Spatial verification of global precipitation forecasts

TL;DR

This paper introduces a fast, spherical-adapted spatial verification metric for global precipitation forecasts, enabling detailed analysis of forecast accuracy and improvements over time.

Contribution

It adapts the Precipitation Attribution Distance (PAD) metric for Earth's spherical geometry, making global precipitation forecast verification computationally feasible and more accurate.

Findings

Location errors grow with forecast lead time.

Forecast performance varies regionally and over time.

The method effectively identifies forecast improvements or deteriorations.

Abstract

Verification of global high-resolution precipitation forecasts is challenging. Spatial verification techniques address some shortcomings of traditional verification. However most existing methods do not account for the non-planar geometry of a global domain, or their computational complexity is too large for global assessments. 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 employing 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 idealized cases and 7 years of operational global 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-series 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.