Sea surface winds from reanalysis （NCEP-2 and ERA-40 datasets） and satellite-based products （QuikSCAT and NCDC blended sea winds） are evaluated using in situ ship measurements from the Chinese National Antarctic R...Sea surface winds from reanalysis （NCEP-2 and ERA-40 datasets） and satellite-based products （QuikSCAT and NCDC blended sea winds） are evaluated using in situ ship measurements from the Chinese National Antarctic Research Expeditions （CH1NAREs） from 1989 through 2006, with emphasis on the Southern Ocean （south of 45°S）. Compared with ship observations, the reanalysis winds have a positive mean bias （0.32 m·s-1 for NCEP-2 and 0.13 m·s-1 for ERA-40）, and this bias is more pronounced in the Southern Ocean （0.57 m·s-1 and 0.45 m·s-1, respectively）. However, mean biases are negative in the tropics and subtropics. The satellite-based winds also show positive mean biases, larger than those of the reanalysis data. All four wind products overestimate ship wind speed for weak winds （〈4 m·s-1） but underestimate for strong winds （〉10 m·s-1）. Differences between the reanalysis and satellite winds are examined to identify regions with large discrepancies.展开更多
Studies on the reconstruction of global mean temperature series are reviewed by introducing three series, Had- CRUT3, NCDC, and GISS in details. Satellite data have been used since 1982 in NCDC and GISS series. NCDC s...Studies on the reconstruction of global mean temperature series are reviewed by introducing three series, Had- CRUT3, NCDC, and GISS in details. Satellite data have been used since 1982 in NCDC and GISS series. NCDC series has the most complete spatial coverage among the three by using statistic interpolation technique. The weakened global warming in 2000-2009 as revealed in HadCRUT3 data is possibly caused by the lack of data coverage of this dataset over the Arctic. GISS and NCDC series showed much stronger warming trends during the last 10 years （-0.1 ℃ per 10 years）. Three series yielded almost the same warming trend for 1910-2009 （ 0.70-0.75 ℃ per 100 years）.展开更多
Recently, due to the rapid growth increment of data sensors, a massive volume of data is generated from different sources. The way of administering such data in a sense storing, managing, analyzing, and extracting ins...Recently, due to the rapid growth increment of data sensors, a massive volume of data is generated from different sources. The way of administering such data in a sense storing, managing, analyzing, and extracting insightful information from the massive volume of data is a challenging task. Big data analytics is becoming a vital research area in domains such as climate data analysis which demands fast access to data. Nowadays, an open-source platform namely MapReduce which is a distributed computing framework is widely used in many domains of big data analysis. In our work, we have developed a conceptual framework of data modeling essentially useful for the implementation of a hybrid data warehouse model to store the features of National Climatic Data Center (NCDC) climate data. The hybrid data warehouse model for climate big data enables for the identification of weather patterns that would be applicable in agricultural and other similar climate change-related studies that will play a major role in recommending actions to be taken by domain experts and make contingency plans over extreme cases of weather variability.展开更多
基金supported by the National Natural Science Foundation of China(Grant nos.41006115,41076128,41206184)the Marine Science Youth Fund of SOA(Grant no.2010215)the Chinese Polar Environmental Comprehensive Investigation and Assessment Programmes (Grant no.CHINARE2013-04-01).
文摘Sea surface winds from reanalysis （NCEP-2 and ERA-40 datasets） and satellite-based products （QuikSCAT and NCDC blended sea winds） are evaluated using in situ ship measurements from the Chinese National Antarctic Research Expeditions （CH1NAREs） from 1989 through 2006, with emphasis on the Southern Ocean （south of 45°S）. Compared with ship observations, the reanalysis winds have a positive mean bias （0.32 m·s-1 for NCEP-2 and 0.13 m·s-1 for ERA-40）, and this bias is more pronounced in the Southern Ocean （0.57 m·s-1 and 0.45 m·s-1, respectively）. However, mean biases are negative in the tropics and subtropics. The satellite-based winds also show positive mean biases, larger than those of the reanalysis data. All four wind products overestimate ship wind speed for weak winds （〈4 m·s-1） but underestimate for strong winds （〉10 m·s-1）. Differences between the reanalysis and satellite winds are examined to identify regions with large discrepancies.
基金supported by LASG Open Research Program and National Natural Science Foundation of China (No41005035/D0507)
文摘Studies on the reconstruction of global mean temperature series are reviewed by introducing three series, Had- CRUT3, NCDC, and GISS in details. Satellite data have been used since 1982 in NCDC and GISS series. NCDC series has the most complete spatial coverage among the three by using statistic interpolation technique. The weakened global warming in 2000-2009 as revealed in HadCRUT3 data is possibly caused by the lack of data coverage of this dataset over the Arctic. GISS and NCDC series showed much stronger warming trends during the last 10 years （-0.1 ℃ per 10 years）. Three series yielded almost the same warming trend for 1910-2009 （ 0.70-0.75 ℃ per 100 years）.
文摘Recently, due to the rapid growth increment of data sensors, a massive volume of data is generated from different sources. The way of administering such data in a sense storing, managing, analyzing, and extracting insightful information from the massive volume of data is a challenging task. Big data analytics is becoming a vital research area in domains such as climate data analysis which demands fast access to data. Nowadays, an open-source platform namely MapReduce which is a distributed computing framework is widely used in many domains of big data analysis. In our work, we have developed a conceptual framework of data modeling essentially useful for the implementation of a hybrid data warehouse model to store the features of National Climatic Data Center (NCDC) climate data. The hybrid data warehouse model for climate big data enables for the identification of weather patterns that would be applicable in agricultural and other similar climate change-related studies that will play a major role in recommending actions to be taken by domain experts and make contingency plans over extreme cases of weather variability.
