The Global Ocean Data Assimilation Experiment High-resolution Sea Surface Temperature Pilot Project

A new generation of integrated sea surface temperature (SST) data products are being provided by the Global Ocean Data Assimilation Experiment (GODAE) High-Resolution SST Pilot Project (GHRSST-PP). These combine in near-real time various SST data products from several different satellite sensors and in situ observations and maintain the fine spatial and temporal resolution needed by SST inputs to operational models. The practical realization of such an approach is complicated by the characteristic differences that exist between measurements of SST obtained from subsurface in-water sensors, and satellite microwave and satellite infrared radiometer systems. Furthermore, diurnal variability of SST within a 24-h period, manifested as both warm-layer and cool-skin deviations, introduces additional uncertainty for direct intercomparison between data sources and the implementation of data-merging strategies. The GHRSST-PP has developed and now operates an internationally distributed system that provides operational feeds of regional and global coverage high-resolution SST data products (better than 10 km and ~6 h). A suite of online satellite SST diagnostic systems are also available within the project. All GHRSST-PP products have a standard format, include uncertainty estimates for each measurement, and are served to the international user community free of charge through a variety of data transport mechanisms and access points. They are being used for a number of operational applications. The approach will also be extended back to 1981 by a dedicated reanalysis project. This paper provides a summary overview of the GHRSST-PP structure, activities, and data products. For a complete discussion, and access to data products and services see the information online at www.ghrsst-pp.org.

Download Full-text

Data Assimilation of the High-Resolution Sea Surface Temperature Obtained from the Aqua-Terra Satellites (MODIS-SST) Using an Ensemble Kalman Filter

Remote Sensing ◽

10.3390/rs5063123 ◽

2013 ◽

Vol 5 (6) ◽

pp. 3123-3139 ◽

Cited By ~ 13

Author(s):

Yasumasa Miyazawa ◽

Hiroshi Murakami ◽

Toru Miyama ◽

Sergey Varlamov ◽

Xinyu Guo ◽

...

Keyword(s):

Kalman Filter ◽

High Resolution ◽

Data Assimilation ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Ensemble Kalman Filter ◽

Sea Surface

Download Full-text

Air-Sea Coupled Data Assimilation Experiment for Typhoons Kilo, Etau and the September 2015 Kanto-Tohoku Heavy Rainfall with the Advanced Microwave Scanning Radiometer 2 Sea Surface Temperature

Journal of the Meteorological Society of Japan Ser II ◽

10.2151/jmsj.2019-029 ◽

2019 ◽

Vol 97 (3) ◽

pp. 553-575 ◽

Cited By ~ 4

Author(s):

Akiyoshi WADA ◽

Hiroshige TSUGUTI ◽

Kozo OKAMOTO ◽

Naoko SEINO

Keyword(s):

Data Assimilation ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Heavy Rainfall ◽

Sea Surface ◽

Assimilation Experiment ◽

Coupled Data Assimilation

Download Full-text

THE GODAE HIGH-RESOLUTION SEA SURFACE TEMPERATURE PILOT PROJECT

Oceanography ◽

10.5670/oceanog.2009.64 ◽

2009 ◽

Vol 22 (3) ◽

pp. 34-45 ◽

Cited By ~ 62

Author(s):

Craig Donlon ◽

Kenneth Casey ◽

Ian Robinson ◽

Chelle Gentemann ◽

Richard Reynolds ◽

...

Keyword(s):

High Resolution ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Pilot Project ◽

Sea Surface

Download Full-text

High resolution 3-D temperature and salinity fields derived from in situ and satellite observations

Ocean Science ◽

10.5194/os-8-845-2012 ◽

2012 ◽

Vol 8 (5) ◽

pp. 845-857 ◽

Cited By ~ 107

Author(s):

S. Guinehut ◽

A.-L. Dhomps ◽

G. Larnicol ◽

P.-Y. Le Traon

Keyword(s):

High Resolution ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Large Scale ◽

Sea Surface ◽

Temperature Fields ◽

Global Ocean ◽

In Situ Observations ◽

Temperature Observations

Abstract. This paper describes an observation-based approach that efficiently combines the main components of the global ocean observing system using statistical methods. Accurate but sparse in situ temperature and salinity profiles (mainly from Argo for the last 10 yr) are merged with the lower accuracy but high-resolution synthetic data derived from satellite altimeter and sea surface temperature observations to provide global 3-D temperature and salinity fields at high temporal and spatial resolution. The first step of the method consists in deriving synthetic temperature fields from altimeter and sea surface temperature observations, and salinity fields from altimeter observations, through multiple/simple linear regression methods. The second step of the method consists in combining the synthetic fields with in situ temperature and salinity profiles using an optimal interpolation method. Results show the revolutionary nature of the Argo observing system. Argo observations now allow a global description of the statistical relationships that exist between surface and subsurface fields needed for step 1 of the method, and can constrain the large-scale temperature and mainly salinity fields during step 2 of the method. Compared to the use of climatological estimates, results indicate that up to 50% of the variance of the temperature fields can be reconstructed from altimeter and sea surface temperature observations and a statistical method. For salinity, only about 20 to 30% of the signal can be reconstructed from altimeter observations, making the in situ observing system essential for salinity estimates. The in situ observations (step 2 of the method) further reduce the differences between the gridded products and the observations by up to 20% for the temperature field in the mixed layer, and the main contribution is for salinity and the near surface layer with an improvement up to 30%. Compared to estimates derived using in situ observations only, the merged fields provide a better reconstruction of the high resolution temperature and salinity fields. This also holds for the large-scale and low-frequency fields thanks to a better reduction of the aliasing due to the mesoscale variability. Contribution of the merged fields is then illustrated to describe qualitatively the temperature variability patterns for the period from 1993 to 2009.

Download Full-text

Using Sea Surface Temperature Observations to Constrain Upper Ocean Properties in an Arctic Sea Ice‐Ocean Data Assimilation System

Journal of Geophysical Research Oceans ◽

10.1029/2019jc015073 ◽

2019 ◽

Vol 124 (7) ◽

pp. 4727-4743

Author(s):

Xi Liang ◽

Martin Losch ◽

Lars Nerger ◽

Longjiang Mu ◽

Qinghua Yang ◽

...

Keyword(s):

Data Assimilation ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Arctic Sea Ice ◽

Sea Surface ◽

Ocean Data Assimilation ◽

Arctic Sea ◽

Ocean Properties ◽

Data Assimilation System ◽

Assimilation System

Download Full-text

Impacts of OSTIA Sea Surface Temperature in Regional Ocean Data Assimilation System

The Sea ◽

10.7850/jkso.2015.20.1.1 ◽

2015 ◽

Vol 20 (1) ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Ji Hye Kim ◽

Hyun-Min Eom ◽

Jong-Kuk Choi ◽

Sang-Min Lee ◽

Young-Ho Kim ◽

...

Keyword(s):

Data Assimilation ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Surface ◽

Ocean Data Assimilation ◽

Data Assimilation System ◽

Assimilation System

Download Full-text

High-Resolution Sea Surface Temperature Retrieval from Landsat 8 OLI/TIRS Data at Coastal Regions

Remote Sensing ◽

10.3390/rs11222687 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2687 ◽

Cited By ~ 5

Author(s):

Jae-Cheol Jang ◽

Kyung-Ae Park

Keyword(s):

High Resolution ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Korean Peninsula ◽

Sea Surface ◽

Global Ocean ◽

Small Scale ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Coastal Regions

High-resolution sea surface temperature (SST) images are essential to study the highly variable small-scale oceanic phenomena in a coastal region. Most previous SST algorithms are focused on the low or medium resolution SST from the near polar orbiting or geostationary satellites. The Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) makes it possible to obtain high-resolution SST images of coastal regions. This study performed a matchup procedure between 276 Landsat 8 images and in-situ temperature measurements of buoys off the coast of the Korean Peninsula from April 2013 to August 2017. Using the matchup database, we investigated SST errors for each formulation of the Multi-Channel SST (MCSST) and the Non-Linear SST (NLSST) by considering the satellite zenith angle (SZA) and the first-guess SST. The retrieved SST equations showed a root-mean-square error (RMSE) from 0.59 to 0.72 °C. The smallest errors were found for the NLSST equation that considers the SZA and uses the first-guess SST, compared with the MCSST equations. The SST errors showed characteristic dependences on the atmospheric water vapor, the SZA, and the wind speed. In spite of the narrow swath width of the Landsat 8, the effect of the SZA on the errors was estimated to be significant and considerable for all the formations. Although the coefficients were calculated in the coastal regions around the Korean Peninsula, these coefficients are expected to be feasible for SST retrieval applied to any other parts of the global ocean. This study also addressed the need for high-resolution coastal SST, by emphasizing the usefulness of the high-resolution Landsat 8 OLI/TIRS data for monitoring the small-scale oceanic phenomena in coastal regions.

Download Full-text

Diffusion Filters for Variational Data Assimilation of Sea Surface Temperature in an Intermediate Climate Model

Advances in Meteorology ◽

10.1155/2015/751404 ◽

2015 ◽

Vol 2015 ◽

pp. 1-17

Author(s):

Xuefeng Zhang ◽

Dong Li ◽

Peter C. Chu ◽

Lianxin Zhang ◽

Wei Li

Keyword(s):

Data Assimilation ◽

Surface Temperature ◽

Sea Surface Temperature ◽

State Estimation ◽

The State ◽

Variational Data Assimilation ◽

Sea Surface ◽

Superior Performance ◽

Assimilation Experiment ◽

Gradient Diffusion

Sequential, adaptive, and gradient diffusion filters are implemented into spatial multiscale three-dimensional variational data assimilation (3DVAR) as alternative schemes to model background error covariance matrix for the commonly used correction scale method, recursive filter method, and sequential 3DVAR. The gradient diffusion filter (GDF) is verified by a two-dimensional sea surface temperature (SST) assimilation experiment. Compared to the existing DF, the new GDF scheme shows a superior performance in the assimilation experiment due to its success in extracting the spatial multiscale information. The GDF can retrieve successfully the longwave information over the whole analysis domain and the shortwave information over data-dense regions. After that, a perfect twin data assimilation experiment framework is designed to study the effect of the GDF on the state estimation based on an intermediate coupled model. In this framework, the assimilation model is subject to “biased” initial fields from the “truth” model. While the GDF reduces the model bias in general, it can enhance the accuracy of the state estimation in the region that the observations are removed, especially in the South Ocean. In addition, the higher forecast skill can be obtained through the better initial state fields produced by the GDF.

Download Full-text