Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013
Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global S...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-01-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/10/2/168 |
| _version_ | 1819121796961533952 |
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| author | Qiaolin Zeng Yongqian Wang Liangfu Chen Zifeng Wang Hao Zhu Bin Li |
| author_facet | Qiaolin Zeng Yongqian Wang Liangfu Chen Zifeng Wang Hao Zhu Bin Li |
| author_sort | Qiaolin Zeng |
| collection | DOAJ |
| description | Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global Satellite Mapping of Precipitation] RENALYSIS, CMORPH [Climate Prediction Center’s morphing technique] BLD and CMORPH_RAW) were compared with in situ measurements over China for the period of 2005 to 2013. To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAW underestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer. |
| first_indexed | 2024-12-22T06:42:16Z |
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| id | doaj.art-0ff0ab285e5c4defb7cd711198eb6423 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-22T06:42:16Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0ff0ab285e5c4defb7cd711198eb64232022-12-21T18:35:24ZengMDPI AGRemote Sensing2072-42922018-01-0110216810.3390/rs10020168rs10020168Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013Qiaolin Zeng0Yongqian Wang1Liangfu Chen2Zifeng Wang3Hao Zhu4Bin Li5State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, ChinaBeijing Huayun Shinetek Science and Technology Co., Ltd., Beijing 100081, ChinaPrecipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global Satellite Mapping of Precipitation] RENALYSIS, CMORPH [Climate Prediction Center’s morphing technique] BLD and CMORPH_RAW) were compared with in situ measurements over China for the period of 2005 to 2013. To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAW underestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer.http://www.mdpi.com/2072-4292/10/2/168precipitationstatistics methodsChinaTibetan PlateauSouth China’s |
| spellingShingle | Qiaolin Zeng Yongqian Wang Liangfu Chen Zifeng Wang Hao Zhu Bin Li Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013 Remote Sensing precipitation statistics methods China Tibetan Plateau South China’s |
| title | Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013 |
| title_full | Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013 |
| title_fullStr | Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013 |
| title_full_unstemmed | Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013 |
| title_short | Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013 |
| title_sort | inter comparison and evaluation of remote sensing precipitation products over china from 2005 to 2013 |
| topic | precipitation statistics methods China Tibetan Plateau South China’s |
| url | http://www.mdpi.com/2072-4292/10/2/168 |
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