Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of anomalous hydrometeorological events over East Africa

Shukla, Shraddhanand; Roberts, J. Brent; Hoell, Andrew; Funk, Christopher C.; Robertson, Franklin R.; Kirtman, Ben P.

doi:10.1007/s00382-016-3296-z

Cited by 57 publications

(63 citation statements)

References 44 publications

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“…In the SOND season for GHAS ( Figure 11) the skill of both direct and indirect forecasts is substantially higher than in the MAM season. (This is consistent with, e.g., Shukla et al, 2016, who noted higher skill for OND versus MAM in their assessment of multi-model direct forecasts in a similar region). For the Dry category the pure indirect forecasts have ROC skill approaching 80%, versus just over 65% for pure direct forecasts.…”

Section: Roc Scores From a Multi-modelsupporting

confidence: 90%

“…In order to sample a range of outcomes and account for uncertainty in the model formulation and in the initial conditions, both hindcasts and real-time forecasts are produced as ensembles by running the systems many times with slightly differing initial conditions and sometimes different model parameters to reflect the uncertainty in these factors. 1996:19961960-1988:1989-20051981-2001-20041981-2004:2005-20071981-2007-20101981-2010:2011-20131981-2013:2014-20161981-2016:2017Verification period 19961989-2005-2017 Verification For this study hindcast data from several dynamical longrange forecast systems and centres have been used, as listed in Table 1. For the example in Section 3 illustrating the use of CCA to construct indirect forecasts, the Met Office GloSea5 system (MacLachlan et al, 2015) was selected.…”

Section: Model Datamentioning

confidence: 99%

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Direct and indirect seasonal rainfall forecasts for East Africa using global dynamical models

Colman

Graham

Davey

2019

Intl Journal of Climatology

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Regional‐scale seasonal climate outlooks are typically produced using forecast information either local to the region or from another area with teleconnections to the region. Dynamical global long‐range forecast (LRF) systems can provide both types of information, and these two approaches are compared in the context of seasonal rainfall forecasts for two adjoining areas in the Greater Horn of Africa region in tropical East Africa. The direct method utilizes the unprocessed LRF outputs for the region. For the “indirect” method, canonical correlation analysis is used and works by identifying patterns in historical LRF predictions over large tropical domains that relate to observed variability in the East Africa regions. For a given year, projections of the LRF forecasts onto those patterns can then be employed to construct the regional forecasts. This approach takes advantage of the tendency for LRF systems to have greater skill for large‐scale variability than for smaller regional‐scale features. Using case studies, it is found the two approaches contain complementary information: the indirect approach can provide notable skill benefits in years with strong large‐scale forcing while in some years, particularly when large‐scale signals are weak, the “direct” forecast are superior. Skill comparisons over many years found that, although results are region/season dependent, in general the indirect approach has higher skill overall—with improvements equal to or greater than those afforded by a 1‐month reduction in lead time with the direct approach. Results for both methods used separately and in combination are provided for the March–April–May and September‐to‐December seasons in the two regions, using data from currently operational dynamical LRF systems. Skill was best for the September‐to‐December season in the southern region, using “indirect” forecasts. The “direct” approach was better than “indirect” for the March‐to‐May season in the northern region. In general, combination did not produce a substantial benefit.

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Section: Roc Scores From a Multi-modelsupporting

confidence: 90%

Section: Model Datamentioning

confidence: 99%

Direct and indirect seasonal rainfall forecasts for East Africa using global dynamical models

Colman

Graham

Davey

2019

Intl Journal of Climatology

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“…The rainfall prediction of the most skillful model from ECMWF has a correlation of 0.53 with observations, and the multimodel prediction has a correlation of 0.45. These values are comparable to previous research (Jury 2014, Shukla et al 2016. However, the majority of the 11 models studied here are not skillful.…”

Section: Discussionsupporting

confidence: 91%

Seasonal predictability of Kiremt rainfall in coupled general circulation models

Gleixner

Keenlyside

Demissie

et al. 2017

Environ. Res. Lett.

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The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

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“…If proven skillful, these could be used to increase the advance warning time for impending outbreaks. The present work limits the scope of the predictions to an advance warning (lead time) of 4 months, since previous research has shown that this is likely the upper limit for skillful prediction for the present generation of seasonal prediction systems (particularly for precipitation, e.g., Ogutu et al, 2017;Shukla et al, 2016), while noting that this time frame may still be inadequate for some operational decisions.…”

Section: 1029/2018gh000157mentioning

confidence: 99%

Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead

et al. 2019

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Malaria forecasts from dynamical systems have never been attempted at the health district or local clinic catchment scale, and so their usefulness for public health preparedness and response at the local level is fundamentally unknown. A pilot preoperational forecasting system is introduced in which the European Centre for Medium Range Weather Forecasts ensemble prediction system and seasonal climate forecasts of temperature and rainfall are used to drive the uncalibrated dynamical malaria model VECTRI to predict anomalies in transmission intensity 4 months ahead. It is demonstrated that the system has statistically significant skill at a number of sentinel sites in Uganda with high‐quality data. Skill is also found at approximately 50% of the Ugandan health districts despite inherent uncertainties of unconfirmed health reports. A cost‐loss economic analysis at three example sentinel sites indicates that the forecast system can have a positive economic benefit across a broad range of intermediate cost‐loss ratios and frequency of transmission anomalies. We argue that such an analysis is a necessary first step in the attempt to translate climate‐driven malaria information to policy‐relevant decisions.

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Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of anomalous hydrometeorological events over East Africa

Cited by 57 publications

References 44 publications

Direct and indirect seasonal rainfall forecasts for East Africa using global dynamical models

Direct and indirect seasonal rainfall forecasts for East Africa using global dynamical models

Seasonal predictability of Kiremt rainfall in coupled general circulation models

Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead

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