The value of satellite observations in the analysis and short-range prediction of Asian dust
<p>Asian dust is a seasonal meteorological phenomenon which affects east Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-01-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/19/987/2019/acp-19-987-2019.pdf |
| Summary: | <p>Asian dust is a seasonal meteorological phenomenon which
affects east Asia, and has severe consequences on the air quality of China,
North and South Korea and Japan. Despite the continental extent, the
prediction of severe episodes and the anticipation of their consequences is
challenging. Three 1-year experiments were run to assess the skill of the
model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in
monitoring Asian dust and understand its relative contribution to the aerosol
load over China. Data used were the Moderate Resolution Imaging
Spectroradiometer (MODIS) Dark Target and the Deep Blue aerosol optical depth
(AOD). In particular the experiments aimed at understanding the added value
of data assimilation runs over a model run without any aerosol data. The year
2013 was chosen as representative of the availability of independent AOD data
from two established ground-based networks (AERONET, Aerosol Robotic Network,
and CARSNET, China Aerosol Remote Sensing Network), which could be used to
evaluate experiments. Particulate matter (PM) data from the China
Environmental Protection Agency were also used in the evaluation. Results
show that the assimilation of satellite AOD data is beneficial to predict the
extent and magnitude of desert dust events and to improve the short-range
forecast of such events. The availability of observations from the MODIS Deep
Blue algorithm over bright surfaces is an asset, allowing for a better
localization of the sources and definition of the dust events. In general
both experiments constrained by data assimilation perform better than the
unconstrained experiment, generally showing smaller normalized mean bias and
fractional gross error with respect to the independent verification datasets.
The impact of the assimilated satellite observations is larger at analysis
time, but lasts into the forecast up to 48 h. The performance of the global
model in terms of particulate matter does not show the same degree of skill
as the performance in terms of optical depth. Despite this, the global model
is able to capture some regional pollution patterns. This indicates that the
global model analyses may be used as boundary conditions for regional air
quality models at higher resolution, enhancing their performance in
situations in which part of the pollution may have originated from
large-scale mechanisms. While assimilation is not a substitute for model
development and characterization of the emission sources, results indicate
that it can play a role in delivering improved monitoring of Asian dust
optical depth.</p> |
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| ISSN: | 1680-7316 1680-7324 |