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

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 detailed program will be published in the course of the semester as long as we get confirmation from the speakers.

 

10, 11 Oct 17.00-18.00/17.00-18.00

Lecturer: Ali Aydogdu

CMCC, Italy. ali.aydogdu@cmcc.it

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

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.

 

17 Oct, 14, 21 Nov 17.00-18.00

Lecturer: Federico Fabiano

Istituto nazionale di scienze dell’atmosfera e del clima (ISAC-CNR), IT

Climate sensitivity, feedbacks and warming patterns in models and observations

One fundamental property of the climate system is the Equilibrium Climate Sensitivity (ECS), defined as the global mean temperature change in response to a doubling of the CO₂ concentration with respect to pre-industrial levels. The quantity is of paramount importance for understanding societal impacts, since the allowed carbon budget to remain below a given warming level is directly proportional to it. Yet it is striking to observe that the assessed likely range in the most recent IPCC report (2.5-4°C) still shows a very large uncertainty, just slightly reduced since the first estimates were made about 50 years ago.

We will review recent literature exploring the role of the different climate feedbacks in this uncertainty, and will try to understand the complexity behind the physics of climate sensitivity through simple conceptual estimates. We will then take a glimpse of current lines of research that aim to tackle the problem, in various directions. ECS assessment through climate model projections is limited by models' resolution and approximations done in physical parametrizations, which result in a large inter-model spread. On the other side, observational estimates only show part of the story and are hindered by our limited understanding of current oceanic warming patterns. Will new model generations overcome the issue? Or should we rely on future observations?

 

18 Oct 16.00-18.00

Lecturer: Petros Ampatzidis

Centre for Climate Adaptation and Environment Research, Department of Architecture and Civil Engineering, University of Bath, UK

Wind and wind-induced loads on high-rise buildings over mountainous terrains: The Cattinara Hospital case study

Extreme weather events dominate the disaster landscape of the 21st century and disaster risk is becoming systemic with one event overlapping and influencing another in ways that are testing our resilience to the limit. This is particularly true for critical infrastructure, such as hospitals, that are vital to the functioning of society but have received limited attention in terms of investment in prevention, climate change adaptation and risk reduction. In this talk, we will present findings regarding the Bora-wind-induced atmospheric forces exerted on the high-rise Cattinara hospital in Trieste, Italy, a mountainous location where strong Bora winds often occur during the autumn and winter seasons and an increased risk of functionality loss is present. A series of multiscale numerical simulations were performed and linked together via a downscaling methodology to pass on and incorporate information coming from the climatic time-spatial scale, the meteorological mesoscale and the building-level microscale. The February 2012 Bora wind event that saw gusts of more than 40 m/s has been identified as the most extreme in more than 50 years and, thus, selected here for numerical reproduction. The study was conducted within the remit of the ongoing HORIZON-EU project RISKADAPT (Asset Level Modelling of RISKs in the Face of Climate-Induced Extreme Events and ADAPtation) that seeks to provide solutions to support systemic, risk-informed decisions regarding adaptation to climate change induced compound events at the asset level.

 

 

24-25 Oct 17.00-18.00 / 16.00-18.00

Lecturer: Alice Portal

Istituto di scienze dell'atmosfera e del clima (CNR-ISAC),
Consiglio Nazionale delle Ricerche, Bologna

Atmospheric Phenomena Causing Precipitation Extremes in the Mediterranean Region and What Stakeholders (Want to) Know About Them

In the Mediterranean region precipitation is mainly brought by two types of atmospheric systems, namely extratropical cyclones and thunderstorms. Cyclones are normally associated with extensive, long-lasting and low-intensity « stratiform » precipitation events, while thunderstorms induce short and localised high-intensity « convective » precipitation events. Although rainfall is generally beneficial for the water budget of the Mediterranean region, when falling for long time spans or at very high rates it can have disastrous impacts, for example giving rise to flooding and landslides. These are the cases when weather centers issue alarm warnings, when the Civil Protection (in Italy) is activated to prevent and manage the emergency and when insurances pay for (insured) losses.

This three-hour lecture intends to :

- provide a phenomenological explanation of extratropical cyclones and thunderstorms;

- explain what type of precipitation cyclones and thunderstorms are associated with;

- describe the circumstances in which these weather systems induce extreme rainfall and societal impacts;

- give an idea of what type of data and analyses stakeholders use to take decisions related to weather hazards of this kind.

 

08 Nov 16.00-18.00

Lecturer: Prof Alberto Troccoli

Co-founder and Managing Director of the World Energy & Meteorology Council, co-founder and CEO of Inside Climate Service srl, and a visiting professor at the University of East Anglia (UK).

Navigating the Global Energy Transition with Climate Insights

The energy sector is a major contributor to greenhouse gas emissions and must play a central role in mitigating the escalating impacts of climate change. The global energy transition towards renewable energy (RE) is critical to achieving the decarbonization targets outlined in the 2015 Paris Agreement and beyond. This ongoing shift aims to move the world’s energy systems away from fossil fuels toward renewable sources like solar, wind, and hydropower. Given that these RE resources are highly dependent on weather conditions, it is crucial to understand and predict the variability of meteorological factors that influence RE generation and energy demand.

WEMC has been at the forefront of this transition, providing critical insights and tools to understand the complex interplay between weather, climate, and energy systems. Our work has focused on improving the resilience of energy systems, ensuring continuity of supply, and mitigating the risks posed by extreme weather events. By integrating cutting-edge climate science with energy sector needs, WEMC has helped advance digitalization for smarter decisions, enhance energy efficiency, and drive investment in low-carbon innovations.

In my presentation, I will showcase the significant strides made in the energy transition over the past two decades, highlighting the challenges and opportunities ahead. I will also share specific contributions from WEMC, demonstrating how our initiatives are helping to accelerate this transition, lower costs, and pave the way for a sustainable energy future.

 

15 Nov 16.00-18.00

Lecturer: Luca Spogli

Istituto nazionale di geofisica e vulcanologia (INGV), IT

Long term trend on the upper atmosphere

 

22 Nov 16.00-18.00

Lecturer: Susanna Corti

Istituto di scienze dell'atmosfera e del clima (CNR-ISAC),
Consiglio Nazionale delle Ricerche, Bologna

Predictability of Weather and Climate

I will address the matter of climate (and weather) predictability, trying to highlight what we should (and should not) expect from climate predictions. Simulations of the mean global temperature trend during the 20th century will be shown. The difference between climate and weather predictions will be discussed. After that I will consider climate change predictions and model developments. A discussion on the role of deterministic chaos, non-linearity and flow regimes in weather and climate predictability will conclude the lecture.

 

28 Nov 17.00-18.00

Lecturer: Tijana Janjic, Yvonne Ruckstuhl, and Stefanie Legler

KU Eichstätt Ingolstadt, Ingolstadt, Germany ( [mailto:tijana.janjic@ku.de])

Learning model parameters from observations by combining data assimilation and machine learning

Parametrization of microphysics as well as parametrization of processes in the surface and boundary layers typically contain several tunable parameters. The parameters are not observed and are only crudely known. Traditionally, the numerical values of these model parameters are chosen by manual model tuning, leading to model errors in convection permitting numerical weather prediction models. More objectively, parameters can be estimated from observations by the augmented state approach during the data assimilation or by combing data assimilation with machine learning (ML).

If the parameters are updated objectively according to observations, they are flexible to adjust to recent conditions, their uncertainty is considered, and therefore the uncertainty of the model output is more accurate. To illustrate benefits of online augmented state approach with ensemble Kalman filter, Ruckstuhl and Janjic (2020) show in an operational convection-permitting configuration that the prediction of clouds and precipitation can be improved if the two-dimensional roughness length parameter is estimated. This could lead to improved forecasts of up to 6 h of clouds and precipitation. However, when parameters are estimated by the augmented state approach, stochastic model for the parameters needs to be pre-specified to keep the spread in parameters. Alternatively, Legler and Janjic (2022) investigate a possibility of using data assimilation for the state estimation while using ML for parameter estimation in order to overcome this problem. We train two types of artificial neural networks as a function of the observations or analysis of the atmospheric state. The test case uses perfect model experiments with the one-dimensional modified shallow-water model, which was designed to mimic important properties of convection. Through perfect model experiments we show that Bayesian neural networks (BNNs) and ensemble of point estimate neural networks (NNs) are able to estimate model parameters and their relevant statistics. The estimation of parameters combined with data assimilation for the state decreases the initial state errors even when assimilating sparse and noisy observations.

 

29 Nov 16.00-18.00

Lecturer: Prof Annika Oertel

Institute of Meteorology and Climate Research
Troposphere Research (IMKTRO)
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

The 'Swabian MOSES 2023' research campaign: Thunderstorm initiation and mesoscale circulations from observational and numerical weather prediction model perspectives.

Forecasting intense summer-time thunderstorm in Central and Southern Europe remains a challenge. Recently developed convective-scale data assimilation and ensemble forecasting systems aim at providing high-resolution forecasts of such extremes. A key question in this context is whether suitable high-resolution observations can improve convective-scale numerical weather prediction through their assimilation in operational systems or through the process of model evaluation and model development. As convective initiation and development is often influenced by local air mass convergence linked to mesoscale circulations, improving the representation of mesoscale circulations in numerical weather prediction models has the potential to improve forecasts on short lead times. Observations of such mesoscale circulations in complex terrain are available from the so-called 'Swabian MOSES 2023' field campaign, including, among others, a large network of Doppler wind lidars. In this contribution we introduce the interdisciplinary field campaign, illustrate the observation strategies, and present ongoing research activities. Current research topics include the verification of operational high-resolution weather forecast products with campaign observations and the assimilation of campaign observations in a quasi-operational data assimilation and forecasting system.

 

06, 12, 13, 19, 20 Dic 17.00-18.00/16.00-18.00

Lecturer: Claudia Acquistapace

Institute for geophysics and meteorology, University of Cologne (Germany).

(Preliminary Program)

Lecture series: Featuring AI methods applied to cloud satellite observations

Total number of hours: 7

Description of the seminar series. This series of lectures focuses on the topic of computer vision since this research branch, already finding huge applications in very diverse fields, has an enormous potential also for advancing in the atmospheric field. We will go through the working principle of convolutional neural networks, exploring the different aspects of the so-called deep learning methods. We will also introduce recent works that exploit deep learning methods to analyze satellite cloud and precipitation observations. We will show how classification methods can be used to characterize cloud fields and how the Long Short Term Memory models can help in the predicting precipitation. The course is presenting very recent research results and it will create an occasion for everyone, students and lecturer as well, to learn more about these topics.

Resources: In preparation, contact the lecturer for additional info. Detailed material and references will be provided on the first lecture and made available online at this page https://www.claudiaacquistapace.it/activities/teaching.html and/or on the website https://expats-ideas4s.com/

Classification of images using a data driven approach: Convolutional neural networks (CNN) explained

In this first lecture, we will start by discussing how to perform the task of assigning a label (from a given ensemble of labels) to an image with a computer. We will introduce a linear classifier (fully connected) based on a score function that maps images to labels and a loss function that can quantify how good is the agreement between the assigned labels and the image truth. Then we will understand how the network is learning with a process called optimization, that includes various processes behind it: stochastic gradient descent, backpropagation etc. Finally, we will look at the neural network architecture and its different layers and how they are spatially arranged, together with an overview of the functions and parameters involved. We will conclude with some methods to visualize convolutional neural networks and corresponding examples.

Keywords: Data driven approach, k-nearest neighbor, classification and optimization tasks with stochastic gradient descent, backpropagation, neural network architecture, activation functions, spatial arrangement, layer sizing patterns, hyperparameters

Learning and evaluation of a CNN: babysitting the learning process

We will start with a small recap from the previous lecture and we will then dig into how to prepare the data, initialize weights and run the network. We will discuss the batch normalization and will present tips and tricks that reduce the risks of overfitting and improve the network performance, like regularization, L2 dropout and data augmentation. We will then introduce one example, Resnet, which is often used in meteorological applications.

Keywords: Preprocessing, weight initialization, batch normalization, regularization and L2 dropout, loss functions, data augmentation. Overview of some checks to perform for monitoring the CNN algorithm, using one example. Resnet, fine-tuning, transfer learning

Applying CNN to improve our understanding about clouds and precipitation.

After the first three hours of theory on CNN, it is time to see how this powerful method can contribute to increase our understanding of cloud and precipitation processes. In this lecture we will give a detailed look at recent research works that exploited CNN to classify cloud mesoscale patterns. We will introduce supervised, unsupervised and self-supervised methods and we will describe how they are used in the different research works. Finally, we will also present current research work done by the EXPATS research group and present the main open research questions they are currently working on.

Keywords: supervised, unsupervised, self-supervised learning, human-label

Recurrent neural networks (RNN): LSTM models and their application for nowcasting precipitation

In this lecture we will introduce the usage of recurrent neural networks to model sequences of data. We will talk about the architecture of the RNN, the problems associated with backpropagation and the Long Short Term Memory model, that tries to mitigate the issues that RNN can encounter. We will then conclude the lecture with an example of application of the LSTM model in the prediction of precipitation fields.

Keywords: LSTM, recurrent neural networks, ifog, exploding gradients, vanishing gradients, gradient clipping

From CNN to attention model and vision transformers for image classification tasks

In this last lecture, we will introduce the vision transformers (ViT). ViTs are models that recently outperformed CNN in many computer vision tasks. For years and until 2017, the CNN models represented the most capable model in performing image classification tasks. ViTs are deep learning models that weight the input data in a differential way based on self-attention mechanisms. We will describe their architecture and explain in what they differ from RNNs, trying to understand the implication of such differences. Finally, we will show some of the main computer vision tasks they are able to achieve. If time allows, we will conclude our seminar series with a brief overview of the deep learning methods for video prediction.

Keywords: Vision transformers, self-attention, frame prediction, patches, linear embeddings

 

21 Mar 15.00-16.00

 Lecturer: Susanne Crewell

Institute for Geophysics and Meteorology, University of Cologne, DE. susanne.crewell@uni-koeln.de

Arctic amplification: observations and modelling

Within the last 25 years a remarkable increase of the Arctic near–surface air temperature exceeding the global warming by a factor of two to three has been observed. This phenomenon is commonly referred to as Arctic Amplification. The Arctic climate has several unique features, for example, the mostly low solar elevation, regularly occurring polar day and night, high surface albedo, large sea ice covered areas, an often very shallow atmospheric boundary layer, and the frequent abundance of low–level mixed–phase clouds. These characteristics influence the physical and bio–geochemical processes (such as feedback mechanisms of water vapor, clouds, temperature, and lapse–rate), atmospheric composition (trace gases, aerosol particles, clouds and precipitation), as well as meteorological (including energy fluxes) and surface parameters. In addition, meridional atmospheric and oceanic transports and exchanges between ocean, troposphere, and stratosphere largely control the Arctic climate. Although many individual consequences of changes in the above parameters and processes are known, their combined influence and relative importance for Arctic Amplification are complicated to quantify and difficult to disentangle. As a result, there is no consensus about the mechanisms dominating Arctic Amplification. To improve this situation the scientific expertise and competency of several German research institutes and three universities are combined in the framework of the Transregional Collaborative Research Centre TR 172. Observations from instrumentation on satellites, aircraft, tethered balloons, research vessels, and a selected set of ground–based sites are being integrated in dedicated campaigns and long–term measurements. The field studies are conducted in different seasons and meteorological conditions, covering a suitably wide range of spatial and temporal scales. They are performed in an international context, e.g., the MOSAiC expedition with the research vessel Polarstern drifting across the Arctic during polar night, and in close collaboration with modelling activities on different scales. The seminar will highlight how observations and modelling have to work hand in hand to solve the puzzle of Arctic Amplification.

Link for online streaming:

https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZTA0ZmJhNTUtNGQ5My00Y2Q4LTlmNzYtYmZhYzBlNDIxZmUw%40thread.v2/0?context=%7b%22Tid%22%3a%22e99647dc-1b08-454a-bf8c-699181b389ab%22%2c%22Oid%22%3a%220427d660-a66e-430e-a229-497c54fa1fee%22%7d

 

03, 04 Apr 16.00-18.00/16.00-18.00

Lecturer: Daniele Marinazzo

Ghent University, Belgium. Daniele.Marinazzo@UGent.be

Connectivity through dynamics: surprise and variance reductio

I will describe a series of approaches to study collective dynamics in complex systems, based on reduction in variance and in informational surprise, with applications ranging from neuroscience to social systems, to climate science.

This framework can easily be extended to study interactions across temporal scales, and to look for higher-order interactions of groups of more variables sharing common information about the dynamics of the system.

 

Link for online streaming:

  3 April: https://teams.microsoft.com/l/meetup-join/19%3ameeting_N2YzNzYxYjYtNjJkZC00ZjljLTg2NmQtMjAwZWJmNWIxZjgz%40thread.v2/0?context=%7b%22Tid%22%3a%22e99647dc-1b08-454a-bf8c-699181b389ab%22%2c%22Oid%22%3a%220427d660-a66e-430e-a229-497c54fa1fee%22%7d

  4 April: https://teams.microsoft.com/l/meetup-join/19%3ameeting_YTM0NTI3YjctYjlkMC00M2Q4LWFiMjQtNGM3M2E2ODE2YTY1%40thread.v2/0?context=%7b%22Tid%22%3a%22e99647dc-1b08-454a-bf8c-699181b389ab%22%2c%22Oid%22%3a%220427d660-a66e-430e-a229-497c54fa1fee%22%7d

 

10 April 16.00-18.00

Lecturer: Thomas Gastaldo

ARPA Emilia Romagna and Italia Meteo, Italy. tgastaldo@arpae.it

Operational numerical weather prediction at Agenzia Italia Meteo and Arpae Emilia-Romagna based on the ICON model

The operational numerical weather prediction framework adopted by Agenzia ItaliaMeteo and Arpae Emilia-Romagna is centered on the ICON-2I model and integrates state-of-the-art forecasting techniques. This talk will provide an overview of the collaboration between European meteorological centers within consortia and describe the operational workflow, including the data assimilation system KENDA, based on the LETKF scheme, the Rapid Update Cycle (RUC) and the Ensemble Prediction System (EPS). The modeling chain also includes verification processes and the generation of products, which are crucial for ensuring forecast quality and usability.The discussion will also cover cascading applications, such as downstream modeling, highlighting their role in enhancing forecast accuracy and practical applications.

 

11 April 16.00-18.00

Lecturer: Julia Thomas

Institute of Meteorology and Climate Research Troposphere Research (IMKTRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, DE. julia.thomas@kit.edu

The ‘Swabian MOSES 2023’ campaign re-analysis: How do high-resolution observations change the analysis of a convective-scale numerical weather prediction system?

The Swabian Jura region in southwestern Germany is frequently affected by hailstorms. The initiation and development of these events is closely related to the mesoscale flow and the thermodynamic conditions in the lower troposphere. However, forecasting convective events remains a challenge, even for the latest generation of convective-scale numerical weather prediction models. The forecast quality may in such cases benefit from improved initial conditions in form of an improved analysis. Thus, the assimilation of high-resolution observations of the lower troposphere has potential to enhance the predictability of convective events. The ‘Swabian MOSES’ 2023 campaign took place from June to August 2023 in the Black Forest and Swabian Jura mountain range. It deployed a spatially distributed network of instruments to observe the dynamic and thermodynamic characteristics of the lower troposphere. Among them was a network of several Doppler wind lidars (DWL), which together have not been used for data assimilation experiments before. Our campaign re-analysis utilizes 3-months of ground-based remote sensing and in-situ campaign observations. These observations are assimilated in the regional forecasting system of the German weather service, which employs the non-hydrostatic model ICON at 2 km resolution (ICON-D2) and the Kilometer Scale Ensemble Data Assimilation system (KENDA) with 40 ensemble members. In addition to the operationally available observations we assimilate (1) vertical profiles of the horizontal wind retrieved from the DWLs, (2) reflectivity from an X-Band radar, (3) targeted radiosoundings released from two sites during intensive observation periods, (4) ground based zenith path-delay observations from a (not yet operationally assimilated) German-wide network of Global Navigation Satellite Systems receivers, and (5) 2-meter temperature and relative humidity observations from campaign sites. The talk starts with the different observation types used in the re-analysis, explains the data assimilation setup and shows how additional observations influence the analysis. Our main focus is on the assimilation of DWL wind profiles. Here, we will first look at the observation space perspective, and secondly compare the 4D campaign re-analysis with a quasi-operational reference re-analysis that does not include additional campaign observations. Hereby, we will see how the mesoscale flow pattern over and around the Black Forest mountain range differs within the two datasets.

 

 

08 May 16.00-18.00

Lecturer: Virginia Poli

ARPA Emilia Romagna and Italia Meteo, Italy. vpoli@arpae.it

Assimilation of radar data in the ICON model

Data assimilation plays a key role in numerical modelling by integrating observational data with numerical models to improve accuracy and forecasting capabilities. In particular, radar observations have emerged as a significant data source, offering high-resolution, real-time insights into atmospheric and environmental processes. The use of radar data within the assimilation system used in the ICON-2I model by Agenzia ItaliaMeteo and Arpae Emilia-Romagna is then presented. In particular, the quality of the data, its pre-processing and the definition of the error associated with the data itself are discussed. Finally, the impact of the assimilation of these data in forecasting will be considered.

 

15 May 16.00-18.00

Lecturer: Mattia Zaramella

In-Climate-Service, Italy. mattia.zaramella@inclimateservice.com  

Development of an Operational Decision Support System for Climate Risk Assessment and Monitoring in the Veneto Region

Climate risk assessment, monitoring and strategic planning are based on the analysis of large volumes of data and models, which are often heterogeneous fragmented, and difficult to interpret. Decision Support Systems (DSS) can serve as valuable tools for decision-makers and technicians by integrating information from various sources, developing scenarios, and providing targeted analyses. The majority of European projects include among their objectives the development of DSS platforms, often aimed at the same decision-makers and pursuing similar goals. The development of these platforms therefore occurs in parallel and is constrained by the specific objectives of each project, limiting their use over time and space, with the risk of creating fragmented and non-interoperable solutions. The seminar focuses on one of the various projects aimed at developing forecasting systems in the Veneto region and its neighboring provinces, the operational DSS developed for the "Decentralized Functional Center of the Veneto Region", a regional operational hub responsible for monitoring, forecasting, and managing emergencies, such as weather-related risks or natural disasters, within the Veneto Region in Italy.

16 May 16.00-18.00

Lecturer: Annalisa Di Bernardino

Physics Department, Sapienza University of Rome, Italy. Annalisa.DiBernardino@uniroma1.it  

Ground-based atmospheric observations: a key approach for urban climate studies

Currently, urban areas accommodate approximately 55% of the global population, with projections suggesting a significant increase in the coming decades. The intricate layout of buildings, combined with high levels of air pollutant emissions that directly impact human health and cities’ livability, makes studying atmospheric processes in urban environments particularly challenging.

In this context, the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) have promoted the establishment of an experimental research infrastructure with unique features in Rome (Italy), named Boundary Layer Air Quality-Analysis Using Network of Instruments (BAQUNIN). BAQUNIN is one of the world's first “distributed” supersites, integrating multiple passive and active ground-based instruments installed in three locations in the Rome area, across diverse urban and suburban environments. Since 2016, BAQUNIN has been collecting surface and columnar atmospheric measurements of thermodynamic variables, particulate matter, and trace gases. This setup allows the supersite to: (i) characterize the urban boundary layer, (ii) monitor atmospheric pollution and key atmospheric constituents, and (iii) provide reference measurements for calibrating and validating numerical models and satellite products in urban, semi-rural, and rural settings within the Mediterranean basin, an area with distinct anemological characteristics, including coastal weather regimes.

I will show how high-resolution and quality-checked observations are essential to conduct long-term studies of atmospheric dynamics and air quality and to validate numerical models and satellite atmospheric products.

 

22 May 16.00-18.00

Lecturer: Alessio Bellucci

ISAC-CNR, Italy. a.bellucci@isac.cnr.it

Abrupt Climate Shifts: mechanisms, impacts and predictability.

Abrupt climate change refers to rapid, large-scale shifts in the Earth’s climate system, often occurring over a period of decades or centuries. Such shifts have profound implications for ecosystems, human societies, and the global climate system. This presentation explores the mechanisms (and potential predictability) of these abrupt changes, with a particular focus on the oceanic components.

 

29 May 16.00-18.00

Lecturer: Daniele Bigoni

Data assimilation via transport couplings

We use semi-parametric couplings between lag-1 smoothing distributions to devise a sequential algorithm for nonlinear Bayesian filtering and smoothing in high-dimensional state-space models, with a computational cost constant in time. We exploit the fact that many dynamical systems exhibit updates that act only on lower dimensional subspaces, allowing the construction of couplings that encode nonlinearities only in the interaction of a handful of important directions. The methodology will be showcased on chaotic dynamical systems.

 

Modalità di verifica e valutazione dell'apprendimento

There will not be assessment, however the attendance of at least 70% is required.

Orario di ricevimento

Consulta il sito web di Natale Alberto Carrassi