Spatial mode-based calibration (SMoC) of forecast precipitation fields from numerical weather prediction models

Abstract

Statistical calibration of precipitation forecasts from numerical weather prediction (NWP) models is routinely performed grid-cell by grid-cell, aiming to produce accurate and reliable ensemble forecasts for precipitation fields. Calibrated ensemble members from different grid-cells are then connected using ensemble reordering to form spatially structured ensemble forecasts. However, ensemble reordering approaches, such as the well-known Schaake shuffle method, are often criticized for not considering real physical atmospheric states of precipitation events. In this paper, we propose a spatial mode-based calibration (SMoC) model for post-processing forecast precipitation fields and producing ensemble forecasts with an inbuilt spatial structure, so that ensemble reordering is not required. The SMoC model is developed based on spatial modes derived from empirical orthogonal function (EOF) analyses of precipitation fields and linear regressions of derived EOF expansion coefficients of the first few dominant modes. Unlike conventional calibration models that are applied to forecast grid-cells individually, SMoC is applied to the whole forecast fields, and the spatial structure is inherently present in calibrated ensemble forecasts. There is therefore no need to rely on ensemble reordering for spatial reconstruction. The performance of SMoC is evaluated by applying it to NWP forecasts of substantive precipitation events over the Brisbane drainage basin in eastern Australia. Cross-validated results show that SMoC calibrated forecasts are of high quality at both grid-cell and basin scales. The spatial structure of precipitation is found to be well embedded in the ensemble members of the calibrated forecasts for the basin.