{"id":2884,"date":"2024-09-20T17:11:46","date_gmt":"2024-09-20T16:11:46","guid":{"rendered":"https:\/\/apolo.loa.univ-lille.fr\/?post_type=mp-event&p=2884"},"modified":"2024-11-18T03:23:30","modified_gmt":"2024-11-18T02:23:30","slug":"poster-session","status":"publish","type":"mp-event","link":"https:\/\/apolo.loa.univ-lille.fr\/timetable\/event\/poster-session\/","title":{"rendered":"Poster Session"},"content":{"rendered":"

1. Upcoming and current satellite missions and field campaigns<\/h4>\n

1.1 Assessment of retrieval capabilities of two polarimeters in the NASA PACE mission using synthetic and real measurements for the joint retrieval of aerosol and ocean color information over coastal waters<\/b><\/a>
\nKamal Aryal, University of Maryland Baltimore County (United States)<\/em><\/p>\n

1.2 Estimation of Cloud Condensation Nuclei (CCN) from SPEXone on PACE<\/b><\/a>
\nNeranga Hannadige, SRON Netherlands Institute for Space Research (The Netherlands)<\/em><\/p>\n

1.3 Above Cloud Aerosol Retrieval from Multi-Angular Polarimetric Satellite Measurements using a Neural Network Ensemble Approach<\/b><\/a>
\nZihao Yuan, SRON and Leiden University (The Netherlands)<\/em><\/p>\n

1.4 BRDF correction for PACE\u2019s OCI using multi-angle polarimetry<\/b><\/a>
\nXiaodong Zhang, University of Southern Mississippi (United States)<\/em><\/p>\n

1.5 Comparative Analysis of Aerosol Layer Heights: PACE vs. MPLNET Surface-Based Lidars<\/b><\/a>
\nMaurice Roots, University of Maryland, Baltimore County (United States)<\/em><\/p>\n

1.6 PACE-HARP2 Pre-Launch Calibration Uncertainties<\/b><\/a>
\nNoah Sienkiewicz, University of Maryland, Baltimore County (United States)<\/em><\/p>\n

1.7 LWIR Spectro-Polarimeter Cloud Top Observations and Ice-Water Discriminatory Studies<\/b><\/a>
\nJaclyn John, University of Arizona (United States)<\/em><\/p>\n

1.8 Tip of iceberg: indirect forcing from ship emissions from bottom-up and top-down approaches<\/b><\/a>
\nTianle Yuan, UMBC \/ NASA (United States)<\/em><\/p>\n

<\/h4>\n

2. Advances in the theory of polarimetric remote sensing<\/h4>\n

2.1 Development of advanced hyperangular polarimetric cloud retrievals for current and future NASA missions<\/b><\/a>
\nDaniel Miller, NASA GSFC (United States)<\/em><\/p>\n

2.2 Polarized discrete ordinate adding approximation (POLDDA) for radiative transfer<\/b><\/a>
\nFeng Zhang, Fudan University (China)<\/em><\/p>\n

2.3 Comparison of polarization for sky radiance simulations with two radiative transfer models<\/b><\/a>
\nDaniel Gonz\u00e1lez-Fern\u00e1ndez, Group of Atmospheric Optics of the University of Valladolid (Spain)<\/em><\/p>\n

2.4 Retrieving Liquid Cloud Droplet Size Distribution from the Geometric Parameters of Polarized Cloudbow: Novel Algorithm Development and Demonstrations<\/b><\/a>
\nRachel Smith, University of Maryland Baltimore County (United States)<\/em><\/p>\n

3. Scattering of light by terrestrial aerosols, clouds, oceans, and land surfaces<\/h4>\n

3.1 Contribution of underlying terrain to sunlight scattered by atmospheric aerosols<\/b><\/a>
\nEvgenij Zubko, Planetary Atmospheres Group, Institute for Basic Science (IBS) (South Korea)<\/em><\/p>\n

3.2 Randomly oriented fractal-like clusters as a model of smoke particles: implementation into GRASP algorithm and tests on AERONET measurements<\/b><\/a>
\nLiudmyla Berdina, Laboratoire d\u2019Optique Atmosph\u00e9rique (France)<\/em><\/p>\n

3.3 The Emergence of Non-Principal Meridian Neutral Points<\/b><\/a>
\nEdward Blocker, SSAI\/NASA GSFC (United States)<\/em><\/p>\n

3.4 Effect of aerosol optical scattering and absorbing properties on the Urban heat island intensity during summertime in Rome, Italy<\/b><\/a>
\nMonica Campanelli, ISAC-CNR (Italy)<\/em><\/p>\n

3.5 Cloud thermodynamic phase measurements from low-cost pixelated polarization cameras<\/b><\/a>
\nErica Venkatesulu, Montana State University (United States)<\/em><\/p>\n

3.6 Development and evaluation of the angular polarization feature for marine observation<\/b><\/a>
\nZihan Zhang, Peking University (China)<\/em><\/p>\n

3.7 Comparison between two 3D polarized radiative transfer models: MCstar and MYSTIC<\/b><\/a>
\nRoberto Rom\u00e1n, Universidad de Valladolid (Spain)<\/em><\/p>\n

4. Polarimetric applications in astrophysics and planetary science<\/h4>\n

4.1 Modelling the degree of linear polarization (DoLP) of the Moon’s light with a Cimel CE318-TP9<\/b><\/a>
\nAfrica Barreto, Meteorological State Agency of Spain (Spain)<\/em><\/p>\n

4.2 Imaging Stokes polarimeters for the BOOTES Global Telescope Network<\/b><\/a>
\nIvan Syniavskyi, Main Astronomical Observatory of NAS of Ukraine (Ukraine)<\/em><\/p>\n

4.3 Polarimetric Characteristics of Lunar Simulant JSC-1A Depending on Incidence Angle<\/b><\/a>
\nMinsup Jeong, Korea Astronomy and Space Science Institute (South Korea)<\/em><\/p>\n

5. Improvement of polarimetric instrumentation quality and information content<\/h4>\n

5.1 Application of atmospheric correction algorithm to enhance the accuracy of on-orbit geometric calibration<\/b><\/a>
\nGuangfeng Xiang, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences (China)<\/em><\/p>\n

5.2 Improvement on the retrieval of aerosol properties when accounting for light polarization in inversion algorithms<\/b><\/a>
\nSara Herrero Anta, University of Valladolid (Spain)<\/em><\/p>\n

5.3 Evaluation of the improvements in the aerosol retrieval adding polarization information in the sky radiances<\/b><\/a>
\nCelia Herrero del Barrio, University of Valladolid (Spain)<\/em><\/p>\n

5.4 A new method for direct measurement of polarization characteristics of water-leaving radiation<\/b><\/a>
\nJia Liu, Xi\u2019an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences (China)<\/em><\/p>\n

6. Development of advanced retrieval algorithms and data processing<\/h4>\n

6.1 MOCMAC: Polarimetric ocean color atmospheric correction with Bayesian inference<\/b><\/a>
\nJames Allen, GSFC\/GESTAR II (United States)<\/em><\/p>\n

6.2 Complementing Quality Assurance in Satellite Data using GRASP’s Dynamic Error Estimates<\/b><\/a>
\nMilagros Herrera, GRASP-Earth (France)<\/em><\/p>\n

6.3 Comparing MAIAC and MISR Surface Bidirectional Reflectane Factors: Implications for MAIA Aerosol Retrievals<\/b><\/a>
\nTaozhong Huang, University of Oklahoma (United States)<\/em><\/p>\n

6.4 A three-dimensional atmospheric retrieval framework using an ensemble Kalman filter and three-dimensional radiative transfer<\/b><\/a>
\nKen Hirata, Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder (United States)<\/em><\/p>\n

6.5 Atmospheric compensation algorithm for high-resolution satellite images with extreme oblique observations<\/b><\/a>
\nTangyu Sui, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences (China)<\/em><\/p>\n

6.6 New Remote Sensing Inversion and Numerical Assimilation Technique of the Vertical Structure of PM2.5 Ammonium Concentration<\/b><\/a>
\nTing Yang, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences (China)<\/em><\/p>\n

6.7 Information Content Analysis for multiangle satellite ultraviolet polarimetric measurements<\/b><\/a>
\nHaoran Gu, Aerospace Information Research Institute, Chinese Academy of Sciences (China)<\/em><\/p>\n

6.8 Potassium Emission Lines as Tool for Remote Sensing of Biomass Burning<\/b><\/a>
\nWilliam Julstrom, University of Iowa (United States)<\/em><\/p>\n

6.9 Retrieval of nighttime Aerosol Optical Depth with a PREDE POM radiometer<\/b><\/a>
\nGaurav Kumar, Universidad de Valencia (Spain)<\/em><\/p>\n

6.10 Versatile Aerosol and Cloud Obstruction Mask (ACOM) for Diverse Remote<\/b><\/a>
\nChristian Matar, GRASP-Earth (France)<\/em><\/p>\n

6.11 Advancing Ocean and Atmospheric Research through Polarimetric Remote Sensing Innovations<\/b><\/a>
\nTianfeng Pan, Zhejiang University (China)<\/em><\/p>\n

6.12 An optimal inverse method with regularization for retrieving dust aerosol microphysical properties from extinction, backscattering and depolarization lidar measurements<\/b><\/a>
\nYuyang Chang, Laboratoire d’Optique Atmosph\u00e9rique (France)<\/em><\/p>\n

7. In situ and ground-based validation observations<\/h4>\n

7.1 Harmonization of aerosol inversion products from AERONET and SKYNET sun-sky radiometer observations with GRASP algorithm<\/b><\/a>
\nMasahiro Momoi, GRASP-Earth (France)<\/em><\/p>\n

7.2 Exploring the use of ground-based remote sensing to identify new particle formation events: A case study in the Beijing area<\/b><\/a>
\nYing Zhang, Aerospace Information Research Institute, Chinese Academy of Sciences (China)<\/em><\/p>\n

8. Calibration and validation<\/h4>\n

8.1 Pre-Launch and On-Orbit Testing of SMAC on-board GFDM satellite<\/b><\/a>
\nMengfan Li, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences (China)<\/em><\/p>\n

8.2 Onboard polarization calibration and polarimetric accuracy verification method for the particulate observing scanning polarimeter (POSP)<\/b><\/a>
\nXuefeng Lei, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences (China)<\/em><\/p>\n

9. Models, In situ and Remote sensing of Aerosols (MIRA) Collaborations<\/h4>\n

9.1 High-Resolution Retrieval of Local Road Emissions from Coarse Satellite Images Using CFD Modeling<\/b><\/a>
\nKonstantin Kuznetsov, GRASP-Earth (France)<\/em><\/p>\n

9.2 Black Carbon on a High-Resolution Map with Coupled Chemistry-Transport Models: Improving Regional Emissions with Adjoint GEOS-Chem and WRF-CHEM Calculations<\/b><\/a>
\nAbhinna Behera, GRASP-Earth (France)<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

1. Upcoming and current satellite missions and field campaigns 1.1 Assessment of retrieval capabilities of two polarimeters in the NASA PACE mission using synthetic and real measurements for the joint retrieval of aerosol and ocean color information over coastal waters Kamal Aryal, University of Maryland Baltimore County (United States) 1.2 Estimation of Cloud Condensation Nuclei […]<\/p>\n","protected":false},"author":1,"featured_media":3139,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","mp-event_category":[49],"mp-event_tag":[],"class_list":["post-2884","mp-event","type-mp-event","status-publish","has-post-thumbnail","hentry","mp-event_category-posters","mp-event-item"],"_links":{"self":[{"href":"https:\/\/apolo.loa.univ-lille.fr\/wp-json\/wp\/v2\/mp-event\/2884","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/apolo.loa.univ-lille.fr\/wp-json\/wp\/v2\/mp-event"}],"about":[{"href":"https:\/\/apolo.loa.univ-lille.fr\/wp-json\/wp\/v2\/types\/mp-event"}],"author":[{"embeddable":true,"href":"https:\/\/apolo.loa.univ-lille.fr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/apolo.loa.univ-lille.fr\/wp-json\/wp\/v2\/comments?post=2884"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/apolo.loa.univ-lille.fr\/wp-json\/wp\/v2\/media\/3139"}],"wp:attachment":[{"href":"https:\/\/apolo.loa.univ-lille.fr\/wp-json\/wp\/v2\/media?parent=2884"}],"wp:term":[{"taxonomy":"mp-event_category","embeddable":true,"href":"https:\/\/apolo.loa.univ-lille.fr\/wp-json\/wp\/v2\/mp-event_category?post=2884"},{"taxonomy":"mp-event_tag","embeddable":true,"href":"https:\/\/apolo.loa.univ-lille.fr\/wp-json\/wp\/v2\/mp-event_tag?post=2884"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}