Energy stakeholder workshop - Gaps in collaboration

2 Stakeholder and industry challenges

2.2 Task 5.2: The industry energy challenge

2.2.4 Gaps in collaboration

2.2.4.1 Energy stakeholder workshop

The platform has been presented at a solar renewable energy stakeholder workshop through a dedicated session “Europe through the ConnectinGEO project funded under the Horizon 2020 program supports future developments for exchange and dissemination of in situ measurements - SOS, standards and interoperability - Discussion” (http://soda-pro.com/research/training-session-2016). This workshop gathered nineteen engineers from SMEs from Algeria, Belgium, France, Germany and Portugal, as well as twelve academics from the European Union, Egypt, Morocco, Oman and South

Korea. During the session a first round of outcomes of the presentation have been gathered. This include discussions that took place at the end of the session were several question were asked:

 ConnectinGEO platform

o Usefulness of the existing functionalities?

o Do you need other functionalities?

o Benefit to your daily work?

o Other comments?

 ConnectinGEO Quality check, model and export o Relevancy and usefulness in your business?

o Benefit to your daily work?

o Completeness?

 ConnectinGEO Contribution of your data to the platform o Data access?

o Data usage policies

o Will you be interested to be a data provider?

o Will you allow to provide access to your data to everyone?

o Could we create an exchange hub possibly with the support of MINES ParisTech?

The following excerpt feedback has been gathered during the stakeholder workshop:

 Several participants have already expressed in writing their agreement to provide data to the platform hosted by MINES ParisTech. MINES ParisTech in turn will provide reports on the quality of data. Different licenses of data use and IPR were discussed briefly. The companies AKUO and Martifer expressed interest and asked for more details on the data MINES ParisTech is interested in.

 Practitioners see the interest of such a tool. Most of them are ready to use it. It is a valuable tool for them to discover data, to explore these data and eventually take decision about sitting and sizing a sun powered plant.

 There was a suggestion made by AKUO backed by Sunpower of using this tool as a means to discover the stations that are measuring the irradiation. A map displaying the locations of the stations would be helpful and if not contained in the MINES ParisTech database, each station will be described not necessarily in details.

 Suggestion made by Sunpower. What about providing a tool that estimates the amount of radiation contained in a specific spectral band with the measurements as input to this estimating algorithm?

The general opinion of session’s leaders can be summarized as follow:

 The principle of the tool has been well received. There were no questions on the possible benefits but suggestions on improvements that demonstrate indirectly that the tool may be adopted. Several

suggestions made may take the tool a bit far from its original objectives.

Following the workshop the same set of questions has been sent by email to enable a more complete test of the platform. The first general trends of email feedback analysis after testing the platform is summarized as follow:

 Platform:

o It is a great idea, and can be useful for a variety of actors. The main benefit to my work would be pyranometric data, downloadable in .csv format (as current BSRN data has different formats, sometimes hard to understand).

 Quality Check (QC):

o It is a great method to convince institutions with ground stations to share their data, while saving them money and time potentially spent on instruments and algorithm developments.

o QC is crucial as data may be used for comparison. As a future time-series provider, we would use this service to better qualify our measurements.

o It is necessary to have the information of % of good data after QC.

o Vital. Need to have reference to QC methods and be able to flag QC’ed.

 Data Contribution:

o I am all for sharing of data. We operate large PV plants but are not owner of the data. So we would need to validate with our customer case by case to provide you with our data.

o Solaïs a French SME PV operator has started to share its measurements coming from their 13 stations in operation.

The full version is available in the mid-term report.

A video providing a summary of this training is also available

Figure 14: Solar Training video (https://youtu.be/p-NCtwkGlZg) 2.2.4.2 Targeting the private sector

The platform has been promoted through the “Avenir Energy PME” program targeting French stakeholders in the field of renewable energies.

(http://www.oie.mines-paristech.fr/Recherche/Projets-de-recherche/Projets-de-recherche-acheves/#dissemination).

A one-day workshop Called “Rencontre Avenir Energie PME 2016” has taken place in Grenoble on 28^th September. A promotion video of the platform has been realized with the support of the French SMEs Solaïs and THIRDSTEP involved in solar renewable energies exploitation.

Figure 15: Private sector video (https://youtu.be/WpC5C6qnYkg) 2.2.4.3 Scientific dissemination

The platform and been widely presented at scientific events as well as through several publications:

 Ménard L, Nüst D, Jirka S, Masó J, Ranchin T, Wald L (2015) Open Surface Solar Irradiance Observations - A Challenge. Geophysical Research Abstracts Vol. 17, EGU2015-6607, 2015:

http://meetingorganizer.copernicus.org/EGU2015/EGU2015-6607.pdf.

European Geosciences Union General Assembly 2015 - EGU 2015, Vienna (Austria).

 Ménard, Lionel, Daniel Nüst, Simon Jirka, Joan Masó, Thierry Ranchin, and Lucien Wald. Open Surface Solar Irradiance Observations - A Challenge. European Geosciences Union General Assembly 2015, Vienna, Austria, 12-17 April 2015, EGU2015-6607.

 Ménard, L., D. Nüst, K. -M Ngo, P. Blanc, S. Jirka, J. Masó, T.

Ranchin, and L. Wald. 2015. "Interoperable Exchange of Surface Solar

Irradiance Observations: A Challenge".

doi:10.1016/j.egypro.2015.07.867.

 Matthes Rieke, Raquel Casas, Oscar Garcia, Simon Jirka, Lionel Menard, Thierry Ranchin, Christoph Stasch and Lucien Wald - Sensor Web Standards for Interoperability between in-situ Earth Observation Networks - Geophysical Research Abstracts Vol. 18, EGU2016-12647, 2016: http://meetingorganizer.copernicus.org/EGU2016/EGU2016-12647.pdf. EGU General Assembly 2016.

 Menard Lionel, Blanc Philippe, Gschwind Benoit, Wald Lucien, A spatial data infrastructure dedicated to the interoperable exchange of meteorological measurements in renewable energies. 16th EMS

Annual Meeting, 12-16 September 2016, Trieste, Italy, EMS Annual Meeting Abstracts, 13, EMS2016-369.

 GEO XII Ministerial - AIP-8 and Citizen GEO Sessions (Nov. 2015 Mexico).

 IEA Task SHC 46: Solar Resource Assessment and Forecasting meeting Sophia Antipolis (France) 6-8 April 2016.

 Rencontres Avenir Energie PME 2016 – MINATEC Grenoble (Sep.

2016)

 ENEON Plenary Workshop – Building a collaborative ENEON to inform policies and actions to address complex societal challenge - Meta network a contribution to ENEON in solar energy (Oct. 2016 Laxenburg, Austria).

 GEO XIII Plenary - In-situ EO networks and its relation to GEOSS (ENEON) and 1st Data Providers Workshop side events sessions.

2.2.4.4 Link with the GEOSS Common Infrastructure (GCI)

At a more technical level, the platform has been registered on the webservice-energy.org SDI catalog (http://geocatalog.webservice-webservice-energy.org). A dedicated ISO 19139 metadata record describing the platform and the available interoperable services is available on the catalog

(http://geocatalog.webservice-energy.org/geonetwork/srv/eng/metadata.show?id=3926&currTab=simple).

This enables to promote the GEO recommendations on interoperability.

Indeed the webservice-energy.org catalog is a registered resource of the GCI (GEOSS Common Infrastructure) and it is harvested weekly by the DAB (Discovery and Access Broker). Consequently any resources such as the in-situ platform metadata record that are available on the webservice-energy.org catalog are visible on the GEO Web Portal (GWP) for a larger dissemination and for the good of the solar energy community.

Figure 16: ISO 19139 metadata record describing the platform and the available interoperable services (1)

Figure 17: ISO 19139 metadata record describing the platform and the available interoperable services (2)

Figure 18: Same ISO 19139 metadata record as available in the DAB

Figure 19: Same raw ISO 19139 metadata record as available in the DAB (1)

Figure 20: Same raw ISO 19139 metadata record as available in the DAB (2)

Figure 21: Same ISO 19139 metadata record as available in the GWP

Figure 22: Same raw ISO 19139 metadata record as available in the GWP 2.2.4.5 Meta network

As presented at the mid-term review meeting in Brussels on April 8^th 2016, new array of potential providers seem to emerge as we move forward presenting the benefit of the platform and its underlying concept of openness and interoperability to the community. This is the case for private companies in the field of PV electricity production. In the European Union hundreds of

private ground station are associated with PV plants. Such companies operate PV plants and daily operate in-situ measurement from various types of sensors (Direct, diffuse and global irradiation, wind speed and direction, temperature, humidity…) available on-site for the monitoring and forecast of the production of these plants. They consequently hold very precious time-series that could be further valorized. These in-situ measurements should complement those coming from existing meteorological networks such as WRDC (World radiation Data Center), BSRN (Baseline Surface radiation Network), GAW (Global Atmospheric Watch) for a valued added offer to solar energy stakeholders. The concept we came-up with is called “Meta-Network”

for a combination between well establish Meteorological Network an spread Micro Network. It could be roughly sketch as follow with interactions between such networks, the platform and the relation to gaps assessment and the ENEON effort within the ConnectinGEO project.

To enable such Meta Network further efforts should be carried out that are currently out of the ConnectinGEO objectives.

Figure 23: Meta Network concept diagram

2.2.4.6 Relation with other projects’ tasks

Regarding interactions between this work-package with others tasks of the project the development and the release of the platform as well as all the outreach activities have supported:

• The tasks 2.2 regarding essential variable definition for solar energy.

• The debate of the task 3.2 regarding to shape the ENEON.

• The task 3.3 about Copernicus satellite in-situ relation as the platform is currently used under the MACC/CAMS Copernicus program.

• The tasks 4.4 and 6.2.

• The task 7.2 “Exploitation” as illustrated by the numerous references above.

2.3 Task 5.3: In-situ data compatible to satellite mission data challenge

The goal of this study is to assess the compatibility and functional gaps among measured satellite data and in-situ data providers exploitable by the Copernicus program. The study will first analyze the main discrepancies among in-situ measurements on similar quantities performed by different facilities, highlighting the main difficulties for users to exploit those datasets. It will then review relevant projects for harmonizing in-situ repositories, assessing in-situ data quality and making results of the assessment comments on the accessibility and usability of in-situ networks, assessed during our research.

2.3.1 In-situ data providers gap analysis

In-situ is a general definition, which includes all the measurements not taken by a satellite. Ground stations, air-borne and sea-borne sensors, such as ship or buoy based observations, are an example of in-situ data.

In-situ data are indispensable to complement satellite data, as these data provide:

1. Calibration and validation for space-borne information, i.e. as an independent source for detecting trends in instrument performance/degradation.

2. Complement satellite data and fill any kind of gaps that might be present in space sources.

In-situ data are also useful as a primary source of data, because they provide more accurate information on regional scales than satellite data, even if on a less extended spatial scale. Therefore, they are commonly used for local scientific analysis and assimilated into forecasting models to provide high-resolution regional constraints.

The study of the main discrepancies and issues in in-situ data providers has been carried out through the analysis of the intermediate results and conclusions of the HORIZON 2020 GAIA-CLIM project. This section has been written for D3.4 [5] of ConnectinGEO and is also reported here for its relevance to this study.

GAIA-CLIM (Gap Analysis for Integrated Atmospheric ECV CLImate Monitoring, http://www.gaia-clim.eu/) established sound methods for the characterization of satellite-based Earth Observation data by surface-based and sub-orbital measurement platforms.

ECV (Essential Climate Variables) are defined in [1] as a list of variables for which data products are required to support climate monitoring, forecasting, research, service provision and policy and whose observation must be technically feasible and cost effective. A non-exhaustive list of ECV for Atmosphere, Land and Ocean is the following:

 Atmosphere: Air temperature, wind speed and direction, water vapour, pressure, precipitation, surface radiation budget, Carbondioxide, methane, other long-lived green house gases, ozone and aerosols.

 Land: River discharge, water use, groundwater, lakes, snow cover, glaciers and ice caps, ice sheets, permafrost, albedo, land cover (including vegetation type), fraction of absorbed photosynthetically active radiation, leaf area index, above-ground biomass, soil carbon, fire disturbance, soil moisture.

 Sea/Ocean: Sea-surface temperature, sea-surface salinity, sea level, sea state, sea ice, surface current, ocean colour, carbon dioxide partial pressure, ocean acidity, phytoplankton, surface temperature, sub-surface salinity, sub-sub-surface current, nutrients, oxygen.

GAIA-CLIM identified, catalogued and studied a selected set of atmospheric ECVs.

In the intermediate results of the project, the assessment of the gaps is presented together with an estimate of their impact and a set of recommendations. In this study, we generalize the outcome of GAIA-CLIM in order to make it applicable to other domains. For more details about the GAIA-CLIM project itself we refer the reader to [2].

Six generic gap types can be identified and applied to any non-satellite data provider. They are:

 Coverage: In order to use a network of in-situ repositories as Cal/Val provider to satellite measurements, it is crucial to have a wide distribution of locations as well as consistent long time series. Therefore, the most common gaps falling in this category are:

 Geographical/Temporal Coverage: A comprehensive scientific approach assessing the gaps in the current observing capabilities of the system of systems does not exist. Assessments are commonly performed without a scientific basis or using an ad hoc (non-systematic) approach. Often this is done on the basis of the experience gained by the international experts in the frame of research projects.

 Knowledge of uncertainties: Limited availability or poor information of uncertainty estimates affects several repositories and propagates to applications when the data are assimilated into models. Progress here is critical to have long time series of consistent data samples, insensitive to the method of measurement and geographically uniform. Moreover, in

order to assimilate data into models two aspects are important: First the filtering of bad data. This means that in-situ data should have been consolidated and/or come with enough quality information to allow filtering out the corrupted measurements. The second aspect is the weighing of the data during the assimilation process. When bringing data into a model, a weight factor is needed to compare what the model is predicting and what the measured data is saying. This weighing is determined by the uncertainty of the measurement (usually a standard deviation). So having proper quality assurance and having correct uncertainty information is important. A system for the implementation and evaluation of QA measures for satellite-derived ECV datasets is described in Section 3.

 Knowledge of comparator measurements (i.e. validation uncertainties): In order to align and compare satellite data and in-situ data there are several steps that need to be taken. This includes quantity conversion (not only unit conversion, but also going from e.g. volume mixing ratios to number densities), regridding (vertical, spatial, and/or temporal) and/or smoothing (e.g. 'vertical smoothing' by applying an averaging kernel), etc. For each of these steps uncertainty needs to be propagated in order to give a confidence interval between the in-situ and satellite data. For uncertainty propagation uncertainties for all influential variables are needed as well (e.g. temperature and pressure profiles), and for regridding also covariance data (for the dimension in which regridding is performed). This additional uncertainty and/or covariance information is not always available. Furthermore, there are sometimes different algorithms to align the quantities (i.e. different ways of doing regridding). It shall be ensured that the estimation (and subsequent propagation) of uncertainties is discussed within the product documentation.

 Technical: For in-situ data there are often multiple data providers delivering data for the same kind of instrument (i.e. within the same network). This is something that does not happen for satellite data, where there is only one provider for a specific data product. In in-situ networks, measurements are often provided in a sparse way and with an uncoordinated effort, leading to technical inconsistencies between products from different providers. A harmonization effort has been done within the context of the NORS project, which is described in Section 2.

The main inconsistencies found in the in-situ data repositories are:

o Data policies: Different repositories have different data policies, lack of easy access or low speed access. They don’t follow the same guidelines for levels of data and associated names.

o Lack of metadata harmonization: Metadata is defined as a set of data providing information about the observations or other derived data. Metadata in our case is an increasingly central tool in the data provider’s environment, enabling large-scale, distributed management of resources. However, metadata standards have not been able to meet the needs of interoperability between independent standardization communities. Observations without metadata are of very limited use: it is only when accompanied by adequate metadata that the full potential of the observations can be utilized. Several efforts

have been spent to improve the harmonization of metadata across the networks and international programs.

o Harmonization and consistency of data representation in tools across all repositories: the consequences of lack of harmonization in data representation among tools includes:

 Different interpretation of quantities and variables

 Different and/or unspecified units of measures

 Different ordering of data in multi-dimensional arrays

 Different representation of averaging kernels (e.g.

providing the matrix in transposed form)

 Non-compliance problems with regard to the specification

 Moreover, different processing algorithms or differently calibrated instruments can also break the consistency of the dataset.

 Governance: In many repositories there is a general lack of documentation. This leads to missing Quality Indicators in many validation studies and to incoherent and poorly traceable validation results. It shall be ensured that all documentation of processing information provides accurate and clear details about the generation of a product and allows users to adequately assess the fitness-for-purpose of a product.

 Parameters gaps: These gaps concern parameters that are missing or hardly traceable in the monitored ECV. This can include also auxiliary data to the ECV monitoring. These gaps are closely related to lack of knowledge of comparator measurements since one of the main sources of gaps in ECV is the lack of comparator measurements, even when data are available.

Initiatives for harmonizing in-situ data networks and assessing the quality of ECVs are reported in the following sections.

2.3.2 In-situ data networks harmonization: the NORS project

One of the most extensive networks of ground-based observations for verifying remote sensing in support of the Copernicus missions has been built through the FP 7 NORS project (Demonstration Network Of ground-based Remote Sensing Observations in support of the GMES Atmospheric Service, http://nors.aeronomie.be). NORS is a demonstration project, whose main goal is improving the quality and validation of the Copernicus Atmospheric Service (CAS) using the ground-based data from the Network for the Detection of Atmospheric Composition Change (NDACC). Due to its pioneering nature, the project focuses only on a limited amount of NDACC stations, representative of

Dans le document EU Framework Program for Research and Innovation (SC5-18a-2014 - H2020) (Page 24-0)