La structure multimodale de la distribution de probabilité de la réflectivité radar des précipitations

Un ensemble de données radar collectées sur divers sites du réseau américain de radars bande S, Nexrad (Next Generation Weather Radar), est utilisé pour analyser la fonction de distribution de probabilité (fdp) du facteur de réflectivité radar (Z) des précipitations, soit P(Z). Nous avons étudié et comparé divers types de systèmes précipitants : 1) orages grêlifères sur le site continental de Little Rock (Arkansas), 2) convection péninsulaire et côtière à Miami (Floride), 3) convection côtière et transition terre/mer à Brownsville (Texas) , 4) convection maritime tropicale à Hawaii, 5) convection maritime des latitudes moyennes à Eureka (Californie), 6) neige associée aux systèmes frontaux continentaux d'hiver à New York City (New York) et 7) neige à Middleton Island (Alaska), une zone maritime des hautes latitudes. On montre que chaque type de système précipitant a une signature spécifique au niveau de la forme de P(Z). La distribution P(Z) a une forme complexe. Nous montrons qu'il s'agit d'un mélange de plusieurs composantes gaussiennes, chacune étant attribuable à un type de précipitation. Avec l'algorithme EM (Expectation Maximisation) de Dempster et al. 1977, basé sur la méthode du maximum devraisemblance, on décompose la fdp des systèmes précipitants en quatre compo-santes : 1) le nuage et les précipitations de très faible intensité ou drizzle, 2) les précipitations stratiformes, 3) les précipitations convectives et 4) la grêle. Chaque composante est représentée par une gaussienne définie par sa moyenne, sa variance et la proportion de l'aire qu'elle occupe dans le mélange. On a mis en évidence l'absence de composante grêle dans les P(Z) des cas de systèmes convectifs maritimes et côtiers. Les chutes de neige correspondent à des distributions P(Z) plus régulières. La présence de plusieurs composantes dans P(Z) est liée à des différences dans la dynamique et la microphysique propres à chaque composante. Une combinaison linéaire des différentes composantes gaussiennes a permis d'obtenir un très bon ajustement de P(Z). Nous présentons ensuite une application des résultats de la décomposition de P(Z). Nous avons isolé chaque composante, et pour chacune d'elles, la distribution de réflectivité est convertie en une distribution d'intensité de précipitation (R), soit P(R) ayant comme paramètres µR et sR2 qui sont respectivement la moyenne et la variance. On montre, sur le le graphe (µR ,sR2), que chaque composante occupe une région spécifique, suggérant ainsi que les types de précipitation identifiés constituent des populations distinctes. Par exemple, la position des points représentatifs de la neige montre que cette dernière est statistiquement différente de la pluie. Le coefficient de variation de P(R), CVR = sR /µR est constant pour chaque type de précipitation. Ce résultat implique que la connaissance de CVR et la mesure de l'un des paramètres de P(R) permet de déterminer l'autre et de définir la distributionde l'intensité de précipitation pour chaque composante. L'influence des coefficients a et b de la relation Z = aRb sur P(R) a été également discutée.A set of radar data gathered over various sites of the US Nexrad (Next Generation Weather Radar) S band radar network is used to analyse the probability distribution function (pdf) of the radar reflectivity factor (Z) of precipitation, P(Z). Various storm types are studied and a comparison between them is made: 1) hailstorms at the continental site of Little Rock (Arkansas), 2) peninsular and coastal convection at Miami (Florida), 3) coastal convection and land/sea transition at Brownsville (Texas), 4) tropical maritime convection at Hawaii, 5) midlatitude maritime convection at Eureka (California), 6) snowstorms from winter frontal continental systems at New York City (New York), and 7) high latitude maritime snowstorms at Middleton Island (Alaska). Each storm type has a specific P(Z) signature with a complex shape. It is shown that P(Z) is a mixture of Gaussian components, each of them being attribuable to a precipitation type. Using the EM (Expectation Maximisation) algorithm of Dempster et al. 1977, based on the maximum likelihood method, four main components are categorized in hailstorms: 1) cloud and precipitation of very low intensity or drizzle, 2) stratiform precipitation, 3) convective precipitation, and 4) hail. Each component is described by the fraction of area occupied inside P(Z) and by the two Gaussian parameters, mean and variance. The absence of hail component in maritime and coastal storms is highlighted. For snowstorms, P(Z) has a more regular shape. The presence of several components in P(Z) is linked to some differences in the dynamics and microphysics of each precipitation type. The retrieval of the mixed distribution by a linear combination of the Gaussian components gives a very stisfactory P(Z) fitting. An application of the results of the split-up of P(Z) is then presented. Cloud, rain, and hail components have been isolated and each corresponding P(Z) is converted into a probability distribution of rain rate P(R) which parameters are µR and sR2 , respectively mean and variance. It is shown on the graph (µR ,sR2) that each precipitation type occupies a specific area. This suggests that the identified components are distinct. For example, the location of snowstorms representative points indicates that snow is statistically different from rain. The P(R) variation coefficient, CVR = sR/µR is constant for each precipitation type. This result implies that knowing CVR and measuring only one of the P(R) parameters enable to determine the other one and to define the rain rate probability distribution. The influence of the coefficients a and b of the relation Z = aRb on P(R) is also discussed

Nowcasting convective activity for the Sahel: a simple probabilistic approach using real‐time and historical satellite data on cloud‐top temperature

Flash flooding from intense rainfall frequently results in major damage and loss of life across Africa. In the Sahel, automatic prediction and warning systems for these events, driven by Mesoscale Convective Systems (MCSs), are limited, and Numerical Weather Prediction (NWP) forecasts continue to have little skill. The ground observation network is also sparse, and very few operational meteorological radars exist to facilitate conventional nowcasting approaches. Focusing on the western Sahel, we present a novel approach for producing probabilistic nowcasts of convective activity out to six hours ahead, using the current location of observed convection. Convective parts of the MCS, associated with extreme and heavy precipitation, are identified from 16 years of Meteosat Second Generation thermal–infrared cloud-top temperature data, and an offline database of location-conditioned probabilities calculated. From this database, real-time nowcasts can be quickly produced with minimal calculation. The nowcasts give the probability of convection occurring within a square neighbourhood surrounding each grid point, accounting for the inherent unpredictability of convection at small scales. Compared to a climatological reference, formal verification approaches show the nowcasts to be skilful at predicting convective activity over the study region, for all times of day and out to the six-hour lead time considered. The nowcasts are also skilful at capturing extreme 24-hour rain gauge accumulations over Dakar, Senegal. The nowcast skill peaks in the afternoon, with a minimum in the evening. We find that the optimum neighbourhood size varies with lead time, from 10 km at the nowcast origin to around 100 km at a six-hour lead time. This simple and skilful nowcasting method could be highly valuable for operational warnings across West Africa and other regions with long-lived thunderstorms, and help to reduce the impacts from heavy rainfall and flooding

Transport and Deposition of Saharan Dust Observed from Satellite Images and Ground Measurements

Haboob occurrence strongly impacts the annual variability of airborne desert dust in North Africa. In fact, more dust is raised from erodible surfaces in the early summer (monsoon) season when deep convective storms are common but soil moisture and vegetation cover are low. On 27 June 2018, a large dust storm is initiated over North Africa associated with an intensive westward dust transport. Far away from emission sources, dust is transported over the Atlantic for the long distance. Dust plume is emitted by a strong surface wind and further becomes a type of haboob when it merges with the southwestward deep convective system in central Mali at 0200 UTC (27 June). We use satellite observations to describe and estimate the dust mass concentration during the event. Approximately 93% of emitted dust is removed the dry deposition from the atmosphere between sources (10°N–25°N; 1°W–8°E) and the African coast (6°N–21°N; 16°W–10°W). The convective cold pool has induced large economic and healthy damages, and death of animals in the northeastern side of Senegal. ERA5 reanalysis has shown that the convective mesoscale impacts strongly the climatological location of the Saharan heat low (SHL)

Institutionalising co-production of weather and climate services: learning from the African SWIFT and ForPAc projects

There is growing recognition of the multiple benefits of co-production for forecast producers, researchers and users in terms of increasing understanding of the skill, decision-relevance, uptake and use of forecasts. This policy brief identifies lessons learnt from two operational research projects, African SWIFT and ForPAc, on pathways for embedding co-production into operational weather and climate services as the new standard operational procedure. Experiences across these projects identifies the following potential pathways for institutionalising co-production practises within operational weather and climate services: • Changing mindsets and systems to enable co-production of enhanced forecasts and systematic approaches for their use. • Strengthening in-country institutional links between operational forecasting centres and academic institutions to develop sustainable and improved forecasting capacities to meet users’ evolving weather and climate information needs. • Ensuring continued access to raw forecast data from global forecasting centres to continue and further develop new and improved decision-relevant forecasts. • Formalising user engagement in co-production, through agreeing standard and continuity of representation and commitment to providing regular feedback. • Mainstreaming stakeholder engagement and co-production in meteorological training, forecasting operations and environmental research. • Working through existing channels, such as agricultural and livestock extension services, and harnessing social media and remote ways of working to develop sustainable forms of continuous user engagement. • Establishing monitoring systems to demonstrate the benefits of investing in forecasting capacities. • Incentivising collaboration between complementary initiatives. • Addressing the risks of operationalising new and improved weather and climate services in resource- constrained environments

Nowcasting for Africa: advances, potential and value

The high frequency of intense convective storms means there is a great demand to improve predictions of high-impact weather across Africa. The low skill of numerical weather prediction over Africa, even for short lead times highlights the need to deliver nowcasting based on satellite data. The Global Challenges Research Fund African SWIFT (Science for Weather Information and Forecasting Techniques) project is working to improve the nowcasting of African convective systems and so the ability to provide timely warnings

The African SWIFT project: growing science capability to bring about a revolution in weather prediction

Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The GCRF African SWIFT project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps. Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training; modelling and operational prediction; communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather-forecasting “Testbeds” – the first of their kind in Africa – which have been used to bring new evaluation tools, research insights, user perspectives and communications pathways into a semi-operational forecasting environment