Cloud Detection over the Arctic Region Using Airborne Imaging Spectrometer Data during the Daytime

1998 ◽  
Vol 37 (11) ◽  
pp. 1421-1429 ◽  
Author(s):  
Bo-Cai Gao ◽  
Wei Han ◽  
Si Chee Tsay ◽  
North F. Larsen
Keyword(s):  
Arctic Region ◽  
The Arctic ◽  
Cloud Detection ◽  
Imaging Spectrometer ◽  
Airborne Imaging ◽  
Spectrometer Data ◽  
The Arctic Region

Cirrus cloud detection from Airborne Imaging Spectrometer data using the 1.38 µm water vapor band

1993 ◽  
Vol 20 (4) ◽  
pp. 301-304 ◽  
Author(s):  
Bo-Cai Gao ◽  
Alexander F. H. Goetz ◽  
Warren J. Wiscombe
Keyword(s):  
Water Vapor ◽  
Cirrus Cloud ◽  
Cloud Detection ◽  
Imaging Spectrometer ◽  
Airborne Imaging ◽  
Spectrometer Data

scholarly journals Arctic Cloud Characteristics as Derived from MODIS, CALIPSO, and CloudSat

2013 ◽  
Vol 26 (10) ◽  
pp. 3285-3306 ◽  
Author(s):  
Mark Aaron Chan ◽  
Josefino C. Comiso
Keyword(s):  
Sea Ice ◽  
Cloud Cover ◽  
Arctic Region ◽  
The Arctic ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
Percentage Difference ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
High Level ◽  
The Arctic Region

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), and CloudSat Cloud Profiling Radar (CPR) set of sensors, all in the Afternoon Constellation (A-Train), has been regarded as among the most powerful tools for characterizing the cloud cover. While providing good complementary information, the authors also observed that, at least for the Arctic region, the different sensors provide significantly different statistics about cloud cover characteristics. Data in 2007 and 2010 were analyzed, and the annual averages of cloud cover in the Arctic region were found to be 66.8%, 78.4%, and 63.3% as derived from MODIS, CALIOP, and CPR, respectively. A large disagreement between MODIS and CALIOP over sea ice and Greenland is observed, with a cloud percentage difference of 30.9% and 31.5%, respectively. In the entire Arctic, the average disagreement between MODIS and CALIOP increased from 13.1% during daytime to 26.7% during nighttime. Furthermore, the MODIS cloud mask accuracy has a high seasonal dependence, in that MODIS–CALIOP disagreement is the lowest during summertime at 10.7% and worst during winter at 28.0%. During nighttime the magnitude of the bias is higher because cloud detection is limited to the use of infrared bands. The clouds not detected by MODIS are typically low-level (top height <2 km) and high-level clouds (top height >6 km) and, especially, those that are geometrically thin (<2 km). Geometrically thin clouds (<2 km) accounted for about 95.5% of all clouds that CPR misses. As reported in a similar study, very low and thin clouds (<0.3 km) over sea ice that are detected by MODIS are sometimes not observed by CPR and misclassified by CALIOP.

Nuclear icebreaker fleet and power supply on the basis of floating power units

2018 ◽  
Vol 35 (4) ◽  
pp. 110-113
Author(s):  
V. A. Tupchienko ◽  
H. G. Imanova
Keyword(s):  
Foreign Policy ◽  
Power Supply ◽  
Nuclear Power ◽  
Arctic Region ◽  
Nuclear Industry ◽  
The Arctic ◽  
Far North ◽  
The Arctic Region

The article deals with the problem of the development of the domestic nuclear icebreaker fleet in the context of the implementation of nuclear logistics in the Arctic. The paper analyzes the key achievements of the Russian nuclear industry, highlights the key areas of development of the nuclear sector in the Far North, and identifies aspects of the development of mechanisms to ensure access to energy on the basis of floating nuclear power units. It is found that Russia is currently a leader in the implementation of the nuclear aspect of foreign policy and in providing energy to the Arctic region.

Chemical Composition of Atmospheric Aerosol in the Arctic Region and Adjoining Seas along the Routes of Marine Expeditions in 2018–2019

2020 ◽  
Vol 33 (5) ◽  
pp. 480-489
Author(s):  
L. P. Golobokova ◽  
T. V. Khodzher ◽  
O. N. Izosimova ◽  
P. N. Zenkova ◽  
A. O. Pochyufarov ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Atmospheric Aerosol ◽  
Arctic Region ◽  
The Arctic ◽  
The Arctic Region

Estimating Friction Reduction ForCasing Operations in High-Angle Wells in The Arctic Region - A Russia Case History

2011 ◽  
Author(s):  
Chimerebere Onyekwere Nkwocha ◽  
Evgeny Glebov ◽  
Alexey Zhludov ◽  
Sergey Galantsev ◽  
David Kay
Keyword(s):  
Case History ◽  
Arctic Region ◽  
Friction Reduction ◽  
The Arctic ◽  
High Angle ◽  
The Arctic Region

scholarly journals Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region

2021 ◽  
Vol 13 (10) ◽  
pp. 1884
Author(s):  
Jingjing Hu ◽  
Yansong Bao ◽  
Jian Liu ◽  
Hui Liu ◽  
George P. Petropoulos ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Real Time ◽  
Wide Spectrum ◽  
Warm Season ◽  
Arctic Region ◽  
Cold Season ◽  
The Arctic ◽  
Practical Applications ◽  
A New Technique ◽  
The Arctic Region

The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic’s climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60°N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D’s operational implementation range from within 60°N to the Arctic region.

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