Variational Data Assimilation for the Global Ocean

Cummings, James; Smedstad, Ole Martin

doi:10.1007/978-3-642-35088-7_13

Cited by 264 publications

(218 citation statements)

References 34 publications

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“…The shortwave radiation forcing has an analytic diurnal cycle superimposed. Three-dimensional multivariate data assimilation is performed via the Navy Coupled Ocean Data Assimilation system (Cummings 2005, Cummings & Smedstad 2013.…”

Section: Physical Model Descriptionmentioning

confidence: 99%

Factors controlling the seasonal distribution of pelagic Sargassum

Brooks¹,

Coles²,

Hood³

et al. 2018

Mar. Ecol. Prog. Ser.

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Section: Physical Model Descriptionmentioning

confidence: 99%

Factors controlling the seasonal distribution of pelagic Sargassum

Brooks¹,

Coles²,

Hood³

et al. 2018

Mar. Ecol. Prog. Ser.

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“…[46] ( Table 2). The HYCOM model uses the Navy Coupled Ocean Data Assimilation (NCODA) system [47] to incorporate information from satellite altimeters and SSTs, as well as data from ocean floats and buoys through the application of a three-dimensional variation (3DVAR) scheme [48]. Atmospheric aerosols-specifically mineral dust which contains iron (Fe), phosphorus (P), and other micronutrients-have been postulated to play an important role in the fertilization of the global oceans, especially in nutrient-limited areas such as oligotrophic seas [49,50].…”

Section: The Ocean Color Climate Change Initiative (Oc-cci) Datamentioning

confidence: 99%

Synergistic Use of Remote Sensing and Modeling to Assess an Anomalously High Chlorophyll-a Event during Summer 2015 in the South Central Red Sea

El-Askary

Manikandan

et al. 2017

Remote Sensing

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An anomalously high chlorophyll-a (Chl-a) event (>2 mg/m 3 ) during June 2015 in the South Central Red Sea (17.5 • to 22 • N, 37 • to 42 • E) was observed using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellite platforms. This differs from the low Chl-a values (<0.5 mg/m 3 ) usually encountered over the same region during summertime. To assess this anomaly and possible causes, we used a wide range of oceanographical and meteorological datasets, including Chl-a concentrations, sea surface temperature (SST), sea surface height (SSH), mixed layer depth (MLD), ocean current velocity and aerosol optical depth (AOD) obtained from different sensors and models. Findings confirmed this anomalous behavior in the spatial domain using Hovmöller data analysis techniques, while a time series analysis addressed monthly and daily variability. Our analysis suggests that a combination of factors controlling nutrient supply contributed to the anomalous phytoplankton growth. These factors include horizontal transfer of upwelling water through eddy circulation and possible mineral fertilization from atmospheric dust deposition. Coral reefs might have provided extra nutrient supply, yet this is out of the scope of our analysis. We thought that dust deposition from a coastal dust jet event in late June, coinciding with the phytoplankton blooms in the area under investigation, might have also contributed as shown by our AOD findings. However, a lag cross correlation showed a two-month lag between strong dust outbreak and the high Chl-a anomaly. The high Chl-a concentration at the edge of the eddy emphasizes the importance of horizontal advection in fertilizing oligotrophic (nutrient poor) Red Sea waters.

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“…An adjoint-based procedure to determine the impact of assimilation of observations on reducing ocean model forecast error has been integrated into the Navy's global HYCOM ocean analysis/forecast system (Cummings and Smedstad, 2013). Adjoint sensitivity gradients and actual model-data differences are used to estimate the impact of each observation assimilated on a measure of model forecast error (Langland and Baker, 2004).…”

Section: Resultsmentioning

confidence: 99%

Multi-sensor Improved Sea-Surface Temperature (MISST) for IOOS - Navy Component

Barron¹

2013

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Award Number: N000141221311 https://www.ghrsst.org/ LONG-TERM GOALSEnsure that Navy capabilities for a new generation of sea surface temperature products are coordinated with and benefitting from international research and development embodied by the Group for High Resolution Sea Surface Temperature (SST; GHRSST) and domestic capabilities demonstrated in the U.S. Integrated Ocean Observing System (IOOS) regions and applicable to regions around the world. This project is the Navy component of a broad national NOPP/IOOS/NASA MISST for IOOS project led by Chelle Gentemann. The Navy Participants under the ONR funding are Charlie Barron (NRL), James Cummings (NRL), Bruce McKenzie (NAVOCEANO), and Doug May (NAVOCEANO). OBJECTIVESThe overarching objectives in MISST for IOOS are to continue producing GHRSST compliant satellite SSTs from existing and new sensors and to produce multi-sensor blended gap-free SSTs from US and international GHRSST datasets. The objectives of the Navy component are to coordinate Navy research and development with GHRSST through complementary tasks and interaction at the annual meetings, use of GHRSST data sets for assimilation and validation, and intercomparison of Navy and other GHRSST products. The Navy participants have work elements that support the five MISST for IOOS project tasks:1. Computation of sensor-specific observational error characteristics required for optimal application and data fusion techniques.2. Parameterization of IR and MW retrieval differences, with consideration of diurnal warming and cool-skin effects required for multi-sensor blending.3. Retrospective reanalysis, continued NRT production, and dissemination of sensor-specific SST products with associated retrieval confidence, standard deviation (STD), and diurnal warming estimates to the application user community in the new GDS 2.0 GHRSST format.4. Retrospective reanalysis, continued NRT production, and dissemination of improved multisensor high-resolution SST analyses in the new GDS 2.0 format, to demonstrate and optimize utility in IOOS and operational applications.

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Variational Data Assimilation for the Global Ocean

Cited by 264 publications

References 34 publications

Factors controlling the seasonal distribution of pelagic Sargassum

Factors controlling the seasonal distribution of pelagic Sargassum

Synergistic Use of Remote Sensing and Modeling to Assess an Anomalously High Chlorophyll-a Event during Summer 2015 in the South Central Red Sea

Multi-sensor Improved Sea-Surface Temperature (MISST) for IOOS - Navy Component

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