scholarly journals The impact of aerosol optical depth assimilation on aerosol forecasts and radiative effects during a wild fire event over the United States

2014 ◽  
Vol 7 (3) ◽  
pp. 3851-3866
Author(s):  
D. Chen ◽  
Z. Liu ◽  
C. S. Schwartz ◽  
H.-C. Lin ◽  
J. D. Cetola ◽  
...  

Abstract. The Gridpoint Statistical Interpolation three-dimensional variational data assimilation (DA) system coupled with the Weather Research and Forecasting/Chemistry (WRF/Chem) model was utilized to improve aerosol forecasts and study aerosol direct and semi-direct radiative feedbacks during a US wild fire event. Assimilation of MODIS total 550 nm aerosol optical depth (AOD) retrievals clearly improved WRF/Chem forecasts of surface PM2.5 and organic carbon (OC) compared to the corresponding forecasts without aerosol data assimilation. The scattering aerosols in the fire downwind region typically cooled layers both above and below the aerosol layer and suppressed convection and clouds, which led to an average 2% precipitation decease during the fire week. This study demonstrated that even with no input of fire emissions, AOD DA improved the aerosol forecasts and allowed a more realistic model simulation of aerosol radiative effects.

2014 ◽  
Vol 7 (6) ◽  
pp. 2709-2715 ◽  
Author(s):  
D. Chen ◽  
Z. Liu ◽  
C. S. Schwartz ◽  
H.-C. Lin ◽  
J. D. Cetola ◽  
...  

Abstract. The Gridpoint Statistical Interpolation three-dimensional variational data assimilation (DA) system coupled with the Weather Research and Forecasting/Chemistry (WRF/Chem) model was utilized to improve aerosol forecasts and study aerosol direct and semi-direct radiative feedbacks during a US wild fire event. Assimilation of MODIS total 550 nm aerosol optical depth (AOD) retrievals clearly improved WRF/Chem forecasts of surface PM2.5 and organic carbon (OC) compared to the corresponding forecasts without aerosol data assimilation. The scattering aerosols in the fire downwind region typically cooled layers both above and below the aerosol layer and suppressed convection and clouds, which led to an average of 2% precipitation decrease during the fire week. This study demonstrated that, even with no input of fire emissions, AOD DA improved the aerosol forecasts and allowed a more realistic model simulation of aerosol radiative effects.


2020 ◽  
Vol 20 (10) ◽  
pp. 6015-6036
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu ◽  
Wei Sun ◽  
Yonghee Lee ◽  
Limseok Chang

Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea–United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2.5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2.5 observations. Consistent with previous studies, the assimilation of surface PM2.5 measurements alone still underestimates surface PM2.5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.


2020 ◽  
Vol 12 (18) ◽  
pp. 3099
Author(s):  
Jean-François Léon ◽  
Nadège Martiny ◽  
Sébastien Merlet

Due to a limited number of monitoring stations in Western Africa, the impact of mineral dust on PM10 surface concentrations is still poorly known. We propose a new method to retrieve PM10 dust surface concentrations from sun photometer aerosol optical depth (AOD) and CALIPSO/CALIOP Level 2 aerosol layer products. The method is based on a multi linear regression model that is trained using co-located PM10, AERONET and CALIOP observations at 3 different locations in the Sahel. In addition to the sun photometer AOD, the regression model uses the CALIOP-derived base and top altitude of the lowermost dust layer, its AOD, the columnar total and columnar dust AOD. Due to the low revisit period of the CALIPSO satellite, the monthly mean annual cycles of the parameters are used as predictor variables rather than instantaneous observations. The regression model improves the correlation coefficient between monthly mean PM10 and AOD from 0.15 (AERONET AOD only) to 0.75 (AERONET AOD and CALIOP parameters). The respective high and low PM10 concentration during the winter dry season and summer season are well produced. Days with surface PM10 above 100 μg/m3 are better identified when using the CALIOP parameters in the multi linear regression model. The number of true positives (actual and predicted concentrations above the threshold) is increased and leads to an improvement in the classification sensitivity (recall) by a factor 1.8. Our methodology can be extrapolated to the whole Sahel area provided that satellite derived AOD maps are used in order to create a new dataset on population exposure to dust events in this area.


2019 ◽  
Author(s):  
Milija Zupanski ◽  
Anton Kliewer ◽  
Ting-Chi Wu ◽  
Karina Apodaca ◽  
Qijing Bian ◽  
...  

Abstract. Strongly coupled data assimilation frameworks provide a mechanism for including additional information about aerosols through the coupling between aerosol and atmospheric variables, effectively utilizing atmospheric observations to change the aerosol analysis. Here, we investigate the impact of these observations on aerosol using the Maximum Likelihood Ensemble Filter (MLEF) algorithm with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) which includes the Godard Chemistry Aerosol Radiation and Transport (GOCART) module. We apply this methodology to a dust storm event over the Arabian Peninsula and examine in detail the error covariance and in particular the impact of atmospheric observations on improving the aerosol initial conditions. The assimilated observations include conventional atmospheric observations and Aerosol Optical Depth (AOD) retrievals. Results indicate a positive impact of using strongly coupled data assimilation and atmospheric observations on the aerosol initial conditions, quantified using Degrees of Freedom for Signal.


2018 ◽  
Author(s):  
Angela Benedetti ◽  
Francesca Di Giuseppe ◽  
Luke Jones ◽  
Vincent-Henri Peuch ◽  
Samuel Rémy ◽  
...  

Abstract. 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 one-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 air quality over China. Data used were the MODIS Dark Target and the Deep Blue Aerosol Optical Depth. 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 for the availability of independent Aerosol Optical Depth (AOD) data from two established ground-based networks (AERONET and CARSNET), which could be used to evaluate experiments. Particulate Matter (PM) data from the China Environmental Protection Agency (CEPA) 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 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 that the unconstrained experiment, generally showing smaller mean normalized 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 well into the forecast. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a big role in delivering improved forecasts of Asian Dust.


2020 ◽  
Vol 16 (1) ◽  
pp. 1-14
Author(s):  
Monim Jiboori ◽  
Nadia Abed ◽  
Mohamed Abdel Wahab

Author(s):  
Qijiao Xie ◽  
Qi Sun

Aerosols significantly affect environmental conditions, air quality, and public health locally, regionally, and globally. Examining the impact of land use/land cover (LULC) on aerosol optical depth (AOD) helps to understand how human activities influence air quality and develop suitable solutions. The Landsat 8 image and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products in summer in 2018 were used in LULC classification and AOD retrieval in this study. Spatial statistics and correlation analysis about the relationship between LULC and AOD were performed to examine the impact of LULC on AOD in summer in Wuhan, China. Results indicate that the AOD distribution expressed an obvious “basin effect” in urban development areas: higher AOD values concentrated in water bodies with lower terrain, which were surrounded by the high buildings or mountains with lower AOD values. The AOD values were negatively correlated with the vegetated areas while positively correlated to water bodies and construction lands. The impact of LULC on AOD varied with different contexts in all cases, showing a “context effect”. The regression correlations among the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and AOD in given landscape contexts were much stronger than those throughout the whole study area. These findings provide sound evidence for urban planning, land use management and air quality improvement.


2020 ◽  
Author(s):  
Stephanie P. Rusli ◽  
Otto Hasekamp ◽  
Joost aan de Brugh ◽  
Guangliang Fu ◽  
Yasjka Meijer ◽  
...  

Abstract. Atmospheric aerosols have been known to be a major source of uncertainties in CO2 concentrations retrieved from space. In this study, we investigate the added value of multi-angle polarimeter (MAP) measurements in the context of the Copernicus candidate mission for anthropogenic CO2 monitoring (CO2M). To this end, we compare aerosol-induced XCO2 errors from standard retrievals using spectrometer only (without MAP) with those from retrievals using both MAP and spectrometer. MAP observations are expected to provide information about aerosols that is useful for improving XCO2 accuracy. For the purpose of this work, we generate synthetic measurements for different atmospheric and geophysical scenes over land, based on which XCO2 retrieval errors are assessed. We show that the standard XCO2 retrieval approach that makes no use of auxiliary aerosol observations returns XCO2 errors with an overall bias of 1.12 ppm, and a spread (defined as half of the 15.9th to the 84.1th percentile range) of 2.07 ppm. The latter is far higher than the required XCO2 accuracy (0.5 ppm) and precision (0.7 ppm) of the CO2M mission. Moreover, these XCO2 errors exhibit a significantly larger bias and scatter at high aerosol optical depth, high aerosol altitude, and low solar zenith angle, which could lead to a worse performance in retrieving XCO2 from polluted areas where CO2 and aerosols are co-emitted. We proceed to determine MAP instrument specifications in terms of wavelength range, number of viewing angles, and measurement uncertainties that are required to achieve XCO2 accuracy and precision targets of the mission. Two different MAP instrument concepts are considered in this analysis. We find that for either concept, MAP measurement uncertainties on radiance and degree of linear polarization should be no more than 3 % and 0.003, respectively. Adopting the derived MAP requirements, a retrieval exercise using both MAP and spectrometer measurements of the synthetic scenes delivers XCO2 errors with an overall bias of −0.004 ppm and a spread of 0.54 ppm, implying compliance with the CO2M mission requirements; the very low bias is especially important for proper emission estimates. For the test ensemble, we find effectively no dependence of the XCO2 errors on aerosol optical depth, altitude of the aerosol layer, and solar zenith angle. These results indicate a major improvement in the retrieved XCO2 accuracy with respect to the standard retrieval approach, which could lead to a higher data yield, better global coverage, and a more comprehensive determination of CO2 sinks and sources. As such, this outcome underlines the contribution of, and therefore the need for, a MAP instrument onboard the CO2M mission.


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