A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments
Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-03-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/22/1615/2018/hess-22-1615-2018.pdf |
| Summary: | Rainfall forecasts are an integral part of hydrological forecasting systems
at sub-seasonal to seasonal timescales. In seasonal forecasting, global
climate models (GCMs) are now the go-to source for rainfall forecasts.
For hydrological applications however, GCM forecasts are often biased and
unreliable in uncertainty spread, and calibration is therefore required
before use. There are sophisticated statistical techniques for calibrating
monthly and seasonal aggregations of the forecasts. However, calibration of
seasonal forecasts at the daily time step typically uses very simple
statistical methods or climate analogue methods. These methods generally lack
the sophistication to achieve unbiased, reliable and coherent forecasts of
daily amounts and seasonal accumulated totals. In this study, we propose and
evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating
daily forecasts and the Schaake Shuffle for connecting the daily ensemble
members of different lead times. We apply the method to post-process ACCESS-S
forecasts for 12 perennial and ephemeral catchments across Australia and for
12 initialisation dates. RPP-S significantly reduces bias in raw forecasts
and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been
post-processed using quantile mapping, especially for monthly and seasonal
accumulations. Several opportunities to improve the robustness and skill of
RPP-S are identified. The new RPP-S post-processed forecasts will be used in
ensemble sub-seasonal to seasonal streamflow applications. |
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| ISSN: | 1027-5606 1607-7938 |