99524 - GUEST LECTURES AND SEMINARS ON CLIMATE SCIENCE

Conoscenze e abilità da conseguire

This course made in the format of seminars and guest lecturers will expose the student to the frontier of knowdledge of climate and will apprenhend what are the topics available for the final thesis. The student will be able to grasp what are the emerging areas on climate science and be able to select the topic for future deepening of the knowldge.

Contenuti

 IMPORTANT:

- The Guest Lecture course lasts 2 semesters. In order to get it pass it is needed to attend them both.

- The exact schedule is given in the list of lectures below. Students are asked to check the program frequently given that it is usually updated in the course of the year based on availability of speakers. Therefore DO NOT only look at the time schedule but check the actual program below.

- In some, sporadic, instances the lectures will be also given online and the students will be allowed to attend remotely.

 

Structure:

The course is structured with 1- or 2-hours long time slots and with three types of offers:

1) Seminars: >=1 hour on current research/technological challenges, delivered by specialist.

2) Lecture: >=2 hours on a more general topic of broader relevance and less technical details.

3) Short course: >=3 hours on an additional supplementary skill. Examples may include a focus on programming or on an area of transversal interest.

The program is published in the course of the semesters and then constantly updated based on the availability of external guest lectures and the coherence of the content.

PROGRAM

 

24 Oct 16.00-18.00

1st SEMINAR

Title: Modeling the Unpredictable: Why Nat Cat Models Matter for Insurance and Resilience

Speakers:

Andrea Leoncini - Unipol Assicurazioni S.p.A., Bologna, Italy

Gianluca Mussetti - Unipol Assicurazioni S.p.A., Bologna, Italy

Abstract:

It is well known that the Law of Large Numbers breaks down in the presence of heavy-tailed probability distributions, where extreme events disproportionately drive aggregate outcomes. Catastrophe insurance is shaped by rare and severe natural events that challenge conventional actuarial models; as a result, pricing and risk management tools based on the Law of Large Numbers and the Central Limit Theorem often prove inadequate. Natural Catastrophe (Nat Cat) models have therefore become indispensable for insurers, reinsurers, and policymakers in assessing the financial risks of hazards such as storms, floods, and earthquakes. By integrating hazard science, exposure data, and vulnerability analysis, these models inform premium setting, capital allocation, and regulatory compliance. Professionals in Nat Cat modelling—spanning atmospheric scientists, engineers, and actuaries—play a vital role in bridging research and practice, improving model accuracy, and addressing uncertainties, particularly in the context of climate change.

2nd SEMINAR

Title: Unipol research projects: near-term changes in rainfall extremes and severe thunderstorms in Italy

Speakers:

Simone Persiano - Unipol Assicurazioni S.p.A., Bologna, Italy

Lorenzo Aiazzi - Unipol Assicurazioni S.p.A., Bologna, Italy

Abstract:

As reported by the European Environment Agency (EEA, 2024), economic losses due to weather- and climate-related extreme events have increased over time. As natural extreme events are expected to intensify further, there is a growing interest in the insurance industry in modeling hydrological hazards (floods) and meteorological hazards (storms) and understanding how they are expected to change in the future.

A considerable share of the economic losses in Europe is associated with floods caused by extreme rainfall events, that have been steadily increasing over recent decades, mainly driven by climate change and the increase in exposure to flooding due to land-use and land-cover modifications. Urban areas are particularly vulnerable to the impacts of these events and, with their high spatial concentration of assets, represent a key concern for the insurance industry. Thus, a thorough analysis of changes in the magnitude of daily and sub-daily rainfall extremes is crucial: gridded rainfall datasets from reanalysis products can help identify hotspots and inform targeted insurance strategies.

When it comes to severe thunderstorms, a change in their frequency and intensity has been observed in recent years, with a significant increase of hail across most of Europe. Northern Italy represents a hotspot for increasing hail hazard (Battaglioli et al., 2023), within an area characterized by high urbanization and thus of great interest from an insurance perspective. In this context, we aim to assess changes in thunderstorm-related hazard in the near-term (i.e., over the next 5–10 years) in Italy. To achieve this goal, we are planning to make use of seasonal and decadal predictions.

07 Nov 16.00-17.00

Title: Implementation of solar UV and energetic particle precipitation within the LINOZ scheme in ICON-ART

Speaker: Maryam Ramezani Ziarani (Catholic University of Eichstätt-Ingolstadt, Mathematical Institute for Machine Learning and Data Science, Ingolstadt, Germany)

Abstract:

Our study aims to present a new method for incorporating top-down solar forcing into stratospheric ozone by relying on a linearized ozone scheme in the ICON (ICOsahedral Nonhydrostatic)–ART (Aerosols and Reactive Trace gases) model system. The addition of geomagnetic forcing leads to significant ozone loss in the polar upper stratosphere of both hemispheres due to catalytic cycles involving NOy. In addition to the particle precipitation effect, accounting for solar UV variability in the ICON-ART model results in changes in ozone in the tropical stratosphere. The changes in radiative heating rates induced by both the direct modulation of UV radiation at the tropical stratopause and the indirect modulation through ozone changes alter the temperatures and dynamics of the middle atmosphere. These radiative heating changes modify the meridional temperature gradient, thereby affecting the zonal wind. As a result, changes in the zonal wind can modulate the behavior of planetary waves, which can propagate further downward to the Earth’s surface, eventually impacting lower atmospheric circulation patterns such as the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO).

10 Nov 13.00-14.00

Title: Advancing Global Ocean Reanalysis and Forecast Systems: Convolutional Neural Network and the Assimilation of Diurnal Satellite Retrievals of Sea Surface Temperature

Speaker: Matteo Broccoli (CMCC, Italy)

Abstract:

A variety of SST datasets have been produced in the satellite era. Each one of them has almost unique characteristics and differ from the SST variable of ocean general circulation models, so that assimilating such datasets in an optimal way requires a mapping to the first model level. However, this projection is non-trivial as it depends on the specific characteristics of the dataset, as well as on surface physical processes that impact the (sub)skin SST measured from satellite. Therefore, proper assimilation requires dataset-dependent operators.

In this talk we present a data-driven approach to construct the projection operator with machine learning (ML). The benefit is that it can be used over different SST dataset by only re-training the network. We compare conventional methodologies (climatology, linear regression) and ML networks with increasing complexity (random forest, U-Net, pix2pix) trained with L3 global diurnal subskin SST derived from AVHRR’s infrared channels on MetOp satellites to reproduce the ESA SST CCI product as target. Conventional approaches are able to correct the first momentum (bias) but fail in correcting the second momentum (RMSE) in a distribution sense. The pix2pix is the most effective operator and we test it over several one-year-long reanalysis-like experiments at 1/4° using different ML-assisted approaches for SST assimilation. The ML-based observation operator is beneficial mainly in the tropics but degrades in the areas of strong mesoscale activities that probably require higher resolution or longer training. The ML-unbiased approach instead improves globally the subsurface vertical temperature RMSE up to 20% against independent in situ data, when SST is the only variable assimilated. In a realistic set-up, assimilating also in situ observation and Sea Level Anomaly retrievals, the global improvement is about 10%.

Finally, we discuss work in progress to include such operator in the data assimilation system of CMCC’s Global Ocean Forecast System (GOFS16). GOFS16 is a short-term prediction system (6-day) based on a global eddy-resolving configuration at 1/16° resolution, whose purpose is to represent the full dynamics and life cycle of baroclinic eddies in most of the global ocean. Currently, the assimilation system ingests multiple data sources (in situ, altimetry and SST data) on the same model grid, and in particular the L3 subskin SST data are placed directly on the first model level. The application of the ML operator is meant to improve the skill of the system by reducing the error, especially within the model’s surface layers.

14 Nov 16.30-18.00

Title: Lost (and found) in latent land

Speaker: Pierre Gentine (Columbia University, USA)

Abstract:

Machine learning and artificial intelligence have been revolutionising weather and climate modelling and data analysis over the past few years. However, it remains unclear how much understanding has been gained from those models, even though they are reaching unprecedented accuracy. Through the study and analysis of carefully chosen latent spaces, I will demonstrate how we can get new understanding on the terrestrial water and carbon cycle as well as on atmospheric processes, specifically on convection. Those latent spaces can also be used to better characterise complex stochastic processes, such as turbulence, and combined with data assimilation in order to achieve improved performance in those models.

17 Nov 13.00-14.00

Title: Advances in Seasonal Sea Ice Forecasting: Dynamical Models, Machine Learning, and Applications

Speaker: Yiguo Wang (NERSC, Norway)

Abstract:

Sea ice plays a critical role in the Earth’s climate system, influencing heat exchange, ocean circulation, and ecosystems in polar regions. Its rapid decline under global warming not only signals profound climate change but also impacts human activities such as shipping, fisheries, and offshore operations. Reliable seasonal sea ice prediction has therefore become an essential scientific and societal challenge. In this lecture, I will introduce the fundamentals of sea ice, including its key physical properties, variability, and the observed impacts of climate change on Arctic and Antarctic sea ice cover. We will then explore the importance of seasonal sea ice forecasting and its applications, e.g., supporting Arctic shipping routes. The lecture will present an overview of the main approaches to sea ice prediction, including physics-based dynamical models and emerging machine learning (ML) techniques. As a concrete example, I will present the Norwegian Climate Prediction Model (NorCPM) and its application to Arctic and Antarctic sea ice prediction. Also, we will highlight recent advances in ML-based post-processing, focusing on error-correction methods that enhance the skill of dynamical forecasts.

21 Nov 16.00-18.00

Title: Climate Prediction: advances and challenges

Speaker: Noel Keenlyside (Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Nansen Environmental and Remote Sensing Center, Norway)

Abstract:

Climate prediction focuses on timescales from weeks to decades. Bridging the gap between weather forecasts and climate change projections, it combines elements of both. Like in weather forecasting, the state of system at the beginning of the forecast is crucial. However, for these timescales the state of the ocean, land surface, and sea ice become more important. Like in climate change projections, changes in external radiative forcing, such as induced from increasing greenhouse gas concentrations and aerosol loadings, need to be considered. This lecture will not only introduce the basic concepts in climate prediction but will emphasize the developments that have made it possible to predict phenomena such as El Niño and the Atlantic Multidecadal Variability, and their global impacts. For example, we can now predict droughts years in advance, such as the devastating ones that gripped sub-Saharan Africa in the 1980’s. The lecture will also cover the current challenges limiting climate predictions and discuss exciting new ways to address them. I will introduce supermodelling and machine learning approaches that can reduce long-standing model errors and enhance climate prediction.

Abstract:

24 Nov 13.00-14.00

Title: Expanding CMCC Seasonal Prediction System v3.5 applications to the local scale through statistical downscaling techniques

Speakers: Leonardo Aragao (CMCC, Italy)

Abstract:

The Italian Peninsula’s climate is highly influenced by its complex topography and diverse regional weather systems, making high-resolution seasonal forecasting crucial for agriculture, water management, and energy sectors. Traditional seasonal prediction models, such as the CMCC Seasonal Prediction System v3.5 (SPS), provide valuable insights but lack the spatial resolution necessary to capture local-scale climatic details. Recent advances in statistical downscaling show promise for enhancing these coarse-resolution forecasts by generating more localised and accurate predictions. Thus, this study aims to provide a high-resolution seasonal forecast for the Italian Peninsula by enhancing the SPS model through statistical downscaling techniques tailored to the region’s demand for finer-scale climate information. The downscaling method involves a three-step process that utilises observational datasets and machine-learning techniques to refine SPS forecasts. The first regards the ground truth, composed of high-resolution observational data from ERA5 reanalysis for 2m temperature, sea surface temperature, and 10m wind components, and from CHIRPS for precipitation. Then, SPS daily predictions are spatially interpolated from 1° to 1/4° to match the observational data's grid. Finally, both datasets are combined through two different machine-learning methods based on the Empirical Quantile Mapping (EQM) and the k-Nearest Neighbours (kNN) technique, translating SPS into high-resolution fields by matching predicted conditions to observed patterns. Finally, both statistical downscaling methods were assessed over the Italian Peninsula domain through cross-validation along the 24-year hindcast period available for SPS (1993-2016). The results indicate that statistical downscaling significantly enhances seasonal predictions for the Italian Peninsula, achieving biases about 5-6 times smaller than the original SPS for all evaluated predictands. The main component of this improvement is the spatial accuracy promoted by downscaling, allowing the identification of domain characteristics unnoticed in SPS. Even though the statistical indices show appreciable values for the domain as a whole, when we evaluate smaller portions of this same domain, the original seasonal predictions are still far from the desired. As expected, bias increases with lead time also for EQM and kNN, with accuracy declining progressively from lead month 1 onward. For example, 2m temperature bias increased from -0.14/-0.31/-0.85°C in lead month 1 to -0.68/-0.71/-1.41°C in lead month 6 (kNN/EQM/SPS). This trend highlights the ongoing challenge of maintaining prediction skills over extended periods and the importance of adaptive correction strategies to extend lead-time reliability. Integrating statistical downscaling techniques with SPS outputs provides a promising solution for generating high-resolution seasonal predictions, offering valuable support for climate-sensitive applications by reducing forecast bias and enhancing spatial accuracy. This work demonstrates the potential of statistical downscaling as an effective tool for bridging the gap between coarse seasonal predictions and the localised weather information necessary for effective decision-making.

 

05 Dec 16.00-18.00

Title: Operational numerical weather prediction with the ICON model at Agenzia Italia Meteo and Arpae Emilia-Romagna

Speaker: Thomas Gastaldo (Italia Meteo, Italy)

Abstract:
This seminar will present the operational numerical weather prediction system implemented by Agenzia Italia Meteo and Arpae Emilia-Romagna, centered on the ICON-2I model. The talk will outline the collaborative framework with European meteorological centers and describe the end-to-end operational workflow, including the KENDA data assimilation system based on the LETKF scheme, the Rapid Update Cycle (RUC), and the Ensemble Prediction System (EPS). Key aspects of forecast verification and product generation will be discussed, emphasizing their importance for forecast reliability and practical usability. Finally, the presentation will explore cascading applications and downstream modeling, illustrating how these tools contribute to improved forecast accuracy and operational decision-making.

15 Dec 13.00-14.00

Title: Genesis, precursors and forecasting of extreme precipitation in Italy

Speaker: Federico Grazzini (Arpae Struttura IdroMeteoClima, Dirigente Sala Operativa e Centro Funzionale, Italy)

Abstract:

 In this seminar, we will examine the processes and weather conditions conducive to intense precipitation, also in the context of climate change induced by global warming. We will also speak about operational forecasting procedures, including the experimental use of machine learning techniques to improve the anticipation of these events.

 

19 Dec 16.00-18.00 (2 Seminars)

Seminar 1 - Title: Advancing Weather Prediction: NCAR Efforts in Post-Processing and Machine Learning

Speakers: Stefano Alessandrini (National Centre for Atmospheric Research, USA)

Abstract:
NCAR is advancing a suite of post-processing and machine-learning (ML) approaches to improve environmental predictions across weather, climate, renewable energy, and air-quality applications. A significant component of this effort is the Analog Ensemble (AnEn) technique, which produces reliable probabilistic forecasts from a single deterministic model and a historical database of predictions and observations. Across applications, from wind and solar power forecasting to precipitation reconstruction and meteorological reanalysis, AnEn consistently reduces systematic and random errors, increases correlation with observations, and delivers intrinsically reliable ensembles without requiring model perturbations. Both deterministic metrics (RMSE, bias, MAE) and probabilistic attributes (reliability, sharpness, resolution) demonstrate clear advantages over traditional post-processing and numerical weather prediction (NWP) ensemble systems.
NCAR is also expanding its evaluation and integration of machine-learning weather prediction (MLWP) models. Recent work assesses ECMWF’s Artificial Intelligence Forecasting System (AIFS) against raw and bias-corrected outputs from IFS, GFS, and HRRR over CONUS, with emphasis on near-surface variables critical for renewable-energy use. These comparisons highlight the importance of applying consistent bias-correction methods to ensure fair benchmarking and provide insights into the strengths and operational readiness of emerging MLWP systems.
In parallel, NCAR is developing CREDIT, a next-generation ML-based post-processing and downscaling framework that blends physical constraints with data-driven learning. CREDIT employs transformer architectures, analog-inspired feature selection, and flow-aware calibration to enhance local-scale predictions for meteorology and air quality.
Together, these efforts illustrate NCAR’s strategy for next-generation environmental prediction: integrating statistical methods, analog techniques, and modern AI/ML architectures to improve accuracy, uncertainty quantification, and computational efficiency across a broad range of applications.

Seminar 2 - Title: Urban Meteorology Across Scales: Processes, Models, and Applications

Speakers: Matteo Zonato (CIMA Foundation, IT)

Abstract:

 Urban areas profoundly modify the atmosphere through complex exchanges of energy, heat, and momentum. This seminar will provide a multi-scale overview of urban meteorology, from street-level processes to city and regional scales, combining a phenomenological and modeling perspective.

The discussion will focus on three main aspects:

1. Physical mechanisms governing turbulence, radiation, and heat fluxes in the urban canopy and their role in shaping local weather and microclimates.

2. Advances in modeling and parameterization, including the integration of urban morphology and land-surface data into high-resolution mesoscale models to improve urban forecasts.

3. Applications and mitigation strategies, exploring how changes in urban form, materials, and vegetation influence temperature, humidity, and boundary-layer dynamics

Drawing on recent studies from European and international cities, the seminar will highlight open research questions and state-of-the-art developments that aim to better represent urban processes in weather and climate models. The final part will discuss how improved modeling of urban mitigation and forecasting techniques can support more resilient and sustainable cities.

 

11 Mar 14.00

Title: Satellite Soil Moisture Data Assimilation in Southeastern South America

Speaker: Fabricio Obregon (UNNE, Argentina)

Abstract

 Soil moisture is a variable of direct interest for agricultural production. However, its high spatio-temporal variability makes its estimation difficult. L-Band microwave based remote sensors aboard satellites enable for soil moisture retrievals over large areas with good spatial and temporal resolution. Despite these advantages, the satellite measurements are indirect, limited, biased, and might be spurious, requiring corrections. Data assimilation techniques provide a robust approach to estimating soil moisture variability by integrating numerical soil models—i.e., land surface models—with observations from satellite retrievals. Within data assimilation, the ensemble Kalman filter (EnKF) has been widely employed for soil moisture estimation. This algorithm assumes unbiased observations; thus, bias correction becomes necessary to ensure optimal satellite data assimilation. We are currently working on the assessment of bias correction in soil moisture data assimilation. Three off-line bias correction techniques are applied to correct SMAP and SMOS retrievals prior to data assimilation. We show that improving the statistical sampling by including soil texture information has a non-negligible impact on the SSM estimates resulting from EnKF, particularly during dry periods. The corrected samplings show better alignment and stronger correlation with the time series of the independent in-situ soil moisture measurements. With our work, we emphasize the need for context-aware bias-correction techniques to enhance data assimilation in regions with strong seasonal precipitation variations.

 

18 Mar 14.00

Title: AI-enhanced climate prediction of cyclone activity

Speaker: Leone Cavicchia (CMCC, Italy)

Abstract:

As climate records are being surpassed every year, there is a growing demand from different stakeholders for accessible and reliable short-term (seasonal to multi-annual) climate predictions of extremes. Despite major advances in the development of numerical Earth system models and the increase of available computer power, the prediction of extreme events at (sub)seasonal and longer time scales remain challenging. In recent developments, AI and machine learning techniques are being increasingly used to improve the accuracy of such predictions.

In this talk, I will first review the state of the art of numerical climate predictions systems and their limitations. Focusing on cyclones in the Tropics and the Mediterranean, I will then show how AI techniques can be exploited to increase the skill of climate predictions – in particular by exploiting large observational datasets to train algorithms to detect connections between the extreme events and their large-scale drivers.

19 Mar 11.00

Title: Satellite-Based Thunderstorm Nowcasting with Deep Neural Networks

Speaker: Christoph Metzl (German Aerospace Center, Institute of Atmospheric Physics, Oberpfaffenhofen, Germany)

Abstract

Forecasting severe weather events such as thunderstorms with high spatial and temporal accuracy is crucial. For very short lead times (up to a few hours) weather services have traditionally relied on data-driven nowcasting rather than numerical weather prediction. I will introduce the classical nowcasting approach and show how it is applied to satellite observations, then discuss its key limitation. I will present an alternative based on deep neural networks, compare both approaches in terms of forecast skill, and conclude with concrete directions for improving short-range forecast quality.

8 Apr 14.00

Title: Direct assimilation of differential reflectivity in an idealised setup

Speaker: Tatsiana Bardachova (Katholische Universität Eichstätt-Ingolstadt, MIDS, Germany)

Abstract

Radar data assimilation (DA) is critical for convective-scale forecasting, as it provides real-time, high-resolution information on precipitation, wind, and convective system dynamics that is not captured by surface observations or satellite data. Polarimetric radar observations complement conventional reflectivity (Zh) and radial velocity (Vr) measurements by enabling the determination of hydrometeor types and particle size characteristics. Differential reflectivity (ZDR) is one of the key polarimetric radar variables, defined as the ratio between horizontal and vertical reflectivity, that provides information on hydrometeor shape and size. Despite its strong potential to better constrain storm microphysics and improve convective-scale forecasts, the assimilation of ZDR remains challenging. Challenges associated with observation operators, error characterisation, and data quality underscore the need for further research in this area.
The current study investigates the direct assimilation of ZDR in an observing system simulation experiment (OSSE) of a long-lived supercell. The OSSE is conducted using the ICOsahedral Nonhydrostatic (ICON) model with a two-moment microphysics scheme and the Local Ensemble Transform Kalman Filter (LETKF), employing both hydrometeor mixing ratios and number concentrations as analysis variables. Synthetic observations are generated using the polarimetric radar forward operator EMVORADO developed at the Deutscher Wetterdienst. The synthetic ZDR observations are assimilated in addition to the non-polarimetric variables, namely Zh and Vr, while a reference experiment assimilating only non-polarimetric synthetic observations was conducted for comparison. A series of sensitivity experiments are performed to assess the impact of DA settings on assimilation performance, for varying observation error, localisation radius, and ensemble size. In addition, appropriate thresholds and no-reflectivity (clear air) equivalents for ZDR observations are examined.

30 Apr 11.00

Title: Assimilating Doppler wind lidar observations from ‘Swabian MOSES 2023’ reveals substantial wind biases in the ICON-D2 model over the Black Forest

Speaker: Julia Thomas (IMKTRO, Germany)

Abstract

Forecasting convective events remains difficult, even with the newest convective-scale numerical weather prediction models. One option to improve the predictability of convective events is assimilating additional observations of lower-tropospheric thermodynamic and dynamic variables at the mesoscale. The ‘Swabian MOSES’ 2023 campaign was conducted in June, July and August 2023 and deployed a variety of observation systems to capture lower-tropospheric dynamics and thermodynamics in the Black Forest and Swabian Jura in southwestern Germany, a region particularly prone to hailstorms and their impacts. Using DWD’s regional forecasting system, we assimilated (i) DWL-retrieved vertical profiles of the horizontal wind components, (ii) X-band radar reflectivity, (iii) targeted radiosoundings from two sites during intensive observation periods, (iv) ground-based zenith path-delay data from a domain-wide Global Navigation Satellite Systems receiver network, and (v) 2m-temperature and relative humidity from meteorological masts at the campaign sites, in addition to operationally available observations. In this talk, I will compare the campaign re-analysis with a quasi-operational reference re-analysis. Overall, the additional observations lead to differences in temperature, humidity, and wind with the most pronounced differences in the campaign region. The dense wind lidar observations in the campaign reanalysis lead to systematic differences in wind speed and direction between the campaign and the reference analysis, i.e. revealing a persistent model bias. I will present our current efforts to identify the sources of the wind bias by stratifying the analysis increments by time of day, flow regimes, wind shear and background wind direction, and give an outlook of which physical parametrizations might be causing this persistent model wind bias.

7 May 11.00

Title: Research activities at the regional Hydro-Meteo-Climate Service of Arpae

Speaker: Chiara Marsigli (ARPAE, Italy)

Abstract

The Hydro-Meteo-Climate Service of the Emilia-Romagna region is appointed to carry out operational activities, analyses, planning and warning support, and research and development in the hydro-meteo-climatology sector. It provides forecasting services in meteorology, hydrology, operational climatology, oceanography and air quality and it maintains the regional hydro-meteorological measurement network and two weather radars. In order to provide state-of-the-art products and services, a great effort is spent in research and development activities, which are carried out in the framework of the national and international research community, engaging in a number of projects and taking part in several coordinating bodies. In the talk, it is provided an overview of the research activities, highlighting the needs to which they respond, the scientific and technical challenges and the open issues, which determine the lines of research to be pursued in the next future.

20 May 14.00

Title: Ocean Data Assimilation: Practical aspects and case of Mediterranean analysis and reanalysis systems.

Speaker: Ali Aydogdu (CMCC, Italy)

Abstract

Ocean data assimilation (DA) has several aspects differing from other DA applications such as in the atmosphere, due to for example coastal boundaries, relatively slower time variability, less number and limited coverage of observations, model resolution. In this seminar, we will present state-of-the-art data assimilation (DA) techniques commonly used in ocean DA. Some differences with other DA applications in Earth Sciences will be highlighted. Some considerations that are differing from Global Ocean to regional seas to coastal scales will be discussed while available and commonly used observation types will be introduced. Examples from various ocean data assimilation systems either using Kalman filter based schemes or variational schemes be presented with a focus on the Mediterranean Sea analysis and reanalysis systems, a component of Copernicus Marine Service, based on a 3D ocean variational data assimilation scheme OceanVar, developed and maintained at CMCC.

Modalità di verifica e valutazione dell'apprendimento

At the end of both semesters the students will edit a short report on 3 lectures at their choice and send it for evaluation.

A mandatory attendance of at least 70% is required.

Strumenti a supporto della didattica

Students with learning disabilities (LD) or temporary or permanent disabilities: please contact the relevant University office promptly (https://site.unibo.it/studenti-con-disabilita-e-dsa/it). They will be responsible for suggesting any adjustment to the students concerned. However, these adjustments must be submitted to the instructor for approval 15 days in advance, who will evaluate their suitability also in relation to the educational objectives of the course.

Orario di ricevimento

Consulta il sito web di Natale Alberto Carrassi