OCEAN MODEL ANALYSIS AND PREDICTION SYSTEM (OCEANMAPS): OPERATIONAL OCEAN FORECASTING BASED ON NEAR REAL-TIME SATELLITE ALTIMETRY AND ARGO

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1 OCEAN MODEL ANALYSIS AND PREDICTION SYSTEM (OCEANMAPS): OPERATIONAL OCEAN FORECASTING BASED ON NEAR REAL-TIME SATELLITE ALTIMETRY AND ARGO Gary B. Brassington Bureau of Meteorology Research Centre, 700 Collins Street, Melbourne, 3000, Australia ABSTRACT/RESUME BLUElink> is a joint Australian government initiative to develop Australia s first operational ocean forecasting system called OceanMAPS. The project has transitioned to the implementation and trial phase using the infrastructure of the Bureau of Meteorology. OceanMAPS has a global grid with 1/10 by 1/10 resolution in the Australian region (90E-180E, 70S- 16N) and uses the Modular Ocean Model version 4 optimised for the NEC SX6. The analysis uses an ensemble based multi-variate optimal interpolation scheme where model error covariances are derived from a 72-member ensemble of intra-seasonal anomalies based on a 12-year ocean only model integration. The scheme has been formulated to assimilate near real-time sea level height anomalies processed from Jason-1, ENVISAT and Geosat Follow-On and profile observations including Argo, XBT and the TAO array. The operational configuration including the data management of the near real-time observations is reviewed. 1. INTRODUCTION In 2003, the Australian Government initiated a $15M project called BLUElink Ocean Forecasting Australia to deliver operational short-range ocean forecasts for the Asian-Australian region. The partners assigned to accomplish the task included the Bureau of Meteorology (the Bureau), the Royal Australian Navy (RAN), and the Commonwealth Scientific and Industrial Research Organisation (CSIRO). CSIRO has lead the collaborative development of both the Ocean Forecast Australia Model (OFAM) [1] and the BLUElink Ocean Data Assimilation System (BODAS) [2]. This development culminated in BLUElink s first major achievement, a 15 year ocean reanalysis experiment referred to as the BLUElink ReANalysis (BRAN). The Bureau has lead the development of the ocean prediction system referred to here as the Ocean Model Analysis and Prediction System (OceanMAPS) [3]. Developments include extending the model and assimilation system, developing the system infrastructure including data management of real-time observations; quality control; archive management; and data servicing. The principle user of the operational products and jointpartner in the project is the RAN. However the operational system being developed at the Bureau is aimed at a more general public service for the benefit of the wider Australian maritime community. Australia is an island continent surrounded by three of the worlds major oceans. Australia s 200nm Exclusive Economic Zone ranks as the third largest in the world behind the United States and France. Guiding the sustainable management of this vast resource is a critical activity for the Australian government. Important users will include: the Australian Maritime Safety Authority for ocean surface tracking; marine management such as the Great Barrier Reef Marine Park Authority; recreational ocean racing such as the Sydney to Hobart Yacht race and other related activities including tourism, angling diving and boating. 2. OCEAN OBSERVING SYSTEM Advances in developing the global ocean observing system have been critical to Australia realising an operational ocean prediction capability. Australia continues to actively engage in international ocean observing programmes that contribute to the real-time system through SOOP and Argo. Investment by the Australian government in satellite ocean observing systems has largely been restricted to scientific contributions. The BLUElink project is providing a focal point for evaluating the quality of this system in Australia s regional sea. 2.1 Argo Argo was initiated in 1999 with a target of maintaining a network of 3000 autonomous profiling floats. At Feb 2006 the deployment had reached ~2400 floats. Australia has contributed 117 deployments to the network with an on-going commitment to manage the real-time and delayed mode retrievals. Argo science team provide images of the float distribution as well as maps of the density updated monthly as shown in Fig.1. Focusing on the density found in Australia s marginal

2 seas reveals three regions that have consistently shown a low density of floats: the North West shelf; Coral Sea; and the Great Australian Bight. Forecasts of heat content in the Coral Sea are of particular interest for applications of tropical cyclone forecasting and GBR marine park management for coral bleaching. Figure 1: Argo worldwide density at the 31 January (Courtesy of SIO/JCOMM) The bathymetry of the Coral Sea shown in Fig, 2 shows that its eastward boundary is defined by several island groups formed along the edge of the continental plate including the Solomon Islands and Bank Islands. Sea with part of the northern domain showing a counter current. This suggests that on average there is a low probability for drifters moving into the region during these months. However, in August (and September not shown) the streamlines demonstrate a broad region of penetrating streamlines along both sections indicating a high probability for westward propagation of drifters into the Coral Sea. All streamlines that penetrate into the Coral Sea, across all seasons, show a dominant westward propagation of ~6 /month, many of which reach the GBR. This indicates a high attrition rate for Argo drifters and a relatively short residence time. (Trajectories from the global surface drifter program ( show similar behaviour, not shown). Figure 2: Ocean bathymetry for the Coral Sea. Land is shown in white and the 1000m depth contour line is shown in black. This chain of islands act as a significant barrier to the South Equatorial Current (SEC) that is a feature of the general circulation of this region. The Australian continent represents the western boundary to the SEC however the equatorial return flow is yet to be clearly observed and defined. BRAN has provided the best available model analysis for the Australian region for the period Streamlines derived from the monthly means for February and August averaged for the years are shown in Fig.3a and 3b respectively Sreamlines have been traced from the west Pacific along the 172.5E meridion and from the equatorial Pacific along the latitude line of 7S. The velocity field represents the average over the top 200m. Streamlines are not equivalent to Lagrangian paths in unsteady flow, however the persistence of westward flow throughout the climatological model year suggests a quasi-steady mean circulation. February is typical of a signifcant portion of the climatological model year (Oct- May) where few streamlines penetrate into the Coral Figure 3: Streamlines of the horizontal currents and temperature averaged over the top 200m. The streamlines are initiated from the 172.5E meridion and the latitude line 7S at 0.25 degree intervals. The data is based on a monthly average of BRAN from (a) February and (b) August. The BRAN analysis and the use of streamlines are not definitive evidence in of themselves to evaluate the Argo observing system for the Coral Sea. The findings indicate that a more comprehensive study based on tracing historical records of surface drifters and Argo floats is required. These results are suggestive only that the low density shown in Fig.1 for this region may continue to persist when Argo reaches full deployment and optimal distribution. It also indicates that the GBR is likely to be a net sink for both the Argo and global surface drifter program. 2.2 Satellite altimetry At present there are three instruments available for operational oceanography, Jason, ENVISAT and GFO.

3 Each platform has unique orbit characteristics as shown in Tab. 1 that impact the resolution of sampling and the coverage of the total observing system. Platform Period (days) Along-track Resolution (km) Latitude range Jason ~9.92 ~7.6 66S 66N ENVISAT ~35 ~5.8 79S - 79N GFO ~16.9 ~7.0 72S - 72N Table 1: Characteristics of orbit tracks for satellite altimeter platforms The global coverage of satellite platforms make them a crucial component to the observing system for countries with modest in-situ programs like Australia. However the coverage of sub-orbits is not uniformly distributed over the domain of interest. An example of one days coverage for all three platforms is shown in Fig. 4 for the Australian region. Figure 5: Satellite altimetry passes for Jason, ENVISAT and GFO over the Australian domain from 1 st, 2 nd and 3 rd Aug The quality of the distribution of altimeter observations is a function of the scale of interest and gaps remain at scales on the order of 4!4. The spatial scale for BLUElink is determined by the model error covariances combined together with localisation (L=8 for BRAN [2]) used in the data assimilation scheme. Figure 4: Satellite altimetry passes for Jason, ENVISAT and GFO over the Australian domain for 3 rd Aug, The distribution of satellite observations for the 3 rd August 2003 is an example where the entire East Australia Current (a key area for maritime activity) was not observed. However, in other regions such as (130E, 55S) there is a convergence of multiple tracks from multiple platforms. This pattern changes from day to day however during March 2003 the large data gap in the EAC region occurred as frequently as every three days. If we round up the orbit periods to 10 and 17 days for Jason and GFO respectively the combined repeat period of the observing system is approximately 3.25 years and the pattern in Fig. 4 should occur again later this year (Oct. 2006). The strategy that was adopted for BRAN to improve this distribution was to widen the window to 3 days. An example of which is shown in Fig. 5 for the 1 st 3 rd Aug Figure 6: Average number of sea level height observations from Jason, ENVISAT and GFO per day per 2!2 bin. Black squares indicate zero observations. The average number of observation per day per 2! 2 area for sea surface height observations from Jason, ENVISAT and GFO combined is shown in Fig. 6. The higher density of observations at high latitudes is consistent with standard orbit tracks. The noticeable checkerboard indicates the gaps between orbit crossings. The relatively low density in the tropics and equatorial region is a result of larger separation of passes and the interference from precipitation. The large volume and coverage of quality observations from these three platforms makes this the dominant observing system for BLUElink.

4 2.3 Other observations Sea surface temperature from satellite platforms has a longer history than altimetry and provides a much larger volume of quality observations. BLUElink is supporting research for the development of high resolution sea surface temperature analyses as a contribution to the GHRSST project. However these quality controlled observations are not directly assimilated into OceanMAPS. The SST analyses are instead used to apply surface restoring. Plans for a follow-on project to BLUElink include assimilating SST as part of the prediction system. The global drifter program is another significant global observing program that is targeted toward observing mesoscale oceanography. BLUElink has not included these into the assimilation system and are used only as an independent validation. 2.4 Real-time data management The GTS is the primary operational communication for in-situ ocean observations. The Bureau have established infrastructure to receive and manage this stream. There are essentially two formats for ocean observations, BATHY (bathythermal observations) and TESAC (TEmperature, SAlinity and Currents). These formats have the advantage of compact size but prove less convenient for data handling compared with NetCDF the standard adopted by the Argo program. The Bureau have developed an internal program to convert the GTS message into the Argo format. A priority for BLUElink has been to ensure the maximum number of Argo retrievals available in real-time. The Argo program have devised a distributed data management system with each partner forming a Data Assembly Centre to manage the retrieval, real-time and delayed mode quality control and distribution through both the GTS and the two GDAC s (USGODAE and Coriolis). Analysis of the three sources of real-time Argo files have revealed that none of these sources contain a complete superset of all the real-time observations. In order to maximise the number of Argo observations available to BLUElink the Bureau have developed a system based on all three sources. A duplicate checker was developed to locate all duplicates and form a single file containing the best copy (the highest level of quality control) amongst the duplicates for each day. The sorting is performed in the order of USGODAE, Coriolis then GTS. Initial processing amongst the USGODAE and Coriolis highlighted the differences in the updating cycles and the high number of duplicates that could be found within each source. Current behaviour is shown in Fig.7 where the relative contribution of unique profiles and timeliness in days behind real-time of each source is given for the 2 nd March Argo peaks at ~2 days behind real-time. Additional profile observations from Coriolis peak 3-4 days behind real-time. Figure 7: The total number of unique observations based on retrievals from USGODAE, Coriolis and GTS for 2 nd Mar and the timeliness of those profile data. None of the satellite altimeter platforms are rated as operational systems and delivery of products is under a best efforts basis. The first operational platform to be managed by NOAA will be Jason-2, scheduled for launch in mid The dependence of BLUElink on the existing altimetry makes this a clear vulnerability to the prediction system. This status will likely prevent OceanMAPS being certified as a fully operational system at the Bureau in The BLUElink project does not process the raw satellite altimetry observations and relies on the managing centres JPL, ESA and NOAA providing a processed sea surface height anomaly product. The quality of these products is impacted by the algorithm used to compute the Geophysical Data Record. The faster algorithms required for the near real-time delivery impact the quality of the products. Sensitivity of analyses to realtime ssha products will be assessed during system trials. Each of the three sources of sea level anomaly products are obtained in a unique way. Jason is obtained via OCEANIDS and ftp push service provided by PO.DAAC. ENVISAT is obtained via an ftp push from ESA. GFO is retrieved by ftp from NOAA. Both Jason and GFO have alternative retrieval pathways through USGODAE providing useful backup. Each data stream has its own unique file format which is processed at the Bureau and passed through a series of quality control checks and reformatted to BUFR for storage and netcdf for use in the analysis. 3. SURFACE FLUXES

5 The Bureau maintain a set of operational NWP forecast systems including: GASP [4] a global assimilation and prediction system based on a spectral model and LAPS [5] a limited area prediction system for the Australian region. GASP has a horizontal resolution of 0.75!0.75 resolution in the tropics and uses a gaussian grid in high latitudes and has 33 sigma levels. The analysis uses a generalised multi-variate statistical interpolation scheme (GenSI) which has been recently upgraded to include scatterometer observations from QuikSCAT. The analysis for GASP is performed on a 6 hourly cycle with 10day forecasts issued every 12 hours as 06Z and 18Z. Analyses have demonstrated modest improvements in bias and skill in forecast winds [6, 7]. LAPS has a horizontal resolution of 0.375!0.375 over the Australian domain E E and S N. LASP uses a GASP background state to perform an analysis using the GenSI scheme. LAPS performs a 3 day forecast every 12 hours at 0Z and 12Z. GASP fluxes are retrieved from the operational disk storage and interpolated onto the OFAM grid using Delaunay triangulation. The broad features of shallow mixed layers over much of the central domain; deeper mixed layers in the equatorial and high latitude region show good correspondence. OFAM shows evidence of deeper mixed layers over the tropics indicating stronger westerlies and La Nina like conditions. There is also evidence of significant penetration of SEC into the Coral Sea compared with CARS. The high latitudes in OFAM are characterised by very distinct narrow filaments that retain this characteristic after averaging. This could be a realistic feature determined by bathymetric steering. The same region in CARS is smooth consistent with the 1 resolution. BLUElink has developed a high resolution climatology for the Australian region that is due to be published in OCEAN FORECAST AUSTRALIA MODEL BLUElink have implemented the GFDL Modular Ocean Model version 4 (MOM4) [1, 8] with specific enhancements for mixed layer physics [9] and the algorithm for calculating penetrative solar radiation. In addition, the software was vectorised for use on the NEC SX6 computing architecture a joint facility of the Bureau and CSIRO. OFAM uses a global grid with a horizontal resolution of 0.1!0.1 in the Australian region defined by 90E-180E and 75S-16N which is designed to resolve mesoscale ocean dynamics. Outside this region the grid is stretched up to a resolution of 0.9 in the Indian Ocean and South Pacific Ocean and tropical North Pacific. Beyond this domain the resolution in the North Atlantic is 2!2. This configuration has 71% of the total grid points within the Australian region and 72% of all model grid points are water cells. The OFAM grid provides dynamically consistent and numerically well behaved boundary conditions to the Australian region. The OFAM model has been integrated over the period using ERA40 and ECMWF forecast fluxes. These spinup runs have been used to diagnose model performance such as mixed layer depth against the CSIRO Atlas of Regional Seas climatology [10] as shown in Fig. 8. The CARS dataset is derived from historical observations from the NODC world ocean atlas 1998 and CSIRO data holdings. The results for OFAM are based on the period which is beyond the CARS climatology so that a portion of variation will be attributable to interannual variability. Figure 8: Depth the of temperature that is 1.0 C cooler than the surface. (a) mean January from CARS, (b) mean January from OFAM based on BLUELINK OCEAN DATA ASSIMILATION SYSTEM BLUElink has developed a new software for ocean data assimilation which uses an ensemble based multi-variate optimal interpolation scheme [2]. A specific feature of the scheme is the use of an ensemble of mesoscale anomalies from OFAM to form the statistics for the model error covariance. BRAN was implemented based on a 72 member ensemble which was constrained by the length of the free model run available and the computational cost of the scheme. In general, the undersampling of the statistics gave rise to large far field covariances. These were assumed to be unrealistic features for mesoscale variability and were controlled through localisation. It is critical to this scheme that the free ocean model driven by surface fluxes maintains both a realistic mean ocean state and a realistic distribution of ocean variability. A complete analysis of OFAM is a significant on-going task. Analyses to date have discovered some biases in surface fluxes and in the mixed layer parameterisation leading to improvements that are seen in Fig. 8 (and other diagnostics not shown).

6 BRAN was the first test of the OFAM and BODAS system. It was conducted for a period determined by the availability of altimeter observations, July 1992 March This included ERS1 and Topex-Poseidon in addition to those in Tab. 1 and all available quality controlled in-situ profile data. The primary independent observation used to compare with BRAN has been surface drifters. In regions where both tidal induced and wave induced surface motion are weak, BRAN shows remarkably good agreement with drifter tracks [2]. BRAN was performed on 6 nodes of an NEC SX6 using priority queues and was completed in 5 months generating ~8Tbytes for daily averaged prognostic variables. Analyses of BRAN are on-going and followon analysis BRANII is scheduled for OCEANMAPS/CONCLUSION BRAN was an important milestone in the BLUElink project that has provided a level of confidence that the ocean prediction system being developed will have some level of forecast skill. The on-going analysis of BRAN will continue to lead to refinements of parameter choices and further improvements in skill. However, there are also several distinctions between BRAN and OceanMAPS that are likely to deteriorate forecast skill. These include: (1) a larger forecast period of 7 days; (2) the use of NWP forecasts; (3) real-time observations; and (4) operational scheduling for delivery of forecasts. The Bureau s experience in supporting operational systems was the logical choice for developing and supporting OceanMAPS. A large array of operationally supported infrastructure is in place that have been adapted into the ocean prediction system. These include the high performance computing facility, existing communication networks for retrieval of GTS and other real-time observations, real-time databases and archive devices. Key issues addressed by the BLUElink team include handling non-standard file formats that occur in the ocean community and servicing of the large data volumes. In particular the WMO standards of GRIB and BUFR are not used in the ocean community. BLUElink is developing internet server solutions based on OPeNDAP intended to insulate the maritime user community from the details of the data management system. OceanMAPS is in trial mode with a focus on testing the robustness of infrastructure components. The first system trial is focusing on selected target periods including Jan-Mar 2005 which overlaps with BRAN and includes dynamical features such as tropical cyclone Ingrid and southern extension of the EAC. 7. ACKNOWLEDGEMENTS 8. REFERENCES 1. A Schiller, G B Brassington, R Fiedler, D A Griffin, J Mansbridge, P R Oke, K R Ridgway, and N R Smith, Eddy-resolving ocean circulation in the Asian- Australian region inferred from an ocean reanalysis effort. manuscript in preparation. 2. P R Oke, A Schiller, D A Griffin and G B Brassington, Ensemble data assimilation for an eddyresolving ocean model of the Australian region, Q. J. Roy. Meteorol. Soc., in press. 3. G. B. Brassington, G. Warren, N. Smith, A. Schiller, P. R. Oke, BLUElink> Progress on operational ocean prediction for Australia, Bulletin of the Australian Meteorological and Oceanographic Society, 18, pp , Seaman, R., Bourke, W., Steinle, P., Hart, T., Embery, G., Naughton, M and Rikus, L., Evolution of the Bureau of Meteorology's Global Assimilation and Prediction System, Part 1: Analyses and Initialization, Aust. Met. Mag., 44, 1-18, Puri, K., Dietachmayer, G., Mills, G. A., Davidson, N. E., Bowen, R. A. and Logan, L. W., The new BMRC Limited Area Prediction System, LAPS, Aust. Met. Mag., 47, , J D Kepert, D J M Greenslade and G D Hess. Assessing and improving the marine surface winds in the Bureau of Meteorology numerical weather prediction systems. BMRC Research Report No. 10, Eric W. Schulz, Jeffrey D. Kepert, and Diana J. M. Greenslade, An Assessment of Marine Surface Winds in Numerical Weather Prediction Systems at the Australian Bureau of Meteorology (in press) 8. S M Griffies, M J Harrison, R C Pacanowski, and A Rosati, A technical guide to MOM4. GFDL ocean group technical Report No. 5, 339pp, D Chen, L M Rothstein and A J Busalacchi. A hybrid vertical mixing scheme and its application to tropical ocean models. J. Phys. Oceanogr., 24, pp , Ridgway K.R., J.R. Dunn, and J.L. Wilkin, Ocean interpolation by four-dimensional least squares - Application to the waters around Australia, J. Atmos. Ocean. Tech., Vol 19, No 9, , 2002 BLUElink science team and Dr. Spillman for Fig. 7.