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Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses., Nature Methods, 19 (8), ISSN:1548-7105, ISSN:1548-7091

We propose a new forward electricity market framework that admits heterogeneous market participants with second-order cone strategy sets, who accurately express the nonlinearities in their costs and constraints through conic bids, and a network operator facing conic operational constraints. In contrast to the prevalent linear-programming-based electricity markets, we highlight how the inclusion of second-order cone constraints improves uncertainty-, asset- and network-awareness of the market, which is key to the successful transition towards an electricity system based on weather-dependent renewable energy sources. We analyze our general market-clearing proposal using conic duality theory to derive efficient spatially-differentiated prices for the multiple commodities, comprising of energy and flexibility services. Under the assumption of perfect competition, we prove the equivalence of the centrally-solved market-clearing optimization problem to a competitive spatial price equilibrium involving a set of rational and self-interested participants and a price setter. Finally, under common assumptions, we prove that moving towards conic markets does not incur the loss of desirable economic properties of markets, namely market efficiency, cost recovery and revenue adequacy. Our numerical studies focus on the specific use case of uncertainty-aware market design and demonstrate that the proposed conic market brings advantages over existing alternatives within the linear programming market framework., Comment: Final accepted manuscript with supplementary material (revised 15.12.2022)

Wind power forecasts have been operationally used for over 25 years. Despite this fact, there are still many possibilities to improve and enhance forecasts, both from the weather prediction side and in the use of the forecasts. Until now, most applications have focused on deterministic forecast methods. This is likely to change in the future as penetration levels increase and weather conditions become more unstable due to climate change. Probabilistic methods are therefore receiving more attention from users. The International Energy Agency (IEA) Wind Task 36 on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, ...), forecast vendors and forecast users to facilitate scientific exchange to be prepared for future challenges.Collaboration is open to IEA Wind member states; 12 countries are already actively collaborating. The Task is divided in three work packages: Work Package (WP) 1 is a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction (NWP) model physics, but also widely distributed information on accessible datasets. This WP will also organise benchmarks for NWP models. The efforts of WP2 resulted in the publication of an international pre-standard (an IEA Recommended Practice) on how to select an optimal wind power forecast solution for a specific application. The focus of WP3 is on the engagement of end users to disseminate the best practice in the use of wind power predictions, especially probabilistic forecasts.The paper presents an overview of the recently completed first phase and the ongoing second phase of IEA Task 36 on Wind Power Forecasting, which provides a forum forinternational collaboration in this important field for meteorologists, wind power forecasters and end users. For collaboration, please contact the author (grgi@dtu.dk) and seethe website at www.ieawindforecasting.dk.

The aim of this paper is to present the objectives, research directions and first highlight results of the Smart4RES project, which was launched in November 2019, under the Horizon 2020 Framework Programme. Smart4RES is a research project that aims to bring substantial performance improvements to the whole model and value chain in renewable energy (RES) forecasting, with particular emphasis placed on optimizing synergies with storage and to support power system operation and participation in electricity markets. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of renewable energy forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in data science and approaches to meteorological forecasting. Smart4RES concentrates on novel developments towards very high-resolution and dedicated weather forecasting solutions. It makes optimal use of varied and distributed sources of data e.g. remote sensing (sky imagers, satellites, etc), power and meteorological measurements, as well as high-resolution weather forecasts, to yield high-quality and seamless approaches to renewable energy forecasting. The project accommodates the fact that all these sources of data are distributed geographically and in terms of ownership, with current restrictions preventing sharing. Novel alternative approaches are to be developed and evaluated to reach optimal forecast accuracy in that context, including distributed and privacy-preserving learning and forecasting methods, as well as the advent of platform-enabled data-markets, with associated pricing strategies. Smart4RES places a strong emphasis on maximizing the value from the use of forecasts in applications through advanced decision making and optimization approaches. This also goes through approaches to streamline the definition of new forecasting products balancing the complexity of forecast information and the need of forecast users. Focus is on developing models for applications involving storage, the provision of ancillary services, as well as market participation.

International audience; We propose a framework for evaluating the quality of solar irradiance probabilistic forecasts. The verification framework is based on visual diagnostic tools and a set of scoring rules mostly originating from the weather forecast verification community. Two types of probabilistic forecasts are used as a basis to illustrate the application of these verification approaches. The first one consists in ensemble forecasts commonly provided by national or international meteorological centres. The second one originates from statistical methods and produces a set of discrete quantile forecasts, the nominal proportions of which span the unit interval. These probabilistic forecasts are evaluated for two selected sites that experience very different climatic conditions. The first site is located in the continental US while the second one is situated on La Réunion Island. Although visual diagnostic tools can help identify deficiencies in generated forecasts, it is recommended that a set of numerical scores be used to assess the quality of probabilistic forecasts. In particular, the Continuous Ranked Probability Score (CRPS) seems to have all the features needed to evaluate a probabilis-tic forecasting system and, as such, may become a standard for verifying solar irradiance probabilistic forecasts and by extension probabilistic forecasts of solar power generation.

International audience; Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The International Energy Agency (IEA) Task on Wind Power Forecasting organises international collaboration, among national weather centres, forecast vendors and forecast users. The Task looks back on the first 3 years, and just started the second three-year period. Collaboration is open to IEA Wind member states, 13 countries are already therein.The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets and a benchmark. Secondly, we try to improve the derived power forecasts and deal with forecast vendor related matters to bring the entire industry forward. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions.The main result of the first phase is the IEA Recommended Practice for Selecting Renewable Power Forecasting Solutions. This document in three parts (Forecast solution selection process, Designing and executing forecasting benchmarks and trials and Benchmark metrics) takes its outset from the recurrent problem at forecast user companies of how to choose a forecast vendor. The first report describes how to tackle the general situation, while the second report specifically describes how to set up a forecasting trial so that the result is what the client intended. Many of the pitfalls we have seen over the years, are avoided. Other results include a comprehensive review paper on the use of uncertainty forecasts in the power industry and an information portal related to forecasting.In short, the poster presents the IEA Task 36 on Wind Power Forecasting, opening a forum for international collaboration in this important field for meteorologists, wind power forecasters and end users. For collaboration, please contact the author (grgi@dtu.dk) and see the website at www.ieawindforecasting.dk.

International audience; Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The International Energy Agency (IEA) Task on Wind Power Forecasting organises international collaboration, among national weather centres, forecast vendors and forecast users. The Task looks back on the first 3 years, and just started the second three-year period. Collaboration is open to IEA Wind member states, 13 countries are already therein. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets and a benchmark. Secondly, we try to improve the derived power forecasts and deal with forecast vendor related matters to bring the entire industry forward. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions. The main result of the first phase is the IEA Recommended Practice for Selecting Renewable Power Forecasting Solutions. This document in three parts (Forecast solution selection process, Designing and executing forecasting benchmarks and trials and Benchmark metrics) takes its outset from the recurrent problem at forecast user companies of how to choose a forecast vendor. The first report describes how to tackle the general situation, while the second report specifically describes how to set up a forecasting trial so that the result is what the client intended. Many of the pitfalls we have seen over the years, are avoided. Other results include a comprehensive review paper on the use of uncertainty forecasts in the power industry and an information portal related to forecasting. In short, the poster presents the IEA Task 36 on Wind Power Forecasting, opening a forum for international collaboration in this important field for meteorologists, wind power forecasters and end users. For collaboration, please contact the author (grgi@dtu.dk) and see the website at www.ieawindforecasting.dk.

International audience; This poster gives anoverview of the IEA Wind Task for Wind Power Forecasting.The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Will Shaw of PNNL. Collaboration in the task is solicited from everyone interested in the forecasting business. The task runs for three years, 2016-2018, but will see a second phase for 2019-2021. The main deliverables are an up-to-date list of current projects and main project results,including data sets which can be used by researchers around the world to improve their own models, and an IEA Recommended Practice on the forecast solution selection process, a position paper regarding the use of probabilistic forecasts. Additionally, spreading of relevant information in both the forecasters and the users community is paramount, e.g. through common workshops or webinars. Participation is open for all institutions in member states of the IEA Annex on WindPower, see ieawind.org for the up-to-date list.

International audience

International audience; Dissolution dynamic nuclear polarization (D-DNP) has become a method of choice to enhance signals in nuclear magnetic resonance (NMR). Recently, we have proposed to combine cross-polarization (CP) with D-DNP to provide high polarization P(13C) in short build-up times. In this paper, we show that switching microwave irradiation off for a few hundreds of milliseconds prior to CP can significantly boost the efficiency. By implementing microwave gating, 13C polarizations on sodium [1-13C]acetate as high as 64% could be achieved with a polarization build-up time constant as short as 160 s. A polarization of P(13C) = 78% could even be reached for [13C]urea.

In the frame of the NetLander mission, developed by an European consortium under CNES leadership, a network of 4 landers will be deployed at the Mars surface. Among other instruments, the geophysical package of each lander will include a magnetometer. The different possible contributions of magnetic measurements onboard the NetLander stations are presented. The MagNet consists in continuous recording of the transient variations in the three components of the magnetic field observed at each NetLander. The transient variations of the magnetic field at the surface of a planet has a primary external source, the interaction between the environment of the planet and solar radiation, and a secondary source, the electric currents induced in the conductive planet. The continuous recording of the time variations of the magnetic fields at the surface of Mars by means of three component magnetometers installed onboard NetLander stations will therefore allow study of both the internal structure of Mars and the dynamic of its ionised environment. As is the case for the Earth, different possible controling plasma processes will lead to different convection patterns inside the magnetosphere and therefore different magnetic signatures at the planetary surface. Continuous recordings of the transient variations of the magnetic field onboard landers will then provide constraints on the convection within the Martian magnetosphere, that is a small magnetosphere where the ionosphere lies at great heights relative to the dimensions of the magnetospheric cavity. The use of the network magnetic data to probe the internal structure of Mars is also considered. The information that can be thus obtained on the Mars permafrost, lithosphere and mantle structure illustrated by numerical simulations. Finally, the specifications of the instrument allowing to achieve these objectives are discussed, and the instrument described.

This poster gives an overview of IEA Wind Task 36 for Wind Power Forecasting. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Will Shaw of PNNL. Collaboration in the task is solicited from everyone interested in the forecasting business. The second phase of Task 36 runs for three years, 2019-2021. The main deliverables from phase 1 (2016-2018) were (1) an IEA Wind Recommended Practice on the forecast solution selection and evaluation process, (2) an up-to-date list of current wind forecasting projects and their main project results, including datasets which can be used by researchers around the world to improve their own models, and (3) two review journal papers regarding the use of probabilistic forecasts and minute-scale forecasting and one Power & Energy magazine publication. Through workshops, webinars and meetings, dissemination of relevant information in both forecaster and end-user communities has been a focus area. Participation is open for all organisations in member states of the IEA Annex on Wind Power, see ieawind.org for the up-to-date list.