Spatial verification of global precipitation forecasts

Abstract

Spatial verification of global high-resolution weather forecasts remains a considerable challenge. Most existing spatial verification techniques either do not properly account for the non-planar geometry of a global domain or their computation complexity becomes too large. We present an adaptation of the recently developed Precipitation Attribution Distance (PAD) metric, designed for verifying precipitation, enabling its use on the Earth's spherical geometry. PAD estimates the magnitude of location errors in the forecasts and is related to the mathematical theory of Optimal Transport as it provides a close upper bound for the Wasserstein distance. The method is fast and flexible with time complexity $O(n \log(n))$. Its behavior is analyzed using a set of idealized cases and 7 years of operational global high-resolution deterministic 6-hourly precipitation forecasts from the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts. The summary results for the whole period show how location errors in the IFS model grow steadily with increasing lead time for all analyzed regions. Moreover, by examining the time evolution of the results, we can determine the trends in the score's value and identify the regions where there is a statistically significant improvement (or worsening) of the forecast performance. The results can also be analyzed separately for different intensities of precipitation. Overall, the PAD provides meaningful results for estimating location errors in global high-resolution precipitation forecasts at an affordable computational cost.