Masahisa Kubota

Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO)

We have constructed ocean surface data sets using mainly satellite data and called them Japanese Ocean Flux data sets with Use of Remote sensing Observations (J-OFURO). The data sets include shortwave radiation, longwave radiation, latent heat flux, sensible heat flux, and momentum flux etc. This article introduces J-OFURO and compares it with other global flux data sets such as European Centre for Medium Range Weather Forecasting (ECMWF) and National Center for Environmental Prediction (NCEP) reanalysis data and da Silva et al. (1994). The usual ECMWF data are used for comparison of zonal wind. The comparison is carried out for a meridional profile along the dateline for January and July 1993. Although the overall spatial variation is common for all the products, there is a large difference between them in places. J-OFURO shortwave radiation in July shows larger meridional contrast than other data sets. On the other hand, J-OFURO underestimates longwave radiation flux at low- and mid-latitudes in the Southern Hemisphere. J-OFURO latent heat flux in January overestimates at 10°N–20°N and underestimates at 25°N–40°N. Finally, J-OFURO shows a larger oceanic net heat loss at 10°N–20°N and a smaller loss north of 20°N in January. The data of da Silva et al. in July show small net heat loss around 20°S and large gain around 20°N, while the NCEP reanalysis (NRA) data show the opposite. The da Silva et al. zonal wind speed overestimates at low-latitudes in January, while ECMWF wind data seem to underestimate the easterlies.

Intercomparison of Various Surface Latent Heat Flux Fields

Abstract The Japanese Ocean Flux Data Sets with use of Remote Sensing Observations (J-OFURO) latent heat flux field is compared with the Hamburg Ocean–Atmosphere Parameters and Fluxes from Satellite Data (HOAPS), the Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF), ECMWF, NCEP–NCAR reanalysis (NCEP1), and da Silva et al.s fields. All products qualitatively reveal a similar pattern in the average fields. Although the latent heat fluxes of J-OFURO and GSSTF are quite similar, they are larger than those of HOAPS in the tropical regions. The difference between J-OFURO and the da Silva data is large, and the temporal correlation is extremely low in the Southern Hemisphere. This suggests that the da Silva product hardly reproduces accurate variability in the data-sparse regions. Also the time correlation between J-OFURO and ECMWF or NCEP1 is considerably lower in the Southern Hemisphere than in the Northern Hemisphere. The ECMWF and NCEP1 fields may be affected by the lack of ship observations there. ...

Pleading for the use of biodegradable polymers in favor of marine environments and to avoid an asbestos-like problem for the future

Research results about the movement and accumulation of floating marine debris drifting throughout the world’s oceans are reviewed in this paper. A mechanism for this accumulation and movement is strongly associated with surface currents consisting of the Ekman drift and the geostrophic current, because all floating marine debris is passive to surface currents. The basic published mechanism for the North Pacific is common across the world’s ocean. After marine debris accumulates in the narrow Ekman convergence zone, it is moved to the east by geostrophic currents. The most important thing is that floating marine debris concentrates in some specific regions, independent of the initial quantity of marine debris. In order to resolve this problem and to avoid an asbestos-like problem, the use of biodegradable polymers is important in our daily life.

Inter-comparison and evaluation of global sea surface temperature products

To clarify the characteristics of global sea surface temperature (SST) products, we have compared the Reynolds product with four other products: the Center for Atmospheric and Oceanic Studies (CAOS) SST, the microwave optimum interpolation (MWOI) SST, the merged satellite and in‐situ data global daily (MGD) SST and the real time global (RTG) SST. Furthermore, we have validated these five products with SST data observed by moored buoys. The CAOS SST and the MWOI SST show significant underestimation in several regions. The underestimation is related to the characteristics of the original microwave data. It should be noted that the MGD SST provides the best statistics, although the high‐frequency variations are removed by a low‐pass filter. Moreover, we have investigated the impact of the differences between the SST products on estimated latent heat flux. In regions with strong SST gradients, the average differences are significantly large.

Relationship between an el niño event and the interannual variability of significant wave heights in the north pacific

Abstract A new global I° × 1° gridded monthly significant wave heights (SWHs) dataset was produced by applying an optimum interpolation method (OIM) to the TOPEX/Poseidon (TP) SWH data obtained from 1993 to 2000. We ascertain the high accuracy of this dataset by comparing it with SWH data obtained from the National Data Buoy Center (NDBC). We then investigate the interannual variability of SWH by using the empirical orthogonal function (EOF) analysis and the relationship of interannual variability between SWH and marine surface winds by using linear regression analysis. When an El Niño event occurs, significant changes can be observed in the SWH east of the Philippines (120°E‐135°E, 10°N‐25°N) and off the US West Coast (140°W‐120°W, 35°N‐45°N). The SWH variability east of the Philippines in winter (January‐March) depends strongly on the northerly winds in relation to the appearance of the Pacific‐North American (PNA) pattern. Off the US West Coast, both the appearance of the PNA pattern and the westerly wind associated with the Aleutian low are very important to wintertime SWH variability. On the other hand, SWH in summer (July‐September) is not related to the PNA pattern either east of the Philippines or off the US West Coast. It is interesting to note that summer SWH off the US West Coast is very low compared with the winter SWH, although the wind speed in summer is almost the same as that in winter. This result suggests that the winter westerlies would lead to higher SWH because of the longer fetch.

Sampling error of daily mean surface wind speed and air specific humidity due to Sun-synchronous satellite sampling and its reduction by multi-satellite sampling

To quantify the sampling error of wind speed (W) and the surface air specific humidity (Q a) resulting from Sun-synchronous polar-orbit satellite sampling and the effect of single- and multi-satellite sampling, we compared satellite-simulated data with true daily mean data using buoy data. True daily mean data were obtained by averaging buoy data at all available times over 24 h, while satellite-simulated data were the averages of buoy data sampled at satellite passing times (once or twice each day). The difference between true and satellite-simulated data was defined as the sampling error. The sampling error of the daily mean data of W and Q a depends considerably on the satellite observation time and location. Although the sampling error is fairly reduced if multi-satellite sampling is employed, a noticeable sampling error remains in some cases if a wrong sampling combination is employed. Therefore, multi-satellite data should be carefully used to obtain more accurate global data.

Mechanism of the Seasonal Transport Variation through the Tokara Strait

To investigate an mechanism of the seasonal variation of transport through the Tokara Strait, two numerical experiments with real geometry and wind forcing were carried out. The models are linear barotropic models which are a North Pacific Ocean model and a limited-area model with a fine grid. The seasonal variation of volume transport with a maximum in the summer and a minimum in the autumn could be well reproduced by both models. The results demonstrate the wind stress component normal to a gradient vector of bottom topography is crucial for determining the seasonal variation. The similar seasonal variation widely covers the East China Sea and has a large amplitude near the Tokara Strait. Finally, it can be concluded that winds north of 35°N have little influence on the seasonal response of our model at the Tokara Strait.

A New Cloud Detection Algorithm for Nighttime AVHRR/HRPT Data

A new cloud detection algorithm for nighttime Advanced Very High Resolution Radiometer (AVHRR) data has been developed and applied to a large number of images from various locations around Japan. The algorithm is characterized by a recovery function and the use of a two-dimensional histogram. Results obtained after applying the algorithm are presented and compared with those of previous algorithms. The comparison reveals that the new algorithm appears to be more successful than the previous algorithms.

A statistical method to get surface level air-temperature from satellite observations of precipitable water

Ten-day mean surface level air-temperature from SSMI precipitable water (SSMI-Ta) has been derived and compared with the temperature from two ocean data buoys (Buoy-Ta) of Japan Meteorological Agency (JMA) for a period of six months (July–December, 1988). Statistical relations between air-temperature and mixing ratio, using data from ocean data buoys are used to derive air-temperature from mixing ratio, obtained from SSMI precipitable water. For getting the mixing ratio from precipitable water, regional mixing ratio-precipitable water relations have been used, instead of global relation proposed by Liu (1986). The rms errors (standard deviation of the difference between SSMI-Ta and Buoy-Ta) for two buoy locations are found to be 1.15 and 1.12°C, respectively. Surface level temperature for the two buoy locations are also derived using direct regression relation between Buoy-Ta and precipitable water. The rms errors of the SSMI-Ta, in this case are found to be reduced to 1.0°C.

Retrieval of Surface Air Specific Humidity Over the Ocean Using AMSR-E Measurements

We have developed a new algorithm to estimate the surface air specific humidity over the ocean from AMSR-E data. It should be noted that remarkably reduced random errors of the estimated surface air specific humidity result from using the surface air specific humidity provided by reanalysis data. We validated our new algorithm using independent ship and buoy data. The bias, RMS error, and correlation coefficient of the products obtained using our algorithm for global buoys are 0.38 g/kg, 0.61 g/kg and 0.99, respectively. It should be noted that surface specific humidity having similar accuracy to the reanalysis data near in situ data could be derived from AMSR-E data by the present algorithm.