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A. B. White, M. L. Anderson, M. D. Dettinger, F. M. Ralph, A. Hinojosa, D. R. Cayan, R. K. Hartman, D. W. Reynolds, L. E. Johnson, T. L. Schneider, R. Cifelli, Z. Toth, S. I. Gutman, C. W. King, F. Gehrke, P. E. Johnston, C. Walls, D. Mann, D. J. Gottas, and T. Coleman

goals: 1) to install a twenty-first-century observing system to help address California’s water and emergency management needs, 2) to provide a state-of-the-art numerical weather forecast model ensemble with a high-resolution nest over California, and 3) to develop decision support tools for weather and river forecasters and water managers. This project is part of the California Department of Water Resources (CA-DWR) Enhanced Flood Response and Emergency Preparedness Program. The HMT-Legacy project

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Michael E. Gorbunov, A. V. Shmakov, Stephen S. Leroy, and Kent B. Lauritsen

European Centre for Medium-Range Forecasts (ECMWF). The ECMWF analyses are only used as a reference. We do not discuss any possible biases of ECMWF data. Our system for processing GPS RO data has been developed by the first author (MEG) starting from his participation in the GPS Meteorology Network (GPSMet) project. Currently, it is capable of handling GPS radio occultation data from all the known missions, including GPSMet, CHAMP, the Satellite de Aplicaciones Cientificas-C (SAC-C), COSMIC, and the

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Ganesh Gopalakrishnan, Bruce D. Cornuelle, Matthew R. Mazloff, Peter F. Worcester, and Matthew A. Dzieciuch

Abstract

A strongly nonlinear eddy field is present in and around the Subtropical Countercurrent in the Northern Philippine Sea (NPS). A regional implementation of the Massachusetts Institute of Technology general circulation model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system is found to be able to produce a series of two-month-long dynamically-consistent optimized state estimates between April 2010 and April 2011 for the eddy-rich NPS region. The assimilation provides a stringent dynamical test of the model, showing that a free run of the model forced using adjusted controls remains consistent with the observations for two months. The 4DVAR iterative optimization reduced the total cost function for the observations and controls by 40–50% from the reference solution, initialized using the Hybrid Coordinate Ocean Model 1/12° global daily analysis, achieving residuals approximately equal to the assumed uncertainties for the assimilated observations. The state estimates are assessed by comparing with assimilated and withheld observations and also by comparing one-month model forecasts with future data. The state estimates and forecasts were more skillful than model persistence and the reference solutions. Finally, the continuous state estimates were used to detect and track the eddies, analyze their structure, and quantify their vertically-integrated meridional heat and salt transports.

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D. Lynch, K. Smith, B. Blanton, R. Luettich, and F. Werner

1. Introduction Our objective is to construct a data assimilative forecast system in the South Atlantic Bight (SAB) shelf region, between St. Augustine, Florida, and Charleston, South Carolina, and offshore to roughly the shelf break (70–200-m isobath; Fig. 1a ). Boicourt et al. (1998) provide a contemporary oceanographic review of these waters. Three-dimensional baroclinic physics are to be used on a high-resolution, local area mesh. Local forcings (river, wind, atmospheric heating, and

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Régis Borde, Olivier Hautecoeur, and Manuel Carranza

biases (middle), and speed root-mean-square (RMS) (m s −1 ) against collocated forecast fields from the ECMWF Integrated Forecast System (IFS) model. Results are split into five latitudinal bands: the North Pole (NP) between 60° and 90°N, the northern midlatitudes (NH) between the Tropic of Cancer (23.47°N) and 60°N, the intertropical region (TR), the Southern midlatitudes (SH) between the Tropic of Capricorn (23.47°S) and 60°S, and the South Pole (SP) between 90° and 60°S. Only AMVs having a QI > 60

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Rick Lumpkin, Luca Centurioni, and Renellys C. Perez

) prediction and evaluation of seasonal to interannual ocean and climate forecast systems, were required at a temporal resolution of one observation per month at a comparable spatial resolution, at an accuracy of 2 cm s −1 , in order to resolve seasonal and time-mean currents to 10% of the eddy variability ( Needler et al. 1999 ). The requirement for in situ SST observations has subsequently ( Zhang et al. 2009 ) been refined to focus on reducing the potential bias in satellite-derived measurements below

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Kalpesh Patil and M. C. Deo

–atmosphere general circulation models (COAGCMs) in the form of boundary conditions. Such models require regularly predicted SST fields for model integration. Accurate SST predictions can help in understanding rainfall patterns a few months earlier and also can assist in tracking the cyclone genesis and in planning the marine ecosystem. In the past SST predictions have been reported by many researchers using complex physics-based models, statistical methods, and nonlinear forecasting techniques. The physics

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Conor McNicholas and Clifford F. Mass

1. Introduction Over the past decade, increasing computational resources have enabled the development of high-resolution convection-allowing numerical weather prediction ( Pinto et al. 2015 ; Seity et al. 2011 ; Baldauf et al. 2011 ; Lean et al. 2008 ). Although, advances in model resolution have fostered more realistic representations of convective systems, they have not produced improvements in forecast location, timing, and intensity ( Weisman et al. 2008 ). Such forecast deficiencies

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Alan F. Blumberg, Nickitas Georgas, Larry Yin, Thomas O. Herrington, and Philip M. Orton

more meaningful results. A potential shortcoming of the static approach is that the horizontal movement of flood waters over land areas is determined by friction, wind, and other dynamic factors that are likely captured better using hydrodynamic modeling. This is the reason why the Federal Emergency Management Agency (FEMA) utilizes hydrodynamic models for its flood mapping studies ( FEMA 2014 ) and why NOAA uses them for forecasting neighborhood flooding during hurricanes. Moreover, prior

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Tao Song, Zihe Wang, Pengfei Xie, Nisheng Han, Jingyu Jiang, and Danya Xu

1. Introduction Sea surface salinity (SSS) is one of the most critical factors in the study of climate forecasting ( Cronin and McPhaden 1999 ), global water cycle ( Batteen et al. 1995 ), sea ice observation, marine disaster monitoring ( Reul et al. 2012 ; Hasson et al. 2013 ), marine ecosystems ( Gabarró et al. 2004 ) and military field. Accurate and real-time SSS prediction is an elemental part of marine environmental monitoring. Computational methods for predicting SSS values can be

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