A5084725110- Precipitation Measurement and Analysis
- Soil Moisture and Remote Sensing
- Meteorological Phenomena and Simulations
- Cryospheric studies and observations
- Climate variability and models
- Climate change and permafrost
- Geophysics and Gravity Measurements
- Image and Signal Denoising Methods
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Radio Wave Propagation Studies
- Landslides and related hazards
- Geophysical and Geoelectrical Methods
- Geophysical Methods and Applications
- Plant Water Relations and Carbon Dynamics
- Hydrology and Watershed Management Studies
- Advanced Computational Techniques and Applications
- Computational Physics and Python Applications
- Image Processing and 3D Reconstruction
- Wind and Air Flow Studies
- Tropical and Extratropical Cyclones Research
- Flood Risk Assessment and Management
- Fluid Dynamics and Turbulent Flows
- Smart Agriculture and AI
- Microplastics and Plastic Pollution
- Sparse and Compressive Sensing Techniques
Saint Anthony College of Nursing
2013-2025
University of Minnesota
2014-2025
Minnesota State University Moorhead
2024
University of Minnesota System
2021-2022
Twin Cities Orthopedics
2011-2021
Grantmakers for Effective Organizations
2020
Georgia Institute of Technology
2015-2017
Boise State University
2017
Utah State University
2016
Passive microwave remote sensing at L-band (1.4 GHz) provides an unprecedented opportunity to estimate global surface soil moisture (SM) and vegetation water content (via the optical depth, VOD), which are essential monitor Earth carbon cycles. Currently, only two space-borne radiometer missions operating: Soil Moisture Ocean Salinity (SMOS) Active (SMAP) in orbit since 2009 2015, respectively. This study presents a new mono-angle retrieval algorithm (called SMAP-INRAE-BORDEAUX, hereafter...
Abstract Understanding the role of dynamic, thermodynamic, and cloud microphysical parameters governing occurrence magnitude convective precipitation at sub-grid scales is crucial for reducing uncertainties in extreme properties projected by global climate models. This study evaluates efficacy eXtreme Gradient Boosting decision trees detecting features (CPFs) estimating their extent using convection-allowing simulations based on observations across contiguous United States (CONUS). Our...
Integration of machine learning with a classic Bayesian algorithm is investigated for passive microwave precipitation retrievals using coincidences from the Global Precipitation Measurement core satellite and CloudSat Profiling Radar (CPR). Among several models, eXtreme Gradient Boosting Decision Tree (XGBDT), equipped weighted cross entropy loss function, exhibits highest accuracy in detection occurrence phase true positive rate greater than 94 (98)% false smaller 1 (1)% rainfall (snowfall)...
Abstract Satellites are playing an ever‐increasing role in estimating precipitation over remote areas. Improving satellite retrievals of requires increased understanding its passive microwave signatures different land surfaces. Snow‐covered surfaces notoriously difficult to interpret because they exhibit both emission from the below and scattering ice crystals. Using data Global Precipitation Measurement (GPM) satellite, we demonstrate that brightness temperatures rain snowfall transition a...
Abstract Snowfall is one of the primary drivers global cryosphere and declining in many regions world with widespread hydrological ecological consequences. Previous studies have shown that probability snowfall occurrence well described by wet-bulb temperatures below 1°C (1.1°C) over land (ocean). Using this relationship, from three reanalysis products as multisatellite precipitation data are analyzed 1979 to 2017 study changes potential areas, snowfall-to-rainfall transition latitude,...
Abstract In-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification depletion recharge groundwater resources. Net cannot be directly measured, its estimates as a residual individual components often suffer from mass conservation errors due to accumulated systematic biases fluxes. Here, first time, we provide direct NWF based on near-surface satellite soil moisture retrievals Soil Moisture Ocean Salinity (SMOS) Active Passive...
The Global Precipitation Measurement (GPM) Dual-Frequency Radar (DPR) (Ku- and Ka-band, or 14 35 GHz) provides the capability to resolve precipitation structure under moderate heavy conditions. In this manuscript, use of near-coincident observations between GPM CloudSat Profiling (CPR) (W-band, 94 are demonstrated extend representing light rain cold-season from DPR passive microwave constellation sensors. These unique triple-frequency data have opened up applications related precipitation,...
[1] Improved estimation of hydrometeorological states from down-sampled observations and background model forecasts in a noisy environment has been subject growing research the past decades. Here we introduce unified variational framework that ties together problems downscaling, data fusion, assimilation as ill-posed inverse problems. This seeks solutions beyond classic least squares paradigms by imposing proper regularization, expressed constraint consistent with degree smoothness and/or...
Monitoring changes of precipitation phase from space is important for understanding the mass balance Earth's cryosphere in a changing climate. This paper examines Bayesian nearest neighbor approach prognostic detection and its using passive microwave observations Global Precipitation Measurement (GPM) satellite. The method uses weighted Euclidean distance metric to search through an priori database populated with coincident GPM radiometer radar as well ancillary snow-cover data. algorithm...
Abstract A fully global satellite-based precipitation estimate that can transition across changing Earth surface and complex land/water conditions is an important capability for many hydrological applications, independent evaluation of the derived from weather climate models. This inherently challenging owing to complexity geophysical properties upon which instruments view. To date, these satellite observations originate primarily a variety wide-swath passive microwave (MW) imagers sounders....
Downscaling of remotely sensed precipitation images and outputs general circulation models has been a subject intense interest in hydrometeorology. The problem downscaling is basically one resolution enhancement, that is, appropriately adding details or high frequency features onto low‐resolution observation simulated rainfall field. Invoking the property self similarity, this mathematically ill‐posed approached past within stochastic framework resulting ensemble possible high‐resolution...
Understanding and reducing the uncertainties in inversion of first-order radiative transfer models at L-band are important for improved spaceborne retrievals soil moisture (SM) vegetation optical depth (VOD) over dense canopy. This article quantifies compares sensitivity dual-channel two-stream (2S) τ-ω proposes a new approach simultaneous SM, VOD, vegetation-scattering albedo (ω) from single satellite overpass. In particular, algorithm incorporates information nearby spatial observations,...
Abstract Classical variational data assimilation methods address the problem of optimally combining model predictions with observations in presence zero‐mean Gaussian random errors. However, many natural systems, uncertainty structure and/or parameters often results systematic errors or biases. Prior knowledge about such error for parametric removal is not always feasible practice, limiting efficient use improved prediction. The main contribution this work to advocate relevance...
Abstract This study characterizes the space‐time structure of soil moisture background error covariance and paves way for development a variational data assimilation system Noah land surface model coupled to Weather Research Forecasting (WRF) model. The over contiguous United States exhibits strong seasonal regional variability with largest values occurring in uppermost layer during summer. Large biases were identified, particularly southeastern States, caused mainly by discrepancy between...
This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research Forecasting (WRF) Noah land surface model through variational approaches. The authors tested by assimilating data from Tropical Rainfall Measuring Mission (TRMM) Soil Moisture Ocean Salinity (SMOS) satellite. results show both TRMM SMOS can effectively improve forecast skills precipitation, top 10-cm moisture, 2-m temperature...
ABSTRACT Developing high‐quality long‐term data sets at uniform space–time resolution is essential for improved climate studies. This article processes the outputs from two global and regional models, Community Climate System Model ( CCSM3 ) Regional driven by Hadley Centre Coupled RegCM3 ). The results are bias‐corrected time series of atmospheric variables corresponding to Intergovernmental Panel on Change IPCC 's) historical 20C3M future A2 scenarios over Amazon Basin. We use a simple but...
This paper studies the role of sparse regularization in a properly chosen basis for variational data assimilation (VDA) problems. Specifically, it focuses on noisy and down-sampled observations while state variable interest exhibits sparsity real or transformed domain. We show that presence sparsity, $\ell_{1}$-norm produces more accurate stable solutions than classic methods. To motivate further developments proposed methodology, experiments are conducted wavelet spectral domain using...