Francesca Di Giuseppe

Generalizing Cloud Overlap Treatment to Include Solar Zenith Angle Effects on Cloud Geometry

Abstract Shortwave radiative transfer depends on the cloud field geometry as viewed from the direction of the sun. To date, the radiation schemes of large-scale models only consider a zenith view of the cloud field, and the apparent change in the cloud geometry with decreasing solar zenith angle is neglected. A simple extension to an existing cloud overlap scheme is suggested to account for this for the first time. It is based on the assumption that at low sun angles, the overlap between cloud elements is random for an unscattered photon. Using cloud scenes derived from radar retrievals at two European sites, it is shown that the increase of the apparent cloud cover with a descending sun is reproduced very well with the new scheme. Associated with this, there is a marked reduction in the mean radiative biases averaged across all solar zenith angles with respect to benchmark calculations. The scheme is implemented into the ECMWF global forecast model using imposed sea surface temperatures, and while the im...

Potential Predictability of Malaria in Africa Using ECMWF Monthly and Seasonal Climate Forecasts

Idealized model experiments investigate the advance warning for malaria that may be presently possible using temperature and rainfall predictions from state-of-the-art operational monthly and seasonal weatherprediction systems. The climate forecasts drive a dynamical malaria model for all of Africa, and the predictions are evaluated using reanalysis data. The regions and months for which climate is responsible for significant interannual malaria transmission variability are first identified. In addition to epidemic-prone zones these also include hyperendemic regions subject to high variability during specific months of the year, often associatedwith the monsoon onset.In manyof these areas,temperatureanomalies are predictablefrom 1 to 2 months ahead, and reliable precipitation forecasts are available in eastern and southern Africa 1 month ahead. The inherent lag between the rainy seasons and malaria transmission results in potential predictability in malaria transmission 3‐4 months in advance, extending the early warning available from environmental monitoring by 1‐2 months, although the realizable forecast skill will be less than this because of an imperfect malaria model.A preliminaryexaminationofthe forecastsfor the highlands ofUganda andKenyashows that the system is able to predict the years during the last two decades in which documented highland outbreaks occurred, in particular the major event of 1998, but that the timing of outbreaks was often imprecise and inconsistent across lead times. In addition to country-level evaluation with district health data, issues that need addressing to integrate such a climate-based prediction system into health-decision processes are briefly discussed.

An interpretation of cloud overlap statistics

AbstractObservational studies have shown that the vertical overlap of cloudy layers separated by clear sky can exceed that of the random overlap assumption, suggesting a tendency toward minimum overlap. In addition, the rate of decorrelation of vertically continuous clouds with increasing layer separation is sensitive to the horizontal scale of the cloud scenes used. The authors give a heuristic argument that these phenomena result from data truncation, where overcast or single cloud layers are removed from the analysis. This occurs more frequently as the cloud sampling scale falls progressively below the typical cloud system scale. The postulate is supported by sampling artificial cyclic and subsequently more realistic fractal cloud scenes at various length scales. The fractal clouds indicate that the degree of minimal overlap diagnosed in previous studies for discontinuous clouds could result from sampling randomly overlapped clouds at spatial scales that are 30%–80% of the cloud system scale. Removing ...

Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model

The severity and timing of seasonal malaria epidemics is strongly linked with temperature and rainfall. Advance warning of meteorological conditions from seasonal climate models can therefore potentially anticipate unusually strong epidemic events, building resilience and adapting to possible changes in the frequency of such events. Here we present validation of a process-based, dynamic malaria model driven by hindcasts from a state-of-the-art seasonal climate model from the European Centre for Medium-Range Weather Forecasts. We validate the climate and malaria models against observed meteorological and incidence data for Botswana over the period 1982–2006; the longest record of observed incidence data which has been used to validate a modeling system of this kind. We consider the impact of climate model biases, the relationship between climate and epidemiological predictability and the potential for skillful malaria forecasts. Forecast skill is demonstrated for upper tercile malaria incidence for the Botswana malaria season (January–May), using forecasts issued at the start of November; the forecast system anticipates six out of the seven upper tercile malaria seasons in the observational period. The length of the validation time series gives confidence in the conclusion that it is possible to make reliable forecasts of seasonal malaria risk, forming a key part of a health early warning system for Botswana and contributing to efforts to adapt to climate change.

The Potential Predictability of Fire Danger Provided by Numerical Weather Prediction

AbstractA global fire danger rating system driven by atmospheric model forcing has been developed with the aim of providing early warning information to civil protection authorities. The daily predictions of fire danger conditions are based on the U.S. Forest Service National Fire-Danger Rating System (NFDRS), the Canadian Forest Service Fire Weather Index Rating System (FWI), and the Australian McArthur (Mark 5) rating systems. Weather forcings are provided in real time by the European Centre for Medium-Range Weather Forecasts forecasting system at 25-km resolution. The global system’s potential predictability is assessed using reanalysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 yr of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire, and low values correspond to nonobserved events. A more quantitat...

Generalizing Cloud Overlap Treatment to Include the Effect of Wind Shear

AbstractSix months of CloudSat and CALIPSO observations have been divided into over 8 million cloud scenes and collocated with ECMWF wind analyses to identify an empirical relationship between cloud overlap and wind shear for use in atmospheric models. For vertically continuous cloudy layers, cloud decorrelates from maximum toward random overlap as the layer separation distance increases, and the authors demonstrate a systematic impact of wind shear on the resulting decorrelation length scale. As expected, cloud decorrelates over smaller distances as wind shear increases. A simple, empirical linear fit parameterization is suggested that is straightforward to add to existing radiation schemes, although it is shown that the parameters are quite sensitive to the processing details of the cloud mask data and also to the fitting method used. The wind shear–overlap dependency is implemented in the radiation scheme of the ECMWF Integrated Forecast System. It has a similar-magnitude impact on the radiative budget...

Improving Forecasts of Biomass Burning Emissions with the Fire Weather Index

AbstractIn the absence of a dynamical fire model that could link the emissions to the weather dynamics and the availability of fuel, atmospheric composition models, such as the European Copernicus Atmosphere Monitoring Services (CAMS), often assume persistence, meaning that constituents produced by the biomass burning process during the first day are assumed constant for the whole length of the forecast integration (5 days for CAMS). While this assumption is simple and practical, it can produce unrealistic predictions of aerosol concentration due to an excessive contribution from biomass burning. This paper introduces a time-dependent factor , which modulates the amount of aerosol emitted from fires during the forecast. The factor is related to the daily change in fire danger conditions and is a function of the fire weather index (FWI). The impact of the new scheme was tested in the atmospheric composition model managed by the CAMS. Experiments from 5 months of daily forecasts in 2015 allowed for both the...

Development of dynamical weather-disease models to project and forecast malaria in Africa

Background Weather and climate play an important role in the spread of malaria. Suitable weather conditions for malaria are found in sub-Saharan Africa, where most of the worldwide malaria cases and deaths are found. For this reason, integrated weather-disease malaria models are useful tools to project the malaria future and to provide monthly-toseasonal forecasts. Methods Malaria projections and forecasts are undertaken by two dynamical mathematical-biological malaria models: (i) the LMM (Liverpool Malaria Model) [1-3] and (ii) VECTRI (VECtor-borne disease community model of the International Centre for Theoretical Physics, TRIeste). Both models are driven by daily temperature and precipitation values. An improved version of the LMM was introduced by [2], which was calibrated by malaria field observations from West Africa [3]. Regarding the assessment of the impact of climate change on malaria [4], the LMM was driven by data from the REgional MOdel (REMO) including the effect of land surface changes. For the QWeCI (Quantifying Weather and Climate Impacts on health in developing countries) project, a seamless weather prediction system has been developed at ECMWF by appending the first 25 days of the monthly forecasting system with the Seasonal Forecasting System 4 to provide a continuous 120 day lead time prediction. The forecast is calibrated to correct for displacement errors of West African monsoonal precipitation. Results and outlook The malaria projections up to 2050 [4] based on the integrated REMO-LMM reveal a southward shift of the epidemic malaria area in West Africa due to the precipitation decline. The increased temperatures lead to an increase of transmission in highland territories. Formerly, malaria free areas become epidemic, whereas the epidemic risk is

A dynamical climate-driven malaria early warning system evaluated in Uganda, Rwanda and Malawi

Background Malaria is a climate-sensitive disease with a significant socio-economic impact. As monthly and seasonal dynamical climate prediction systems have improved their skill in the tropics over recent years, there is now the potential to use these forecasts to drive dynamical malaria modelling systems to provide early warnings of climate-related transmission hazards in holo and hyper endemic settings. Materials and methods A new pilot dynamical malaria prediction system was introduced. Multiple temperature and precipitation forecasts from the ECMWF monthly and seasonal prediction systems were used to drive the spatially explicit, dynamical malaria model VECTRI that accounts for population density and climate to produce forecasts of up to 4 months ahead. The malaria forecasts were started from realistic initial conditions derived from climate observations. The parameter predicted is the logarithm of the entomological inoculation rate (EIR). Forecasts are made on a grid mesh with a spatial resolution of 25km, but are then aggregated at the administrative district level, and normalized to be evaluated using normalized district level crude incidence data or sentinel site cases (suspected or confirmed) for three countries: Uganda, Rwanda and Malawi. The forecasts are evaluated over a period of approximately a decade, depending on the length of the data record available. Results The results show that for a number of districts in which interventions have been limited over the evaluation period, the forecasts were statistically significantly correlated with observed malaria outcomes, with the forecasts predicting the years of anomalous transmission and the sub-seasonal anomalies well. This was true for districts of both low and higher endemicity. However, for the majority of locations, significance levels were lower. The potential reasons for this are numerous, including incorrect climate forecasts, errors or missing physics (e.g. lack of immunity) in the malaria model, inaccuracies in the cases data itself, and interventions that have reduced prevalence and transmission risk as a function of time. We demonstrate, using confirmed sentinel site data, that data inaccuracies in the district statistics can be significant. Interestingly, the analysis and forecast system appear to indicate that the reduction in malaria prevalence in Rwanda over the past decade has been achieved against a backdrop of increasing transmission hazard due to climate. We conclude by discussing the weaknesses in the system that require addressing and the next steps to pilot this system in an operational context.