The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
Daniel J. Mcdonald - One of the best experts on this subject based on the ideXlab platform.
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AAAI - Algorithms for Estimating Trends in Global Temperature Volatility
Proceedings of the AAAI Conference on Artificial Intelligence, 2019Co-Authors: Arash Khodadadi, Daniel J. McdonaldAbstract:Trends in terrestrial temperature variability are perhaps more relevant for species viability than trends in mean temperature. In this paper, we develop methodology for estimating such trends using multi-resolution climate data from polar orbiting Weather Satellites. We derive two novel algorithms for computation that are tailored for dense, gridded observations over both space and time. We evaluate our methods with a simulation that mimics these data’s features and on a large, publicly available, global temperature dataset with the eventual goal of tracking trends in cloud reflectance temperature variability.
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Algorithms for Estimating Trends in Global Temperature Volatility
arXiv: Machine Learning, 2018Co-Authors: Arash Khodadadi, Daniel J. McdonaldAbstract:Trends in terrestrial temperature variability are perhaps more relevant for species viability than trends in mean temperature. In this paper, we develop methodology for estimating such trends using multi-resolution climate data from polar orbiting Weather Satellites. We derive two novel algorithms for computation that are tailored for dense, gridded observations over both space and time. We evaluate our methods with a simulation that mimics these data's features and on a large, publicly available, global temperature dataset with the eventual goal of tracking trends in cloud reflectance temperature variability.
Arash Khodadadi - One of the best experts on this subject based on the ideXlab platform.
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AAAI - Algorithms for Estimating Trends in Global Temperature Volatility
Proceedings of the AAAI Conference on Artificial Intelligence, 2019Co-Authors: Arash Khodadadi, Daniel J. McdonaldAbstract:Trends in terrestrial temperature variability are perhaps more relevant for species viability than trends in mean temperature. In this paper, we develop methodology for estimating such trends using multi-resolution climate data from polar orbiting Weather Satellites. We derive two novel algorithms for computation that are tailored for dense, gridded observations over both space and time. We evaluate our methods with a simulation that mimics these data’s features and on a large, publicly available, global temperature dataset with the eventual goal of tracking trends in cloud reflectance temperature variability.
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Algorithms for Estimating Trends in Global Temperature Volatility
arXiv: Machine Learning, 2018Co-Authors: Arash Khodadadi, Daniel J. McdonaldAbstract:Trends in terrestrial temperature variability are perhaps more relevant for species viability than trends in mean temperature. In this paper, we develop methodology for estimating such trends using multi-resolution climate data from polar orbiting Weather Satellites. We derive two novel algorithms for computation that are tailored for dense, gridded observations over both space and time. We evaluate our methods with a simulation that mimics these data's features and on a large, publicly available, global temperature dataset with the eventual goal of tracking trends in cloud reflectance temperature variability.
Neeti Panchal - One of the best experts on this subject based on the ideXlab platform.
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Third Navigational Satellite of India Indian Regional Navigational Satellite System-1C
International Journal of Research, 2014Co-Authors: Leena Rani, Preeti Narula, Neeti PanchalAbstract:A Satellite is an artificial object which has been intentionally placed into orbit. Satellites are used for a large number of purposes. Common types include military and civilian Earth observation Satellites, communications Satellites, navigation Satellites, Weather Satellites, and research Satellites. A satellite navigation or satnav system is a system of Satellites that provide autonomous geo-spatial positioning with global coverage. It allows small electronic receivers to determine their location (longitude, latitude, and altitude) to high precision (within a few metres) using time signals transmitted along a line of sight by radio from Satellites.
William G. Pichel - One of the best experts on this subject based on the ideXlab platform.
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mesoscale oceanic and atmospheric feature detection through fusion of radarsat sar with goes imager data
International Geoscience and Remote Sensing Symposium, 1998Co-Authors: K.s. Friedman, William G. PichelAbstract:Synthetic aperture radar (SAR) data from the Canadian RADARSAT satellite, along with infrared and visible data from operational Weather Satellites, is being collected over the ocean off the U.S. East, Gulf of Mexico, Gulf of Alaska, and Bering Sea coasts. Mesoscale features observed in the SAR data are identified using coincident Geostationary Operational Environment Satellite (GOES) data to decide whether the signature is atmospheric or oceanic in origin. Combining the two data sources allows information in the marine atmospheric boundary layer (MABL) to be observed. This technique of feature detection using multiple data sets is referred to as data fusion. This paper focuses on a single storm event that is imaged by RADARSAT SAR in the Bering Sea on February 5, 1998 at 06:00 UTM. From the fusion of SAR and GOES data, frontal boundaries can be deciphered.
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Mesoscale oceanic and atmospheric feature detection through fusion of RADARSAT SAR with GOES/Imager data
IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174), 1998Co-Authors: K.s. Friedman, William G. PichelAbstract:Synthetic aperture radar (SAR) data from the Canadian RADARSAT satellite, along with infrared and visible data from operational Weather Satellites, is being collected over the ocean off the U.S. East, Gulf of Mexico, Gulf of Alaska, and Bering Sea coasts. Mesoscale features observed in the SAR data are identified using coincident Geostationary Operational Environment Satellite (GOES) data to decide whether the signature is atmospheric or oceanic in origin. Combining the two data sources allows information in the marine atmospheric boundary layer (MABL) to be observed. This technique of feature detection using multiple data sets is referred to as data fusion. This paper focuses on a single storm event that is imaged by RADARSAT SAR in the Bering Sea on February 5, 1998 at 06:00 UTM. From the fusion of SAR and GOES data, frontal boundaries can be deciphered.
Robert A. Schiffer - One of the best experts on this subject based on the ideXlab platform.
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ISCCP Cloud Data Products
Bulletin of the American Meteorological Society, 1991Co-Authors: William B. Rossow, Robert A. SchifferAbstract:The operational data collection phase of the International Satellite Cloud Climatology Project (ISCCP) began in July 1983. Since then, visible and infrared images from an international network of Weather Satellites have been routinely processed to produce a global cloud climatology. This report outlines the key steps in the data processing, describes the main features of the data products, and indicates how to obtain these data. We illustrate some early results of this analysis.
