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

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.

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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.

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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

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Numerical Simulation of Heavy Rainfall in August 2014 over Japan and Analysis of Its Sensitivity to Sea Surface Temperature

Atmosphere ◽

10.3390/atmos9030084 ◽

2018 ◽

Vol 9 (3) ◽

pp. 84 ◽

Cited By ~ 2

Author(s):

Yuki Minamiguchi ◽

Hikari Shimadera ◽

Tomohito Matsuo ◽

Akira Kondo

Keyword(s):

Numerical Simulation ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Heavy Rainfall ◽

Sea Surface

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

Climate of the Past ◽

10.5194/cp-9-887-2013 ◽

2013 ◽

Vol 9 (2) ◽

pp. 887-901 ◽

Cited By ~ 28

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.

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Local Atmosphere–Ocean Predictability: Dynamical Origins, Lead Times, and Seasonality

Journal of Climate ◽

10.1175/jcli-d-18-0817.1 ◽

2019 ◽

Vol 32 (21) ◽

pp. 7507-7519 ◽

Cited By ~ 2

Author(s):

Eviatar Bach ◽

Safa Motesharrei ◽

Eugenia Kalnay ◽

Alfredo Ruiz-Barradas

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Phase Relationship ◽

Sea Surface ◽

Low Frequencies ◽

Model Studies ◽

Almost Everywhere ◽

Summer Hemisphere ◽

Coupled Data Assimilation ◽

The Tropics

Abstract Due to the physical coupling between atmosphere and ocean, information about the ocean helps to better predict the future of the atmosphere, and in turn, information about the atmosphere helps to better predict the ocean. Here, we investigate the spatial and temporal nature of this predictability: where, for how long, and at what frequencies does the ocean significantly improve prediction of the atmosphere, and vice versa? We apply Granger causality, a statistical test to measure whether a variable improves prediction of another, to local time series of sea surface temperature (SST) and low-level atmospheric variables. We calculate the detailed spatial structure of the atmosphere-to-ocean and ocean-to-atmosphere predictability. We find that the atmosphere improves prediction of the ocean most in the extratropics, especially in regions of large SST gradients. This atmosphere-to-ocean predictability is weaker but longer-lived in the tropics, where it can last for several months in some regions. On the other hand, the ocean improves prediction of the atmosphere most significantly in the tropics, where this predictability lasts for months to over a year. However, we find a robust signature of the ocean on the atmosphere almost everywhere in the extratropics, an influence that has been difficult to demonstrate with model studies. We find that both the atmosphere-to-ocean and ocean-to-atmosphere predictability are maximal at low frequencies, and both are larger in the summer hemisphere. The patterns we observe generally agree with dynamical understanding and the results of the Kalnay dynamical rule, which diagnoses the direction of forcing between the atmosphere and ocean by considering the local phase relationship between simultaneous sea surface temperature and vorticity anomaly signals. We discuss applications to coupled data assimilation.

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

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-12-00238.1 ◽

2013 ◽

Vol 30 (12) ◽

pp. 2926-2943 ◽

Cited By ~ 4

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.

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Evaluation and Preprocess of Chinese Fengyun-3A Sea Surface Temperature Experimental Product for Data Assimilation

Atmospheric and Oceanic Science Letters ◽

10.1080/16742834.2013.11447068 ◽

2013 ◽

Vol 6 (3) ◽

pp. 128-132 ◽

Cited By ~ 1

Author(s):

Zhou Wei ◽

Cheng Yan-Jie ◽

Wang Su-Juan ◽

Wang Zai-Zhi

Keyword(s):

Data Assimilation ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Surface

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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

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Oceanic and radiative forcing of medieval megadroughts in the American Southwest

Science Advances ◽

10.1126/sciadv.aax0087 ◽

2019 ◽

Vol 5 (7) ◽

pp. eaax0087 ◽

Cited By ~ 14

Author(s):

Nathan J. Steiger ◽

Jason E. Smerdon ◽

Benjamin I. Cook ◽

Richard Seager ◽

A. Park Williams ◽

...

Keyword(s):

Global Warming ◽

Data Assimilation ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Radiative Forcing ◽

American Southwest ◽

Sea Surface ◽

Comprehensive Theory ◽

The Common ◽

Common Era

Multidecadal “megadroughts” were a notable feature of the climate of the American Southwest over the Common era, yet we still lack a comprehensive theory for what caused these megadroughts and why they curiously only occurred before about 1600 CE. Here, we use the Paleo Hydrodynamics Data Assimilation product, in conjunction with radiative forcing estimates, to demonstrate that megadroughts in the American Southwest were driven by unusually frequent and cold central tropical Pacific sea surface temperature (SST) excursions in conjunction with anomalously warm Atlantic SSTs and a locally positive radiative forcing. This assessment of past megadroughts provides the first comprehensive theory for the causes of megadroughts and their clustering particularly during the Medieval era. This work also provides the first paleoclimatic support for the prediction that the risk of American Southwest megadroughts will markedly increase with global warming.

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