scholarly journals Optimal Assimilation of Daytime SST Retrievals from SEVIRI in a Regional Ocean Prediction System

2019 ◽  
Vol 11 (23) ◽  
pp. 2776 ◽  
Author(s):  
Andrea Storto ◽  
Paolo Oddo
Keyword(s):  
Data Assimilation ◽  
Mixed Layer ◽  
Bias Correction ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Prediction System ◽  
Ligurian Sea ◽  
Skill Scores ◽  
Correction Scheme ◽  
Ocean Prediction

Exploiting the potential of space-borne oceanic measurements to characterize the sub-surface structure of the ocean becomes critical in areas where deployment of in situ sensors might be difficult or expensive. Sea Surface Temperature (SST) observations potentially provide enormous amounts of information about the upper ocean variability. However, the assimilation of daytime SST retrievals, e.g., from infrared sensors into ocean prediction systems, requires a specific treatment of the diurnal cycle of skin SST, which is generally under-estimated in current ocean models due to poor vertical resolution at the air–sea interface and lack of proper parameterizations. To this end, a simple off-line bias correction scheme is proposed, where the bias predictors include, among others, the warm layer and cool skin warming/cooling deduced from a prognostic model. Furthermore, a localization procedure that limits the vertical penetration of the SST information in a hybrid variational-ensemble data assimilation system is formulated. These two novelties are implemented and assessed within a regional ocean prediction system in the Ligurian Sea for the assimilation of daytime SST data retrieved with hourly frequency from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary satellite Meteosat-10. Experiments are validated against independent measurements collected by gliders, moorings, and drifters during the Long-term Glider Missions for Environmental Characterization (LOGCMEC17) sea trial. Results suggest that the simple bias correction scheme is effective in improving both the sea surface and mixed layer accuracy, correctly thinning the mixed layer compared to the control experiment, outperforming experiments with night-only data assimilation, and improving the forecast skill scores. Localization further improves the prediction of the mixed layer depth. It is therefore recommended that sophisticated bias correction and localization procedures are adopted for fruitfully assimilating daytime SST data in operational oceanographic analysis systems.

scholarly journals Can wave coupling improve operational regional ocean forecasts for the North-West European Shelf?

2018 ◽  
Author(s):  
Huw W. Lewis ◽  
Juan Manuel Castillo Sanchez ◽  
John Siddorn ◽  
Robert R. King ◽  
Marina Tonani ◽  
...  
Keyword(s):  
Mixed Layer ◽  
Sea Surface ◽  
Ocean Wave ◽  
Prediction System ◽  
Wave Coupling ◽  
North West ◽  
The North ◽  
European Shelf ◽  
Ocean Prediction ◽  
The Impact

Abstract. Operational ocean forecasts are typically produced by modelling systems run using a forced mode approach. The evolution of the ocean state is not directly influenced by surface waves, and the ocean dynamics are driven by an external source of meteorological data which is independent of the ocean state. Model coupling provides one approach to increase the extent to which ocean forecast systems can represent the interactions and feedbacks between ocean, waves and the atmosphere seen in nature. This paper demonstrates the impact of improving how the effect of waves on the momentum exchange across the ocean-atmosphere interface is represented through ocean-wave coupling on the performance of an operational regional ocean prediction system. This study focuses on the eddy-resolving (1.5 km resolution) Atlantic Margin Model (AMM15) ocean model configuration for the North-West European Shelf (NWS) region. A series of two-year duration forecast trials of the Copernicus Marine Environment Monitoring Service (CMEMS) North-West Shelf regional ocean prediction system are analysed. The impact of including ocean-wave feedbacks via dynamic coupling on the simulated ocean is discussed. The main interactions included are the modification of surface stress by wave growth and dissipation, Stokes–Coriolis forcing and wave height dependent ocean surface roughness. Given the relevance to operational forecasting, trials with and without ocean data assimilation are considered. Summary forecast metrics demonstrate that the ocean-wave coupled system is a viable evolution for future operational implementation. When results are considered in more depth, wave coupling was found to result in an annual cycle of relatively warmer winter and cooler summer sea surface temperatures for seasonally stratified regions of the NWS. This is driven by enhanced mixing due to waves, and a deepening of the ocean mixed layer during summer. The impact of wave coupling is shown to be reduced within the mixed layer with assimilation of ocean observations. Evaluation of salinity and ocean currents against profile measurements in the German Bight demonstrates improved simulation with wave coupling relative to control simulations. Further, evidence is provided of improvement to simulation of extremes of sea surface height anomalies relative to coastal tide gauges.

scholarly journals Global sensitivity analysis of the Indian monsoon during the Pleistocene

2015 ◽  
Vol 11 (1) ◽  
pp. 45-61 ◽  
Author(s):  
P. A. Araya-Melo ◽  
M. Crucifix ◽  
N. Bounceur
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mixed Layer ◽  
Global Sensitivity Analysis ◽  
Indian Monsoon ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Layer Depth ◽  
Global Sensitivity

Abstract. The sensitivity of the Indian monsoon to the full spectrum of climatic conditions experienced during the Pleistocene is estimated using the climate model HadCM3. The methodology follows a global sensitivity analysis based on the emulator approach of Oakley and O'Hagan (2004) implemented following a three-step strategy: (1) development of an experiment plan, designed to efficiently sample a five-dimensional input space spanning Pleistocene astronomical configurations (three parameters), CO2 concentration and a Northern Hemisphere glaciation index; (2) development, calibration and validation of an emulator of HadCM3 in order to estimate the response of the Indian monsoon over the full input space spanned by the experiment design; and (3) estimation and interpreting of sensitivity diagnostics, including sensitivity measures, in order to synthesise the relative importance of input factors on monsoon dynamics, estimate the phase of the monsoon intensity response with respect to that of insolation, and detect potential non-linear phenomena. By focusing on surface temperature, precipitation, mixed-layer depth and sea-surface temperature over the monsoon region during the summer season (June-July-August-September), we show that precession controls the response of four variables: continental temperature in phase with June to July insolation, high glaciation favouring a late-phase response, sea-surface temperature in phase with May insolation, continental precipitation in phase with July insolation, and mixed-layer depth in antiphase with the latter. CO2 variations control temperature variance with an amplitude similar to that of precession. The effect of glaciation is dominated by the albedo forcing, and its effect on precipitation competes with that of precession. Obliquity is a secondary effect, negligible on most variables except sea-surface temperature. It is also shown that orography forcing reduces the glacial cooling, and even has a positive effect on precipitation. As regards the general methodology, it is shown that the emulator provides a powerful approach, not only to express model sensitivity but also to estimate internal variability and detect anomalous simulations.

Statistical features of the Oceanographic area off south-western Australia, obtained from Bathythermograph data

1986 ◽  
Vol 37 (4) ◽  
pp. 421 ◽  
Author(s):  
LJ Hamilton
Keyword(s):  
Mixed Layer ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Temperature Fields ◽  
Surface Mixed Layer ◽  
Statistical Features ◽  
Layer Depth ◽  
Leeuwin Current ◽  
South West

A statistical analysis has been made of 26 years of bathythermograph (BT) data to 1980 for the south-west Australian area bounded by 30-35�s. and 110-115�E., a region influenced by the Leeuwin Current. The data indicate that a surface mixed layer exists all year round, with average depth 55 m and standard deviation 37 m. All but 2% of BT casts show a mixed-layer depth (MLD) less than 150 m. MLD are deepest in mid-year, particularly from July to September. Sea surface temperatures (SST) are significantly related to temperature values down to 200 m depth, especially in mid-year, for both eastern and western parts of the area separated by 113�E. Correlations of MLD with SST are significant only in the western part, and then only from January to March, and April to June. Long-term horizontally averaged temperature fields are broadly related through the water column from the surface to 200 m. All results indicate that, especially in mid-year, SST fields are related to subsurface temperature fields, which may be representative of flow structure. Seasonal differences exist between the eastern and western areas, caused by the Leeuwin Current.

Assimilation of Satellite-retrieved Sea Ice Concentration and Prospects for September Predictions of Arctic Sea Ice

2020 ◽  
pp. 1-49
Author(s):  
Yong-Fei Zhang ◽  
Mitchell Bushuk ◽  
Michael Winton ◽  
Bill Hurlin ◽  
Xiaosong Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Sea Surface ◽  
Prediction System ◽  
Observational Error ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Ice Concentration ◽  
Analysis Errors

AbstractThe current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea-ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice-ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SST) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (∼10%) from up to 3 times larger than this (∼30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from 5-daily to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.

scholarly journals Mixed Layer Lateral Eddy Fluxes Mediated by Air–Sea Interaction

2011 ◽  
Vol 41 (1) ◽  
pp. 130-144 ◽  
Author(s):  
Emily Shuckburgh ◽  
Guillaume Maze ◽  
David Ferreira ◽  
John Marshall ◽  
Helen Jones ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Mixed Layer ◽  
Mixed Layer Depth ◽  
Effective Diffusivity ◽  
Eddy Diffusivity ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Temperature Fields ◽  
Small Scale ◽  
Eddy Diffusivities

Abstract The modulation of air–sea heat fluxes by geostrophic eddies due to the stirring of temperature at the sea surface is discussed and quantified. It is argued that the damping of eddy temperature variance by such air–sea fluxes enhances the dissipation of surface temperature fields. Depending on the time scale of damping relative to that of the eddying motions, surface eddy diffusivities can be significantly enhanced over interior values. The issues are explored and quantified in a controlled setting by driving a tracer field, a proxy for sea surface temperature, with surface altimetric observations in the Antarctic Circumpolar Current (ACC) of the Southern Ocean. A new, tracer-based diagnostic of eddy diffusivity is introduced, which is related to the Nakamura effective diffusivity. Using this, the mixed layer lateral eddy diffusivities associated with (i) eddy stirring and small-scale mixing and (ii) surface damping by air–sea interaction is quantified. In the ACC, a diffusivity associated with surface damping of a comparable magnitude to that associated with eddy stirring (∼500 m2 s−1) is found. In frontal regions prevalent in the ACC, an augmentation of surface lateral eddy diffusivities of this magnitude is equivalent to an air–sea flux of 100 W m−2 acting over a mixed layer depth of 100 m, a very significant effect. Finally, the implications for other tracer fields such as salinity, dissolved gases, and chlorophyll are discussed. Different tracers are found to have surface eddy diffusivities that differ significantly in magnitude.

scholarly journals The Regional Ice Ocean Prediction System v2: a pan-Canadian ocean analysis system

2020 ◽  
Author(s):  
Gregory C. Smith ◽  
Yimin Liu ◽  
Mounir Benkiran ◽  
Kamel Chikhar ◽  
Dorina Surcel Colan ◽  
...  
Keyword(s):  
Gulf Stream ◽  
Water Mass ◽  
Grid Resolution ◽  
Sea Surface ◽  
Prediction System ◽  
Sea Level Anomaly ◽  
Tidal Harmonic ◽  
Mass Properties ◽  
Ocean Prediction ◽  
Analysis System

Abstract. Canada has the longest coastline in the world and includes a diversity of ocean environments, from the frozen waters of the Canadian Arctic Archipelago to the confluence region of Labrador and Gulf Stream waters on the East Coast. There is a strong need for a pan-Canadian operational regional ocean prediction capacity covering all Canadian coastal areas, in support of marine activities including emergency response, search and rescue as well as safe navigation in ice-infested waters. Here we present the first pan-Canadian operational regional ocean analysis system developed as part of the Regional Ice Ocean Prediction System version 2 (RIOPSv2) running in operations at the Canadian Centre for Meteorological and Environmental Prediction (CCMEP). The RIOPSv2 domain extends from 26° N in the Atlantic Ocean through the Arctic Ocean to 44° N in the Pacific Ocean, with a model grid-resolution that varies between 3 and 8 km. RIOPSv2 includes a multi-variate data assimilation system based on a reduced-order extended Kalman filter together with a 3DVar bias correction system for water mass properties. The analysis system assimilates satellite observations of sea level anomaly and sea surface temperature, as well as in situ temperature and salinity measurements. Background model error is specified in terms of seasonally varying model anomalies from a 10-year forced model integration allowing inhomogeneous anisotropic multi-variate error covariances. A novel online tidal harmonic analysis method is introduced that uses a sliding-window approach to reduce numerical costs and to allow time-varying harmonic constants, necessary in seasonally ice-infested waters. As compared to the Global Ice Ocean Prediction System (GIOPS) running at CCMEP, RIOPSv2 also includes a spatial filtering of model fields as part of the observation operator for sea surface temperature. In addition to the tidal harmonic analysis, the observation operator for sea level anomaly is also modified to remove the inverse barometer effect due to the application of atmospheric pressure forcing fields. RIOPSv2 is compared to GIOPS and shown to provide similar innovation statistics over a 3-year evaluation period. Specific improvements are found in the vicinity of the Gulf Stream for all model fields due to the higher model grid-resolution, with smaller root-mean-squared (RMS) innovations for RIOPSv2 of about 5 cm for SLA and 0.5 °C for SST. Verification against along-track satellite observations demonstrates the improved representation of meso-scale features in RIOPSv2 compared to GIOPS, with increased correlations of SLA (0.83 compared to 0.73) and reduced RMS differences (12 cm compared to 14 cm). While the RIOPSv2 grid resolution is 3 times higher than GIOPS, the power spectral density of surface kinetic energy provides an indication that the effective resolution of RIOPSv2 is roughly double that of the global system (35 km as compared to 66 km). Observations made as part of the Year of Polar Prediction (2017–19) provide a rare glimpse at errors in Arctic water mass properties and show salinity biases of 0.3–0.4 psu in the eastern Beaufort Sea in RIOPSv2.

scholarly journals Interannual Variability of the Wind-Wave Regime Parameters in the Black Sea

2020 ◽  
Vol 27 (5) ◽  
Author(s):  
P. N. Lishaev ◽  
V. V. Knysh ◽  
G. K. Korotaev ◽  
◽  
◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Black Sea ◽  
Mixed Layer ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Temperature Fields ◽  
The Black Sea ◽  
Layer Depth ◽  
Upper Mixed Layer

Purpose. The investigation is aimed at increasing accuracy of the temperature field reconstruction in the Black Sea upper layer. For this purpose, satellite observations of the sea surface temperature and the three-dimensional fields of temperature (in the 50–500 m layer) and salinity (in the 2.5–500 m layer) pseudo-measurements, previously calculated by the altimetry and the Argo floats data, were jointly assimilated in the Marine Hydrophysical Institute model. Methods and Results. Assimilation of the sea surface temperature satellite observations is the most effective instrument in case the discrepancies between the sea surface and the model temperatures are extrapolated over the upper mixed layer depth up to its lower boundary. Having been analyzed, the temperature profiles resulted from the forecast calculation for 2012 and from the Argo float measurements made it possible to obtain a simple criterion (bound to the model grid) for determining the upper mixed layer depth, namely the horizon on which the temperature gradient was less or equal to ≤ 0.017 °C/m. Within the upper mixed layer depth, the nudging procedure of satellite temperature measurements with the selected relaxation factor and the measurement errors taken into account was used in the heat transfer equation. The temperature and salinity pseudo-measurements were assimilated in the model by the previously proposed adaptive statistics method. To test the results of the sea surface temperature assimilation, the Black Sea hydrophysical fields were reanalyzed for 2012. The winter-spring period (January – April, December) is characterized by the high upper mixed layer depths, well reproducible by the Pacanowski – Philander parameterization, and also by the low values (as compared to the measured ones) of the basin-averaged monthly mean square deviations of the simulated temperature fields. The increased mean square deviations in July – September are explained by absence of the upper mixed layer in the temperature profiles measured by the Argo floats that is not reproduced by the Pacanowski – Philander parameterization. Conclusions. The algorithm for assimilating the sea surface temperature together with the profiles of the temperature and salinity pseudo-measurements reconstructed from the altimetry data was realized. Application of the upper mixed layer depths estimated by the temperature vertical profiles made it possible to correct effectively the model temperature by the satellite-derived sea surface temperature, especially for a winter-spring period. It permitted to reconstruct the temperature fields in the sea upper layer for 2012 with acceptable accuracy.

scholarly journals STUDY OF OCEAN PRIMARY PRODUCTIVITY USING OCEAN COLOR DATA AROUND JAPAN

2010 ◽  
Vol 2 (-) ◽  
Author(s):  
TAKAHIRO OSAWA ◽  
CHAO FANG ZHAO ◽  
I WAYAN Nuarsa ◽  
I Ketut Swardika ◽  
YASUHIRO SUGIMORI
Keyword(s):  
Neural Network ◽  
Chlorophyll A ◽  
Mixed Layer ◽  
Primary Productivity ◽  
Mixed Layer Depth ◽  
Ocean Color ◽  
Sea Surface ◽  
Layer Depth ◽  
Concentration Data ◽  
Gaussian Shape

Ocean primary production is an important factor for determining the ocean's role in global carbon cycle. In recent years, much more chlorophyll-a concentration data in the euphotic layer were derived from the satellite ocean color sensors. The primary productivity algorithms have been proposed based on satellite chlorophyll measurements (Piatt, 1988; Morel, 1991) and other environmental parameters such as sea surface temperature or mixed layer depth (Behrenfeld and Falkowski, 1997; Esaias, 1996; Asanuma, 2002). In order to estimate integrated primary productivity in the whole water column, the vertical distribution of chlorophyll concentration below the sea surface should be reconstructed based on satellite data. In this paper, the vertical profile data of chlorophyll-a (Chl-a) measured around Japan Islands from 1974 to 1994 were reanalyzed based on the shifted-Gaussian shape proposed by Piatt et al (1988). Using this statistical model (neural network) and the photosynthesis irradiance parameters from Asanuma (2002), the distribution of primary productivity and its seasonal variation around Japan islands were estimated from SeaWiFS data, and the results were compared with in situ data and the other two models estimated from VGPM and mixed layer depth model. Keywords: ocean color, primary productivity, chlorophyll profile, artificial neural network

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.

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