Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter

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 can forecast the full evolution of dust storms. However, large...

Clustering-based spatial transfer learning for short-term ozone forecasting

Ground-level ozone is a critical atmospheric pollutant, and high concentrations of ozone can damage human health, affect plant growth and cause ecological harm. Traditional chemical transport models and popular machine learning models have difficulty in predicting ozone concentrations, especially in times with high concentrations. We proposes a...

Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large...

Inverse modeling of the 2021 spring super dust storms in East Asia

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, and profound impacts on the Earth system, but also to reveal the...

Machine learning based bias correction for numerical chemical transport models

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 of these CTMs are still limited because of many imperfections, e.g.,...

Conditioning of Deep-Learning Surrogate Models to Image Data with Application to Reservoir Characterization

Efficient estimation of space varying parameters in numerical models using non-intrusive subdomain reduced order modeling

A reduced order modeling algorithm for the estimation of space varying parameter patterns in numerical models is proposed. In this approach domain decomposition is applied to construct separate approximations to the numerical model in every subdomain. We introduce a new local parameterization that decouples the computational cost of the...

Position correction in dust storm forecasting using LOTOS-EUROS v2.1: Grid-distorted data assimilation v1.0

When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity er rors arise mainly from uncertain emission and sedimenta tion strengths, while position errors are attributed either to imperfect emission timing or to uncertainties in the trans port. Though many studies have been conducted on the cal...

Global greenhouse gas emissions from residential and commercial building materials and mitigation strategies to 2060

Building stock growth around the world drives extensive material consumption and envir onmental impacts. Future impacts will be dependent on the level and rate of socioeconomic development, along with material use and supply strategies. Here we evaluate material related greenhouse gas (GHG) emissions for residential and commercial buildings...

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 dust storms which usually have great spatial and temporal variability. The quality of the data has been verified through a...

Future scenarios of variable renewable energies and flexibility requirements for thermal power plants in China

In 2017 about 37% of the world's wind turbines and 50% of the world's photovoltaic (PV) panels are installed in China. But at the same time a huge amount of wind power and PV power is wasted mainly because of insufficient flexibility of thermal power which is the dominant source in China's electricity system. This paper aims to assess the...

Non-intrusive subdomain POD-TPWL for reservoir history matching

Spatially varying parameter estimation for dust emissions using reduced-tangent-linearization 4DVar

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 friction velocity threshold parameterization in the emission process....

Evaluation criteria on the design for assimilating remote sensing data using variational approaches

Remote sensing, as a powerful tool for monitoring atmospheric phenomena, has been playing an increasingly important role in inverse modeling. Remote sensing instruments measure quantities that often combine several state variables as one. This creates very strong correlations between the state variables that share the same observation variable....

Estimation of volcanic ash emissions through assimilating satellite data and ground-based observations

In this paper, we reconstruct the vertical profile of volcanic ash emissions by assimilating satellite data and ground-based observations using a modified trajectory-based 4D-Var (Trj4DVar) approach. In our previous work, we found that the lack of vertical resolution in satellite ash column data can result in a poor estimation of the injection...

Model-based aviation advice on distal volcanic ash clouds by assimilating aircraft in situ measurements

The forecast accuracy of distal volcanic ash clouds is important for providing valid aviation advice during volcanic ash eruption. However, because the distal part of volcanic ash plume is far from the volcano, the influence of eruption information on this part becomes rather indirect and uncertain, resulting in inaccurate volcanic ash forecasts...

Assimilating aircraft-based measurements to improve forecast accuracy of volcanic ash transport

The 2010 Eyjafjallajökull volcano eruption had serious consequences to civil aviation. This has initiated a lot of research on volcanic ash transport forecast in recent years. For forecasting the volcanic ash transport after eruption onset, a volcanic ash transport and diffusion model (VATDM) needs to be run with Eruption Source Parameters (ESP)...