138 research outputs found
A Limited area model intercomparison on the "Montserrat-2000" flash-flood event using statistical and deterministic methods
In the scope of the European project Hydroptimet, INTERREG IIIB-MEDOCC programme, limited area model (LAM) intercomparison of intense events that produced many damages to people and territory is performed. As the comparison is limited to single case studies, the work is not meant to provide a measure of the different models' skill, but to identify the key model factors useful to give a good forecast on such a kind of meteorological phenomena. This work focuses on the Spanish flash-flood event, also known as "Montserrat-2000" event.
The study is performed using forecast data from seven operational LAMs, placed at partners' disposal via the Hydroptimet ftp site, and observed data from Catalonia rain gauge network. To improve the event analysis, satellite rainfall estimates have been also considered.
For statistical evaluation of quantitative precipitation forecasts (QPFs), several non-parametric skill scores based on contingency tables have been used. Furthermore, for each model run it has been possible to identify Catalonia regions affected by misses and false alarms using contingency table elements. Moreover, the standard "eyeball" analysis of forecast and observed precipitation fields has been supported by the use of a state-of-the-art diagnostic method, the contiguous rain area (CRA) analysis. This method allows to quantify the spatial shift forecast error and to identify the error sources that affected each model forecasts.
High-resolution modelling and domain size seem to have a key role for providing a skillful forecast. Further work is needed to support this statement, including verification using a wider observational data set
Eddy-Permitting Ocean Circulation Hindcasts of Past Decades
International audienc
Deep learning for internet of underwater things and ocean data analytics
The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes
Confronting Grand Challenges in environmental fluid mechanics
Environmental fluid mechanics underlies a wealth of natural, industrial and,
by extension, societal challenges. In the coming decades, as we strive towards
a more sustainable planet, there are a wide range of grand challenge problems
that need to be tackled, ranging from fundamental advances in understanding and
modeling of stratified turbulence and consequent mixing, to applied studies of
pollution transport in the ocean, atmosphere and urban environments. A workshop
was organized in the Les Houches School of Physics in France in January 2019
with the objective of gathering leading figures in the field to produce a road
map for the scientific community. Five subject areas were addressed: multiphase
flow, stratified flow, ocean transport, atmospheric and urban transport, and
weather and climate prediction. This article summarizes the discussions and
outcomes of the meeting, with the intent of providing a resource for the
community going forward
Accelerating large-eddy simulations of clouds with Tensor Processing Units
Clouds, especially low clouds, are crucial for regulating Earth's energy
balance and mediating the response of the climate system to changes in
greenhouse gas concentrations. Despite their importance for climate, they
remain relatively poorly understood and are inaccurately represented in climate
models. A principal reason is that the high computational expense of simulating
them with large-eddy simulations (LES) has inhibited broad and systematic
numerical experimentation and the generation of large datasets for training
parametrization schemes for climate models. Here we demonstrate LES of low
clouds on Tensor Processing Units (TPUs), application-specific integrated
circuits that were originally developed for machine learning applications. We
show that TPUs in conjunction with tailored software implementations can be
used to simulate computationally challenging stratocumulus clouds in conditions
observed during the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS)
field study. The TPU-based LES code successfully reproduces clouds during
DYCOMS and opens up the large computational resources available on TPUs to
cloud simulations. The code enables unprecedented weak and strong scaling of
LES, making it possible, for example, to simulate stratocumulus with
speedup over real-time evolution in domains with a horizontal cross section. The results open up new avenues for
computational experiments and for substantially enlarging the sample of LES
available to train parameterizations of low clouds
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