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Gui-Ying Yang, Samantha Ferrett, Steve Woolnough, John Methven, and Chris Holloway


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.

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Carlo Cafaro, Beth J. Woodhams, Thorwald H. M. Stein, Cathryn E. Birch, Stuart Webster, Caroline L. Bain, Andrew Hartley, Samantha Clarke, Samantha Ferrett, and Peter Hill


Convection-permitting ensemble prediction systems (CP-ENS) have been implemented in the midlatitudes for weather forecasting time scales over the past decade, enabled by the increase in computational resources. Recently, efforts are being made to study the benefits of CP-ENS for tropical regions. This study examines CP-ENS forecasts produced by the Met Office over tropical East Africa, for 24 cases in the period April–May 2019. The CP-ENS, an ensemble with parameterized convection (Glob-ENS), and their deterministic counterparts are evaluated against rainfall estimates derived from satellite observations (GPM-IMERG). The CP configurations have the best representation of the diurnal cycle, although heavy rainfall amounts are overestimated compared to observations. Pairwise comparisons between the different configurations reveal that the CP-ENS is generally the most skillful forecast for both 3- and 24-h accumulations of heavy rainfall (97th percentile), followed by the CP deterministic forecast. More precisely, probabilistic forecasts of heavy rainfall, verified using a neighborhood approach, show that the CP-ENS is skillful at scales greater than 100 km, significantly better than the Glob-ENS, although not as good as found in the midlatitudes. Skill decreases with lead time and varies diurnally, especially for CP forecasts. The CP-ENS is underspread both in terms of forecasting the locations of heavy rainfall and in terms of domain-averaged rainfall. This study demonstrates potential benefits in using CP-ENS for operational forecasting of heavy rainfall over tropical Africa and gives specific suggestions for further research and development, including probabilistic forecast guidance.

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Samantha Ferrett, Thomas H. A. Frame, John Methven, Christopher E. Holloway, Stuart Webster, Thorwald H. M. Stein, and Carlo Cafaro


Forecasting rainfall in the tropics is a major challenge for numerical weather prediction. Convection-permitting (CP) models are intended to enable forecasts of high-impact weather events. Development and operation of these models in the tropics has only just been realized. This study describes and evaluates a suite of recently developed Met Office Unified Model CP ensemble forecasts over three domains in Southeast Asia, covering Malaysia, Indonesia, and the Philippines. The fractions skill score is used to assess the spatial scale dependence of skill in forecasts of precipitation during October 2018–March 2019. CP forecasts are skillful for 3-h precipitation accumulations at spatial scales greater than 200 km in all domains during the first day of forecasts. Skill decreases with lead time but varies depending on time of day over Malaysia and Indonesia, due to the importance of the diurnal cycle in driving rainfall in those regions. Skill is largest during daytime when precipitation is over land and is constrained by orography. Comparison of CP ensembles using 2.2-, 4.5-, and 8.8-km grid spacing and an 8.8-km ensemble with parameterized convection reveals that varying resolution has much less effect on ensemble skill and spread than the representation of convection. The parameterized ensemble is less skillful than CP ensembles over Malaysia and Indonesia and more skillful over the Philippines; however, the parameterized ensemble has large drops in skill and spread related to deficiencies in its diurnal cycle representation. All ensembles are underspread indicating that future model development should focus on this issue.

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