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David G. Long - One of the best experts on this subject based on the ideXlab platform.
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Extension of the QuikSCAT Sea Ice Extent Data Set With OSCAT Data
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Jordan C. Hill, David G. LongAbstract:The Ku-band Oceansat-2 Scatterometer (OSCAT) is very similar to the Quick Scatterometer (QuikSCAT), which operated from 1999 to 2009. OSCAT continues the Ku-band scatterometer data record through 2014 with an overlap of 19 days with QuikSCAT’s mission in 2009. This letter discusses a particular climate application of the time series for sea ice extent observation. In this letter, a QuikSCAT sea ice extent algorithm is modified for OSCAT. Gaps in OSCAT data are accounted for and filled in to support sea ice extent mapping. The OSCAT sea ice extent data are validated with QuikSCAT and Special Sensor Microwave/Imager sea ice extent data.
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ASCAT and QuikSCAT Azimuth Modulation of Backscatter Over East Antarctica
IEEE Geoscience and Remote Sensing Letters, 2016Co-Authors: Richard D. Lindsley, David G. LongAbstract:For most land and ice surfaces, the measured radar backscatter at the spatial resolution of a wind scatterometer is insensitive to the azimuth angle. However, for regions of East Antarctica, the backscatter strongly depends on the azimuth angle. This relationship between backscatter and azimuth angle is often modeled with a Fourier series. Although previous work has separately examined the data from QuikSCAT, a Ku-band scatterometer, and the Advanced Scatterometer (ASCAT), a C-band scatterometer, this letter compares the two on the same high-resolution grid. We find that, although QuikSCAT has superior azimuth angle coverage (due to its measurement geometry) compared to ASCAT, both are suitable to estimate the radar backscatter azimuth angle modulation of East Antarctica. The ASCAT data exhibit a much larger azimuth modulation than the QuikSCAT data. This is attributed to the different wavelengths of the microwave signal: At C-band (5.7 cm), East Antarctica has features that show radar backscatter to be more dependent on azimuth angle than at Ku-band (2.2 cm). This letter also examines the ASCAT and QuikSCAT azimuth modulation over previously identified regions of wind glaze. Although azimuth modulation is expected to be minimal over wind glaze, we find the wind-glaze regions to contain more structure than previously suggested.
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Multiyear Arctic Sea Ice Classification Using OSCAT and QuikSCAT
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: David B. Lindell, David G. LongAbstract:Arctic sea ice can be classified as first-year (FY) or multiyear (MY) based on data collected by satellite microwave scatterometers. The Oceansat-2 Ku-band Scatterometer (OSCAT) was operational from 2009 to 2014 and is here used to classify ice as FY or MY during these years. Due to similarities in backscatter measurements from sea ice and open water, a NASA Team ice concentration product derived from passive microwave brightness temperatures is used to restrict the classification area to within the sea ice extent. Classification of FY and MY ice is completed with OSCAT by applying a temporally adjusted threshold on backscatter values. The classification method is also applied to the Quick Scatterometer (QuikSCAT) data set, and ice age classifications are processed using QuikSCAT for 1999–2009. The combined QuikSCAT and OSCAT classifications represent a 15-year record, which extends from 1999 to 2014. The classifications show a decrease in MY ice, while the total area of the ice cover remains consistent throughout winter seasons over the time series.
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Simultaneous Wind and Rain Estimation for QuikSCAT at Ultra-High Resolution
IEEE Transactions on Geoscience and Remote Sensing, 2011Co-Authors: Michael P. Owen, David G. LongAbstract:Although originally designed solely for wind retrieval, the QuikSCAT scatterometer has proved to be a useful tool for rain estimation as well. Resolution enhancement algorithms designed for QuikSCAT allow for ultra-high-resolution (UHR) (2.5 km) simultaneous wind and rain (SWR) retrieval. The principle advantage of UHR SWR estimation is that compared to conventional resolution, the higher resolution allows for identification of much smaller rain events and their effects on the wind field. To enable SWR retrieval, we adjust the geophysical model function to account for rain effects such as attenuation and increased backscatter due to increased surface roughness. Two possible rain models are proposed, a phenomenological rain model and an effective rain model. Both models are compared by evaluating data fit and rain estimation performance. Comparisons of a co-located data set show that QuikSCAT UHR SWR integrated rain rates are comparable to those from tropical rain measuring mission precipitation radar (TRMM PR) but have higher variance. Buoy comparisons reveal improved wind estimates in the presence of rain. The theoretic estimator bounds are compared to both the simulated estimator variance and the actual estimator variance. The estimator bounds indicate that despite high-noise levels, wind and rain information is still retrievable at UHR, although certain directions have degraded estimator bounds. Both rain models are compared to truth data and are shown to have comparable performance for most rain rates. Comparison with buoy measurements shows that in the presence of rain, QuikSCAT UHR SWR wind estimates have less bias and variability than wind-only estimates. Although QuikSCAT UHR SWR rain estimates are noisier than TRMM PR rain rates, they provide a useful rain flag for QuikSCAT winds.
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Determining Selected Tropical Cyclone Characteristics Using QuikSCAT's Ultra-High Resolution Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011Co-Authors: Faozi Said, David G. LongAbstract:Operational SeaWinds on QuikSCAT data can be enhanced to yield a 2.5 km ultra-high resolution (UHR) wind product, which can be used to help estimate tropical cyclone (TC) characteristics such as TC center and wind radii. This paper provides the results of two studies in which the QuikSCAT UHR wind product's effectiveness in estimating these TC characteristics is evaluated. First, a comparison is made between an analyst's choice of center location based on UHR images and interpolated best track position. In this analysis, the UHR images are divided into two categories based on the analyst's confidence level of finding the center location. In each category, statistical error quantities between the analyst's choice of center location and interpolated best track location are computed. UHR images within the high-confidence category can provide, for a given year and basin, mean error distance as small as 19 km with a 10 km standard deviation. Second, a comparison of QuikSCAT's performance in estimating wind radii is made. QuikSCAT's performance is gauged against the H*wind dataset and the extended best track (EBT) dataset. Results show that QuikSCAT UHR data yields the correct 34 kt wind radius most of the time regardless of the TC category when compared to both H*wind and EBT, whereas the 50 kt and 64 kt wind radii estimates do not always agree with H*wind and EBT. A more sophisticated method is implemented to automatically estimate wind radii based on a model fit to QuikSCAT data. Results from this method are compared with EBT wind radii. The 50 kt and 64 kt wind radii obtained from QuikSCAT model fit are generally highly correlated with EBT estimated wind radii.
D.g. Long - One of the best experts on this subject based on the ideXlab platform.
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progress toward validation of QuikSCAT ultra high resolution rain rates using trmm pr
International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Michael P. Owen, D.g. LongAbstract:Although originally designed solely for wind retrieval, the QuikSCAT scatterometer has also proved to be a useful tool for rain retrieval. Resolution enhancement algorithms designed for QuikSCAT allow for ultra-high-resolution (UHR) (2.5 km) simultaneous wind and rain (SWR) retrieval. To enable SWR retrieval, we adjust the geophysical model function (GMF) to account for rain effects such as attenuation or increased backscatter due to increased surface roughness. Comparisons of a co-located data set show that QuikSCAT UHR SWR rain rates are comparable to those from Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) but have higher variance. The noise level of the QuikSCAT rain estimates can be reduced by forming the reduced resolution rain rate estimates. As expected, rain estimates are significantly worse in regions where wind dominates the backscatter.
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a comparison of hurricane eye determination using standard and ultra high resolution QuikSCAT winds
International Geoscience and Remote Sensing Symposium, 2006Co-Authors: R R Halterman, D.g. LongAbstract:Space-borne scatterometers are instruments designed to measure the radar backscatter of the earth's surface at a variety of azimuth angles from orbiting satel- lites. Empirical model functions relate these backscatter measurements to geophysical parameters such as wind speed and direction. For SeaWinds on QuikSCAT, stan- dard wind retrieval is performed on wind vector cells of 25 x 25 kilometers in size. Because of the inherent spatial over-sampling, image reconstruction techniques may be applied to enhance the resolution of the backscatter images. Using such methods, higher resolution winds are possi- ble. This paper compares the use of standard resolution QuikSCAT wind information with ultra-high resolution QuikSCAT data for the observation of hurricanes.
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calibrating seawinds and QuikSCAT scatterometers using natural land targets
IEEE Geoscience and Remote Sensing Letters, 2005Co-Authors: L B Kunz, D.g. LongAbstract:The SeaWinds-on-QuikSCAT (QuikSCAT) and SeaWinds-on-ADEOS-2 (SeaWinds) scatterometers measure the normalized radar backscatter (/spl sigma//sup o/) of the earth's surface. These identical radar sensors are on different spaceborne platforms in similar orbits. QuikSCAT and SeaWinds data are used to infer near-surface wind vectors, polar sea-ice extent, polar-ice melt events, etc. In order to verify the relative calibration of these sensors, a simple cross calibration based on land backscatter measurements is performed. A first-order polynomial model is used to remove the incidence angle dependence of /spl sigma//sup o/ for selected regions of the Amazon rainforest and the Sahara Desert. It is shown that the two sensors are well-calibrated to each other and require no bias corrections. Additionally, evidence of a diurnal cycle in the Amazon rainforest backscatter is given.
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validation of sea ice motion from QuikSCAT with those from ssm i and buoy
IEEE Transactions on Geoscience and Remote Sensing, 2002Co-Authors: Yunhe Zhao, Antony K Liu, D.g. LongAbstract:Arctic sea ice motion for the period from October 1999 to March 2000 derived from QuikSCAT and ocean buoy observations.Special Sensor Microwave/Imager (SSM/I) data using the wavelet analysis method agrees well with ocean buoy observations. Results from QuikSCAT and SSM/I are compatible when compared with buoy observations and complement each other. Sea ice drift merged from daily results from QuikSCAT, SSM/I, and buoy data gives more complete coverage of sea ice motion. Based on observations of six months of sea ice motion maps, the sea ice motion maps in the Arctic derived from QuikSCAT data appear to have smoother (less noisy) patterns than those from NSCAT, especially in boundary areas, possibly due to constant radar scanning incidence angle. For late summer, QuikSCAT data can provide good sea ice motion information in the Arctic as early as the beginning of September. For early summer, QuikSCAT can provide at least partial sea ice motion information until mid-June. In the Antarctic, a case study shows that sea ice motion derived from QuikSCAT data is consistent with pressure field contours.
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Validation of sea ice motion from QuikSCAT with those from SSM/I and buoy
IEEE Transactions on Geoscience and Remote Sensing, 2002Co-Authors: Yunhe Zhao, D.g. LongAbstract:Arctic sea ice motion for the period from October 1999 to March 2000 derived from QuikSCAT and ocean buoy observations.Special Sensor Microwave/Imager (SSM/I) data using the wavelet analysis method agrees well with ocean buoy observations. Results from QuikSCAT and SSM/I are compatible when compared with buoy observations and complement each other. Sea ice drift merged from daily results from QuikSCAT, SSM/I, and buoy data gives more complete coverage of sea ice motion. Based on observations of six months of sea ice motion maps, the sea ice motion maps in the Arctic derived from QuikSCAT data appear to have smoother (less noisy) patterns than those from NSCAT, especially in boundary areas, possibly due to constant radar scanning incidence angle. For late summer, QuikSCAT data can provide good sea ice motion information in the Arctic as early as the beginning of September. For early summer, QuikSCAT can provide at least partial sea ice motion information until mid-June. In the Antarctic, a case study shows that sea ice motion derived from QuikSCAT data is consistent with pressure field contours.
K. Gopala Reddy - One of the best experts on this subject based on the ideXlab platform.
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TropFlux wind stresses over the tropical oceans: evaluation and comparison with other products
Climate Dynamics, 2013Co-Authors: B. Praveen Kumar, Jérôme Vialard, Matthieu Lengaigne, V. S. N. Murty, Michael J. Mcphaden, M. F. Cronin, Françoise Pinsard, K. Gopala ReddyAbstract:In this paper, we present TropFlux wind stresses and evaluate them against observations along with other widely used daily air-sea momentum flux products (NCEP, NCEP2, ERA-I and QuikSCAT). TropFlux wind stresses are computed from the COARE v3.0 algorithm, using bias and amplitude corrected ERA-I input data and an additional climatological gustiness correction. The wind stress products are evaluated against dependent data from the TAO/TRITON, PIRATA and RAMA arrays and independent data from the OceanSITES mooring networks. Wind stress products are more consistent amongst each other than surface heat fluxes, suggesting that 10 m-winds are better constrained than near-surface thermodynamical parameters (2 m-humidity and temperature) and surface downward radiative fluxes. QuikSCAT overestimates wind stresses away from the equator, while NCEP and NCEP2 underestimate wind stresses, especially in the equatorial Pacific. QuikSCAT wind stress quality is strongly affected by rain under the Inter Tropical Convergence Zones. ERA-I and TropFlux display the best agreement with in situ data, with correlations >0.93 and rms-differences
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TropFlux wind stresses over the tropical oceans: evaluation and comparison with other products
Climate Dynamics, 2012Co-Authors: B. Praveen Kumar, Jérôme Vialard, Matthieu Lengaigne, V. S. N. Murty, Michael J. Mcphaden, M. F. Cronin, Françoise Pinsard, K. Gopala ReddyAbstract:International audienceIn this paper, we present TropFlux wind stresses and evaluate them against observations along with other widely used daily air-sea momentum flux products (NCEP, NCEP2, ERA-I and QuikSCAT). TropFlux wind stresses are computed from the COARE v3.0 algorithm, using bias and amplitude corrected ERA-I input data and an additional climatological gustiness correction. The wind stress products are evaluated against dependent data from the TAO/TRITON, PIRATA and RAMA arrays and independent data from the OceanSITES mooring networks. Wind stress products are more consistent amongst each other than surface heat fluxes, suggesting that 10 m-winds are better constrained than near-surface thermodynamical parameters (2 m-humidity and temperature) and surface downward radiative fluxes. QuikSCAT overestimates wind stresses away from the equator, while NCEP and NCEP2 underestimate wind stresses, especially in the equatorial Pacific. QuikSCAT wind stress quality is strongly affected by rain under the Inter Tropical Convergence Zones. ERA-I and TropFlux display the best agreement with in situ data, with correlations >0.93 and rms-differences
Dudley B. Chelton - One of the best experts on this subject based on the ideXlab platform.
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Chelton (2004), Scatterometer and model wind and wind stress in the Oregon–Northern California coastal zone
2014Co-Authors: Natalie Perlin, R M Samelson, Dudley B. CheltonAbstract:Measurements of surface wind stress by the SeaWinds scatterometer on NASA’s Quick Scatterometer (QuikSCAT) satellite are analyzed and compared with several different atmospheric model products, from an operational model and two high-resolution nested regional models, during two summer periods, June through September 2000 and 2001, in the coastal region west of Oregon and northern California. The mean summer wind stress had a southward component over the entire region in both years. Orographic intensifications of both the mean and fluctuating wind stress occurred near Cape Blanco, Cape Mendocino, and Point Arena. Substantial differences between the model products are found for the mean, variable, and diurnal wind stress fields. Temporal correlations with the QuikSCAT observations are highest for the operational model, and are not improved by either nested model. The highest-resolution nested model most accurately reproduced the mean observed stress fields, but slightly degrades the temporal correlations due to incoherent high-frequency (0.5–2 cpd) fluctuations. The QuikSCAT data reveal surprisingly strong diurnal fluctuations that extend offshore 150 km or more with magnitudes that are a significant fraction of the mean wind stress. Wind stress curl fields from QuikSCAT and the models show local cyclonic and anticyclonic maxima associated with the orographic wind intensification around the capes. The present results are consistent with the hypothesis of a wind-driven mechanism for coastal jet separation and cold water plume and anticyclonic eddy formation in the California Current System south of Cape Blanco. 1
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on the use of QuikSCAT scatterometer measurements of surface winds for marine weather prediction
Monthly Weather Review, 2006Co-Authors: Dudley B. Chelton, Michael H. Freilich, Joseph Sienkiewicz, Joan M. Von AhnAbstract:The value of Quick Scatterometer (QuikSCAT) measurements of 10-m ocean vector winds for marine weather prediction is investigated from two Northern Hemisphere case studies. The first of these focuses on an intense cyclone with hurricane-force winds that occurred over the extratropical western North Pacific on 10 January 2005. The second is a 17 February 2005 example that is typical of sea surface temperature influence on low-level winds in moderate wind conditions in the vicinity of the Gulf Stream in the western North Atlantic. In both cases, the analyses of 10-m winds from the NCEP and ECMWF global numerical weather prediction models considerably underestimated the spatial variability of the wind field on scales smaller than 1000 km compared with the structure determined from QuikSCAT observations. The NCEP and ECMWF models both assimilate QuikSCAT observations. While the accuracies of the 10-m wind analyses from these models measurably improved after implementation of the QuikSCAT data assimilation, the information content in the QuikSCAT data is underutilized by the numerical models. QuikSCAT data are available in near–real time in the NOAA/NCEP Advanced Weather Interactive Processing System (N-AWIPS) and are used extensively in manual analyses of surface winds. The high resolution of the QuikSCAT data is routinely utilized by forecasters at the NOAA/NCEP Ocean Prediction Center, Tropical Prediction Center, and other NOAA weather forecast offices to improve the accuracies of wind warnings in marine forecasts.
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scatterometer based assessment of 10 m wind analyses from the operational ecmwf and ncep numerical weather prediction models
Monthly Weather Review, 2005Co-Authors: Dudley B. Chelton, Michael H. FreilichAbstract:Wind measurements by the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) and the SeaWinds scatterometer on the NASA QuikSCAT satellite are compared with buoy observations to establish that the accuracies of both scatterometers are essentially the same. The scatterometer measurement errors are best characterized in terms of random component errors, which are about 0.75 and 1.5 m s 1 for the along-wind and crosswind components, respectively. The NSCAT and QuikSCAT datasets provide a consistent baseline from which recent changes in the accuracies of 10-m wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Centers for Environmental Prediction (NCEP) operational numerical weather prediction (NWP) models are assessed from consideration of three time periods: September 1996–June 1997, August 1999–July 2000, and February 2002–January 2003. These correspond, respectively, to the 9.5-month duration of the NSCAT mission, the first 12 months of the QuikSCAT mission, and the first year after both ECMWF and NCEP began assimilating QuikSCAT observations. There were large improvements in the accuracies of both NWP models between the 1997 and 2000 time periods. Though modest in comparison, there were further improvements in 2002, at least partly attributable to the assimilation of QuikSCAT observations in both models. There is no evidence of bias in the 10-m wind speeds in the NCEP model. The 10-m wind speeds in the ECMWF model, however, are shown to be biased low by about 0.4 m s 1 . While it is difficult to eliminate systematic errors this small, a bias of 0.4 m s 1 corresponds to a typical wind stress bias of more than 10%. This wind stress bias increases to nearly 20% if atmospheric stability effects are not taken into account. Biases of these magnitudes will result in significant systematic errors in ocean general circulation models that are forced by ECMWF winds.
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scatterometer and model wind and wind stress in the oregon northern california coastal zone
Monthly Weather Review, 2004Co-Authors: Natalie Perlin, R M Samelson, Dudley B. CheltonAbstract:Measurements of surface wind stress by the SeaWinds scatterometer on NASA’s Quick Scatterometer (QuikSCAT) satellite are analyzed and compared with several different atmospheric model products, from an operational model and two high-resolution nested regional models, during two summer periods, June through September 2000 and 2001, in the coastal region west of Oregon and northern California. The mean summer wind stress had a southward component over the entire region in both years. Orographic intensifications of both the mean and fluctuating wind stress occurred near Cape Blanco, Cape Mendocino, and Point Arena. Substantial differences between the model products are found for the mean, variable, and diurnal wind stress fields. Temporal correlations with the QuikSCAT observations are highest for the operational model, and are not improved by either nested model. The highest-resolution nested model most accurately reproduced the mean observed stress fields, but slightly degrades the temporal correlations due to incoherent high-frequency (0.5‐2 cpd) fluctuations. The QuikSCAT data reveal surprisingly strong diurnal fluctuations that extend offshore 150 km or more with magnitudes that are a significant fraction of the mean wind stress. Wind stress curl fields from QuikSCAT and the models show local cyclonic and anticyclonic maxima associated with the orographic wind intensification around the capes. The present results are consistent with the hypothesis of a wind-driven mechanism for coastal jet separation and cold water plume and anticyclonic eddy formation in the California Current System south of Cape Blanco.
Stephen E. L. Howell - One of the best experts on this subject based on the ideXlab platform.
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Development of a water clear of sea ice detection algorithm from enhanced SeaWinds/QuikSCAT and AMSR-E measurements
Remote Sensing of Environment, 2010Co-Authors: Stephen E. L. Howell, Chris Derksen, Adrienne TivyAbstract:Abstract We develop and evaluate water clear of sea ice (open water following ice cover) detection algorithms that make use of Scatterometer Image Reconstruction (SIR) SeaWinds/QuikSCAT (QuikSCAT) backscatter (σ°) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) brightness temperature (TB) measurements. Algorithm validation was performed within Canadian Arctic waters using the Canadian Ice Service Digital Archive (CISDA) ice charts, NASATeam ice concentration estimates, extended AVHRR Polar Pathfinder (APP-x) albedo data, RADARSAT-1 imagery, and MODIS imagery. Results indicate that the temporal evolution of QuikSCAT σ°, AMSR-E polarization ratio (PR18), and AMSR-E vertical spectral gradient ratio (GR3618) can detect water clear of sea ice events, however mean differences due to frequency dependent characteristics of the data (spatial resolution; sensitivity to open water) were apparent. All water clear of sea ice algorithms are in good agreement with the timing and clearing patterns given by the CISDA. The QuikSCAT algorithm provided a more representative ice edge and more details on the ice clearing process due to higher spatial resolution, however, transient clearing events were better represented by the AMSR-E PR(18) or (GR3618) algorithm. By exploiting the strengths of each sensor, we found that a QuikSCAT and AMSR-E fused algorithm provide improved open water area estimates by as much as 11%. The fusion of QuikSCAT and AMSR-E PR(18) yielded in the most spatially representative open water detection. The residual surface of the water clear of sea ice algorithms was found to provide another measure of the average September minimum pan-Arctic sea ice extent within 6% of the NASATeam algorithm estimates.
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application of a seawinds QuikSCAT sea ice melt algorithm for assessing melt dynamics in the canadian arctic archipelago
Journal of Geophysical Research, 2006Co-Authors: Stephen E. L. Howell, John J. Yackel, Adrienne Tivy, Randall K ScharienAbstract:[1] A remotely sensed sea ice melt algorithm utilizing SeaWinds/QuikSCAT (QuikSCAT) data is developed and applied to sea ice the Canadian Arctic Archipelago (CAA) from 2000 to 2004. The extended advanced very high resolution radiometer Polar Pathfinder (APP-x) data set is used to identify spatially coupled relationships between sea ice melt and radiative forcings. In situ data from the Collaborative Interdisciplinary Cryospheric Experiment (C-ICE) (2000, 2001, and 2002) and the Canadian Arctic Shelf Exchange Study (CASES) (2004) are used to validate APP-x data during the melt period. QuikSCAT-detected maps of melt onset, pond onset, and drainage are created from 2000 to 2004, and results indicate considerable interannual variability of melt dynamics within the CAA. In some years, melt stages are positively spatially autocorrelated, whereas other years exhibit a negative or no spatial autocorrelation. QuikSCAT-detected stages of melt are found to be influenced by interannual varying amounts and timing of radiative forcing making prediction difficult. The spatiotemporal variability of ice melt also influences the distribution of ice within the CAA. The lower-latitude regions of the CAA are shown to have accumulated increasing concentrations of multiyear ice from 2000 to 2005. This paper concludes with a discussion of the interplay between thermodynamic and dynamic sea ice processes likely to have contributed to this trend.
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on the utility of seawinds QuikSCAT data for the estimation of the thermodynamic state of first year sea ice
IEEE Transactions on Geoscience and Remote Sensing, 2005Co-Authors: Stephen E. L. Howell, John J. Yackel, R. De Abreu, Torsten Geldsetzer, C. BrenemanAbstract:The thermodynamic state of sea ice is important to accurately and remotely monitor in order to provide improved geophysical variable parameterizations in sea ice thermodynamic models. Operationally, monitoring the thermodynamic state of sea ice can facilitate eased ship navigation routing. SeaWinds/QuikSCAT (QuikSCAT) dual-polarization [i.e., horizontal (HH) and vertical (VV)] active microwave data are available at a sufficiently large spatial scale and high temporal resolution to provide estimates of sea ice thermodynamics. This analysis evaluated the temporal evolution of the backscatter coefficient (/spl sigma//spl deg/) and VV/HH copolarization ratio from QuikSCAT for estimating sea ice thermodynamics. QuikSCAT estimates were compared against RADARSAT-1 synthetic aperture radar (SAR) imagery and the Canadian Ice Service (CIS) prototype operational ice strength algorithm. In situ data from the Collaborative Interdisciplinary Cryospheric Experiment (C-ICE) for 2000, 2001, and 2002 were used as validation. Results indicate that the temporal evolution of /spl sigma//spl deg/ from QuikSCAT is analogous to RADARSAT-1. The QuikSCAT /spl sigma//spl deg/ temporal evolution has the ability to identify winter, snow melt, and ponding thermodynamic states. Moreover, the copolarization VV/HH ratio of QuikSCAT could provide a second estimate of the ponding state in addition to identifying the drainage state that is difficult to detect by single-polarization SAR. QuikSCAT detected thermodynamic states that were found to be in reasonable agreement to that of in situ observations at the C-ICE camp for all years. Operational implications of this analysis suggest QuikSCAT is a more effective and efficient medium for monitoring ice decay compared to RADARSAT-1 and can be utilized to provide more robust modeled ice strength thresholds.
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On the utility of SeaWinds/QuikSCAT data for the estimation of the thermodynamic state of first-year sea ice
IEEE Transactions on Geoscience and Remote Sensing, 2005Co-Authors: Stephen E. L. Howell, John J. Yackel, R. De Abreu, Torsten Geldsetzer, C. BrenemanAbstract:The thermodynamic state of sea ice is important to accurately and remotely monitor in order to provide improved geophysical variable parameterizations in sea ice thermodynamic models. Operationally, monitoring the thermodynamic state of sea ice can facilitate eased ship navigation routing. SeaWinds/QuikSCAT (QuikSCAT) dual-polarization [i.e., horizontal (HH) and vertical (VV)] active microwave data are available at a sufficiently large spatial scale and high temporal resolution to provide estimates of sea ice thermodynamics. This analysis evaluated the temporal evolution of the backscatter coefficient (/spl sigma//spl deg/) and VV/HH copolarization ratio from QuikSCAT for estimating sea ice thermodynamics. QuikSCAT estimates were compared against RADARSAT-1 synthetic aperture radar (SAR) imagery and the Canadian Ice Service (CIS) prototype operational ice strength algorithm. In situ data from the Collaborative Interdisciplinary Cryospheric Experiment (C-ICE) for 2000, 2001, and 2002 were used as validation. Results indicate that the temporal evolution of /spl sigma//spl deg/ from QuikSCAT is analogous to RADARSAT-1. The QuikSCAT /spl sigma//spl deg/ temporal evolution has the ability to identify winter, snow melt, and ponding thermodynamic states. Moreover, the copolarization VV/HH ratio of QuikSCAT could provide a second estimate of the ponding state in addition to identifying the drainage state that is difficult to detect by single-polarization SAR. QuikSCAT detected thermodynamic states that were found to be in reasonable agreement to that of in situ observations at the C-ICE camp for all years. Operational implications of this analysis suggest QuikSCAT is a more effective and efficient medium for monitoring ice decay compared to RADARSAT-1 and can be utilized to provide more robust modeled ice strength thresholds.
