SP-636 Envisat Symposium



INTRODUCTION OF JAPANESE OCEAN FLUX DATA WITH USE OF REMOTE SENSING OBSERVATIONS (J-OFURO) VERSION 2

Masahisa Kubota (1),Hiroyuki Tomita (2), Kunio Kutsuwada (1), Masatoshi Akiyama (1), Shinsuke Iwasaki (1)

,(1) School of Mar.Sci. and Tech., Tokai Univ., 3-20-1, Shimizu, Shizuoka, Shizuka, JAPAN, 424-8610, Email:Kubota@mercury.oi.u-tokai.ac.jp

(2) Research Institute for Global Change, Japan Agency for Marine and Earth Science Technology, Yokosuka, Japan, Email:tomitah@jamstec.go.jp

INTRODUCTION

The ocean actively exchanges heat, water and momentum with the atmosphere at the ocean surface. The exchanged heat, water and momentum are transported by ocean and atmospheric circulations on a global scale. Since the exchange and transport processes play important roles in the global climate, the estimation of the fluxes between the atmosphere and the ocean and those transports by the ocean and atmospheric circulations are critical for understanding the mechanism of the global climate. However, it is difficult to globally estimate the fluxes and transports by using in situ observation data such as ship observation data because they are extremely sparse in time and space. Recently, we can obtain considerably homogeneous data with high resolution using analysis and satellite data. Therefore, it is considered that analysis and satellite data are suitable for obtaining accurate globally covered fluxes between the ocean and the atmosphere.

We have constructed ocean surface flux data sets mainly using satellite data. The data set named Japanese Ocean Flux data sets with Use of Remote sensing Observations (J-OFURO) has been provided to scientists in the world since 2002 [1] and has been used in many research studies. Recently, new surface flux data sets have been constructed in J-OFURO, thereby upgrading it to version 2 (J-OFURO2). Version 2 has many improvements over version 1 for the estimation of surface fluxes. Here, we introduce the J-OFURO Version 2.

MOMENTUM FLUX

We have constructed gridded products of surface wind/wind stress over the world ocean using satellite scatterometer (QuikScat/SeaWinds). Original swath data by the QuikScat/SeaWinds are referred to [2]. Construction procedures in detail are given by [1] and [3]. In construction of surface wind/wind-stress vector products with spatial resolution of 1°x 1° grid over the almost entire world oceans (60°N-80°S) and daily in time from the scatterometer data which have inhomogeneous distribution in space depending upon the orbital motion of the satellite, we adopt an averaging method using a weighting function varying with time and space. The weighting has non-zero values within a radius of influence around each calculated grid point and magnitudes of decreasing monotonously with the distance between the grid point and each datum point and between maximum (=1.0) on the grid point and zero along the radius. The radius of influence is a parameter of depending on the spatial coherency in wind field with selected time resolution, so generally is taken to be larger in the zonal direction than in the meridional one. We use the radii of influence of 300km and 150km for the zonal and meridional, respectively, directions. The weighting function also depends upon time and has its Gaussian-type character with a maximum at a noon of each calculated day and non-zero values for three days including the previous and subsequent days. By taking each scatterometer wind as the wind vector at 10-m level above the sea surface, we calculate wind-stress vector from the conventional bulk formula. The drag coefficient depending only on wind speed based on [4] is adopted.

The swath data (level 2B) of the QuikScat/ SeaWinds based on the new processing software used for the construction of our new product has been provided by the National Aeronautics and Space Administration (NASA) Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory. This data set involves the following three points: improvement of flagging for rain contamination, improvement of performance at high wind speeds, and supply of level 2 data with higher spatial resolution (12.5km) (See PO.DAAC website). Using this data set, we reconstruct a new gridded product of wind/wind-stress over the world ocean. In our new product, we construct gridded products for additional parameters: scalar-averaged wind speed and wind-stress magnitudes as well as the zonal and meridional components of the wind/wind-stress.

Their reliabilities have been verified by comparisons with in-situ measurements mainly obtained by moored buoys on open ocean [5,6], and it has been revealed that the QuikScat product has relatively high reliability compared with numerical weather prediction (NWP) products (National Center for Environmental Prediction Reanalysis: NRA -1 and 2). Validation study was also made for the open ocean region of the mid- and high-latitudes where there had been few moored buoys using the Kuroshio Extension Observatory (KEO) buoy [7,8]. The results reveal that our products have much smaller mean difference in the study areas than the NWP ones, meaning higher reliability compared with the NWP products.

HEAT FLUX

The temporal resolution of heat flux products is improved from a 3-day mean to a daily mean. Moreover, the data period is extended from 1992–2000 to 1988–2005. The radiation data in J-OFURO1 covers the region of the eastern Indian Ocean and the western and central Pacific Ocean between 60°N and 60°S, 80°E and 160°W because we use only the Japanese geostationary satellite for the estimation of radiation flux. However, in many cases, scientists require global flux data. Therefore, we provide the global net heat flux data, which is estimated by our turbulent heat flux data and International Satellite Cloud Climatology Project (ISCCP) radiation data, in J-OFURO2. However, we have estimated the upward longwave radiation by using the Merged satellite and in-situ data Global Daily (MGD) SST data constructed by JMA. The MGD SST data set has been constructed by using infrared radiation SSTs (National Oceanic and Atmospheric Administration/Advanced Very high Resolution Radiometer), Microwave SSTs (Aqua/ Advanced Microwave Scanning Radiometer-EOS:AMSR-E) and in-situ data [9]. Also not only turbulent heat flux data but also the related meteorological variables such as wind speed, saturated specific humidity and air specific humidity are provided in J-OFURO2. The information about the related variables must be useful for most scientists.

There are several major differences between the latent heat flux (LHF) values of J-OFURO1 and 2. First, multi-satellite data are used in J-OFURO2, while data from only one Defense Meteorological Satellite Program (DMSP)/ Special Sensor Microwave Imager

(SSM/I) sensor has been used for the estimation of turbulent heat fluxes in J-OFURO1. This is based on the results provided by [10], who demonstrated a remarkable reduction in the sampling error by using multi-satellite data, particularly, in regions with large daily variability. In J-OFURO2, we have used all available SSM/Is (F08-F15), European Remote Sensing satellite (ERS) 1/2, QuikSCAT, AMSR-E and Tropical Rainfall Measuring Mission Microwave Imager (TMI) for the estimation of daily wind speeds. In J-OFURO2, we could recognize that the usage of multi-satellite data is quite effective, even if bias adjustments are not applied to each sensor. If only one satellite is used, the time series shows many gaps, resulting in less accurate observations. On the other hand, with multi-satellite data, there are no gaps in the time series, and the accuracy is considerably higher. On the other hand, only available SSMIs (F08-F15) are used for estimation of air specific humidity at present because it is not easy to estimate accurate air specific humidity using AMSR-E or TMI data still now. This is a future issue for the construction of J-OFURO3. Various data are merged by an optimum interpolation method [11] into daily mean data with a spatial resolution of 1°. Second, we changed gridded sea surface temperature (SST) data from Reynolds SST data [12] to MGD SST data provided by Japan Meteorological Agency (JMA). This change is based on the results of [13], wherein five types of global SST products are compared and evaluated. Third, the bulk formula suggested by [14] was also changed to coupled ocean-atmosphere response experiment (COARE) 3.0 [15]. However, warm layer and cool skin effects are not included in J-OFURO2. The estimation method for the sensible heat flux (SHF) has been drastically changed. In J-OFURO1, we use a method proposed by [16] for calculating the SHF by multiplying the LHF by the climatological Bowen ratio derived from the European Centre for Medium Range Weather Forecasting (ECMWF) data. However, in J-OFURO2, the SHF is estimated from COARE 3.0 using our merged wind speed, MGD SST data and NRA1 air temperature data.

The general feature of the global distribution of the J-OFURO2 LHF is similar to that of not only J-OFURO1 but also other products such as Goddard satellite-Based Surface Turbulent Fluxes (GSSTF) 2, NRA1, ECMWF reanalysis (ERA) 40 and OAFlux. However, the amplitude in J-OFURO2 is relatively less than that in J-OFURO1, particularly in mid-latitudes called as ocean desert. We have compared the J-OFURO2 LHF with the in situ LHF data observed by the KEO buoy. In June 2004, the KEO buoy was deployed in the Kuroshio Extension recirculation gyre at 144.6°E, 32.4°N by NOAA/ Pacific Marine Environmental Laboratory (PMEL) to monitor air-sea heat, moisture and momentum fluxes and upper-ocean temperature and salinity. Figure 1 shows the daily mean time series of the J-OFURO2 and the differences between J-OFURO2 and KEO from June 2004 to December 2007[17]. Overall, the J-OFURO2 LHF and SHF show good agreement with the KEO observations. The bias and the root-mean-square (RMS) error for a daily mean value of the J-OFURO2 LHF (SHF) are 3 (-4) W m–2 and 40 (11) W m–2, respectively [17].  References [17] and [18] showed that the atmospheric reanalysis products such as NRA1/2 has large bias and RMS. For example, the bias and the RMS for NRA1 LHF (SHF) are 26 (4) W m-2 and 47 (17) W m-2, respectively. Therefore, the both of J-OFURO2 LHF and SHF are considerably more accurate than atmospheric reanalysis products.

It is well known that many famous many warm eddies exist in the Kuroshio and Kuroshio Extension regions. Interestingly, we can find a fine-scaled distribution of LHF associated with that of the sea surface anomaly observed by the TOPES/Poseidon altimeter. Such characteristics cannot be found in other LHF fields such as J-OFURO1, OAFlux and ERA40. This feature suggests that the ocean plays an active role in the atmosphere over the meso-scale eddies. As mentioned earlier, the use of multi-satellite data improves the

accuracy of turbulent heat fluxes. Moreover, the use of multi-satellite data contributes to the representation of the fine structure of heat fluxes over the Kuroshio/Kuroshio Extension Regions.

SUMMARY

We described our new surface flux products, J-OFURO2 and showed the good accuracy compared with other products. J-OFURO2 data is available for scientific use and can be freely obtained from the website . J-OFURO2 data are provided by various formats such as NetCDF, Binary, ASCII and so on. Also detailed information about J-OFURO2 can be found in this website.

[pic]

Figure.1 Daily mean time series of (a) J-OFURO2 and (b) J-OFURO2 minus KEO differences for LHF (black) and, SHF (red). A positive value indicates an upward heat flux [17].

[pic]

Figure 2. Monthly-mean values of sea surface height anomaly derived from altimeter data and LHF distribution by J-OFURO2, OAFlux and ERA40 in the Kuroshio and Kuroshio Extension regions on November, 2000.

REFERENCES

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2. Jet Propulsion Laboratory. (2001) QuikSCAT science data product user’s manual, overview and geophysical data products, version 2.2, December 2001, Jet Propulsion Laboratory, 91 pp.

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6. Kutsuwada, K., Kasahara M., & Aoki, K. (2004). Gridded surface wind-stress product over the world ocean constructed by satellite scatterometer data and its comparison with NWP products, Proc. Pan Ocean Remote-sensing Conference 2004, 68(2), 348-354.

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