School of Earth Sciences - Research Publications
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ItemSurface Radiative Flux Bias Reduction Through Regionally Varying Cloud Fraction Parameter Nudging in a Global Coupled Forecast System(AMER GEOPHYSICAL UNION, 2021-04)Abstract A simple parameter nudging procedure is described that systematically reduces near‐analysis time errors in the surface net shortwave flux in the Navy ESPC (Earth System Prediction Capability) system, a global coupled forecast system that is the product of a continuing development effort at the U. S. Naval Research Laboratory. The procedure generates geographically varying perturbations to one of the cloud fraction parameters in the atmospheric model component of the system during the data assimilation cycle, resulting in large improvements in near‐analysis time surface net shortwave flux biases. After a several week spin‐up period, the global RMSE of the succeeding 10‐day mean bias computed for lead times of 6–12 hours is reduced by 40 percent. Results from application of the approach in a series of 45‐day integrations show that improvements are realized at longer forecast lead times as well. The global RMSE of the surface net shortwave flux averaged over these integrations improves by 37 percent for lead times from 1–5 days, decreasing to 18 percent for lead times from 31–45 days. The corresponding longwave flux errors are slightly degraded, ranging from a 2 percent increase for lead times from 1–5 days to a 0.5 percent increase for lead times from 31–45 days. Global‐mean reductions in ground and sea surface temperature errors are obtained through most of the 45‐day integration period due to improvements over ocean and polar regions. Potential steps for extension and operational application of the method are discussed.
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ItemThe Navy's Earth System Prediction Capability: A New Global Coupled Atmosphere-Ocean-Sea Ice Prediction System Designed for Daily to Subseasonal Forecasting(American Geophysical Union, 2021-04-01)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. Two configurations of the system are validated: (1) a low-resolution 16-member ensemble system and (2) a high-resolution deterministic system. The Navy-ESPC ensemble system became operational in August 2020, and this is 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 past 16 days, and ocean and sea ice ensemble forecasts. 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 high-resolution 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 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 midlatitudes 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.
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ItemEmpirical determination of the covariance of forecast errors: An empirical justification and reformulation of hybrid covariance models(Wiley, 2021-04-01)During the last decade, the replacement of static climatological forecast error covariance models with hybrid error covariance models that linearly combine localised ensemble covariances with static climatological error covariances has led to significant forecast improvements at several major forecasting centres. Here, a deeper understanding of why the hybrid's superficially ad hoc mix of ensemble-based and climatological covariances yields such significant improvements is pursued. In practice, ensemble covariances are not equal to the true flow-dependent forecast error covariance matrix. Here, the relationship between actual forecast error covariance and the corresponding ensemble covariance is empirically demonstrated. Using a simplified global circulation model and the local ensemble transform Kalman filter (LETKF), the covariance of the set of actual forecast errors corresponding to ensemble covariances close to a fixed target value is computed. By doing this for differing target values, an estimate of the actual forecast error covariance as a function of ensemble covariance is obtained. A demonstration that the hybrid is a much better approximation to this estimate than either the static climatological covariance or the localised ensemble covariance is given. The empirical estimate has two features that current hybrid error covariance models fail to represent: (i) The weight given to the static covariance matrix is an increasing function of the horizontal separation distance of the covarying model variables, and (ii) for small ensemble sizes and ensemble covariances near zero but negative, the actual forecast error covariance is a decreasing function of increasing ensemble covariance. While the first finding has been anticipated by other authors, the second finding has not been anticipated, as far as the authors are aware. Here, (ii) is hypothesised to be a consequence of spurious sample correlations and variances associated with reduced ensembles. Consistent with this hypothesis, the non-monotonicity of this relationship is almost eliminated by quadrupling the ensemble size.