Statistical generation of SST perturbations with spatio‐temporally coherent growing patterns

Abstract

The medium‐range ensemble prediction system (EPS) at the Japan Meteorological Agency (JMA), like other operational EPSs, suffers from overconfident probabilistic forecasts in the lower troposphere and over the oceans in particular. To alleviate this issue, a new scheme to perturb the sea surface temperature (SST) is proposed and tested. The proposed method statistically generates SST perturbations that account for time‐varying errors of the SST that is prescribed to the control member. Despite its simplicity and purely statistical nature, the new method is able to produce growing perturbations whose spatial patterns appear to reflect ocean dynamics. Their growth with forecast lead‐time is completely flow‐independent but is statistically consistent with the past records of the error growth of the prescribed SST. Perturbing the SST with the proposed scheme is shown to improve the probabilistic forecast skills of JMA's medium‐range EPS. The improvement is statistically significant over the Tropics throughout the entire troposphere but the impact on the extratropical forecast is found to be neutral. The improvement is largely attributed to enhanced reliability which in turn is achieved through an increase in the ensemble spread. Perturbing SST incurs no harmful impact on the quality of ensemble mean forecast.

RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction

“…Hotta (2019) proposed a method to improve the prediction accuracy of EPS in JMA by seawater temperature perturbation. The experiment proved that the perturbation of SST did not affect the ensemble mean forecast quality [13]. Jacox (2017) used the NMME Global Climate Prediction System to evaluate the maximum prediction period of seasonal SST in the California Ocean Current System (CCS) for up to 4 months and found that ENSO has varying degrees of impact on the sustainability of SST prediction [14].…”

Long-term SST Prediction Based on RSST-ARGM

“…Kale (2020) used monthly temperature, evaporation and precipitation data as input and combined with a variety of statistical methods to build an adaptive neuro-fuzzy reasoning system for regional SST, and verified the accuracy of the prediction from statistical standards [12]. Hotta (2019) proposed a method to improve the prediction accuracy of EPS in JMA by seawater temperature perturbation. The experiment proved that the perturbation of SST did not affect the ensemble mean forecast quality [13].…”

“…Hotta (2019) proposed a method to improve the prediction accuracy of EPS in JMA by seawater temperature perturbation. The experiment proved that the perturbation of SST did not affect the ensemble mean forecast quality [13]. Jacox (2019) used the NMME Global Climate Prediction System to evaluate the maximum prediction period of seasonal SST in the California Ocean Current System (CCS) for up to 4 months and found that ENSO has varying degrees of impact on the sustainability of SST prediction [14].…”

RSST-ARGM: A Data-Driven Approach to Long-term Sea Surface Temperature Prediction

“…Kale (2020) used monthly temperature, evaporation and precipitation data as input and combined with a variety of statistical methods to build an adaptive neuro-fuzzy reasoning system for regional SST, and verified the accuracy of the prediction from statistical standards [12]. Hotta (2019) proposed a method to improve the prediction accuracy of EPS in JMA by seawater temperature perturbation. The experiment proved that the perturbation of SST did not affect the ensemble mean forecast quality [13].…”