evaluation of the diurnal warming of sea surface ... journal of oceanography, vol. 58, pp. 805 to...

805

Journal of Oceanography, Vol. 58, pp. 805 to 814, 2002

Keywords:⋅ Sea surfacetemperature,

⋅ satellite observa-tion,

⋅ diurnal warming,⋅ solar radiation,⋅ wind speed,⋅ numerical model.

* Corresponding author. E-mail: [email protected]

Copyright © The Oceanographic Society of Japan.

Evaluation of the Diurnal Warming of Sea SurfaceTemperature Using Satellite-Derived MarineMeteorological Data

YOSHIMI KAWAI* and HIROSHI KAWAMURA

Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University,Aoba-ku, Sendai 980-8578, Japan

(Received 14 September 2001; in revised form 1 March 2002; accepted 9 March 2002)

In order to produce a high-quality sea surface temperature (SST) data set, the dailyamplitude of SST (∆SST) should be accurately known. The purpose of this study wasto evaluate the diurnal variation of sea surface temperature in a simple manner. Theauthors first simulated ∆SST with a one-dimensional numerical model using buoy-observed meteorological data and satellite-derived solar radiation data. When inso-lation is strong, the model-simulated 1-m-depth ∆SST becomes much smaller thanthe in situ value as wind speed decreases. By forcibly mixing the sea surface layer, themodel ∆SST becomes closer to the in situ value. It can be considered that part of thisdifference is due to the turbulence induced by the buoy hull. Then, on the assumptionthat the model results were reliable, the authors derived a regression equation toevaluate ∆SST at the skin and 1-m depth from daily mean wind speed (U) and dailypeak solar radiation (PS). ∆SST is approximately proportional to In(U) and (PS)2,and the skin ∆SST estimated by the equation is not inconsistent with in situ observa-tion results reported in past studies. The authors prepared maps of PS and U usingonly satellite data, and demonstrated the ∆SST evaluation over a wide area. The re-sult showed that some wide patchy areas where the skin ∆SST exceeds 3.0 K canappear in the tropics and the mid-latitudes in summer.

derived SSTs are simply averaged to make a daily SSTmap without considering the diurnal variation, unnaturalpatches or streaks may appear because of the warming.

Our purpose in this study is to develop a method toevaluate ∆SST from satellite-derived data only. For ex-ample, Price et al. (1987) proposed an empirical equa-tion to evaluate the SST amplitude using wind stress andair-sea heat flux data observed with a moored buoy in theSargasso Sea. Webster et al. (1996) produced another typeof equation that consists of daily peak solar radiation,daily mean wind speed and precipitation rate. Since it isdifficult to obtain daily air-sea heat flux accurately fromsatellite data only, we use an empirical equation similarto that of Webster et al. (1996).

We first describe the data and a numerical model usedin this paper (Section 2), and simulate the diurnal varia-tion of SST using buoy data and satellite-derived hourlysolar radiation data by the simple numerical model (Sec-tion 3). A regression equation to evaluate the daily SSTamplitude is then produced from the model results (Sec-tion 4). The model ∆SST is better for the purpose thanthe in situ data, which may include warming or cooling

1. IntroductionAn operational, higher-resolution sea surface tem-

perature (SST) product is required by several groups, in-cluding the Global Ocean Data Assimilation Experiment(GODAE) and numerical weather prediction. This prod-uct should have a spatial resolution near or better than 10km, temporal resolution of 24 hours or less, and shouldinclude proper account of skin temperature effects (http://www.bom.gov.au/bmrc/ocean/GODAE/2ndIGST/IGST_Report.html). In making such a high-resolutionSST product, one of important problems is how to dealwith the diurnal variation of SST (Smith, 2001). Manyresearchers have indicated that the amplitude of the SSTdiurnal variation (∆SST) caused by solar heating some-times goes up to 3 K or more under calm and clear condi-tions (e.g., Stramma et al., 1986; Price et al., 1987;Yokoyama et al., 1995; Fairall et al., 1996b). If satellite-

806 Y. Kawai and H. Kawamura

caused by advection. In this study, the temperature varia-tion owing to advection is not taken into consideration.Finally, we demonstrate the ∆SST evaluation in practiceusing satellite-derived wind speed and solar radiation databy the regression equation (Section 5).

2. Data and Procedure

2.1 Solar radiation dataTanahashi et al. (2000, 2001) have developed a

method to evaluate solar radiation at the earth’s surfacefrom Geostationary Meteorological Satellite/Visible In-frared Spin Scan Radiometer (GMS/VISSR) data. Thissensor observes the earth every hour from about thirtyminutes after the hour, in the observation area 60°N–60°Sand 80°E–160°W. The spatial resolution of the visiblechannel is 1.25 km at nadir, and that of the infrared chan-nel is 5.0 km at nadir. Tanahashi et al.’s estimated solarradiation image has a spatial resolution of 0.01° and atime resolution of one hour. This study used the solar ra-diation product produced from GMS/VISSR data byTanahashi et al.

2.2 Buoy dataWe used the global drifting-buoy data set distributed

by the Marine Environmental Data Service (MEDS) inCanada. MEDS acquires, processes, quality controls andarchives drifting buoy messages reporting continuouslyover the global telecommunications system (GTS). Thedata of the TRITON (Triangle Trans-Ocean buoy Net-work) and TAO (Tropical Atmosphere Ocean) buoysmoored in the equatorial area are included in the data set.We also used the data of the moored buoys managed byJapan Meteorological Agency (JMA), which were notincluded in the MEDS data set.

This study used the data during Jan. 1998 to Dec.1999. We selected the buoy data that included at least airtemperature and wind speed besides sea surface tempera-ture, and made a daily time series for each buoy if thereare more than four observations in the daytime per day. Acorresponding time series of solar radiation derived fromGMS/VISSR data was matched up with the daily buoytime series. The solar radiation was averaged in a bin of

0.40° × 0.40° the center of which was a daily mean posi-tion of a drifting buoy. In the case of the JMA buoys, thesize of the bin was 0.10° × 0.10°. If the daily time seriesof solar radiation was incomplete (an observation ofVISSR had failed, or the beginning or end of the timeseries was greater than 150 W/m2), this data set was ex-cluded. Since there solar radiation product contains onlydata during 06–19 LST (Japan Standard Time), equato-rial buoy data far from the meridian of 135°E could notbe matched up with the solar radiation data. Most of theselected data were those of the TOGA (Tropical Oceanand Global Atmosphere)-style drifting buoys and the JMAones, and some of the TRITON buoy data were included.Information about these buoys and their sensors is listedin Table 1. The values of solar radiation, air temperature,humidity and wind speed between observations were cal-culated by linear interpolation. If there was no observa-tion at 0000 or 2400 LST, the data at the nearest time ofthe day were substituted. If humidity data were not in-cluded, relative humidity was assumed to be a constantvalue of 75.0% in a model simulation. Using these mete-orological data we simulated the diurnal variation oftemperature in the sea surface layer by the numericalmodel.

2.3 Model simulationIn order to simulate the diurnal variation of tempera-

ture in the vicinity of the sea surface we used the Kondo-Sasano-Ishii (KSI) model, which is a simple one-dimen-sional numerical one described in Kawai and Kawamura(2000). The skin temperature was computed separatelyusing the Fairall et al. (1996b) parametric skin model.The calculation of air-sea heat and momentum fluxes wasimproved in this study. Fairall et al. (1996a)’s methodwas adopted here to compute heat and momentum turbu-lence transfer at the sea surface.

Integration was initiated from 0000 LST. The initialvalues of current speed and eddy diffusion coefficientswere set to zero. The initial profile of salinity was as-sumed to be vertically homogeneous, and set to 34.0 psu.That of seawater temperature was given as follows: thevertical temperature gradient is 0.1 K/50 m in 0~50-mdepth, and 1-m-depth temperature is set to the buoy-ob-

Table 1. Positions of buoy sensors and size of buoy hulls.

TOGA-style buoy TRITON JMA buoy

Type Drifting Moored MooredHeight of anemometer 1.5 m (1.0 m or 0.5 m for several buoys) 3.5 m 7.5 mDepth of SST sensor 1.0 m 1.5 m 1.0 mLength of buoy 3.2–3.7 m 5.53 m 9.0 mMaximum diameter of buoy About 0.7 m 2.4 m 10.0 m

Evaluation of the Diurnal Warming of SST 807

served value at 0000 LST. The bottom depth was set to50.0 m, and it was assumed that momentum and sensibleheat flux are zero at the bottom. The SST diurnal varia-tion is small when the wind is strong. Hence, we did amodel simulation only for the case that daily mean windspeed is equal to or less than 10.0 ms–1. The total numberof data points used for the model simulations was 1718.Figure 1 shows the positions of the data used.

3. Reliability of Simulated SST Diurnal WarmingWe define ∆SST as the difference between the maxi-

mum after 0900 LST and the minimum before 0900 LST.Figure 2 shows the relation between buoy-observed ∆SSTand model-simulated values. The model-simulated tem-perature at the same depth as the corresponding buoy SSTsensor was compared with the buoy SST (Table 1). Windspeeds measured with the buoy sensors at various heightsabove the sea surface were converted to the equivalentneutral wind speeds at a height of 10 m:

Uu z

k10

101

0m

In( ) =

( )[ ] ( )* /,

where u* is the friction velocity, z0 is the surface rough-ness length, k is the von Kármán constant (0.4). u* and z0can be computed by iteration (Fairall et al., 1996a). Av-eraged U(10m) during 09–15 LST is used, and U refersthe time-averaged U(10m) hereafter.

In the case of buoy ∆SST < 1.5 K, the model ∆SSTsagree well with the in situ ones, and most of the differ-

ences between them are within ±0.5 K. (Note that thescales of the axes in Fig. 2(b) are different from those inFig. 2(a).) However, all the model ∆SSTs are smaller thanthe in situ ones in the case of buoy ∆SST ≥ 1.5 K. Thisdifference could be caused by the platform effect de-scribed by Kawai and Kawamura (2000). When condi-tions are clear and wind speed is very low, a vertical tem-perature gradient develops in the vicinity of the sea sur-face in the daytime. If there is a large vertical tempera-ture gradient above 1-m depth, it suppresses heat transfer

Fig. 1. Positions of buoy observations used for the modelsimulations. Solid circles represent drifting buoys andTRITION buoys, and asterisks represent JMA buoys.

Fig. 2. Relation between buoy-observed ∆SST and model-simu-lated value (both in Kelvin). (a) Drifting buoys (open cir-cles) and TRITON buoys (plus signs), (b) JMA buoys.


from the sea surface to 1-m depth. Hence, it becomes moredifficult to increase the 1-m-depth temperature as windspeed decreases under clear conditions. On the other hand,the buoy-observed 1-m-depth temperature increases mo-notonously as wind speed decreases (Fig. 3(a)). This maybe because the hull of a buoy disturbs the temperaturefield and breaks a large vertical gradient around the buoy.The model ∆SST does not increase when U becomes lessthan about 2.5 ms–1 (Fig. 3(b)). We then added artificialmixing to the sea surface layer in the model simulation.Figures 4 and 5 show the cases when the minimum of theeddy diffusion coefficients were set to 1.0 × 10–5 m2s–1

(weak mixing) and 8.0 × 10–5 m2s–1 (strong mixing) above1-m depth, respectively. The eddy diffusion coefficientscould not be less than the minimum value in the model.If the artificial mixing is added, the model ∆SST becomescloser to the in situ values in the case of buoy ∆SST ≥ 1.5

K. When the strong mixing was forcibly added in thesimulations of the drifting-buoy data, some of the model∆SST values became too large compared with the in situvalues. The minimum value of 8.0 × 10–5 m2s–1 is toolarge for the drifting buoys. In the case of the JMA-buoydata, the model ∆SSTs with the strong mixing agree withthe in situ values better than those with the weak mixing.However, the model ∆SSTs are still smaller than the insitu values when the latter are over 2.0 K.

In order to examine whether these in situ data wereaffected by advection of heat, we calculated the possible

Fig. 3. Relation between ∆SST (K) and wind speeds averagedduring 09–15 LST (ms–1). Only the data of PS (Peak Solarradiation) ≥ 800 Wm–2 are plotted. (a) Buoy-observed val-ues, (b) model results (1-m-depth temperature).

Fig. 4. As Fig. 2, except for setting the minimum eddy diffu-sion coefficients of 1.0 × 10–5 m2s–1 above 1-m depth in themodel simulation.


maximum ∆SST (∆SSTmax) at 1-m depth defined by thefollowing formula:

∆SSTmax ,= −( ) ( )∫1

20c

S Q dtt

tm

ρ

where c is the specific heat of seawater at constant pres-sure, ρ is the density of seawater, S is the solar radiationabsorbed between the sea surface and 1-m depth, Q is thenet heat flux at the sea surface, which is the sum of the

sensible and latent heat fluxes and the net longwave ra-diation (upward is positive). The time when S is equal toQ in the morning is specified by t0, and tm is the timewhen SST reaches its peak. Q was calculated in the model,and S was here assumed to be 61% of the radiation at thesea surface. ∆SST is expected to be ∆SSTmax if there isno advection of heat, no heat exchange at 1-m depth, andtemperature is completely constant above 1-m depth.∆SST never exceeds ∆SSTmax to the extent that heatadvection can be neglected.

Figure 6 shows the relation between the buoy ∆SSTand ∆SSTmax. Almost all of the ∆SST over 2.0 K are nearlyequal to or greater than ∆SSTmax, which means that thosein situ data should be affected by heat advection. Fur-thermore, most of the data clearly affected by heatadvection are the JMA-buoy data. Murakami andKawamura (2001) used wavelet analysis to investigaterelations between SST and heat flux including solar ra-diation at the sea surface based on JMA-buoy data. Theypointed out that the heat flux changes SST in the dailycycle, and heat advection sometimes becomes effectiveon the change of SST in longer-term periods. The shift ofKuroshio recirculation or the polar front causes such heatadvection in the marginal seas around Japan, where theJMA buoys were moored. SST in a coastal area is easilyaffected by heat advection.

It is very difficult to estimate the SST warming dueto advection of heat in a time scale of one day. We ne-glect this warming by using the model-simulated ∆SST.Although there is still slight uncertainty under weak wind

Fig. 5. As Fig. 2, except for setting the minimum eddy diffu-sion coefficients of 8.0 × 10–5 m2s–1 above 1-m depth in themodel simulation.

Fig. 6. Relation between buoy-observed ∆SST and possiblemaximum ∆SST (both in Kelvin). Open circles representJMA-buoy data and asterisks represent drifting-buoy data.


conditions, we can almost rely on the results of the modelsimulations. The model-simulated temperatures are con-sidered as the true ones hereafter, and are used to con-struct a simple regression equation to evaluate ∆SST fromwind speed and solar radiation only.

4. Empirical Estimation of SST Diurnal WarmingFigure 7 shows the relation between the daily ampli-

tude of the skin temperature (skin ∆SST) and U in thecase of PS ≥ 800 Wm–2, where PS is the daily peak solarradiation. Although the skin ∆SST is about 1 K at mostwhen U is about 5.0 ms–1, it increases abruptly as U de-creases. The skin ∆SST has a linear relation with In(U).The relation between the skin ∆SST and daily peak solarradiation is shown in Fig. 8. It also rapidly becomes largeras PS increases. A linear function of (PS)2 can approxi-mate this relation. We therefore propose the followingform of a regression equation relating ∆SST to PS and U:

∆SST In In= ( ) + ( )[ ] + ( ) ( )[ ] + ( )a PS b U c PS U d2 2 3.

This form was determined with reference to Webster etal. (1996)’s regression equation:

∆SST In In= ( ) + ( ) + ( )[ ] + ( ) ( )[ ] + ( ) +

( )a PS b P c U d PS U e U f ,

4

where a, b, c, d, e and f are regression coefficients, and Pis the daily average precipitation rate. We do not use Phere because it is difficult to obtain precipitation data over

a wide region. The coefficients in Eq. (3) were determinedby a least squares fit with the restriction that ∆SST mustbe equal to or less than zero when PS = 0. If the righthand of Eq. (3) is negative, ∆SST is evaluated to be zero.The coefficients are listed in Table 2. Following Websteret al. (1996), we also determined difference coefficientsfor U > 2.5 ms–1 and U ≤ 2.5 ms–1. This threshold valuecomes from Fig. 3 in Section 3.

The regression equation is shown as a surface in Fig.9. If U is less than 0.5 ms–1, we regarded this U as 0.5ms–1 in determining the coefficients because an anemom-eter attached to a buoy cannot accurately measure ex-tremely low wind speed. The results of our modelsimulations show that the skin ∆SST can be up to morethan 6 K when it is almost windless, and daily maximumsolar radiation is greater than 950 Wm–2. This maximum∆SST is much larger than that given in Webster et al.(1996). However, our model results do not contradict pastobservation results. Stramma et al. (1986) reported anexample of day-night SST difference of more than 4.0 Kin the Sargasso Sea using satellite data (their figure 5).

Fig. 7. Relation between skin ∆SST (K) and wind speeds aver-aged during 09–15 LST (ms–1). Only the data of PS ≥ 800Wm–2 are plotted.

Fig. 8. Relation between skin ∆SST (K) and daily peak solarradiation (Wm–2). Only the data of U ≤ 2.5 ms–1 are plot-ted.

U > 2.5 ms–1 U ≤ 2.5 ms–1

a 3.0494 × 10–6 5.0109 × 10–6

b –2.8258 × 10–2 2.2063 × 10–1

c –1.1987 × 10–6 –3.3394 × 10–6

d –2.5893 × 10–2 –2.0216 × 10–1

Table 2. Regression coefficients for skin ∆SST.


Yokoyama et al. (1995) observed the temperature at about2-cm depth in Mutsu Bay at the northern end of HonsyuIsland, Japan. According to their data, the SST amplitudeon 7 July 1992 was up to 5.2 K.

We also determined the regression coefficients forthe 1-m-depth ∆SST in the same manner. Satellite SST isusually derived by using 1-m-depth SST observed withdrifting buoys as the ground truth. Hence, the 1-m-depth∆SST is also worth evaluating. The ∆SSTs simulated withthe forcible mixing are necessary to determine the coef-ficients because these ∆SSTs are closer to the in situ val-

Fig. 9. Surface plot of skin ∆SST (K) by the regression equa-tion.

Fig. 10. Relation between 1-m-depth ∆SST (K) simulated withthe weak forcible mixing (the minimum eddy diffusion co-efficients are 1.0 × 10–5 m2s–1) and wind speeds averagedduring 09–15 LST (ms–1). Only the data of PS ≥ 800 Wm–2

are plotted. Fig. 11. As Fig. 9 except for 1-m-depth ∆SST (K).

ues than those with no artificial mixing. According to Figs.4 and 5, the platform effect of the drifting buoys will beweaker than that of the JMA buoys since the drifting bu-oys are much smaller than the moored ones (Table 1).The regression coefficients were determined using themodel ∆SSTs simulated with the weak artificial mixing(eddy diffusion coefficients of 1.0 × 10–5 m2s–1, Fig. 4),which gives better agreement between the model ∆SSTsof the drifting buoys and the in situ ones. The relationbetween this model ∆SST and wind speed is shown inFig. 10. The ∆SST simply increases as wind speed de-creases, even when the wind is very weak (see Fig. 3(b)).These coefficients for 1-m-depth ∆SST are listed in Ta-ble 3, and the regression equation is shown in Fig. 11.The ∆SST evaluated by the regression equation is about2.0 K at most. This is similar to Price et al. (1987)’s re-sult, but the regression results are a little smaller than thePrice et al.’s one because Price et al. used buoy-observed0.6-m-depth temperatures as SST, not 1-m-depth values.The ∆SST evaluated by Webster et al. (1996) is close toour 1-m-depth ∆SST rather than our skin ∆SST.

5. Application of the Regression EquationWe have evaluated ∆SST in practice from satellite-

derived solar radiation and wind speed through the re-gression equation. We can draw daily peak solar radia-

Table 3. Regression coefficients for 1-m-depth ∆SST.

U > 2.5 ms–1 U ≤ 2.5 ms–1

a 2.4069 × 10–6 1.8265 × 10–6

b 7.5810 × 10–2 –6.6016 × 10–2

c –9.2014 × 10–7 –2.8672 × 10–7

d –1.8838 × 10–1 –5.8428 × 10–2


tion maps from the GMS solar radiation data set describedin Section 2. Daily mean wind speed maps are drawn fromthe following satellite data.

5.1 Satellite wind speed dataWind speed can be measured from space with a mi-

crowave radiometer or a scatterometer. The Special Sen-sor Microwave Imager (SSM/I) is flown by the DefenseMeteorological Satellite Program (DMSP). The SSM/Isensors were available on three or four polar orbiting sat-ellites in 1999 and 2000: DMSP F-11, F-13, F-14 and F-15. The local times for the ascending equatorial crossingof the F-11, F-13, F-14 and F-15 satellites are approxi-mately 0730, 0530, 0830 and 0930, respectively. A de-tailed description of the SSM/I and the algorithms to de-rive wind speed can be found in Wentz (1997). Anothermicrowave radiometer onboard the Tropical RainfallMeasuring Mission (TRMM) satellite is available to mea-sure wind speed (Wentz and Meissner, 2000). This radi-ometer, the TRMM Microwave Imager (TMI), is well-calibrated and similar to the SSM/I. Observation by theTMI covers a global region extending from 40°S to 40°N.Since the orbit of the TRMM satellite is not sun-synchro-nous, the local time of the observation changes for anygiven earth location. SeaWinds is a microwavescatterometer onboard the QuikSCAT (QSCAT) satellite.The QSCAT satellite flies in a sun-synchronous orbit withthe local equator crossing time at the ascending node of0600±30 minutes (JPL/PO.DAAC, 2001a, b).

Wind speed data are derived from SSM/I and TMIdata by Remote Sensing Systems. QSCAT/SeaWinds winddata products are produced by the National Aeronauticsand Space Administration (NASA) Scatterometer

Projects, and distributed by NASA/Physical Oceanogra-phy Distributed Active Archive Center (PO.DAAC).These data are available on their web sites. The spatialresolution of all the data is 0.25° and the reference levelof the wind speed is 10 m. In this study we used the level3 product of the SeaWinds wind speed, and simply aver-aged all the above-mentioned wind speed data for eachday.

5.2 Regression coefficients and ∆SST mapsThe regression coefficients in Eq. (3) were deter-

mined using the wind speed averaged only during 09–15LST (Tables 2 and 3). However, in order to make dailymean maps that completely cover the area from 60°N to60°S and from 80°E to 160°W, we had to collect the sat-ellite-derived wind speed data throughout a whole day.Therefore, a new set of regression coefficients was deter-mined in this section using the daily mean wind speedinstead of the daytime-mean wind speed. The regressioncoefficients for the skin ∆SST and the 1-m-depth valueare shown in Tables 4 and 5, respectively. The correla-tion coefficients are lower than those when the daytime-mean wind speed is used (Table 6). This means that thewind speed at the time when insolation is strong is moreimportant in evaluating ∆SST. Furthermore, it is expectedthat the accuracy of the evaluation declines as ∆SST in-creases since fewer data points are used for the regres-sion.

Figures 12 and 13 show the maps of daily peak solarradiation and mean wind speed on 27 July 1999, respec-tively. Both data sets were averaged in each 0.25° × 0.25°bin. The skin and 1-m-depth ∆SSTs evaluated from thesedata are shown in Fig. 14. The spatial resolution of ∆SST

U > 2.5 ms–1 U ≤ 2.5 ms–1

a 3.2708 × 10–6 5.6814 × 10–6

b –7.9982 × 10–2 4.0052 × 10–1

c –1.3329 × 10–6 –3.9637 × 10–6

d 7.3287 × 10–2 –3.6700 × 10–1

U > 2.5 ms–1 U ≤ 2.5 ms–1

a 2.3989 × 10–6 1.9361 × 10–6

b 5.7289 × 10–2 1.4576 × 10–2

c –9.2463 × 10–7 –4.1966 × 10–7

d –1.4236 × 10–1 –1.0322 × 10–1

Wind speed Depth Correlation coefficient Root mean square error (K)

Daytime mean Skin 0.919 0.271-m-depth 0.938 0.13

Daily mean Skin 0.854 0.351-m-depth 0.920 0.14

Table 4. As Table 2, except that U is a daily mean. Table 5. As Table 3, except that U is a daily mean.

Table 6. Statistics of ∆SST evaluated by the regression equation.


Fig. 14. Map of ∆SST (K) on 27 July 1999 evaluated fromdaily peak solar radiation and daily mean wind speed bythe regression equation. (a) Skin, (b) 1-m-depth.

Fig. 12. Daily peak solar radiation (Wm–2) on 27 July 1999produced from GMS/VISSR data.

Fig. 13. Daily mean wind speed (ms–1) on 27 July 1999 pro-duced from QSCAT/SeaWinds, TMI and SSM/I data.

is also 0.25°. Areas with large ∆SST almost correspondto weak-wind areas. We can see patches of the skin ∆SSTof more than 3.0 K in the tropics and the mid-latitudes ofthe northern hemisphere. The skin ∆SSTs at the centersof the patches are up to 4.0–5.0 K, and the 1-m-depth∆SSTs in these patch areas are about 1.5 K. (Note thatthe evaluated skin ∆SST has an RMS error of 0.35 K.)

6. ConclusionsOur purpose in this study was to evaluate the SST

diurnal variation in a simple manner. We first simulated

∆SST with a one-dimensional model using buoy-observedmeteorological data and solar radiation produced fromGMS/VISSR data by Tanahashi et al. (2001). The model-simulated ∆SST agrees well with the in situ value whenthe latter is less than 1.5 K. On the other hand, in caseswhen the in situ ∆SST is over 1.5 K, the former is smallerthan the latter, and the difference between them becomeslarger as the in situ ∆SST increases. It can be inferredthat the platform effect described by Kawai andKawamura (2000) causes the large difference when a largetemperature gradient develops near the sea surface underclear and calm conditions. By adding artificial mixing inthe sea surface layer the model ∆SST becomes closer tothe in situ value.


Since we could be certain that the model results arereliable, we then derived a regression equation to evalu-ate ∆SST from U and PS only. Our regression equation issimilar to that of Webster et al. (1996). However, the termdescribing daily mean precipitation rate was not includedin our equation, and (PS)2 is used instead of PS. ∆SST isapproximately proportional to In(U) and (PS)2. Althoughthe skin ∆SST evaluated by our equation is much greaterthan that found by Webster et al. (1996), our greater skin∆SST is not inconsistent with the observation results pub-lished by other researchers. We determined another setof the regression coefficients for the 1-m-depth ∆SSTsince the reference depth of satellite-derived SST is usu-ally 1 m.

The regression equation was used in practice to ob-tain ∆SST distribution over a wide area. We made dailymaps of peak solar radiation, and daily mean wind speedby simply averaging SSM/I, TMI and SeaWinds windspeed data. The result showed that some wide patchy ar-eas where the skin ∆SST exceeds 3.0 K can appear in thetropics and the mid-latitudes in summer.

AcknowledgementsThe moored buoy data around Japan used in this

study were collected by the Japan Meteorological Agency,and are distributed by the Japan Meteorological BusinessSupport Center. The global drifting buoy data set is pro-duced and distributed by the Marine Environmental DataService in Canada. We would like to acknowledge EtienneCharpentier of the Data Buoy Co-operation Panel(DBCP), Meteorological Service of NZ Ltd., the Bureauof Meteorology in Australia, Environment Canada and theNaval Oceanographic Office in the United States for pro-viding us with information about drifting buoys. The SSM/I and TMI wind data are produced by Remote SensingSystems and sponsored in part by NASA’s Earth ScienceInformation Partnerships (ESIP): a federation of infor-mation sites for Earth science; and by the NOAA/NASAPathfinder Program for early EOS products; principalinvestigator: Frank Wentz. The QSCAT/SeaWinds winddata products are produced by the National Aeronauticsand Space Administration (NASA) ScatterometerProjects, and are distributed by NASA/Physical Oceanog-raphy Distributed Active Archive Center (PO.DAAC). Wewould like to acknowledge Syuichi Tanahashi for pro-viding us with solar radiation data derived from GMS/VISSR. The present study is supported by ADEOS-I andADEOS-II projects of the National Space DevelopmentAgency of Japan.

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