Retrieval of Sea Surface Temperature from TRMM VIRS

Transcription

1 Journal of Oceanography, Vol. 59, pp. 245 to 249, 2003 Short Contribution Retrieval of Sea Surface Temperature from TRMM VIRS LEI GUAN 1,2 *, HIROSHI KAWAMURA 1 and HIROSHI MURAKAMI 3 1 Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Sendai , Japan 2 Ocean Remote Sensing Institute, Ocean University of China, Qingdao , China 3 Earth Observation Research Center, National Space Development Agency of Japan, Harumi Island Triton Square X-23, Harumi, Chuo-ku, Tokyo , Japan (Received 3 June 2002; in revised form 29 July 2002; accepted 30 July 2002) The Visible and Infrared Scanner (VIRS) aboard the Tropical Rainfall Measuring Mission (TRMM) is a five-channel radiometer with wavelength from 0.6 to 12 µm. Daily sea surface temperature (SST) data from VIRS were first produced at the National Space Development Agency (NASDA) for comparison with SST from TRMM Microwave Imager (TMI). In order to obtain accurate high spatial resolution SST for the merging of SST from infrared and microwave measurements, new SST retrieval coefficients of the Multichannel SST (MCSST) algorithm were generated using the global matchups from VIRS brightness temperature (BT) and Global Telecommunications System (GTS) SST. Cloud detection was improved and striping noise was eliminated. One-year global VIRS level-1b data were reprocessed using the MCSST algorithm and the advanced cloud/noise treatments. The bias and standard deviation between VIRS split-window SST and in situ SST are 0.10 C and 0.63 C, and for triple-window SST, are 0.06 C and 0.48 C. The results indicate that the reprocessing algorithm is capable of retrieving high quality SST from VIRS data. Keywords: SST, TRMM VIRS, cloud detection, removal of striping. 1. Introduction The objective of the Global Ocean Data Assimilation Experiment (GODAE) is to demonstrate the practicality and feasibility of routine, real-time global ocean data assimilation and prediction (Smith and Lefebvre, 1997). GODAE requires global high-resolution sea surface temperature (SST) in near real time for validation purpose and assimilation into ocean models. The new generation satellite SST products are based on the merging of satellite infrared and microwave measurements (Guan and Kawamura, 2002). The Tropical Rainfall Measuring Mission (TRMM) is a joint mission of the National Space Development Agency (NASDA) of Japan and the National Aeronautics and Space Administration (NASA) of the United States, designed to measure rainfall and energy exchange in tropical and subtropical regions of the world (Kummerow et al., 1998). Both the Visible and Infrared Scanner (VIRS) and Microwave Imager (TMI) aboard TRMM are capa- * Corresponding author. leiguan@orsi.ouqd.edu.cn Copyright The Oceanographic Society of Japan. ble of providing SST measurements. TMI can measure accurate SST under the clouds (Shibata et al., 1999; Wentz et al., 2000; Kachi et al., 2001). The spatial resolution of TMI SST is relatively coarse, about 50 km. VIRS is a five-channel radiometer with bands in the wavelength range from 0.6 to 12 µm, which is similar to the Advanced Very High Resolution Radiometer (AVHRR) aboard the National Oceanic and Atmospheric Administration (NOAA) Polar-orbiting Operational Environmental Satellites. The spatial resolution of VIRS is 2 km at nadir and 3 km at the 45 scan angle (Kummerow et al., 1998). VIRS SST was first derived at the Earth Observation Research Center (EORC), NASDA for the comparison with TMI SST (Kachi et al., 2001). The daily mean VIRS SST products with a spatial resolution of were generated. The bias and RMSD are 0.12 C and 0.77 C compared with in situ SST data obtained from the Global Telecommunications System (GTS). The cloud-free measurements of SST from TMI and high spatial resolution measurements from VIRS provide us the capability to investigate new generation SST from one platform. In order to merge SSTs from VIRS and TMI, one needs high spatial resolution VIRS SST data. The 245

2 purpose of the present study is to retrieve high spatial resolution SST of high quality from VIRS radiance data. The VIRS data have been reprocessed using improved methods. 2. Data VIRS Level-1B data were provided by the Earth Observation Center (EOC) of NASDA. The VIRS Level- 1B products, VIRS Radiance data, were written in Hierarchical Data Format (HDF). The radiance in each channel, geolocation, scan time, local direction, calibration information etc. were included in the Level-1B products. The data used in this study were acquired during the period May 1999 to April Drifting and moored buoy data reported though GTS during the same period were used to obtain the SST retrieval coefficients and validate the reprocessed products. 3. Retrieval of VISR SST SST was retrieved using Multichannel SST (MCSST) algorithm at EORC (Kachi et al., 2001). It is given by SST = a0 + a1t11 + a2( T11 T12 )+ a3( T11 T12 )( secθ 1) + a ( T T )+ a ( T T ) sec θ 1, ( ) () where a 0, a 1, a 2, a 3, a 4, a 5 are constants, T 3.7, T 11, T 12 are brightness temperature (BT) of 3.7 µm, 11 µm and 12 µm channels, θ is satellite zenith angle. The coefficients, a 4 and a 5, are set to zero for the split-window algorithm. The coefficients, a 0, a 1, a 2, a 3, a 4, a 5, were determined by regressing VIRS BT on the National Meteorological Center (NMC) weekly global SST data set. Figures 1(a) and (b) show the VIRS split-window and triple-window (a) (b) (c) (d) Fig. 1. SST images extracted from orbit on 2 Jan. 2000: (a) original split-window SST; (b) original triple-window SST; (c) reprocessed split-window SST; (d) reprocessed triple-window SST. Fig. 2. Location of the matchups. 246 L. Guan et al.

3 SST extracted from orbit on 2 January 2000 using the algorithm developed at EORC. The striping in the images is significant. Moreover, the difference between the two images is obvious, viz., the split-window SST is warmer than the triple-window SST. GTS buoy SST was used to evaluate the split-window and triple-window algorithms, respectively. Matchups were selected within the temporal window of 30 minutes from VIRS SST and buoy SST during May, August, November 1999 and February The bias and standard deviation of the split-window algorithm are 0.06 C and 0.73 C, and those of the triple-window algorithm are 0.28 C and 0.53 C. The result is consistent with the difference shown in Figs. 1(a) and (b), i.e., negative bias exits in the triple-window algorithm. The purpose of our study is to generate high quality VIRS SST, suitable for merging with TMI SST. Improvements are made to the algorithm. Fig. 3. The histogram of buoy SST of the matchups. 3.1 Estimation of new SST retrieval coefficients First, more accurate matchups were generated using VIRS BT and GTS buoy SST. The VIRS BT, satellite zenith angle, solar zenith angle and universal time arranged in ascending and descending maps were produced first. GTS buoy data in the corresponding bin of cloud-free VIRS data within the temporal window of 30 minutes were selected. The SST calculated from the original coefficients was used as the first-guess SST. The data were rejected if the difference between buoy SST and firstguess SST was greater than 3 C. The matchups were generated during the first and third weeks in May 1999, August 1999, November 1999 and February Figure 2 shows the geographical distribution of the matchups. A histogram of the matchup buoy SST is shown in Fig. 3. The matchups represent the characteristics of the whole TRMM coverage. The forms of MCSST split-window and triple-window algorithms expressed in Eqs. (2) and (3) were used (Kiddwell, 1998). Split-window SST for both daytime and nighttime: SST = a + a T + a ( T T )+ a T T ( )( θ ) sec Triple-window SST for nighttime: ( 2) SST = a0 + a1t11 + a2( T3. 7 T12)+ a3 sec θ 1. 3 ( ) () The coefficients in the two equations were estimated by multiple regression using the matchups generated from the VIRS data and GTS buoy data. The results are shown in Table 1. Figures 1(c) and (d) show the reprocessed SST images. The results indicate that the disparity of SST derived from the original split-window and triple-window algorithms was removed when the new split-window and triple-window algorithms were applied to the same data. 3.2 Cloud detection Figure 4(a) shows an example of the VIRS SST extracted from orbit acquired on 4 January 2000 using the original EORC processing. Striping noise appears in the image. The removal of striping in the SST will be discussed next. Here we only focus on the removal of artificial striping cloud. Some pixels were flagged falsely as clouds due to the striping noise in BT, especially in the 11 µm and 12 µm channels. Modifications were done to remove the false cloud detection due to the striping in the 11 µm and 12 µm channels. First, estimated SST minus previous 9-day mean of VIRS SST data and reflectance of 0.63 µm channel were used to screen the cloud coarsely (Kachi et al., 2001). Secondly, the BT data of 11 Table 1. Split-window and triple-window SST coefficients. SST coefficients Number of matchups a 0 a 1 a 2 a 3 Split-window Triple-window Retrieval of Sea Surface Temperature from TRMM VIRS 247

4 (a) (b) Fig. 4. SST images extracted from orbit on 4 Jan. 2000: (a) original SST; (b) reprocessed SST. µm and 12 µm were averaged using 5 5 neighboring cloud-free values. To minimize the mixing of the average of cloudy and cloud-free pixels, only those pixels within 0.2 K temperature difference of the center pixel were included. If the number of pixels included in the average was less than 3, the center pixel was flagged as cloud. The smoothed BT values of 11 µm and 12 µm were then used for the following cloud tests (Kachi et al., 2001). An additional cloud test shown in Eq. (4) was used during the nighttime (Sakaida et al., 2000, b = 2.0, c = 1.0): T 2T T b and T 2T T c 4 + < + > ( ) Taking account of TRMM coverage with more water vapor content, the value of c was increased to 2.0 K. 3.3 Removal of striping in SST images Figures 1(a) and 4(a) show significant striping in the split-window SST images. To remove the striping, the calculated SSTs were smoothed using the following equation, ( ) ( ) SST = T + SST T n n Here, n is 5 for split-window SST retrieval and 3 for triple-window SST retrieval in the current reprocessing scheme. Figures 1(c), 1(d) and 4(b) show the reprocessed SST images. The striping of SST was reduced significantly and most of the false striping clouds were eliminated. 4. Validation of VIRS SST The VIRS Level-1B data during the one-year period were reprocessed by the parallel computing server, Exemplar/HPUX at Tohoku University to generate VIRS swath SST and mapped SST products. Figures 1(c), 1(d) and 4(b) are examples of the high-spatial resolution SST images extracted from swath products, which show fine structures such as fronts and filaments around the western boundary currents. The GTS buoy SST was used to quantitatively evaluate the accuracy of reprocessed SST products. Matchups were generated in the second week of each month during the one-year period. These periods were different from those used in the regressions of the SST retrieval coefficients, i.e., the matchups for the coefficient retrieval and evaluation are independent. The results are shown in Fig. 5. The mean and standard deviation of the difference between VIRS split-window SST and buoy SST are 0.10 C and 0.63 C. For triple-window SST, the bias is 0.06 C and the standard deviation is 0.48 C. The negative bias in the triple-window SST was removed using the reprocessing algorithm. The standard deviations of the reprocessed SST are lower than the original one, which benefits from the removal of striping. 248 L. Guan et al.

5 of 0.10 ± 0.63 C for the split-window algorithm and 0.06 ± 0.48 C for the triple-window algorithm. The accuracy of reprocessed VIRS SST products is comparable to NOAA/NASA AVHRR Pathfinder SST (Kilpatrick et al., 2001). The products will be used for the merging of satellite SST measurements and other purposes. Acknowledgements The study was supported by ADEOS and ADEOS-II projects of NASDA. The authors would like to thank EOC of NASDA for the provision of TRMM VIRS data and JMA for GTS buoy data. This study is partly supported by the Category 7 of MEXT RR2002 Project for Sustainable Coexistence of Human, Nature and the EarthRR2002 Symbiosis Project of MEXT Japan. Fig. 5. Comparisons of VIRS SST and buoy SST: (a) splitwindow SST, bias = 0.10 C, Std. Dev. = 0.63 C; (b) triplewindow SST, bias = 0.06 C, Std. Dev. = 0.48 C. 5. Summary This paper presents the algorithm for reprocessing VIRS data. First, new SST retrieval coefficients of the MCSST algorithm were generated using global matchups from VIRS data and GTS SST. An additional cloud test was used for nighttime processing. The influence of striping in BT on cloud detection and SST retrieval was eliminated. VIRS SST products with high spatial resolution were generated using the reprocessing algorithm. Comparison of VIRS SST and GTS SST reveals an accuracy References Guan, L. and H. Kawamura (2002): SST availabilities of satellite infrared and microwave measurements. J. Oceanogr. (accepted). Kachi, M., H. Murakami, K. Imaoka and A. Shibata (2001): Sea surface temperature retrieved from TRMM Microwave Imager and Visible Infrared Scanner. J. Meteorol. Soc. Japan (submitted). Kiddwell, K. B. (1998): NOAA Polar Orbiter Data User s Guide (TIROS-N, NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11, NOAA-12, NOAA-13 AND NOAA- 14) November 1998 Revision. NOAA/NESDIS/NCDC. Kilpatrick, K. A., G. P. Podestá and R. Evans (2001): Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database. J. Geophys. Res., 106, Kummerow, C., W. Barnes, T. Kozu, J. Shiue and J. Simpson (1998): The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Ocean. Technol., 15, Sakaida, F., J. Kudoh and H. Kawamura (2000): A-HIGHERS the system to produce the high spatial resolution sea surface temperature maps of the western north Pacific using the AVHRR/NOAA. J. Oceanogr., 56, Shibata, A., K. Imaoka, M. Kachi and H. Murakami (1999): SST observation by TRMM Microwave Imager aboard Tropical Rainfall Measuring Mission. Umi no Kenkyu, 8, (in Japanese with English abstract). Smith, N. and M. Lefebvre (1997): The global Ocean Data Assimilation Experiment (GODAE). Monitoring the Oceans in the 2000s: An Integrated Approach, International Symposium, Biarritz, October 15 17, Wentz, F. J., C. Gentemann, D. Smith and D. Chelton (2000): Satellite measurements of sea surface temperature through clouds. Science, 288, Retrieval of Sea Surface Temperature from TRMM VIRS 249