Implementing a Parallel Version of a Variational Scheme in a Global Assimilation System at Eddy-Resolving Resolution

2020 ◽  
Vol 37 (10) ◽  
pp. 1865-1876
Author(s):  
Andrea Cipollone ◽  
Andrea Storto ◽  
Simona Masina
Keyword(s):  
Global Ocean ◽  
Error Statistics ◽  
Observation Network ◽  
Ocean Prediction ◽  
Realistic Representation ◽  
Artificial Boundaries ◽  
Prediction Systems ◽  
Set Up ◽  
Mesoscale Processes

AbstractRecent advances in global ocean prediction systems are fostered by the needs of accurate representation of mesoscale processes. The day-by-day realistic representation of its variability is hampered by the scarcity of observations as well as the capability of assimilation systems to correct the ocean states at the same scale. This work extends a 3DVAR system designed for oceanic applications to cope with global eddy-resolving grid and dense observational datasets in a hybridly parallelized environment. The efficiency of the parallelization is assessed in terms of both scalability and accuracy. The scalability is favored by a weak-constrained formulation of the continuity requirement among the artificial boundaries implied by the domain decomposition. The formulation forces possible boundary discontinuities to be less than a prescribed error and minimizes the parallel communication relative to standard methods. In theory, the exact solution is recovered by decreasing the boundary error toward zero. In practice, it is shown that the accuracy increases until a lower bound arises, because of the presence of the mesh and the finite accuracy of the minimizer. A twin experiment has been set up to estimate the benefit of employing an eddy-resolving grid within the assimilation step, as compared with an eddy-permitting one, while keeping the eddy-resolving grid within the forecast step. It is shown that the use of a coarser grid for data assimilation does not allow an optimal exploitation of the present remote sensing observation network. A global decrease of about 15% in the error statistics is found when assimilating dense surface observations, and no significant improvement is seen for sparser observations (in situ profilers).

A statistical interpolation code for ocean analysis and forecasting

2021 ◽  
Author(s):  
T. M. Chin ◽  
E. P. Chassignet ◽  
M. Iskandarani ◽  
N. Groves
Keyword(s):  
Data Assimilation ◽  
Ocean Circulation ◽  
Markov Random Fields ◽  
Information Matrix ◽  
Ocean Model ◽  
Global Ocean ◽  
Error Statistics ◽  
Data Format ◽  
Covariance Models

Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse - the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov Random Fields are provided and its relationship to finite difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in-situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4°ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multi-core architectures and uses Climate and Forecasts compliant Network Common Data Format for Input/Output. The package is freely available with an open source license from www.tendral.com/tsis/

scholarly journals Determination of AATSR Biases Using the OSTIA SST Analysis System and a Matchup Database

2008 ◽  
Vol 25 (7) ◽  
pp. 1208-1217 ◽  
Author(s):  
J. D. Stark ◽  
C. Donlon ◽  
A. O’Carroll ◽  
G. Corlett
Keyword(s):  
European Space Agency ◽  
Pilot Project ◽  
Global Ocean ◽  
Bias Error ◽  
Cold Bias ◽  
Error Statistics ◽  
Zero Bias ◽  
Diurnal Variability ◽  
Warm Bias

Abstract Sea surface temperature (SST) analyses are produced on a daily basis at the Met Office using the Operational SST and Sea Ice Analysis (OSTIA) system. OSTIA uses satellite SST data, provided by international agencies via the Global Ocean Data Assimilation Experiment (GODAE) High-Resolution SST Pilot Project (GHRSST-PP) regional/global task sharing (R/GTS) framework, which includes an estimate of bias error (available online at http://www.ghrsst-pp.org). The OSTIA system produces a foundation SST estimate (SSTfnd), which is the SST that is free of diurnal variability, at a resolution of 1/20° (∼6 km). Global coverage outputs are provided each day in GHRSST-PP L4 netCDF format. The verification and intercomparison of the OSTIA analysis, with observations and analyses, has revealed a cold bias of approximately 0.1 K in the OSTIA outputs. Because OSTIA uses the operational 1-km Envisat Advanced Along-Track Scanning Radiometer (AATSR) ATS_NR_2P data [via the GHRSST-PP/European Space Agency (ESA) Medspiration Project, available online at http://www.medspiration.org] as a reference dataset for bias adjustment of other satellite data, the AATSR data were identified as the likely cause of the observed bias. To test this, a series of experiments were carried out in June 2006 using the Medspiration AATSR observations in which the Single Sensor Error Statistics (SSES) bias estimate was assigned fixed magnitudes of 0.0, 0.05, 0.15, and 0.2 K. The authors find that the AATSR data have approximately zero bias relative to in situ buoys. Because AATSR measures the SST skin temperature (SSTskin) and was given a mean global SSTskin deviation of −0.17 K (based on in situ radiometer data), this result suggests that ATS_NR_2P SSTskin data have a warm bias of 0.17 K. Using a matchup database of near-contemporaneous 10 arc min AATSR and in situ data, the authors find that the AATSR SSTskin dual- and triple-window retrievals have a warm bias of 0.14 and 0.17 K, respectively, between August 2002 and July 2006. The results of the experiments confirm that the current Medspiration SSES bias correction provided with the Medspiration AATSR L2P observations is poorly specified. The database was not configured to test the relationship between the cloud proximity confidence value and the AATSR bias error. Based on the matchup database and reanalysis results, the authors suggest that Medspiration be modified to use an SSES bias estimate of 0.17 K for all category 2–6 proximity confidence values for the current AATSR dual-view SST ATS_NR_2P products to provide a correct SSTskin estimate. In response to the results presented in this study, operational changes have been made to the Medspiration processing, which improve the bias estimates provided in the AATSR data. The authors suggest that a concerted effort be invested to develop the most appropriate SSES for the AATSR class of sensors that have specific characteristics that must be included in the SSES estimation scheme. The main elements of such a scheme are presented in this paper.

DESIGN AND IMPLEMENTATION OF THE OCEANOGRAPHIC MODELING AND OBSERVATION NETWORK (REMO) FOR PHYSICAL OCEANOGRAPHY STUDIES AND OCEAN FORECASTING

2014 ◽  
Vol 31 (2) ◽  
Author(s):  
Jose Antonio Moreira Lima
Keyword(s):  
Atlantic Ocean ◽  
Large Scale ◽  
North Pacific Ocean ◽  
Integrated Approach ◽  
Sea Surface ◽  
Global Ocean ◽  
Height Anomaly ◽  
The North ◽  
Observation Network ◽  
Ocean Forecasting

This paper is concerned with the planning, implementation and some results of the Oceanographic Modeling and Observation Network, named REMO, for Brazilian regional waters. Ocean forecasting has been an important scientific issue over the last decade due to studies related to climate change as well as applications related to short-range oceanic forecasts. The South Atlantic Ocean has a deficit of oceanographic measurements when compared to other ocean basins such as the North Atlantic Ocean and the North Pacific Ocean. It is a challenge to design an ocean forecasting system for a region with poor observational coverage of in-situ data. Fortunately, most ocean forecasting systems heavily rely on the assimilation of surface fields such as sea surface height anomaly (SSHA) or sea surface temperature (SST), acquired by environmental satellites, that can accurately provide information that constrain major surface current systems and their mesoscale activity. An integrated approach is proposed here in which the large scale circulation in the Atlantic Ocean is modeled in a first step, and gradually nested into higher resolution regional models that are able to resolve important processes such as the Brazil Current and associated mesoscale variability, continental shelf waves, local and remote wind forcing, and others. This article presents the overall strategy to develop the models using a network of Brazilian institutions and their related expertise along with international collaboration. This work has some similarity with goals of the international project Global Ocean Data Assimilation Experiment OceanView (GODAE OceanView).

scholarly journals Comparison of Hydrocarbon-Degrading Consortia from Surface and Deep Waters of the Eastern Mediterranean Sea: Characterization and Degradation Potential

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2246
Author(s):  
Georgia Charalampous ◽  
Efsevia Fragkou ◽  
Konstantinos A. Kormas ◽  
Alexandre B. De Menezes ◽  
Paraskevi N. Polymenakou ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Oil Spills ◽  
Eastern Mediterranean ◽  
Sole Source ◽  
Eastern Mediterranean Sea ◽  
Deep Waters ◽  
Degradation Rates ◽  
Polycyclic Aromatic ◽  
Set Up

The diversity and degradation capacity of hydrocarbon-degrading consortia from surface and deep waters of the Eastern Mediterranean Sea were studied in time-series experiments. Microcosms were set up in ONR7a medium at in situ temperatures of 25 °C and 14 °C for the Surface and Deep consortia, respectively, and crude oil as the sole source of carbon. The Deep consortium was additionally investigated at 25 °C to allow the direct comparison of the degradation rates to the Surface consortium. In total, ~50% of the alkanes and ~15% of the polycyclic aromatic hydrocarbons were degraded in all treatments by Day 24. Approximately ~95% of the total biodegradation by the Deep consortium took place within 6 days regardless of temperature, whereas comparable levels of degradation were reached on Day 12 by the Surface consortium. Both consortia were dominated by well-known hydrocarbon-degrading taxa. Temperature played a significant role in shaping the Deep consortia communities with Pseudomonas and Pseudoalteromonas dominating at 25 °C and Alcanivorax at 14 °C. Overall, the Deep consortium showed a higher efficiency for hydrocarbon degradation within the first week following contamination, which is critical in the case of oil spills, and thus merits further investigation for its exploitation in bioremediation technologies tailored to the Eastern Mediterranean Sea.

scholarly journals Air-Sea Interactions over Eddies in the Brazil-Malvinas Confluence

2021 ◽  
Vol 13 (7) ◽  
pp. 1335
Author(s):  
Ronald Souza ◽  
Luciano Pezzi ◽  
Sebastiaan Swart ◽  
Fabrício Oliveira ◽  
Marcelo Santini
Keyword(s):  
Latent Heat ◽  
Large Scale ◽  
Surface Wind ◽  
Heat Fluxes ◽  
Lower Atmosphere ◽  
Meteorological Variables ◽  
Global Ocean ◽  
Study Region ◽  
Cold Core

The Brazil–Malvinas Confluence (BMC) is one of the most dynamical regions of the global ocean. Its variability is dominated by the mesoscale, mainly expressed by the presence of meanders and eddies, which are understood to be local regulators of air-sea interaction processes. The objective of this work is to study the local modulation of air-sea interaction variables by the presence of either a warm (ED1) and a cold core (ED2) eddy, present in the BMC, during September to November 2013. The translation and lifespans of both eddies were determined using satellite-derived sea level anomaly (SLA) data. Time series of satellite-derived surface wind data, as well as these and other meteorological variables, retrieved from ERA5 reanalysis at the eddies’ successive positions in time, allowed us to investigate the temporal modulation of the lower atmosphere by the eddies’ presence along their translation and lifespan. The reanalysis data indicate a mean increase of 78% in sensible and 55% in latent heat fluxes along the warm eddy trajectory in comparison to the surrounding ocean of the study region. Over the cold core eddy, on the other hand, we noticed a mean reduction of 49% and 25% in sensible and latent heat fluxes, respectively, compared to the adjacent ocean. Additionally, a field campaign observed both eddies and the lower atmosphere from ship-borne observations before, during and after crossing both eddies in the study region during October 2013. The presence of the eddies was imprinted on several surface meteorological variables depending on the sea surface temperature (SST) in the eddy cores. In situ oceanographic and meteorological data, together with high frequency micrometeorological data, were also used here to demonstrate that the local, rather than the large scale forcing of the eddies on the atmosphere above, is, as expected, the principal driver of air-sea interaction when transient atmospheric systems are stable (not actively varying) in the study region. We also make use of the in situ data to show the differences (biases) between bulk heat flux estimates (used on atmospheric reanalysis products) and eddy covariance measurements (taken as “sea truth”) of both sensible and latent heat fluxes. The findings demonstrate the importance of short-term changes (minutes to hours) in both the atmosphere and the ocean in contributing to these biases. We conclude by emphasizing the importance of the mesoscale oceanographic structures in the BMC on impacting local air-sea heat fluxes and the marine atmospheric boundary layer stability, especially under large scale, high-pressure atmospheric conditions.

Use of a crystallization robot to set up sitting-drop vapor-diffusion crystallization andin situcrystallization screens

2000 ◽  
Vol 33 (2) ◽  
pp. 344-349 ◽  
Author(s):  
Christopher F. Snook ◽  
Michael D. Purdy ◽  
Michael C. Wiener
Keyword(s):  
Membrane Proteins ◽  
Protein Crystallization ◽  
Integral Membrane Proteins ◽  
Vapor Diffusion ◽  
Liquid Handling ◽  
General Utility ◽  
Crystallization Experiments ◽  
Automated Screening ◽  
Set Up

A commercial crystallization robot has been modified for use in setting up sitting-drop vapor-diffusion crystallization experiments, and for setting up protein crystallization screensin situ. The primary aim of this effort is the automated screening of crystallization of integral membrane proteins in detergent-containing solutions. However, the results of this work are of general utility to robotic liquid-handling systems. Sources of error that can prevent the accurate dispensing and mixing of solutions have been identified, and include local environmental, machine-specific and solution conditions. Solutions to each of these problems have been developed and implemented.

Estimating Global Ocean Heat Content Changes in the Upper 1800 m since 1950 and the Influence of Climatology Choice*

2014 ◽  
Vol 27 (5) ◽  
pp. 1945-1957 ◽  
Author(s):  
John M. Lyman ◽  
Gregory C. Johnson
Keyword(s):  
Heat Content ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Ocean Temperature ◽  
Vertical Separation ◽  
Ocean Heat Uptake ◽  
Expendable Bathythermograph ◽  
Heat Uptake ◽  
The Mean

Abstract Ocean heat content anomalies are analyzed from 1950 to 2011 in five distinct depth layers (0–100, 100–300, 300–700, 700–900, and 900–1800 m). These layers correspond to historic increases in common maximum sampling depths of ocean temperature measurements with time, as different instruments—mechanical bathythermograph (MBT), shallow expendable bathythermograph (XBT), deep XBT, early sometimes shallower Argo profiling floats, and recent Argo floats capable of worldwide sampling to 2000 m—have come into widespread use. This vertical separation of maps allows computation of annual ocean heat content anomalies and their sampling uncertainties back to 1950 while taking account of in situ sampling advances and changing sampling patterns. The 0–100-m layer is measured over 50% of the globe annually starting in 1956, the 100–300-m layer starting in 1967, the 300–700-m layer starting in 1983, and the deepest two layers considered here starting in 2003 and 2004, during the implementation of Argo. Furthermore, global ocean heat uptake estimates since 1950 depend strongly on assumptions made concerning changes in undersampled or unsampled ocean regions. If unsampled areas are assumed to have zero anomalies and are included in the global integrals, the choice of climatological reference from which anomalies are estimated can strongly influence the global integral values and their trend: the sparser the sampling and the bigger the mean difference between climatological and actual values, the larger the influence.

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