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- Author or Editor: Steve Woolnough x
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Abstract
A set of five-member ensemble forecasts initialized daily for 48 days during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment period are performed with the ECMWF monthly forecasting system in order to assess its skill in predicting a Madden–Julian oscillation (MJO) event. Results show that the model is skillful in predicting the evolution of the MJO up to about 14 days, but the amplitude of the MJO is damped after a few days of integration. In addition, the model has some deficiencies in propagating the MJO through the Maritime Continent. The same experiment framework is used to quantify the impacts of changing the model physics, the ocean model, the atmospheric horizontal resolution, and the initial conditions on the skill of the monthly forecasting system. Results show that there is a scope for extending the skillful range of the operational monthly forecasting system to predict the evolution of the MJO by at least a week. This is achieved by using an improved cloud parameterization together with a better representation of the mixing of the upper ocean. An additional set of experiments suggests that degrading the quality of the initial conditions (by using the 15-yr ECMWF Re-Analysis instead of the 40-yr ECMWF Re-Analysis) significantly degrades the skill of the model to predict an MJO event and that increasing the horizontal resolution of the atmospheric mode had only a minor impact on the MJO forecasts. In addition, results show that there is a significant sensitivity to the initial perturbations of the ensemble members, and therefore, targeting perturbations on the MJO could improve the skill of the monthly forecasting system. While the propagation of the MJO was sensitive to most of the changes described in this paper, only the change in cloud parameterization improved the strength of the MJO. The propagation of the MJO over the Maritime Continent remains an issue.
Abstract
A set of five-member ensemble forecasts initialized daily for 48 days during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment period are performed with the ECMWF monthly forecasting system in order to assess its skill in predicting a Madden–Julian oscillation (MJO) event. Results show that the model is skillful in predicting the evolution of the MJO up to about 14 days, but the amplitude of the MJO is damped after a few days of integration. In addition, the model has some deficiencies in propagating the MJO through the Maritime Continent. The same experiment framework is used to quantify the impacts of changing the model physics, the ocean model, the atmospheric horizontal resolution, and the initial conditions on the skill of the monthly forecasting system. Results show that there is a scope for extending the skillful range of the operational monthly forecasting system to predict the evolution of the MJO by at least a week. This is achieved by using an improved cloud parameterization together with a better representation of the mixing of the upper ocean. An additional set of experiments suggests that degrading the quality of the initial conditions (by using the 15-yr ECMWF Re-Analysis instead of the 40-yr ECMWF Re-Analysis) significantly degrades the skill of the model to predict an MJO event and that increasing the horizontal resolution of the atmospheric mode had only a minor impact on the MJO forecasts. In addition, results show that there is a significant sensitivity to the initial perturbations of the ensemble members, and therefore, targeting perturbations on the MJO could improve the skill of the monthly forecasting system. While the propagation of the MJO was sensitive to most of the changes described in this paper, only the change in cloud parameterization improved the strength of the MJO. The propagation of the MJO over the Maritime Continent remains an issue.
Abstract
A connection is found between African easterly waves (AEWs), equatorial westward-moving mixed Rossby–gravity (WMRG) waves, and equivalent barotropic Rossby waves (RWs) from the Southern Hemisphere (SH). The amplitude and phase of equatorial waves is calculated by projection of broadband-filtered ERA-Interim data onto a horizontal structure basis obtained from equatorial wave theory. Mechanisms enabling interaction between the wave types are identified. AEWs are dominated by a vorticity wave that tilts eastward below the African easterly jet and westward above: the tilt necessary for baroclinic wave growth. However, a strong relationship is identified between amplifying vorticity centers within AEWs and equatorial WMRG waves. Although the waves do not phase lock, positive vorticity centers amplify whenever the cross-equatorial motion of the WMRG wave lies at the same longitude in the upper troposphere (southward flow) and east of this in the lower troposphere (northward flow). Two mechanisms could explain the vorticity amplification: vortex stretching below the upper-tropospheric divergence and ascent associated with latent heating in convection in the lower-tropospheric moist northward flow.
In years of strong AEW activity, SH and equatorial upper-tropospheric zonal winds are more easterly. Stronger easterlies have two effects: (i) they Doppler shift WMRG waves so that their period varies little with wavenumber (3–4 days) and (ii) they enable westward-moving RWs to propagate into the tropical waveguide from the SH. The RW phase speeds can match those of WMRG waves, enabling sustained excitation of WMRG. The WMRG waves have an eastward group velocity with wave activity accumulating over Africa and invigorating AEWs at similar frequencies through the vorticity amplification mechanism.
Abstract
A connection is found between African easterly waves (AEWs), equatorial westward-moving mixed Rossby–gravity (WMRG) waves, and equivalent barotropic Rossby waves (RWs) from the Southern Hemisphere (SH). The amplitude and phase of equatorial waves is calculated by projection of broadband-filtered ERA-Interim data onto a horizontal structure basis obtained from equatorial wave theory. Mechanisms enabling interaction between the wave types are identified. AEWs are dominated by a vorticity wave that tilts eastward below the African easterly jet and westward above: the tilt necessary for baroclinic wave growth. However, a strong relationship is identified between amplifying vorticity centers within AEWs and equatorial WMRG waves. Although the waves do not phase lock, positive vorticity centers amplify whenever the cross-equatorial motion of the WMRG wave lies at the same longitude in the upper troposphere (southward flow) and east of this in the lower troposphere (northward flow). Two mechanisms could explain the vorticity amplification: vortex stretching below the upper-tropospheric divergence and ascent associated with latent heating in convection in the lower-tropospheric moist northward flow.
In years of strong AEW activity, SH and equatorial upper-tropospheric zonal winds are more easterly. Stronger easterlies have two effects: (i) they Doppler shift WMRG waves so that their period varies little with wavenumber (3–4 days) and (ii) they enable westward-moving RWs to propagate into the tropical waveguide from the SH. The RW phase speeds can match those of WMRG waves, enabling sustained excitation of WMRG. The WMRG waves have an eastward group velocity with wave activity accumulating over Africa and invigorating AEWs at similar frequencies through the vorticity amplification mechanism.
Abstract
A novel technique is developed to identify equatorial waves in analyses and forecasts. In a real-time operational context, it is not possible to apply a frequency filter based on a wide centered time window due to the lack of future data. Therefore, equatorial wave identification is performed based primarily on spatial projection onto wave mode horizontal structures. Spatial projection alone cannot distinguish eastward- from westward-moving waves, so a broadband frequency filter is also applied. The novelty in the real-time technique is to off-center the time window needed for frequency filtering, using forecasts to extend the window beyond the current analysis. The quality of this equatorial wave diagnosis is evaluated. First, the “edge effect” arising because the analysis is near the end of the filter time window is assessed. Second, the impact of using forecasts to extend the window beyond the current date is quantified. Both impacts are shown to be small referenced to wave diagnosis based on a centered time window of reanalysis data. The technique is used to evaluate the skill of the Met Office forecast system in 2015–18. Global forecasts exhibit substantial skill (correlation > 0.6) in equatorial waves, to at least day 4 for Kelvin waves and day 6 for westward mixed Rossby–gravity (WMRG), and meridional mode number n = 1 and n = 2 Rossby waves. A local wave phase diagram is introduced that is useful to visualize and validate wave forecasts. It shows that in the model Kelvin waves systematically propagate too fast, and there is a 25% underestimate of amplitude in Kelvin and WMRG waves over the central Pacific.
Abstract
A novel technique is developed to identify equatorial waves in analyses and forecasts. In a real-time operational context, it is not possible to apply a frequency filter based on a wide centered time window due to the lack of future data. Therefore, equatorial wave identification is performed based primarily on spatial projection onto wave mode horizontal structures. Spatial projection alone cannot distinguish eastward- from westward-moving waves, so a broadband frequency filter is also applied. The novelty in the real-time technique is to off-center the time window needed for frequency filtering, using forecasts to extend the window beyond the current analysis. The quality of this equatorial wave diagnosis is evaluated. First, the “edge effect” arising because the analysis is near the end of the filter time window is assessed. Second, the impact of using forecasts to extend the window beyond the current date is quantified. Both impacts are shown to be small referenced to wave diagnosis based on a centered time window of reanalysis data. The technique is used to evaluate the skill of the Met Office forecast system in 2015–18. Global forecasts exhibit substantial skill (correlation > 0.6) in equatorial waves, to at least day 4 for Kelvin waves and day 6 for westward mixed Rossby–gravity (WMRG), and meridional mode number n = 1 and n = 2 Rossby waves. A local wave phase diagram is introduced that is useful to visualize and validate wave forecasts. It shows that in the model Kelvin waves systematically propagate too fast, and there is a 25% underestimate of amplitude in Kelvin and WMRG waves over the central Pacific.
Abstract
The skill of precipitation forecasts from global prediction systems has a strong regional and seasonal dependence. Quantifying the skill of models for different regions and time scales is important, not only to improve forecast skill, but to enhance the effective uptake of forecast information. The Subseasonal to Seasonal Prediction project (S2S) database contains near-real-time forecasts and reforecasts from 11 operational centers and provides a great opportunity to evaluate and compare the skill of operational S2S systems. This study evaluates the skill of these state-of-the-art global prediction systems in predicting monthly precipitation over the Greater Horn of Africa. This comprehensive evaluation was performed using deterministic and probabilistic forecast verification metrics. Results from the analysis showed that the prediction skill varies with months and region. Generally, the models show high prediction skill during the start of the rainfall season in March and lower prediction skill during the peak of the rainfall in April. ECCC, ECMWF, KMA, NCEP, and UKMO show better prediction skill over the region for most of the months compared with the rest of the models. Conversely, BoM, CMA, HMCR, and ISAC show poor prediction skill over the region. Overall, the ECMWF model performs best over the region among the 11 models analyzed. Importantly, this study serves as a baseline skill assessment with the findings helping to inform how a subset of models could be selected to construct an objectively consolidated multimodel ensemble of S2S forecast products for the Greater Horn of Africa region, as recommended by the World Meteorological Organization.
Abstract
The skill of precipitation forecasts from global prediction systems has a strong regional and seasonal dependence. Quantifying the skill of models for different regions and time scales is important, not only to improve forecast skill, but to enhance the effective uptake of forecast information. The Subseasonal to Seasonal Prediction project (S2S) database contains near-real-time forecasts and reforecasts from 11 operational centers and provides a great opportunity to evaluate and compare the skill of operational S2S systems. This study evaluates the skill of these state-of-the-art global prediction systems in predicting monthly precipitation over the Greater Horn of Africa. This comprehensive evaluation was performed using deterministic and probabilistic forecast verification metrics. Results from the analysis showed that the prediction skill varies with months and region. Generally, the models show high prediction skill during the start of the rainfall season in March and lower prediction skill during the peak of the rainfall in April. ECCC, ECMWF, KMA, NCEP, and UKMO show better prediction skill over the region for most of the months compared with the rest of the models. Conversely, BoM, CMA, HMCR, and ISAC show poor prediction skill over the region. Overall, the ECMWF model performs best over the region among the 11 models analyzed. Importantly, this study serves as a baseline skill assessment with the findings helping to inform how a subset of models could be selected to construct an objectively consolidated multimodel ensemble of S2S forecast products for the Greater Horn of Africa region, as recommended by the World Meteorological Organization.
