scholarly journals Ice Concentration Retrieval from the Analysis of Microwaves: Evaluation of a New Methodology Optimized for the Copernicus Imaging Microwave Radiometer

2020 ◽  
Vol 12 (10) ◽  
pp. 1594
Author(s):  
Catherine Prigent ◽  
Lise Kilic ◽  
Filipe Aires ◽  
Victor Pellet ◽  
Carlos Jimenez
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Fine Tuning ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
High Frequencies ◽  
Ice Concentration

A new methodology has been described in Kilic et al. (Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer, Remote Sensing 2020, 12, 1060, Part 1 of this study) to estimate Sea Ice Concentration (SIC) from satellite passive microwave observations between 6 and 36 GHz. The Ice Concentration Retrieval from the Analysis of Microwaves (IceCREAM) algorithm is based on an optimal estimation, with a simple radiative transfer model derived from satellite observations at 0% and 100% SIC. Observations at low and high frequencies have different spatial resolutions, and a scheme is developed to benefit from the low errors of the low frequencies and the high spatial resolutions of the high frequencies. This effort is specifically designed for the Copernicus Imaging Microwave Radiometer (CIMR) project, equipped with a large deployable antenna to provide a spatial resolution of ∼5 km at 18 and 36 GHz, and ∼15 km at 6 and 10 GHz. The algorithm is tested with Advanced Microwave Scanning Radiometer 2 (AMSR2) observations, for a clear scene over the north polar region, with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) estimates and the Ocean Sea Ice—Satellite Application Facilities (OSI SAF) operational products. Several algorithm options are tested, and the study case shows that both high spatial resolution and low errors are obtained with the IceCREAM method. It is also tested for the full polar regions, winter and summer, under clear and cloudy conditions. Our method is globally applicable, without fine-tuning or further weather filtering. The systematic use of all channels from 6 to 36 GHz makes it robust to changes in ice surface conditions and to weather interactions.

scholarly journals Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer

2020 ◽  
Vol 12 (7) ◽  
pp. 1060 ◽  
Author(s):  
Lise Kilic ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Georg Heygster ◽  
Victor Pellet ◽  
...  
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Special Focus ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Ice Concentration

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.

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 VALIDATION OF ASMR2 SEA ICE CONCENTRATION DATA USING MODIS DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1741-1746
Author(s):  
K. Cho ◽  
R. Nagao ◽  
K. Naoki
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Threshold Level ◽  
Passive Microwave ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Free Condition ◽  
Modis Data ◽  
Ice Concentration

<p><strong>Abstract.</strong> Passive microwave radiometer AMSR2 was launched by JAXA in May 2012 on-board GCOM-W satellite. The antenna diameter of AMSR2 is 2.0&amp;thinsp;m which provide highest spatial resolution as a passive microwave radiometer in space. The sea ice concentration images derived from AMSR2 data allow us to monitor the detailed sea ice distributions of whole globe every day. The AMSR bootstrap algorithm developed by Dr. Josefino Comiso is used as the standard algorithm for calculating sea ice concentration from AMSR2 data. Under the contract with JAXA, the authors have been evaluating the performance of the algorithm. The sea ice concentration estimated from AMSR2 data were evaluated using MODIS data observed from Aqua satellite within few minutes after AMSR2 observation from GCOM-W. Since the spatial resolution of MODIS is much higher than that of AMSR2, under the cloud free condition, the ice concentration corresponds to the size of a pixel of AMSR2 can be calculated much accurately with MODIS data. The procedures of the evaluation are as follows. Firstly, MODIS band 1 reflectance were binarized to discriminate sea ice(1) from open water(0) and sea ice concentration of each pixel size of AMSR2 were calculated. In calculating sea ice concentration from MODIS data, the selection of the threshold level of MODIS band 1 reflectance is critical. Through the detailed evaluation, the authors selected 5% as the optimum threshold level. Then the AMSR2 sea ice concentration of each pixel was compared with the sea ice concentration calculated from MODIS data. The result suggested the possibility of estimating sea ice concentration from AMSR2 data with less than 10% error under the cloud free condition.</p>

Direct Assimilation of AMSR-E Brightness Temperatures for Estimating Sea Ice Concentration

2012 ◽  
Vol 140 (3) ◽  
pp. 997-1013 ◽  
Author(s):  
K. Andrea Scott ◽  
Mark Buehner ◽  
Alain Caya ◽  
Tom Carrieres
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Ocean Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sea Ice Concentration ◽  
Brightness Temperatures ◽  
Ice Concentration

Abstract In this paper a method to directly assimilate brightness temperatures from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) to produce ice concentration analyses within a three-dimensional variational data assimilation system is investigated. To assimilate the brightness temperatures a simple radiative transfer model is used as the forward model that maps the state vector to the observation space. This allows brightness temperatures to be modeled for all channels as a function of the total ice concentration, surface wind speed, sea surface temperature, ice temperature, vertically integrated water vapor, and vertically integrated cloud liquid water. The brightness temperatures estimated by the radiative transfer model are sensitive to the specified values for the sea ice emissivity. In this paper, two methods of specifying the sea ice emissivity are compared. The first uses a constant value for each polarization and frequency, while the second uses a simple emissivity parameterization. The emissivity parameterization is found to significantly improve the fit to the observations, reducing both the bias and the standard deviation. Results from the assimilation of brightness temperatures are compared with those from assimilating a retrieved ice concentration in the context of initializing a coupled ice–ocean model for an area along the east coast of Canada. It is found that with the emissivity parameterization the assimilation of brightness temperatures produces ice concentration analyses that are in slightly better agreement with operational ice charts than when assimilating an ice concentration retrieval, with the most significant improvements during the melt season.

scholarly journals Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Cost Effective ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Neural Network Approach ◽  
Ice Concentration ◽  
L Band ◽  
Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

scholarly journals A Blended Sea Ice Concentration Product from AMSR2 and VIIRS

2021 ◽  
Vol 13 (15) ◽  
pp. 2982
Author(s):  
Richard Dworak ◽  
Yinghui Liu ◽  
Jeffrey Key ◽  
Walter N. Meier
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Infrared Imaging ◽  
High Spatial Resolution ◽  
Sea Ice Concentration ◽  
Clear Sky ◽  
Ice Concentration ◽  
Best Linear Unbiased ◽  
The Individual ◽  
Sky Conditions

An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point.

Sea–ice concentration at 1 km resolution in summer from merged visible and microwave radiometer observations

2021 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen
Keyword(s):  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Statistical Consistency ◽  
Microwave Data ◽  
Infrared Data

&lt;p&gt;Sea&amp;#8211;ice concentration, the surface fraction of ice in a given area, is a key component of the Arctic climate system, governing for example the ocean&amp;#8211;atmosphere heat exchange. Satellite&amp;#8211;based remote sensing offers the possibility for large&amp;#8211;scale monitoring of the sea&amp;#8211;ice concentration. Using passive microwave measurements, it is possible to observe the sea&amp;#8211;ice concentration all year long, almost independently of cloud coverage. The spatial resolution of these measurements is limited to 5 km and coarser. Data from the visible and thermal infrared spectrum offer finer resolutions of 250 m&amp;#8211;1 km, but need clear&amp;#8211;sky scenes and, in case of visible data, sunlight. In previous work, we developed and analysed a merged dataset of passive microwave and thermal infrared data, combining AMSR2 and MODIS satellite data at 1 km spatial resolution. It has benefits over passive microwave data in terms of the finer spatial resolution and an enhanced potential for lead detection. At the same time, it outperforms thermal infrared data due to its spatially continuous coverage and the statistical consistency with the extensively evaluated passive microwave data. Due to higher surface temperatures in summer, the thermal&amp;#8211;infrared based retrieval is limited to winter and spring months. In this contribution, we present first results of extending the existing dataset to summer by using visible data instead of thermal infrared data. The reflectance contrast between ice and water is used for the sea&amp;#8211;ice concentration retrieval and results of merging visible and microwave data at 1 km spatial resolution are presented. Difficulties for both, the microwave and visual, data are surface melt processes during summer, which make sea&amp;#8211;ice concentration retrieval more challenging. The merged microwave, infrared and visual dataset opens the possibility for a year&amp;#8211;long, spatially continuous sea ice concentration dataset at a spatial resolution of 1 km.&lt;/p&gt;

scholarly journals The impact of atmospheric mineral aerosol deposition on the albedo of snow and sea ice: are snow and sea ice optical properties more important than mineral aerosol optical properties?

2015 ◽  
Vol 15 (16) ◽  
pp. 23131-23172
Author(s):  
M. L. Lamare ◽  
J. Lee-Taylor ◽  
M. D. King
Keyword(s):  
Optical Properties ◽  
Sea Ice ◽  
Atmospheric Aerosols ◽  
Aerosol Deposition ◽  
Shortwave Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Polar Regions ◽  
Polar Snow ◽  
The Impact

Abstract. Knowledge of the albedo of polar regions is crucial for understanding a range of climatic processes that have an impact on a global scale. Light absorbing impurities in atmospheric aerosols deposited on snow and sea ice by aeolian transport absorb solar radiation, reducing albedo. Here, the effects of five mineral aerosol deposits reducing the albedo of polar snow and sea ice are considered. Calculations employing a coupled atmospheric and snow/sea ice radiative-transfer model (TUV-snow) show that the effects of mineral aerosol deposits is strongly dependent on the snow or sea ice type rather than the differences between the aerosol optical characteristics. The change in albedo between five different mineral aerosol deposits with refractive indices varying by a factor of 2 reaches a maximum of 0.0788, whereas the difference between cold polar snow and melting sea ice is 0.8893 for the same mineral loading. Surprisingly, the thickness of a surface layer of snow or sea ice loaded with the same mass-ratio of mineral dust has little effect on albedo. On the contrary, multiple layers of mineral aerosols deposited during episodic events evenly distributed play a similar role in the surface albedo of snow as a loading distributed throughout, even when the layers are further apart. The impact of mineral aerosol deposits is much larger on melting sea ice than on other types of snow and sea ice. Therefore, the higher input of shortwave radiation during the summer melt cycle associated with melting sea ice accelerates the melt process.

Sign in / Sign up

Export Citation Format

Share Document