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

The new device, described in the journal Advanced Composites and Hybrid Materials, is called a multifunctional meta-absorber. It is built from two stacked functional layers, each with a carefully assigned job. The top layer is an infrared shielding layer, implemented as a frequency-selective surface, and its task is to suppress the thermal signature of whatever sits beneath it. The bottom layer is a radar absorption and microwave emissivity layer, a multilayer stacked structure that swallows incoming radar waves while radiating strongly in the microwave band. The elegance of the design lies in how these two layers divide the spectrum between them without interfering with each other’s work.

Start with the infrared problem. Every object warmer than absolute zero emits thermal radiation, and in the infrared atmospheric windows this emission is what thermal imagers detect. Metals are notoriously conspicuous in these bands because they behave almost like perfect infrared mirrors with very high effective emissivity contrast against natural backgrounds once heated. The researchers tackled this with their frequency-selective surface, which is engineered to present a low infrared emissivity of just 0.334. In practical terms, the surface emits only about a third of the thermal radiation that a conventional surface at the same temperature would, dramatically shrinking the thermal contrast that an infrared camera would register.

Crucially, that infrared shielding cannot come at the cost of blocking the layers below. Many materials that suppress infrared emission, such as continuous metallic films, also reflect or absorb microwaves, which would ruin the radar and radiometric performance of the underlying structure. The frequency-selective surface solves this by being spatially patterned rather than continuous: it behaves differently at different frequencies and for different propagation directions, remaining essentially transparent to microwaves while still delivering its low-emissivity thermal behavior. This frequency-selective transparency is what allows a single laminate to serve two masters at once, a constraint that has long limited multispectral camouflage designs.

The second layer carries the microwave burden, and it does so through a mechanism as old as electrical engineering itself: ohmic loss. When an electromagnetic wave enters a lossy conductor, the induced currents dissipate its energy as heat rather than allowing it to reflect back toward the source. By stacking multiple layers with tuned properties, the team created a structure that achieves broadband absorption exceeding 90 percent across the range from 4.62 to 28.29 gigahertz. That span covers much of the radar spectrum used for detection and tracking, from lower-frequency surveillance bands through the higher frequencies favored by modern fire-control and imaging radars.

Broadband absorption translates directly into a smaller radar cross section, the measure of how visible a target is to radar. In their experiments, the meta-absorber achieved a 10 dB reduction in radar cross section across the band from 4.38 to 21.18 gigahertz. A 10 dB reduction means the returning echo carries only one-tenth of the power it otherwise would, which can shrink the detection range a radar achieves against the target by a substantial margin. The researchers also report that the structure maintains its performance over wide angles of incidence and is insensitive to polarization, meaning it cannot be defeated simply by illuminating the target from an oblique angle or with a differently oriented antenna.

The most conceptually interesting part of the work addresses microwave radiometric detection, a passive technique that is increasingly being folded into integrated sensor suites. Unlike radar, a radiometer emits nothing; it simply listens, measuring the microwave brightness temperature of the scene. Kirchhoff’s law of thermal radiation ties together absorption and emission: a body that absorbs strongly at a given frequency must also emit strongly there. Metals, being poor emitters at microwave frequencies, appear cold against the warm microwave background of the natural environment, and that cold spot betrays them to a radiometer even when their radar echo has been suppressed.

Here the meta-absorber turns an apparent conflict into an advantage. Because the radar-absorbing layer is designed to absorb more than 90 percent of incident microwave radiation, Kirchhoff’s law dictates that it also emits strongly in the same band. Instead of appearing as a cold metallic anomaly, the coated target radiates at a brightness temperature close to that of its surroundings, blending into the radiometric background. The team quantified this effect: the average brightness temperature contrast between a metallic target and its background fell by 40.33 percent when the meta-absorber was applied. In other words, the very loss mechanism that hides the target from radar simultaneously camouflages it from passive microwave sensors, resolving a trade-off that has plagued earlier single-purpose designs.

Taken together, the reported properties form an unusually complete camouflage package: low infrared emissivity, high microwave absorptivity, high microwave emissivity, wide-angle stability, and polarization insensitivity, all in a layered structure that can be fabricated and tested experimentally rather than existing only in simulation. The authors argue that this combination offers a viable route to protecting high-value ground targets against the integrated, multispectral detection systems that are becoming the norm rather than the exception. The work was supported by the National Natural Science Foundation of China, the Natural Science Foundation of Hunan Province, and the Fundamental Research Funds for the Central Universities of Central South University.

The broader significance of the study lies in its systems-level thinking. Stealth research has historically optimized one spectral channel at a time, often leaving targets exposed to whichever sensor the designer ignored. As detection platforms fuse infrared imaging, active radar, and passive radiometry into a single kill chain, camouflage must become equally multispectral, and the physics of that task is unforgiving because absorption, emission, and reflection are bound together by thermodynamic law. By exploiting those very laws, using a frequency-selective surface to decouple infrared from microwave behavior and ohmic loss to couple radar absorption to radiometric emission, the Central South University team has shown that the constraints can be turned into design tools. If such meta-absorbers can be manufactured at scale, on flexible substrates, and with the durability that fielded equipment demands, the era of hiding in plain sight across the entire spectrum may be moving from theory toward hardware.

Subject of Research: Multispectral camouflage metamaterials that counter infrared, radar, and microwave radiometric detection

Article Title: Multifunctional meta-absorber for multispectral camouflage against infrared, radar, and microwave radiometric detection

Article References: Wu, R., Dong, J., Xiao, C., Wang, M., & Luo, H. (2026). Multifunctional meta-absorber for multispectral camouflage against infrared, radar, and microwave radiometric detection. Advanced Composites and Hybrid Materials. https://doi.org/10.1007/s42114-026-02108-8

Image Credits: AI Generated

DOI: 10.1007/s42114-026-02108-8

Keywords: meta-absorber, metamaterials, multispectral camouflage, infrared emissivity, radar cross section, microwave radiometry, brightness temperature, frequency-selective surface, ohmic loss, stealth materials, Kirchhoff's law, electromagnetic absorption