{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/20611"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/20611","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Development of a Python-based Data Assimilation Framework (PyDAF). Case Study: Refining Ammonia Emissions Through Observation Data","abstract":"Data assimilation combines models with observations to improve predictions, reduce uncertainties, and support better decisions. To meet the need for a comprehensive framework that supports multiple approaches and models, we are developing the Python-based Data Assimilation Framework (PyDAF). In the first study, we introduced PyDAF version 1, supporting CMAQ and WRF-Chem models with iFDMB, 3D-VAR, 4D-VAR, and adjoint methods, using IASI, CrIS, satellite, and Nexrad radar data. For the validation, the Complex Variable Method and pseudo observations are employed. Applying PyDAF, we analyzed an ozone (O3) exceedance in Seoul on June 3, 2019, estimating contributions up to four days ahead. Korean emissions contributed 31.1 ppb, while emissions from Shandong, the Yangtze River Delta, Central China, and Beijing-Tianjin-Hebei contributed 11.42, 4.28, 1.24, and 0.9 ppb, respectively, with 19.3 ppb from background O3 beyond eastern China. In the second study, we used PyDAF:iFDMB to update NH3 emissions over East Asia with CrIS data for July, August, and September 2019. Revised emissions increased in China, especially the North China Plain, and decreased in South Korea in September. Higher NH3 emissions raised NH3 concentrations by 5 ppb. In July and September, ammonium (NH4) and nitrate (NO3) increased by 5 µg m−3, while in August they decreased. Sulfate (SO4) concentrations fell across most of China and Taiwan in August–September due to ammonium sulfate formation, but rose over South Korea, Japan, and southern Chengdu with higher humidity. In July, SO4 increased across much of China. In the third study, we refined 2019 NH3 emissions over the south-central U.S. with PyDAF:iFDMB and CrIS data, evaluating impacts on inorganic PM2.5. We also compared emissions constrained by IASI, CrIS, and both combined. Notably, we showed satellite-based refinement over open water in the northwestern Gulf of Mexico (NWGOM). Annual NH3 emissions rose 2.5-fold (1.43 Gg N a−1), increasing NH3 (3.4-fold), NH4 (1.26-fold), SO4 (1.01-fold), and NO3 (2-fold), especially in Texas, New Mexico, and Oklahoma. Combined IASI/CrIS estimates best matched surface observations. Over NWGOM, NH3 rose 1.4 ppb, mainly due to biological nitrogen fixation.","abstract_html":"Data assimilation combines models with observations to improve predictions, reduce uncertainties, and support better decisions. To meet the need for a comprehensive framework that supports multiple approaches and models, we are developing the Python-based Data Assimilation Framework (PyDAF). In the first study, we introduced PyDAF version 1, supporting CMAQ and WRF-Chem models with iFDMB, 3D-VAR, 4D-VAR, and adjoint methods, using IASI, CrIS, satellite, and Nexrad radar data. For the validation, the Complex Variable Method and pseudo observations are employed. Applying PyDAF, we analyzed an ozone (O3) exceedance in Seoul on June 3, 2019, estimating contributions up to four days ahead. Korean emissions contributed 31.1 ppb, while emissions from Shandong, the Yangtze River Delta, Central China, and Beijing-Tianjin-Hebei contributed 11.42, 4.28, 1.24, and 0.9 ppb, respectively, with 19.3 ppb from background O3 beyond eastern China. In the second study, we used PyDAF:iFDMB to update NH3 emissions over East Asia with CrIS data for July, August, and September 2019. Revised emissions increased in China, especially the North China Plain, and decreased in South Korea in September. Higher NH3 emissions raised NH3 concentrations by 5 ppb. In July and September, ammonium (NH4) and nitrate (NO3) increased by 5 µg m−3, while in August they decreased. Sulfate (SO4) concentrations fell across most of China and Taiwan in August–September due to ammonium sulfate formation, but rose over South Korea, Japan, and southern Chengdu with higher humidity. In July, SO4 increased across much of China. In the third study, we refined 2019 NH3 emissions over the south-central U.S. with PyDAF:iFDMB and CrIS data, evaluating impacts on inorganic PM2.5. We also compared emissions constrained by IASI, CrIS, and both combined. Notably, we showed satellite-based refinement over open water in the northwestern Gulf of Mexico (NWGOM). Annual NH3 emissions rose 2.5-fold (1.43 Gg N a−1), increasing NH3 (3.4-fold), NH4 (1.26-fold), SO4 (1.01-fold), and NO3 (2-fold), especially in Texas, New Mexico, and Oklahoma. Combined IASI/CrIS estimates best matched surface observations. Over NWGOM, NH3 rose 1.4 ppb, mainly due to biological nitrogen fixation.","abstract_has_math":false,"creators":["Momeni, Mahmoudreza"],"institution":"University of Houston","degree_name":"Doctor of Philosophy","degree_level":null,"degree_discipline":"Atmospheric Sciences","degree_department":null,"school":null,"contributors":[],"advisors":["Yunsoo Choi"],"committee_chairs":[],"committee_members":["Rappenglueck, Bernhard","Liu, Junjie","Jiang, Xun"],"year":2025,"date_issued":"2025-08","date_published":"2025-08","updated_at":"2026-07-24T02:32:34Z","subjects":["Atmospheric science"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/20611","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Yunsoo Choi"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Rappenglueck, Bernhard","Liu, Junjie","Jiang, Xun"]},{"key":"dc:creator","label":"Author","values":["Momeni, Mahmoudreza"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-21T06:15:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Atmospheric Sciences"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Atmospheric science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/20611"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Data assimilation combines models with observations to improve predictions, reduce uncertainties, and support better decisions. To meet the need for a comprehensive framework that supports multiple approaches and models, we are developing the Python-based Data Assimilation Framework (PyDAF). In the first study, we introduced PyDAF version 1, supporting CMAQ and WRF-Chem models with iFDMB, 3D-VAR, 4D-VAR, and adjoint methods, using IASI, CrIS, satellite, and Nexrad radar data. For the validation, the Complex Variable Method and pseudo observations are employed. Applying PyDAF, we analyzed an ozone (O3) exceedance in Seoul on June 3, 2019, estimating contributions up to four days ahead. Korean emissions contributed 31.1 ppb, while emissions from Shandong, the Yangtze River Delta, Central China, and Beijing-Tianjin-Hebei contributed 11.42, 4.28, 1.24, and 0.9 ppb, respectively, with 19.3 ppb from background O3 beyond eastern China. In the second study, we used PyDAF:iFDMB to update NH3 emissions over East Asia with CrIS data for July, August, and September 2019. Revised emissions increased in China, especially the North China Plain, and decreased in South Korea in September. Higher NH3 emissions raised NH3 concentrations by 5 ppb. In July and September, ammonium (NH4) and nitrate (NO3) increased by 5 µg m−3, while in August they decreased. Sulfate (SO4) concentrations fell across most of China and Taiwan in August–September due to ammonium sulfate formation, but rose over South Korea, Japan, and southern Chengdu with higher humidity. In July, SO4 increased across much of China. In the third study, we refined 2019 NH3 emissions over the south-central U.S. with PyDAF:iFDMB and CrIS data, evaluating impacts on inorganic PM2.5. We also compared emissions constrained by IASI, CrIS, and both combined. Notably, we showed satellite-based refinement over open water in the northwestern Gulf of Mexico (NWGOM). Annual NH3 emissions rose 2.5-fold (1.43 Gg N a−1), increasing NH3 (3.4-fold), NH4 (1.26-fold), SO4 (1.01-fold), and NO3 (2-fold), especially in Texas, New Mexico, and Oklahoma. Combined IASI/CrIS estimates best matched surface observations. Over NWGOM, NH3 rose 1.4 ppb, mainly due to biological nitrogen fixation."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Development of a Python-based Data Assimilation Framework (PyDAF). Case Study: Refining Ammonia Emissions Through Observation Data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Yunsoo Choi"],"dc:contributor.committeemember":["Rappenglueck, Bernhard","Liu, Junjie","Jiang, Xun"],"dc:creator":["Momeni, Mahmoudreza"],"dc:date.accessioned":["2025-09-21T06:15:48Z"],"dc:date.issued":["2025-08"],"dc:description.abstract":["Data assimilation combines models with observations to improve predictions, reduce uncertainties, and support better decisions. To meet the need for a comprehensive framework that supports multiple approaches and models, we are developing the Python-based Data Assimilation Framework (PyDAF). In the first study, we introduced PyDAF version 1, supporting CMAQ and WRF-Chem models with iFDMB, 3D-VAR, 4D-VAR, and adjoint methods, using IASI, CrIS, satellite, and Nexrad radar data. For the validation, the Complex Variable Method and pseudo observations are employed. Applying PyDAF, we analyzed an ozone (O3) exceedance in Seoul on June 3, 2019, estimating contributions up to four days ahead. Korean emissions contributed 31.1 ppb, while emissions from Shandong, the Yangtze River Delta, Central China, and Beijing-Tianjin-Hebei contributed 11.42, 4.28, 1.24, and 0.9 ppb, respectively, with 19.3 ppb from background O3 beyond eastern China. In the second study, we used PyDAF:iFDMB to update NH3 emissions over East Asia with CrIS data for July, August, and September 2019. Revised emissions increased in China, especially the North China Plain, and decreased in South Korea in September. Higher NH3 emissions raised NH3 concentrations by 5 ppb. In July and September, ammonium (NH4) and nitrate (NO3) increased by 5 µg m−3, while in August they decreased. Sulfate (SO4) concentrations fell across most of China and Taiwan in August–September due to ammonium sulfate formation, but rose over South Korea, Japan, and southern Chengdu with higher humidity. In July, SO4 increased across much of China. In the third study, we refined 2019 NH3 emissions over the south-central U.S. with PyDAF:iFDMB and CrIS data, evaluating impacts on inorganic PM2.5. We also compared emissions constrained by IASI, CrIS, and both combined. Notably, we showed satellite-based refinement over open water in the northwestern Gulf of Mexico (NWGOM). Annual NH3 emissions rose 2.5-fold (1.43 Gg N a−1), increasing NH3 (3.4-fold), NH4 (1.26-fold), SO4 (1.01-fold), and NO3 (2-fold), especially in Texas, New Mexico, and Oklahoma. Combined IASI/CrIS estimates best matched surface observations. Over NWGOM, NH3 rose 1.4 ppb, mainly due to biological nitrogen fixation."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/20611"],"dc:language.iso":["English"],"dc:subject":["Atmospheric science"],"dc:title":["Development of a Python-based Data Assimilation Framework (PyDAF). Case Study: Refining Ammonia Emissions Through Observation Data"],"dc:type":["Thesis"],"thesis:degree_discipline":["Atmospheric Sciences"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:32:34Z"}