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

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.

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

2007 ◽  
Vol 88 (8) ◽  
pp. 1197-1214 ◽  
Author(s):  
C. Donlon ◽  
I. Robinson ◽  
K. S. Casey ◽  
J. Vazquez-Cuervo ◽  
E. Armstrong ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Pilot Project ◽  
Sea Surface ◽  
Global Ocean ◽  
Assimilation Experiment ◽  
Ocean Data Assimilation ◽  
Data Products

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.

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

2013 ◽  
Vol 5 (6) ◽  
pp. 3123-3139 ◽  
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

scholarly journals Using data assimilation to investigate the causes of Southern Hemisphere high latitude cooling from 10 to 8 ka BP

2013 ◽  
Vol 9 (2) ◽  
pp. 887-901 ◽  
Author(s):  
P. Mathiot ◽  
H. Goosse ◽  
X. Crosta ◽  
B. Stenni ◽  
M. Braida ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Atmospheric Circulation ◽  
Southern Hemisphere ◽  
Sea Surface ◽  
Freshwater Flux ◽  
West Antarctic ◽  
The Impact

Abstract. From 10 to 8 ka BP (thousand years before present), paleoclimate records show an atmospheric and oceanic cooling in the high latitudes of the Southern Hemisphere. During this interval, temperatures estimated from proxy data decrease by 0.8 °C over Antarctica and 1.2 °C over the Southern Ocean. In order to study the causes of this cooling, simulations covering the early Holocene have been performed with the climate model of intermediate complexity LOVECLIM constrained to follow the signal recorded in climate proxies using a data assimilation method based on a particle filtering approach. The selected proxies represent oceanic and atmospheric surface temperature in the Southern Hemisphere derived from terrestrial, marine and glaciological records. Two mechanisms previously suggested to explain the 10–8 ka BP cooling pattern are investigated using the data assimilation approach in our model. The first hypothesis is a change in atmospheric circulation, and the second one is a cooling of the sea surface temperature in the Southern Ocean, driven in our experimental setup by the impact of an increased West Antarctic melting rate on ocean circulation. For the atmosphere hypothesis, the climate state obtained by data assimilation produces a modification of the meridional atmospheric circulation leading to a 0.5 °C Antarctic cooling from 10 to 8 ka BP compared to the simulation without data assimilation, without congruent cooling of the atmospheric and sea surface temperature in the Southern Ocean. For the ocean hypothesis, the increased West Antarctic freshwater flux constrainted by data assimilation (+100 mSv from 10 to 8 ka BP) leads to an oceanic cooling of 0.7 °C and a strengthening of Southern Hemisphere westerlies (+6%). Thus, according to our experiments, the observed cooling in Antarctic and the Southern Ocean proxy records can only be reconciled with the reconstructions by the combination of a modified atmospheric circulation and an enhanced freshwater flux.

scholarly journals A New Method to Produce Sea Surface Temperature Using Satellite Data Assimilation into an Atmosphere–Ocean Mixed Layer Coupled Model

2013 ◽  
Vol 30 (12) ◽  
pp. 2926-2943 ◽  
Author(s):  
Eunjeong Lee ◽  
Yign Noh ◽  
Naoki Hirose
Keyword(s):  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mixed Layer ◽  
Satellite Data ◽  
Weather Prediction ◽  
Coupled Model ◽  
Sea Surface ◽  
New Method ◽  
Ocean Mixed Layer

Abstract A new method of producing sea surface temperature (SST) data for numerical weather prediction is suggested, which is obtained from the assimilation of satellite-derived SST into an atmosphere–ocean mixed layer coupled model. The Weather Research and Forecasting (WRF) Model and the Noh mixed layer model are used for the atmosphere and ocean mixed layer models, respectively. Data assimilation (DA) is carried out in two steps, based on the estimation from the covariance matching method that the daily mean SST of satellite data is more accurate than the model data, if the number of data in a grid per day is sufficiently large—that is, the daily mean SST bias correction in the first DA and the sequential SST anomaly correction in the second DA. For the second DA, the model restarts from the initial condition corrected by the first DA, and DA is applied every 30 min using the nudging method. The daily mean and the diurnal variation of satellite SST are assimilated to the bulk and skin SST, respectively. The modeled results with the new data assimilation scheme are validated by statistical comparison with independent satellite and buoy data such as correlation coefficient, root-mean-square difference, and bias. Furthermore, the sensitivity and seasonal variation of the weighting factor in the second DA are examined. The new approach illustrates the possibility of applying the atmosphere–ocean mixed layer coupled model for the production of SST data combined with the assimilation of satellite data.

scholarly journals RETRIEVING COASTAL SEA SURFACE TEMPERATURE FROM LANDSAT-8 TIRS FOR WANGI-WANGI ISLAND, WAKATOBI, SOUTHEAST SULAWESI, INDONESIA

2019 ◽  
Vol 16 (1) ◽  
pp. 13
Author(s):  
Eko Susilo ◽  
Rizki Hanintyo ◽  
Adi Wijaya
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coastal Zone ◽  
Single Band ◽  
Sea Surface ◽  
Superior Performance ◽  
Landsat 8 ◽  
Infrared Sensors ◽  
In Situ Data

The new Landsat generation, Landsat-8, is equipped with two bands of thermal infrared sensors (TIRS). The presence of two bands provides for improved determination of sea surface temperature (SST) compared to existing products. Due to its high spatial resolution, it is suitable for coastal zone monitoring. However, there are still significant challenges in converting radiance measurements to SST, resulting from the limitations of in-situ measurements. Several studies into developing SST algorithms in Indonesia waters have provided good performance. Unfortunately, however, they have used a single-band windows approach, and a split-windows approach has yet to be reported. In this study, we investigate both single-band and split-window algorithms for retrieving SST maps in the coastal zone of Wangi-Wangi Island, Wakatobi, Southeast Sulawesi, Indonesia. Landsat-8 imagery was acquired on February 26, 2016 (01: 51: 44.14UTC) at position path 111 and and row 64. On the same day, in-situ SST was measured by using Portable Multiparameter Water Quality Checker – 24. We used the coefficient of correlation (r) and root mean square error (RMSE) to determine the best algorithm performance by incorporating in-situ data and the estimated SST map. The results showed that there were differences in brightness temperature retrieved from TIRS band10 and band 11. The single-band algorithm based on band 10 for Poteran Island clearly showed superior performance (r = 69.28% and RMSE = 0.7690°C). This study shows that the split-window algorithm has not yet produced a accurate result for the study area.

scholarly journals Updating a Crucial Source of Sea Surface Temperature Data

Eos ◽  
2019 ◽  
Vol 100 ◽  
Author(s):  
Terri Cook
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Systematic Errors ◽  
Temperature Data ◽  
The State ◽  
Climate System ◽  
Sea Surface ◽  
Data Set ◽  
Earth’S Climate ◽  
Surface Temperature Data

A new version of a major sea surface temperature data set reduces systematic errors in measurements of one of the most important indicators of the state of Earth’s climate system.

State Estimation of the North Pacific Ocean by a Four-Dimensional Variational Data Assimilation Experiment

2003 ◽  
Vol 59 (6) ◽  
pp. 931-943 ◽  
Author(s):  
Toshiyuki Awaji ◽  
Shuhei Masuda ◽  
Yoichi Ishikawa ◽  
Nozomi Sugiura ◽  
Takahiro Toyoda ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
Data Assimilation ◽  
State Estimation ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Variational Data Assimilation ◽  
Assimilation Experiment ◽  
The North ◽  
The North Pacific
Sign in / Sign up

Export Citation Format

Share Document