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Forecast skills were improved after application of a lagged ensemble prediction system.
We expect the acquired data to complement current measurement methods and to be instrumental in improving the numerical models' forecast skills.
To improve the heavy rain forecast skills, a hybrid Breeding Growing Mode (BGM)- three-dimensional variational (3DVAR) Data Assimilation (DA) scheme is designed on running the Advanced Research WRF (ARW WRF) model using the Advanced Microwave Sounder Unit A (AMSU-A) satellite radiance data.
It is often desirable to characterize forecast skills by a scalar value, the so-called loss function.
This analysis reveals that, despite an unavoidable increase of noise, there is no systematic erosion of the forecast skills of a model during the combining procedure.
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Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level.
As forecast skill diminishes with increasing lead time, the monthly forecasts approach climatology.
The merged forecasts exhibit considerable seasonal and spatial variability in forecast skill.
The results indicate the significance of forecast skill in forecast-based reservoir operation.
Forecast skill and reliability are assessed through leave-one-year-out cross validation.
In contrast, forecast skill in most wet summer and dry winter periods is generally low.
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