The Experts below are selected from a list of 43941 Experts worldwide ranked by ideXlab platform
Davi De Carvalho Diniz Melo - One of the best experts on this subject based on the ideXlab platform.
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performance evaluation of rainfall estimates by trmm multi satellite precipitation analysis 3b42v6 and v7 over brazil
Journal of Geophysical Research, 2015Co-Authors: Davi De Carvalho Diniz Melo, Alexandre Cândido Xavier, Thiago Bianchi, Paulo S Oliveira, Bridget R Scanlon, Murilo Cesar Lucas, Edson WendlandAbstract:Time series precipitation data generated by the Tropical Rainfall Measuring Mission (TRMM) have been used as a possible solution for providing rainfall information for ungauged regions. We evaluated the quality of TRMM Multi-satellite Precipitation Analysis (TMPA) Version 6 (3B42V6) and Version 7 (3B42V7) products on a daily and Monthly Basis for a 14 year time series by comparing with gridded ground-based rainfall data from ~3625 rain gauges distributed throughout Brazil. The results show that daily estimates are inaccurate for both Versions 6 and 7 (the refined index of agreement, dr, was less than 0.6 in most of the analyzed pixels). In general, both versions perform well on Monthly Basis (dr > 0.75), but no significant improvement between them could be identified with the exception of local areas. TMPA performed poorly in the northwest region, where rainfall depths are higher in Brazil; however, the quality of the ground-based data is poor in this region because of low gauge density. Based on a seasonal analysis, we found that TMPA performed better during the dry seasons and that some improvements, although not significant, between successive versions took place over the northeast, southeast, and south regions. This study shows the value of remote sensing precipitation for providing reliable, spatiotemporally continuous precipitation at Monthly timescales.
Edson Wendland - One of the best experts on this subject based on the ideXlab platform.
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performance evaluation of rainfall estimates by trmm multi satellite precipitation analysis 3b42v6 and v7 over brazil
Journal of Geophysical Research, 2015Co-Authors: Davi De Carvalho Diniz Melo, Alexandre Cândido Xavier, Thiago Bianchi, Paulo S Oliveira, Bridget R Scanlon, Murilo Cesar Lucas, Edson WendlandAbstract:Time series precipitation data generated by the Tropical Rainfall Measuring Mission (TRMM) have been used as a possible solution for providing rainfall information for ungauged regions. We evaluated the quality of TRMM Multi-satellite Precipitation Analysis (TMPA) Version 6 (3B42V6) and Version 7 (3B42V7) products on a daily and Monthly Basis for a 14 year time series by comparing with gridded ground-based rainfall data from ~3625 rain gauges distributed throughout Brazil. The results show that daily estimates are inaccurate for both Versions 6 and 7 (the refined index of agreement, dr, was less than 0.6 in most of the analyzed pixels). In general, both versions perform well on Monthly Basis (dr > 0.75), but no significant improvement between them could be identified with the exception of local areas. TMPA performed poorly in the northwest region, where rainfall depths are higher in Brazil; however, the quality of the ground-based data is poor in this region because of low gauge density. Based on a seasonal analysis, we found that TMPA performed better during the dry seasons and that some improvements, although not significant, between successive versions took place over the northeast, southeast, and south regions. This study shows the value of remote sensing precipitation for providing reliable, spatiotemporally continuous precipitation at Monthly timescales.
Tao Wang - One of the best experts on this subject based on the ideXlab platform.
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multiperiod corporate default prediction a forward intensity approach
Journal of Econometrics, 2012Co-Authors: Jinchuan Duan, Jie Sun, Tao WangAbstract:Abstract A forward intensity model for the prediction of corporate defaults over different future periods is proposed. Maximum pseudo-likelihood analysis is then conducted on a large sample of the US industrial and financial firms spanning the period 1991–2011 on a Monthly Basis. Several commonly used factors and firm-specific attributes are shown to be useful for prediction at both short and long horizons. Our implementation also factors in momentum in some variables and documents their importance in default prediction. The model’s prediction is very accurate for shorter horizons. Its accuracy deteriorates somewhat when the horizon is increased to two or three years, but the performance still remains reasonable. The forward intensity model is also amenable to aggregation, which allows for an analysis of default behavior at the portfolio and/or economy level.
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multiperiod corporate default prediction a forward intensity approach
Social Science Research Network, 2012Co-Authors: Jinchuan Duan, Jie Sun, Tao WangAbstract:A forward intensity model for the prediction of corporate defaults over different future periods is proposed. Maximum pseudo-likelihood analysis is then conducted on a large sample of the US industrial and financial firms spanning the period 1991-2011 on a Monthly Basis. Several commonly used factors and firm-specific attributes are shown to be useful for prediction at both short and long horizons. Our implementation also factors in momentum in some variables and documents their importance in default prediction. The prediction is very accurate for shorter horizons. The accuracy deteriorates somewhat when the horizon is increased to two or three years, but its performance still remains reasonable. The forward intensity model is also amenable to aggregation, which allows for an analysis of default behavior at the portfolio and/or economy level.
Paulo S Oliveira - One of the best experts on this subject based on the ideXlab platform.
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performance evaluation of rainfall estimates by trmm multi satellite precipitation analysis 3b42v6 and v7 over brazil
Journal of Geophysical Research, 2015Co-Authors: Davi De Carvalho Diniz Melo, Alexandre Cândido Xavier, Thiago Bianchi, Paulo S Oliveira, Bridget R Scanlon, Murilo Cesar Lucas, Edson WendlandAbstract:Time series precipitation data generated by the Tropical Rainfall Measuring Mission (TRMM) have been used as a possible solution for providing rainfall information for ungauged regions. We evaluated the quality of TRMM Multi-satellite Precipitation Analysis (TMPA) Version 6 (3B42V6) and Version 7 (3B42V7) products on a daily and Monthly Basis for a 14 year time series by comparing with gridded ground-based rainfall data from ~3625 rain gauges distributed throughout Brazil. The results show that daily estimates are inaccurate for both Versions 6 and 7 (the refined index of agreement, dr, was less than 0.6 in most of the analyzed pixels). In general, both versions perform well on Monthly Basis (dr > 0.75), but no significant improvement between them could be identified with the exception of local areas. TMPA performed poorly in the northwest region, where rainfall depths are higher in Brazil; however, the quality of the ground-based data is poor in this region because of low gauge density. Based on a seasonal analysis, we found that TMPA performed better during the dry seasons and that some improvements, although not significant, between successive versions took place over the northeast, southeast, and south regions. This study shows the value of remote sensing precipitation for providing reliable, spatiotemporally continuous precipitation at Monthly timescales.
Murilo Cesar Lucas - One of the best experts on this subject based on the ideXlab platform.
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performance evaluation of rainfall estimates by trmm multi satellite precipitation analysis 3b42v6 and v7 over brazil
Journal of Geophysical Research, 2015Co-Authors: Davi De Carvalho Diniz Melo, Alexandre Cândido Xavier, Thiago Bianchi, Paulo S Oliveira, Bridget R Scanlon, Murilo Cesar Lucas, Edson WendlandAbstract:Time series precipitation data generated by the Tropical Rainfall Measuring Mission (TRMM) have been used as a possible solution for providing rainfall information for ungauged regions. We evaluated the quality of TRMM Multi-satellite Precipitation Analysis (TMPA) Version 6 (3B42V6) and Version 7 (3B42V7) products on a daily and Monthly Basis for a 14 year time series by comparing with gridded ground-based rainfall data from ~3625 rain gauges distributed throughout Brazil. The results show that daily estimates are inaccurate for both Versions 6 and 7 (the refined index of agreement, dr, was less than 0.6 in most of the analyzed pixels). In general, both versions perform well on Monthly Basis (dr > 0.75), but no significant improvement between them could be identified with the exception of local areas. TMPA performed poorly in the northwest region, where rainfall depths are higher in Brazil; however, the quality of the ground-based data is poor in this region because of low gauge density. Based on a seasonal analysis, we found that TMPA performed better during the dry seasons and that some improvements, although not significant, between successive versions took place over the northeast, southeast, and south regions. This study shows the value of remote sensing precipitation for providing reliable, spatiotemporally continuous precipitation at Monthly timescales.
