The Regional Ocean Modeling System (ROMS) 4-dimensional variational (4D-Var) data 1 assimilation tool has been used to compute two sequences of circulation analyses for the 2 U.S. west coast. One sequence of analyses spans the period 1980-2010 and is subject to 3 surface forcing derived from relatively low resolution atmospheric products from the Cross-4 Calibrated Multi-Platform wind product (CCMP) and the European Centre for Medium 5 Range Weather Forecasts (ECMWF) reanalysis project. The second sequence spans the 6 shorter period 1999-2012 and is subject to forcing derived from a high resolution product from 7 the Naval Research Laboratory Coupled Ocean Atmosphere Mesoscale Prediction System 8 (COAMPS). The two analyses periods are divided into eight day windows, and all available 9 satellite observations of sea surface temperature and sea surface height, as well as in situ 10 hydrographic profiles are assimilated into ROMS using 4D-Var. The performance of the 11 system is monitored in terms of the cost function and the statistics of the innovations, and 12 the impact of data assimilated on the circulation is assessed by comparing the posterior 13 circulation estimates with the prior circulation and the circulation from a run of the model 14 without data assimilation, with particular emphasis on eddy kinetic energy. This is part I 15 of a two part series, and the circulation variability of the 4D-Var analyses is documented in 16 part II.17

This paper illustrates that analysis corrections, when applied as a model tendency term, can be used to improve non-linear model forecasts and is consistent with the hypothesis that they represent an additive 6-hour accumulation of model error. Comparison of mean analysis corrections with observational estimates of bias further illustrates the fidelity with which mean analysis corrections capture the model bias. While most previous implementations have explored the use of analysis corrections to correct forecast biases in short-range forecasts, this is the first implementation of the correction method using both a seasonal mean and random analysis correction for medium range forecasts (out to 10-days). Overall, the analysis correction based perturbations are able to improve forecast skill in ensemble and deterministic systems, especially in the first 5 days of the forecast where bias and RMSE in both lower tropospheric temperature and 500 hPa geopotential height are significantly improved across all experiments. However, while the method does provide some significant improvement to forecast skill, some degradation in bias can occur at later lead-times when the average bias at analysis time trends toward zero over the length of the forecast, leading to an over correction by the analysis correction-based additive inflation (ACAI) method. Additionally, it is shown that both the mean and random component of the ACAI perturbations play a role in adjusting the model bias, and that the two components can have a complicated and sometimes non-linear interaction.

This paper describes the new global Navy Earth System Prediction Capability (Navy-ESPC) coupled atmosphere-ocean-sea ice prediction system developed at the Naval Research Laboratory (NRL) for operational forecasting for timescales of days to the subseasonal. Navy-ESPC will become operational in late 2020, and this system will be the first time the NRL operational partner, Fleet Numerical Meteorology and Oceanography Center, will provide global coupled atmosphere-ocean-sea ice forecasts, with atmospheric forecasts extending to 16 days, and ocean and sea ice ensemble forecasts. Two configurations of the system are validated: (1) a low-resolution 16-member ensemble system and (2) a highresolution deterministic system. A unique aspect of the Navy-ESPC is that the global ocean model is eddy resolving at 1/12° in the ensemble and at 1/25° in the deterministic configurations. The component models are current Navy operational systems: NAVy Global Environmental Model (NAVGEM) for the atmosphere; HYbrid Coordinate Ocean Model (HYCOM) for the ocean; and Community Ice CodE (CICE) for the sea ice. Physics updates to improve the simulation of equatorial phenomena, particularly the Madden Julian Oscillation (MJO) were introduced into NAVGEM. The low resolution ensemble configuration and highresolution deterministic configuration are evaluated based on analyses and forecasts from January 2017 to January 2018. Navy-ESPC ensemble forecast skill for large-scale atmospheric phenomena, such as the Madden-Julian Oscillation (MJO), North Atlantic Oscillation (NAO), Antarctic Oscillation (AAO), and other indices, is comparable to that of other numerical weather prediction (NWP) centers. Ensemble forecasts of ocean sea surface temperatures perform better than climatology in the tropics and mid-latitudes out to 60 days. In addition, the Navy-ESPC Pan-Arctic and Pan-Antarctic sea ice extent predictions perform better than climatology out to about 45 days, although the skill is dependent on season.

High-fidelity analyses and forecasts of integrated vapor transport (VT) are central to the study of earth’s hydrological cycle as well as high-impact phenomena such as monsoons and atmospheric rivers. The impact of the in-line Analysis Correction-based Additive Inflation (ACAI) on IVT biases and forecast errors is examined within the Navy Earth System Prediction Capability (Navy ESPC) global coupled system. The ACAI technique uses atmospheric analysis corrections from the data assimilation system to approximate model bias and as a representation of stochastic model error to simultaneously reduce systematic and random errors and improve ensemble performance. ACAI reduces the global average magnitude of the 7-day and 14-day IVT bias by 16-17% during Northern Hemisphere summer, reaching 70% reductions in some tropical regions. The global average IVT bias reduction is similar to the bias reduction for low-level wind speed bias and considerably smaller than the bias reduction in total precipitable water. The localized regions where ACAI increases IVT bias occur where the control IVT biases change sign and structure with increasing forecast lead time, such as the south Asian monsoon region. Substituting analyzed wind or moisture fields for the forecast fields when calculating the forecast IVT confirms that, on average, wind errors dominate the IVT error calculation in the tropics, although wind and moisture error contributions are comparable in the extratropics. The existence of regions where using either analyzed winds or analyzed moisture increases IVT bias or mean absolute error reveal areas with compensating errors.

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