CHG - Forecasts
FORECASTS

The Climate Hazards Group develops forecasts in conjuction with the FEWS Network and affiliates around the world. These forecasts are made available for easy distrubtion among local decision makers to mitigate or prevent food insecurity in vulnerable and developing regions.

GEFS-CHIRPS

Skill with respect to CHIRPS.

Summary

Medium range precipitation forecasts are being provided through the CHG's Early Warning Explorer. The forecasts are based on NCEP's Global Ensemble Forecast System (GEFS). The forecasts are available on EWX in terms of two products (i) GEFS-Predict and (ii) GEFS-CHIRPS-predict products. The GEFS-Predict contains the raw GEFS prediction at 1 degree resolution. The GEFS-CHIRPS-Predict product is the GEFS prediction bias-corrected to the CHIRPS resolution (0.05 degree). The bias-correction is currently being performed following the quantile-quantile approach which involves bias-correction based on matching of cumulative distribution ranking for both the CHIRPS and GEFS.

NMME

Skill with respect to CHIRPS.

Summary

The North American Multi-Model Ensemble (NMME) is an experimental multi-model seasonal forecasting system consisting of coupled models from US modeling centers including NOAA/NCEP, NOAA/GFDL, IRI, NCAR, NASA, and Canada's CMC. The CHG plans to incorporate CHIRPS into the NMME similarly to how GEFS was integrated with CHIRPS to create the GEFS-CHIRPS product.

GEFS
GEFS Skill

Skill with respect to CHIRPS (images coming soon).

Summary

The Global Ensemble Forecast System (GEFS), previously known as the GFS Global ENSemble (GENS), is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental Prediction (NCEP) started the GEFS to address the nature of uncertainty in weather observations, which is used to initialize weather forecast models. The proverbial butterfly flapping her wings can have a cascading effect leading to wind gusts thousands of miles away. This extreme example illustrates that tiny, unnoticeable differences between reality and what is actually measured can, over time, lead to noticeable differences between what a weather model forecast predicts and reality itself. The GEFS attempts to quantify the amount of uncertainty in a forecast by generating an ensemble of multiple forecasts, each minutely different, or perturbed, from the original observations.