Data Quality Evaluation of Sentinel-1 and GF-3 SAR for Wind Field Inversion

Synthetic aperture radar (SAR) is an important means to observe the sea surface wind field. Sentinel-1 and GF-3 are located on orbit SAR satellites, but the SAR data quality of these two satellites has not been evaluated and compared at present. This paper mainly studies the data quality of Sentinel-1 and GF-3 SAR satellites used in wind field inversion. In this study, Sentinel-1 SAR data and GF-3 SAR data located in Malacca Strait, Hormuz Strait and the east and west coasts of the United States are selected to invert wind fields using the C-band model 5.N (CMOD5.N). Compared with reanalysis data called ERA5, the root mean squared error (RMSE) of the Sentinel-1 inversion results is 1.66 m/s, 1.37 m/s and 1.49 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively; the RMSE of GF-3 inversion results is 1.63 m/s, 1.45 m/s and 1.87 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively. Based on the data of Sentinel-1 and GF-3 located on the east and west coasts of the United States, CMOD5.N is used to invert the wind field. Compared with the buoy data, the RMSE of the Sentinel-1 inversion results is 1.20 m/s, and the RMSE of the GF-3 inversion results is 1.48 m/s. The results show that both Sentinel-1 SAR data and GF-3 SAR data are suitable for wind field inversion, but the wind field inverted by Sentinel-1 SAR data is slightly better than GF-3 SAR data. When applied to wind field inversion, the data quality of Sentinel-1 SAR is slightly better than the data quality of GF-3 SAR. The SAR data quality of GF-3 has achieved a world-leading level.

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Research Productivity of Chinese Young Thousand Talents

International Higher Education ◽

10.6017/ihe.2019.97.10944 ◽

2019 ◽

pp. 17-18 ◽

Cited By ~ 4

Author(s):

Lili Yang ◽

Giulio Marini

Keyword(s):

United States ◽

Research Productivity ◽

Research Performance ◽

Research Capacity ◽

The United States ◽

Similar Rate ◽

Assessment System ◽

Better Than

While it is commonly agreed that globally bred talent returning to China greatly contributes to the enhancement of research capacity, whether returnees perform better than those who stay overseas remains to be examined. We compared the research productivity of Chinese “Young Thousand Talents” (Y1000Ts) and Chinese researchers remaining in the United States. The results of our analysis demonstrate that while the two groups publish at a similar rate, Y1000T lag slightly behind their US-affiliated counterparts in terms of quality of publications. This could be explained by the assessment system of research performance in China.

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Intergenerational Occupational Mobility in Great Britain and the United States Since 1850: Comment

The American Economic Review ◽

10.1257/aer.103.5.2003 ◽

2013 ◽

Vol 103 (5) ◽

pp. 2003-2020 ◽

Cited By ~ 47

Author(s):

Yu Xie ◽

Alexandra Killewald

Keyword(s):

United States ◽

Great Britain ◽

Comparative Study ◽

Data Quality ◽

Survey Data ◽

Social Mobility ◽

Occupational Mobility ◽

The United States ◽

Significant Decline

Using historical census and survey data, Long and Ferrie (2013) found a significant decline in social mobility in the United States from 1880 to 1973. We present two critiques of the Long-Ferrie study. First, the data quality of the Long-Ferrie study is more limiting than the authors acknowledge. Second, and more critically, they applied a method ill-suited for measuring social mobility of farmers in a comparative study between 1880 and 1973, a period in which the proportion of farmers dramatically declined in the United States. We show that Long and Ferrie's main conclusion is all driven by this misleading result for farmers. (JEL J62, N31, N32, N51, N52)

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Evaluating the Suitability of Century-Long Gridded Meteorological Datasets for Hydrological Modeling

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0113.1 ◽

2020 ◽

Vol 21 (11) ◽

pp. 2565-2580

Author(s):

Carolina Massmann

Keyword(s):

United States ◽

Time Series ◽

Hydrological Modeling ◽

Hydrological Model ◽

The United States ◽

Model Driven ◽

Conceptual Hydrological Model ◽

Better Than

AbstractRecent advances in climate reanalyses have led to the development of meteorological products providing information from the beginning of the last century or even before. As these data sources might be of interest to practitioners in the event of missing data from meteorological stations, it is important to assess their usefulness for different applications. The main objective of this study is to investigate the ability of two long-term reanalysis datasets (CERA-20C and 20CR) and one long-term interpolated dataset (Livneh) for supporting hydrological modeling. The precipitation and temperature data of the three datasets were first compared, downscaled, and then used as inputs to the conceptual hydrological model HBV in 168 basins in the United States. The findings suggest that the quality of all three datasets decreases the further we go back in time. Models calibrated at the beginning of the time series, where the data quality is worse, are only able to capture the general properties of the time series and thus do not show a decrease in performance as the period between calibration and validation becomes larger. The opposite is true for models calibrated at the end of the time series, which show a clear decrease in performance toward the beginning of the century. While the hydrological model driven with the interpolated datasets achieved the best performance, the results obtained with the reanalysis datasets were still informative (i.e., better than the long-term monthly mean), and they matched the performance of the interpolated dataset in a few catchments in the northwestern United States.

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