Examination of the Merged Sea Surface Temperature Using Wavelet Analysis

Transcription

1 Journal of Oceanography, Vol. 6, pp. 843 to 852, Examination of the Merged Sea Surface Temperature Using Wavelet Analysis KOHTARO HOSODA* and HIROSHI KAWAMURA Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Aoba, Sendai , Japan (Received 2 September ; in revised form 29 October ; accepted November ) In the previous study, merged sea surface temperature (SST) dataset called New Generation SST has been produced from several infrared and microwave satellite SSTs through an objective mapping. Here we examine the merged SST by comparison with moored buoy SST at m depth, which is treated as true sea surface temperature. Comparison between wavelet spectra of merged and buoy SSTs shows that the former have larger amplitudes than those of the latter, which is partly explained as an aliasing effect due to TRMM Microwave Imager (TMI) aboard Tropical Rainfall Measuring Mission (TRMM) sampling on merged products. Coherency between wavelet-decomposed merged and buoy SSTs has high values in autumn and low ones in winter to spring. In winter, phase differences between them are positive, meaning that wavelet components of merged SST lag those of buoy SST. Reasons for delay and low coherency are: () seasonal components of merged SSTs are strongly affected by a lack of infrared SSTs due to clouds in winter, and (2) small-scale oceanic features, undetectable by coarse-resolution microwave SSTs, are blurred by the merging process. Improvements of merging methodology are discussed with regard to present study results. Keywords: Merged SST, satellite SST, wavelet analysis, Kuroshio recirculation region.. Introduction Sea surface temperature (SST) fields are useful as a boundary condition for atmospheric and ocean models, as tools for the analysis of oceanographic phenomena, and for comparison with SSTs produced by ocean models. For these purposes, one requires SST datasets with no missing observations, including those for the area under clouds. In addition, the spatial and temporal resolution requirements of the dataset are km and day for numerical weather prediction (WMO World Weather Watch Fourth Long Term Plan, ) and km and less than day (diurnal cycle resolved) for ocean data assimilation (Le Traon et al., ). Satellite observation offers the great advantage of spatial and temporal coverage when producing such SST datasets. Mariano and Brown (992) and Reynold and Smith (994) developed the SST dataset with no missing * Corresponding author. hosoda@eorc.jaxa.jp Present address: Earth Observation Research and Application Center, Japan Aerospace Exploration Agency, -8-, Harumi, Chuo-ku, Tokyo 4-6, Japan. Copyright The Oceanographic Society of Japan. data from infrared measurements, the original spatial resolution of which is as high as km. However, infrared measurements are limited by the presence of cloud. In spite of their low spatial resolution, microwave measurements are critical for reproducing SST fields under cloudcover in cases such as SST-cooling produced by hurricanes (Wentz et al., 2). Guan and Kawamura () evaluated SST availabilities (existing rate to total observations) quantitatively for AVHRR (Advanced Very High Resolution Radiometer) aboard NOAA (National Oceanic and Atmospheric Administration), S-VISSR (Stretched- Visible Infrared Spin Scan Radiometer) aboard GMS (Geostationary Meteorological Satellite), and TMI (TRMM Microwave Imager) aboard TRMM (Tropical Rainfall Measuring Mission). Their daily SST availabilities were calculated in overlapping coverage from 2 N to 38 N and 2 E to 6 E. Annual-mean SST availabilities of AVHRR, S-VISSR and TMI are 48%, 56% and 78%, respectively. There are large seasonal variations in infrared measurement availabilities, e.g., low availability in winter (December to March). Therefore, merging of microwave and infrared measurements is needed for high-resolution and high-availability SST datasets to fulfill the requirements. 843

2 Guan and Kawamura () and Kawamura () produced one of the pilot products of high-resolution datasets which include the SST under cloud cover. This merged product, which is called New Generation SST (NGSST) version., utilizes high spatial-resolution SST products from infrared measurements of NOAA/ AVHRR and GMS/S-VISSR and a cloud-free SST product from microwave measurement of TRMM/TMI. As a merging methodology, they used objective mapping, or optimal interpolation (Gandin, 963; Bretherton et al., 976), which is based on the correlation function of the SST field. In the Kuroshio region, this product comprises two datasets: daily mean SST dataset (Guan and Kawamura,, Oct. 999 Sep. 2), and the daily minimum SST dataset (Kawamura,, Oct. 999 May 2). Daily peak solar radiation and daily mean wind speed are used to remove SST diurnal variations by the method proposed by Kawai and Kawamura (). Guan and Kawamura () demonstrated the highquality of the version. NGSST products. Its bias is. C and its root mean square error (RMSE) is.95 C against the daily mean SST of buoy measurements around Japan. The daily minimum SST has been compared with buoy measurement at 6: a.m. local time, which shows that the bias and RMSE are. C and.5 C, respectively. These products are available through the internet for application development, especially for numerical ocean forecasts ( ~ adeos/sst/). However, their evaluation is only a simple statistical comparison. The temporal characteristics are not well understood. Further investigation of temporal and spatial characteristics of the cloud-free high-resolution SST product is needed to improve the quality of the new SST product. This study investigates its temporal variation features. The variation is partly caused by insertion of artificial treatments of satellite SSTs, the merging procedure and atmospheric/oceanic disturbances. Comparison with in situ SST is useful for distinguishing the causes of errors in merged SST, which will contribute to the improvement of merged products. A time series of SST variation without breaks is assigned to each grid point because the new merged SSTs are cloud-free daily images. As true SST, we use in situ SST observations at a fixed platform, i.e., an ocean observation buoy robot operated by the Japan Meteorological Agency (JMA). The reference point where the buoy was deployed is in the recirculation gyre of the Kuroshio south of Japan. In wintertime, large upward heat fluxes decrease SST significantly in this area because of the winter monsoon blowing from the Eurasian continent. In contrast, solar radiation and monsoon winds from the southeast oceanic region gradually warm the ocean in summertime. Synoptic atmospheric disturbances with periods from several Fig.. Region of NGSST version. with annual mean SST field. Cross point is the location of the buoy operated Japan Meteorological Agency. Box shows the region of Figs. 7 and. days to several tens of days pass from west to east to cause temporal SST variations. Oceanic phenomena also generate SST variations; mesoscale eddies and frontal disturbances in the recirculation gyre transfer temperature anomalies. Does the merged SST product represent these phenomena? Section 2 describes the data and the wavelet analysis method, which is introduced to investigate the temporal characteristics of SST fields. Subsection 3. discusses wavelet power spectra and wavelet-coherency/phase to elucidate differences between in situ SST and merged SST time series. The seasonality of this difference is discussed in Subsection 3.2. Oceanic phenomena influencing the merged SST product are investigated in Subsections 3.3 and 3.4. A summary of the study and a discussion of way to improve the merging method are given in Section Data and Method 2. Data For the Kuroshio region (2 38 N, 2 6 E: Fig. ) south of Japan, two types of NGSST ver.. product were generated: the daily minimum (Kawamura, ) and daily mean SST (Guan and Kawamura, ) with spatial resolution of.5.5. The SST fields were produced by an objective mapping method for SST data within three days. The correlation function for objective mapping is assumed as homogeneous and isotropic with e-folding scales of L = km spatially and T = 3 days temporally. This product uses SST data observed by NOAA/AVHRR, GMS/S-VISSR and TRMM/TMI in the Kuroshio region. Sakaida et al. (2) and Tanahashi et al. (2) developed Multi-Channel SST (MCSST) methods for AVHRR and S-VISSR infrared measurements near Japan, respectively. The method is based on a simple relationship between brightness temperature difference of split- 844 K. Hosoda and H. Kawamura

3 window channels and the amount of atmospheric vapor. For cloud detection, they used some threshold tests for the ratio of albedo (only for AVHRR), brightness temperature, and brightness temperature differences. Thresholds and coefficients for tests are determined empirically. It should be noted that these infrared SST datasets might be contaminated with residual atmospheric effects because there is a nonlinear relationship between atmospheric water vapor and brightness temperature difference (e.g., Walton, 988; Emery et al., 994). Shibata et al. (999) developed an algorithm for retrieving SST from TRMM/ TMI at GHz. The influences of water vapor and clouds are removed using GHz and 37 GHz measurements. They also pointed out that microwave measurement at GHz has low sensitivity toward SST if SST is less than C. Diurnal warming of SST is another obstacle to deriving daily SST fields from satellite observation. Kawai and Kawamura () obtained a regression equation to evaluate diurnal amplitude of SST at the skin and m depth from daily mean wind speed and daily peak solar radiation. They also showed that diurnal SST amplitude exceeds 3. C in tropics and mid-latitudes in summer. Kawamura () utilized their regression equation to remove diurnal SST variation using daily peak solar radiation observed by GMS and daily mean wind speed combined from SSM/I, TMI, and SeaWinds. We use daily minimum SST (hereafter, merged SST ) in the present study to minimize diurnal effects on SST variation in analysis. The merged SST was produced for October 999 to May 2. The ocean observation buoy in the sea off Shikoku (28.9 N, 34.9 E), operated by the Japan Meteorological Agency, records marine meteorological observations every three hours. We used observed water temperature at m depth, which is treated as true SST (hereafter, buoy SST ). It is expected that buoy SST variability is identical to that of merged SST because satellite SST retrieval algorithms are usually tuned by regression against buoy SST data. This procedure converts satellite measurements of skin temperature to buoy measurements of bulk temperature. Webster et al. (996) pointed out that the difference between skin SST and bulk SST is up to.3 C and that there is a small phase lag between their diurnal cycle. These might cause a difference between the satellite-derived SST and in situ SST. We produced daily time series of the buoy SST at 6: a.m. local time and of the merged SST at the buoy location for further examination. The time series is given for 99% of the study period because the merged product is cloud-free. Linear interpolation is used for days when merged SSTs are not obtained. Figure 2 shows the time series of the buoy and merged SST. Less sensitivity of TMI at lower temperature is not significant at this buoy point because these SST( C) SST are more than 5 C. The bias and standard deviation between the buoy SST and merged SST are.5 C (merged SST is greater than buoy SST) and.79 C, respectively. 2.2 Wavelet analysis method We employed a wavelet transform to decompose SST time series in order to investigate the merged-sst temporal characteristics. The continuous wavelet transform is commonly used in the geosciences (Meyers, 993; Lau and Weng, 995; Torrence and Compo, 998). The Morlet wavelet transform is used as the mother wavelet in this study; it has been used to analyze SST by Murakami and Kawamura (). Details of wavelet analyses methods are described in Torrence and Compo (998) and Murakami and Kawamura (). The Morlet mother wavelet comprises a complex ( exponential modulated by a Gaussian, e t 2 / s 2 + i ω t / s ), where t is the time, s is the wavelet scale, and ω (ω = 6 in this study) is a non-dimensional frequency. The wavelet scale is almost identical to the corresponding period if ω = 6. The Paul wavelet, another complex wavelet transform, was tested and yielded the same quantitative results as the present study. The wavelet power spectrum is defined as the squared absolute value of the wavelet transform; it gives a measure of the time series at each scale (period) and each time. Wavelet-coherency and phase difference are defined by Torrence and Webster (999) as follows. Given two time series X(t) and Y(t), with their wavelet transforms W X (t, s) and W Y (t, s), the cross-wavelet spectrum is defined as, ( )= ( ) ( ) ( ) XY X Y W t, s W t, s W t, s, Bias:.5 K RMSE:.79 K Fig. 2. Time series of merged SST (solid line) and buoy SST (broken line) at 28.9 N, 34.9 E for October 999 to May 2. where indicates a complex conjugate. The squared wavelet coherency is defined as the squared absolute value of Wavelet Analysis of Merged Sea Surface Temperature 845

4 Period(days) Period(days) (a) Buoy SST (b) Merged SST(min) (K) 2 day - Period(days) Period(days) (c).5.75 Coherency (d) Phase Fig. 3. (a) Wavelet power spectrum of the buoy SST. The thick contour shows the 95% confidence level, estimated from the corresponding red-noise spectrum. (b) As (a), but of the merged SST. (c) Wavelet squared coherency between the buoy SST and the merged SST. Contour interval is.25. The thick contour is the 95% confidence level estimated from Monte Carlo simulation of wavelet coherency between, sets (two each) of white time series. (d) Phase difference between the buoy SST and the merged SST. Contour interval is.5 (radian), and dashed line denotes the negative phase difference (merged SST leads). White dashed lines in all figures indicate the cone of influence. the smoothed cross-wavelet spectrum, normalized by the smoothed wavelet spectra: R 2 ( t, s)= XY ( ) s W t, s X Y ( ) ( ) s W ts, s W ts, 2, ( 2) where < > indicates smoothing in both time and scale. In the present study, time and scale smoothing are moving averages over 5 days and 3 scales, respectively. In addition, the wavelet-coherency phase difference is given as Θ( ts, )= tan XY s W ( t, s) ( ), XY s W ( t, s) ( ) () 3 where and represent the imaginary and real parts, respectively. Henceforth, the term coherency refers to the squared wavelet coherency. This wavelet method is applied to the time series of the buoy and merged SSTs in the following section. If temporal variation of merged SST gives a good representation of the in situ SST variation, wavelet-coherency between the buoy and merged SSTs becomes significantly high and the phase difference is small. The time series of merged SST is insufficiently long to analyze annual variation of SST because its period is eight months. To remove low-frequency variation, the eighth-order Butterworth-type band-pass filter is used for both time series. The half-power points of the filter are 2 and 6 days. The SST time series have been also analyzed without the filter, which yields identical results to those presented in the following section. 3. Result 3. Wavelet spectra Figures 3(a) and (b) show wavelet power spectra for the buoy and merged SST. An autoregressive process model with lag- correlation of.7 was used for the 5% significance level for wavelet spectra in the figures. Wavelet power spectra for both buoy and merged SST are large at about one-week and 2 3 day periods. Murakami (979) reported that atmospheric parameters (pressure and temperature) have two spectrum peaks of 4 6 and 2 3 days in this area in winter. Murakami and Kawamura () pointed out that SST observed at buoy in the sea off Shikoku shows corresponding wavelet spectra peaks in wintertime. The present results also confirms the SST responses to atmospheric forcing, as pointed out in previous studies. Wavelet power spectra for merged SST are larger than those for buoy SST in almost all periods and times, especially at a period of a few days and a 6 day period. 846 K. Hosoda and H. Kawamura

5 25 Spectrum Density(K 2 day). UTC (min) Period (day) Fig. 4. Time-averaged wavelet spectra (global wavelet spectra) of the buoy SST (solid line) and the merged SST (broken line). This is clear in Fig. 4, which shows time-averaged wavelet spectra over the whole period: this is referred to as a global wavelet spectrum by Torrence and Compo (998). One reason might be contamination of atmospheric effects in SSTs. If cloud detection fails, or if the nonlinear relationship between atmospheric water vapor and brightness temperature difference is large, estimation from infrared channels is lower than exact SST. Satellite-observed SST might have high variances at a few-day to -day periods because the time scale of these effects is one meteorological feature. The TMI observation times at the buoy location change slowly (Fig. 5, top panel). It should be noted that the sampling might cause an aliasing effect on the SST time series. To investigate the aliasing effect, we examine an artificial SST time series. The SST time series, derived from buoy SST, is simulated observation at the TMI observed time. Since the buoy observation was recorded at three hourly intervals, linear interpolation is used to estimate SST between the observation. Power spectra for this artificial SST time series and the buoy SST time series were calculated after resampling (Fig. 5, bottom panel). The power of the former, rather than the latter, is enhanced at around a few days and at 4 9 days. This experiment indicates that an aliasing effect of TMI SST observation may be included in merged SSTs. The wavelet-coherency and phase difference of buoy SST and merged SST are shown in Figs. 3(c) and (d). A positive phase difference indicates that buoy SST variation leads to that of merged SST. The 5% significance level for coherency was calculated from Monte Carlo simulation of, sets of two white noise time series with identical length to buoy SST and merged SST. High coherency shows that merged SSTs are good representation of real SST variations detected by buoy Spectrum Density (K 2 day) Period (day) Fig. 5. Top: Observation times of TRMM/TMI at the buoy position (in UTC). Bottom: Spectra of the three-hourly buoy SST (dashed line) and of the buoy SST re-sampled at the TMI observation times (solid line). The vertical bar shows 9% confidence level. observations, regardless of wavelet power amplitude. There are highly coherent peaks at 3-day and 2-day periods in January and February, while the amplitudes of corresponding wavelet spectra are small (Figs. 3(a) and (b)). This result suggests that overall temporal variations of in situ SSTs are well reproduced in merged SST, even when temperature fluctuations are not so large as those of the merged SST. In contrast, wavelet coherency at a period of a few days is small during the studied period; this fact can be attributed to the aliasing effect and atmospheric effects in SSTs. Other aliasing peaks at a period of about a week also have lower wavelet-coherency values. In general, wavelet-coherency is high in autumn and low in winter to spring. However, phase differences become positive at 2 3 day periods when wavelet-coherency is significantly high in winter. It is about π/4, which corresponds to 3 5 days delay of merged SST against buoy SST. Low coherency in spring is also caused by oceanic phenomena, as discussed in Subsection 3.4. Wavelet Analysis of Merged Sea Surface Temperature 847

6 3.2 Seasonal variation Apart from wavelet analysis described above, a time series of difference between buoy SST and merged SST (Fig. 6(a)) is useful for evaluating the difference between them. The difference changes seasonally though its amplitude is small. It is positive (merged SST is higher than buoy SST) in winter (December to March), while it is negative in spring (April and May). These tendencies have some relationship to cloud cover, which has a strong influence on infrared SST availability with large seasonal variation (Guan and Kawamura, ). Figure 6(b) shows SST availability of AVHRR around the buoy using the same definition as that given by Guan and Kawamura () and the clear-sky rate, which is defined as Cl, where Cl is the dailymean total cloud cover rate derived from NCEP-NCAR Reanalysis daily product (Kistler et al., ). The AVHRR availability correlates well with the clear-sky rate, but there are some differences due to the cloud detection scheme of AVHRR-SST and the definition of Cl. In addition, the different horizontal resolution of Cl (about 2 2 around this region) might be a reason for the difference. Figures 6(a) and (b) show that positive biases of difference in winter correspond well with low values of AVHRR availability (infrared SST availability), which suggests that the amount of cloud affects merged SST values. This is not explained by cloud contamination and atmospheric correction error to infrared SST because these effects make SST colder than in situ SST. Sakaida (personal communication) studied the seasonality of the bias between AVHRR SST and in situ SST observed by JMA and drifting buoys. The number of match-up data is more than 7, in the region 5 35 N, 7 E from Jan. 999 to Dec. 2. He pointed out that the bias shows a small seasonal change: The bias is about zero from January to March, while it is.3 C (AVHRR SST is lower than in situ SST) in autumn. Although this result implies that the residual atmospheric contamination in the MCSST method affects the merged SST timeseries seasonally, it is necessary to consider other effects of seasonal variation between the merged SST and the buoy SST since it is larger than C. Next, we address another influence of cloud cover to the merged SST fields. The merged SST product is produced from the SST observed within three days (Guan and Kawamura, ). On a cloudy day, merged SST fields are merged from () low resolution TMI SST, and (2) high resolution infrared SSTs in other clear days. In the cooling (warming) season, infrared SSTs on the previous day are warmer (colder) than the SST on the target day. If infrared data from the previous day are abundant, merged SST data are strongly influenced by these biased data. This phenomenon leads to a phase delay of merged SST variation, which lags the SST (K) (a) (b) Fig. 6. (a) Time series of SST differences defined as the merged SST minus the buoy SST. (b) Data availability of AVHRR SST at the buoy location (shaded) and the clear-sky rate, which is defined as Cl (broken line) where Cl is the total cloud cover rate derived from NCEP-NCAR Reanalysis daily mean product. buoy SST in the cloudy season. Cloud effects on infrared measurements may explain the behavior of merged SST in wintertime by AVHRR and S-VISSR. 3.3 Small scale cold eddy During the study period, a survey of cloud-free SST images revealed a small eddy characterized by cold SST moving around in the Kuroshio recirculation region. The spatial and temporal scales of the eddy are 3 4 km and 3 days, respectively. The SST images in autumn and spring show that this cold eddy propagates westward at 5 6 cm/s speed along 3 N. Figures 7(a) (c) show snapshots of this eddy when it passed through the area around the buoy location. Figures 7(a) and (c) clearly show the horizontal structure of the eddy, while it is ambiguous in Fig. 7(b). The SST at the center of the eddy in Fig. 7(b) exceeds that in Fig. 7(a), which may explain the ambiguous appearance. Time series of buoy and merged SSTs for 3 December 999 are shown in Fig. 7(d) together with oceanic net heat loss due to atmospheric forcing calculated from the NCEP/ NCAR Reanalysis daily product (Kistler et al., ). During 5 27 December, in situ SST in this region decreased because of severe net heat loss at the surface. However, merged SST at the buoy location shows different variation and is generally larger than the buoy SST. 848 K. Hosoda and H. Kawamura

7 (a) 2/ o 35. o 37.5 o 32.5 o 35. o 37.5 o 32.5 (c) 2/ (d) SST ( o c) Fig. 7. (a) (c): Daily snapshot images of the merged SST in the area around the buoy (28.9 N, 34.9 E). Counter interval is.25 C. Thick cross point is the location of the buoy operated Japan Meteorological Agency. In (a) and (c), small crosses (+) superimposed in images show grids in which AVHRR SST are not used for the merging process (AVHRR SST are not obtainable for all points in (b)). (d) Time series of the buoy SST (red line), the merged SST at the buoy position (black line) and the net heat loss of the ocean (blue shaded) derived from NCEP-NCAR Reanalysis daily mean product. (b) 2/ Dec Net Heat flux ( 2 W/m 2 ) To investigate reasons for this difference, grid points, for which AVHRR SSTs are not used for the merging process, are indicated by crosses (+) in Figs. 7(a) and (c). Small areas are crossed in Figs. 7(a) and (c). In contrast, AVHRR SST is not available in the whole area of Fig. 7(b), which means that only low spatial-resolution TMI SSTs is used to generate Fig. 7(b) image. For this reason, the higher TMI-derived SSTs in the outside grids of the eddy are contaminated into merged SSTs at grids in the eddy area (increasing SST at the center of the eddy); this may weaken horizontal SST gradients around it (creating the ambiguous appearance of the eddy in the image). The low sensitivity of TMI GHz toward SST results in a positive bias of TMI SST at less than C. Because the SST observed in this region is more than 2 C, this is not the reason for the positive difference between the merged SST and in situ SST. Actually, the bias between the AVHRR SST and TMI SST in the merged SST region (Fig. ) is.38 C (TMI higher) in SST < 5 C,.34 C in 5 C SST < 25 C and.76 C in SST 25 C. The relationship between AVHRR availability and positive difference of merged SST is not caused by bias of TMI SST because SST in the region discussed is within 5 C SST < 28 C. 3.4 Propagating waves in spring In May 2, buoy SST time series show that a high SST signal with a time scale of 3 days passed through the buoy point, which is shown by index in Fig. 2. However, the merged SST does not show this warming event clearly. This section presents an investigation of this difference between buoy and merged SSTs. Figure 8 is the longitude-time diagram of merged SSTs along 28.9 N. Higher wavenumber components of SST data are extracted by a Gaussian filter with an e- folding scale of 5. This figure shows westward propagating disturbances with higher SST, the spatial and temporal scales of which are about 5 km and 3 days. One of these signals passes through the buoy location (34.9 E) in May, which corresponds to the warming event shown in Fig. 2. Figure 9 shows a time series of buoy-observed temperatures at m, 5 m, and m depths. The warming event is associated with an oceanic structure deeper than m. Kurasawa et al. (983) reported that, from March to May, change of heat content at the buoy point is determined by advection of water masses, which are bounded by sharp front. Warming events caused by propagating disturbances shown in Fig. 8 may correspond to change of heat content due to oceanic phenomena. Wavelet Analysis of Merged Sea Surface Temperature 849

8 Feb. Mar. Apr. May o 35. o 37.5 o 4. o Fig. 8. Longitude-time diagram of the merged SST along 28.9 N. Higher wavenumber components of the SST variations extracted using the Gaussian filter are displayed (see text). Counter interval is.5 C o Figures (a), (b) and (c) show snapshots of daily minima of AVHRR, merged SST, and the difference between them, respectively. Figure (a) shows a streamer with higher SSTs elongated from around 3 N, 34 E to the buoy location. Its zonal width is 5 km. Though a similar feature of high-sst streamer is also shown in Fig. (b), the signal was weakened and its appearance becomes ambiguous. Its tip does not reach the buoy point in the merged SST image in Fig. (b), while the buoy SST does detect its arrival. The SST differences between them are about C around the buoy location, which cor- Temperature( o C) Apr. May Fig. 9. Time series of water temperatures observed by the buoy at m (thick line), 5 m (solid line) and m (dashed line) depths for April to June 2. Jun o (a) (b) 3. o o 32.5 o 35. o 37.5 o 32.5 o (c) 32.5 o 35. o 37.5 o o o 32.5 o 35. o 37.5 o -. Fig.. (a) Daily minimum AVHRR SST image on May 2, (b) Daily snapshot image of the merged SST, and (c) SST difference between the images of (a) and (b). Contour interval in (a) and (b) is.5 C. Cross point in each figures indicates the buoy location. 85 K. Hosoda and H. Kawamura

9 responds to a difference between the merged SST and the buoy SST in Fig. 2. The blurring problem of oceanic phenomena is illustrated by two examples: the cold eddy and the propagating warm disturbance. These can be detected by infrared SST images with higher spatial resolution, but observation under cloud-cover cannot be obtained. This blurring problem is left for future study, along with other obstacles to further improvement of merged SST products in aspects of high-resolution and cloud-free images. 4. Summary and Discussion The temporal characteristics of merged SST product have been investigated by comparison against buoy-observed SST at 28.9 N, 34.9 E. We introduced wavelet analysis to investigate the higher frequency variability. Our conclusions are as follows. () Wavelet spectra revealed that the merged SST has higher variance than the buoy SST. This is partly caused by an aliasing effect by TRMM/TMI, which is almost cloud-free and provides SST information with regular intervals. (2) In general, wavelet-coherency between merged SST and buoy SST is high in autumn and low in winter to spring. High coherency indicates that the merging process works well to represent variation of the true SST, i.e., the buoy observation. The reasons for low coherency in winter and spring have been examined and clarified. (a) Low coherency in winter In addition to low coherency, the difference defined as the merged SST minus the buoy SST is also positive; phase differences at 2 3 day periods are also positive when coherency is significantly high. It can be inferred that these are caused by frequent cloud-cover, which hinders infrared measurement of SST. (b) Low coherency associated with oceanic phenomena in winter and spring Merged and buoy SSTs deviated widely in winter and spring. Examinations of AVHRR snapshot images and other data indicated that: ) the cold eddy of 3 4 km scale is not well identified by merged SST images under cloudy conditions for which only low-resolution microwave measurements are available (merged SST buoy SST); and 2) the oceanic structure of a warm streamer several tens of kilometers in length is blurred through the merging process (merged SST buoy SST). The temporal characteristics of the new merged SST product were revealed throughout this study. These can form the basis for further improvement of a merged SST product. Initially, the aliasing effect introduced from the cloudfree TMI SST should be removed. Introduction of advanced data processing technology may be effective in removing the aliasing because aliasing noises can be characterized. A simple assimilation using some mixed-layer model would decrease the phase delay of the merged SST. However, such a process may be difficult because accurate heat flux data are not easily obtainable, especially for an operational merging in the future. The blurring problem has now been clearly identified. Though the studied area has rather quiet oceanic conditions, propagating disturbances with sharp SST fronts produce large deviations of merged SST from true SST. The blurring problem could be partly mitigated if the spatial resolution of satellite observation is taken into account in the merging process. While errors of observations, or inverse of signal-to-noise ratio, are assumed to be. for all sensors in the version. product, accuracies of data observed by TMI and AVHRR should differ in regard to their spatial resolution. This strategy may improve merged SST product quality. To avoid blurring of oceanic spatial structures, a more beneficial method improvement may entail use of a regionally/seasonally dependent correlation function for SST merging. Correlation function characteristics are very important in the objective mapping method. Bretherton et al. (976) suggested that objective analysis itself can be used to compute the correlation matrix. Although Reynold and Smith (994) obtained decorrelation scales for optimum interpolation of SST, they are useful for a larger scale than the synoptic one. To retain small-scale features such as eddy, frontal waves and meander of currents in the merged SST field, appropriate decorrelation scales for objective analyses should be obtained. The function can be calculated from sequential time series of SST with high spatial and temporal resolution in which we will obtain the signal-to-noise ratio (Reynold and Smith, 994). However, this has proved difficult because high spatial resolution data (AVHRR) has low availability caused by clouds, while high availability data (TMI) offers low spatial resolution. Although the present merged SST product has several weak points, it may be useful to obtain the first set of regional/temporal dependent correlation function. Acknowledgements The authors wish to express their sincere thanks to members of the Physical Oceanography Group at Tohoku University for useful discussion. This study was supported by Special Coordination Funds for Promoting Science and Technology of MEXT, Japan and the MEXT RR Project for Sustainable Coexistence of Human, Nature and Earth (category 7), and partly by the ADEOS-I and ADEOS-II projects of NASDA, Japan. We express our appreciation to the Japan Meteorological Agency for providing the observation data of the Ocean Data Buoy. NCEP Reanalysis data are provided by the NOAA-CIRES Climate Diagnostic Center, Boulder, Corolado, USA, from Wavelet Analysis of Merged Sea Surface Temperature 85

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