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Numerical weather prediction (NWP) models, which attempt to simulate the full state of the atmosphere, come with many options for dealing with processes that are unable to be explicitly resolved by the model. These model parameterizations are an ongoing area of research in atmospheric science. However, with continuous contributions from the research community and subsequent upgrade of the NWP codes, there are often many options for each unresolved process--leaving the user confronted with potentially thousands of ways to configure the model. We use the weather research and forecasting (WRF) model to forecast global horizontal irradiance (GHI) and undertake the task of narrowing down these options for a location in Qinghai, China. We show that optimizing the configuration of the WRF model based on the type of day (sunny, partly cloudy, or cloudy) is 13.6% better than using a single best configuration for all types of days. We also show that this performance improvement holds true for a longer 3-month test period (17.8% improvement). [ABSTRACT FROM AUTHOR]

ABSTRACT This study presents a semi-objective set of criteria for filtering the time series of a self-organizing map ( SOM, a method for collapsing a complex data set onto a series of typical instances/nodes). The purpose of the filter (or selection process) is to avoid the problem of associating anomalous time steps with SOM nodes that do not adequately describe that time step. The filter is based on measures of the match of the original field to the nearest SOM node. We demonstrate the method by using 21 years of MSLP reanalysis data for the Australian region from the European Centre for Medium Range Weather Forecasting product, ERA-Interim. We show that there are instances in which the SOM is incapable of describing the original data and that these instances can be removed using a set of criteria. We then show that this filtering/removal process improves both the quantitative and qualitative matching of the SOM to the MSLP data. Finally, we show that SOM-associated precipitation at three Australian cities (Melbourne, Sydney and Perth) is significantly influenced by the MSLP distributions that do not relate to the SOM and were removed by the filtering. The results show that care is required when interpreting trends inferred from data sorted into SOM nodes. [ABSTRACT FROM AUTHOR]