A5041237796- Hydrology and Watershed Management Studies
- Hydrological Forecasting Using AI
- Atmospheric aerosols and clouds
- Groundwater flow and contamination studies
- Atmospheric chemistry and aerosols
- Meteorological Phenomena and Simulations
- Energy Load and Power Forecasting
- Hydrology and Sediment Transport Processes
- Flood Risk Assessment and Management
- Geophysical Methods and Applications
- Precipitation Measurement and Analysis
- Atmospheric and Environmental Gas Dynamics
- Seismology and Earthquake Studies
- Geophysical and Geoelectrical Methods
- Water-Energy-Food Nexus Studies
- Atmospheric Ozone and Climate
University of Arizona
2018-2023
Abstract This study reports a characterization of the real part dry particle refractive index ( n ) at 532 nm based on airborne measurements over United States, Canada, Pacific Ocean, and Gulf Mexico from 2012 Deep Convective Clouds Chemistry (DC3) 2013 Studies Emissions Atmospheric Composition, Climate Coupling by Regional Surveys (SEAC 4 RS) campaigns. Effective values are reported, with limitations uncertainties discussed. Eight air mass types were identified criteria related to gas‐phase...
Abstract Access to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis products; (ii) machine learning-based approaches; (iii) a gap-filling software explicitly developed for filling the gaps daily records. This study evaluated all approaches over sparsely gauged basin East Africa. Among examined products, PERSIANN-CDR...
This study provides a detailed characterization of stratocumulus clearings off the US West Coast using remote sensing, reanalysis, and airborne in situ data. Ten years (2009-2018) Geostationary Operational Environmental Satellite (GOES) imagery data are used to quantify monthly frequency, growth rate total area (GRArea), dimensional characteristics 306 clearings. While there is interannual variability, summer (winter) months experienced most (least) clearing events, with lowest cloud...
We confirm that energy dissipation weighting provides the most accurate approach to determining effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained pattern from an image distribution, although it was less for cases highly localized structures controlled flow. Furthermore, learned even if not directly on this information. However, weights were represented...
We examine the ability of machine learning (ML) and deep (DL) algorithms to infer surface/ground exchange flux based on subsurface temperature observations. The observations fluxes are produced from a high-resolution numerical model representing conditions in Columbia River near Department Energy Hanford site located southeastern Washington State. Random measurement error, varying magnitude, is added synthetic results indicate that both ML DL methods can be used flux. methods, especially...
We confirm that energy dissipation weighting provides the most accurate approach to determining effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained pattern from an image distribution fidelity, although it was less for cases highly localized structures controlled flow. Furthermore, learned even if not on this information directly. However, weights...
Abstract. This study provides a detailed characterization of stratocumulus clearings off the U.S. West Coast using remote sensing, reanalysis, and airborne in situ data. Ten years (2009–2018) Geostationary Operational Environmental Satellite (GOES) imagery data are used to quantify monthly frequency, growth rate total area (GRArea), dimensional characteristics 306 clearings. While there is interannual variability, summer (winter) months experienced most (least) clearing events with lowest...
We demonstrate the application of two simple machine learning tools - regression tree and gradient boosting analyses to a hydrologic inference problem address objectives. The first goal was infer flux between river subsurface based on high temporal resolution (5-minute) observations pressure temperature. second identify an optimal set support these inferences. Specifically, we examine how many what type (pressure and/or temperature) were necessary at depths. Using synthetic surface fluxes...
Earth and Space Science Open Archive This preprint has been submitted to is under consideration at Water Resources Research. ESSOAr a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Can Machine Learning Extract Useful Information Energy Dissipation Effective Hydraulic Conductivity from Gridded Fields?AuthorsMohammad AMoghaddamiDTyFerreiDJEFFREYKLAKOVICHHoshin...
Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical numerical models temperature time series measured at multiple depths daily average flux. These seen wide use in hydrologic science despite strong simplifying assumptions including lack of consideration model structural error or impacts multidimensional flow transient streambed hydraulic properties. We performed...