Title: On the use of Deep Generative Models for Perfect Prognosis Climate Downscaling.
Deep Learning has recently emerged as a perfect prognosis downscaling
technique to compute high-resolution fields from large-scale coarse atmospheric
data. Despite their promising results to reproduce the observed local
variability, they are based on the estimation of independent [...]
Authors: Jose González-Abad, Jorge Baño-Medina, Ignacio Heredia Cachá
Deep Learning has recently emerged as a perfect prognosis downscaling technique to compute high-resolution fields from large-scale coarse atmospheric data. Despite their promising results to reproduce the observed local variability, they are based on the estimation of independent distributions at each location, which leads to deficient spatial structures, especially when downscaling precipitation. This study proposes the use of generative models to improve the spatial consistency of the high-resolution fields, very demanded by some sectoral applications (e.g., hydrology) to tackle climate change.
Title: Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions.
Recently, deep learning has emerged as a promising tool for statistical
downscaling, the set of methods for generating high-resolution climate fields
from coarse low-resolution variables. Nevertheless, their ability to generalize
to climate change conditions remains q [...]
Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution variables. Nevertheless, their ability to generalize to climate change conditions remains questionable, mainly due to the stationarity assumption. We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models. By better capturing uncertainty, statistical downscaling models allow for superior planning against extreme weather events, a source of various negative social and economic impacts. Since no observational future data exists, we rely on a pseudo reality experiment to assess the suitability of deep ensembles for quantifying the uncertainty of climate change projections. Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change.
Title: Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations.
Flood simulation and forecast capability have been greatly improved thanks to
advances in data assimilation (DA) strategies incorporating various types of
observations; many are derived from spatial Earth Observatio [...]
Authors: Thanh Huy Nguyen, Sophie Ricci, Andrea Piacentini, Ehouarn Simon, Raquel Rodriguez Suquet, Santiago Peña Luque
Flood simulation and forecast capability have been greatly improved thanks to advances in data assimilation (DA) strategies incorporating various types of observations; many are derived from spatial Earth Observation. This paper focuses on the assimilation of 2D flood observations derived from Synthetic Aperture Radar (SAR) images acquired during a flood event with a dual state-parameter Ensemble Kalman Filter (EnKF). Binary wet/dry maps are here expressed in terms of wet surface ratios (WSR) over a number of subdomains of the floodplain. This ratio is further assimilated jointly with in-situ water-level observations to improve the flow dynamics within the floodplain. However, the non-Gaussianity of the observation errors associated with SAR-derived measurements break a major hypothesis for the application of the EnKF, thus jeopardizing the optimality of the filter analysis. The novelty of this paper lies in the treatment of the non-Gaussianity of the SAR-derived WSR observations with a Gaussian anamorphosis process (GA). This DA strategy was validated and applied over the Garonne Marmandaise catchment (South-west of France) represented with the TELEMAC-2D hydrodynamic model, first in a twin experiment and then for a major flood event that occurred in January-February 2021. It was shown that assimilating SAR-derived WSR observations, in complement to the in-situ water-level observations significantly improves the representation of the flood dynamics. Also, the GA transformation brings further improvement to the DA analysis, while not being a critical component in the DA strategy. This study heralds a reliable solution for flood forecasting over poorly gauged catchments thanks to available remote-sensing datasets.
Title: Exact expressions for available potential energy and available potential vorticity.
Exact analytical expressions for available potential energy density (APE) and
available potential vorticity (APV) are derived from first principles. These
APE and APV expressions align with previously known quantities found using
perturbation expansions in Hollid [...]
Authors: Jeffrey J. Early, Gerardo Hernández-Dueñas, Leslie M. Smith, M.-Pascale Lelong
Exact analytical expressions for available potential energy density (APE) and available potential vorticity (APV) are derived from first principles. These APE and APV expressions align with previously known quantities found using perturbation expansions in Holliday & McIntyre (1981) and Wagner & Young (2015), respectively. The key is to recast the equations of motion and their conservation laws in terms of the coordinate label $z-η$, where z is a fluid parcels height at the current time, and $η$ is the isopycnal deviation from the parcels current height after adiabatic rearrangement of all parcels into the no-motion state. In addition to their intuitive appeal and simplicity, the new APE and APV expressions are easily implemented in numerical computations of Boussinesq dynamics with non-constant stratification.
Title: A sewage management proposal for Luruaco lake, Colombia.
This study presents numerical simulations of faecal coliforms dynamics in
Luruaco lake, located in Atl\'antico Department, Colombia. The velocity field
is obtained through a two-dimensional horizontal (2DH) model of Navier-Stokes
equations system. The transport equation of f [...]
Authors: T. M. Saita, P. L. Natti, E.R. Cirilo, N.M.L. Romeiro, M.A.C. Candezano, R.B. Acuña, L.C.G. Moreno
This study presents numerical simulations of faecal coliforms dynamics in Luruaco lake, located in Atlántico Department, Colombia. The velocity field is obtained through a two-dimensional horizontal (2DH) model of Navier-Stokes equations system. The transport equation of faecal coliforms concentration is provided from a convective-diffusive-reactive equation. The lake's geometry is built through cubic spline and multi-block methods. The discretization method by Finite Differences and the First Order Upwind (FOU) are applied to the 2DH model. The Mark and Cell (MAC) method is used to determine numerically the velocity field of water flow. Numerical simulations are carried out for a 72-hour period in order to understand the influence of faecal coliforms injections from each tributary. From the qualitative and quantitative analysis of the factors that influence faecal coliforms dynamics, proposals are presented, which aim to reduce contamination in some regions of Lake Luruaco. The numerical simulations show that the best option to improve water quality in the lake is the implementation of two actions, the diversion of the Limón stream to the Negro stream and the installation of a sewage treatment plant at the mouth of the Negro stream. Other less expensive proposals are also presented.
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