sea ice concentrations.

Evalu-Cited by:   Comiso’s Bootstrap method produced a much smaller and insignificant increase in Antarctic sea ice while the NASA Team algorithm produced a larger, statistically significant increase. Another analysis, by Watkins and Simmonds () produced a trend that agreed better with the NASA Team results than Comiso’s Bootstrap results.

“Daily and monthly total ice-covered area data and total sea ice extent spanning the SMMR and SSM/I-SSMIS record from October through the most recent processing date are provided by Joey Comiso of the NASA GSFC Oceans and Ice Branch, and are produced from the Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS data.Bootstrap sea ice refers to a well-known algorithm used to estimate sea ice concentration from passive microwave brightness temperatures.

It can be applied to data from many satellite instruments, such as SMMR, SSMI, and AMSR-E. This page discusses the algorithm itself and the long-term data set (". This function plots daily Antarctic sea ice concentration from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data or Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations.

does not plot any data, but returns a gridded sea ice concentration. This may be useful for populating a 3D matrix of sea ice concentrations. [ci Reviews: 3.