Estimation of Insolation over the Pacific Ocean off the Sanriku Coast

Transcription

1 Journal of Oceanography, Vol. 54, pp. 457 to Estimation of Insolation over the Pacific Ocean off the Sanriku Coast HIROSHI KAWAMURA 1,4, SHUICHI TANAHASHI 2,4 and TOMOYUKI TAKAHASHI 3,4 1 Center for Atmospheric and Oceanic Studies, Faculty of Science, Tohoku University, Sendai 980, Japan 2 Science Systems Division, Fujitsu Limited, Nakase, Mihama-ku, Chiba 261, Japan 3 Systems Division, Fujitsu Tohoku Systems Engineering Limited, Ichiban-cho, Aoba-ku, Sendai 980, Japan 4 Sendai Research Center of Telecommunications Advancement Organization of Japan (TAO), Minamiyoshinari, Aoba-ku, Sendai , Japan (Received 31 March 1998; in revised form 22 June 1998; accepted 29 June 1998) Surface solar radiation over the Pacific Ocean off the Sanriku coast has been estimated using Visible and Infrared Spin Scan Radiometer data supplied by the Geostationary Meteorological Satellite 5 for September, 1996 to June, 1997, when the Ocean Color and Temperature Scanner was functioning. The hourly and daily insolation is estimated with a spatial resolution of 0.01-degree grid. The in situ surface short wave radiation obtained by the research vessel, Kofu-Maru belonging to the Japan Meteorological Agency is used for validation of the estimated insolation. It is shown that the estimated hourly and daily insolation has an rms (root mean square) error of 17.05% and 8.13%, respectively, which are the ratios between the rms error (W/m 2 ) and the mean insolation (W/m 2 ). Keywords: Insolation, remote sensing, Visible and Infrared Spin Scan Radiometer (VISSR), Geostationary Meteorological Satellite (GMS), bio-optical interaction. 1. Introduction The Ocean Color and Temperature Scanner (OCTS) aboard ADEOS has observed the distribution of chlorophylla (Chl-a) and sea surface temperature (SST) in the global ocean surface from October 1996 to June The ADEOS mission was planned to contribute to the global environmental monitoring, and one of the roles of OCTS is to provide key parameters for researches of carbon cycle relating to the ocean primary production. Phytoplankton converts dissolved CO 2 into compounds through photosynthesis, and plays an important role in the marine bio-geochemical cycles and the global environment changes. Therefore, a reliable estimate of the primary production in the oceans is desired in order to understand the marine material cycle and to assess the global warming issues. Photosynthesis by phytoplankton is carried out in the euphotic zone, where the sun light penetrates to some degree. The processes of primary production are under the control of light and nutrients in the euphotic zone. Several algorithms for estimating the primary production have been proposed in conjunction with the satellite chlorophyll measurement (e.g., Eppley et al., 1985; Platt, 1986; Balch et al., 1989), and most of them need information about the light levels in water. In order to estimate the primary production in the euphotic zone by using the OCTS products, we need the surface solar radiation (insolation) with spatial and temporal resolutions consistent with those of the OCTS Chla and SST. Since direct measurements of insolation over the oceans are few, it is difficult to produce a reliable insolation field for various oceanographic purposes. A primary factor that controls the insolation is cloud. Therefore, many bulk formulae utilizing the ship reports of cloud cover were developed (e.g., Reed, 1977) and used to estimate the insolation fields for climatic researches. During the last two decades, efforts to estimate the insolation using visible radiometers on board operational meteorological satellites have been continued (e.g., Tarpley, 1979; Gautier et al., 1980, 1984; Kizu, 1995; Dubayah and Loechel, 1997). Since these methods use images from the visible sensors aboard the operational geostationary meteorological satellites, the temporal and spatial coverage of estimated insolation is superior to those achieved by the other methods. Using Visible and Infrared Spin Scan Radiometer (VISSR) aboard the Japanese GMS-3, Kizu (1995) has developed a simple radiative transfer model of the atmosphere to estimate the insolation over the Pacific Ocean. He employed three-hourly GMS visible histogram data binned in 0.25-degree grids. The estimated insolation was compared to the ground measurements made at routine meteorological networks widely scattered in the VISSR observation coverage, and it was shown that rms error is 20% for daily values and 10% for monthly means. Combining direct-reception of satellite data, Copyright The Oceanographic Society of Japan. 457

2 supercomputing and computer networking, a system to distribute satellite products to users in real time has been developed at the Sendai Research Center (Tanahashi et al., 1998). Geostationary Meteorological Satellite data are received every hour, and the insolation images with a highspatial resolution are produced by using a supercomputer. The insolation data over the Tohoku area have been distributed through the internet to the agriculture community. The hourly insolation is obtained for 0.01-degree grids, which meets the requirements of agricultural applications in the northern Japan. The estimated daily values are validated by the ground measurements, showing a root-mean-square error of 12%. In the present study we use the same system for estimation of the insolation over the Pacific Ocean off the Tohoku area. Kizu (1995) compared the estimated results over the ocean with the insolation measurements made at the JMA buoys. However, since the buoy measurements were instantaneous values and the sensors were not well calibrated (Endoh et al., 1987), the comparisons were made qualitatively. Therefore, the insolation estimates based on the method developed by Kizu (1995) have not been validated systematically by using reliable in situ data. Since the present estimation is based on an algorithm that has been tuned against the ground measurements (Tanahashi et al., 1998), the insolation product needs to be verified over the open ocean. Furthermore, spatial and temporal resolutions of the present estimates are further improved from those of Kizu (1995), i.e., hourly insolation estimate for 0.01-degree grid is realized by using an advanced computer system. Since the difference between the resolutions may reflect on its accuracy, quantitative validation of this insolation is required for the application studies. These are the purposes of the present study. During October, 1996 to June, 1997, the sea-truth insolation data were collected by the research vessel, Kofu- Maru, belonging to the Hakodate Marine Observatory of the Japan Meteorological Agency (JMA). We use them for validation. The hourly and daily insolation over the Pacific Ocean off the Sanriku coast is estimated with a spatial resolution of 0.01-degree grids, which corresponds to those of the OCTS regional products. Using the satellite insolation and the OCTS Chl-a and SST, the primary production has been estimated by Ishizaka et al. (1998). 2. VISSR Aboard GMS-5, Processing System, and Algorithm to Estimate the Insolation over the Ocean 2.1 VISSR aboard GMS-5 The VISSR aboard GMS-5 has come into full operation since 21 June, The observational function of VISSR/ GMS-5 has been improved, compared with that of GMS-4, i.e., (1) the visible channel sensor has an improved S/N ratio and sensitivity, (2) split-window infrared channels ( µm and µm) are provided, and (3) a water vapor channel ( µm) has been added (Meteorological Satellite Center, 1996). Characteristics of the new VISSR are summarized in Table 1. The VISSR visible band, which we use to estimate the insolation, has a spatial resolution of 1.25 km at nadir. 2.2 Processing system In order to receive the VISSR data for computation of the insolation products every hour, an automated highperformance system has been developed at the Sendai Research Center (SRC), which is a research laboratory established in 1995 by Telecommunications Advancement Organization (TAO) of Japan and Sendai city. The land-based S-VISSR system (Model TL800, Sea Space Co., Ltd.) for the direct reception of VISSR/GMS-5 data was introduced into SRC in April The insolation is derived through pixel-by-pixel computation for all the received counts, which requires adequate computer resources. The Fujitsu compact supercomputer VX has been employed to conduct the insolation computation. It possesses 2.2 Gflops of vector facility, 2 GB of main memory, and a sophisticated vector compiler. The insolation images, produced within 3.5 minutes after data recep- Table 1. Characteristics of VISSR/GMS H. Kawamura et al.

3 tion, have been distributed via the Internet to the regional agricultural community in real time (Tanahashi et al., 1998). The images can be accessed through the internet within 5 minutes after GMS-data reception ( or.jp/apl). In order to validate the insolation products in the Tohoku area, the SRC has developed the Tohoku Pyranometer Network, consisting of seven automated pyranometers with the data transmission system. The in situ insolation measured by the distributed pyranometers are collected through wireless phones linked to the public line in real time, and enable us to validate the insolation products over the ground. 2.3 Algorithm to estimate the insolation over the ocean Based on work by Gautier et al. (1980, 1984) and by Kizu (1995), a physical retrieval scheme to estimate the insolation using the VISSR visible images has been developed (Tanahashi et al., 1998). First, each pixel of the VISSR image is classified as cloud-free or cloudy using VISSR visible and infrared bands. Then, the clear-sky model or the cloudy sky model is applied to estimate the insolation. According to Kizu (1995), the present models are described as follows and the parameters used in the equations are listed in Table 2 together with their sources. For the clear sky condition, the insolation can be obtained through the following equations; S S = S I + S R + S A, () 1 S I = S τ 0 τ R ( 1 α W ) τ A, ( 2) S R = S τ 0 ( 0.5 ( 1 τ R )) τ A, ( 3) S A = S τ 0 τ R ( 1 α W ) F c ω 0 ( 1 τ A ), ( 4) S = I ( d M / d) 2 cosθ. ( 5) In these formulas, S S is the total insolation, S I the direct irradiance, S R the diffuse irradiance due to Rayleigh scattering and S A the diffuse irradiance due to scattering by aerosols. For the cloudy sky case, the surface insolation is estimated by taking account of the effects of reflection and absorption by the clouds, i.e., S S = (S I + S R + S A ) (1 a A). (6) Table 2. Parameters used in the equations for insolation estimation. Parameters Values References S S total insolation S I direct irradiance Paltridge and Platt (1976) S R diffuse irradiance due to Rayleigh-scattering ditto S A diffuse irradiance due to scattering by aerosols ditto θ solar zenith angle Nagasawa (1985) I solar constant 1367 W/m 2 Frohlich and Wehrli (1981) d M sun-earth distance (annual mean) Spencer (1971) d sun-earth distance ditto τ 0 transmittance due to absorption by ozone Lacis and Hansen (1974) τ R transmittance due to Rayleigh scattering Kizu (1995) τ A transmittance due to attenuation by aerosols Macher (1983) α Ångstrom s turbidity parameter 1.3 Ångstrom (1929, 1930) β Ångstrom s turbidity parameter 0.1, 0.2 Ångstrom (1929, 1930) α W absorptance of water vapor Lacis and Hansen (1974) W precipitable water Chester et al. (1987) T 1 brightness temperature of VISSR IRI 1 T 2 brightness temperature of VISSR IRI 2 T a air temperature (= T 2 2.2) Chester et al. (1987) F c ratio of forward to total scattering by aerosols Robinson (1962) ω 0 single scattering albedo 1 R broad-band reflectance of the GMS visible band 0~1 a insolation attenuation coefficient by cloud 0~1.4 A albedo (= R/cos θ) 0~1 U amount of ozone 0.238~0.414 cm Suzuki (1996) Estimation of Insolation over the Pacific Ocean off the Sanriku Coast 459

4 (c) (a) Fig. 1. (continued). The transmittance due to attenuation by aerosols is given by Macher (1983) as, τ A = α ( α) exp( βm(1.089α )). (7) In this formula, the Ångstrom turbidity parameters are set α = 1.3, and β = 0.1 (for November March) and =0.2 (for April October), which correspond to a visibility of about 28 km and 11 km (Iqbal, 1983). Estimation of the absorptance of water vapor, α W, requires the precipitable water, W, which is derived, using the VISSR IR data, through the splitwindow algorithm (Chester et al., 1987) for cloud-free conditions as: W = (1/0.095)[(1/secθ)ln[(T 1 T a )/(T 2 T a )] 0.025]. (8) The estimated W shows fairly good agreement with radiosonde observations. Using W, the absorptance for the cloudfree conditions are given by (b) Fig. 1. Sea-truth insolation observation points in the Pacific ocean off the Sanriku coast. (a) October to December, 1996, (b) January to March, 1997 and (c) April to June, α W = 2.9 Wm /(( Wm) Wm). (9) The precipitable water is set 3.0 g/cm 2 for cloudy conditions according to the results of our model simulations and Chester et al. (1987). The influence of the precipitable water on the absorptance is significant for the cloud-free conditions, but negligible for the cloudy conditions since the 460 H. Kawamura et al.

5 cloud effects become dominant. This is the main reason for setting the constant value for the cloudy conditions. It is known that designation and classification of the clouds are critical for the retrieval of insolation using both models. In the present scheme, the bispectral threshold technique is used with respect to the visible brightness count (albedo) and the infrared temperature (equivalent blackbody temperature: TBB). In order to determine clear sky radiance as a reference of the threshold technique, visible images at a specific time of day are analyzed to select the minimum visible albedo for each pixel for a year. These extremes are assumed to be the clear sky brightness values for the specific time of day for each month. The threshold albedo value for the clouds designation is set as the clear-sky brightness plus some margin of confidence which is determined through manual cloud designation. TBB of VISSR IR-band 1 (see Table 1) is used together with the visible data for the cloud classification. The cloud absorption coefficient a is set for each of the classified clouds, according to the theoretical calculations by Liou (1976) and the aircraft measurements by Reynolds (1975). Although this model has been developed for insolation estimation over the ground (Tanahashi et al., 1998), it has proved to be applicable for the estimation over the ocean, as shown in Section 3. Therefore, we did not tune the model especially for the ocean insolation. 2.4 Sea-truth insolation and match-up data set The Hakodate Marine Observatory has conducted several observation cruises in a year to monitor the sea south of Hokkaido island and east of Honshu island (north-western Pacific) using the research vessel, Kofu-Maru. During the cruises, the insolation is observed every hour by the thermoelectric pyranometer, the accuracy of which is evaluated as 8% for hourly values (JMA, 1993). The insolation observation points along the cruise tracks for October, 1996 June, 1997 are shown in Fig. 1. Although the observation number was reduced during January March, 1997, the number and coverage are adequate for the present study. We used the observations taken from 00 UT to 07 UT, considering change of day time length and the measurement accuracy of pyranometer and VISSR. The total number of sea-truth insolation data is 583. The Kofu-Maru cruise days, for which observations more than six were carried out, are selected and used to evaluate the estimates of daily-mean insolation. The number of selected days is 76. The match-up data set is produced to validate the estimated insolation; 4-pixels 4-pixels of the GMS visible image around the sea-truth point is extracted from the VISSR data received at the time close to the in situ observation time. The mutch-up is produced as a pair of the extracted GMS-visible image and the sea-truth insolation, and the visible values are converted to the insolation for comparison. 3. Results Figure 2 shows the comparison between the hourly in situ and estimated insolation. It is evident that both of the insolations agree well with each other over a wide range, although they are scattered. Statistics of the comparison for each hour are indicated in Table 3. Because of sun-altitude Fig. 2. Validation of hourly insolation over the Pacific Ocean off the Sanriku coast. Fig. 3. Validation of daily insolation over the Pacific Ocean off the Sanriku coast. Estimation of Insolation over the Pacific Ocean off the Sanriku Coast 461

6 (b) (a) Fig. 4. (a) VISSR/GMS-5 visible image obtained at 01 UT on 26 April, 1997, and (b) insolation image calculated from (a). Fig. 5. Daily insolation image on 26 April, change, the rms error varies with time, ranging from to W/m 2. However, the ratio between the rms error and the mean value (designated as rmse (%) in Table 3) is smaller than 20%, except for 06 and 07 UT. The large error ratio at 07 UT may be attributed to the small mean value due to the low sun altitude. The bias for all the hourly comparison is 3.34 W/m 2, the rms error 17.05%, and the correlation coefficient Both of the hourly insolations are averaged for a day considering the change of day time length, and compared as presented in Fig. 3. The bias is 1.47 W/m 2, and the rms error 8.13%, which is the ratio between the rms error of W/ m 2 and the mean value. The correlation coefficient between them is Since the daily bias is negligible and absolute values of the hourly biases scattering around zero are much smaller than the rms errors (Table 3), we judge that this algorithm, tuned against ground measurements, is applicable for the insolation estimate over the open ocean. The VISSR visible image taken at 01 UT on 26 April, 1997 is shown in Fig. 4(a), and the insolation image calculated from it in Fig. 4(b). ADEOS flew over this area around 01 UT, when the OCTS image capturing phytoplankton blooming was taken. It is almost cloud-free over the sea except for the southern parts. The corresponding insolation image shows that the insolation of W/m 2 covers most of the sea area off the Sanriku coast. Decreases of the 462 H. Kawamura et al.

7 Table 3. Statistics of comparison between the hourly in situ and estimated insolation. Hour (UT) All Obs. number Bias (W/m 2 ) rmse (W/m 2 ) rmse (%) Correlation insolation are seen around the areas where the clouds are located in Fig. 4(b). The estimated insolation indicates an arched pattern, which demonstrates that the solar zenith angle is the primary factor to determine the insolation field. The image of daily-mean insolation for 26 April, 1997 is made by integrating the hourly insolation, and is shown in Fig. 5. The daily mean values have W/m 2 in a wide area of the studied ocean. The zonal feature of insolation decreasing with latitudes is again the reflection of the dailymean solar zenith angle because of the clear day. 4. Conclusions and Discussions Using the models and facilities in the Sendai Research Center, the insolation over the Pacific Ocean off the Sanriku Coast is estimated using VISSR data from GMS5 for September, 1996 to June, The hourly and daily insolation is estimated with a spatial resolution of 0.01-degree grid. The estimated insolation is validated by high-quality in situ sea-truth data, which cover a year and the whole study area. It is shown that the estimated hourly and daily insolation has an rms (root mean square) error of 17.05% and 8.13%, respectively. The insolation estimates based on the method developed by Kizu (1995) is validated systematically throughout this study. Furthermore, it has been proved that the estimation algorithm tuned against the ground measurements is applicable for the estimates over the open ocean. The primary production by phytoplankton is under the control of light and nutrients in the euphotic zone. For example, a simple model, proposed by Platt (1986), is that the primary production in the surface water column is proportional to the products of the vertically integrated Chla and light intensity, which can be obtained from the satellite insolation. Since the satellite Chl-a has a considerable error (exceeding 30%), the present validation results strongly suggest that the estimated hourly and daily insolation have sufficient accuracy for the estimation of primary production. Further discussion in terms of primary production derivation using the estimated insolation is beyond the scope of present study, and is left for future research. Acknowledgements This work has been done as a part of the Multi-point Environment Estimate System Project of the Sendai Research Center of Telecommunication Advancement Organization of Japan, and also a part of the ADEOS Sanriku Campaign. We thank to the Hakodate Marine Observatory for providing high-quality sea-truth insolation data. References Ångstrom, A. (1929): On the atmospheric transmission of sun radiation and on dust in the air. Geografis. ANNal., 2, Ångstrom, A. (1930): On the atmospheric transmission of sun radiation. Geografis. ANNal., 2-3, Balch, W. M., R. W. Eppley and M. R. Abbott (1989): Remote sensing of primary production II. A semi-analytical algorithm based on pigment, temperature and light. Deep-Sea Res., 36, Chester, D., W. D. Robmson and L. W. Uccellini (1987): Optimized retrievals of precipitable water from the VAS Split windows. J. Climate Appl. Meteor., 26, Dubayah. R. and S. Loechel (1997): Modeling topographic solar radiation using GOES data. J. Appl. Meteor., 36, Endoh, M., Y. Kimura and N. Yoshioka (1987): On the accuracy of estimation of global solar radiation using the solar sensor installed on the JMA Marine Meteorological Buoy. J. Meteor. Res., 39, (in Japanese with English abstract). Eppley, R. W., E. Stewart, M. R. Abbott and U. Heyman (1985): Estimating ocean primary production from satellite chlorophyll. Introduction to regional differences and statistics for the Southern California Bight. J. Plankton Res., 7, Frohlich, C. and C. Wehrli (1981): Spectral Distribution of Solar Irradiance from 25,000 nm to 250 nm. World Radiation Center, Davos, Switzerland (cited from Iqbal, 1983). Gautier, C., G. Diak and S. Masse (1980): A simple physical model to estimate incident solar radiation at the surface from GOES satellite data. J. Appl. Meteor., 19, Gautier, C., G. Diak and S. Masse (1984): An investigation of the effects of spatially averaging satellite brightness measurements on the calculations of insulation. J. Appl. Meteor., 23, Iqbal, M. (1983): An Introduction to Solar Radiation. Academic Press, Canada, 390 pp. Ishizaka, J. (1998): Spatial distribution of primary production off Sanriku, northwestern Pacific, during spring estimated by Ocean Color and Temperature Scanner (OCTS). J. Oceanogr., 54, this volume, Japan Meteorological Agency (1993): Measurements of Solar Radiation in Guide to Meteorological Observations on Ground, (in Japanese). Kizu, S. (1995): A study on thermal response of ocean surface layer to solar radiation using satellite sensing. Doctoral The- Estimation of Insolation over the Pacific Ocean off the Sanriku Coast 463

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