Cambazoglu M.K.,University of Southern Mississippi |
Blain C.A.,U.S. Navy |
Smith T.A.,U.S. Navy |
Linzell R.S.,Vencore Services and Solutions Inc.
Ocean Engineering | Year: 2016
The impact of resolution on wind predictions within regions of complex coastal geometry is evaluated using a quadruple nest of COAMPS® (27 km to 1 km) to find an optimal configuration of spatial and temporal resolution. Two regions, Turkish Straits System and Chesapeake Bay, are selected because of their diverse coastal environments, the availability of wind observations and to determine if the relationships between resolution and wind prediction accuracy would be valid for geographically different regions. The coarse resolution model successfully simulates the general trend of the surface wind variation, but cannot capture peak events accurately. Increased spatial resolution results in more accurate wind predictions. The coastline representation and land features impact friction over land and blocking of the winds and affect accuracy of wind predictions. 27-km resolution products lack important details over coastal waters and are not adequate to force high resolution ocean models. No evident improvement in accuracy is observed when increasing the resolution from 3-km to 1-km. An increase in frequency of the wind records from 3-hourly to hourly is required to capture frontal events with strong wind speeds and sharp gradients. Our analysis for both regions suggests the use of hourly atmospheric products at 3-km resolution for oceanic forcing purposes. © 2015 Elsevier Ltd. All rights reserved.
Hebert D.A.,U.S. Navy |
Allard R.A.,U.S. Navy |
Metzger E.J.,U.S. Navy |
Posey P.G.,U.S. Navy |
And 4 more authors.
Journal of Geophysical Research: Oceans | Year: 2015
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. © Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
Posey P.G.,U.S. Navy |
Metzger E.J.,U.S. Navy |
Wallcraft A.J.,U.S. Navy |
Hebert D.A.,U.S. Navy |
And 7 more authors.
Cryosphere | Year: 2015
This study presents the improvement in ice edge error within the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System - ACNFS) went into operations with a horizontal resolution of ∼ 3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed, resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements in ice edge forecasting in both of the Navy's sea ice forecasting systems. © Author(s) 2015.
Jacobs G.A.,U.S. Navy |
Bartels B.P.,Vencore Services and Solutions Inc. |
Bogucki D.J.,Texas A&M University-Corpus Christi |
Beron-Vera F.J.,University of Miami |
And 31 more authors.
Ocean Modelling | Year: 2014
Ocean prediction systems rely on an array of assumptions to optimize their data assimilation schemes. Many of these remain untested, especially at smaller scales, because sufficiently dense observations are very rare. A set of 295 drifters deployed in July 2012 in the north-eastern Gulf of Mexico provides a unique opportunity to test these systems down to scales previously unobtainable. In this study, background error covariance assumptions in the 3DVar assimilation process are perturbed to understand the effect on the solution relative to the withheld dense drifter data. Results show that the amplitude of the background error covariance is an important factor as expected, and a proposed new formulation provides added skill. In addition, the background error covariance time correlation is important to allow satellite observations to affect the results over a period longer than one daily assimilation cycle. The results show the new background error covariance formulations provide more accurate placement of frontal positions, directions of currents and velocity magnitudes. These conclusions have implications for the implementation of 3DVar systems as well as the analysis interval of 4DVar systems. © 2014.