Parisa Heidary

Working on the West Coast Operational Forecasting System (WCOFS) to improve forecast accuracy, especially for salinity. I focus on enhancing data assimilation using ROMS 4D-Var to reduce biases, including incorporating additional river inputs (reducing salinity bias by 0.5 PSU), re-evaluating background error covariances to have better representation of surface mixed layer, assimilating in-situ salinity observations, and adjusting model viscosity to better manage horizontal eddy variability. My work aims to advance operational forecasting and deliver more reliable oceanographic predictions for the West Coast.

Taught an upper-level course focused on the principles and applications of water resources engineering and management. Covered topics such as water sustainability, hydraulic and hydrologic processes, flow in pressurized pipes, open channel flow, surface runoff, streamflow routing, stormwater management, and sedimentation and erosion hydraulics.

Hydrology: - Assisted in teaching hydrology principles and practical applications to undergraduate students. - Provided guidance and support to students in understanding hydrological processes, data analysis, and modeling techniques. - Conducted tutorials, workshops, and lab sessions to reinforce learning objectives and enhance students' understanding of hydrological concepts. Hydraulics and Fluid Mechanics: - Supported faculty in teaching fundamental principles of hydraulics and fluid mechanics to undergraduate students. - Facilitated interactive sessions, demonstrations, and experiments to illustrate fluid behavior and hydraulic systems' principles. - Assisted students in conducting laboratory experiments, data collection, analysis, and interpretation.

Variational Data Assimilation (4D-Var) in Earth System Modeling - Developed an optimization model using the Reduced-Adjoint Variational Data assimilation technique for highly nonlinear models (e.g. numerical weather prediction, global climate, and earth system modeling) - Estimated the unknown parameters of the root zone (the initial condition of the soil moisture profile, and soil hydraulic parameters) Statistical analysis for validating and implementing the model at a large (regional) scale - Performed statistical analysis to validate the model with field datasets such as ARM and Mesonet soil data - Implemented the model for Large-scale coupled estimation of the initial soil moisture profile and the effective soil hydraulic parameters using spaceborne satellite data over the SGP region. Surface-subsurface interaction study - Investigated the impact of soil, climate, and vegetation on groundwater recharge within a study of surface-subsurface interactions, contributing valuable insights for effective groundwater management

Modeling of Snow cover and snow water equivalent - Evaluated existing techniques for measuring snow parameters through remote sensing, aiming at identifying the most effective and implementable method for Iran’s conditions. - Implemented, verified, and calibrated remote sensing algorithms to accurately estimate snow depth and snow water equivalent, resulting in more precise snowfall assessments. - Prioritized methods that require minimal field data, addressing the challenge of limited accurate and continuous measurements available in Iran, leading to more sustainable and low-maintenance solutions. Land Use and Land Cover Modeling- object-based classification techniques: - Contributed to a significant project called the Iranian Land Use Land Cover Monitoring Service. - Utilized object-based classification techniques to create LU/LC maps. - Created detailed Land Use/Land Cover map for the Karkheh basin as part of the Iranian Land Use Land Cover Monitoring Service Project, employing 30-m-spatial-resolution Landsat-8 OLI multispectral images and object-based classification techniques, enhancing regional environmental planning and resource management. - Scrutinized changes in Land Use and Land Cover in the Karkheh basin from 2000 to 2014 using 30-m-spatial-resolution Landsat-8 OLI multispectral images and pixel-based classification methods, informing future conservation and land development strategies.

Lake Urmia restoration program: - Analyzed spatiotemporal dust patterns in Lake Urmia, pinpointing spatial dust sources with a specialized numerical algorithm, leading to enhanced monitoring and environmental management strategies. - Adapted high-resolution Simplified Aerosol Retrieval Algorithm (SARA) to effectively identify dust sources in high-concentration conditions over reflective desert terrains, resulting in improved accuracy and reliability for aerosol measurements. - Collaborated on the analysis of daily Aerosol Optical Depth (AOD) data from MODIS over northwestern Iran, focusing on Lake Urmia. Revealed that regional AOD dynamics are predominantly influenced by transported pollution, emphasizing implications for public health, climate, and pollution control efforts amid the lake's desiccation

GIS and Remote Sensing: - Provided instructional support for courses covering GIS and remote sensing technologies, with a focus on ArcGIS mapping software. - Assisted students in learning ArcGIS software functionalities, spatial analysis techniques, and map creation. - Conducted training sessions, workshops, and one-on-one consultations to enhance students' proficiency in ArcGIS mapping and spatial analysis.

- Executed land-use simulations for the Zaribar Lake Ecological Restoration Project in Kordestan, Iran, to support the long-term stability of the ecosystem and enhance biodiversity. - Implemented effective stormwater management strategies in the western region of Tehran, Iran, resulting in improved water quality and a reduction in flood risks. - Conducted conceptual studies to analyze the network gathering surface water and associated facilities in the Tehran basin, Iran. - Conducted assessments and implemented improvements to enhance the efficiency of existing runoff control facilities in Tehran, Iran, with the primary goal of mitigating flood risks.