AMSR2 using in situ data and global products

Remote Sensing of Environment 164 (2015) 170–178

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Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse

Evaluation of sea surface temperature and wind speed observed by GCOM-W1/AMSR2 using in situ data and global products Tsutomu Hihara a,⁎, Masahisa Kubota a, Atsushi Okuro a,b a b

School of Marine Science and Technology, Tokai University, Shizuoka, Japan Oceanographic Command, Maritime Self-Defense Force, Yokosuka, Kanagawa, Japan

a r t i c l e

i n f o

Article history: Received 9 May 2014 Received in revised form 30 March 2015 Accepted 5 April 2015 Available online xxxx Keywords: AMSR2/GCOM-W1 Sea surface temperature Sea surface wind speed Marine meteorology Microwave radiometer

a b s t r a c t The Global Change Observation Mission (GCOM) 1st-Water (GCOM-W1) satellite, equipped with the Advanced Microwave Scanning Radiometer 2 (AMSR2), was launched on May 18, 2012, by the Japan Aerospace Exploration Agency (JAXA), and began observing on July 3, 2012. In this study, we evaluated the sea surface temperature (SST) and sea surface wind speed (SSW) data from the AMSR2 (ver. 1) standard product, provided by JAXA, by comparison with mooring buoy data or global products. From a comparison of the AMSR2 and Triangle TransOcean Buoy Network (TRITON) data, mean differences of 0.21 °C and 0.30 m/s, and root mean square (RMS) differences of 0.49 °C and 1.25 m/s, were inferred for SST and SSW, respectively. During daytime low wind-speed conditions, SSTs from AMSR2 are significantly higher than from TRITON. This implies that SSTs from AMSR2 are strongly affected by diurnal heating. From an intercomparison of global products for the Southern Ocean, the annual-mean SSW from AMSR2 is as much as 1 m/s lower than values from other SSW products. Although the SSW data from AMSR2 have a similar accuracy to data from other products, the AMSR2 data have the advantage of fewer missing data compared with other products. Furthermore, we checked for significant differences in annual means by comparing data from WindSat, AMSR2, and AMSR for the Earth Observing System (AMSR-E) provided by JAXA. For the Southern Ocean, we found remarkable differences in the annual-mean values of SST and SSW. SSTs from AMSR2 are lower than from WindSat in regions of strong current in the Northern Hemisphere, and SSTs from AMSR-E JAXA are higher than from WindSat in the same regions. However, the results also suggest a risk of artificial trends in the satellite data if AMSR2, AMSR-E JAXA, and WindSat data are used continuously. © 2015 Elsevier Inc. All rights reserved.

1. Introduction Sea surface temperature (SST) and sea surface wind speed (SSW) are two of the most important parameters used in climate research and prediction. Therefore, accurate observation of these parameters is necessary, especially for understanding air–sea interaction. Typically, meteorological data observed directly by ships or buoys are used in studies of air–sea interaction (e.g., Kubota, Iwabe, Cronin, & Tomita, 2008). However, these data suffer from serious spatial sampling deficiencies due to sparseness of observations. On the other hand, global products based on satellite or reanalysis data are used widely by researchers to analyze large-scale phenomena. Nevertheless, it is necessary to identify whether a global product is sufficiently accurate for reliable analysis using in situ data because SST and SSW, in most global products, are derived from either remote sensing or numerical model data. ⁎ Corresponding author at: School of Marine Science and Technology, Tokai University, 3-20-1 Orido, Shimizu, Shizuoka, Japan. E-mail address: [email protected] (T. Hihara).

http://dx.doi.org/10.1016/j.rse.2015.04.005 0034-4257/© 2015 Elsevier Inc. All rights reserved.

The Global Change Observation Mission (GCOM) 1st-Water (GCOMW1) satellite is the first GCOM satellite. GCOM-W1 was launched and put into the “A-Train” orbit on May 18, 2012, and began acquiring scientific data on July 3, 2012. GCOM-W1 is equipped with the Advanced Microwave Scanning Radiometer 2 (AMSR2). AMSR2 is the successor to the previous AMSR models onboard the Advanced Earth Observing Satellite II (ADEOS-II) and for the Earth Observing System (AMSR-E) onboard Aqua. From May 17, 2013, the Japan Aerospace Exploration Agency (JAXA) has provided datasets of global physical parameters (total precipitable water, cloud liquid water, precipitation, SSW, SST, sea ice concentration, snow depth, and soil moisture content) as the GCOM-W1/AMSR2 standard product. In this paper, we compare the SST and SSW data from AMSR2 with mooring buoy data in the western tropical Pacific, and global distributions of the AMSR2 products with those of other global products. Furthermore, we investigate differences between the AMSR2 and AMSR-E data. We describe these data in Section 2. Results of the comparison between the GCOM-W1 and in situ data and the intercomparison of global products are presented in Sections 3 and 4, respectively. Finally, a summary and a discussion are presented in Section 5.

T. Hihara et al. / Remote Sensing of Environment 164 (2015) 170–178

2. Data 2.1. AMSR2 products In this study, we used the AMSR2 products (Version 1.0) for SST and SSW from the GCOM-W1 Data Providing Service (https://gcom-w1. jaxa.jp) in JAXA. For each geophysical product, JAXA provides standard and release accuracies, defined as “a useable and standard level of accuracy based on past experience with AMSR, AMSR-E, etc.,” and “the minimum accuracy of data that can be released for use in climate change analyses,” respectively. These standard and release accuracies are expressed as the root mean square (RMS) error of instantaneous values, validated by in situ data, and are 0.5 °C and 0.8 °C, and 1.0 m/s and 1.5 m/s, for SST and SSW, respectively. The spatial resolutions of SST and SSW are 50 km and 15 km, respectively, relative to the footprint of brightness temperature observed by AMSR2. AMSR2 acquires the SST data by monitoring the 6-GHz band (Shibata, 2004, 2006, 2007). The brightness temperature obtained by monitoring this frequency band is sensitive to sea temperature up to several millimeters below the surface, which is known as the “subskin” or “near-skin” sea temperature. JAXA provides the AMSR2 SSW product as wind-speed data in neutral-equivalent conditions at 10 m above the sea surface. The equatorial-crossing times of GCOM-W1 and Aqua are approximately 1:30 a.m. and 1:30 p.m. Accordingly, data obtained from AMSR2 and AMSR-E are recorded in the middle of the night and day (JAXA, 2013). We used the “Level 2 (swath data)” products for comparisons with in situ data, and the “Level 3 (gridded data)” products for the intercomparison of global products and the investigation of differences between microwave radiometer products during the period from July 2012 to June 2013. Although JAXA provides “Level 3” products with both high (0.1°) and low (0.25°) resolutions, we used the low-resolution data. 2.2. TRITON data We obtained in situ data observed by the Triangle Trans-Ocean Buoy Network (TRITON), which uses buoys moored at 12 sites in the western Tropical Pacific along longitudes of 137°E, 147°E, and 156°E. We mainly used the SST, SSW and short wave radiation (SWR) data to validate the satellite data in this study. We also used the air temperature, relative humidity and sea surface pressure data for height correction of SSW. The SST, SSW (anemometer) and SWR sensors have accuracy of 0.02 °C, 0.3 m/s and ± 2%, and are located at a depth of 1.5 m (below the surface), at a height of 3.5 m and 3 m (above the surface), respectively (http://www.jamstec.go.jp/jamstec/TRITON/real_time/php/top. php). TRITON SSW data are converted to their corresponding values at a height of 10 m by assuming neutral atmospheric conditions, using the Coupled Ocean Atmosphere Response Experiment (COARE) 3.0 (Fairall, Bradley, Hare, Grachev, & Edson, 2003). Daily and hourly data observed by TRITON are provided by the Tropical Atmosphere Ocean (TAO) project (http://www.pmel.noaa.gov/tao/index.shtml). In this study, we used hourly data (averages computed for each hour from samples taken at 10-minute intervals) for analysis. 2.3. Other products In addition to the AMSR2 data, we also evaluated satellite data from other sources (Tables 1 and 2), by comparison with TRITON data. The satellite products provided by the Remote Sensing Systems (RSS) consist of orbital data mapped onto a grid with a spatial resolution of 0.25°. The period of analysis was the same as for the AMSR2 products, except for the AMSR-E products, for which the period of analysis was from July 2010 to June 2011. The reason for this difference is that AMSR-E has not functioned properly since Oct. 4, 2011. However, JAXA has obtained the AMSR-E data with slow rotation (2 rotations per minute). These data, known as AMSR-E Slow Rotation Data, are

171

Table 1 List of satellite SST products used for comparison with TRITON data. Sensor

Level

Version

Organization

Ascending equatorial crossing time

AMSR2 TMI WindSat AMSR-E AMSR-E

2 3 3 3 2

1 4 7.01 7 7

JAXA RSS RSS RSS JAXA

Around 13:30 Variable Around 18:00 Around 13:30

provided on the web (http://sharaku.eorc.jaxa.jp/AMSR/products/ amsre_slowdata.html) with the goal of validating the AMSR2 data. We used various other global products for the intercomparison of the SST and SSW data (Tables 3 and 4). Definitions of SST differed from one product to another, and the SST diurnal-change feature varied with observation depth. This is because seawater temperature changes with depth owing to solar irradiance at the surface. Donlon et al. (2007) provided a schematic diagram of the vertical temperature structure from the interface to a depth of 10 m. In this diagram, they refer to temperatures at a depth of about 1 m as depth temperatures (SSTdepth), and to those at a depth of about 10 m as foundation temperatures (SSTfnd). It is well known that large diurnal warming occurs in regions of low wind and high insolation (e.g., Price, Weller, & Pinkel, 1986; Stramma, Cornillon, Weller, Price, & Briscoe, 1986). Stuart-Menteth, Robinson, and Challenor (2003) showed, using the model introduced by Kawai and Kawamura (2002), that the western tropical Pacific, where TRITON buoys are located, is one of the largest diurnal warming regions. SSTs obtained from the Advanced Very High Resolution Radiometer (Reynolds SST; Reynolds et al., 2007) and the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA SST; Donlon et al., 2012) are defined as “SSTdepth” and “SSTfnd,” respectively. These SSTs indicate the temperature several meters below the sea surface. Using a diurnal model, the SST obtained by Microwave Optimum Interpolation (MWOI SST) is “normalized” to a daily minimal SST, defined to occur at approximately 8:00 a.m., local time (Gentemann, Donlon, Sturart-Menteth, & Wentz, 2003; Okuro, Kubota, Tomita, & Hihara, 2014). Reynolds, OSTIA, and MWOI SST were constructed by using an optimal interpolation (OI) method. It is noted that it is highly possible that sub-mesoscale (and potentially mesoscale) structures, having a horizontal scale of 100 to 200 km, are smoothed in the OI SST field. They also included buoy-adjustment methods, in which the daily satellite SST fields are adjusted to the in situ SST measurements. For example, in the Reynolds SST, the average zonal and meridional spatial correlation scales, which define the maximum distance between the grid point and the location of data, are assumed to be 151 and 155 km, respectively. Spatial modes of SST anomalies, which were detected by the empirical orthogonal teleconnection functions (Van den Dool, Saha, & Johansson, 2000), were used to bias correct the satellite data of Reynolds SST, as pre-processing for OI (Reynolds et al., 2007). WindSat SSTs are near-skin temperatures of seawater (thickness of layer is ~ 1 mm) (http://www.remss.com/missions/WindSat). Thus,

Table 2 List of satellite SSW products used for comparison with TRITON data. Sensor

Level Version Organization Ascending equatorial crossing time

AMSR2 SSMIS F16 SSMIS F17 TMI11GHz TMI37GHz WindSat LF WindSat MF WindSat AW AMSR-E LF AMSR-E MF AMSR-E

2 3 3 3 3 3 3 3 3 3 2

1 7 7 4 4 7.01 7.01 7.01 7 7 7

JAXA RSS RSS RSS RSS RSS RSS RSS RSS RSS JAXA

Around 13:30 Around 17:30 Around 17:45 Variable Around 18:00

Around 13:30

172

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Table 3 List of global SST products used for intercomparison. Name

Space resolution

Type

Input data

Organization

Version

AMSR2 AVHRR MWOI SST OSTIA WindSat

0.25° 0.25° 0.25° 0.05° 0.25°

Sub-skin Depth Minimum Foundation Sub-skin

AMSR2 AVHRR, in situ TMI, AMSR-E, WindSat MODIS (Tera, Aqua) in situ AVHRR, AATSR, TMI, SEVIRI, AMSR-E, in situ WindSat

JAXA NOAA RSS UK Met Office RSS

1 2 3 1 7.01

Table 4 List of Global SSW products used for intercomparison. Name

Space resolution

Sensor

Input data

Organization

Version

AMSR2 SSMIS F16 SSMIS F17 ASCAT WindSat MF

0.25° 0.25° 0.25° 0.25° 0.25°

Radiometer Radiometer Radiometer Scatterometer Radiometer

AMSR2 SSMIS F16 SSMIS F17 ASCAT WindSat

JAXA RSS RSS IFREMER RSS

1 7 7 1 7.01

the WindSat SST product is similar to the AMSR2 SST product in this respect. To make use of the OSTIA SST product, we mapped its daily data onto a grid with a spatial resolution of 0.25° by applying a simple averaging method to the original data with a resolution of 0.05°. For the intercomparison of SSWs, we used the Advanced Scatterometer (ASCAT) SSW product provided by the Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) (Bentamy & Fillon, 2012) and three microwave radiometer SSW products provided by RSS. The equator-crossing time of the ASCAT onboard the first polar orbiting satellite of the Meteorological Operational Satellite Program (MetOp-A) is 9:30 a.m. and 9:30 p.m. The Special Sensor Microwave/ Imager (SSM/I) is the first operational satellite-mounted sensor that can observe SSW (Wentz, 1997). Furthermore, the Special Sensor Microwave Imager Sounder (SSMIS) improves upon the surface and atmospheric retrievals of SSM/I and upon the atmospheric temperature and water-vapor sounding capabilities of both the Special Sensor Microwave Temperature Sounder (SSM/T-1) and the Special Sensor Microwave Humidity Sounder (SSM/T-2). RSS provides three kinds of WindSat SSW products: low frequency (LF), medium frequency (MF), and all weather (AW) products. In addition to both the LF and MF products, the AW provides the SSW data even under rainy conditions using

(a) 35

6.8-GHz channel data. For the intercomparison, we used only the WindSat MF product. In order to investigate differences between the AMSR-E and AMSR2 data, we used the AMSR-E and AMSR2 “Level 3” data provided by JAXA and the WindSat “Level 3” data provided by RSS. The periods of analysis used to compare the AMSR-E and AMSR2 data were from July 2010 to June 2011 and from July 2012 to June 2013, respectively. Furthermore, we used the WindSat SST and MF SSW data covering the same periods as the AMSR-E and AMSR2 data. Among the three types of WindSat SSW data, WindSat MF SSWs are most similar to AMSR2 SSWs when considering the channels used to derive SSWs for each sensor; we therefore only used the WindSat MF product. WindSat MF SSWs are calculated using the raw data from frequency channels at 18.7-GHz and above, and AMSR2 SSWs, provided by JAXA, are calculated using the raw data from frequency channels at 36.5-GHz. 3. Comparison of satellite and TRITON data First, we interpolated hourly TRITON SST, SSW and SWR data into the data at the satellite observation time for temporal comparison. Second, we spatially matched satellite and TRITON data, either by averaging samples within a radius of 25 km around buoys (for JAXA products), or by selecting the grid values closest to buoy locations (for RSS products). 3.1. Sea surface temperature Fig. 1a shows the statistics and an SST scatter plot of TRITON and the AMSR2 data. The RMS difference is 0.49 °C, which is smaller than the standard accuracy (0.5 °C) given by JAXA. However, the mean difference is positive (as much as 0.21 °C). This positive mean difference was particularly evident when a buoy recorded an SST higher than 30 °C. The

(b) Mean difference : 0.218 [°C] RMS difference : 0.492 [°C] Correlation coefficient : 0.853

14

34

Mean difference : 0.304 [m/s] RMS difference : 1.257 [m/s] Correlation coefficient : 0.891

12

AMSR2 SSW [m/s]

AMSR2 SST [°C]

33 32 31 30 29

10 8 6 4

28 2

27 26 26

27

28

29

30

31

32

TRITON SST [°C]

33

34

35

0 0

2

4

6

8

10

12

14

TRITON SSW [m/s]

Fig. 1. Scatter plots of TRITON data and AMSR2 data: (a) SST and (b) SSW. Broken lines indicate the regression line for each scatter plot.

T. Hihara et al. / Remote Sensing of Environment 164 (2015) 170–178

173

SATELLITE SST - BUOY SST [°C]

(a) 4 3 2 1 0 -1 -2 -3 -4 0

1

2

3

4

5

6

7

8

9

10

11

12

8

9

10

11

12

8

9

10

11

12

SATELLITE SST - BUOY SST [°C]

TRITON SSW [m/s] 4 3 2 1 0 -1 -2 -3 -4 0

(b)

1

2

3

4

5

6

7

SATELLITE SST - BUOY SST [°C]

TRITON SSW [m/s]

(c) 4 3 2 1 0 -1 -2 -3 -4 0

1

2

3

4

5

6

7

TRITON SSW [m/s] Fig. 2. Scatter plots of TRITON SSW and SST differences. (a) AMSR2, (b) AMSR-E JAXA, and (c) AMSR-E RSS.

mean differences are 0.35 °C for daytime data and 0.08 °C for nighttime data. Furthermore, a significant positive difference was found for wind speeds lower than 3 m/s (Fig. 2a). Solar irradiance increases the near-

800 1.5 600

0.5

400

200

0

2

4

6

8

10

12

AMSR2 SST − TRITON SST [°C]

TRITON short wave radiation [W/m2]

2.5 1000

−0.5

TRITON SSW [m/s] Fig. 3. Glyph scatterplot of the SST differences between AMSR2 and TRITON for the values of TRITON SSW and SWR.

skin temperature for low wind-speed conditions during the day compared with SSTdepth (Donlon et al., 2007). Fig. 3 gives dependence of SST differences of AMSR2 and TRITON data on SSW and SWR data. In this figure, it is clear that the large differences between AMSR2 SST and TRITON SST occur when wind speed is lower and SWR is larger. Therefore, it is likely that the significant and positively valued difference in mean AMSR2 SST values is related to near-skin temperature changes due to solar irradiance. The amount of short wave radiation reaching the sea surface depends on the total cloud amount (Kasten & Czeplak, 1980). Over the warm pool in the tropical Pacific, the diurnal cycle of cloudiness (e.g., a development of cumulonimbus clouds in afternoon) was reported (Chen & Houze, 1997). Therefore, this relation could be one of the error sources in the comparison. In Tables 5 and 6, we show the evaluation of AMSR2 and other satellite products using TRITON data. The accuracy of AMSR2 SSTs is almost

Table 5 Evaluation results for satellite SST data. Sensor

Mean difference [°C]

RMS difference [°C]

Correlation coefficient

Number

AMSR2 TMI WindSat AMSR-E RSS AMSR-E JAXA

0.218 0.123 0.107 0.010 0.207

0.492 0.646 0.393 0.414 0.536

0.853 0.644 0.820 0.870 0.850

1873 2509 1367 2194 2048

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Table 6 Evaluation results for satellite SSW data. Sensor

Mean difference [m/s]

RMS difference [m/s]

Correlation coefficient

Number

AMSR2 SSM/I F16 SSM/I F17 TMI11GHz TMI37GHz WindSat LF WindSat MF Windsat AW AMSR-E LF AMSR-E MF AMSR-E JAXA

0.304 −0.043 −0.082 0.088 0.033 0.132 −0.027 0.440 −0.045 −0.178 0.120

1.257 1.048 0.974 1.322 1.008 0.848 0.853 1.639 0.897 0.898 1.261

0.891 0.904 0.917 0.862 0.919 0.938 0.936 0.798 0.910 0.909 0.825

2946 2541 2548 2639 2565 1599 1655 2411 2244 2250 1983

(a)

identical to that of AMSR-E JAXA SSTs. The accuracy of Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) SSTs is the lowest, with an RMS difference of 0.65 °C and a correlation coefficient of 0.64. The RMS difference for WindSat SSTs is lower than for AMSR2 SSTs by about 0.1 °C. However, the number of WindSat observations is considerably less than for AMSR2 (by 638) because WindSat SST data are far from complete. It is interesting that the accuracy of AMSR-E RSS SSTs is higher than that of AMSR-E JAXA SSTs. Furthermore, from Fig. 2b–c, it can be seen that AMSR-E JAXA SSTs tend to show warming of the top layer during daytime compared with AMSR-E RSS SSTs. This may be caused by differences between the algorithms of Wentz and Meissner (2000, 2007) and Shibata (2004, 2006, and 2007).

(b)

[°C] 1.0

0.5

(c)

(d)

0.0

-0.5

-1.0

(e)

Fig. 4. Differences of annual-mean SSTs: (a) Reynolds − AMSR2, (b) MW OI SST − AMSR2, (c) OSTIA − AMSR2, (d) WindSat − AMSR2, and (e) WindSat − AMSR-E JAXA.

T. Hihara et al. / Remote Sensing of Environment 164 (2015) 170–178

3.2. Sea surface wind speed In Fig. 1b, we show the results of the comparison between AMSR2 SSWs and TRITON data. The RMS difference for AMSR2 SSWs is 1.25 m/s, which is higher than the standard accuracy (1.0 m/s) but lower than the release accuracy (1.5 m/s). We notice that AMSR2 SSWs tend to be higher than TRITON SSWs when wind speeds are high. Moreover, we confirm that the AMSR2 SSW data also show a positive mean difference, depending on the season. The average monthly mean difference in SSWs over the period from November 2012 to May 2013 is 0.22 m/s. The monthly mean SSW values for TRITON in this period are lower than 0.5 m/s. On the other hand, in other periods, they are higher than 0.5 m/s, and the average monthly mean difference is 0.43 m/s. Statistics for satellite SSW products are shown in Table 6. The RMS difference for the AMSR2 SSW product is almost identical to that of the AMSR-E JAXA SSW product. On the other hand, the positive mean difference shown by the AMSR2 SSW product is not found in the

(a)

175

AMSR-E JAXA SSW product. The most accurate products are the WindSat products, except for the WindSat AW SSW product. However, the number of observations is lower for WindSat products than for other products because RSS provides only rain-free SSW data for these products. The WindSat AW SSW product has the lowest accuracy. SSMIS, AMSR-E, and TMI 37 G-Hz products, provided by RSS, all have better accuracy than the AMSR2 product. The number of observations comprising the AMSR2 SSW product is considerably higher than for other products, and its accuracy is lower. These results may be explained by the fact that quality control of the AMSR2 SSW product allows worse atmospheric states to be included than are permitted for other SSW products. 4. Intercomparison of satellite data In this section, we present daily averaged data, calculated from both ascending and descending data, used to derive annual-mean data. The

(b)

[°C] 1.0

0.5

0.0

(c)

(d) -0.5

-1.0

(e)

Fig. 5. Differences of annual-mean SSWs: (a) SSMIS F16 − AMSR2, (b) SSMIS F17 − AMSR2, (c) ASCAT − AMSR2, (d) WindSat MF − AMSR2, and (e) WindSat MF − AMSR-E JAXA.

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Table 7 Swath widths and data acquisition rates of satellites in the TRITON region. Sensor

AMSR2

WindSat

SSMIS F16

SSMIS F17

TMI

Swath width

1450 km

1000 km

1707 km

1707 km

878 km after Aug. 24 2001 (760 km before Aug. 7 2001)

Average data acquisition rate calculated individually from ascending and descending maps Average of data acquisition rate calculated from daily map Number of missing observation days

0.58 0.72 4

0.47 0.68 45

0.58 0.87 18

0.57 0.87 8

0.52 0.72 3

distributions of annual-mean differences between AMSR2 and other SST products are shown in Fig. 4a–d. Large positive differences, indicating that the AMSR2 SST data are lower than other SST data on average, are found in strong-current regions in the Northern Hemisphere (e.g., the Kuroshio Extension and the Gulf Stream). On the other hand, large negative differences in annual means may be found in the Maritime Continent. The polar distributions of annual-mean differences vary with each SST product. In the Southern Ocean, the AMSR2 SST data are almost identical to MWOI and OSTIA data, but tend to be underestimated compared to Reynolds and WindSat SST data. On the other hand, for the Arctic Ocean, the AMSR2 SST data tend to be underestimated compared to MWOI and WindSat SST data, but overestimated compared to OSTIA SST data. For the western tropical Indian Ocean and the central North Atlantic Ocean, only positive annual-mean differences can be found (Fig. 4a and c). Fig. 5a–d shows spatial distributions of annual-mean differences between AMSR2 and other SSW products. The AMSR2 product gives lower SSWs than other products in the northern Pacific Ocean, along the equator, and in the high latitudes of the Southern Hemisphere. In particular, large positive differences are found for the Southern Ocean. On the other hand, in subtropical regions, the AMSR2 product tends to overestimate SSWs compared with other products (by about 0.5 m/s). The characteristics of the AMSR2 SSW product differ from other products, as indicated in Fig. 5a–d, the maps had similar spatial distributions. Therefore, it may be necessary to improve the current version of the AMSR2 SSW product. AMSR2 is the successor to AMSR-E JAXA; however, there is a data gap of nine months between the AMSR-E and AMSR2 data because AMSR-E has not functioned properly since October 4, 2011. On the other hand, WindSat, acquiring scientific data with the same type of microwave radiometer as the AMSR series, has continuously observed since January 2003 and may be used to fill in the data gap. However, continuously using several kinds of satellite data carries the risk of creating artificial trends by shifting from satellite to satellite. Therefore, we checked for annual-mean differences in the WindSat and AMSR-E JAXA data, and in WindSat and AMSR2 data, averaged from July 2010 to June 2011, and from July 2012 to Jun 2013, respectively. After comparing the two maps of annual-mean differences in SST, shown in Fig. 4d and e, we found that AMSR-E JAXA SSTs are higher than WindSat SSTs in areas of western boundary currents and extension regions, especially in the Northern Hemisphere, while AMSR2 SSTs are lower than WindSat SSTs in same regions. Furthermore, in large parts of the Southern Ocean, the annual-mean differences in AMSR-E JAXA and WindSat SSTs are more than 0.5 °C. Such a large value was not found for the difference in AMSR2 and WindSat SSTs. We detected different features in the two maps of annual-mean differences in the SSW data, shown in Fig. 5d and e. Furthermore, we revealed large differences between the AMSR2 SSW product and other SSW products for most regions of the Southern Ocean, where AMSR2 SSWs are as much as 1 m/s lower than WindSat SSWs. In the northern North Pacific, AMSR2 SSWs are lower than WindSat SSWs, whereas AMSR-E JAXA SSWs are higher than WindSat SSWs. We should use caution with the data for the Southern Ocean because of the large difference between AMSR-E JAXA and AMSR2 for both SST and SSW data. In meteorology and oceanology, the middle latitudes

are very important because of the high variability common to these latitudes. However, this high variability also makes it very difficult to perform sound statistical analyses, as it sometimes causes artificial trends in SST data for this region, which is undesirable for many studies using SST as their fundamental source of data.

5. Summary and discussion In this study, we evaluated AMSR2 SST and SSW standard products provided by JAXA. First, we compared the AMSR2 SST and SSW data with TRITON data. From this comparison, we found that the RMS difference for AMSR2 SSTs (0.49 °C) was smaller than the standard accuracy (0.5 °C) defined by JAXA, whereas that for AMSR2 SSWs (1.25 m/s) was larger than the standard accuracy (1.0 m/s) but smaller than the release accuracy (1.5 m/s). AMSR2 SSTs are much higher than TRITON SSTs during daytime low wind-speed conditions. This result has implications for understanding the influence of solar irradiance, and is also similar to results from the AMSR-E SST product provided by JAXA. In order to validate near-skin SST data obtained from satellite observations, near-skin temperature data from buoys or ships are required. The mean difference between AMSR2 and TRITON SSWs is 0.30 m/s. Although the accuracy of the AMSR2 SSW product is slightly lower than for other products, it has the advantage of having fewer missing observations. This means that quality control may not be adequate in the process of AMSR2 SSW products. The number of data and the severity of quality control are opposite sides of the same coin. Even though the accuracy decreases to some extent, some may think that a large number of data are most important. On the other hand, some may feel that inaccurate data are fatal for their research. Accordingly, which is more important depends on the purpose of each user. We examined the data acquisition rate of the satellite to discuss the difference in the amount of satellite data used for comparison with buoy data. We suggest that five main factors affect the coverage of the different satellite datasets: swath width, footprint of sensor, quality control related to atmospheric state, difference of satellite orbit, and data missing due to failed observation. For example, the broader the swath width, the more data are acquired. Swath widths for each sensor are shown in Table 7. Additionally, we also show (in the second and third lines of Table 7) the annual-mean data acquisition rates around TRITON buoys (4°S–10°N, 135°E–160°E). The values in the second (third) line were average of data acquisition rates calculated individually from ascending and descending maps (daily maps). The data acquisition rate of WindSat, which is the satellite with the narrowest swath width, is the lowest. On the other hand, the data acquisition rates of AMSR2 and SSMISs, shown in the second line of Table 7, are almost identical,

Table 8 Data acquisition rate of SST data in the TRITON region.

All condition Only rain condition

AMSR2

WindSat

TMI

0.398 0.047

0.35 0.047

0.529 0.071

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Table 9 Data acquisition rate of SSW data in the TRITON region.

All condition Only rain condition

AMSR2

WindSat LF

WindSat MF

WindSat AF

SSMIS F16

SSMIS F17

TMI11GHz

TMI37GHz

0.621 0.105

0.463 0.064

0.501 0.071

0.603 0.182

0.663 0.067

0.659 0.060

0.53 0.071

0.539 0.073

although the swath width of AMSR2 is narrower than for SSMISs. The data acquisition rates of SSMISs, however, are higher than for AMSR2, as shown in the third line of Table 7. Comparing maps of the data (not shown here) shows that the land-mask area of SSMIS data is wider than for the AMSR2 data. This difference may contribute to the small differences in values between the SSMISs and the AMSR2 data seen in the second line of Table 7. Although the swath width of TMI is narrower than for WindSat, the data acquisition rate is higher because of the unique pass of TMI, which is suitable for observation in tropical regions. The last line of Table 7 shows the number of days on which missing observations occurred between July 2012 and Jun 2013. WindSat had missing observations more frequently than other satellites. On the other hand, AMSR2 observed continuously, except from May 11 to May 14, 2013. In the top line of Tables 8 and 9, we show the average of data acquisition rates of the SST data and the SSW data. These values were calculated from data acquisition rates of ascending and descending maps. The data acquisition rate for SSTs is lower than for SSWs. The reason for this may be that the channels used to derive SSTs have lower frequencies than the channels used to derive SSWs, leading to a difference in footprints. Additionally, data acquisition rates in rainy conditions (see the lowest line of Tables 8 and 9) show that the data acquisition rate for AMSR2 SSWs is the highest, except for that of WindSat AW. Therefore, strict quality control for the atmospheric state is not one of the causes for the high data acquisition rate of the AMSR2 SSW data. Results of the intercomparison of global SST products show that AMSR2 SSTs are lower in the strong-current regions of the Northern Hemisphere and higher in the Maritime Continent compared with SSTs from other products. In the polar regions, there are large SST differences between AMSR2 and other products. Since information on the polar regions is important for climate research, the accuracy of SST data in the polar regions provided by global products should be improved. The differences among the SST datasets are due to various factors. One of these factors is diurnal warming. In the regions where diurnal

warming strongly affects SSTs, large differences can be found if satellite data observed at different times are compared. Therefore, we examined the amplitude of diurnal warming using the AMSR2 SST data. Fig. 6 shows the annual-mean SST difference (daytime − nighttime). Most regions show positive values. In particular, large temperature differences between daytime and nighttime occur around the Maritime Continent, along the west coast of Mexico and Central America, and in part of the tropical Atlantic. Fig. 4 shows that there are large differences between the AMSR2 SST product and other SST products in these regions. Accordingly, we suggest that diurnal warming is strongly related to the characteristics of the SST product. SSWs from the AMSR2 product are lower than values from other global products for the northern Pacific Ocean, oceans along the equator, and in high latitudes of the Southern Hemisphere. Furthermore, care is required when using the current version of the AMSR2 SSW product in the Southern Ocean because SSWs are significantly lower than values from other products. We investigated differences between the AMSR2 and AMSR-E JAXA data by comparing the AMSR2 data or the AMSR-E JAXA data with the WindSat data. From this comparison, we found large differences in the Southern Ocean for both SSTs and SSWs. Furthermore, a significant difference could be observed in the strong-current regions of the Northern Hemisphere. SSTs from the AMSR2 product were lower than values from the WindSat product in these regions, whereas SSTs from the AMSR-E JAXA product were higher. These results suggest that artificial trends may exist in local areas if the AMSR2, AMSR-E JAXA, and WindSat data are used continuously. It is important to constantly accumulate earth-observing satellite data because these data provide crucial information for understanding air–sea interaction. However, the accuracy or quality of such data depends not only on the satellite itself but also on the algorithm used to obtain geophysical data. Thus, it is necessary to understand the different characteristics of the satellite products to construct a long-term multisatellite product. Therefore, in situ data play an important role in

Fig. 6. Distribution of annual-mean SST differences (daytime − nighttime) using AMSR2 SST data.

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