Retrieval of sea surface specific humidity based on AMSR-E satellite data

Zong, Hong-Shi; Liu, Yuguang; Rong, Zengrui; Cheng, Yongcun

doi:10.1016/j.dsr.2007.04.008

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…; 0.99. Both values are much better than those obtained by the algorithm of [7], which was developed for AMSR-E data. The biases of the products obtained by our algorithms are larger than those of the products obtained by Zong's algorithm for the KEO buoy.…”

Section: Discussionmentioning

confidence: 67%

“…The algorithm developed by [7], which was developed for AMSR-E data, results in a relatively small bias, shown in Table 5, as compared to the bias for the products obtained by our algorithm. Figure 4 shows a scatter diagram for the KEO data and surface air specific humidity obtained by Zong's algorithm and the present algorithm 002.…”

Section: Discussionmentioning

confidence: 86%

“…J-OFURO 2 [21] and HOAPS3 are satellite products, which utilize the data of DMSP/SSMI. We estimated the specific humidity by applying the algorithms of [7] and [18] to AMSR-E data. NRA1, NCEP/Department of Energy reanalysis (NRA2) [22], and Japanese Re-Analysis 25 years (JRA25) [23] data are all types of reanalysis data.…”

Section: Comparison With Global Buoy Datamentioning

confidence: 99%

“…If we can estimate the specific humidity using AMSR-E data, we can reduce the sampling error in the daily mean value of specific humidity because the observation time of AMSR-E (1 a.m. and 1 p.m.) is quite different from those of DMSP/SSMIs (around 6 a.m. and 6 p.m.). However, to date, only [7] has developed an algorithm for estimation of specific humidity using AMSR-E data. They developed the algorithm by assuming that the values of the reanalysis data are the true values.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is not clear whether reanalysis data are more accurate than satellite-derived data. Therefore, it is necessary to evaluate the accuracy of the air specific humidity derived from AMSR-E data using [7]'s algorithm, by comparing with ocean observation data.…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of Surface Air Specific Humidity Over the Ocean Using AMSR-E Measurements

Kubota

Hihara

2008

Sensors

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We have developed a new algorithm to estimate the surface air specific humidity over the ocean from AMSR-E data. It should be noted that remarkably reduced random errors of the estimated surface air specific humidity result from using the surface air specific humidity provided by reanalysis data. We validated our new algorithm using independent ship and buoy data. The bias, RMS error, and correlation coefficient of the products obtained using our algorithm for global buoys are 0.38 g/kg, 0.61 g/kg and 0.99, respectively. It should be noted that surface specific humidity having similar accuracy to the reanalysis data near in situ data could be derived from AMSR-E data by the present algorithm.

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Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 86%

Section: Comparison With Global Buoy Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Retrieval of Surface Air Specific Humidity Over the Ocean Using AMSR-E Measurements

Kubota

Hihara

2008

Sensors

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Fluxes through the air–sea interface

Robinson

Fangohr

2010

Discovering the Ocean From Space

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Algorithms for estimation of air-specific humidity using TMI data

Iwasaki

Kubota

2012

International Journal of Remote Sensing

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We developed two empirical algorithms for estimating the surface air-specific humidity (Q a ) at 10 m over the ocean using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) brightness temperature data. We used the in situ data included in the International Comprehensive Ocean-Atmosphere Data Set for 2003-2006 as true values in this study. We estimated the Q a using the developed regression formulae and validated the results by comparing with moored buoy data. The biases of our product were relatively small, i.e. 0.03 and 0.29 g kg -1 for TMI_4CH and TMI_9CH, respectively, when compared with other Q a retrievals. Moreover, we investigated the relationship between the brightness temperature observed by each channel and the in situ Q a . We concluded that the use of the brightness temperature determined by the 85 GHz polarized radiation can considerably reduce the bias.

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Retrieval of sea surface specific humidity based on AMSR-E satellite data

Cited by 6 publications

References 13 publications

Retrieval of Surface Air Specific Humidity Over the Ocean Using AMSR-E Measurements

Retrieval of Surface Air Specific Humidity Over the Ocean Using AMSR-E Measurements

Fluxes through the air–sea interface

Algorithms for estimation of air-specific humidity using TMI data

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