A Segers
29 records found
1
Authored
Ozone exceedance forecasting with enhanced extreme instance augmentation
A case study in Germany
Accurately forecasting ozone levels that exceed specific thresholds is pivotal for mitigating adverse effects on both the environment and public health. However, predicting such ozone exceedances remains challenging due to the infrequent occurrence of high-concentration ozone ...
Advecting Superspecies
Efficiently Modeling Transport of Organic Aerosol With a Mass-Conserving Dimensionality Reduction Method
The chemical transport model LOTOS-EUROS uses a volatility basis set (VBS) approach to represent the formation of secondary organic aerosol (SOA) in the atmosphere. Inclusion of the VBS approximately doubles the dimensionality of LOTOS-EUROS and slows computation of the advect ...
Improving Air Pollution Modelling in Complex Terrain with a Coupled WRF–LOTOS–EUROS Approach
A Case Study in Aburrá Valley, Colombia
Super dust storms re-occurred over East Asia in 2021 spring and casted great health damages and property losses. It is essential to achieve an accurate dust forecast to reduce the damage for early warning. The forecasting system fundamentally relies on a numerical model which ...
Last spring, super dust storms reappeared in East Asia after being absent for one and a half decades. The event caused enormous losses in both Mongolia and China. Accurate simulation of such super sandstorms is valuable for the quantification of health damage, aviation risks, ...
An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation
Exploiting prior knowledge
In this paper, we propose an efficient and practical implementation of the ensemble Kalman filter via shrinkage covariance matrix estimation. Our filter implementation combines information brought by an ensemble of model realizations, and that based on our prior knowledge abou ...
Position correction in dust storm forecasting using LOTOS-EUROS v2.1
Grid-distorted data assimilation v1.0
The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-co ...
Tropospheric ozone is a secondary pollutant which can affect human health and plant growth. In this paper, we investigated transferred convolutional neural network long short-term memory (TL-CNN-LSTM) model to predict ozone concentration. Hourly CNN-LSTM model is used to extra ...
Air quality warning and forecasting systems are usually based on numerical chemical transport models (CTMs). Those dynamic models perform predictions by simulating the life cycles of the atmospheric components, including emission, transport and removal. However, the accuracy o ...
Source backtracking for dust storm emission inversion using an adjoint method
Case study of Northeast China
Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulat ...
Dust Emission Inversion Using Himawari-8 AODs Over East Asia
An Extreme Dust Event in May 2017
Aerosol optical depths (AODs) from the new Himawari-8 satellite instrument have been assimilated in a dust simulation model over East Asia. This advanced geostationary instrument is capable of monitoring the East Asian ...
In previous studies, a number of model-based dust forecasts and early warning systems have been developed for the prevention of environmental impacts due to dusts. However, the accuracy of the model is limited by imperfect identification of dust emissions, in particular by the ...