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Spatio-temporal analysis of the accuracy of tropical multisatellite precipitation analysis 3B42 precipitation data in mid-high latitudes of China.

Cai Y, Jin C, Wang A, Guan D, Wu J, Yuan F, Xu L - PLoS ONE (2015)

Bottom Line: Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms.Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions.Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, People's Republic of China; Graduate University of Chinese Academy of Sciences, Beijing, People's Republic of China.

ABSTRACT
Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product over high latitude areas beyond the TRMM satellites latitude band (38°NS). This study attempts to statistically assess TMPA V7 data over the region beyond 40°NS using data obtained from numerous weather stations in 1998-2012. Comparative analysis at three timescales (daily, monthly and annual scale) indicates that adoption of a monthly adjustment significantly improved correlation at a larger timescale increasing from 0.63 to 0.95; TMPA data always exhibits a slight overestimation that is most serious at a daily scale (the absolute bias is 103.54%). Moreover, the performance of TMPA data varies across all seasons. Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions. Special attention should be paid to its application in arid areas and in winter with poor scores of accuracy indices. Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation. The systematic evaluation of TMPA over mid-high latitudes provides a broader understanding of satellite-based precipitation estimates, and these data are important for the rational application of TMPA methods in climatic and hydrological research.

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The frequency distribution of the six statistical indices for three regions during 1998–2012: (a) CC, (b) ME, (c) MAE, (d) POD, (e) POFD, (f) ETS.Each statistical index is computed for individual rain gauge for each year.
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pone.0120026.g014: The frequency distribution of the six statistical indices for three regions during 1998–2012: (a) CC, (b) ME, (c) MAE, (d) POD, (e) POFD, (f) ETS.Each statistical index is computed for individual rain gauge for each year.

Mentions: In addition, the frequency distribution of statistical indices for Liaoning and Shandong was also investigated to access TMPA’s performance in different areas (Fig. 14). Each statistical indice was calculated based on daily precipitation for every year in individual station in each region. The frequency of six indices in Liaoning is similar to that in Shandong. Usually, TMPA got a good score with a high correlation, desirable POD and ETS. By contrast, the distribution of statistical indices in Xinjiang was unique, except for ME and POFD. Generally, a poor statistical score dominated in this region, especially CC, POD and ETS. Thus, TMPA has a limited accuracy to detect precipitation characteristic in arid area.


Spatio-temporal analysis of the accuracy of tropical multisatellite precipitation analysis 3B42 precipitation data in mid-high latitudes of China.

Cai Y, Jin C, Wang A, Guan D, Wu J, Yuan F, Xu L - PLoS ONE (2015)

The frequency distribution of the six statistical indices for three regions during 1998–2012: (a) CC, (b) ME, (c) MAE, (d) POD, (e) POFD, (f) ETS.Each statistical index is computed for individual rain gauge for each year.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4382316&req=5

pone.0120026.g014: The frequency distribution of the six statistical indices for three regions during 1998–2012: (a) CC, (b) ME, (c) MAE, (d) POD, (e) POFD, (f) ETS.Each statistical index is computed for individual rain gauge for each year.
Mentions: In addition, the frequency distribution of statistical indices for Liaoning and Shandong was also investigated to access TMPA’s performance in different areas (Fig. 14). Each statistical indice was calculated based on daily precipitation for every year in individual station in each region. The frequency of six indices in Liaoning is similar to that in Shandong. Usually, TMPA got a good score with a high correlation, desirable POD and ETS. By contrast, the distribution of statistical indices in Xinjiang was unique, except for ME and POFD. Generally, a poor statistical score dominated in this region, especially CC, POD and ETS. Thus, TMPA has a limited accuracy to detect precipitation characteristic in arid area.

Bottom Line: Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms.Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions.Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, People's Republic of China; Graduate University of Chinese Academy of Sciences, Beijing, People's Republic of China.

ABSTRACT
Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product over high latitude areas beyond the TRMM satellites latitude band (38°NS). This study attempts to statistically assess TMPA V7 data over the region beyond 40°NS using data obtained from numerous weather stations in 1998-2012. Comparative analysis at three timescales (daily, monthly and annual scale) indicates that adoption of a monthly adjustment significantly improved correlation at a larger timescale increasing from 0.63 to 0.95; TMPA data always exhibits a slight overestimation that is most serious at a daily scale (the absolute bias is 103.54%). Moreover, the performance of TMPA data varies across all seasons. Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions. Special attention should be paid to its application in arid areas and in winter with poor scores of accuracy indices. Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation. The systematic evaluation of TMPA over mid-high latitudes provides a broader understanding of satellite-based precipitation estimates, and these data are important for the rational application of TMPA methods in climatic and hydrological research.

Show MeSH
Related in: MedlinePlus