Articles | Volume 10, issue 1
https://doi.org/10.5194/acp-10-39-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-10-39-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Data assimilation of CALIPSO aerosol observations
T. T. Sekiyama
Meteorological Research Institute, Tsukuba, Japan
T. Y. Tanaka
Meteorological Research Institute, Tsukuba, Japan
A. Shimizu
National Institute for Environmental Studies, Tsukuba, Japan
T. Miyoshi
Japan Meteorological Agency, Tokyo, Japan
now at: Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Viewed
Total article views: 5,066 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 04 Mar 2009)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
2,811 | 2,121 | 134 | 5,066 | 138 | 101 |
- HTML: 2,811
- PDF: 2,121
- XML: 134
- Total: 5,066
- BibTeX: 138
- EndNote: 101
Total article views: 4,493 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 05 Jan 2010)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
2,577 | 1,798 | 118 | 4,493 | 126 | 97 |
- HTML: 2,577
- PDF: 1,798
- XML: 118
- Total: 4,493
- BibTeX: 126
- EndNote: 97
Total article views: 573 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 04 Mar 2009)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
234 | 323 | 16 | 573 | 12 | 4 |
- HTML: 234
- PDF: 323
- XML: 16
- Total: 573
- BibTeX: 12
- EndNote: 4
Cited
141 citations as recorded by crossref.
- NHM-Chem, the Japan Meteorological Agency's Regional Meteorology – Chemistry Model: Model Evaluations toward the Consistent Predictions of the Chemical, Physical, and Optical Properties of Aerosols M. KAJINO et al. 10.2151/jmsj.2019-020
- Ensemble filter based estimation of spatially distributed parameters in a mesoscale dust model: experiments with simulated and real data V. Khade et al. 10.5194/acp-13-3481-2013
- Lidar methods for observing mineral dust N. Sugimoto & Z. Huang 10.1007/s13351-014-3068-9
- RETRACTED ARTICLE: A neural network–based rainfall trend in plain areas and a personalized French learning system G. Xiao 10.1007/s12517-021-07419-2
- Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting J. Rubin et al. 10.5194/acp-16-3927-2016
- Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0 E. Di Tomaso et al. 10.5194/gmd-10-1107-2017
- An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences P. Lynch et al. 10.5194/gmd-9-1489-2016
- Development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: aiming to improve air quality forecasting and diagnose model deficiencies W. Sun et al. 10.5194/acp-20-9311-2020
- Impact of local and regional emission sources on air quality in foothills of the Himalaya during spring 2016: An observation, satellite and modeling perspective M. Mehra et al. 10.1016/j.atmosenv.2019.116897
- Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model H. Ye et al. 10.1016/j.atmosres.2021.105877
- A Review of Data Assimilation on Aerosol Optical, Radiative, and Climatic Effects Study Y. Cheng et al. 10.1007/s41810-022-00142-9
- Estimation of aerosol particle number distributions with Kalman Filtering – Part 1: Theory, general aspects and statistical validity T. Viskari et al. 10.5194/acp-12-11767-2012
- Influence of aerosols and thin cirrus clouds on the GOSAT-observed CO<sub>2</sub>: a case study over Tsukuba O. Uchino et al. 10.5194/acp-12-3393-2012
- Evaluation of MERRA-2 data for aerosols patterns over the Kingdom of Saudi Arabia A. Labban & M. Butt 10.1016/j.heliyon.2023.e17047
- Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model T. Dai et al. 10.1016/j.envpol.2014.06.021
- Chemical Data Assimilation—An Overview A. Sandu & T. Chai 10.3390/atmos2030426
- Improvement of the Aerosol Forecast and Analysis Over East Asia With Joint Assimilation of Two Geostationary Satellite Observations Y. Cheng et al. 10.1029/2022GL099908
- ANISORROPIA: the adjoint of the aerosol thermodynamic model ISORROPIA S. Capps et al. 10.5194/acp-12-527-2012
- Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook T. Yang et al. 10.1016/j.jes.2022.04.012
- Assimilating spaceborne lidar dust extinction can improve dust forecasts J. Escribano et al. 10.5194/acp-22-535-2022
- Forecasting of Asian dust storm that occurred on May 10–13, 2011, using an ensemble-based data assimilation system K. Yumimoto et al. 10.1016/j.partic.2015.09.001
- Assessing the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts: Sand dust events in northeast China Y. Bao et al. 10.1016/j.atmosenv.2019.02.026
- Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China C. Ma et al. 10.1029/2019JD030421
- Assimilation of Both Column‐ and Layer‐Integrated Dust Opacity Observations in the Martian Atmosphere T. Ruan et al. 10.1029/2021EA001869
- Inverse modeling of black carbon emissions over China using ensemble data assimilation P. Wang et al. 10.5194/acp-16-989-2016
- A methodology for investigating dust model performance using synergistic EARLINET/AERONET dust concentration retrievals I. Binietoglou et al. 10.5194/amt-8-3577-2015
- Impact of anomalous forest fire on aerosol radiative forcing and snow cover over Himalayan region K. Bali et al. 10.1016/j.atmosenv.2016.11.061
- Nighttime smoke aerosol optical depth over U.S. rural areas: First retrieval from VIIRS moonlight observations M. Zhou et al. 10.1016/j.rse.2021.112717
- Dust Emission Estimated with an Assimilated Dust Transport Model Using Lidar Network Data and Vegetation Growth in the Gobi Desert in Mongolia N. Sugimoto et al. 10.2151/sola.2010-032
- Horizontal Resolution Dependence of Atmospheric Simulations of the Fukushima Nuclear Accident Using 15-km, 3-km, and 500-m Grid Models T. SEKIYAMA et al. 10.2151/jmsj.2015-002
- How much information do extinction and backscattering measurements contain about the chemical composition of atmospheric aerosol? M. Kahnert & E. Andersson 10.5194/acp-17-3423-2017
- Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity A. Gumber et al. 10.5194/amt-16-2547-2023
- Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP) P. Xian et al. 10.1002/qj.3497
- Data Assimilation of Himawari-8 Aerosol Observations: Asian Dust Forecast in June 2015 T. Sekiyama et al. 10.2151/sola.2016-020
- Identifying chemical aerosol signatures using optical suborbital observations: how much can optical properties tell us about aerosol composition? M. Kacenelenbogen et al. 10.5194/acp-22-3713-2022
- A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM<sub>2.5</sub> prediction Z. Li et al. 10.5194/acp-13-4265-2013
- Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin Y. Wang et al. 10.5194/acp-14-12031-2014
- Assimilating aerosol optical properties related to size and absorption from POLDER/PARASOL with an ensemble data assimilation system A. Tsikerdekis et al. 10.5194/acp-21-2637-2021
- Status and future of numerical atmospheric aerosol prediction with a focus on data requirements A. Benedetti et al. 10.5194/acp-18-10615-2018
- Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
- Temperate grasslands as a dust source: Knowledge, uncertainties, and challenges M. Shinoda et al. 10.1016/j.aeolia.2011.07.001
- Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system Y. Kong et al. 10.1016/j.atmosres.2020.105366
- Integrated Study of AD-Net Mie-Lidar Network and Data Assimilated CTM for Asian Dust Epidemiology in Japan A. Shimizu et al. 10.1051/epjconf/201611908007
- Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic K. Han et al. 10.3390/app11041959
- Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China J. Jin et al. 10.5194/acp-20-15207-2020
- An improved method for retrieving nighttime aerosol optical thickness from the VIIRS Day/Night Band T. McHardy et al. 10.5194/amt-8-4773-2015
- Evaluating the Impact of Emissions Regulations on the Emissions Reduction During the 2015 China Victory Day Parade With an Ensemble Square Root Filter K. Chu et al. 10.1002/2017JD027631
- Decadal trends of MERRA-estimated PM2.5 concentrations in East Asia and potential exposure from 1990 to 2019 S. Yin 10.1016/j.atmosenv.2021.118690
- A nonlinear least squares four-dimensional variational data assimilation system for PM2.5 forecasts (NASM): Description and preliminary evaluation S. Zhang et al. 10.1016/j.apr.2021.03.003
- Detection of Aerosols in Antarctica From Long‐Range Transport of the 2009 Australian Wildfires J. Jumelet et al. 10.1029/2020JD032542
- Impact of the OMI aerosol optical depth on analysis increments through coupled meteorology–aerosol data assimilation for an Asian dust storm E. Lee et al. 10.1016/j.rse.2017.02.013
- Stereoscopic hyperspectral remote sensing of the atmospheric environment: Innovation and prospects C. Liu et al. 10.1016/j.earscirev.2022.103958
- JRAero: the Japanese Reanalysis for Aerosol v1.0 K. Yumimoto et al. 10.5194/gmd-10-3225-2017
- Introducing the MISR level 2 near real-time aerosol product M. Witek et al. 10.5194/amt-14-5577-2021
- Statistical downscaling of water vapour satellite measurements from profiles of tropical ice clouds G. Carella et al. 10.5194/essd-12-1-2020
- Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa Y. Shi et al. 10.5194/amt-6-949-2013
- CALIOP near-real-time backscatter products compared to EARLINET data T. Grigas et al. 10.5194/acp-15-12179-2015
- Application of linear minimum variance estimation to the multi-model ensemble of atmospheric radioactive Cs-137 with observations D. Goto et al. 10.5194/acp-20-3589-2020
- The Effects of Snow Cover and Soil Moisture on Asian Dust: II. Emission Estimation by Lidar Data Assimilation T. Sekiyama et al. 10.2151/sola.7A-011
- The SPRINTARS version 3.80/4D-Var data assimilation system: development and inversion experiments based on the observing system simulation experiment framework K. Yumimoto & T. Takemura 10.5194/gmd-6-2005-2013
- PM2.5 concentration distribution patterns and influencing meteorological factors in the central and eastern China during 1980–2018 J. Ma et al. 10.1016/j.jclepro.2021.127565
- Decadal changes in PM2.5-related health impacts in China from 1990 to 2019 and implications for current and future emission controls S. Yin 10.1016/j.scitotenv.2022.155334
- The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 2: Evaluation of aerosol optical thickness P. Bhattacharjee et al. 10.5194/gmd-11-2333-2018
- Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al. 10.1029/2018MS001475
- Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations S. Zhai et al. 10.5194/acp-21-16775-2021
- Inverting the East Asian Dust Emission Fluxes Using the Ensemble Kalman Smoother and Himawari-8 AODs: A Case Study with WRF-Chem v3.5.1 T. Dai et al. 10.3390/atmos10090543
- Feasibility Study for Future Spaceborne Coherent Doppler Wind Lidar, Part 2: Measurement Simulation Algorithms and Retrieval Error Characterization P. BARON et al. 10.2151/jmsj.2017-018
- Assimilation of POLDER observations to estimate aerosol emissions A. Tsikerdekis et al. 10.5194/acp-23-9495-2023
- Statistical Evaluation of the Temperature Forecast Error in the Lower‐Level Troposphere on Short‐Range Timescales Induced by Aerosol Variability A. Yamagami et al. 10.1029/2022JD036595
- Improving estimation of a record-breaking east Asian dust storm emission with lagged aerosol Ångström exponent observations Y. Cheng et al. 10.5194/acp-24-12643-2024
- Estimating Aerosol Emissions by Assimilating Remote Sensing Observations into a Global Transport Model N. Schutgens et al. 10.3390/rs4113528
- Long‐term inverse modeling of Asian dust: Interannual variations of its emission, transport, deposition, and radiative forcing K. Yumimoto & T. Takemura 10.1002/2014JD022390
- Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations J. Hong et al. 10.1016/j.envpol.2020.114451
- Improved Hourly Estimates of Aerosol Optical Thickness Using Spatiotemporal Variability Derived From Himawari-8 Geostationary Satellite M. Kikuchi et al. 10.1109/TGRS.2018.2800060
- The characterization of Taklamakan dust properties using a multiwavelength Raman polarization lidar in Kashi, China Q. Hu et al. 10.5194/acp-20-13817-2020
- Evaluating the impact of multisensor data assimilation on a global aerosol particle transport model J. Zhang et al. 10.1002/2013JD020975
- Measurement Report: Determination of aerosol vertical features on different timescales over East Asia based on CATS aerosol products Y. Cheng et al. 10.5194/acp-20-15307-2020
- An accuracy assessment of the CALIOP/CALIPSO version 2/version 3 daytime aerosol extinction product based on a detailed multi-sensor, multi-platform case study M. Kacenelenbogen et al. 10.5194/acp-11-3981-2011
- Evolving patterns of arctic aerosols and the influence of regional variations over two decades K. Lee et al. 10.1016/j.scitotenv.2024.177465
- Evaluating nighttime CALIOP 0.532 μm aerosol optical depth and extinction coefficient retrievals J. Campbell et al. 10.5194/amt-5-2143-2012
- Better prediction of surface ozone by a superensemble method using emission sensitivity runs in Japan M. Kajino et al. 10.1016/j.aeaoa.2021.100120
- Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street R. Kahn et al. 10.1029/2022RG000796
- Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia Z. Liu et al. 10.1029/2011JD016159
- Quantifying the low bias of CALIPSO's column aerosol optical depth due to undetected aerosol layers M. Kim et al. 10.1002/2016JD025797
- Spatially varying parameter estimation for dust emissions using reduced-tangent-linearization 4DVar J. Jin et al. 10.1016/j.atmosenv.2018.05.060
- The Impact of Ground-Based Observations on the Inverse Technique of Aeolian Dust Aerosol T. Maki et al. 10.2151/sola.7A-006
- Machine learning for observation bias correction with application to dust storm data assimilation J. Jin et al. 10.5194/acp-19-10009-2019
- The impact of multi-species surface chemical observation assimilation on air quality forecasts in China Z. Peng et al. 10.5194/acp-18-17387-2018
- Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing X. Cheng et al. 10.1016/j.scitotenv.2019.05.186
- Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM<sub>2.5</sub> forecasts across China Y. Liang et al. 10.5194/gmd-13-6285-2020
- Three-dimensional climatology, trends, and meteorological drivers of global and regional tropospheric type-dependent aerosols: insights from 13 years (2007–2019) of CALIOP observations K. Gui et al. 10.5194/acp-21-15309-2021
- Utilizing Cloud Computing to address big geospatial data challenges C. Yang et al. 10.1016/j.compenvurbsys.2016.10.010
- Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum J. Zhang et al. 10.5194/gmd-14-27-2021
- The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies V. Buchard et al. 10.1175/JCLI-D-16-0613.1
- Operation-Oriented Ensemble Data Assimilation of Total Column Ozone T. Sekiyama et al. 10.2151/sola.2011-011
- Preliminary investigations toward nighttime aerosol optical depth retrievals from the VIIRS Day/Night Band R. Johnson et al. 10.5194/amt-6-1245-2013
- Ensemble Dispersion Simulation of a Point-Source Radioactive Aerosol Using Perturbed Meteorological Fields over Eastern Japan T. Sekiyama et al. 10.3390/atmos12060662
- Space-Borne Lidar Technology for Global Environmental Observation Research D. SAKAIZAWA et al. 10.2184/lsj.39.12
- Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model N. Schutgens et al. 10.5194/acp-10-2561-2010
- Air quality modeling in East Asia: present issues and future directions R. Park & S. Kim 10.1007/s13143-014-0030-9
- The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation C. Randles et al. 10.1175/JCLI-D-16-0609.1
- Efficacy of dust aerosol forecasts for East Asia using the adjoint of GEOS-Chem with ground-based observations J. Jeong & R. Park 10.1016/j.envpol.2017.12.025
- Aerosol data assimilation in the MOCAGE chemical transport model during the TRAQA/ChArMEx campaign: lidar observations L. El Amraoui et al. 10.5194/amt-13-4645-2020
- Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter Y. Cheng et al. 10.5194/acp-19-13445-2019
- Minimum aerosol layer detection sensitivities and their subsequent impacts on aerosol optical thickness retrievals in CALIPSO level 2 data products T. Toth et al. 10.5194/amt-11-499-2018
- Spatiotemporal Variations in Summertime Arctic Aerosol Optical Depth Caused by Synoptic‐Scale Atmospheric Circulation in Three Reanalyses A. Yamagami et al. 10.1029/2022JD038007
- CALIPSO-Derived Three-Dimensional Structure of Aerosol over the Atlantic Basin and Adjacent Continents A. Adams et al. 10.1175/JCLI-D-11-00672.1
- RETRACTED ARTICLE: Evaluation of agricultural climate and regional agricultural economic efficiency based on remote sensing analysis X. Lu 10.1007/s12517-021-07153-9
- Comparative inverse analysis of satellite (MODIS) and ground (PM10) observations to estimate dust emissions in East Asia B. Ku & R. Park 10.1007/s13143-013-0002-5
- Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method S. Zhang et al. 10.1016/j.atmosenv.2021.118896
- Lidar Measurements for Desert Dust Characterization: An Overview L. Mona et al. 10.1155/2012/356265
- Dust Emission Inversion Using Himawari‐8 AODs Over East Asia: An Extreme Dust Event in May 2017 J. Jin et al. 10.1029/2018MS001491
- Development of the RAQM2 aerosol chemical transport model and predictions of the Northeast Asian aerosol mass, size, chemistry, and mixing type M. Kajino et al. 10.5194/acp-12-11833-2012
- Bayesian Merging of MISR and MODIS Aerosol Optical Depth Products Using Error Distributions From AERONET M. Singh et al. 10.1109/JSTARS.2017.2734331
- Machine learning based bias correction for numerical chemical transport models M. Xu et al. 10.1016/j.atmosenv.2020.118022
- Evaluating the impact of assimilating CALIOP-derived aerosol extinction profiles on a global mass transport model J. Zhang et al. 10.1029/2011GL047737
- Performance comparisons of the three data assimilation methods for improved predictability of PM2·5: Ensemble Kalman filter, ensemble square root filter, and three-dimensional variational methods U. Dash et al. 10.1016/j.envpol.2023.121099
- Global simulations of aerosol amount and size using MODIS observations assimilated with an Ensemble Kalman Filter J. Rubin & W. Collins 10.1002/2014JD021627
- Sensitivity tests for an ensemble Kalman filter for aerosol assimilation N. Schutgens et al. 10.5194/acp-10-6583-2010
- The value of satellite observations in the analysis and short-range prediction of Asian dust A. Benedetti et al. 10.5194/acp-19-987-2019
- Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth N. Miatselskaya et al. 10.3390/atmos14010032
- Direct comparison of extinction coefficients derived from Mie-scattering lidar and number concentrations of particles, subjective weather report in Japan A. Shimizu et al. 10.1016/j.jqsrt.2014.12.005
- A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations D. Wang et al. 10.5194/gmd-15-1821-2022
- A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. 10.5194/gmd-16-5949-2023
- Improvement in Dust Storm Simulation by Considering Stone Coverage Effects for Stony Deserts in East Asia T. Sekiyama et al. 10.1029/2022JD037295
- The Effects of Snow Cover and Soil Moisture on Asian Dust: I. A Numerical Sensitivity Study T. Tanaka et al. 10.2151/sola.7A-010
- Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth B. Sič et al. 10.5194/amt-9-5535-2016
- Estimating aerosol emission from SPEXone on the NASA PACE mission using an ensemble Kalman smoother: observing system simulation experiments (OSSEs) A. Tsikerdekis et al. 10.5194/gmd-15-3253-2022
- Assimilation of Aerosol Optical Depth Into the Warn‐on‐Forecast System for Smoke (WoFS‐Smoke) T. Jones et al. 10.1029/2022JD037454
- Investigating the Long-Term Variation Trends of Absorbing Aerosols over Asia by Using Multiple Satellites D. Li et al. 10.3390/rs14225832
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- Yearlong first measurements of black carbon in the western Indian Himalaya: Influences of meteorology and fire emissions C. Pandey et al. 10.1016/j.apr.2020.04.015
- A new approach for optimizing air pollutant emissions using Newtonian relaxation and the coupled WRF-CAMx model: a case study in Xuzhou city, China Y. Li et al. 10.1007/s12517-020-06002-5
- Spatial boundaries of Aerosol Robotic Network observations over the Mediterranean basin A. Mishra et al. 10.1002/2015GL067630
- Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM<sub>2.5</sub> S. Park et al. 10.5194/gmd-15-2773-2022
- Dust cycle: An emerging core theme in Earth system science Y. Shao et al. 10.1016/j.aeolia.2011.02.001
- Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter Z. Peng et al. 10.5194/acp-17-4837-2017
- Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program J. Reid et al. 10.1016/j.atmosres.2012.06.005
- Common Retrieval of Aerosol Properties for Imaging Satellite Sensors M. YOSHIDA et al. 10.2151/jmsj.2018-039
- CALIOP Aerosol Subset Processing for Global Aerosol Transport Model Data Assimilation J. Campbell et al. 10.1109/JSTARS.2010.2044868
- Dust Model Intercomparison Between ADAM and CFORS/Dust For Asian Dust Case in 2007 (March 28 - April 3) S. Kim et al. 10.2151/sola.7A-007
139 citations as recorded by crossref.
- NHM-Chem, the Japan Meteorological Agency's Regional Meteorology – Chemistry Model: Model Evaluations toward the Consistent Predictions of the Chemical, Physical, and Optical Properties of Aerosols M. KAJINO et al. 10.2151/jmsj.2019-020
- Ensemble filter based estimation of spatially distributed parameters in a mesoscale dust model: experiments with simulated and real data V. Khade et al. 10.5194/acp-13-3481-2013
- Lidar methods for observing mineral dust N. Sugimoto & Z. Huang 10.1007/s13351-014-3068-9
- RETRACTED ARTICLE: A neural network–based rainfall trend in plain areas and a personalized French learning system G. Xiao 10.1007/s12517-021-07419-2
- Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting J. Rubin et al. 10.5194/acp-16-3927-2016
- Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0 E. Di Tomaso et al. 10.5194/gmd-10-1107-2017
- An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences P. Lynch et al. 10.5194/gmd-9-1489-2016
- Development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: aiming to improve air quality forecasting and diagnose model deficiencies W. Sun et al. 10.5194/acp-20-9311-2020
- Impact of local and regional emission sources on air quality in foothills of the Himalaya during spring 2016: An observation, satellite and modeling perspective M. Mehra et al. 10.1016/j.atmosenv.2019.116897
- Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model H. Ye et al. 10.1016/j.atmosres.2021.105877
- A Review of Data Assimilation on Aerosol Optical, Radiative, and Climatic Effects Study Y. Cheng et al. 10.1007/s41810-022-00142-9
- Estimation of aerosol particle number distributions with Kalman Filtering – Part 1: Theory, general aspects and statistical validity T. Viskari et al. 10.5194/acp-12-11767-2012
- Influence of aerosols and thin cirrus clouds on the GOSAT-observed CO<sub>2</sub>: a case study over Tsukuba O. Uchino et al. 10.5194/acp-12-3393-2012
- Evaluation of MERRA-2 data for aerosols patterns over the Kingdom of Saudi Arabia A. Labban & M. Butt 10.1016/j.heliyon.2023.e17047
- Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model T. Dai et al. 10.1016/j.envpol.2014.06.021
- Chemical Data Assimilation—An Overview A. Sandu & T. Chai 10.3390/atmos2030426
- Improvement of the Aerosol Forecast and Analysis Over East Asia With Joint Assimilation of Two Geostationary Satellite Observations Y. Cheng et al. 10.1029/2022GL099908
- ANISORROPIA: the adjoint of the aerosol thermodynamic model ISORROPIA S. Capps et al. 10.5194/acp-12-527-2012
- Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook T. Yang et al. 10.1016/j.jes.2022.04.012
- Assimilating spaceborne lidar dust extinction can improve dust forecasts J. Escribano et al. 10.5194/acp-22-535-2022
- Forecasting of Asian dust storm that occurred on May 10–13, 2011, using an ensemble-based data assimilation system K. Yumimoto et al. 10.1016/j.partic.2015.09.001
- Assessing the impact of Chinese FY-3/MERSI AOD data assimilation on air quality forecasts: Sand dust events in northeast China Y. Bao et al. 10.1016/j.atmosenv.2019.02.026
- Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China C. Ma et al. 10.1029/2019JD030421
- Assimilation of Both Column‐ and Layer‐Integrated Dust Opacity Observations in the Martian Atmosphere T. Ruan et al. 10.1029/2021EA001869
- Inverse modeling of black carbon emissions over China using ensemble data assimilation P. Wang et al. 10.5194/acp-16-989-2016
- A methodology for investigating dust model performance using synergistic EARLINET/AERONET dust concentration retrievals I. Binietoglou et al. 10.5194/amt-8-3577-2015
- Impact of anomalous forest fire on aerosol radiative forcing and snow cover over Himalayan region K. Bali et al. 10.1016/j.atmosenv.2016.11.061
- Nighttime smoke aerosol optical depth over U.S. rural areas: First retrieval from VIIRS moonlight observations M. Zhou et al. 10.1016/j.rse.2021.112717
- Dust Emission Estimated with an Assimilated Dust Transport Model Using Lidar Network Data and Vegetation Growth in the Gobi Desert in Mongolia N. Sugimoto et al. 10.2151/sola.2010-032
- Horizontal Resolution Dependence of Atmospheric Simulations of the Fukushima Nuclear Accident Using 15-km, 3-km, and 500-m Grid Models T. SEKIYAMA et al. 10.2151/jmsj.2015-002
- How much information do extinction and backscattering measurements contain about the chemical composition of atmospheric aerosol? M. Kahnert & E. Andersson 10.5194/acp-17-3423-2017
- Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity A. Gumber et al. 10.5194/amt-16-2547-2023
- Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP) P. Xian et al. 10.1002/qj.3497
- Data Assimilation of Himawari-8 Aerosol Observations: Asian Dust Forecast in June 2015 T. Sekiyama et al. 10.2151/sola.2016-020
- Identifying chemical aerosol signatures using optical suborbital observations: how much can optical properties tell us about aerosol composition? M. Kacenelenbogen et al. 10.5194/acp-22-3713-2022
- A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM<sub>2.5</sub> prediction Z. Li et al. 10.5194/acp-13-4265-2013
- Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin Y. Wang et al. 10.5194/acp-14-12031-2014
- Assimilating aerosol optical properties related to size and absorption from POLDER/PARASOL with an ensemble data assimilation system A. Tsikerdekis et al. 10.5194/acp-21-2637-2021
- Status and future of numerical atmospheric aerosol prediction with a focus on data requirements A. Benedetti et al. 10.5194/acp-18-10615-2018
- Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
- Temperate grasslands as a dust source: Knowledge, uncertainties, and challenges M. Shinoda et al. 10.1016/j.aeolia.2011.07.001
- Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system Y. Kong et al. 10.1016/j.atmosres.2020.105366
- Integrated Study of AD-Net Mie-Lidar Network and Data Assimilated CTM for Asian Dust Epidemiology in Japan A. Shimizu et al. 10.1051/epjconf/201611908007
- Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic K. Han et al. 10.3390/app11041959
- Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China J. Jin et al. 10.5194/acp-20-15207-2020
- An improved method for retrieving nighttime aerosol optical thickness from the VIIRS Day/Night Band T. McHardy et al. 10.5194/amt-8-4773-2015
- Evaluating the Impact of Emissions Regulations on the Emissions Reduction During the 2015 China Victory Day Parade With an Ensemble Square Root Filter K. Chu et al. 10.1002/2017JD027631
- Decadal trends of MERRA-estimated PM2.5 concentrations in East Asia and potential exposure from 1990 to 2019 S. Yin 10.1016/j.atmosenv.2021.118690
- A nonlinear least squares four-dimensional variational data assimilation system for PM2.5 forecasts (NASM): Description and preliminary evaluation S. Zhang et al. 10.1016/j.apr.2021.03.003
- Detection of Aerosols in Antarctica From Long‐Range Transport of the 2009 Australian Wildfires J. Jumelet et al. 10.1029/2020JD032542
- Impact of the OMI aerosol optical depth on analysis increments through coupled meteorology–aerosol data assimilation for an Asian dust storm E. Lee et al. 10.1016/j.rse.2017.02.013
- Stereoscopic hyperspectral remote sensing of the atmospheric environment: Innovation and prospects C. Liu et al. 10.1016/j.earscirev.2022.103958
- JRAero: the Japanese Reanalysis for Aerosol v1.0 K. Yumimoto et al. 10.5194/gmd-10-3225-2017
- Introducing the MISR level 2 near real-time aerosol product M. Witek et al. 10.5194/amt-14-5577-2021
- Statistical downscaling of water vapour satellite measurements from profiles of tropical ice clouds G. Carella et al. 10.5194/essd-12-1-2020
- Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa Y. Shi et al. 10.5194/amt-6-949-2013
- CALIOP near-real-time backscatter products compared to EARLINET data T. Grigas et al. 10.5194/acp-15-12179-2015
- Application of linear minimum variance estimation to the multi-model ensemble of atmospheric radioactive Cs-137 with observations D. Goto et al. 10.5194/acp-20-3589-2020
- The Effects of Snow Cover and Soil Moisture on Asian Dust: II. Emission Estimation by Lidar Data Assimilation T. Sekiyama et al. 10.2151/sola.7A-011
- The SPRINTARS version 3.80/4D-Var data assimilation system: development and inversion experiments based on the observing system simulation experiment framework K. Yumimoto & T. Takemura 10.5194/gmd-6-2005-2013
- PM2.5 concentration distribution patterns and influencing meteorological factors in the central and eastern China during 1980–2018 J. Ma et al. 10.1016/j.jclepro.2021.127565
- Decadal changes in PM2.5-related health impacts in China from 1990 to 2019 and implications for current and future emission controls S. Yin 10.1016/j.scitotenv.2022.155334
- The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 2: Evaluation of aerosol optical thickness P. Bhattacharjee et al. 10.5194/gmd-11-2333-2018
- Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al. 10.1029/2018MS001475
- Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations S. Zhai et al. 10.5194/acp-21-16775-2021
- Inverting the East Asian Dust Emission Fluxes Using the Ensemble Kalman Smoother and Himawari-8 AODs: A Case Study with WRF-Chem v3.5.1 T. Dai et al. 10.3390/atmos10090543
- Feasibility Study for Future Spaceborne Coherent Doppler Wind Lidar, Part 2: Measurement Simulation Algorithms and Retrieval Error Characterization P. BARON et al. 10.2151/jmsj.2017-018
- Assimilation of POLDER observations to estimate aerosol emissions A. Tsikerdekis et al. 10.5194/acp-23-9495-2023
- Statistical Evaluation of the Temperature Forecast Error in the Lower‐Level Troposphere on Short‐Range Timescales Induced by Aerosol Variability A. Yamagami et al. 10.1029/2022JD036595
- Improving estimation of a record-breaking east Asian dust storm emission with lagged aerosol Ångström exponent observations Y. Cheng et al. 10.5194/acp-24-12643-2024
- Estimating Aerosol Emissions by Assimilating Remote Sensing Observations into a Global Transport Model N. Schutgens et al. 10.3390/rs4113528
- Long‐term inverse modeling of Asian dust: Interannual variations of its emission, transport, deposition, and radiative forcing K. Yumimoto & T. Takemura 10.1002/2014JD022390
- Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations J. Hong et al. 10.1016/j.envpol.2020.114451
- Improved Hourly Estimates of Aerosol Optical Thickness Using Spatiotemporal Variability Derived From Himawari-8 Geostationary Satellite M. Kikuchi et al. 10.1109/TGRS.2018.2800060
- The characterization of Taklamakan dust properties using a multiwavelength Raman polarization lidar in Kashi, China Q. Hu et al. 10.5194/acp-20-13817-2020
- Evaluating the impact of multisensor data assimilation on a global aerosol particle transport model J. Zhang et al. 10.1002/2013JD020975
- Measurement Report: Determination of aerosol vertical features on different timescales over East Asia based on CATS aerosol products Y. Cheng et al. 10.5194/acp-20-15307-2020
- An accuracy assessment of the CALIOP/CALIPSO version 2/version 3 daytime aerosol extinction product based on a detailed multi-sensor, multi-platform case study M. Kacenelenbogen et al. 10.5194/acp-11-3981-2011
- Evolving patterns of arctic aerosols and the influence of regional variations over two decades K. Lee et al. 10.1016/j.scitotenv.2024.177465
- Evaluating nighttime CALIOP 0.532 μm aerosol optical depth and extinction coefficient retrievals J. Campbell et al. 10.5194/amt-5-2143-2012
- Better prediction of surface ozone by a superensemble method using emission sensitivity runs in Japan M. Kajino et al. 10.1016/j.aeaoa.2021.100120
- Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street R. Kahn et al. 10.1029/2022RG000796
- Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia Z. Liu et al. 10.1029/2011JD016159
- Quantifying the low bias of CALIPSO's column aerosol optical depth due to undetected aerosol layers M. Kim et al. 10.1002/2016JD025797
- Spatially varying parameter estimation for dust emissions using reduced-tangent-linearization 4DVar J. Jin et al. 10.1016/j.atmosenv.2018.05.060
- The Impact of Ground-Based Observations on the Inverse Technique of Aeolian Dust Aerosol T. Maki et al. 10.2151/sola.7A-006
- Machine learning for observation bias correction with application to dust storm data assimilation J. Jin et al. 10.5194/acp-19-10009-2019
- The impact of multi-species surface chemical observation assimilation on air quality forecasts in China Z. Peng et al. 10.5194/acp-18-17387-2018
- Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing X. Cheng et al. 10.1016/j.scitotenv.2019.05.186
- Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM<sub>2.5</sub> forecasts across China Y. Liang et al. 10.5194/gmd-13-6285-2020
- Three-dimensional climatology, trends, and meteorological drivers of global and regional tropospheric type-dependent aerosols: insights from 13 years (2007–2019) of CALIOP observations K. Gui et al. 10.5194/acp-21-15309-2021
- Utilizing Cloud Computing to address big geospatial data challenges C. Yang et al. 10.1016/j.compenvurbsys.2016.10.010
- Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum J. Zhang et al. 10.5194/gmd-14-27-2021
- The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies V. Buchard et al. 10.1175/JCLI-D-16-0613.1
- Operation-Oriented Ensemble Data Assimilation of Total Column Ozone T. Sekiyama et al. 10.2151/sola.2011-011
- Preliminary investigations toward nighttime aerosol optical depth retrievals from the VIIRS Day/Night Band R. Johnson et al. 10.5194/amt-6-1245-2013
- Ensemble Dispersion Simulation of a Point-Source Radioactive Aerosol Using Perturbed Meteorological Fields over Eastern Japan T. Sekiyama et al. 10.3390/atmos12060662
- Space-Borne Lidar Technology for Global Environmental Observation Research D. SAKAIZAWA et al. 10.2184/lsj.39.12
- Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model N. Schutgens et al. 10.5194/acp-10-2561-2010
- Air quality modeling in East Asia: present issues and future directions R. Park & S. Kim 10.1007/s13143-014-0030-9
- The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation C. Randles et al. 10.1175/JCLI-D-16-0609.1
- Efficacy of dust aerosol forecasts for East Asia using the adjoint of GEOS-Chem with ground-based observations J. Jeong & R. Park 10.1016/j.envpol.2017.12.025
- Aerosol data assimilation in the MOCAGE chemical transport model during the TRAQA/ChArMEx campaign: lidar observations L. El Amraoui et al. 10.5194/amt-13-4645-2020
- Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter Y. Cheng et al. 10.5194/acp-19-13445-2019
- Minimum aerosol layer detection sensitivities and their subsequent impacts on aerosol optical thickness retrievals in CALIPSO level 2 data products T. Toth et al. 10.5194/amt-11-499-2018
- Spatiotemporal Variations in Summertime Arctic Aerosol Optical Depth Caused by Synoptic‐Scale Atmospheric Circulation in Three Reanalyses A. Yamagami et al. 10.1029/2022JD038007
- CALIPSO-Derived Three-Dimensional Structure of Aerosol over the Atlantic Basin and Adjacent Continents A. Adams et al. 10.1175/JCLI-D-11-00672.1
- RETRACTED ARTICLE: Evaluation of agricultural climate and regional agricultural economic efficiency based on remote sensing analysis X. Lu 10.1007/s12517-021-07153-9
- Comparative inverse analysis of satellite (MODIS) and ground (PM10) observations to estimate dust emissions in East Asia B. Ku & R. Park 10.1007/s13143-013-0002-5
- Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method S. Zhang et al. 10.1016/j.atmosenv.2021.118896
- Lidar Measurements for Desert Dust Characterization: An Overview L. Mona et al. 10.1155/2012/356265
- Dust Emission Inversion Using Himawari‐8 AODs Over East Asia: An Extreme Dust Event in May 2017 J. Jin et al. 10.1029/2018MS001491
- Development of the RAQM2 aerosol chemical transport model and predictions of the Northeast Asian aerosol mass, size, chemistry, and mixing type M. Kajino et al. 10.5194/acp-12-11833-2012
- Bayesian Merging of MISR and MODIS Aerosol Optical Depth Products Using Error Distributions From AERONET M. Singh et al. 10.1109/JSTARS.2017.2734331
- Machine learning based bias correction for numerical chemical transport models M. Xu et al. 10.1016/j.atmosenv.2020.118022
- Evaluating the impact of assimilating CALIOP-derived aerosol extinction profiles on a global mass transport model J. Zhang et al. 10.1029/2011GL047737
- Performance comparisons of the three data assimilation methods for improved predictability of PM2·5: Ensemble Kalman filter, ensemble square root filter, and three-dimensional variational methods U. Dash et al. 10.1016/j.envpol.2023.121099
- Global simulations of aerosol amount and size using MODIS observations assimilated with an Ensemble Kalman Filter J. Rubin & W. Collins 10.1002/2014JD021627
- Sensitivity tests for an ensemble Kalman filter for aerosol assimilation N. Schutgens et al. 10.5194/acp-10-6583-2010
- The value of satellite observations in the analysis and short-range prediction of Asian dust A. Benedetti et al. 10.5194/acp-19-987-2019
- Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth N. Miatselskaya et al. 10.3390/atmos14010032
- Direct comparison of extinction coefficients derived from Mie-scattering lidar and number concentrations of particles, subjective weather report in Japan A. Shimizu et al. 10.1016/j.jqsrt.2014.12.005
- A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations D. Wang et al. 10.5194/gmd-15-1821-2022
- A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. 10.5194/gmd-16-5949-2023
- Improvement in Dust Storm Simulation by Considering Stone Coverage Effects for Stony Deserts in East Asia T. Sekiyama et al. 10.1029/2022JD037295
- The Effects of Snow Cover and Soil Moisture on Asian Dust: I. A Numerical Sensitivity Study T. Tanaka et al. 10.2151/sola.7A-010
- Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth B. Sič et al. 10.5194/amt-9-5535-2016
- Estimating aerosol emission from SPEXone on the NASA PACE mission using an ensemble Kalman smoother: observing system simulation experiments (OSSEs) A. Tsikerdekis et al. 10.5194/gmd-15-3253-2022
- Assimilation of Aerosol Optical Depth Into the Warn‐on‐Forecast System for Smoke (WoFS‐Smoke) T. Jones et al. 10.1029/2022JD037454
- Investigating the Long-Term Variation Trends of Absorbing Aerosols over Asia by Using Multiple Satellites D. Li et al. 10.3390/rs14225832
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- Yearlong first measurements of black carbon in the western Indian Himalaya: Influences of meteorology and fire emissions C. Pandey et al. 10.1016/j.apr.2020.04.015
- A new approach for optimizing air pollutant emissions using Newtonian relaxation and the coupled WRF-CAMx model: a case study in Xuzhou city, China Y. Li et al. 10.1007/s12517-020-06002-5
- Spatial boundaries of Aerosol Robotic Network observations over the Mediterranean basin A. Mishra et al. 10.1002/2015GL067630
- Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM<sub>2.5</sub> S. Park et al. 10.5194/gmd-15-2773-2022
- Dust cycle: An emerging core theme in Earth system science Y. Shao et al. 10.1016/j.aeolia.2011.02.001
- Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter Z. Peng et al. 10.5194/acp-17-4837-2017
- Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program J. Reid et al. 10.1016/j.atmosres.2012.06.005
- Common Retrieval of Aerosol Properties for Imaging Satellite Sensors M. YOSHIDA et al. 10.2151/jmsj.2018-039
2 citations as recorded by crossref.
Saved (final revised paper)
Saved (final revised paper)
Saved (preprint)
Latest update: 13 Dec 2024
Altmetrics
Final-revised paper
Preprint