Validation of Daily Amplitude of Sea Surface Temperature Evaluated with a Parametric Model Using Satellite Data

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1 Journal of Oceanography, Vol. 59, pp. 637 to 644, 003 Short Contribution Validation of Daily Amplitude of Sea Surface Temperature Evaluated with a Parametric Model Using Satellite Data YOSHIMI KAWAI* and HIROSHI KAWAMURA Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Aoba, Sendai , Japan (Received 1 July 00; in revised form 3 February 003; accepted 10 February 003) The authors have verified a regression model for the evaluation of the daily amplitude of sea surface temperature ( SST) proposed by Kawai and Kawamura (00). The authors investigated the accuracy of satellite data used for the evaluation and showed that SST error caused by satellite data error is less than ±0.7 K. The evaluated SSTs were compared with in situ values. Its root-mean-square error is about 0.3 K or less, except for a coastal region, and it has a bias of more than +0.1 K in the tropics. This bias can be removed by considering latent heat flux. Keywords: Validation, sea surface temperature, daily amplitude, parametric model, latent heat flux. 1. Introduction Sea surface temperature (SST) is one of the principal variables in the study of both the oceans and the atmosphere. More accurate SST values with a higher spatial and temporal resolution are now needed for numerical models and studies on warming/cooling processes in the upper ocean or the atmosphere. The Global Ocean Data Assimilation Experiment (GODAE), which is an international endeavor to develop operational ocean analysis and prediction systems for the global oceans, needs SST products with a spatial resolution of better than 10 km and a temporal resolution of 4 hours or less (Smith, 001; Donlon, 00). GODAE will utilize such SST products in order to properly constrain the upper ocean circulation and thermal structure. A proper treatment of the daily variation of SST is one of the important issues for the development of high-quality SST products with a fine temporal resolution. Several researchers have developed methods to estimate the diurnal amplitude of SST ( SST) (e.g., Price et al., 1987; Webster et al., 1996). Kawai and Kawamura (00) (hereafter referred to as KK0) simulated the diurnal SST variation using buoy data and satellite-derived solar radiation. They also proposed a parametric model * Corresponding author. kawai@ocean.caos.tohoku. ac.jp Copyright The Oceanographic Society of Japan. to evaluate SST from peak solar radiation and daily mean wind speed. The spatial distribution of SST can be obtained with their parametric model using satellite data only. The purpose of this study is to validate satellitederived data used for the SST evaluation, and SST evaluated by the KK0 model. An explanation of the KK0 parametric model and the data used here are given in Section. We validate the accuracy of the satellitederived wind speed and solar radiation in Section 3. Section 4 compares the evaluated SST with the in situ value and discusses the reason for a bias (mean of the errors) between them found in the tropics. In Section 5 we describe some improvements to the KK0 model to adapt it to the tropics.. Model and Data.1 Parametric model KK0 proposed the following equation to evaluate SST: = ( ) + [ ( )]+ ( ) [ ( )]+ () 1 SST a PS b ln U c PS ln U d, where a, b, c and d are regression coefficients, PS is the daily peak solar radiation, and U is the daily mean wind speed. They first simulated SST using a one-dimensional numerical model developed by Kawai and Kawamura (000), buoy-observed meteorological data and satellitederived hourly solar radiation produced by Tanahashi et 637

2 (a) Table 1. Statistics of the comparisons between daily mean satellite wind speed and in situ values. Buoy type No. Bias (m/s) Scatter (m/s) RMS (m/s) JMA TAO (b) Fig. 1. Positions of buoy observations used for validations from July 1999 to September 000. (a) Wind speed and SST validations. Asterisks, solid circles and plus signs represent JMA buoys, drifting buoys and TAO buoys, respectively. Solid lines represent 14 N and 14 S. (b) Solar radiation validation. Dots represent TRITON buoys. al. (001). When simulating 1-m-depth SST, they forcibly increased the eddy diffusion coefficients between the surface and 1-m depth in the numerical model in order to obtain 1-m-depth temperature affected by buoyinduced turbulence. The regression coefficients of Eq. (1) were determined using the model-simulated SST, buoyobserved U and satellite-derived PS.. Data a. Satellite-derived data PS and U can be obtained from satellite observations. Solar radiation derived from Geostationary Meteorological Satellite/Visible Infrared Spin Scan Radiometer (GMS/ VISSR) data was used here (Tanahashi et al., 001). The GMS/VISSR observation area is 80 E 160 W and 60 N 60 S, and the product has a spatial resolution of We averaged the hourly solar radiation in each grid. This study used the wind speed products of the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) satellite s Microwave Imager (TRMM/MI or TMI) and the SeaWinds instrument aboard the QuikSCAT satellite (Wentz, 1997; JPL/PO.DAAC, 001). We simply averaged all of these wind speed data in each grid and used it as U. The daily peak solar radiation and mean wind speed in grids from July 1999 to September 000 were used to evaluate SST through Eq. (1). b. Buoy data The following in situ SST data were used to verify SST evaluated from the satellite data: the global drifting-buoy dataset distributed by the Marine Environmental Data Service (MEDS) in Canada, the data of the Tropical Atmosphere Ocean (TAO) buoys in the equatorial area, and the Japan Meteorological Agency (JMA) buoys. The MEDS dataset included part of the TAO data, and these were excluded to avoid overlapping. The JMA buoys were moored in the seas around Japan until the middle of 000. The moored buoys all observed wind speed, wind direction, air temperature, humidity and SST. The MEDS dataset does not include humidity. We selected only the buoys that had anemometers and SST sensors at 1-m depth for this study. The locations of the buoys used for the validations of wind speed and SST are shown in Fig. 1(a). We cannot compare the satellite daily peak solar radiation with the in situ values at these buoy sites since they did not observe hourly solar radiation. In this study we investigated the accuracy of the satellite solar radiation using in situ data observed with the Triangle Trans- Ocean buoy Network (TRITON) buoys instead. The TRITON buoys are deployed in the tropical western Pacific (Fig. 1(b)). Wind speed and direction, air temperature, relative humidity, atmospheric pressure, precipitation and short-wave radiation are measured hourly at the buoys (Kuroda, 001). We did not use the TRITON data for the validation of SST because the SST sensors of the buoys are at 1.5-m depth, not 1.0 m. 3. Validation of Satellite-Derived Wind Speed and Solar Radiation We first examined the accuracy of the satellite-derived daily mean wind speed. Wind speeds measured with buoy sensors at various heights were converted to the equivalent neutral wind speeds at a height of 10 m: 638 Y. Kawai and H. Kawamura

3 Bias RMS Error (W /m ) Scatter In situ daily peak sola r r adiation ( W/m ) Fig.. Statistics of the error of satellite daily peak solar radiation as a function of in situ value (W/m ). TRITON-buoy data are used as in situ data. The statistics are calculated in bins of 100 W/m. [ ] ( ) ( )= ( ) U 10m u ln 10 / z0 / k, where u is the friction velocity, z 0 is the surface roughness length and k is the von Kármán constant (0.4). u and z 0 were computed by iteration using buoy-observed meteorological data (Fairall et al., 1996). Table 1 shows the comparison of the daily mean wind speed between the satellite and in situ data. We can obtain daily mean wind speed from the satellite data with a Root-Mean- Square (RMS) of less than 1.3 m/s. These results are almost the same as the validation result of instantaneous wind speed (Wentz, 1997; Ebuchi et al., 00). We then compared the satellite-derived daily peak solar radiation and in situ values observed from July 1999 to September 000. There were 855 matchups in total. The satellite data have an RMS error of 95.3 W/m with a bias of W/m compared with the in situ data. Figure shows the bias, RMS and scatter (standard deviation of the errors) of the satellite data. Though the satellite data agree well with the in situ values in the range 800 < PS < 1000 W/m, the error becomes greater as PS becomes smaller. For PS < 600 W/m the bias and the scatter reach W/m and W/m, respectively. Furthermore, when PS exceeds 1000 W/m the satellite value is smaller than the in situ one by about 55 W/m on average. Kawamura et al. (1998) investigated the accuracy of the satellite solar radiation over the Pacific Ocean off the Sanriku coast between 33 N and 43 N. They showed that the hourly satellite solar radiation values agreed well with in situ ones throughout the entire range with an RMS error of W/m and a bias of Fig. 3. Maximum SST error ( δ( SST) ) as a function of SST (K) (the right side of (3)) W/m. The satellite-derived solar radiation data used here clearly have a greater error with a systematic bias in the tropical western Pacific. By contrast, du Penhoat et al. (00) reported that solar radiation values derived from GMS-4 data were too low for cloudy conditions and too high for clear-sky conditions in this region. We need to improve the satellite solar radiation algorithms using more abundant in situ data. We estimated the uncertainty in SST introduced by the use of the satellite-derived wind speed and solar radiation. The estimation error of SST is expressed by the following equation, derived from Eq. (1): b+ c( PS) δ( SST) δu + ( PS) { a+ c( ln U) } δ( PS), U () 3 where δu is the error of daily mean wind speed, δ(ps) is that of peak solar radiation and δ( SST) is that of SST caused by δu and δ(ps). For δu we used the RMS error of 0.99 m/s at the TAO sites. The satellite-derived PS has a large PS-dependent bias in the tropical western Pacific. Since the purpose here is to estimate the effect of the standard random error of the satellite data on SST, we adopted the scatters shown in Fig. as δ(ps). The error in SST caused by the bias of PS is mentioned in the next section. Here we use the regression coefficients obtained by KK0 for 1-m-depth SST (their table 5). SST and its errors were calculated at 1.0-m/s intervals of U from 1.0 to 10.0 m/s, and 100-W/m intervals of PS from 50 to 1050 W/m. Under a clear and calm condition in the tropics (PS = 1050 W/m, U = 1.0 m/s), the uncer- Validation of Diurnal Amplitude of SST 639

4 Table. Statistics of the comparisons between evaluated SSTs and in situ values. Upper and lower values are the results using Eqs. (1) and (4), respectively. Region Buoy type No. Bias (K) Scatter (K) RMS (K) Japan Sea JMA South of Japan JMA Extratropics ( 14 N/S) Drifting buoys Tropics (14 S 14 N) Drifting buoys Tropics (8 S 8 N) TAO tainties of the satellite PS and U can cause SST errors of ±0.16 K and ±0.45 K, respectively. The maximum δ( SST) derived from the standard random errors of U and PS is always less than 0.7 K (Fig. 3). This means that most of SST errors are expected to be less than ±0.7 K. The ratio of the maximum δ( SST) to SST is low as the wind is weak and insolation is strong. Actual RMS error and scatter of evaluated SST should be smaller than the maximum δ( SST) because the first and second terms on the right-hand side of Eq. (3) are not always cumulative; they also cancel each other out. 4. Validation of SST Evaluated from Satellite Data Then we compared model-evaluated 1-m-depth SST with the in situ value for each type of buoy. The results are shown in Table. As mentioned in the previous section, most of the SST errors are within ±0.7 K, while there are also many errors of lower than 0.7 K in the Japan Sea (Fig. 4(a)) and some outliers can be seen in Fig. 4(b). According to KK0, one can infer that these large errors and greater scatters at the JMA-buoy sites would be due to horizontal heat advection, which cannot be represented in a one-dimensional process. The evaluated SST agrees with the in situ values with an RMS of about K except for the Japan Sea. The biases are nearly zero at the JMA-buoy site south of Japan and drifting-buoy points in the latitudes equal to or higher than 14 S or 14 N, which we call the extratropics hereafter. However, the SST has a warm bias when compared with the drifting-buoy value in the tropics within 14 S 14 N and the TAO-buoy value. Figure 5(a) shows that the SST has a clear trend of being greater than the in situ values in the range SST < 1.0 K. Since SST decreases as the wind becomes stronger, the positive bias of the wind speed shown in Table 1 can never cause the positive bias of SST. The satellite PS has a large positive bias at the TRITON-buoy sites when PS is small. We corrected PS by subtracting the bias shown in (a) Ev aluated SST (K) Ev aluated SST (K) (b) Japan Sea (J MA buoy) South of Japan (J MA buoy) In si tu In situ SST (K) SST (K) Fig. 4. Comparison of SSTs evaluated with the parametric model (1) with in situ values observed with the JMA buoys (K). Dot lines represent errors of ±0.7 K. (a) Japan Sea and (b) south of Japan. Fig. from PS and evaluated SST at the TAO-buoy sites using the bias-corrected PS. The bias and scatter after the correction are +0.3 K and 0.0 K, which are nearly the same as the values before the correction because most of PS distribute between 800 and 1000 W/m. This means 640 Y. Kawai and H. Kawamura

5 be greater than is assumed in the KK0 model at the same wind speed. Greater air-sea latent heat flux suppresses the rise of SST. Considering the dependence of SST on latent heat flux is needed for a more accurate evaluation in the tropics. We do not discuss sensible heat flux here because it is usually much smaller than latent heat flux, and the mean sensible heat flux in KK0 was about 13 W/m, which is about 13% of mean latent heat flux. (a) Tropics (8 S 8 N, Ev a lua t ed SST (K) TAO bu oys) Consideration of Latent Heat Flux in SST Evaluation Based on the fact set out above, we tried to include latent heat flux as a variable in the SST model. Because latent heat transfer to the air must cool the sea surface, we replaced the solar radiation term in Eq. (1) with the term of solar radiation minus latent heat flux: 5. In situ SS T (K) (b) Tropics (8 S 8 N, SST = a( MS + Hl + e) + b[ln(u )] TAO buoys ) + c( MS + Hl + e) [ln(u )] + d, Ev a lua t ed SST (K) In situ SS T (K) Fig. 5. Comparison of SSTs evaluated with (a) the parametric model (1), and (b) the revised model (4) with in situ values observed with the TAO buoys in the tropics (K). Dot lines represent errors of ±0.7 K. that, whatever the main reason for the SST bias might be, it is not PS error. Most of the SST data used to determine the regression coefficients were within 10 N 40 N and 10 S 45 S (see figure 1 in KK0). Equation (1) does not include airsea turbulent heat flux directly, but implicitly assumes average atmospheric conditions for the entire region. The effect of air-sea turbulent heat flux is expressed only in the parameter U. Hence, a difference in average atmospheric conditions in the boundary layer between the tropics and extratropics can be a reason for the bias in the tropics. The average of air-sea specific humidity differences of all data used in KK0 was 5. g/kg. On the other hand, the mean value at the TAO-buoy sites in this study is 6.35 g/kg, and 78% of the data are greater than 5. g/kg. Hence, latent heat flux in the tropics tends to ( 4) where e is a constant to prevent (MS + H l + e) from being negative, H l is the daily mean latent heat flux (upward is negative) and MS is the daily mean solar radiation (both in W/m). PS was replaced with MS in order to be in harmony with the use of the daily mean value of latent heat flux. When a time series of satellite solar radiation was incomplete, MS was calculated after completing the time series by temporal interpolation or extrapolation. The constant e was set to W/m since almost all of (MS + Hl) used here were greater than W/m. Even if this (MS + Hl) falls below W/m, there is no problem since in this case SST becomes zero because of the high wind speed. The regression coefficients in Eq. (4) were determined using the model-simulation data described in KK0 (Table 3). If SST calculated through Eq. (4) became negative, it was set to zero. Near-surface atmospheric specific humidity (Qair), which is necessary to calculate H l, can be obtained from SSM/I and TMI observations. Schlüssel et al. (1995) proposed an improved method relating Q air directly with brightness temperatures measured with SSM/I. Although the accuracy of the Schlüssel et al. method is better than other methods using satellite-derived precipitable water, we adopted Liu s (1986) method in this study for the convenience of our data processing. The precipitable water products derived from SSM/I and TMI data were used to obtain Qair. We calculated H l with the method by Fairall et al. (1996) using the satellite-derived Qair and the National Center for Environmental Prediction (NCEP) Reynolds optimally interpolated weekly SST product (Reynolds and Smith, 1994). Daily mean SST was obtained by temporal interpolation. Validation of Diurnal Amplitude of SST 641

6 (a) RMS Table 3. Regression coefficients of Eq. (4) for 1-m-depth SST. U >.5 m/s U.5 m/s Error (W /m ) Bias Scatter a b c d In situ daily m ean sola r radiation (W /m ) (b) RMS Error (W /m ) Bias Fig. 7. Maximum SST error ( δ( SST) ) as a function of SST (K) (the right side of (5)). In situ laten t h eat flux (W/m ) Fig. 6. Statistics of the errors of (a) daily mean solar radiation and (b) daily mean latent heat flux as a function of in situ value (W/m ). The statistics are calculated in bins of 50 W/m. TRITON-buoy data and TAO-buoy data are used for the comparison of solar radiation and latent heat flux, respectively. The validation results of SSTs evaluated with the revised model (4) are listed in Table, and the scatter plot at the TAO-buoy sites is shown in Fig. 5(b). The new evaluated SSTs agree with the in situ values as well as those evaluated with Eq. (1) in the extratropics, and we no longer see such noticeable bias in the tropics as appeared in Fig. 5(a). Including the effect of latent heat flux explicitly in the parametric model successfully improved the bias. Figure 6 shows the comparison of the satellite-derived MS and H l with in situ values. The satellite MS is overestimated in the entire range, and its RMS error and bias are 36.3 W/m and +9.4 W/m, respectively. The accuracy of MS becomes worse as the in situ MS becomes smaller, like PS in Fig.. The SST bias decreases to +0.0 K in the case that MS is corrected by subtracting the bias. The RMS error and bias of the satellite H l throughout the entire range is 50.3 W/m and +.4 W/m, respectively. The satellite Q air derived in this study has a scatter of 1.84 g/kg, and the effect of the Q air error on H l becomes greater as H l increases. We evaluated SST using the in situ H l rather than the satellite value. The bias and scatter of these SSTs were K and 0.15 K, respectively (the bias of MS was not corrected). These are not very different from the results obtained when the satellite value is used. We also tested the sensitivity of SST to the satellite-derived parameters. δ( SST) is expressed by the following equation: ( ) b+ c MS+ Hl + e δ( SST) U δu ( l ){ + ( )} ( ) ( ){ + ( )} ( ) + MS + H + e a c lnu δ MS + MS + H + e a c ln U δh, 5 l l 64 Y. Kawai and H. Kawamura

7 where δh l is the error of daily mean latent heat flux. SST and each term in (5) were calculated at 1.0-m/s intervals of U from 1.0 to 10.0 m/s, 50-W/m intervals of MS from 50 to 300 W/m, and 50-W/m intervals of H l from 5 to 75 W/m. We adopted the scatters shown in Fig. 6 as δ(ms) and δh l. The values of the first and second terms in (5) are equal to or smaller than those in (3) at the same SST. The values of the δh l term are all less than 0.35 K. Figure 7 shows that the maximum δ( SST) is always less than 0.7 K, like that in Fig Conclusions We have verified the regression model for SST derived by KK0. SSTs were evaluated though the regression equation using the solar radiation derived from GMS/ VISSR data, and the wind speed of SSM/I, TMI and SeaWinds in the region 80 E 160 W and 60 S 60 N. We first examined the accuracy of the satellite data used for the SST evaluation. The satellite daily mean wind speed had a scatter of about m/s and a bias of less than +0. m/s. The daily peak solar radiation derived from GMS/VISSR data had an RMS of 95.3 W/m with a bias of W/m compared with the TRITON-buoy data in the tropics, and its error was greater as solar radiation declined. We estimated SST error caused by the standard error of these satellite data and concluded that the SST error is expected to be less than ±0.7 K at most. The evaluated SSTs were then compared with the in situ values observed with the drifting and moored buoys. The evaluated SSTs agreed well with the in situ values in the latitudes equal to or higher than 14 S or 14 N, while some data in the coastal or near-coastal areas had very large derivations due to horizontal heat advection. The bias in the extratropics was almost zero except for the Japan Sea. On the other hand, the SSTs evaluated in the tropics within 14 S 14 N had a warm bias of about K. It can be considered that the reason for the bias is the difference of latent heat flux between the tropics and extratropics. Air-sea specific humidity difference in the tropics is usually larger than in the extratropics. Hence, latent heat flux in the tropics tends to be greater than in the extratropics at the same wind speed. Greater latent heat flux takes more heat from the sea surface, and it reduces the SST rise. We improved the SST model by including satellite-derived latent heat flux in it. Q air was obtained through Liu s (1986) equation using precipitable water derived from SSM/I and TMI data. The SSTs evaluated with the new equation (4) agreed with the in situ values as well as those with the former equation (1) in the extratropics, and had a bias less than ±0.1 K even in the tropics. The satellite latent heat flux had a scatter of about 50 W/m, which does not have a serious effect on the SST evaluation. Acknowledgements The moored buoy data around Japan used in this study were collected by the Japan Meteorological Agency and are distributed by the Japan Meteorological Business Support Center. The global drifting buoy dataset is produced and distributed by the Marine Environmental Data Service in Canada. The TAO Project Office of the Pacific Marine Environmental Laboratory has been managing the TAO buoys and distributing the data. The TRITON buoy data were provided us by the Japan Marine Science and Technology Center. The SSM/I and TMI products are produced by Remote Sensing Systems and sponsored by the NASA Pathfinder Program for early Earth Observing System (EOS) products, and NASA s Earth Science Information Partnerships (ESIP): a federation of information sites for Earth Science; and by NASA s TRMM Science Team. The QSCAT/SeaWinds wind data products are produced by NASA Scatterometer Projects, and distributed by NASA/Physical Oceanography Distributed Active Archive Center (PO.DAAC). The NCEP Reynolds SST product is also distributed by NASA/PO.DAAC. We also would like to express our appreciation to Hiroyuki Tomita of Tokai University for his advice on satellitederived latent heat flux. This study is supported by ADEOS-II projects of the National Space Development Agency of Japan (NASDA), and the Category 7 of MEXT RR00 Project for Sustainable Coexistence of Human, Nature and the Earth. References Donlon, C. J. (00): The GODAE high resolution SST pilot project Strategy document. Marine Environment Unit, Joint Research Centre, Ispra, Italy, 47 pp. [On-line document available at: Strategy-v1.5.pdf] du Penhoat, Y., G. Reverdin and G. Caniaux (00): A Lagrangian investigation of vertical turbulent heat fluxes in the upper ocean during Tropical Ocean-Global Atmosphere/Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE). J. Geophys. Res., 107, / 001JC Ebuchi, N., H. C. Graber and M. J. Caruso (00): Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data. J. Atmos. Oceanic Technol., 19, Fairall, C. W., E. F. Bradley, D. P. Rogers, J. B. Edson and G. S. Young (1996): Bulk parameterization of air-sea fluxes for tropical ocean-global atmosphere coupled-ocean atmosphere response experiment. J. Geophys. Res., 101, JPL/PO.DAAC (001): SeaWinds on QuikSCAT Level 3 Daily, Gridded Ocean Wind Vectors (JPL SeaWinds project) (Version 1.0). JPL Document D-0335, Jet Propulsion Laboratory, Pasadena, CA. [On-line document available at: podaac.jpl.nasa.gov/quikscat/qscat_doc.html] Validation of Diurnal Amplitude of SST 643

8 Kawai, Y. and H. Kawamura (000): Study on a platform effect in the in situ sea surface temperature observations under weak wind and clear sky conditions using numerical models. J. Atmos. Oceanic Technol., 17, Kawai, Y. and H. Kawamura (00): Evaluation of the diurnal warming of sea surface temperature using satellite-derived marine meteorological data. J. Oceanogr., 58, Kawamura, H., S. Tanahashi and T. Takahashi (1998): Estimation of insolation over the Pacific Ocean off the Sanriku coast. J. Oceanogr., 54, Kuroda, Y. (001): TRITON: Present status and future plan. Japan Marine Science and Technology Center, Yokosuka, Japan, 31 pp. [On-line document available at: Liu, W. T. (1986): Statistical relation between monthly mean precipitable water and surface-level humidity over global oceans. Mon. Wea. Rev., 114, Price, J. F., R. A. Weller, C. M. Bowers and M. G. Briscoe (1987): Diurnal response of sea surface temperature observed at the long-term upper ocean study (34 N, 70 W) in the Sargasso Sea. J. Geophys. Res., 9, Reynolds, R. W. and T. M. Smith (1994): Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7, Schlüssel, P., L. Schanz and G. Englisch (1995): Retrieval of latent heat flux and longwave irradiance at the sea surface from SSM/I and AVHRR measurements. Adv. Space Res., 16(10), Smith, N. (001): Report of the GODAE high-resolution SST Workshop (GODAE Report No. 7), 30 Oct. 1 Nov. 000 Ispra Italy. Bureau of Meteorology, Australia, 66 pp. [Online document available at: GODAE/frames.html] Tanahashi, S., H. Kawamura, T. Matsuura, T. Takahashi and H. Yusa (001): A system to distribute satellite incident solar radiation in real-time. Remote Sens. Environ., 75, Webster, P. J., C. A. Clayson and J. A. Curry (1996): Clouds, radiation, and the diurnal cycle of sea surface temperature in the tropical western Pacific. J. Climate, 9, Wentz, F. J. (1997): A well-calibrated ocean algorithm for SSM/ I. J. Geophys. Res., 10, Y. Kawai and H. Kawamura