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the Canadian operational global NWP model, and found that the 4DEnVar improved upon their operational, nonhybrid 4DVar in the tropics and Southern Hemisphere, but not in the Northern Hemisphere. It was also found that 4DEnVar performed slightly worse than a hybrid 4DVar. Buehner et al. (2013) , again using the operational Canadian system, found that while the use of 4D instead of 3D ensemble covariances did result in small, consistent improvements in their EnVar for deterministic NWP, the gains
the Canadian operational global NWP model, and found that the 4DEnVar improved upon their operational, nonhybrid 4DVar in the tropics and Southern Hemisphere, but not in the Northern Hemisphere. It was also found that 4DEnVar performed slightly worse than a hybrid 4DVar. Buehner et al. (2013) , again using the operational Canadian system, found that while the use of 4D instead of 3D ensemble covariances did result in small, consistent improvements in their EnVar for deterministic NWP, the gains
in the tropics. Two separate studies were carried out to investigate the impact of a hybrid four-dimensional variational data assimilation (4DVar) system relative to 4DVar for the Naval Research Laboratory ( Kuhl et al. 2013 ) and the Met Office ( Clayton et al. 2013 ) global prediction systems. Both studies showed that hybrid 4DVar was an improvement relative to 4DVar for deterministic prediction across a wide variety of variable, levels, and lead times. Kuhl et al. (2013) found that the
in the tropics. Two separate studies were carried out to investigate the impact of a hybrid four-dimensional variational data assimilation (4DVar) system relative to 4DVar for the Naval Research Laboratory ( Kuhl et al. 2013 ) and the Met Office ( Clayton et al. 2013 ) global prediction systems. Both studies showed that hybrid 4DVar was an improvement relative to 4DVar for deterministic prediction across a wide variety of variable, levels, and lead times. Kuhl et al. (2013) found that the
westerly flow. The distance between the peaks of about 40° in longitude is likely a representation of underlying Rossby waves. For example, for an observation placed at a trough these additional peaks represent the adjacent ridges associated with a trough. As observations get closer to the tropics (e.g., observations 4 and 5), RCF begins to take a different, sometimes complex shape, including stretching eastward along the equator upstream of the main flow. Fig . 4. Examples of RCF functions for seven
westerly flow. The distance between the peaks of about 40° in longitude is likely a representation of underlying Rossby waves. For example, for an observation placed at a trough these additional peaks represent the adjacent ridges associated with a trough. As observations get closer to the tropics (e.g., observations 4 and 5), RCF begins to take a different, sometimes complex shape, including stretching eastward along the equator upstream of the main flow. Fig . 4. Examples of RCF functions for seven
forecasts on seasonal and longer time scales (e.g., Neelin et al. 1994 ). Recent research by Johns et al. (2012) and Johns et al. (2015, manuscript submitted to Mon. Wea. Rev .) also shows significant improvements on shorter time scales (out to 15 days) to both atmospheric and ocean forecast skill in the tropics when using a coupled model as opposed to either atmosphere or ocean models run separately. There is an increasing interest within the GODAE Oceanview community in coupled predictions on
forecasts on seasonal and longer time scales (e.g., Neelin et al. 1994 ). Recent research by Johns et al. (2012) and Johns et al. (2015, manuscript submitted to Mon. Wea. Rev .) also shows significant improvements on shorter time scales (out to 15 days) to both atmospheric and ocean forecast skill in the tropics when using a coupled model as opposed to either atmosphere or ocean models run separately. There is an increasing interest within the GODAE Oceanview community in coupled predictions on
observing systems assimilated and the variable skill of the HYCOM forecast. Magnitudes of the instantaneous temperature and salinity forecast errors are greatest in the tropics and western boundary current regions. Fig . 1. Instantaneous forecast error gradients [Eq. (4) ] for (top) temperature (°C) and (bottom) salinity (PSU) at 100-m depth, valid 1800 UTC 1 Nov 2012. Results are presented for each analysis ocean basin. Positive values (warm colors) indicate forecast error growth; negative values
observing systems assimilated and the variable skill of the HYCOM forecast. Magnitudes of the instantaneous temperature and salinity forecast errors are greatest in the tropics and western boundary current regions. Fig . 1. Instantaneous forecast error gradients [Eq. (4) ] for (top) temperature (°C) and (bottom) salinity (PSU) at 100-m depth, valid 1800 UTC 1 Nov 2012. Results are presented for each analysis ocean basin. Positive values (warm colors) indicate forecast error growth; negative values
a preliminary version of the 4DEnVar-based system over the two-month period February–March 2011. The additional channels increased the combined number of assimilated AIRS and IASI observations from about 1.2 million to about 3.8 million day −1 . Results show a modest improvement in the accuracy of temperature and midtropospheric humidity forecasts up to day 5 in the extratropics, but a neutral impact in the tropics. f. Other changes to radiance assimilation Satellite radiance observation
a preliminary version of the 4DEnVar-based system over the two-month period February–March 2011. The additional channels increased the combined number of assimilated AIRS and IASI observations from about 1.2 million to about 3.8 million day −1 . Results show a modest improvement in the accuracy of temperature and midtropospheric humidity forecasts up to day 5 in the extratropics, but a neutral impact in the tropics. f. Other changes to radiance assimilation Satellite radiance observation
intervals, 2004–05 and 2009–11, that had been developed for different research objectives. Although the 2004–05 and 2009–11 solutions were produced independently, all OGCM parameters, such as horizontal and vertical mixing coefficients, were the same in both time intervals and were obtained using a Green’s function approach ( Menemenlis et al. 2005 ). Near-surface vertical mixing in the tropics follows Large et al. (1994) with a background viscosity of 5.7 × 10 −4 m 2 s −1 and a diffusivity of 4
intervals, 2004–05 and 2009–11, that had been developed for different research objectives. Although the 2004–05 and 2009–11 solutions were produced independently, all OGCM parameters, such as horizontal and vertical mixing coefficients, were the same in both time intervals and were obtained using a Green’s function approach ( Menemenlis et al. 2005 ). Near-surface vertical mixing in the tropics follows Large et al. (1994) with a background viscosity of 5.7 × 10 −4 m 2 s −1 and a diffusivity of 4
temporal extension of the forecast impact of PQC. We explored the ability of PQC to reduce the relative integrated forecast error within three latitudinal bands: the NH extratropics, the SH extratropics, and the tropics (30°S–30°N, within which we made no detrimental innovation denial). These statistics were computed for all 20 cases using the allneg criterion ( section 5b ). The PQC-modified forecasts from 6 to 126 h were verified against the GSI analysis that was obtained before applying PQC. The
temporal extension of the forecast impact of PQC. We explored the ability of PQC to reduce the relative integrated forecast error within three latitudinal bands: the NH extratropics, the SH extratropics, and the tropics (30°S–30°N, within which we made no detrimental innovation denial). These statistics were computed for all 20 cases using the allneg criterion ( section 5b ). The PQC-modified forecasts from 6 to 126 h were verified against the GSI analysis that was obtained before applying PQC. The
-error covariance We first checked the background-error standard deviations (BESDs) on the CSG points to determine the structure of background-error covariance. The BESDs of U , V , and T are relatively large over the midlatitudes between 30° and 50°N and the ocean near Antarctica, while those of Q are large in the tropics for both the upper and lower troposphere ( Figs. 6a–h ). Meanwhile, the BESDs of Ps have the most similar spatial distribution with lower-tropospheric temperature BESDs ( Fig. 6i
-error covariance We first checked the background-error standard deviations (BESDs) on the CSG points to determine the structure of background-error covariance. The BESDs of U , V , and T are relatively large over the midlatitudes between 30° and 50°N and the ocean near Antarctica, while those of Q are large in the tropics for both the upper and lower troposphere ( Figs. 6a–h ). Meanwhile, the BESDs of Ps have the most similar spatial distribution with lower-tropospheric temperature BESDs ( Fig. 6i
algorithm of Liu et al. (2008 , 2009 ) was implemented within a global spectral model (called En-4D-Var in their papers) and compared with traditional 4DVar, EnKF, and 4D-Var-Benkf methods. These data assimilation schemes had a similar performance in the northern extratropics and tropics. In the southern extratropics, En-4D-Var was slightly better than EnKF but slightly worse than 4D-Var-Benkf. Here we point out that the En-4D-Var implementation of Buehner et al. (2010a , b ) corresponds to the
algorithm of Liu et al. (2008 , 2009 ) was implemented within a global spectral model (called En-4D-Var in their papers) and compared with traditional 4DVar, EnKF, and 4D-Var-Benkf methods. These data assimilation schemes had a similar performance in the northern extratropics and tropics. In the southern extratropics, En-4D-Var was slightly better than EnKF but slightly worse than 4D-Var-Benkf. Here we point out that the En-4D-Var implementation of Buehner et al. (2010a , b ) corresponds to the
