Fig. 4: Comparative analysis for the 2022 Pakistan floods predictions between the ECMWF subseasonal-to-seasonal (S2S) and FuXi-S2S models as well as the precursor signals that contributed to accurate predictions by the FuXi-S2S model.
From: A machine learning model that outperforms conventional global subseasonal forecast models
Comparison of spatially and temporally averaged standardized total precipitation (TP) anomaly (a) over the 2 weeks from August 16th to August 31st, 2022, showcasing GPCP observations (in black) alongside predictions from ECMWF S2S real-time forecasts (in blue) and FuXi-S2S forecasts (in red), with initialization dates: August 11th (08-11, MM-DD), August 8th (08-08), August 4th (08-04), August 1st (08-01), July 28th (07-28), July 25th (07-25), and July 21st (07-21). The black lines on the bar of ECMWF S2S and FuXi-S2S forecasts represent the 25th and 75th percentiles. For the comparison of temporally averaged standardized TP anomaly maps (b), the first column represents GPCP observations, while the second and third columns display predictions from ECMWF S2S and FuXi-S2S, respectively, both initialized on July 28th, and the fourth and fifth columns correspond to predictions from ECMWF S2S and FuXi-S2S, respectively, with an initialization date of July 21st. Green contour indicates the border line of Pakistan. The saliency maps (c) were generated using the gradient of the negative standardized TP anomaly, averaged over the Pakistan region, in relation to the input SST. These maps correspond to forecasts initialized on July 28th (07-28, first column) and July 21st (07-21, second column). Here, the red and blue colors indicate the positive and negative correlations between the negative of standardized TP and variations in SST. The black lines on the bars in this figure represent the 25th and 75th percentiles of the ensemble forecasts for each start date for both ECMWF and FuXi-S2S models.
