Effective use of general circulation model outputs for forecasting monthly rainfalls to long lead times

Abstract

[1] Long lead rainfall forecasts are highly valuable for planning and management of water resources and agriculture. In this study, we establish multiple statistical calibration and bridging models that use general circulation model (GCM) outputs as predictors to produce monthly rainfall forecasts for Australia with lead times up to 8 months. The statistical calibration models make use of raw forecasts of rainfall from a coupled GCM, and the statistical bridging models make use of sea surface temperature (SST) forecasts of the GCM. The forecasts from the multiple models are merged through Bayesian model averaging to take advantage of the strengths of individual models. The skill of monthly rainfall forecasts is generally low. Compared to forecasting seasonal rainfall totals, it is more challenging to forecast monthly rainfall. However, there are regions and months for which forecasts are skillful. In particular, there are months of the year for which forecasts can be skillfully made at long lead times. This is most evident for the period of November and December. Using GCM forecasts of SST through bridging clearly improves monthly rainfall forecasts. For lead time 0, the improvement is particularly evident for February to March, July and October to December. For longer lead times, the benefit of bridging is more apparent. As lead time increases, bridging is able to maintain forecast skill much better than when only calibration is applied.

The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia

“…the statistical rainfall forecasts may exhibit skill in different seasons or locations to dynamical forecasts; Schepen et al, 2012b), and that merging forecasts of high rainfalls from dynamical and statistical models may improve overall skill. Using climate indices derived from SST forecasts from coupled ocean-atmosphere dynamical climate models shows promise in improving forecasts of monthly rainfall totals at lead times of more than six months (Hawthorne et al, 2013), and avoids the use of lagged climate indices for forecasting. Our forecast method could be adapted to catchments in different regions by including predictors that are relevant to a given region.…”

A new method for post-processing daily sub-seasonal to seasonal rainfall forecasts from GCMs and evaluation for 12 Australian catchments

“…BJP has 20 since been applied to calibrate hourly rainfall forecasts (Shrestha et al, 2015;Robertson et al, 2013b) and seasonal rainfall forecasts (Hawthorne et al, 2013;Khan et al, 2015;Peng et al, 2014;. BJP was most recently adapted for sub-seasonal to seasonal streamflow forecasting Schepen et al, 2016).…”

“…Several conceptually simple statistical correction methods are used for directly post-processing daily GCM rainfall forecasts including: additive bias correction, multiplicative bias correction and quantile mapping (Ines and Hansen, 2006). For example, Crochemore et al (2016) recently evaluated linear scaling and quantile mapping for post-processing ECMWF System4 rainfall forecasts in France.…”

“…In this regard, several Bayesian calibration approaches are known to be effective at post-processing GCM rainfall totals aggregated to monthly and seasonal time scales (Hawthorne et al, 2013;Luo et al, 2007). However, it is apparent that full calibration methods aren't normally applied 20 to the post-processing of daily GCM forecasts of rainfall in the sub-seasonal to seasonal period.…”

SeFo: A Package for Generating Probabilistic Forecasts from NMME Predictive Ensembles

“…A variety of methods have been proposed to convert ensemble outputs to calibrated probabilistic forecasts of future meteorologic variables such as temperature and precipitation [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The workflow for applying, assessing and comparing the performance of different methods involves many steps: downloading historic observations and ensemble outputs; applying the selected methods to produce probabilistic forecasts; comparing the performance of the selected methods over a common past period using any of several possible metrics; and producing summary graphics.…”