Global Ocean Prediction System

1997 ◽  
Author(s):  
Harley E. Hurlburt
Keyword(s):  
Global Ocean ◽  
Prediction System ◽  
Ocean Prediction

scholarly journals Forecast skill score assessment of a relocatable ocean prediction system, using a simplified objective analysis method

Ocean Science ◽  
2017 ◽  
Vol 13 (6) ◽  
pp. 925-945 ◽  
Author(s):  
Reiner Onken
Keyword(s):  
Observational Data ◽  
Skill Score ◽  
Forecast Skill ◽  
Objective Analysis ◽  
Prediction System ◽  
Regional Ocean Modeling System ◽  
Data Set ◽  
Vast Number ◽  
Ocean Prediction ◽  
Forecast Skill Score

Abstract. A relocatable ocean prediction system (ROPS) was employed to an observational data set which was collected in June 2014 in the waters to the west of Sardinia (western Mediterranean) in the framework of the REP14-MED experiment. The observational data, comprising more than 6000 temperature and salinity profiles from a fleet of underwater gliders and shipborne probes, were assimilated in the Regional Ocean Modeling System (ROMS), which is the heart of ROPS, and verified against independent observations from ScanFish tows by means of the forecast skill score as defined by Murphy(1993). A simplified objective analysis (OA) method was utilised for assimilation, taking account of only those profiles which were located within a predetermined time window W. As a result of a sensitivity study, the highest skill score was obtained for a correlation length scale C = 12.5 km, W = 24 h, and r = 1, where r is the ratio between the error of the observations and the background error, both for temperature and salinity. Additional ROPS runs showed that (i) the skill score of assimilation runs was mostly higher than the score of a control run without assimilation, (i) the skill score increased with increasing forecast range, and (iii) the skill score for temperature was higher than the score for salinity in the majority of cases. Further on, it is demonstrated that the vast number of observations can be managed by the applied OA method without data reduction, enabling timely operational forecasts even on a commercially available personal computer or a laptop.

Eddy-resolving global ocean prediction

2008 ◽  
pp. 353-381 ◽  
Author(s):  
Harley E. Hurlburt ◽  
Eric P. Chassignet ◽  
James A. Cummings ◽  
A. Birol Kara ◽  
E. Joseph Metzger ◽  
...  
Keyword(s):  
Global Ocean ◽  
Ocean Prediction

Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017 – CORRIGENDUM

2020 ◽  
Vol 66 (260) ◽  
pp. 1079-1079
Author(s):  
Longjiang Mu ◽  
Xi Liang ◽  
Qinghua Yang ◽  
Jiping Liu ◽  
Fei Zheng
Keyword(s):  
Sea Ice ◽  
Prediction System ◽  
Ocean Prediction ◽  
Arctic Ice

The Distributed Integrated Ocean Prediction System (DIOPS)

2004 ◽  
Author(s):  
R. Allard ◽  
J. Christiansen ◽  
T. Taxon ◽  
S. Williams ◽  
D. Wakeham
Keyword(s):  
Prediction System ◽  
Ocean Prediction

scholarly journals Global Ocean Prediction Using HYCOM

2006 ◽  
Author(s):  
A.J. Wallcraft ◽  
E.J. Metzger ◽  
H.E. Hurlburt ◽  
E.P. Chassignet ◽  
Z.D. Garraffo ◽  
...  
Keyword(s):  
Global Ocean ◽  
Ocean Prediction

scholarly journals Global Ocean Prediction Using HYCOM

2007 ◽  
Author(s):  
A. Wallcraft ◽  
H. Hurlburt ◽  
E.J. Metzger ◽  
E. Chassignet ◽  
J. Cummings ◽  
...  
Keyword(s):  
Global Ocean ◽  
Ocean Prediction

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.

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