NASA team algorithm for sea ice concentration retrieval from Defense Meteorological Satellite Program special sensor microwave imager: Comparison with Landsat satellite imagery

1991 ◽  
Vol 96 (C12) ◽  
pp. 21971 ◽  
Author(s):  
Konrad Steffen ◽  
Axel Schweiger
Keyword(s):  
Sea Ice ◽  
Satellite Imagery ◽  
Sea Ice Concentration ◽  
Defense Meteorological Satellite Program ◽  
Ice Concentration ◽  
Microwave Imager ◽  
Landsat Satellite ◽  
Meteorological Satellite

scholarly journals Reduction of weather effects in the calculation of sea-ice concentration with the DMSP SSM/I

1995 ◽  
Vol 41 (139) ◽  
pp. 455-464 ◽  
Author(s):  
Donald J. Cavalieri ◽  
Karen M. St. Germain ◽  
Calvin T. Swift
Keyword(s):  
Water Vapor ◽  
Sea Ice ◽  
Microwave Radiometer ◽  
Atmospheric Water Vapor ◽  
Surface Roughening ◽  
Atmospheric Water ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Vapor Line ◽  
Microwave Imager

AbstractA problem in mapping the polar sea-ice covers in both hemispheres has been the sporadic false indication of sea ice over the open ocean and at the ice edge. These spurious sea-ice concentrations result from variations in sea-surface roughening by surface winds, atmospheric water vapor and both precipitating and non-precipitating liquid water. This problem was addressed for sea-ice concentrations derived from the Nimbus-7 scanning multi-channel microwave radiometer (SMMR) data through the development of a weather filter based on spectral information from the 18.0 and 37.0 GHz vertical polarization SMMR channels. Application of a similar filter for use with sea-ice concentration maps derived with the special-sensor microwave imager (SSM/I) sensor is less successful. This results from the position of the 19.35 GHz SSM/I channels, which are closer to the center of the 22.2 GHz atmospheric water-vapor line than are the SMMR 18.0 GHz channels. Thus, the SSM/I 19.35 GHz channels are more sensitive to changes in atmospheric water vapor, which results in greater contamination problems. An additional filter has been developed, based on a combination of the 19.35 and 22.2GHz. SSM/I channels. Examples of the effectiveness of the new filter are presented and limitations are discussed.

scholarly journals Trends in the Stability of Antarctic Coastal Polynyas and the Role of Topographic Forcing Factors

2020 ◽  
Vol 12 (6) ◽  
pp. 1043
Author(s):  
Liyuan Jiang ◽  
Yong Ma ◽  
Fu Chen ◽  
Jianbo Liu ◽  
Wutao Yao ◽  
...  
Keyword(s):  
Sea Ice ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Dynamic Factors ◽  
Ice Concentration ◽  
Two Factors ◽  
Microwave Imager ◽  
The Stability ◽  
Relationship Of ◽  
The Antarctic

Polynyas are an important factor in the Antarctic and Arctic climate, and their changes are related to the ecosystems in the polar regions. The phenomenon of polynyas is influenced by the combination of inherent persistence and dynamic factors. The dynamics of polynyas are greatly affected by temporal dynamical factors, and it is difficult to objectively reflect the internal characteristics of their formation. Separating the two factors effectively is necessary in order to explore their essence. The Special Sensor Microwave/Imager (SSM/I) passive microwave sensor has been making observations of Antarctica for more than 20 years, but it is difficult for existing current sea ice concentration (SIC) products to objectively reflect how the inherent persistence factors affect the formation of polynyas. In this paper, we proposed a long-term multiple spatial smoothing method to remove the influence of dynamic factors and obtain stable annual SIC products. A halo located on the border of areas of low and high ice concentration around the Antarctic coast, which has a strong similarity with the local seabed in outline, was found using the spatially smoothed SIC products and seabed. The relationship of the polynya location to the wind and topography is a long-understood relationship; here, we quantify that where there is an abrupt slope and wind transitions, new polynyas are best generated. A combination of image expansion and threshold segmentation was used to extract the extent of sea ice and coastal polynyas. The adjusted record of changes in the extent of coastal polynyas and sea ice in the Southern Ocean indicate that there is a negative correlation between them.

scholarly journals Evaluation of operational SSM/I ice-concentration algorithms

2001 ◽  
Vol 33 ◽  
pp. 102-108 ◽  
Author(s):  
Walter N. Meier ◽  
Michael L. Van Woert ◽  
Cheryl Bertoia
Keyword(s):  
The United States ◽  
Substantial Improvement ◽  
The Arctic ◽  
Ongoing Research ◽  
Defense Meteorological Satellite Program ◽  
Ice Concentration ◽  
Operational Activities ◽  
High Resolution Satellite Imagery ◽  
Microwave Imager ◽  
Meteorological Satellite

AbstractThe United States National Ice Center (NIC) provides weekly ice analyses of the Arctic and Antarctic using information from ice reconnaissance, ship reports and high-resolution satellite imagery. In cloud-covered areas and regions lacking imagery, the higher-resolution sources are augmented by ice concentrations derived from Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) passive-microwave imagery. However, the SSM/I-derived ice concentrations are limited by low resolution and uncertainties in thin-ice regions. Ongoing research at NIC is attempting to improve the utility of these SSM/I products for operational sea-ice analyses. The refinements of operational algorithms may also aid future scientific studies. Here we discuss an evaluation of the standard operational ice-concentration algorithm, Cal/Val, with a possible alternative, a modified NASA Team algorithm. The modified algorithm compares favorably with Cal/Val and is a substantial improvement over the standard NASA Team algorithm in thin-ice regions that are of particular interest to operational activities.

scholarly journals The EUMETSAT sea ice climate record

2016 ◽  
Author(s):  
R. T. Tonboe ◽  
S. Eastwood ◽  
T. Lavergne ◽  
A. M. Sørensen ◽  
N. Rathmann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Open Water ◽  
The Arctic ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Microwave Imager

Abstract. An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of American microwave radiometer data from Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from 1978 to 2014 and updates and further developments are planned for the next phase of the project. The methodology is using: 1) numerical weather prediction (NWP) input to a radiative transfer model (RTM) for correction of the brightness temperatures for reduction of atmospheric noise, 2) dynamical algorithm tie-points to mitigate trends in residual atmospheric, sea ice and water emission characteristics and inter-sensor differences/biases, 3) and a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new algorithm has been developed to estimate the spatially and temporally varying sea ice concentration uncertainties. A comparison to sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate ice concentrations. The sea ice climate dataset is available for download at (www.osisaf.org) including documentation.

scholarly journals Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals

2020 ◽  
Vol 12 (14) ◽  
pp. 2197 ◽  
Author(s):  
Walter N. Meier ◽  
J. Scott Stewart
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Real Time ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Brightness Temperatures ◽  
Ice Concentration ◽  
Orbital Drift ◽  
Microwave Imager ◽  
Mean Differences

Gridded passive microwave brightness temperatures (TB) from special sensor microwave imager and sounder (SSMIS) instruments on three different satellite platforms are compared in different years to investigate the consistency between the sensors over time. The orbits of the three platforms have drifted over their years of operation, resulting in changing relative observing times that could cause biases in TB estimates and near-real-time sea ice concentrations derived from the NASA Team algorithm that are produced at the National Snow and Ice Data Center. Comparisons of TB histograms and concentrations show that there are small mean differences between sensors, but variability within an individual sensor is much greater. There are some indications of small changes due to orbital drift, but these are not consistent across different frequencies. Further, the overall effect of the drift, while not definitive, is small compared to the intra- and interannual variability in individual sensors. These results suggest that, for near-real-time use, the differences in the sensors are not critical. However, for long-term time series, even the small biases should be corrected for. The strong day-to-day, seasonal, and interannual variability in TB distributions indicate that time-varying algorithm coefficients in the NASA team algorithm would lead to improved, more consistent sea ice concentration estimates.

scholarly journals Measuring sea ice concentration in the Arctic Ocean using SMOS

2016 ◽  
Author(s):  
Carolina Gabarro ◽  
Antonio Turiel ◽  
Pedro Elosegui ◽  
Joaquim A. Pla-Resina ◽  
Marcos Portabella
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Radiative Properties ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Wide Range ◽  
Ice Concentration ◽  
The Arctic Ocean ◽  
Ice Conditions ◽  
Microwave Imager

Abstract. We present a new method to estimate sea ice concentration in the Arctic Ocean using brightness temperature observations from the Soil Moisture Ocean Salinity (SMOS) interferometric satellite. The method, which employs a Maximum Likelihood Estimator (MLE), exploits the marked difference in radiative properties between sea ice and seawater, in particular when observed over the wide range of satellite viewing angles afforded by SMOS. Observations at L-band frequencies such as those from SMOS (i.e., 1.4 GHz, or equivalently 21-cm wavelength) are advantageous to remote sensing of sea ice because the atmosphere is virtually transparent at that frequency. We find that sea ice concentration is well determined (correlations of about 0.75) as compared to estimates from other sensors such as the Special Sensor Microwave/Imager (SSM/I and SSMIS). We also find that the efficacy of the method decreases under thin sea ice conditions (ice thickness

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