Does improved SSTA prediction ensure better seasonal rainfall forecasts?

Khan, Zaved; Sharma, Ashish; Mehrotra, R.; Schepen, Andrew; Wang, QJ

doi:10.1002/2014wr015997

Cited by 19 publications

(19 citation statements)

References 42 publications

(65 reference statements)

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“…The procedure followed is outlined below. Read observed and modelled seasonal SSTA forecasts over the entire global SST grid. At each grid point, calculate the seasonal forecast errors of both the models over the calibration period by subtracting the ensemble mean of the model forecast from the observed seasonal SSTA. In order to have sufficient sample size for a fair assessment of model weights, consider a moving window of 2 years for model 2 and augment the number of data points by considering eight nearest grid points around the target grid cell Khan et al . (). The procedure provides 22 events over the calibration period. Model 2 (ECMWF) now has forecast errors for each 2 years period (e.g., 1960–1961, 1961–1962, …, 1981–1982) which moves forwards within the calibration period, while model 1 (MF) has forecast errors for the entire calibration period.…”

Section: Application Of Model and Results Discussionmentioning

confidence: 99%

“…The approach developed to combine multi‐model forecasts of unequal time periods follows the model weighting scheme of Khan et al . (). In this scheme, the weight of an individual model is derived from the dependence structure of model forecast errors defined in terms of a covariance matrix using a common time periods of data across all the models.…”

Section: Methodsmentioning

confidence: 99%

“…The derivation of this equation is provided in Khan et al . () and Timmermann ().

w = {(ι^{'} {normalΣ}_{e}^{- 1} ι)}^{- 1} Σ_{e}^{- 1} ι .

…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Using all data to improve seasonal sea surface temperature predictions: A combination‐based model forecast with unequal observation lengths

Khan

Sharma

Mehrotra

2018

Intl Journal of Climatology

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One way to reduce model uncertainty in climate predictions is to combine forecasts from several models. Recent multi‐model combination approaches combine model forecasts by pooling data for a time period, common across all the models, thus ignoring the additional data available or discarding altogether the models with the shorter time period. This results in the loss of some information which could otherwise be used while combining the models to possibly improve forecast skill. Our research explores this issue in the context of multi‐model sea surface temperature (SST) models predictions and proposes a novel concept that allows a framework for combining models with unequal time period. Here, the unequal time periods imply different range of start and end dates of available model forecasts. A qualitative standpoint of our multi‐model forecasting strategy is to reduce the uncertainty and improve the forecast skill. The utility of the approach is demonstrated by combining the global seasonal NDJ (November–January) SST predictions of two models and also as many as eight models, obtained using both equal and unequal time periods. The proposed approach shows improvement over 62–69% grid cells around the entire globe over the case when the common period of data length across the models is considered.

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Section: Application Of Model and Results Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Using all data to improve seasonal sea surface temperature predictions: A combination‐based model forecast with unequal observation lengths

Khan

Sharma

Mehrotra

2018

Intl Journal of Climatology

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“…Therefore, in case of seasonal SSTA predictions, the multimodel forecasts show clear improvement over a single model prediction around the globe irrespective of seasons. Khan et al [] also showed that the multimodel SSTA forecasts offers consistent and significant improvements for all the seasons over the most of grid cells than any other approach.…”

Section: Resultsmentioning

confidence: 99%

Global seasonal precipitation forecasts using improved sea surface temperature predictions

2017

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Climate models driven by observed or modeled sea surface temperature (SST) or SST anomalies (SSTA) are used as a standard tool in seasonal climate predictions. This study investigates the merit of seasonal rainfall predictions obtained by using multimodel SSTA to drive a climate model over a single model case. The multimodel SSTA predictions are obtained by combining the predictions of five climate models, whereas the stand‐alone model predictions come from the Predictive Ocean Atmosphere Model for Australia. The climate model used is the Australian Community Climate and Earth System Simulator (ACCESS). The use of multimodel SSTA over a single model shows marginal improvements in seasonal rainfall predictions across the entire globe. Further improvements in rainfall forecasts are possible either through improved parameterizations in the ACCESS model or by using another climate model. As a final step, the rainfall forecasts from the multimodel and the single model SSTA (two‐member hierarchical rainfall forecasts, denoted as HIER) are combined and to gain further improvements over the individual multimodel or single model rainfall predictions for most of the grid cells in all seasons.

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“…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).…”

Section: Bayesian Joint Probability Modelsmentioning

confidence: 99%

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

Schepen

Zhao

Wang

2017

Preprint

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Abstract.Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal time scales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. However, for hydrological 10 applications, GCM forecasts are often biased and unreliable in uncertainty spread, and therefore calibration is 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 15 forecasts (RPP-S) based on the Bayesian joint probability approach for calibrating daily forecasts and the Schaake Shuffle approach 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 more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal 20 accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S postprocessed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

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Does improved SSTA prediction ensure better seasonal rainfall forecasts?

Cited by 19 publications

References 42 publications

Using all data to improve seasonal sea surface temperature predictions: A combination‐based model forecast with unequal observation lengths

Using all data to improve seasonal sea surface temperature predictions: A combination‐based model forecast with unequal observation lengths

Global seasonal precipitation forecasts using improved sea surface temperature predictions

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

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