Validation of a hybrid optimal interpolation and Kalman filter scheme for sea surface temperature assimilation

Abstract Sea surface temperature (SST) plays an important role in tropical cyclone (TC) life cycle evolution, but often the uncertainties in SST estimates are not considered in the ensemble Kalman filter (EnKF). The lack of uncertainties in SST generally results in the lack of ensemble spread in the atmospheric states near the sea surface, particularly for temperature and moisture. In this study, the uncertainties of SST are included by adding ensemble perturbations to the SST field, and the impact of the SST perturbations is investigated using the local ensemble transform Kalman filter (LETKF) with the Weather Research and Forecasting Model (WRF) in the case of Typhoon Sinlaku (2008). In addition to the experiment with the perturbed SST, another experiment with manually inflated ensemble perturbations near the sea surface is performed for comparison. The results indicate that the SST perturbations within EnKF generally improve analyses and their subsequent forecasts, although manually inflating the ensemble spread instead of perturbing SST does not help. Investigations of the ensemble-based forecast error covariance indicate larger scales for low-level temperature and moisture from the SST perturbations, although manual inflation of ensemble spread does not produce such structural effects on the forecast error covariance. This study suggests the importance of considering SST perturbations within ensemble-based data assimilation and promotes further studies with more sophisticated methods of perturbing SST fields such as using a fully coupled atmosphere–ocean model.

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Validation of satellite-based sea surface temperature products against in situ observations off the western coast of Sumatra

Scientific Reports ◽

10.1038/s41598-021-04156-0 ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Qoosaku Moteki

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Surface ◽

Optimal Interpolation ◽

Western Coast ◽

Mean Square ◽

Ensemble Spread ◽

In Situ Observations ◽

High Clouds

AbstractThis study validated the sea surface temperature (SST) datasets from the Group for High-Resolution SST Multi Product Ensemble (GMPE), National Oceanic and Atmospheric Administration (NOAA) Optimal Interpolation (OI) SST version 2 and 2.1 (OIv2 and OIv2.1), and Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) in the area off the western coast of Sumatra against in situ observations. Furthermore, the root mean square differences (RMSDs) of OIv2, OIv2.1, and ECCO2 were investigated with respect to GMPE, whose small RMSD < 0.2 K against in situ observations confirmed its suitability as a reference. Although OIv2 showed a large RMSD (1–1.5 K) with a significant negative bias, OIv2.1 (RMSD < 0.4 K) improved remarkably. In the average SST distributions for December 2017, the differences among the 4 datasets were significant in the areas off the western coast of Sumatra, along the southern coast of Java, and in the Indonesian inland sea. These results were consistent with the ensemble spread distribution obtained with GMPE. The large RMSDs of OIv2 corresponded to high clouds, and it was suggested that the change in the satellites used for SST estimation contributed to the improvement in OIv2.1.

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DINEOF reconstruction of clouded images including error maps – application to the Sea-Surface Temperature around Corsican Island

Ocean Science ◽

10.5194/os-2-183-2006 ◽

2006 ◽

Vol 2 (2) ◽

pp. 183-199 ◽

Cited By ~ 45

Author(s):

J.-M. Beckers ◽

A. Barth ◽

A. Alvera-Azcárate

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Empirical Orthogonal Functions ◽

Sea Surface ◽

Optimal Interpolation ◽

Orthogonal Functions ◽

Ligurian Sea ◽

Additional Information ◽

Error Covariance ◽

Data Coverage

Abstract. We present an extension to the Data INterpolating Empirical Orthogonal Functions (DINEOF) technique which allows not only to fill in clouded images but also to provide an estimation of the error covariance of the reconstruction. This additional information is obtained by an analogy with optimal interpolation. It is shown that the error fields can be obtained with a clever rearrangement of calculations at a cost comparable to that of the interpolation itself. The method is presented on the reconstruction of sea-surface temperature in the Ligurian Sea and around the Corsican Island (Mediterranean Sea), including the calculation of inter-annual variability of average surface values and their expected errors. The application shows that the error fields are not only able to reflect the data-coverage structure but also the covariances of the physical fields.

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Validation of Satellite-Based Sea Surface Temperature Products Against in Situ Observations Off the Western Coast of Sumatra

10.21203/rs.3.rs-556590/v1 ◽

2021 ◽

Author(s):

Qoosaku MOTEKI

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Surface ◽

Optimal Interpolation ◽

Western Coast ◽

Mean Square ◽

Ensemble Spread ◽

In Situ Observations ◽

High Clouds

Abstract This study validated the sea surface temperature (SST) datasets from the Group for High-Resolution SST Multi Product Ensemble (GMPE), National Oceanic and Atmospheric Administration (NOAA) Optimal Interpolation (OI) SST version 2 and 2.1 (OIv2 and OIv2.1), and Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) in the area off the western coast of Sumatra against in situ observations. Furthermore, the root mean square differences (RMSDs) of OIv2, OIv2.1, and ECCO2 were investigated with respect to GMPE, whose small RMSD < 0.2 K against in situ observations confirmed its suitability as a reference. Although OIv2 showed a large RMSD (1-1.5 K) with a significant negative bias, OIv2.1 (RMSD < 0.4 K) improved remarkably. In the average SST distributions for December 2017, the differences among the 4 datasets were significant in the areas off the western coast of Sumatra, along the southern coast of Java, and in the Indonesian inland sea. These results were consistent with the ensemble spread distribution obtained with GMPE. The large RMSDs of OIv2 corresponded to high clouds, and it was suggested that the change in the satellites used for SST estimation contributed to the improvement in OIv2.1.

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PREDIKSI CURAH HUJAN BULANAN BERDASARKAN SUHU PERMUKAAN LAUT NINO 3.4 : SUATU PENDEKATAN DENGAN METODE FILTER KALMAN(MONTHLY RAINFALL PREDICTION BASED ON SEA SURFACE TEMPERATURE NINO 3.4 : THE APPROACH WITH KALMAN FILTERING)

Agromet ◽

10.29244/j.agromet.19.2.43-56 ◽

2005 ◽

Vol 19 (2) ◽

pp. 43

Author(s):

Woro Estinigtyas ◽

S. Suciantini ◽

G. Irianto

Keyword(s):

Kalman Filter ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Correlation Coefficient ◽

Kalman Filtering ◽

Sea Surface ◽

Deterministic Models ◽

Rainfall Prediction ◽

Dependent Variables ◽

On Line

Many approaches have been applied to forecast climate using statistical and deterministic models using independent and dependent variables empirically. It is more practical to analyze the parameters, but it needs validation anytime and anywhere. Kalman filtering unites physical and statistical model approaches to stochastic model renewable anytime for objective of on line forecasting. Based on research, sea surface temperature Nino 3.4 have high correlation with rainfall in Indonesia, so it is used to forecast rainfall in Cirebon as area study. Rainfall clustering in Cirebon results 6 groups with rainfall average 1400-1500 mm/year for dry area and 3000-3200 mm/year for wet area. Validation have correlation coefficient validation value more than 94%, correlation coefficient model value more than 78% and fit model value more than 38%. The result of regression gives R2 value of more than 0,8. It implies that predicting model using Kalman Filter is feasible to forecast montly rainfall based on sea surface temperature Nino 3.4. The result of rainfall prediction in Cirebon show increasing in rainfall until February 2005, with correlation coeficient value of model more than 90% and fit model more than 40%.

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Assimilation of sea surface temperature in the Northwest Pacific Ocean and its marginal seas using the ensemble Kalman filter

Ocean Science Journal ◽

10.1007/s12601-010-0021-4 ◽

2010 ◽

Vol 45 (4) ◽

pp. 225-242 ◽

Cited By ~ 13

Author(s):

Gwang-Ho Seo ◽

Byoung-Ju Choi ◽

Yang-Ki Cho ◽

Young Ho Kim ◽

Sangil Kim

Keyword(s):

Kalman Filter ◽

Pacific Ocean ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Ensemble Kalman Filter ◽

Sea Surface ◽

Northwest Pacific ◽

Northwest Pacific Ocean ◽

Marginal Seas ◽

The Northwest Pacific Ocean

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DINEOF reconstruction of clouded images including error maps. Application to the Sea-Surface Temperature around Corsican Island

Ocean Science Discussions ◽

10.5194/osd-3-735-2006 ◽

2006 ◽

Vol 3 (4) ◽

pp. 735-776 ◽

Cited By ~ 2

Author(s):

J.-M. Beckers ◽

A. Barth ◽

A. Alvera-Azcárate

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Empirical Orthogonal Functions ◽

Sea Surface ◽

Optimal Interpolation ◽

Orthogonal Functions ◽

Ligurian Sea ◽

Additional Information ◽

Error Covariance ◽

Data Coverage

Abstract. We present an extension to the Data INterpolating Empirical Orthogonal Functions (DINEOF) which allows not only to fill in clouded images but also to provide an estimation of the error covariance of the reconstruction. This additional information is obtained by an analogy with optimal interpolation. It is shown that the error fields can be obtained with a clever rearrangement of calculations at a cost comparable to that of the interpolation itself. The method is presented on the reconstruction of sea-surface temperature in the Ligurian Sea and around the Corsican Island (Mediterranean Sea), including the calculation of inter-annual variability of average surface values and their expected errors. The application shows that the error fields are not only able to reflect the data-coverage structure but also the covariances of the physical fields.

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Uncertainties in Ocean Latent Heat Flux Variations over Recent Decades in Satellite-Based Estimates and Reduced Observation Reanalyses

Journal of Climate ◽

10.1175/jcli-d-19-0954.1 ◽

2020 ◽

Vol 33 (19) ◽

pp. 8415-8437

Author(s):

Franklin R. Robertson ◽

Jason B. Roberts ◽

Michael G. Bosilovich ◽

Abderrahim Bentamy ◽

Carol Anne Clayson ◽

...

Keyword(s):

Wind Speed ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Latent Heat ◽

Heat Fluxes ◽

Sea Surface ◽

Optimal Interpolation ◽

Regional Patterns ◽

Near Surface ◽

Microwave Imager

AbstractFour state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed intercomparisons are made with other datasets including 1) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; 2) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage, and satellite precipitation; 3) the ECMWF Reanalysis 5 (ERA5); 4) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals; and 5) several sea surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990–2010 period, during which time the interdecadal Pacific oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5, and RedObs are reasonably consistent in identifying contributions by the 10-m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager/Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates, and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s.

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