The impact of data density and data error on the evolution of mesoscale forecast error

The effects of data density and data errors on the accuracy of mesoscale weather forecasts were assessed by simulations of a period in February 1979 when a snowstorm occurred along the U.S. eastern seaboard. The simulations were initiated every 12 hr and the growth of the rms error was tracked as a function of time and varying data densities. A uniform grid of instrumentation separation and perfect boundary conditions were assumed for all the simulations. A bias error representation was defined to account for systematic measurement errors. A brief summary of features of the somewhat complex storm is provided, together with the parameters of the 11 different simulations with high, medium and low data densities. The accuracy of the forecasts were proportional to the data density, although the error decreased for all the simulations over a 24 hr period. The density of the vertical data had a significant impact on the accuracy of the forecast, whereas the horizontal data density did not. Finally, pathways by which errors were transferred among variables were identified.