Emulation and S2S probabilistic prediction of 2-m temperature and precipitation over the global domain using linear inverse modeling

We developed a data-driven system for joint prediction of daily precipitation (Pr) and near-surface temperature (T2m) over the global domain by utilizing NASA’s satellite observations and the associated reanalysis products, with the focus on S2S hydrologic forecasting. Our approach is based on a well-established methodology of linear inverse modeling modified and adapted by our science team for high-resolution modeling of precipitation. The key element of this new methodology is the usage of a so-called pseudo-precipitation (PP) variable, equal to the actual Pr where precipitation is occurring and, otherwise, equal to the (negative) air-column integrated water-vapor saturation deficit — the amount of water vapor to be added to the air column to achieve saturation at each vertical level. The model’s jointly obtained Pr and T2m forecasts are then validated against the observed fields as usual.

The above model is shown to be an efficient tool for emulating daily sequences of global coupled T2m and Pr fields with spatiotemporal characteristics strikingly similar to the observed characteristics. We used a large (100-member) ensemble of our statistical model’s hindcasts of precipitation over global domain to predict probabilities of weekly and biweekly precipitation amounts in one of the three categories (below normal, normal, and above normal) and compared these hindcasts with those based on the NASA GEOSS2S v2p1 model (4-member ensemble), calibrated using extended logistic regression. While the statistical model’s S2S precipitation forecast skill is somewhat lower than that of the reference NASA state-of-the-art system, it exhibits similar geographical and seasonal distributions, which warrants further research. We are currently looking into incorporating automated ML/AI feature identification techniques into our existing set up (with a linear activation function), to fine-tune the model learning and improve its predictive potential.