Browse

You are looking at 1 - 1 of 1 items for :

  • The 1st NOAA Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction x
  • Journal of Hydrometeorology x
  • Refine by Access: Content accessible to me x
Clear All
Hanoi Medina, Di Tian, Fabio R. Marin, and Giovanni B. Chirico

Abstract

This study compares the performance of Global Ensemble Forecast System (GEFS) and European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation ensemble forecasts in Brazil and evaluates different analog-based methods and a logistic regression method for postprocessing the GEFS forecasts. The numerical weather prediction (NWP) forecasts were evaluated against the Physical Science Division South America Daily Gridded Precipitation dataset using both deterministic and probabilistic forecasting evaluation metrics. The results show that the ensemble precipitation forecasts performed commonly well in the east and poorly in the northwest of Brazil, independent of the models and the postprocessing methods. While the raw ECMWF forecasts performed better than the raw GEFS forecasts, analog-based GEFS forecasts were more skillful and reliable than both raw ECMWF and GEFS forecasts. The choice of a specific postprocessing strategy had less impact on the performance than the postprocessing itself. Nonetheless, forecasts produced with different analog-based postprocessing strategies were significantly different and were more skillful and as reliable and sharp as forecasts produced with the logistic regression method. The approach considering the logarithm of current and past reforecasts as the measure of closeness between analogs was identified as the best strategy. The results also indicate that the postprocessing using analog methods with long-term reforecast archive improved raw GEFS precipitation forecasting skill more than using logistic regression with short-term reforecast archive. In particular, the postprocessing dramatically improves the GEFS precipitation forecasts when the forecasting skill is low or below zero.

Full access