G.I. Belchansky

Duration of the Arctic Sea Ice Melt Season: Regional and Interannual Variability, 1979-2001

Abstract Melt onset dates, freeze onset dates, and melt season duration were estimated over Arctic sea ice, 1979–2001, using passive microwave satellite imagery and surface air temperature data. Sea ice melt duration for the entire Northern Hemisphere varied from a 104-day minimum in 1983 and 1996 to a 124-day maximum in 1989. Ranges in melt duration were highest in peripheral seas, numbering 32, 42, 44, and 51 days in the Laptev, Barents-Kara, East Siberian, and Chukchi Seas, respectively. In the Arctic Ocean, average melt duration varied from a 75-day minimum in 1987 to a 103-day maximum in 1989. On average, melt onset in annual ice began 10.6 days earlier than perennial ice, and freeze onset in perennial ice commenced 18.4 days earlier than annual ice. Average annual melt dates, freeze dates, and melt durations in annual ice were significantly correlated with seasonal strength of the Arctic Oscillation (AO). Following high-index AO winters (January–March), spring melt tended to be earlier and autumn fr...

Fluctuating Arctic Sea Ice Thickness Changes Estimated by an In Situ Learned and Empirically Forced Neural Network Model

Abstract Sea ice thickness (SIT) is a key parameter of scientific interest because understanding the natural spatiotemporal variability of ice thickness is critical for improving global climate models. In this paper, changes in Arctic SIT during 1982–2003 are examined using a neural network (NN) algorithm trained with in situ submarine ice draft and surface drilling data. For each month of the study period, the NN individually estimated SIT of each ice-covered pixel (25-km resolution) based on seven geophysical parameters (four shortwave and longwave radiative fluxes, surface air temperature, ice drift velocity, and ice divergence/convergence) that were cumulatively summed at each monthly position along the pixel’s previous 3-yr drift track (or less if the ice was <3 yr old). Average January SIT increased during 1982–88 in most regions of the Arctic (+7.6 ± 0.9 cm yr−1), decreased through 1996 Arctic-wide (−6.1 ± 1.2 cm yr−1), then modestly increased through 2003 mostly in the central Arctic (+2.1 ± 0.6 c...

Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN and RADARSAT data

Abstract The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellites two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.

Classification methods for monitoring Arctic sea ice using OKEAN passive/active two-channel microwave data

Abstract This paper presents methods for classifying Arctic sea ice using both passive and active (2-channel) microwave imagery acquired by the Russian OKEAN 01 polar-orbiting satellite series. Methods and results are compared to sea ice classifications derived from nearly coincident Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) image data of the Barents, Kara, and Laptev Seas. The Russian OKEAN 01 satellite data were collected over weekly intervals during October 1995 through December 1997. Methods are presented for calibrating, georeferencing and classifying the raw active radar and passive microwave OKEAN 01 data, and for correcting the OKEAN 01 microwave radiometer calibration wedge based on concurrent 37 GHz horizontal polarization SSM/I brightness temperature data. Sea ice type and ice concentration algorithms utilized OKEANs two-channel radar and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter, together with a priori knowledge about the scattering parameters and natural emissivities of basic sea ice types. OKEAN 01 data and algorithms tended to classify lower concentrations of young or first-year sea ice when concentrations were less than 60%, and to produce higher concentrations of multi-year sea ice when concentrations were greater than 40%, when compared to estimates produced from SSM/I data. Overall, total sea ice concentration maps derived independently from OKEAN 01, SSM/I, and AVHRR satellite imagery were all highly correlated, with uniform biases, and mean differences in total ice concentration of less than four percent ( sd

Estimating multiyear sea-ice concentration using passive microwave data and MLP neural networks

Three ice-type classification methods utilizing SSM/I passive microwave data were compared. Each applied a multilayer perceptron (MLP) neural network (NN) with OKEAN (radar and passive microwave) sea-ice learning data, a different learning algorithm based on, respectively, error back propagation and simulated annealing (M1), dynamic learning and polynomial basis functions (M2), and dynamic learning with two-step optimization (M3). M2 and M3 methods used the Kalman filtering technique. Our studies demonstrated, that for sea-ice inversions the modified MLP NN with M1 algorithm was more efficient because M2 and M3 algorithms caused overfitting. Multiyear (MY) sea-ice concentration maps were generated from SSM/I Tbs (19 GHz V, 19 GHz H and 37 GHz V channels) using modified MLP NN with M1 algorithm, OKEAN and ERS learning data. These maps were compared with respective MY sea-ice concentration maps developed using NASA Team algorithm (NTA). Our studies demonstrated the superiority of the NN method compared to the NTA.

Comparative analysis of multisensor satellite monitoring of Arctic sea-ice

This report represents comparative analysis of nearly coincident Russian OKEAN-01 polar orbiting satellite data, Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) imagery. OKEAN-01 ice concentration algorithms utilize active and passive microwave measurements and a linear mixture model for measured values of the brightness temperature and the radar backscatter. SSM/I and AVHRR ice concentrations were computed with NASA Team algorithm and visible and thermal-infrared wavelength AVHRR data, accordingly.