BackgroundMalaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model.MethodsThe spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series.Results and discussionThe forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of “high”, “above average” and “low” malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.
The onset of the Indian summer monsoon (ISM) represents one of the most dramatic transitions in the regional circulation pattern. The onset also marks the beginning of the main rainy season for India; advanced and accurate forecast of the date of the onset of monsoon (DOM) thus has application in many sectors. Although the standard deviation (s) in DOM over the past hundred years is only 7 days, nearly 50% of the cases show large (.1s) deviations; forecasting of DOM, especially for the extreme years is thus nontrivial and is rarely attempted because of the poor skill of most GCMs in the long-range prediction of daily ISM rainfall. A primary cause for the poor skill in forecasting parameters like rainfall appears to be the loss of predictability due to noise introduced by local synoptic processes. However, sharp transitions in the regional circulation pattern and associated rainfall, which are likely to be less affected by synoptic noise, may have higher predictability, somewhat similar to the way that monthly mean parameters are more predictable. This premise is explored for advanced forecasting of the onset of ISM over Kerala, India, and it is shown that significant skill is possible in advanced forecasting of DOM. A general circulation model (GCM) with a special feature, variable resolution, and an objective debiasing of daily rainfall forecast, is used to meet the special requirements of forecasting DOM. Based on a set of objective and validated criteria, hindcasts of DOM are generated in a complete operational setting from a five-member ensemble for each year for the period 1980-2003. The hindcasts are evaluated in terms of a number of parameters; as well as against a climatological forecast (null hypothesis), for 70% of the forecasts, the mean absolute error is less than that of the climatological forecasts. Furthermore, in contrast to the climate forecasts, these forecasts capture 7 out of 9 large (.1s in observation) departures from the mean within the mean error, which implies high skill. Implications of the results for predicting certain weather and climate processes are discussed.
Capsule Summary The WMO HIWeather Multiscale Hazard Forecasting project members and collaborators review the current status and future challenges in Observations, Nowcasting, Data Assimilation, Ensemble Forecasting, and Coupled Hazard Modeling.
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