Some Characteristics of the Limited-Area Fine-Mesh (LFM) Model Quantitative Precipitation Forecasts (QPF) during the 1982 and 1983 Warm Seasons

Abstract

The skill of the limited-area fine-mesh (LFM) model in making 24-h areal quantitative precipitation forecasts (QPF) of ? 0.5 in. (12.7 mm) is evaluated and analyzed for two warm seasons; 1982 and 1983. Differences in skill between the eastern and western United States are investigated and the impact of updated initial conditions is explored. The skill in predicting precipitation from cyclonic versus mesoscale systems is also examined. It is found that the model skill changed drastically from the summers of 1982 to 1983. Skill levels for 1982 were about 25% to 35%. In 1983, they dropped to an average value of less than 10% and often to zero on a daily basis. The precipitous drop in skill appears to be the result of changes made in key model threshold parameters. These parameters include grid-resolvable saturation criterion and convective cloud base boost. The introduction of constraints on convective precipitation and the vertical advection of moisture also appear to have reduced model skill. A comparison of skill between the eastern and western United States indicates that the model scored substantially better in the east. It is also evident that, in general, the model skill for quantitative precipitation forecasts for the 12-h period 0000 to 1200 UTC was much greater with initial conditions from 0000 UTC, than with initial conditions from 12 h earlier at 1200 UTC; i.e., updated initial conditions had a large positive impact on the model skill. This was particularly true for mesoscale convective systems. Longer term (> 12 h) predictions of the 0000?1200 UTC period scored better for cyclone-related precipitation than for precipitation from mesoscale systems. When the model was reinitialized at 0000 UTC, there was virtually no difference between skill levels for cyclone-related and mesoscale events. The results indicate that the lack of a realistic diurnal heating cycle in the LFM may be particularly detrimental to the model's ability to forecast convectively dominated warm-season rainfall. The results also indicate that threshold or critical values for initiating resolvable-scale precipitation or making convective adjustments may introduce artificial seasonal, diurnal, and regional variations in quantitative precipitation forecasts. Due to the inherent nonlinearity of numerical models, attempts to improve model skill for a particular region, season, time of day, or precipitation type by adjusting threshold or critical values may introduce bias in other model parameters and negatively impact overall model skill.