PM10 data assimilation over south Korea to Asian dust forecasting model with the optimal interpolation method

Lee, Eun-Hee; Ha, Jong-Chul; Lee, Sang-Sam; Chun, Youngsin

doi:10.1007/s13143-013-0009-y

Cited by 31 publications

(26 citation statements)

References 36 publications

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“…The NRL-UND MODIS AOD product was produced for the purpose of aerosol data assimilation and is based on the NASA operational MODIS Level 2 Collection 5 AOD dataset (Remer et al, 2005;Levy et al, 2007). These data (i.e., Dark Target AODs) have been subjected to extensive quality assurance (QA) and quality check (QC) procedures (Zhang and Reid 2006;Shi et al, 2011;Hyer et al, 2011).…”

Section: Observation Data 231 Assimilation Dataset: the Nrl-und Modmentioning

confidence: 99%

“…The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites is an example of a passive sensor. MODIS has provided aerosol optical properties (AOPs), including aerosol optical depth (AOD) and the Ångström exponent, globally since 2000 (Remer et al, 2005;Levy et al, 2007;Zhang and Reid, 2006). More recently, Himawari 8, a geostationary meteorological satellite launched on 7 October 2014, has been providing full-disk images of AOPs every 10 min (Bessho et al, 2016;Kikuchi et al, 2017;Yumimoto et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Hakami et al (2005) and Yumimoto et al (2007Yumimoto et al ( , 2008 attempted to apply a four-dimensional variational method (a so-called advanced data assimilation method) to inverse modeling of black carbon (BC) and dust aerosols with ground-based observations and regional models. To date, measurements obtained by various observation platforms, including MODIS (Dai et al, 2014;Huneeus et al, 2012;Wang et al, 2012;Zhang et al, 2008), CALIPSO (Sekiyama et al, 2010;Zhang et al, 2011Zhang et al, , 2014, Himawari 8 (Yumimoto et al, 2016), AERONET (Schutgens et al, 2010a), and surface PM 10 (particulate matter with diameters less than 10 µm) monitoring systems (Tombette et al, 2009;Lee et al, 2013;Jiang et al, 2013), have been used in assimilation studies adopting both variational (Benedetti et al, 2009;Dubovik et al, 2008;Hakami et al, 2007;Henze et al, 2007;Yumimoto and Takemura, 2013) and ensemble-based Schutgens et al, 2010b;Di Tomaso et al, 2017;Yumimoto and Takemura, 2011) methods.…”

Section: Introductionmentioning

confidence: 99%

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JRAero: the Japanese Reanalysis for Aerosol v1.0

et al. 2017

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Abstract. A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a twodimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1 • × 1.1 • ). This paper describes the aerosol transport model, the data assimilation system, the observation data, and the setup of the reanalysis and examines its quality with AOD observations.Comparisons with MODIS AODs that were used for the assimilation showed that the reanalysis showed much better agreement than the free run (without assimilation) of the aerosol model and improved under-and overestimation in the free run, thus confirming the accuracy of the data assimilation system. The reanalysis had a root mean square error (RMSE) of 0.05, a correlation coefficient (R) of 0.96, a mean fractional error (MFE) of 23.7 %, a mean fractional bias (MFB) of 2.8 %, and an index of agreement (IOA) of 0.98. The better agreement of the first guess, compared to the free run, indicates that aerosol fields obtained by the reanalysis can improve short-term forecasts.AOD fields from the reanalysis also agreed well with monthly averaged global AODs obtained by the Aerosol Robotic Network (AERONET) (RMSE = 0.08, R = 0.90, MFE = 28.1 %, MFB = 0.6 %, and IOA = 0.93). Site-by-site comparison showed that the reanalysis was considerably better than the free run; RMSE was less than 0.10 at 86.4 % of the 181 AERONET sites, R was greater than 0.90 at 40.7 % of the sites, and IOA was greater than 0.90 at 43.4 % of the sites. However, the reanalysis tended to have a negative bias at urban sites (in particular, megacities in industrializing countries) and a positive bias at mountain sites, possibly because of insufficient anthropogenic emissions data, the coarse model resolution, and the difference in representativeness between satellite and ground-based observations.

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Section: Observation Data 231 Assimilation Dataset: the Nrl-und Modmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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JRAero: the Japanese Reanalysis for Aerosol v1.0

et al. 2017

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“…From the perspective of reducing the uncertainties in the ICs for aerosols, recent efforts have focused on assimilating aerosol observations using optimal interpolation (Collins et al, 2001;Yu et al, 2003;Adhikary et al, 2008;Tombette et al, 2009;Lee et al, 2013) or variational (Kahnert, 2008;Z. Peng et al: Improving PM 2.5 forecast over China et al, 2010a, b;Pagowski and Grell, 2012;Dai et al, 2014;Rubin et al, 2016;Ying et al, 2016;Yumimoto et al, 2016) and the hybrid variational-ensemble DA approach (Schwartz et al, 2014) have also been applied to aerosol predictions.…”

Section: Introductionmentioning

confidence: 99%

Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

et al. 2017

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Abstract. In an attempt to improve the forecasting of atmospheric aerosols, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions (ICs) and emission input. The forecast model, which was expanded by combining the Weather Research and Forecasting with Chemistry (WRF-Chem) model and a forecast model of emission scaling factors, generated both chemical concentration fields and emission scaling factors. The forecast model of emission scaling factors was developed by using the ensemble concentration ratios of the WRF-Chem forecast chemical concentrations and also the time smoothing operator. Hourly surface fine particulate matter (PM 2.5 ) observations were assimilated in this system over China from 5 to 16 October 2014. A series of 48 h forecasts was then carried out with the optimized initial conditions and emissions on each day at 00:00 UTC and a control experiment was performed without data assimilation. In addition, we also performed an experiment of pure assimilation chemical ICs and the corresponding 48 h forecasts experiment for comparison. The results showed that the forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment. In the Yangtze River delta (YRD) and the Pearl River delta (PRD) regions, large reduction of the root-mean-square errors (RMSEs) was obtained for almost the entire 48 h forecast range attributed to assimilation. In particular, the relative reduction in RMSE due to assimilation was about 37.5 % at nighttime when WRF-Chem performed comparatively worse. In the BeijingTianjin-Hebei (JJJ) region, relatively smaller improvements were achieved in the first 24 h forecast but then no improvements were achieved afterwards. Comparing to the forecasts with only the optimized ICs, the forecasts with the joint adjustment were always much better during the night in the PRD and YRD regions. However, they were very similar during daytime in both regions. Also, they performed similarly for almost the entire 48 h forecast range in the JJJ region.

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“…This is the narrow way. The main data assimilation methods from the middle of the last century to the present include the function fitting method [12,13] (objective analysis), the stepwise correction method (SCM) [14,15], the optimal interpolation method (OI) [16,17], the variability method (3Dvar, 4Dvar) [18,19], and the ensemble Kalman filtering (EnKF) method [20][21][22]. In these studies, most scientists optimize the initial conditions by constructing a cost function and find the extreme of this cost function [23], finally obtaining optimization by obtaining a, or a set of maximum possible state/s, and we call this the Data Assimilation method.…”

Section: Introductionmentioning

confidence: 99%

Precipitation Data Assimilation System Based on a Neural Network and Case-Based Reasoning System

Zhang

2018

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There are several methods to forecast precipitation, but none of them is accurate enough since predicting precipitation is very complicated and influenced by many factors. Data assimilation systems (DAS) aim to increase the prediction result by processing data from different sources in a general way, such as a weighted average, but have not been used for precipitation prediction until now. A DAS that makes use of mathematical tools is complex and hard to carry out. In our paper, machine learning techniques are introduced into a precipitation data assimilation system. After summarizing the theoretical construction of this method, we take some practical weather forecasting experiments and the results show that the new system is effective and promising.

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PM10 data assimilation over south Korea to Asian dust forecasting model with the optimal interpolation method

Cited by 31 publications

References 36 publications

JRAero: the Japanese Reanalysis for Aerosol v1.0

JRAero: the Japanese Reanalysis for Aerosol v1.0

Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

Precipitation Data Assimilation System Based on a Neural Network and Case-Based Reasoning System

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PM10 data assimilation over south Korea to Asian dust forecasting model with the optimal interpolation method

Cited by 31 publications

References 36 publications

JRAero: the Japanese Reanalysis for Aerosol v1.0

JRAero: the Japanese Reanalysis for Aerosol v1.0

Improving PM&lt;sub&gt;2. 5&lt;/sub&gt; forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

Precipitation Data Assimilation System Based on a Neural Network and Case-Based Reasoning System

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Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter