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The study uses numerical weather prediction models to predict the occurrence of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) during the period 23–26 August 2017 over Nigeria. Fraction skill score (FSS) and method for object‐based diagnostic evaluation (MODE) verification techniques were applied to verify how well the models predict the high‐impact event and to demonstrate how these tools can support operational forecasting. Ensemble model forecasts at a convective scale from UK Met Office Unified Model (MetUM) and a one‐way nested weather research and forecasting (WRF) model were compared with the integrated multisatellite retrievals for global precipitation measurement (IMERG GPM). The purpose is to examine skills of improved model resolution and ensemble in reproducing rainfall forecasts on useful scales and how the skill varies with spatial scale. WRF 2 and 6 km model forecasts show comparable skill at smaller grid scales. The skill of MetUM improves dramatically when the verification statistics are applied to the ensemble mean of the binary fields of the individual member forecast. The object‐based analysis reveals a similar structure as observed, although displaced eastwards. Most improvement occurred for heavier rainfall events associated with the passage of the AEW. WRF 6 km compares reasonably well with WRF 2 km in terms of shape and structure of rainfall underscoring the ability of the model to reasonably represent convection at 6 km horizontal resolution. The ensemble members in MetUM explicitly reproduce convection at 4 km resolution but are displaced at about 166 km behind observed rainfall. [ABSTRACT FROM AUTHOR]

Performance of all- India summer monsoon rainfall ( June to September) has been studied when June or July or June + July rainfall is reported as being in excess/deficit, using monthly and seasonal rainfall data for the period 1871-2010. The study shows that seasonal rainfall was above/below the long term mean in almost all the years when rainfall of June or July or June + July was in excess/deficit. The probabilities of occurrence of seasonal rainfall below the long term mean are 77, 83, and 92% respectively when June, July, and June + July rainfalls are in deficit. The years in which the rainfalls of June and July were deficit, were mostly El Niño years and their corresponding seasonal rainfalls were below the mean. The deficiencies in June/ July or June + July rainfall during El Niño years thus give an indication of seasonal rainfall being below the mean, and vice versa for La Niña years. The study has brought out an important result that rainfall during June gives a first signal of the performance of seasonal rainfall. In order to explain the relationship between June excess/deficit rainfall and seasonal above/below mean rainfall, an hypothesis is proposed which states that the circulation anomalies developed over the Indian region during an excess/deficit June persist throughout the season. The hypothesis has been proved by examining the composites of excess/deficit June wind anomalies at 850 and 200 hPa and observing their subsequent persistence from July to September. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]