The 2013/14 Thames basin floods: do improved meteorological forecasts lead to more skilful hydrological forecasts at seasonal timescales?

The Thames basin experienced 12 major Atlantic depressions in winter 2013/14 leading to extensive and prolonged fluvial and groundwater flooding. This exceptional weather coincided with highly anomalous meteorological conditions across the globe. Atmospheric relaxation experiments, whereby conditions within specified regions are relaxed towards a reanalysis, have been used to investigate teleconnection patterns. However, no studies have examined whether improvements to seasonal meteorological forecasts translate into more skilful seasonal hydrological forecasts. This study applied relaxation experiments to reforecast the 2013/14 floods for three Thames basin catchments with different hydrogeological characteristics. The tropics played an important role in the development of extreme conditions over the Thames basin. Greatest hydrological forecasting skill was associated with the tropical Atlantic and less with the tropical Pacific, although both captured seasonal meteorological flow anomalies. Relaxation applied over the north-eastern Atlantic produced confident ensemble forecasts, but hydrological extremes were under-predicted; this was unexpected with relaxation applied so close to the UK. Streamflow was most skilfully forecast for the catchment representing a large drainage area with high peak flow. Permeable lithology and antecedent conditions were important for skilfully forecasting groundwater levels. Atmospheric relaxation experiments can improve our understanding of extratropical anomalies and the potential predictability of extreme events such as the Thames 2013/14 floods. Seasonal hydrological forecasts differed to what was expected from the meteorology alone, thus knowledge is gained by considering both components. In the densely populated Thames basin, considering local hydrogeological context can provide an effective early alert of potential high-impact events, allowing for better preparedness

Att välja den vinnande sponsringen - för både företag och samhälle. Hur sponsorer väljer elitklubbar att sponsra samt hur de mäter effekterna

Sponsring har blivit en av de vanligaste marknadsföringsmetoderna och fortsätter att växa i popularitet. Sponsring innebär att ett företag köper rätten att associera sig med en rättighet, till exempel en idrottsklubb. Anledningen till dess popularitet kan ha att göra med dess varumärkesbyggande egenskaper. Tidigare forskning visar att sponsring kan förbättra företagets image, öka försäljning och skapa en stolthet hos medarbetarna, vilket i sin tur är motiverande för dem. Att sponsra professionella idrottslag kan även förbättra sponsorns CSR-image. Dock kan misslyckad sponsring innebära att företaget istället ser negativa effekter som till exempel bojkotter och/eller skadat rykte. Att välja rätt klubb att sponsra blir därför viktigt för att få positiva effekter såväl som att undvika negativa effekter. Att dessutom ha flera alternativ, som det finns i Göteborg, kan göra valet ännu svårare. Från tidigare forskning framkommer det att det finns utmaningar med att mäta effekter av sponsringen och att siffrorna kan variera beroende på vilken mätmetod som används. Det primära syftet med denna studie är att undersöka och öka förståelsen för hur huvudsponsorer av elitklubbar i Göteborg som spelar i högstaligan väljer ändamålsenliga föreningar att sponsra. Det sekundära syftet är att undersöka hur de mäter effekterna av sponsringen. Frågeställningarna är (1) Hur väljer huvudsponsorer i Göteborg ändamålsenliga elitklubbar att sponsra för att uppnå önskvärda effekter? (2) Hur mäter huvudsponsorerna i denna studie effekterna av sin sponsring? Arbetet avgränsas genom att undersöka enbart lagidrotter, sponsorer av elitklubbar i högsta ligan i Göteborg samt att de ska vara huvudsponsorer. För att svara på frågeställningarna användes en kvalitativ ansats med hjälp av fem semistrukturerade intervjuer med fyra sponsorer och en kompletterande intervju med en expert. Därefter gjordes en tematisk analys. Denna studie har kommit fram till att det som ligger till grund för att välja klubb att sponsra är företagets policy och strategi, klubbens nätverk, beslutsfattarens känslor och erfarenhet samt klubbens CSR-arbete. Den sistnämnda är det viktigaste kriteriet för samtliga sponsorer i studien. Kriterierna för att välja klubb stämde överens med tidigare forskning, men att CSR-arbetet var det viktigaste är unikt för denna studie. För att mäta effekter får sponsorerna rapporter till sig från mätföretag och klubbarna själva. De är intresserade av att veta hur de synts, hur många som känner till att de är sponsorer och att öka försäljningen. Dock finns det en avsaknad på specifika mått som används av sponsorerna i denna studie. De gör egna mätningar på till exempel CSR och hållbarhet, men inga specifika mått nämns. Den tidigare forskningen på effektmätning av sponsring stämmer delvis överens med sponsorerna i denna studie. Däremot upplever samtliga sponsorer i denna studie att det är svårt att mäta positiva finansiella effekter av sponsring. De nämner däremot inte hur de mäter sina varumärken eller effekten av CSR

Year-round sub-seasonal forecast skill for Atlantic-European weather regimes

Weather regime forecasts are a prominent use case of sub‐seasonal prediction in the midlatitudes. A systematic evaluation and understanding of year‐round sub‐seasonal regime forecast performance is still missing, however. Here we evaluate the representation of and forecast skill for seven year‐round Atlantic–European weather regimes in sub‐seasonal reforecasts from the European Centre for Medium‐Range Weather Forecasts. Forecast calibration improves regime frequency biases and forecast skill most strongly in summer, but scarcely in winter, due to considerable large‐scale flow biases in summer. The average regime skill horizon in winter is about 5 days longer than in summer and spring, and 3 days longer than in autumn. The Zonal Regime and Greenland Blocking tend to have the longest year‐round skill horizon, which is driven by their high persistence in winter. The year‐round skill is lowest for the European Blocking, which is common for all seasons but most pronounced in winter and spring. For the related, more northern Scandinavian Blocking, the skill is similarly low in winter and spring but higher in summer and autumn. We further show that the winter average regime skill horizon tends to be enhanced following a strong stratospheric polar vortex (SPV), but reduced following a weak SPV. Likewise, the year‐round average regime skill horizon tends to be enhanced following phases 4 and 7 of the Madden–Julian Oscillation (MJO) but reduced following phase 2, driven by winter but also autumn and spring. Our study thus reveals promising potential for year‐round sub‐seasonal regime predictions. Further model improvements can be achieved by reduction of the considerable large‐scale flow biases in summer, better understanding and modeling of blocking in the European region, and better exploitation of the potential predictability provided by weak SPV states and specific MJO phases in winter and the transition seasons.The overall sub‐seasonal forecast performance (biases and skill) for predicting seven year‐round Atlantic–European weather regimes is highest in winter and lowest in summer. The year‐round skill horizon is shortest for the European Blocking and longest for the Zonal Regime and Greenland Blocking (see figure). Furthermore, the winter skill horizon tends to be enhanced following a strong stratospheric polar vortex but reduced following a weak one. Madden–Julian Oscillation phases 4 and 7 tend to increase and phase 2 to decrease the year‐round skill horizon.Helmholtz‐Gemeinschaft http://dx.doi.org/10.13039/50110000165

Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall

A number of studies have investigated the large-scale drivers and upstream precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote from the impacted region in both time and space. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building storylines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high-impact weather and providing early warning of their possible development. However, operational applications of this developing knowledge base are limited, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large-scale precursors of categorical weather events in an automated fashion and reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional- to national-scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large-scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how, for more continental-scale applications, the regionally specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales, and regions

Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall

A number of studies have investigated the large-scale drivers and upstream-precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote in both time and space from the impacted region. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building story-lines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high-impact weather, and providing early warning of their possible development. However, operational applications of this developing knowledge-base is limited so far, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large-scale precursors of categorical weather events in an automated fashion, and for reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional- to national-scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large-scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how for more continental-scale applications the regionally-specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales and regions.Comment: 3 figure SI, 22 manuscript pages, 10 figures, submitted to QJRM

Improving forecasts of precipitation extremes over northern and central Italy using machine learning

The accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. However, direct precipitation forecasting is still challenging. Greater availability of machine-learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user-oriented postprocessing. Here we describe a specific chain, based on a random-forest (RF) pipeline, specialised in recognising favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on northern and central Italy, taken as a testbed region, but is seamlessly extensible to other regions and time-scales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia-Romagna. MalCoX has been trained with the ARCIS gridded high-resolution precipitation dataset as the target truth, using the last 20 years of the European Centre for Medium-Range Weather Forecasts (ECMWF) reforecast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger-scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme

Improving forecasts of precipitation extremes over northern and central Italy using machine learning

The accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. How- ever, direct precipitation forecasting is still challenging. Greater availability of machine-learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user-oriented post- processing. Here we describe a specific chain, based on a random-forest (RF) pipeline, specialised in recognising favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on northern and central Italy, taken as a testbed region, but is seamlessly extensible to other regions and time-scales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia-Romagna. MalCoX has been trained with the ARCIS gridded high-resolution precipitation dataset as the target truth, using the last 20 years of the European Centre for Medium-Range Weather Forecasts (ECMWF) refore- cast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger-scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme

The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback

Convectively coupled equatorial waves (CCEWs) are modes of tropical variability that are often seen as potentially beneficial for sub-seasonal predictions. Quantifying this potential has been proven difficult in operational forecast systems because tropical variability tends to be poorly represented in such models. The European Center for Medium-Range Weather Forecasts (ECMWF) is a potential exception because it has been shown to have much improved representation of the tropics. Here, ECMWF reforecasts are used to investigate the predicted evolution of CCEWs and how they might impact sub-seasonal predictions. It is shown that the ECMWF system is able to generate variability consistent with various modes of CCEWs, but CCEWs tend to be weaker in amplitude and their propagation characteristics often deviate from observations. This study suggests that model advancements aimed at improving the deterministic representation of CCEWs are still needed in order to better understand and utilize tropical sources of sub-seasonal predictive skill. &nbsp;</p

The open innovation research landscape: established perspectives and emerging themes across different levels of analysis

This paper provides an overview of the main perspectives and themes emerging in research on open innovation (OI). The paper is the result of a collaborative process among several OI scholars – having a common basis in the recurrent Professional Development Workshop on ‘Researching Open Innovation’ at the Annual Meeting of the Academy of Management. In this paper, we present opportunities for future research on OI, organised at different levels of analysis. We discuss some of the contingencies at these different levels, and argue that future research needs to study OI – originally an organisational-level phenomenon – across multiple levels of analysis. While our integrative framework allows comparing, contrasting and integrating various perspectives at different levels of analysis, further theorising will be needed to advance OI research. On this basis, we propose some new research categories as well as questions for future research – particularly those that span across research domains that have so far developed in isolation