The Bering Sea Regional Data
Assimilation System: From Climate
Variability to Short Term Hindcasting
Gleb G. Panteleev, Max Yaremchuk, Vladimir Luchin
and Oceana Francis
Introduction
In the last decades, 4d variational (4dVar) data assimilation (DA) has become the
most acknowledged tool for advanced hindcasting and forecasting of the ocean
circulation. Starting from DA systems constrained by simple dynamics (e.g., [1,
16]), the 4dVar approach [9] has gradually evolved into advanced DA systems
based on state-of-the-art ocean models (MIT, ROMS) and is now routinely applied
toward reconstructing the circulation in the Arctic, Pacific, or World Ocean (e.g., [5,
15]). These state-of-the-art DA systems involve the assimilation of a variety of
observations from satellites, surface and Argo drifters and climatological observations from oceanographic databases. Assimilation of these publicly accessible,
near real-time observations is natural since the major goal of the “global” DA
systems is to provide hindcasting of the global circulation and/or analysis of
observed climate changes on a global scale.
Meanwhile, due to the low flexibility of the global DA systems in assimilating
regional data on smaller scales, the regional DA systems have been under extensive
development within the last decade (e.g. [6, 8]). These DA systems routinely
assimilate alongtrack satellite altimetry (Jason-1, Jason-2, CryoSat), hourly SSTs
from GOES, and surface currents from HFRs and provide regional updates and
3-day forecasts of the ocean state. It is necessary to note, that all the regional
G. G. Panteleev ( ✉ ) ⋅ M. Yaremchuk
Naval Research Laboratory, Stennis Space Center, Hancock, USA
e-mail: gleb.panteleev@nrlssc.navy.mil
V. Luchin
Pacific Oceanological Institute, Vladivostok, Russia
e-mail: vlad_luchin@mail.ru
O. Francis
University of Hawaii, Honolulu, USA
e-mail: oceanaf@hawaii.edu
© Springer International Publishing AG, part of Springer Nature 2018
M. G. Velarde et al. (eds.), The Ocean in Motion, Springer Oceanography,
https://doi.org/10.1007/978-3-319-71934-4_32
501
Assimilation System: From Climate
Variability to Short Term Hindcasting
Gleb G. Panteleev, Max Yaremchuk, Vladimir Luchin
and Oceana Francis
Introduction
In the last decades, 4d variational (4dVar) data assimilation (DA) has become the
most acknowledged tool for advanced hindcasting and forecasting of the ocean
circulation. Starting from DA systems constrained by simple dynamics (e.g., [1,
16]), the 4dVar approach [9] has gradually evolved into advanced DA systems
based on state-of-the-art ocean models (MIT, ROMS) and is now routinely applied
toward reconstructing the circulation in the Arctic, Pacific, or World Ocean (e.g., [5,
15]). These state-of-the-art DA systems involve the assimilation of a variety of
observations from satellites, surface and Argo drifters and climatological observations from oceanographic databases. Assimilation of these publicly accessible,
near real-time observations is natural since the major goal of the “global” DA
systems is to provide hindcasting of the global circulation and/or analysis of
observed climate changes on a global scale.
Meanwhile, due to the low flexibility of the global DA systems in assimilating
regional data on smaller scales, the regional DA systems have been under extensive
development within the last decade (e.g. [6, 8]). These DA systems routinely
assimilate alongtrack satellite altimetry (Jason-1, Jason-2, CryoSat), hourly SSTs
from GOES, and surface currents from HFRs and provide regional updates and
3-day forecasts of the ocean state. It is necessary to note, that all the regional
G. G. Panteleev ( ✉ ) ⋅ M. Yaremchuk
Naval Research Laboratory, Stennis Space Center, Hancock, USA
e-mail: gleb.panteleev@nrlssc.navy.mil
V. Luchin
Pacific Oceanological Institute, Vladivostok, Russia
e-mail: vlad_luchin@mail.ru
O. Francis
University of Hawaii, Honolulu, USA
e-mail: oceanaf@hawaii.edu
© Springer International Publishing AG, part of Springer Nature 2018
M. G. Velarde et al. (eds.), The Ocean in Motion, Springer Oceanography,
https://doi.org/10.1007/978-3-319-71934-4_32
501