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The field of Open Science has made scientists agree on the idea that data, workflows and services should be findable, accessible, interoperable, and thus optimally reusable (FAIR). These principles apply to Earth Science communities also, dealing with rapidly evolving natural phenomena. However, there is still a weakness regarding research sharing and re-use through the scientific community, due to lack of technological solutions and their long-term implementation. The H2020 Reliance project (https://www.reliance-project.eu) delivers a suite of innovative and interconnected services that extend European Open Science Cloud (EOSC) capabilities to support the management of the research lifecycle within Earth Science communities, Copernicus users, and beyond. The project has delivered three complementary technologies: Research Object, Data Cubes and AI-based Text Mining. ROHub (https://reliance.rohub.org/) is the Research Object management platform that implements these three technologies and enables researchers to collaboratively manage, share and preserve their research work. ROHub implements the full Research Object model and paradigm: resources associated to a particular research work are aggregated into a single FAIR digital object, and metadata relevant for understanding and interpreting the content is represented as semantic metadata that are user and machine readable. Research Objects are the innovative and interoperable service, open-by-default, and cross-disciplinary research management environment. Research Objects are virtual aggregations of resources that bring together data, methods, results and people to document scientific investigations, according to Open Science principles. To guide researchers, different types of Research Objects can be created: Basic, that can contain anything; Bibliography-centric, including manuals, and/or other material that support research; Data-centric, focused on datasets which can be indexed, discovered, and manipulated; Executable, including code, data and computational environment. This type of Research Objects can be executed and is often used for scripts and/or Jupyter Notebooks. Research Objects can be public/open/private and can be snapshotted or archived with permanent identifier (DOI). Additional information is associated and displayed in ROHub such as number of downloads, additional discovered metadata (automatically generated from the Reliance Text Mining service), keywords and citation. The toolbox and share tools in ROHub allow end-users to download, snapshot and archive the Research Object and/or share it. Any Research Object in ROHub is a FAIR digital object that is for instance findable in OpenAire, including those without DOI associated.The development of ROHub is co-designed and validated through multidisciplinary and thematic real life use cases led by three different Earth Science communities: Geohazards, Sea Monitoring and Climate Change communities. In our presentation, we will showcase different types of Research Objects for the three Earth Science communities represented in Reliance to highlight how the scientists in our respective disciplines fostered their work towards Open Science.The RELIANCE (Research lifecycle management for Earth Science Communities and Copernicus users in EOSC) project has received funding from the European Union’s Horizon 2020 INFRAEOSC programme under grant agreement No 101017501.

Research projects heavily rely on the exchange and processing of data and in this context Pangeo (https://pangeo.io/), a world-wide community of scientists and developers, thrives to facilitate the deployment of ready to use and community-driven platforms for big data geoscience. The European Open Science Cloud (EOSC) is the main initiative in Europe for providing a federated and open multi-disciplinary environment where European researchers, innovators, companies and citizens can share, publish, find and re-use data, tools and services for research, innovation and educational purposes. While a number of services based on Jupyter Notebooks were already available, no public Pangeo deployments providing fast access to large amounts of data and compute resources were accessible on EOSC. Most existing cloud-based Pangeo deployments are USA-based, and members of the Pangeo community in Europe did not have a shared platform where scientists or technologists could exchange know-how. Pangeo teamed up with two EOSC projects, namely EGI-ACE (https://www.egi.eu/project/egi-ace/) and C-SCALE (https://c-scale.eu/) to demonstrate how to deploy and use Pangeo on EOSC and emphasise the benefits for the European community. The Pangeo Europe Community together with EGI deployed a DaskHub, composed of Dask Gateway (https://gateway.dask.org/) and JupyterHub (https://jupyter.org/hub), with Kubernetes cluster backend on EOSC using the infrastructure of the EGI Federation (https://www.egi.eu/egi-federation/). The Pangeo EOSC JupyterHub deployment makes use of 1) the EGI Check-In to enable user registration and thereby authenticated and authorised access to the Pangeo JupyterHub portal and to the underlying distributed compute infrastructure; and 2) the EGI Cloud Compute and the cloud-based EGI Online Storage to distribute the computational tasks to a scalable compute platform and to store intermediate results produced by the user jobs. To facilitate future Pangeo deployments on top of a wide range of cloud providers (AWS, Google Cloud, Microsoft Azure, EGI Cloud Computing, OpenNebula, OpenStack, and more), the Pangeo EOSC JupyterHub deployment is now possible through the Infrastructure Manager (IM) Dashboard (https://im.egi.eu/im-dashboard/login). All the computing and storage resources are currently supplied by CESNET (https://www.cesnet.cz/?lang=en) in the frame of EGI-ACE project (https://im.egi.eu/). Several deployments have been made to serve the geoscience community, both for teaching and for research work. To date, more than 100 researchers have been trained on Pangeo@EOSC deployments and more are expected to join, in particular with easy access to large amounts of Copernicus data through a recent collaboration established with the C-SCALE project. In this presentation, we will provide details on the different deployments, how to get access to JupyterHub deployments and more generally how to contribute to Pangeo@EOSC.

The ever increasing number of scientific datasets made available by authoritative data providers (NASA, Copernicus, etc.) and provided by the scientific community opens new possibilities for advancing the state of the art in many areas of the natural sciences. As a result, researchers, innovators, companies and citizens need to acquire computational and data analysis skills to optimally exploit these datasets. Several educational programs dispense basic courses to students, and initiatives such as “The Carpentries” (https://carpentries.org/) complement this offering but also reach out to established researchers to fill the skill gap thereby empowering them to perform their own data analysis. However, most researchers find it challenging to go beyond these training sessions and face difficulties when trying to apply their newly acquired knowledge to their own research projects. To this regard, hackathons have proven to be an efficient way to support researchers in becoming competent practitioners but organising good hackathons is difficult and time consuming. In addition, the need for large amounts of computational and storage resources during the training and hackathons requires a flexible solution. Here, we propose an approach where researchers work on realistic, large and complex data analysis problems similar to or directly part of their research work. Researchers access an infrastructure deployed on the European Ocean Science Cloud (EOSC) that supports intensive data analysis (large compute and storage resources). EOSC is a European Commission initiative for providing a federated and open multi-disciplinary environment where data, tools and services can be shared, published, found and re-used. We used jupyter book for delivering a collection of FAIR training materials for data analysis relying on Pangeo EOSC deployments as its primary computing platform. The training material (https://pangeo-data.github.io/foss4g-2022/intro.html, https://pangeo-data.github.io/clivar-2022/intro.html, https://pangeo-data.github.io/escience-2022/intro.html) is customised (different datasets with similar analysis) for different target communities and participants are taught the usage of Xarray, Dask and more generally how to efficiently access and analyse large online datasets. The training can be completed by group work where attendees can work on larger scale scientific datasets: the classroom is split into several groups. Each group works on different scientific questions and may use different datasets. Using the Pangeo (http://pangeo.io) ecosystem is not always new for all attendees but applying Xarray (http://xarray.pydata.org) and Dask (https://www.dask.org/) on actual scientific “mini-projects” is often a showstopper for many researchers. With this approach, attendees have the opportunity to ask questions, collaborate with other researchers as well as Research Software Engineers, and apply Open Science practices without the burden of trying and failing alone. We find the involvement of scientific computing research engineers directly in the training is crucial for success of the hackathon approach. Feedback from attendees shows that it provides a solid foundation for big data geoscience and helps attendees to quickly become competent practitioners. It also gives infrastructure providers and EOSC useful feedback on the current and future needs of researchers for making their research FAIR and open. In this presentation, we will provide examples of achievements from attendees and present the feedback EOSC providers have received.

Making assets in scientific research Findable, Accessible, Interoperable and Reusable (FAIR) is still overwhelming for many scientists. When considered as an afterthought, FAIR research is indeed challenging, and we argue that its implementation is by far much easier when considered at an early stage and focusing on improving the researchers' day to day work practices. One key aspect is to bundle all the research artefacts in a FAIR Research Object (RO) using RoHub (https://reliance.rohub.org/), a Research Object management platform that enables researchers to collaboratively manage, share and preserve their research work (data, software, workflows, models, presentations, videos, articles, etc.). RoHub implements the full RO model and paradigm: resources associated to a particular research work are aggregated into a single FAIR digital object, and metadata relevant for understanding and interpreting the content is represented as semantic metadata that are user and machine readable. This approach provides the technical basis for implementing FAIR executable notebooks: the data and the computational environment can be “linked” to one or several FAIR notebooks that can then be executed via EGI Binder Service with scalable compute and storage capabilities. However, the need for defining clear practises for writing and publishing FAIR notebooks that can be reused to build upon new research has quickly arised. This is where a community of practice is required. The Environmental Data Science Book (or EDS Book) is a pan-european community-driven resource hosted on GitHub and powered by Jupyter Book. EDS Book provides practical guidelines and templates that help to translate research outputs into curated, interactive, shareable and reproducible executable notebooks. The quality of the FAIR notebooks is ensured by a collaborative and transparent reviewing process supported by GitHub related technologies. This approach provides immediate benefits for those who adopt it and can feed fruitful discussions to better define a reward system that would benefit Science and scientific communities. All the resources needed for understanding and executing the notebook are gathered into an executable Research Object in RoHub. To date, the community has successfully published ten FAIR notebooks covering a wide range of topics in environmental data science. The notebooks consume open-source python libraries e.g. intake, iris, xarray, hvplot for fetching, processing and interactively visualising environmental research. While these notebooks are currently python-based, EDS Book supports other programming languages such as R and Julia, and we are aiming at engaging with computational notebooks communities alike towards improving the research practices in environmental science.

Overview of the NICEST2 project and reflection on the successes, failures and possible improvements for follow-up projects.

Presentation given at ESIP January meeting 2023 to the session entitled Bringing Cloud-Native Science "Down To Earth". For over 20 years, ESIP meetings have brought together the most innovative thinkers and leaders around Earth science data, forming a community dedicated to making Earth science data more discoverable, accessible and useful to researchers, practitioners, policymakers, and the public. The theme of the January meeting is "Opening Doors to Open Science."

In this study, we focusing on understanding changes in air and water quality during the Covid-19 lockdown in the Venice Lagoon. We are re-using existing Research Objects, and in particular Jupyter Notebooks that were created in previous studies.

Pangeo has been deployed on a number of diverse infrastructures and learning resources are available with for instance the Pangeo Tutorial Gallery (http://gallery.pangeo.io/repos/pangeo-data/pangeo-tutorial-gallery/index.html). However, knowledge of Python is necessary to develop or reuse applications with the Pangeo ecosystem which hinders its wider adoption and reduces potential inter-disciplinary collaborations. Our main objective is to reduce barriers for using the Pangeo ecosystem and allow everyone to understand the fundamental concepts behind Pangeo and offer a Pangeo deployment for teaching and developing reproducible, reusable and fully automated workflows.Most Pangeo tutorials and examples use Jupyter notebooks but the gap between these “toy examples” and real complex applications is still huge: adopting best software practices for Jupyter notebooks and big applications is essential for reuse and automation of workflows.Galaxy project is a worldwide community dedicated to making tools, workflows and infrastructures open and accessible to everyone. Each tool in Galaxy has a wrapper describing the tool itself along with the input and output parameters, citations, and possible annotations thanks to EDAM ontology. Galaxy workflows are also annotated and can contain any kind of Galaxy Tools, including interactive tools such as Pangeo notebooks. Galaxy is also accessible via a web-based interface. The platform is designed to be community and technology agnostic and has gained adoption in various communities, ranging from Climate Science and Biodiversity to Biology and Medicine. By combining Pangeo and Galaxy, we are providing access to the Pangeo ecosystem to everyone, including those who are not familiar with Python and we offer fully automated and annotated Pangeo “tools”. Two main set of tools are currently available in Galaxy:Pangeo notebook (synced with Pangeo notebook with corresponding docker https://github.com/pangeo-data/pangeo-docker-images) Xarray tools to manipulate and visualise netCDF data from Galaxy Graphical User Interface. Training material is being developed and included in the Galaxy Training Network (https://training.galaxyproject.org/):“Pangeo ecosystem 101 for everyone - Introduction to Xarray Galaxy Tools” where anyone can learn about Pangeo and its main concepts and try it out without using any command lines; Pangeo Notebook in Galaxy - Introduction to Xarray:itl is very similar to “Xarray Tutorial” from Pangeo (http://gallery.pangeo.io/repos/pangeo-data/pangeo-tutorial-gallery/xarray.htm) but makes use of Galaxy Pangeo notebooks and offers a different entry point to Pangeo. Galaxy Training Infrastructure as a Service (https://galaxyproject.eu/tiaas.html) with infrastructure at no cost is provided by Galaxy Europe for teachers/instructors. It was used for the FORCeS eScience course “Tools in Climate Science: Linking Observations with Modeling” (https://galaxyproject.eu/posts/2021/11/13/tiaas-anne/) where about 30 students learned about Pangeo (see https://nordicesmhub.github.io/forces-2021/intro.html).Galaxy Pangeo also contributes to the worldwide online training “GTN Smörgåsbord” (last event 14-18 March 2022, https://gallantries.github.io/posts/2021/12/14/smorgasbord2-tapas/) where everyone is welcome as a trainee, trainer or just observer! This will contribute to democratising Pangeo.

The January 2022 NICEST2Newsletteris out and availablehere(HTML and pdf format). Below is a short summary: FAIR for Climate Science in the Nordics The NeIC-NICEST2 staff attended the Metadata for Machines workshop (M4M) organized by EOSC-Nordic for the Climate Community and it was a great success! Diagnostics for Earth System Modelling We are inviting researchers, Research Software Engineers that use or develop diagnostics tools for Earth System Models to fill out our survey:https://forms.gle/AxS7apMXKw6Po5iu5 Lumi and NICEST2: a joint interest for joint resources Our first experience with EuroHPC has been a success and we believe that having joint access to Lumi would benefit the NeIC-NICEST2 staff, and more generally the Nordic climate community. Upcoming seminar series on the use of GPUs in the field of climate Science Our next seminar series will be dedicated to GPUs and Climate science and we are gathering a list of potential presenters. (email jeani-at-uio-no). &nbsp

Presentation (slides) and demo (video) by Anne Fouilloux for the RELIANCE use case on Climate Change. The presentation and demo were given during the pan-europeans digital assets supporting research communities. Agenda of the event: On 5-6 December 2022, EOSC Future and the INFRAEOSC-07 projects (C-SCALE, DICE, EGI-ACE, OpenAIRE Nexus, Reliance) are hosting an online use case showcase. Check out the agenda and register for this online interactive event by 4 December, 23.59 CET. Over 2 half-day webinars, actual EOSC users will present how their research communities are using EOSC digital assets to address scientific and societal challenges related to 3 UN Sustainable Development Goals (SDGs): • Climate action (SDG 13) • Industry, Innovation & infrastructure (SDG 9) • Good health & well-being (SDG 3) There will also be a session with use cases related to Open Science more broadly. Why ‘use cases’? The demonstrative, first-hand format of the event will enable real research communities to show how their work can be leveraged by EOSC. Researchers, disciplinary groups and anyone interested to learn about both EOSC-related tools and services for data sharing and discoverability as well as discipline-related solutions are invited to the webinar. Attendees will also hear first-hand accounts from early-adopter communities that have integrated some of these core EOSC services. 1 programme, 2 days Check out the agenda to get a glimpse of the cases in the programme, in addition to a at the users, EU and UN officials who will be weighing in on discussions. Day 1 – 5 December • 09.30-11.00: Digital assets supporting SDG 13: Climate action • 11.15-12.00: Digital assets supporting SDG 3: Good health and well-being • 12:15-13:00: Discovering services for open science Day 2 – 6 December • 09.00-09.45: Digital assets supporting SDG 9 Industry, innovation and infrastructure • 10.00-10.45: Experiences from Early Adopters approaching EOSC: the RELIANCE Open challenge • 11.00-12.00: Lessons Learnt from use cases and Looking forward

This presentation has been given at the Data Managers Network meeting on Tuesday 22 November 2022. The topic of the meeting was "EOSC in practise" where different speakers gave their perspectives and experience with involvement in EOSC.

Presentation given at the French Institute on Thursday 1st December 2022.

Presentation (same as the google doc) but in pdf format.

Presentation given at ESA-NASA Open Innovation for EO Programmes 2022. This presentation gives an overview of the PAngeo Community and perspectives on creating Open Source user workflows.

This Research Object aggregates all the different Research Objects and resources used for presenting the Environmental Data Science Book at AGU 2022. The Environmental Data Science book is a living, open and community-driven online resource to showcase and support the publication of data, research and open-source tools for collaborative, reproducible and transparent Environmental Data Science. The Environmental Data Science is: a book a community a global collaboration We target to make sense of: environmental systems environmental data and sensors innovative research in Environmental Data Science open-source tools for Environmental Data Science We hope you find the content in the resource helpful. The resource and executable notebooks are free under a CC-BY licence and OSI-approved MIT license, respectively.

The H2020 Reliance project delivers a suite of innovative and interconnected services that extend European Open Science Cloud (EOSC)’s capabilities to support the management of the research lifecycle within Earth Science Communities and Copernicus Users. The project has delivered 3 complementary technologies: Research Objects (ROs), Data Cubes and AI-based Text Mining. RoHub is a Research Object management platform that implements these 3 technologies and enables researchers to collaboratively manage, share and preserve their research work. RoHub implements the full RO model and paradigm: resources associated to a particular research work are aggregated into a single FAIR digital object, and metadata relevant for understanding and interpreting the content is represented as semantic metadata that are user and machine readable. In our presentation at the 1st international FAIR Digital Object Conference, we will showcase different types of ROs for the 3 Earth Science communities represented in Reliance to highlight how the scientists in our respective disciplines changed their working methodology towards Open Science.

We describe here how to package and distribute Earth System Models, generate automatically Docker/Singularity containers, run containerized ESMs on various platforms (laptop, cloud, HPC), distribute workflow tasks (for inputs, execution, quality control, diagnostics) and publish workflows for FAIR (Findable, Accessible, Interoperable and Reusable) and open climate research. We demonstrate that containers provide native performance on HPCs. When added to Galaxy (https://galaxyproject.org), ESMs become available to all, regardless of their technical skills., This presentation was produced for the NICEST2 and EOSC-Nordic projects, that was supported respectively by the Nordic e-Infrastructure Collaboration (NeIC), which is part of NordForsk, and by the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652.

This report summarizes the first NICEST2 hackathon with FAIR experts and Earth System Model specialists to understand what needs to be done to make climate data FAIR. It will help us to define our roadmap for FAIR Climate in the Nordics., This report was produced for the NeIC NICEST2 project, that is supported by the Nordic e-Infrastructure Collaboration (NeIC), which is part of NordForsk.

Presentation given during the CSC GPU hackathon where NorESM/NICEST2 team worked together on the porting of BLOM on GPUs. The presentation shows the work done during the hackathon with the porting of a few routines on GPUs. The focus was not on performance and overall there was no gain (and even degradation of the overall performance because only a portion of the code has been ported to GPUs). Howeer, the hackathon was an excellent experience where the NorESM/NICEST2 team learned a lot about GPUs and discussed on possible methodology for migrating ESMs to GPUs. This work is of course only preliminary. Team Members: Martin L. Sætra (MET Norway) Yanchun He (NERSC) Mats Bentsen (NORCE) Alok Gupta (NORCE) Helge Avlesen (NORCE) Thomas Röblitz (UiB) Tomas Torsvik (UiB) Anne C. Fouilloux (UiO) Jørgen Nordmoen (UiO) Mentors: Mark Abraham (ENCCS) Nisha Agarwal (C-DAC) Gonzalo Gadeschi (NVIDIA) The NorESM/NICEST2 Team would like to thank the 3 mentors involved during the hackathon., This presentation was produced for the NICEST2 project, that was supported by the Nordic e-Infrastructure Collaboration (NeIC), which is part of NordForsk.

Presentation of EOSC-Nordic & NICEST2 projects at the INES (Infrastructure for Norwegian Earth System modelling) annual meeting, September 2020. Presentation given online. The goal was to coordinate with national initiative (INES) for Climate modelling., EOSC-Nordic project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652

The data management process for a research project covers the collection, storage, transformation, analysis and archiving or deletion of the data. Each of these stages can involve some complex and detailed steps. Projects in the same discipline may have very similar, or even the same, series of steps in a given stage. For example, more than one project may make use of the same multi-purpose detector. For each project the transformation from the raw data into more physical quantities (such as pulse signals from a detector into three-dimensional coordinates) could be common. If one project were able to define such a workflow and include it as part of their DMP, other projects would be able to reuse it. Capturing these workflows in the DMP would more accurately reflect the data management process and provide some value to follow-up, or related projects that have similar workflows and which would be able to reuse these workflows. The task we set ourselves for the hackathon was to take some specific workflows from a portion of the data management process for the Climate community in the EOSC-Nordic project and see how we could capture the workflow within the maDMP schema., This report is a summary of work done by the Something team at the RDA Hackathon on machine-actionable Data Management Plans that took place between 27th and 29th May 2020

The Lagrangian particle dispersion model FLEXPART was in its original version in the mid-1990s designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modelling and analysis and has attracted a global user community. Its application fields have been extended to a large range of atmospheric gases and aerosols, e.g. greenhouse gases, short-lived climate forcers like black carbon, or volcanic emissions, and it has also been used to study the atmospheric branch of the water cycle. Given suitable meteorological input data, it can be used for scales from dozens of meters to the global scale. In particular, inverse modelling based on source-receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.3, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecast System (IFS), and data from the United States' National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physico-chemical parametrizations, input/output formats and available pre- and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes. The deviation from 100 % efficiency is almost entirely due to remaining non-parallelized parts of the code, suggesting large potential for further speed-up. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g. to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option for running backward in time from atmospheric concentrations at receptor locations since many years, but this has now been extended to work also for deposition values and may become useful, for instance, for the interpretation of ice core measurements. To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART's customary binary format. In this paper, we describe these new developments. Moreover, we present some tools for the preparation of the meteorological input data and for processing of FLEXPART output data and briefly report on alternative FLEXPART versions.

By combining AVHRR data from the NOAA satellites with information from a database of in situ measurements, large-scale maps can be generated of the microphysical parameters most immediately significant for the modelling of global circulation and climate. From the satellite data, the clouds can be classified into cumuliform, stratiform and cirrus classes and then into further sub-classes by cloud top temperature. At the same time a database of in situ measurements made by research aircraft is classified into the same sub-classes and a statistical analysis is used to derive relationships between the sub-classes and the cloud microphysical properties. These two analyses are then linked to give estimates of the microphysical properties of the satellite observed clouds. Examples are given of the application of this technique to derive maps of the probability of occurrence of precipitating clouds and of precipitating water content derived from a case study within the International Cirrus Experiment (ICE) held in 1989 over the North Sea.

This contribution to the EUCREX mission 206 series aims at evaluating the performances of three methods considered for the retrieval of cloud microphysical properties (optical depth, droplet effective radius and liquid water path (LWP)) from Advanced Very High Resolution Radiometer (AVHRR) satellite images. These procedures, namely a set of empirical parameterizations, a physically based inversion code, and a neuronal approach, are tested with the mission 206 case study. The influence of horizontal averaging on the retrieved cloud properties is also investigated to assess the limitations of the comparison between satellite observations and aircraft measurements performed at a smaller scale.

The classification of pixels making up a satellite image is seen here, not only in order to cluster or discriminate these pixels, but exactly as an inversion procedure. The implemented adjustable combination of neural networks (ACNN) technique is built as a combination of self-organized neural networks in order to be particularly robust. After a sensitivity study, this method was considered for the extraction of cloud optical thickness and droplet effective radius from AVHRR imagery. The validation of this algorithm was conducted in well-documented cases issued from the EUCREX campaigns by comparisons against the CRTVL inversion package developed by Nakajima and Nakajima (1995) and also in situ measurements.

The influence of two initialization schemes implemented for cirrus cloud simulation with a mesoscale model (RAMS) when run at two distinct horizontal resolutions (40 and 13.5 km) is investigated. The first method combines both first guess and analyzed data issued from the European Centre for Medium-Range Weather Forecasts (ECMWF). Indeed, the former provides a good estimation of the amplitude of relative humidity, but the field is phase shifted while the latter gives a correct phase but a bad amplitude (because of the difficulty of measuring relative humidity at pressure levels lower than 400 mb). The second scheme uses information at three scale levels: ECMWF three-dimensional fields, an atmospheric vertical profile, and a cloud cover mask derived from a National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer satellite image. An interpolation algorithm that conserves the power spectrum slopes is used. Finally, several comparisons between the model outputs and observational data collected during the European Cloud and Radiation Experiment campaign are made. At a horizontal scale of 13.5 km, the authors’ new assimilation procedure allows a much better characterization of the whole three-dimensional cloud field (i.e., both horizontally and vertically) in terms of optical thickness and ice water content distributions, whereas the simulations are not improved at a larger horizontal scale (40 km).

As part of the European Cloud and Radiation Experiment campaign, we conducted studies with the regional atmospheric modeling system in order to simulate stratocumulus clouds observed on April 18, 1994, over Brittany (France). This three-dimensional model was used in its non hydrostatic mode, with bulk microphysical and radiative transfer parameterizations. The first initialization employed data provided by the European Center for Medium-range Weather Forecasts (ECMWF) and radiosoundings obtained at the Guipavas ground station. The model outputs were then compared with the corresponding NOAA-AVHRR (advanced very high resolution radiometer) satellite image and aircraft data collected in situ during the experiment. The relative differences between modeled and actual microphysical quantities (i.e., mixing ratio of water vapor, liquid water content, or liquid water path) were found to be less than 25% along the airplane trajectory; this disagreement was as large as 200% elsewhere in the experimental domain, principally because cloud cover was underestimated. The development of a simple time-to-space analysis made it possible to force the mesoscale model with satellite images combined with vertical profiles and tridimensional data of the ECMWF, leading to a more accurate spatial distribution of clouds over a large domain and diminishing these errors to 10%.

[1] This paper reports on the first experiences with Advanced Technology Microwave Sounder (ATMS) data at the European Centre for Medium-Range Weather Forecasts (ECMWF), both in terms of the contribution to the calibration/validation exercise and in terms of initial assimilation trials. Comparisons in antenna temperature space against short-term forecasts are used to establish the fidelity of the data. Monitoring of ATMS data against short-term forecasts shows that the data are generally of good quality, with a noise performance that is well within specification and after appropriate averaging, comparable to or better than that of the Advanced Microwave Sounding Unit-A (AMSU-A). Biases vary smoothly with scan positions, even before an appropriate antenna pattern correction has been established, and ATMS looks better than AMSU-A in this regard. Outer scan positions can be assimilated without restrictions due to biases, and together with the wider swath, this leads to a better coverage from one ATMS compared to one AMSU-A. There are indications of larger interchannel and spatial error correlations in ATMS data than for AMSU-A, possibly linked to a weak striping effect. The analysis and forecast impact in initial assimilation trials over two seasons are significantly positive in the short range over the Southern Hemisphere and in the long range over the Northern Hemisphere, with an otherwise overall neutral impact. Experiments in the context of a depleted observing system suggest that ATMS gives a comparable forecast impact to that from a single AMSU-A/Microwave Humidity Sounder combination.

International audience; In this conclusion paper, remote sensing retrievals of cloud optical thickness performed during the EUCREX mission 206 are analyzed. The comparison with estimates derived from in situ measurements demonstrates that the adiabatic model of cloud microphysics is more realistic than the vertically uniform plane parallel model (VUPPM) for parameterization of optical thickness. The analysis of the frequency distributions of optical thickness in the cloud layer then shows that the adiabatic model provides a good prediction when the cloud layer is thick and homogeneous, while it overestimates significantly the optical thickness when the layer is thin and broken. Finally, it is shown that the effective optical thickness over the whole sampled cloud is smaller than the adiabatic prediction based on the mean geometrical thickness of the cloud layer. The high sensitivity of the optical thickness on cloud geometrical thickness suggests that the effect of aerosol and droplet concentration on precipitation efficiency, and therefore on cloud extent and lifetime, is likely to be more significant than the Twomey effect.