MUR SST

A global, gap-free, gridded sea surface temperature analysis at roughly 1 km resolution — and the containerized pipeline that produces it.

The Multi-scale Ultra-high Resolution (MUR) SST analysis blends infrared and microwave satellite retrievals with in-situ buoy observations into a single daily global field with no gaps. This site describes how the pipeline is put together, how to run it, and how the underlying analysis works.

~1 kmOutput resolution
DailyGlobal, gap-free
4Processing containers
L=2→11Analysis scales

The pipeline at a glance

Three preprocessing stages convert external data into compact binary observation files. The MRVA analysis stage then fits a multi-scale field to all of them at once and writes the NetCDF product.

flowchart TD
    subgraph sources["Data Sources"]
        OSI["OSI-SAF<br/>Ice Concentration"]
        PODAAC["PO.DAAC<br/>L2P Satellite Files"]
        IQUAM["NOAA iQUAM<br/>Buoy Data"]
    end

    subgraph containers["Preprocessing Containers"]
        LANDICE["Land/Ice Container<br/>makeicefiles.m"]
        L2P["L2P Container<br/>l2p2bic.m"]
        BUOY["iQUAM Container<br/>makedailyiquam.m"]
    end

    subgraph outputs["Preprocessed Data"]
        GDS[".gds files<br/>Land/ice masks"]
        BIP[".bip files<br/>Ice SST points"]
        BIC[".bic files<br/>Satellite observations"]
        BII[".bii files<br/>Buoy observations"]
    end

    subgraph analysis["Analysis"]
        MRVA["MRVA Algorithm<br/>Multi-scale variational analysis"]
        NETCDF["MUR SST Product<br/>NetCDF L4 output"]
    end

    OSI --> LANDICE
    PODAAC --> L2P
    IQUAM --> BUOY

    LANDICE --> GDS
    LANDICE --> BIP
    L2P --> BIC
    BUOY --> BII

    GDS --> MRVA
    BIP --> MRVA
    BIC --> MRVA
    BII --> MRVA

    MRVA --> NETCDF

    style MRVA fill:#e1f5ff,stroke:#0288d1,stroke-width:2px
    style NETCDF fill:#e1f5ff,stroke:#0288d1,stroke-width:2px
External sources feed three preprocessing containers; their binary outputs converge on the MRVA analysis, which emits the NetCDF L4 product.

Where to go next

Quick start

Build the container images and run a day through the pipeline.

Architecture

The four containers, the shared MATLAB base image, and the explicit-input contract they all follow.

The MRVA algorithm

Multi-scale variational analysis on a B-spline basis — what the scales mean and how observations enter.

Data lifecycle

Where inputs come from, the temporal windows, caching and stability rules, and storage footprint.

Daily execution

What a scheduled run actually does, and how NRT and reanalysis modes differ.

Adding a sensor

What a new L2P instrument needs in order to join the analysis.

Configuration

Every key in config.json, how each stage is driven, and what to check when a run goes wrong.

MRVA internals

The MATLAB and Fortran behind the analysis, module by module.

Reference material

Everything below is part of this site — the full detail, not a summary of it:

The published method

The analysis behind the product is described in:

  • Chin, T. M., Vazquez-Cuervo, J., and Armstrong, E. M. (2017). “A multi-scale high-resolution analysis of global sea surface temperature.” Remote Sensing of Environment, 200, 154–169.

The product itself is distributed by PO.DAAC, and the pipeline source lives at podaac/mur.