Seasonal predictions: from global to regional information for decision support in water resources management in semi-arid regions

The increasing tension within the water-energy-food nexus in face of climate change requires the sustainable use and management of available water resources. This is particularly relevant in semi-arid regions of South America, Africa and West Asia: already, these regions are facing high precipitation variability, and are additionally exposed to high population growth rates, politically induced unsustainable use of water resources, and transboundary water management conflicts. It is shown that for the climate-sensitive regions of Northeast Brazil, Iran, West Africa, and Northeast Africa, the relative frequency of droughts has increased significantly from 10 to 30 % in recent decades. This calls for proactive measures to aid climate proofing and mitigate climate risks. For water allocation during drought or for flood control, improved knowledge of the upcoming rainy season in semi-arid regions can be the basis for a more robust and sustainable water management. Here, seasonal forecasts with forecast horizons up to seven months ahead can offer great opportunities to support regional water management. State-of-the-art seasonal forecasting systems already reach resolutions that are suitable for regional applications, e.g., the latest seasonal forecasting system version 5 (SEAS5) of the European Centre for Medium-Range Weather Forecasts (ECMWF) with a horizontal resolution of 36 km. Indeed, with skillful and reliable forecasts for the coming months, decision-makers in the water, agriculture, and energy sectors could induce a more timely, proactive and sustainable reservoir management and seed selection, thereby reducing damage and loss. Decision-makers, however, often still hesitate to use seasonal forecasts claiming their lack of reliability and the inherent uncertainty due to their probabilistic nature. In many cases, statistical performance measures for forecast quality cannot provide actual decision support. Therefore, the potential economic value (PEV) is implemented demonstrating the possible relative savings when basing decisions for preventative water management action on seasonal forecasts compared to optimal early action. This measure of forecast value therewith demonstrates the economic benefit of integrating seasonal forecasts in decisions incorporating the user’s cost-loss situation and the fore- cast probability. The methodology used in this study allows the use of unprocessed global seasonal forecasts for regional analysis. It is shown that a proactive approach based on seasonal forecasts in the case of droughts can achieve potential economic savings of up to 70 % of those from optimal early action. For very warm months and droughts, savings of at least 20 % are achieved even with forecast horizons of up to seven months ahead. For one particular large dam in Sudan, the Upper-Atbara Dam, the savings are explicitly quantified in monetary terms if the dam was operated with early drought mitigation. In fact, avoidable losses of 16 Mio US$are revealed in one example year. Though shown to provide substantial economic value to users, for some applications, the seasonal forecasts SEAS5 are still not applicable. If absolute values and high spatial resolution are required, the global seasonal forecasts need to be corrected for biases and model drifts with increasing forecast horizon (lead time), and a spatial refinement is required. In this study, the forecast quality of global seasonal forecasts is considerably improved regionally with statistical correction methods of quantile mapping for daily data. In this approach, more than 100 000 forecast days are corrected for each of the 25 (until 2016) and 51 (since 2017) ensemble members of a 1981-2019 reforecast period for each of the five analyzed variables. The forecast thus provides more accurate information about future precipitation amounts, temperatures and incoming solar radiation, allowing a concrete decision support in practice. Monthly forecast biases and lead-dependent drift effects of up to 4 mm d1 and 2 K are corrected and the forecasts are spatially disaggregated to the higher resolution of 0.1 of the reanalysis dataset ERA5-Land, further increasing the agreement of spatial patterns with ERA5-Land com- pared to the raw forecasts. The computational efficiency of this approach allows to provide region-tailored, corrected hydrometeorological seasonal forecasts for the study regions in Northeast Brazil, Sudan/Ethiopia and Iran operationally in an online decision support system (viewer). In addition to statistical approaches, spatial refinement to higher spatial resolution and reduction of global model errors can be achieved through dynamical downscaling with regional climate models. The dynamical approach has the advantage of resolving or better representing smaller-scale processes that are not captured in the global model products, and thereby reducing possible model biases. Therefore, the potential of a physically-based refinement approach is investigated. In particular, the dependence on the physical parameters in the atmospheric model, which are known to determine the quality of the hydrometeorological information produced, is analyzed. For this purpose, the Weather Research and Forecasting (WRF) model is applied to dynamically downscale reanalysis data in the climatically sensitive study regions of Ecuador/Peru and Brazil with different parameterization setups. Downscaling is performed to a horizontal resolution of 9 km for both domains, and to an additional 3 km for the orographically highly complex domain of Ecuador/Peru. This study reveals that a high uncertainty in the simulated precipitation is introduced with the combination of physical parameterizations of four different cumulus convection (CU) physics, two microphysics (MP), two planetary boundary layer (PBL) physics and two radiation (RA) physics schemes (32 parameterization runs in total). Up to four times the monthly reference precipitation can be modeled according to different setups. A special focus of this study is on spatial patterns, which are analyzed in detail using an ensemble-tailored evaluation approach via the eSAL metric. The pooled results from ensemble-tailored distributional, temporal and the spatial pattern evaluation metrics reveal that the CU schemes, followed by the RA schemes, produce the dominant tendencies in the simulated precipitation within the WRF ensemble (too dry/wet, too large/small precipitation features etc.). No single setup is found to be superior, but the metrics allow for appropriate se- lection based on application needs and reference data. Future application of the setup to SEAS5 seasonal forecasts is proving to be less than straightforward. The double random perturbation of the initial conditions of SEAS5 impedes a necessary selection of ensemble members to reduce the computational cost of the dynamical downscaling. Since the parameterization sensitivity study also shows that biases still exist after dynamical downscaling compared to the reference, whether in distributional, temporal, or spatial patterns, additional dynamical downscaling of long reforecast periods would be required for post-processing, adding an immense computational cost. This work therewith demonstrates the opportunities and highlights the challenges in using seasonal forecasts for decision support in water resources management, and in refining and correcting these forecasts for impact studies and direct operational applications. It enables the final transfer of acquired scientific knowledge into practice and provides the tools to actively contribute to sustainable water resources management in semi-arid regions that urgently need adaptation and mitigation strategies to combat the impacts of climate change and policy-driven water use and management challenges. It turns out that global datasets of seasonal forecasts and reanalyses can provide concrete value for decision support, but depending on the application, further post-processing is needed to make these data regionally usable.Die zunehmende Spannung innerhalb des Wasser-, Energie-, und Ernährungssicherheitsnexus angesichts des Klimawandels erfordert die nachhaltige Nutzung und Bewirtschaftung der verfügbaren Wasserressourcen. Dies ist vor allem in den semiariden Regionen Südamerikas, Afrikas und Westasiens von Bedeutung: Diese Regionen sind bereits jetzt einer hohen Niederschlagsvariabilität ausgesetzt und haben zudem mit hohen Bevölkerungswachstumsraten, politisch veranlasster nicht-nachhaltiger Nutzung von Wasserressourcen und grenzüberschreitenden Wasserbewirtschaftungskonflikten zu kämpfen. Diese Arbeit zeigt, dass in den klimasensiblen Regionen Nordostbrasiliens, Irans, Westafrikas und Nordostafrikas die relative Häufigkeit von Dürren in den letzten Jahrzehnten von 10 auf 30 Mit leistungsfähigen und zuverlässigen Vorhersagen für die kommenden Monate könnten Entscheidungsträger im Wasser-, Landwirtschafts- und Energiesektor das Management von Wasserreservoirs und die Auswahl von Saatgut zeitnaher, proaktiver und nachhaltiger gestalten und so Schäden und Verluste verringern. Entscheidungsträger zögern jedoch oft noch, saisonale Vorhersagen zu verwenden, und verweisen auf deren mangelnde Zuverlässigkeit und die ihnen innewohnende Unsicherheit aufgrund ihres wahrscheinlichkeitsbasierten Charakters. In vielen Fällen können statistische Leistungskennzahlen für die Vorhersagequalität keine wirkliche Entscheidungshilfe bieten. Daher wird der potenzielle wirtschaftliche Wert (PEV) eingeführt. Dieser zeigt die möglichen relativen Einsparungen auf, wenn Entscheidungen für vorbeugende wasserwirtschaftliche Maßnahmen auf saisonale Vorhersagen gestützt werden, im Vergleich zu optimalen frühzeitigen Maßnahmen. Dieses Maß für den Prognosewert zeigt damit den wirtschaftlichen Nutzen der Einbeziehung von saisonalen Vorhersagen in Entscheidungen unter Berücksichtigung der Kosten-Verlust-Situation des Nutzers und der Vorhersagewahrscheinlichkeit. Die in dieser Arbeit angewandte Methodik ermöglicht die Verwendung unbearbeiteter globaler saisonaler Vorhersagen für regionale Analysen. Es wird gezeigt, dass ein proaktiver Ansatz auf der Grundlage von saisonalen Vorher- sagen im Falle von Dürreperioden potenzielle wirtschaftliche Einsparungen von bis zu 70$ festgestellt. Obwohl sie den Nutzern nachweislich einen erheblichen wirtschaftlichen Vorteil bieten, sind die saisonalen Vorhersagen SEAS5 für einige Anwendungen noch immer nicht geeignet. Wenn absolute Werte und eine hohe räumliche Auflösung erforderlich sind, müssen die globalen saisonalen Vorhersagen um Verzerrungen und Modelldrift mit zunehmendem Vorhersagehorizont (Vorlaufzeit) korrigiert werden, und es bedarf einer räumlichen Verfeinerung. In dieser Studie wird die Vorhersagequalität globaler saisonaler Vorhersagen mit statistischen Korrekturmethoden der Quantilabbildung (Quantile Mapping) für tägliche Daten regional bedeutend verbessert. Dabei werden mehr als 100.000 Vorhersagetage für jedes der 25 (bis 2016) und 51 (seit 2017) Ensemblemitglieder des Vorhersagezeitraums 1981-2019 für jede der fünf analysierten Variablen korrigiert. Die Vorhersage liefert damit genauere Informationen über zukünftige Niederschlagsmengen, Temperaturen und die einfallende Sonnenstrahlung, und ermöglicht so eine konkrete Entscheidungshilfe in der Praxis. Monatliche Vorhersageverzerrungen und vorlaufzeitabhängige Drifteffekte von bis zu 4 mm d-1 und 2 K werden korrigiert, und die Vorhersagen werden räumlich auf die höhere Auflösung von 0,1 des Reanalysedatensatzes ERA5-Land disaggregiert, wodurch die Übereinstimmung der räumlichen Muster mit ERA5-Land im Vergleich zu den Rohvorhersagen weiter verbessert wird. Die rechnerische Effizienz dieses Ansatzes ermöglicht es, regional angepasste, verbesserte hydrometeorologische saisonale Vorhersagen für die Untersuchungsregionen in Nordostbrasilien, Sudan/Äthiopien und im Iran in einem Online-Entscheidungsunterstützungssystem (Viewer) bereitzustellen. Zusätzlich zu dem statistischen Ansatz kann eine räumliche Verfeinerung auf eine höhere räumliche Auflösung und die Verringerung der Fehler globaler Modelle durch dynamisches Downscaling mit regionalen Klimamodellen erreicht werden. Der dynamische Ansatz hat den Vorteil, dass er kleinskalige Prozesse, die in den globalen Modellprodukten nicht erfasst werden, auflöst bzw. besser darstellt und dadurch mögliche Modellfehler reduziert. Daher wird in dieser Arbeit auch das Potenzial eines physikalisch basierten Verfeinerungsansatzes untersucht. Insbesondere wird die Abhängigkeit von den physikalischen Parametern im Atmosphärenmodell analysiert, die bekanntlich die Qualität der erzeugten hydrometeorologischen Informationen bestimmen. Dazu wird das Weather Research and Forecasting (WRF) Modell zum dynamischen Downscaling von Reanalysedaten in den klimatisch sensiblen Untersuchungsregionen Ecuador/Peru und Brasilien mit unterschiedlichen Parametrisierungssetups eingesetzt. Das Downscaling wird für beide Gebiete mit einer horizontalen Auflösung von 9 km und für das orografisch hochkomplexe Gebiet von Ecuador/Peru mit einer zusätzlichen Auflösung von 3 km durchgeführt. Diese Studie zeigt, dass die Kombination von unterschiedlichen physikalischen Parametrisierungen mit vier Schemata für die Kumuluskonvektion (CU), je zwei für die Mikrophysik (MP), planetare Grenzschicht (PBL) und Strahlung (RA) (insgesamt 32 Parametrisierungen) eine hohe Unsicherheit in den simulierten Niederschlag einbringt. Dabei kann bis zum Vierfachen des monatlichen Referenzniederschlags mit verschiedenen Konfigurationen modelliert werden. Ein besonderer Schwerpunkt dieser Studie liegt auf den räumlichen Mustern, die mit einem auf Ensemble zugeschnittenen Bewertungsansatz über die Metrik eSAL im Detail analysiert werden. Die zusammengefassten Ergebnisse für Verteilungs-, zeitliche und räumliche Muster zeigen, dass die CU-Schemata, gefolgt von den RA-Schemata, die dominierenden Tendenzen des simulierten Niederschlags innerhalb des WRF- Ensembles erzeugen (zu trocken/nass, zu große/kleine Niederschlagsmuster usw.). Ins- gesamt hat sich keine einzige Konfiguration als deutlich überlegen erwiesen, aber die angewandten Metriken ermöglichen eine angemessene Auswahl auf der Grundlage der Anwendungsanforderungen und der Referenzdaten. Eine zukünftige Übertragung des Setups auf die saisonalen Vorhersagen von SEAS5 erweist sich jedoch als nicht ganz einfach. Die doppelte zufällige Störung der Anfangsbedingungen von SEAS5 erschwert eine notwendige Auswahl von Ensemblemitgliedern, um den Rechenaufwand des dynamischen Downscaling zu reduzieren. Da die Studie zur Sensitivität der Parametrisierung auch zeigt, dass nach dem dynamischen Downscaling im Vergleich zur Referenz immer noch Abweichungen bestehen, sei es bei den Verteilungsmustern, den zeitlichen oder den räumlichen Mustern, wäre ein zusätzliches dynamisches Downscaling langer Vorhersagezeiträume in der Vergangenheit für das Post-Processing erforderlich, was einen immensen Rechenaufwand bedeutet. Diese Arbeit zeigt damit die Möglichkeiten auf und stellt die Herausforderungen heraus, die sich bei der Verwendung von saisonalen Vorhersagen zur Entscheidungsunterstützung in der Wasserwirtschaft und bei der Verfeinerung und Korrektur dieser Vorhersagen für Impaktstudien und direkte operationelle Anwendungen ergeben. Sie ermöglicht den finalen Transfer der gewonnenen wissenschaftlichen Erkenntnisse in die Praxis und liefert die erforderlichen Instrumente, um aktiv zu einer nachhaltigen Bewirtschaftung der Wasserressourcen in semiariden Regionen beizutragen. Denn diese Regionen benötigen dringend Anpassungs- und Abmilderungsstrategien, um die Auswirkungen des Klimawandels und die politisch bedingten Herausforderungen der Wassernutzung und -bewirtschaftung zu bekämpfen. Es zeigt sich, dass globale Datensätze mit saisonalen Vorhersagen und Reanalysen einen konkreten Nutzen für die Entscheidungsfindung bieten können, aber je nach Anwendung sind weitere Nachbearbeitungen erforderlich, um diese Daten regional nutzbar zu machen

Givenness and the Licensing of Object-First Order in German: The Effect of Referential Form

This paper presents two experiments investigating how the referential form of the object NP affects the acceptability of German main clauses starting with either the subject or direct object. The main clause was always preceded by a context sentence introducing a human referent to which the object NP of the following sentence referred. Results show higher acceptability for SO order when the object is a personal pronoun, higher acceptability for OS order when the object is a d-pronoun, and equal acceptability for SO and OS order when the object is a demonstrative NP. We discuss the results in the context of current theories of how word order variation depends on properties of the preceding context

A framework to evaluate and verify the presence of linguistic concepts in the prosody of spoken utterances

With recent developments in controlling the prosodic output of speech synthesizers[1], the quality of synthetic speech has improved considerably. However, determining the prosody required to convey specific linguistic concepts is still a largely unsolved problem. Concept-to-speech systems seem the most promising: additional information (structuring, focussing, affirmation/negation, quotation, enumeration, time/date, salutation, speaker attitude, etc.) is available to the prosody generation algorithm. This paper describes a method for determining which linguistic concepts are present in the prosody of a spoken utterance and which should therefore be taken into account when modelling prosody

Two dimensions of prominence

Wagner P, Portele T. Two dimensions of prominence. In: Proceedings of the ESCA Workshop on Dialogue and Prosody. Eindhoven, The Netherlands; 1999.Prosody fulfills a variety of functions in dialogues. Our study examines the relationship between different levels of perceived prominence of syllables and the linguistic and paralinguistic categories accent and emphasis which are conveyed prosodically. It is still unclear, how a notational system might look like that is able to capture the fine–grained differences between both. The notion of perceptual prominence—defined as a relational parameter on a scale between 0 and 31—seems to be a useful phonetic measure to capture both the subtle differences and shared characteristics of the phenomena commonly referred to as linguistic and paralinguistic. Our data indicate that the overall level of prominence within an utterance reflects the level of emphasis, whereas the relative difference of prominences to each other distinguishes between different linguistic accent types

Data specifications for INSPIRE

In Europe a major recent development has been the entering in force of the INSPIRE Directive in May 2007, establishing an infrastructure for spatial information in Europe to support Community environmental policies, and policies or activities which may have an impact on the environment. INSPIRE is based on the infrastructures for spatial information established and operated by the 27 Member States of the European Union. The Directive addresses 34 spatial data themes needed for environmental applications, with key components specified through technical implementing rules. This makes INSPIRE a unique example of a legislative ¿regional¿ approach. One of the requirements of the INSPIRE Directive is to make existing spatial data sets with relevance for one of the spatial data themes available in an interoperable way, i.e. where the spatial data from different sources in Europe can be combined to a coherent result. Since INSPIRE covers a wide range of spatial data themes, the first step has been the development of a modelling framework that provides a common foundation for all themes. This framework is largely based on the ISO 19100 series of standards. The use of common generic spatial modelling concepts across all themes is an important enabler for interoperability. As a second step, data specifications for the first set of themes has been developed based on the modelling framework. The themes include addresses, transport networks, protected sites, hydrography, administrative areas and others. The data specifications were developed by selected experts nominated by stakeholders from all over Europe. For each theme a working group was established in early 2008 working on their specific theme and collaborating with the other working groups on cross-theme issues. After a public review of the draft specifications starting in December 2008, an open testing process and thorough comment resolution process, the draft technical implementing rules for these themes have been approved by the INSPIRE Committee. After they enter into force they become part of the legal framework and European Member States have to implement these rules. The next step is the development of the remaining 25 spatial data themes, which include many themes of interest for the Earth Sciences including geology, meteorological and oceanographic geographic features, atmospheric conditions, habitats and biotopes, species distribution, environmental monitoring facilities, and land cover to name a few. The process will follow in general the same steps as for the first themes and the working groups are expected to start their work in March/April 2010. The first draft specifications for public comment are expected at the end of 2010 and the work is scheduled to be completed in 2012.JRC.DDG.H.6-Spatial data infrastructure

Prosody generation with a neural network

The use of neural networks in speech synthesis has been especially successful in the domain of prosody generation. The approach presented here differs from others in a) the transformation from a simple input to an output vector consisting of different parameters and b) the use of subcorpora that allow specialized networks. The network operates in a prominence-based synthesis system, where prominence is the most important parameter and is, consequently, the input parameter for the network. The output is not yet evaluated formally but the synthetic speech sounds natural and lively

A mixed inventory structure for German concatenative synthesis

In speech synthesis by unit concatenation a major point is the definition of the unit inventory. Diphone or demisyllable inventories are widely used but both unit types have their drawbacks. This paper describes a mixed inventory structure which is syllable oriented but does not demand a definite decision about the position of a syllable boundary. In the definition process of the inventory the results of a comprehensive investigation of coarticulatory phenomena at syllable boundaries were used as well as a machine readable pronunciation dictionary. An evaluation comparing the mixed inventory with a demisyllable and a diphone inventory confirms that speech generated with the mixed inventory is superior regarding general acceptance. A segmental intelligibility test shows the high intelligibility of the synthetic speech

Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions

Seasonal forecasts have the potential to substantially improve water management particularly in water-scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium-Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface rerun of the latest ECMWF reanalysis ERA5. Thereby, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1∘. This is performed for example over four predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco River (Brazil), the Tekeze–Atbara river and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo–Chira river (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts as well as remarkably reduced lead-dependent drift effects. But our analysis also shows that computing monthly averages from daily bias-corrected forecasts particularly during periods with strong temporal climate gradients or heteroscedasticity can lead to remaining biases especially in the lowest- and highest-lead forecasts. Our SEAS5 BCSD forecasts cover the whole (re-)forecast period from 1981 to 2019 and include bias-corrected and spatially disaggregated daily and monthly ensemble forecasts for precipitation, average, minimum, and maximum temperature as well as for shortwave radiation from the issue date to the next 215 d and 6 months, respectively. This sums up to more than 100 000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the five analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), https://doi.org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD (Domain D02: São Francisco Basin (Brazil), Lorenz et al., 2020c), https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD (Domain D03: basins of the Tekeze–Atbara and Blue Nile (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD (Domain D04: Catamayo–Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions

Synthesizing prosody : a prominence-based approach

A preliminary test exploring 4 emotions showed that conveying emotions by time domain synthesis may be possible. Therefore, a more sophisticated test was carried out in order to determine the influence of the prosodic parameters in the perception of a speaker's emotional state. Six different emotional states were investigated. The stimuli of the second test were used in three different testing procedures: as natural speech, resynthesized and reduced to a sawtooth signal. The recognition rates were lower than in the preliminary test, although the differences between the recognition rates of natural and synthetic speech were comparable for both tests. The outcome of the sawtooth test showed that the amount of information about a speaker's emotional state transported by F_{0}, energy and overall duration is rather small. However, we could determine relations between the acoustic prosodic parameters and the emotional content of speech

Personal Pronouns and D-Pronouns in German: Connecting Comprehension to Production

We review recent experiments and corpus data from our ongoing investigation of p(ersonal) and d(emonstrative) pronouns. Contrary to a widespread belief, d-pronouns did not always refer to non-topical antecedents in case of referential ambiguity. More generally, the data that we review show that a complex interplay of factors governs the processing of p-pronouns and d-pronouns. During language comprehension, syntactic function, position, and topichood of the antecedent are all taken into account when interpreting a p- or d-pronoun. Similarly, the choice between p- and d-pronoun during language production is heavily influenced by these three factors. We propose an interpretation and a production rule for d-pronouns, both based on prominence features. For interpretation, the proposed rule simply counts prominence features and assigns the d-pronoun the antecedent that is least prominent. For production, we propose a similar rule for choosing between a p- and d-pronoun for referring to a given antecedent. In contrast to interpretation, the rule for choosing a pronominal form makes use of weighted prominence features and relates prominence to production frequency in an exponential way