Zobrazeno 1 - 10
of 518
pro vyhledávání: '"H. X. Lin"'
Publikováno v:
Atmospheric Measurement Techniques, Vol 17, Pp 2595-2610 (2024)
This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained
Externí odkaz:
https://doaj.org/article/00b7e6c2d80345a99d6d4f0a890fe35e
Publikováno v:
Geoscientific Model Development, Vol 17, Pp 275-300 (2024)
We discuss the various performance aspects of parallelizing our transient global-scale groundwater model at 30′′ resolution (30 arcsec; ∼ 1 km at the Equator) on large distributed memory parallel clusters. This model, referred to as GLOBGM, is
Externí odkaz:
https://doaj.org/article/2dd70f928ee34d14b7c4f1e36613f9a5
Publikováno v:
Geoscientific Model Development, Vol 16, Pp 4867-4882 (2023)
Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmen
Externí odkaz:
https://doaj.org/article/399d75c8d19049829e55a08e4a1c19f4
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 7791-7807 (2022)
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 stud
Externí odkaz:
https://doaj.org/article/ee20fc8e378f4db98ac0f297a237b702
Publikováno v:
Ocean Science, Vol 18, Pp 881-904 (2022)
Global tide and surge models play a major role in forecasting coastal flooding due to extreme events or climate change. The model performance is strongly affected by parameters such as bathymetry and bottom friction. In this study, we propose a metho
Externí odkaz:
https://doaj.org/article/d299473030bc4133892be2ca5a818b58
Publikováno v:
Atmospheric Chemistry and Physics, Vol 22, Pp 6393-6410 (2022)
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 heal
Externí odkaz:
https://doaj.org/article/b82c478c98ae4c0a82620411e969a92e
Publikováno v:
Geoscientific Model Development, Vol 14, Pp 5607-5622 (2021)
When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timin
Externí odkaz:
https://doaj.org/article/d64a7a9c1321482981a199ffbfb551e1
Publikováno v:
Atmospheric Chemistry and Physics, Vol 20, Pp 15207-15225 (2020)
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 differ
Externí odkaz:
https://doaj.org/article/a092c3c569fd4ce480aa856c7ba38540
Publikováno v:
Atmospheric Chemistry and Physics, Vol 19, Pp 10009-10026 (2019)
Data assimilation algorithms rely on a basic assumption of an unbiased observation error. However, the presence of inconsistent measurements with nontrivial biases or inseparable baselines is unavoidable in practice. Assimilation analysis might diver
Externí odkaz:
https://doaj.org/article/359d3b257bb740699d97ae496defe20b
Publikováno v:
Geoscientific Model Development, Vol 10, Iss 4, Pp 1751-1766 (2017)
In this study, we investigate a strategy to accelerate the data assimilation (DA) algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study of the character
Externí odkaz:
https://doaj.org/article/44bbabf43413473abf934d3a7076cee5