Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China

Peng, Zhaoliang; Wang, Q. J.; Bennett, James; Schepen, Andrew; Pappenberger, Florian; Pokhrel, Prafulla; Wang, Ziru

doi:10.1002/2013jd021162

Cited by 42 publications

(44 citation statements)

References 43 publications

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“…Any operational forecasting system needs to take account of such uncertainties, for example by use of ensemble methods. Nonetheless, seasonal forecasts are expected to continue to improve in the future and additional post-processing may increase the skill of the fire forecast (Peng et al, 2014). Given the considerable effort required in mobilising prevention and preparedness measures in Indonesia, we therefore argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire-management policies.…”

Section: Discussionmentioning

confidence: 99%

Seasonal forecasting of fire over Kalimantan, Indonesia

Spessa

Field

Pappenberger

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, biodiversity, health, and economic activities.In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean-atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire).The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70 % of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.

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Section: Discussionmentioning

confidence: 99%

Seasonal forecasting of fire over Kalimantan, Indonesia

Spessa

Field

Pappenberger

et al. 2015

Nat. Hazards Earth Syst. Sci.

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“…A full description, including detailed equations, is available in and . FoGSS makes use of climate forecasts from the Predictive Ocean and Atmosphere Model for Australia (POAMA) (Hudson et al, 2013;Marshall et al, 2014), post-processed with the method of calibration, bridging and merging (CBaM; Peng et al, 2014) to produce ensemble precipitation forecasts. CBaM corrects biases, removes noise, downscales forecasts to catchment areas and ensures ensembles are statistically reliable.…”

Section: Actual Forecasts: Forecast Guided Stochastic Scenarios (Fogss)mentioning

confidence: 99%

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Turner

Bennett

Robertson

et al. 2017

Hydrol. Earth Syst. Sci.

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106

105

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Abstract. Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made -namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

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“…The method was introduced by Wang et al (2012a) for forecasting seasonal precipitation for Australia and has also been applied to post-processing dynamical climate model forecasts (Hawthorne et al, 2013;Peng et al, 2014), combining statistical and dynamical models , combining multiple international dynamical models and forecasting streamflow extremes . In the context of statistical seasonal precipitation forecasting, the method first establishes multiple statistical models using a Bayesian joint probability (BJP) approach.…”

Section: Introductionmentioning

confidence: 99%