Keynote by Mary M. Glackin, AMS President 2020 (confirmed)
https://www.ametsoc.org/index.cfm/ams/about-ams/ams-election-information/2018-ams-elections/2019-ams-president-elect/mary-m-glackin/

https://www.linkedin.com/in/mary-glackin-91162246/

This session presents and explores the increasingly sophisticated systems developed to aid, and often automate, the forecasting and warning process. The rapid proliferation of data available, including probabilistic and rapidly-updating NWP as well as a plethora of observations, combined with a growing appreciation of user needs and the importance of timely and relevant forecasts, has brought the development of these systems to the fore. The opportunities afforded by the WMO's HiWeather programme will also be discussed in this session.

Topics may include:
• Nowcasting systems
• Links to severe weather and severe weather impacts
• Automated first guess warning systems
• Post-processing techniques
• Seamless deterministic and probabilistic forecast prediction
• Use of machine learning and other advanced analytic techniques

The Sendai Framework for Disaster Risk Reduction 2015-2030 states that the implementation of effective disaster risk reduction measures should be based on an understanding of disaster risks, including all aspects of vulnerability, capacity, exposure of persons and hazard characteristics. Understanding these disaster risks establishes the basis for the development of impact models and impact-based warnings.
In recent years there has been increasing interest in multi-hazard impact-based warning systems to reduce the impact of natural disasters. For example, in 2015 the World Meteorological Organisation (WMO) published ‘WMO Guidelines on Multi-hazard Impact-based Forecast and Warning Services’ for National Meteorological and Hydrological Services and their partner agencies responsible for issuing warnings. Impact-based warnings use a combination of likelihood and potential impact to assess the risk of a hazard event and therefore which warning to issue. The whole decision chain from assessing the likelihood of a hazard and potential impacts to deciding which warning severity to issue to warning communication and verification is complex. For many hydrometeorological events the likelihood can be assessed by using ensemble prediction systems, but an assessment of impacts is often highly subjective. To aid the decision making process hazard impact models and methods for multi-hazard assessment have been developed. These are at the cutting-edge area of research and require a multi-disciplinary partnership approach. This research not only spans hydrometeorology but all natural hazards ensuring that people receive the best advice and information to build their resilience and prepare for natural hazard events.

This session invites presentations from all natural hazard areas on:
• Impact based warning systems that are being developed or have been implemented across the world
• The decision making process of meteorologists and other hazard specialists in issuing warnings
• Research and development of explicit hazard impact models and multi-hazard systems
• How impact models are used to aid the decision making process
• Verification of impacts and collection of impact data
• Communication of this information to governments, civil contingencies, the responder community and the public.

Making Probabilities Work (SPARK session)

For this session we are aiming to organise it according to the SPARK concept:
https://www.ems2020.eu/programme_and_abstracts/on_the_programme/spark_sessions.html

Weather forecasts have matured substantially in providing reliable and sharp predictions and consequently the associated forecast uncertainty. This information can be integrated in downstream models and used to support decision-making processes.

The raw uncertainty information, e.g. as members of one or multiple ensemble prediction systems or as statistically derived probability distributions, has to be postprocessed, combined or visualized before it can serve as input for impact models such as hydrological models, or as decision support for weather forecasters, and for lay or professional end-users, such as emergency managers or energy providers.

In this session, we would like to support a holistic perspective on issues that arise when making use of uncertainty information of weather forecasts in decision processes and applications. To this end, we encourage contributions that investigate the application and interpretation of uncertainty information along any of the following questions:

How does the quality of the final decision depend on forecast uncertainty and uncertainty from other parts of the decision process (e.g., missing information, weather impact assessment, other sources, interactions, misinterpretations)?
Where, along the chain from raw forecast uncertainty to the final decision, do the largest uncertainties arise?
How is the uncertainty information (e.g., from ensemble prediction systems, multi-models etc.) propagated through the production chain up to the final decision?
How can we tailor information about forecast uncertainty to a given decision process or application?
How is uncertainty represented best in a given case (e.g., as ensemble members, PDFs, or worst/best case) to reduce complexity and computational or cognitive cost?
How can we identify the most suitable representation for different user-groups and decision processes?
How can we incorporate vulnerability and exposure data in a risk-based decision framework?
How can we evaluate and quantify the value of uncertainty information for the final decision?
What strategies help the end-user to the right interpretation of the uncertainty forecasts to make informed decisions?
What are the benefits of impact-based or risk-based forecasts and warnings in decision-making (including for disaster risk reduction)?
How can the interaction between scientists and end-users help to overcome reservations about uncertainty forecasts?

This session will focus on data assimilation techniques which are developed or implemented in meteorology, oceanography and land-surface modelling, and on observation impact.

This session will accept papers on any aspect of data assimilation, including (but not limited to) algorithmic developments in variational or ensemble methods; novel nonlinear data assimilation methods; model, background or observation uncertainty estimation and modelling; coupled data assimilation; observation impacts; targeting strategies and observation network design; innovative observing systems.

The session will include an invited talk on a topic in land surface data assimilation.

For this session we are aiming to organise it according to the SPARK concept: https://www.ems2020.eu/programme_and_abstracts/on_the_programme/spark_sessions.html .
The session will focus on the most recent developments in the field of ensemble techniques, ranging from its close connections with data-assimilation and nowcasting at short and medium ranges to their capacity to produce and deliver skillful and reliable forecasts of high-impact extreme events at sub-seasonal to seasonal (S2S) timescales.
As such it may provide a platform for exchanging ideas on how to create and use an ensemble system, techniques varying according to the forecast lead time. In particular, the forecaster perspective and the use of ensembles in predicting hazardous weather will be of interest.

The conveners invite papers on various issues associated with Ensemble Forecasting for weather prediction, such as:
• representation of initial uncertainties in Global and Limited-Area Ensemble Prediction Systems, including interlinks between data-assimilation and probabilistic forecasting;
• representation of model or boundary uncertainties in Global and Limited-Area Ensemble Prediction Systems;
• results from experiments including THORPEX Regional Campaigns, HyMeX, FROST-2014, etc.;
• results from recent studies using TIGGE and TIGGE-LAM databases;
• use, verification and calibration methods of Ensemble Prediction Systems;
• applications of probabilistic forecasts in the sectors of energy, health, transport, agriculture, insurance and finance;
• challenges tackled by the S2S WWRP/THORPEX-WCRP joint project (http://s2sprediction.net), including discussion on S2S sources of predictability, forecasts and socioeconomic applications of high-impact climate services.

Participants are especially encouraged to present contributions and discuss strategies to bridge gaps between stakeholders and actionable S2S tailored products.

This session will handle various aspects of scientific and operational collaboration related to weather and climate modelling. The session will be split into three sub-sessions which will focus on the following topics:

- Challenges in developing high-resolution mesoscale models with a focus on end-users and the EUMETNET forecasting programme. Observation impact studies to assess the importance of different parts of the observing system for global and limited area NWP models.

- Numerics and physics-dynamics coupling in weather and climate models: This encompasses the development, testing and application of novel numerical techniques, the coupling between the dynamical core and physical parameterizations, variable-resolution modelling, as well as performance aspects on current and future supercomputer architectures.

- Model verification: Developments and new approaches in the use of observations and verification techniques. It covers all verification aspects from research to applications to general verification practice and across all time and space scales. Highly welcome verification subjects including high-impact, user oriented applications, warnings against adverse weather events or events with high risk or user relevance.

In every day work operational meteorologists face up interesting, challenging and even dangerous weather phenomena when nowcasting and monitoring short to medium range forecasts. Several meteorologists are also interested in climate and are following extended to seasonal range forecasts. Combining this knowledge, meteorologist are trying to find new ways to improve the predictability and developing new services for both nowcasting and longer range forecasts.
In this session we are focused in case studies on local hazards and how the warning process in these cases has worked, or could be improved. We are also interested in climate information's role in operational forecasting such as the use of climate information in impact warnings and extended-range forecasts. The session focuses firmly on the operational experience and is in association with the Working Group for Cooperation between European Forecasters (WGCEF).

Papers on the following topics are welcome:
· Case studies on local hazards and how the warning process has worked, or could be improved
· Climate information's role in operational forecasting
· Extended-range forecasts: applicability on operational forecasting
· Towards seamless forecasting

The Weather Research and Forecasting model (WRF) is a widely used high-resolution meteorological model for operational weather forecasting, fundamental and applied research in meteorology, air quality, wind energy engineering, and consultancy studies. Its user’s community consists of universities, weather forecasters, and consultancy agencies world-wide. The goal of this session is to create a European forum to discuss research results concerning all aspects of the WRF and MPAS modelling frameworks.
Papers are invited on:
• Initialization, and meteorological and land surface boundary conditions.
• Numerical and grid spacing aspects
• Studies concerning data assimilation.
• Development of physical parameterization schemes.
• Model evaluation and validation against a broad range of available observations.
• Future WRF development.
• Tailored WRF versions, e.g. polar WRF, WRF-LES, WRF-Chem, H-WRF, the WRF single-column model
• WRF applications in weather forecasting, air quality studies, wind energy engineering.
• Regional climate studies
• Mesoscale meteorological phenomena studied with WRF.
• Analogous studies using Model for Prediction Across Scales (MPAS)

Artificial Intelligence (AI) is nowadays a central key for many modern applications and research areas, e.g. autonomous driving, image / face recognition as well as system simulation and optimization. Consequently, AI gains more and more importance also in weather and climate related sciences. This session focuses on machine learning and computer vision techniques and aims at bringing together research with weather and climate related background with relevant contributions from computer sciences using these techniques.

Contributions from all kinds of machine learning and computer vision studies in weather and climate on a wide range of time-scales are encouraged, including
• All kinds of postprocessing studies of Numerical Weather Prediction (NWP) forecasts (including projects such as DeepRain, etc.)
• Nowcasting studies, studies using satellite data, radar data, and observational weather data
• Seasonal forecast studies
• Climate related studies

These studies may e.g. deal with one or more of the fields
• Pre-processing of weather and climate data for machine learning purposes (e.g. forecasts from Numerical Weather Prediction (NWP) models, observational / satellite / radar data, etc.)
• Dimensionality reduction of weather and climate data, extraction of relevant features
• All kinds of supervised and unsupervised learning techniques
• Application of computer vision algorithms
• Regression and classification tasks
• Artificial Neural Networks, Deep / Convolutional / Recurrent Neural Networks, LSTMs, Decision Trees, Support Vector Machines, Ensemble Learning and Random Forests, etc.

Our European transport infrastructure is vulnerable to disruption by the weather and from other natural hazards. For example, we know that fog, snow, thunderstorms and volcanic ash all have potential to severely disrupt aviation. On land, rail and road networks may be greatly affected by factors such as snow, ice, flooding and strong winds. At sea, wind, fog, ice but also wind-driven sea motions such as waves, currents and sea ice can strongly affect traffic. Such disruptions can have significant consequences at both national and international level, and can be one of the most costly effects of bad weather.

Increasingly as transport networks expand, with climate change and as our dependence on technology increases, we see that there is a need to mitigate against the disruption of land, sea and air transport.

This session invites contributions from those involved in developing weather-based solutions for reducing risk to air, sea and/or land transport. In particular, participants are encouraged to discuss strategic risk reduction in transport at organizational or national level, perhaps achieved through engagement with the aviation or marine community, stakeholders and users in road and rail networks.

In addition, the session welcomes presentations on other aspects of transport meteorology, including impact studies and verification of forecasts, meteorological services in the cockpit, and environmental impacts of aviation and other forms of transport.

Weather conditions directly influence agricultural yields. Hail, disease and drought can have devastating effects on crops. However meteorological-related risks can be reduced through better timing of harvests, application of pesticides or through use of irrigation systems. A clear picture of current and future weather conditions, along with appropriate farm actions, can increase the likelihood of improved yields.

Climate change also influences crop suitability in certain regions where livestock can be negatively affected by migrating diseases and available food. To complicate matters the agricultural sector is also trying to become more sustainable and environmentally friendly in an attempt to meet greenhouse gas emission targets.

This session intends to examine our increasing knowledge of agricultural meteorology, while also attempting to identify opportunities in our changing environment.

We invite presentations related but not limited to:
• Agrometeorological modelling (e.g. modelling agrometeorological related diseases, frost protection warning methods, drought indices etc.)
• Impact of weather and climate extremes on agriculture
• Methods of measurements and observations (e.g. ground based equipment, remote sensing products, citizen science, Big Data etc.)
• Decision support systems & the representation of uncertainty
• Interactions/feedback of farmers and other end users
• Use of future climate projections on agrometeorological models

Renewable energy sources are currently investigated worldwide and technologies undergo rapid developments. However, further basic and applied studies in meteorological processes and tools are needed to understand these technologies and better integrate them with local, national and international power systems. This applies especially to wind and solar energy resources as they are strongly affected by weather and climate and highly variable in space and time. Contributions from all energy meteorology fields are invited with a focus on the following topics:

• Wind and turbulence profiles with respect to wind energy applications (measurements and theory) including wakes within a wind farm;
• Clouds and aerosol properties with respect to solar energy applications (measurements and theory);
• Marine renewable energy (wind, wave, tidal, marine current, osmotic, thermal);
• Meteorology and biomass for energy;
• Impact of wind and solar energy farms and biomass crops on local, regional and global meteorology;
• The use of numerical models and remote sensing (ground based and from satellites) for renewable energy assessment studies;
• Research on nowcasting, short term forecasts (minutes to day) and ensemble forecasts and its application in the energy sector;
• Quantification of the variability of renewable resources in space and time and its integration into power systems;
• Impacts of long term climate change and variability on power systems (e.g., changes in renewable resources or demand characteristics);
• Practical experience using meteorological information in energy related applications.

This session “Atmospheric effects on humans” deals with the interactions between atmospheric conditions and humans beings in an interdisciplinary manner. The core question is how atmospheric conditions impact the well-being and health of humans, and how to transfer such knowledge in a widely understandable way in order to ensure the appropriate use of such kind of information. Atmospheric conditions include transient ones driven by weather patterns and long-term climatology but as well how potential climate change trends may affect these interactions.

In this context, the session will address issues concerning health, warning systems and measures in place to mitigate adverse impacts, and the models used to evaluate the heat load and cold stress on organisms. This will include the thermal component from the environment, weather sensitivity, actinic and chemical components of stress factors. Modelling studies and experimental studies on how environmental management, urban planning and design or traffic regulation can improve living conditions and decrease emissions are particularly welcome.

In addition, the session will consider the impacts of weather processes on human well-being and health. Since several methods are in use to compile bio-weather forecasts, we are looking forward to discussing such approaches and the way to convey such information to the public, but also to special target groups. Another aim is to describe ways, how climate data and information should be transferred and addressed for issues on tourism, recreation and other economic sectors.

The session will also address efforts to combine different environmental impacts on humans into one single index, as it is well known that humans react to the whole mix of atmospheric stimuli. Our aim is to improve the requested information and to look for more efficient ways of conveying the message on a regular basis in order to enable citizens to make the best use of such information in their everyday activities.

Robust and reliable climatic studies, particularly those assessments dealing with climate variability and change, greatly depend on availability and accessibility to high-quality/high-resolution and long-term instrumental climate data. At present, a restricted availability and accessibility to long-term and high-quality climate records and datasets is still limiting our ability to better understand, detect, predict and respond to climate variability and change at lower spatial scales than global. In addition, the need for providing reliable, opportune and timely climate services deeply relies on the availability and accessibility to high-quality and high-resolution climate data, which also requires further research and innovative applications in the areas of data rescue techniques and procedures, data management systems, climate monitoring, climate time-series quality control and homogenisation.
In this session, we welcome contributions (oral and poster) in the following major topics:
• Climate monitoring , including early warning systems and improvements in the quality of the observational meteorological networks
• More efficient transfer of the data rescued into the digital format by means of improving the current state-of-the-art on image enhancement, image segmentation and post-correction techniques, innovating on adaptive Optical Character Recognition and Speech Recognition technologies and their application to transfer data, defining best practices about the operational context for digitisation, improving techniques for inventorying, organising, identifying and validating the data rescued, exploring crowd-sourcing approaches or engaging citizen scientist volunteers, conserving, imaging, inventorying and archiving historical documents containing weather records
• Climate data and metadata processing, including climate data flow management systems, from improved database models to better data extraction, development of relational metadata databases and data exchange platforms and networks interoperability
• Innovative, improved and extended climate data quality controls (QC), including both near real-time and time-series QCs: from gross-errors and tolerance checks to temporal and spatial coherence tests, statistical derivation and machine learning of QC rules, and extending tailored QC application to monthly, daily and sub-daily data and to all essential climate variables
• Improvements to the current state-of-the-art of climate data homogeneity and homogenisation methods, including methods intercomparison and evaluation, along with other topics such as climate time-series inhomogeneities detection and correction techniques/algorithms, using parallel measurements to study inhomogeneities and extending approaches to detect/adjust monthly and, especially, daily and sub-daily time-series and to homogenise all essential climate variables
• Fostering evaluation of the uncertainty budget in reconstructed time-series, including the influence of the various data processes steps, and analytical work and numerical estimates using realistic benchmarking datasets

Spatially comprehensive representations of past weather and climate, for example in the form of gridded datasets, are an important basis for analyzing climate variations and for modelling weather-related impacts on the environment and natural resources. They are also indispensable for validation and downscaling of climate models. Increasing demands for, and widespread application of grid data, call for efficient methods of spatial analysis from observations, and profound knowledge of the potential and limitations of these datasets in applications. At the same time, the growing pool of observational data (radar data, satellite based data…) offers the opportunity to improve the accuracy and reduce uncertainty of gridded climate data. Modern spatial climatology therefore deals with a wide range of space and time scales. As a result, actual developments in the field are concerned with a range of challenging issues. These include for example the spatial characteristics and representation of extremes, the representation of small-scale processes (auxiliary variables), the integration of several observational data sources (e.g. station, radar, satellite, re-analysis data), the quantification of uncertainties, the analysis at sub-daily time scales, and the long-term consistency as well as cross-variable consistency in grid datasets.

This session addresses topics related to the development, production, quality assessment and application of gridded climate data with an emphasis on statistical methods for spatial analysis and interpolation applied on observational data. Contributions dealing with modern methodological challenges and applications giving pertinent insights are particularly encouraged. Spatial analysis by applying e.g. GIS is a very strong tool for visualizing and disseminating climate information. Examples showing developments, application and dissemination of products from such analyses for climate services are also very welcome.

The session intends to bring together experts, scientists and other interested people analyzing spatio-temporal characteristics of climatological elements, including spatial interpolation and GIS modeling within meteorology, climatology and other related environmental sciences.

The objective of the session is to provide overview of the current applications of satellite climate and weather datasets with potential downstream applications.
Observations from space have the advantage of broad spatial and temporal coverage that complement in situ measurements. Satellite based data records have reached a high level of maturity in terms of time coverage, accuracy and accessibility. Such products come from long-term international programs that allow continuity and constant quality and are freely and openly available to government, academic, commercial and general public users.

This session intends to promote the usage of such datasets presenting existing and potential applications of products based on satellite-based climate data records and “near real time” data. A broad ensemble of thematic applications is encouraged, such as climate monitoring, extreme events, land and vegetation, hydrology, atmospheric composition.

Contribution may address novel science-driven (operational) applications, methodological approach, data fusion, innovative products and services.

For this session we are aiming to organise it according to the SPARK concept:

Copernicus Climate Change service will combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. The service will benefit from a network of observations, both from in situ and satellite sensors, and modelling capabilities. Moreover, it will provide key indicators on climate change drivers (such as carbon dioxide) and impacts (such as reducing glaciers). This information will serve a number of sectors sensitive to climate change, including energy, water management, agriculture & forestry, tourism, insurance, transport, health, disaster risk reduction, coastal areas and infrastructure.

This session will provide the opportunity to discuss how the service can deliver substantial economic value to Europe by:
• informing policy development to protect citizens from climate-related hazards such as high-impact weather events;
• improving planning of mitigation and adaptation practices for key human and societal activities;
• promoting the development of new services for the benefit of society.

Contributions from climate service providers and sectoral users are solicited, as well as from relevant FP7 and H2020 precursor projects.

The prediction of changes in the climate mean state, variability and extremes remains a key challenge on decadal to centennial timescales. Recent advances in climate modelling and post-processing techniques (statistical downscaling, bias correction and ensemble techniques) provide the basis for developing future climate information on local to regional and global scales. To make such information actionable for users, relevant information needs to be derived and provided in a way that can support decision-making processes. This requires a close dialogue between the producers and users of future climate information.

Over recent years, a number of European countries have established operational climate services in support of climate adaptation. These services – in form of climate scenarios - are typically based on coordinated model ensembles such as CMIP5, CMIP6 and CORDEX, taking into account country-specific circumstances (e.g. KNMI14 and KNMI21 in the Netherlands, UKCP18 in the UK, CH2018 in Switzerland, ÖKS15 in Austria). The session aims at an exchange on the different national approaches and invites papers on:

•Practical challenges and best practices in developing climate scenarios to support adaptation action.

•Developments in dynamical and statistical downscaling techniques, process-based model evaluations and model weighting, methods to quantify uncertainties from climate model ensembles.

•Challenges in the co-production of future climate information for different target audiences. Examples of tailored information for impact assessments and decision-making. Methods for eliciting user requirements.

•Achievements to increase the uptake of future climate information in decision-making (e.g. case studies, targeted communications campaigns)

Skilful climate predictions on seasonal-to-decadal timescales can generate large socio-economic benefits, but current forecast quality is relatively low over Europe and the Mediterranean Sea. In general, our limited understanding of the mechanisms and processes responsible for predictability and model systematic errors hamper our capability to simulate and forecast seasonal-to-decadal climate variability, especially over the Euro–Mediterranean region. Improved global climate model calibration and regionalization techniques, as well as better forecast verification methods need to be developed specifically for this region to both extract as much climate information as possible from operational forecast systems, and tailor this information to produce top–quality climate services for sectors with high societal impacts in the Mediterranean.

This Session is devoted to research and development aimed at improving climate prediction capabilities and related services on seasonal-to-decadal timescales in the Euro–Mediterranean region, and it invites contributions on (but not only restricted to):

- understanding of sources and mechanisms for predictability in the target region,
- novel, process-based methods for bias correction, downscaling, optimal combination of different sources of information,
- innovative empirical forecasting models,
- climate services based on end-user tailored climate forecasts, in relevant socio–economic sectors for the Mediterranean (e.g., water management, renewable energy, agriculture, tourism, forestry and fire risk)

The Session will act as a workshop organized by the JPI–ERA4CS EU–project MEDSCOPE (www. medscope–project.eu)