This paper reviews the historical development of concepts and practices in the science of ocean predictions. It begins with meteorology, which conducted the first forecasting experiment in 1950, followed by wind waves, and continuing with tidal and storm surge predictions to arrive at the first successful ocean mesoscale forecast in 1983. The work of Professor A. R. Robinson of Harvard University, who produced the first mesoscale ocean predictions for the deep ocean regions is documented for the first time. The scientific and technological developments that made accurate ocean predictions possible are linked with the gradual understanding of the importance of the oceanic mesoscales and their inclusion in the numerical models. Ocean forecasting developed first at the regional level, due to the relatively low computational requirements, but by the end of the 1990s, it was possible to produce global ocean uncoupled forecasts and coupled ocean-atmosphere seasonal forecasts.

At the beginning of the 20th century Vilhelm Bjerknes defined the “ultimate problem of meteorology and hydrography” as the discovery of “the laws according to which an atmospheric or hydrospheric state develops out of the preceding one” and the “precalculation of future states” from gridded analyzed observations—that is, forecasting. The development of the electronic computer and the vision of several meteorologists allowed the transformation of meteorology into a sophisticated scientific discipline based on physics and mathematics. The first successful meteorological forecast was carried out in the 1950s. Meteorological forecasting became an operational activity at the end of the 1960s. The contributions to society of such operations have been tremendous. Ocean forecasting began in the 1980s with a joint venture between Harvard University and the Naval Postgraduate School in Monterey, California, that completed the first successful forecast of ocean mesoscales in a limited area of the ocean. Since then, computational ocean modeling and prediction have led to major discoveries across multiple time and space scales, from ocean turbulence to climate. In the first decades of the 21st century, ocean forecasting has become an operational activity. The rapidly increasing interconnectivity between humans and the Earth system suggests that ocean prediction will become ever more vital to society in the years to come. Ocean prediction is now part of the wider human endeavor to understand, monitor, and forecast whole-earth physical and biogeochemical dynamics and cycles. The science of ocean prediction is the systematic development of fundamental knowledge about ocean dynamics in the form of testable ocean models and estimation systems that relate to forecasting the ocean’s evolution. It includes governing laws, model equations, parameterizations, numerical implementation, data-driven computational integration, and evaluation of models and systems outputs against ocean observations. Central to the science is the acquisition of relevant observational data sets and the development of models and numerical techniques, together with data assimilation schemes that quantitatively meld all information sources in accord with the uncertainties of the observations and models. It is the sustained combination

In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by 5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

A popular cigarette advertisement from the 1960s exclaimed, "You've come a long way, Baby!" That sentiment could be applied to Naval Oceanography. The US Navy has navigated the course of developing prediction technology over many fundamental shifts in global geopolitics while addressing the evolving challenges at the forefront of the oceanography mission to ensure the safety of the nation's armed forces. Originally motivated by Soviet-era submarine programs, accurate acoustic prediction necessitated forecasting the positions of ocean fronts and eddies. Since then, the scope of Naval Oceanography has expanded to encompass a littoral focus, including applications that assist Navy SEa Air and Land (SEAL) teams, amphibious vehicle landings, and mine warfare. The fundamental physics governing the universe remains unchanged and so has the Navy's need to understand ocean physics, build numerical representations, connect to data streams, and assimilate observations in order to provide forecasts addressing the challenges of today and tomorrow. A well-planned course is no accident, and the Navy's leading edge in ocean prediction is the result. This paper provides a description of the path to this leading edge, a synthesis of the current operational architecture that enables Naval Oceanography, an analysis of the triumphs of the last 10 years that are part of today's oceanography portfolio, and a prediction of what the next 10 years holds for Naval Oceanography.

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The goal of the Battlespace Environments Institute (BEI) is to integrate Earth and space modeling capabilities into a seamless, whole-Earth common modeling infrastructure that facilitates interservice development of multiple, mission-specific environmental simulations and supports battlefield decisions, improves interoperability, and reduces operating costs.

: Naval Research Laboratory has compared sea ice hindcast for the Arctic Ocean derived from the latest coupled ice-ocean prediction system to observations. The system is based on the HYbrid Coordinate Ocean Model coupled via the Earth System Modeling Framework to the Los Alamos Community Ice CodE and tested using the Navy Coupled Ocean Data Assimilation system. The validation of the 1/12 degree Arctic Cap Nowcast/ Forecast System (ACNFS) was accomplished by comparing hindcast fields to the current operational Polar Ice Prediction System (PIPS 2.0) as well as comparing to observations. Both the ACNFS and PIPS 2.0 output daily fields of ice thickness, ice concentration, and ice drift. The goal of the validation is to determine if the new ACNFS is an improvement over the existing PIPS 2.0 model and should be used, operationally, in its place. NRL validated hindcasts and forecasts of ice edge location, ice thickness, ice draft, and ice drift using observational data sets such as daily ice edge, ice mass balance buoys, ice thickness survey flight data, upward looking sonars data and Special Sensor Microwave/Imager ice concentration data. NRL evaluated the models against these data during the years 2007-2009 depending on the availability of the observations.

Multi-model super-ensemble combines different models and improves atmospheric weather predictions significantly. Prediction skill is still low in the coastal ocean due to high dynamics and the presence of small-scale processes and lack of real-time data. The prediction skill improves and uncertainty reduces through the combination of SEPTR real-time measurements and super-ensemble techniques.

The Naval Research Laboratory (NRL) has developed a global, relocatable, tide/surge forecast system called PCTides. This system was designed in response to a U. S. Navy requirement to rapidly produce tidal predictions anywhere in the world. The system is composed of a two-dimensional barotropic ocean model driven by tidal forcing only or in conjunction with surface wind and pressure forcing. PCTides is unique in its ability to forecast tidal parameters for a user-specified latitude/longitude domain easily and quickly, and is especially useful in areas where observations are nonexistent. PCTides provides short-term (daily to weekly) predictions of water-level elevation and depth-averaged ocean currents. The system has been tested in numerous regions and validated against observations collected in conjunction with several navy exercises.

To provide a short-term ocean forecast for sea level variation, current, temperature, and salinity, an ocean nowcast/forecast system has been developed. The system is an integration of a data-assimilating, dynamical ocean model, a statistical data-analysis model, and various data streams for ocean bathymetry, climatological data, surface forcing, open boundary forcing, and observations. The system assimilates satellite data and in-situ measurements to produce an estimation of the current ocean state or nowcast and is forced with a meteorological forecast to produce an ocean forecast. During the MREA04 sea trial, the system was implemented for a region off the Portuguese coast with two-way nested grids and produced real-time ocean forecasts for the period of the experiment. The high density of real-time, in-situ observations during MREA04 provided a unique opportunity for the system to assimilate the in-situ observations in addition to satellite data and to perform a statistically meaningful evaluation of the system's forecast capability. The evaluation shows that the nowcast/forecast system has good skill in predicting the tide and fair skill in predicting the ocean temperature and salinity with overall rms errors of 0.5 degrees C and 0.15 psu for temperature and salinity, respectively. Assimilating in-situ CTD data produced a better nowcast/forecast than assimilating only satellite data. The forecast error increases as the forecast time increases, but the forecast error does not increase significantly over the nowcast error, which indicates that the error in the nowcast is the major source of the forecast error. Published by Elsevier B.V.

The impact of coastal flooding and inundation on Navy operational missions and the existing Navy requirements for resolution and accuracy relevant to coastal inundation are presented. High resolution (less than 500 m) coastal models exercised operationally at the Naval Oceanographic Office are reviewed, summarizing the advantages, disadvantages and typical spatial resolutions of each. The present state of Navy coastal inundation modeling from a research and development perspective is presented along with highlights of planned advances in the near-future. Lastly, the gaps between users, products, data and research and development are illuminated. Fine-scale bathymetry and topography as well as in situ currents for validation are required for accurate, high resolution inundation modeling. Gaps between the needs of Navy users and the type and form of operationally available inundation products are identified and discussed. The need for usability, relocateability, and expediency in the operational setting contribute in large part to the delay in transitioning model capabilities from research and development to operations.