The dataset, called LETKF-based Ocean Research Analysis version 2.0 for a quasi-global domain, or LORA-QG, was developed by Shun Ohishi of the RIKEN Center for Computational Science, Takemasa Miyoshi, also of RIKEN, and Misako Kachi of the Japan Aerospace Exploration Agency. It covers the ocean between 70 degrees south and 70 degrees north at an eddy-permitting horizontal resolution of 0.25 degrees, and it extends from June 2002 through January 2024. That start date is no accident: it marks the moment when the AMSR-E microwave radiometer aboard NASA’s Aqua satellite began delivering near-global sea surface temperature observations and when the Argo program of profiling floats was dramatically expanding its coverage of in situ temperature and salinity measurements.

What makes LORA-QG unusual among publicly available ocean reanalyses is its assimilation method. Most operational products rely on variational techniques or ensemble optimal interpolation with static error statistics. LORA-QG instead uses the local ensemble transform Kalman filter, or LETKF, running a 128-member ensemble of the Stony Brook Parallel Ocean Model. The ensemble approach allows the system to estimate flow-dependent forecast error covariances, meaning the model effectively learns where it is likely to be wrong as the ocean’s currents and eddies evolve. The team ran the system on the Fugaku supercomputer at RIKEN and the JAXA Supercomputer System Generation 3, with a single assimilation cycle taking roughly 20 to 30 minutes on more than 24,000 processors.

The technical build involved substantial engineering. The team modernized legacy Fortran 77 code into Fortran 90, added OpenMP parallelization alongside existing MPI routines, and implemented a scheme to reduce pressure gradient errors inherent to sigma-coordinate ocean models. The system assimilates satellite sea surface temperature from the AMSR-E, WindSat, and AMSR2 microwave radiometers, sea surface salinity from ESA’s SMOS and NASA’s SMAP missions, satellite sea surface height, and in situ temperature and salinity profiles from Argo floats and the Global Temperature and Salinity Profile Programme. To keep the ensemble from collapsing, the researchers perturbed atmospheric and lateral boundary conditions and applied a technique called relaxation-to-prior perturbation, alongside incremental analysis updates and adaptive observation error inflation.

To find out how well the new product performs, the team validated it against observations that are independent of LORA-QG, including surface drifter buoys, tide gauge stations from the University of Hawaii Sea Level Center, and the Kuroshio Extension Observatory and Ocean Station Papa moorings. They compared the results with three established eddy-permitting global reanalyses: GLORYS2V4 from Mercator Ocean, ORAS5 from the European Centre for Medium-Range Weather Forecasts, and C-GLORSv7 from the Euro-Mediterranean Centre on Climate Change. The validation period spanned January 2003 to December 2023, and differences were tested for statistical significance using a bootstrap method with 1,000 resampling iterations.

The verdict, summarized in a radar chart of error ratios, is that the observations are best reproduced in the order of GLORYS2V4, LORA-QG, C-GLORSv7, and ORAS5. In other words, the newcomer takes second place overall, a remarkable result for a research system built by a small team competing against mature operational products. For surface currents measured by drifters, LORA-QG’s spatiotemporally averaged root-mean-square deviation was 0.172 meters per second for zonal velocity, significantly better than ORAS5 and C-GLORSv7 but significantly worse than GLORYS2V4. For sea surface temperature, LORA-QG achieved an average deviation of 0.519 degrees Celsius, statistically indistinguishable from GLORYS2V4’s 0.530 degrees and significantly better than the other two products.

Notably, LORA-QG avoided a systematic flaw seen in its competitors. GLORYS2V4, ORAS5, and C-GLORSv7 all exhibited bias patterns that tended to weaken the large-scale general circulation, damping both westward and eastward currents in subtropical and mid-latitude regions. LORA-QG showed no such systematic bias structure. It also steered clear of a spurious drift that plagued C-GLORSv7 in tide gauge comparisons, where a substantial global sea level decline, likely tied to an unconstrained freshwater budget, produced errors far larger than those of the other three datasets.

The new product is not without weaknesses. LORA-QG displays significant warm sea surface temperature biases in the tropics, particularly in the western tropical Pacific, a pattern that broadly matches the global rainfall climatology. The authors suggest that microwave SST retrievals, which represent subskin temperature and degrade under heavy precipitation, may be responsible; the competing products assimilate optimally interpolated SST products that blend infrared and microwave data and may mitigate such errors. Sea surface salinity representation is also likely limited because the team applied relatively strong nudging toward the World Ocean Atlas 2018 climatology in the mixed layer to suppress salinity drift, and the ensemble spread is underdispersive throughout the water column, indicating the system is overconfident about its own state.

Even so, the ensemble spread does track actual errors in a meaningful way. Temporal correlations between monthly ensemble spread and analysis errors are positive across most of the open ocean and are especially strong around energetic current systems such as the western boundary currents and the Antarctic Circumpolar Current. That flow-dependent uncertainty information, together with the availability of individual terms of the heat and salinity budget equations saved alongside the standard fields, is precisely what conventional reanalyses lack. The budget terms allow researchers to diagnose the mechanisms driving temperature and salinity changes, while the ensemble spread can seed ensemble ocean forecasts, which the team has already shown significantly outperform deterministic single-run forecasts for predicting the Kuroshio south of Japan.

The team plans to extend the analysis back to 1982, when infrared satellite SST observations became available, and to develop a near-real-time system incorporating the new AMSR3 radiometer on the GOSAT-GW satellite. They have also produced an eddy-resolving North Pacific companion product, LORA-NP, and intend to release both datasets through the JAXA-RIKEN Ocean Analysis website. For oceanographers studying mesoscale dynamics, interannual variability, and predictability, LORA-QG promises to become a genuinely new kind of tool: a reanalysis that not only reconstructs the ocean’s past but tells you how much to trust every grid cell of it.

Subject of Research: Development and validation of the LETKF-based quasi-global ocean reanalysis dataset LORA-QG

Article Title: LETKF-based Ocean Research Analysis version 2.0 for a quasi-global domain (LORA-QG): validation and intercomparison with eddy-permitting global ocean reanalysis datasets

Article References: Ohishi, S., Miyoshi, T., & Kachi, M. (2026). LETKF-based Ocean Research Analysis version 2.0 for a quasi-global domain (LORA-QG): validation and intercomparison with eddy-permitting global ocean reanalysis datasets. Ocean Science, 22(5), 2915-2938. https://doi.org/10.5194/os-22-2915-2026

Image Credits: AI Generated

DOI: 10.5194/os-22-2915-2026

Keywords: ocean reanalysis, data assimilation, LETKF, ensemble Kalman filter, LORA-QG, sea surface temperature, Argo floats, ocean forecasting, GLORYS2V4, ORAS5, microwave radiometry, Fugaku supercomputer