A5101667919- Soil Moisture and Remote Sensing
- Precipitation Measurement and Analysis
- Climate change and permafrost
- Meteorological Phenomena and Simulations
- Multi-Criteria Decision Making
- Climate variability and models
- Remote Sensing in Agriculture
- Cryospheric studies and observations
- Power Systems and Renewable Energy
- Atmospheric and Environmental Gas Dynamics
- Sustainable Industrial Ecology
- Sustainable Supply Chain Management
- Supply Chain and Inventory Management
- Plant Water Relations and Carbon Dynamics
- Calibration and Measurement Techniques
- Evaluation Methods in Various Fields
- Global Energy and Sustainability Research
- Power Systems and Technologies
- Hydrology and Drought Analysis
- Land Use and Ecosystem Services
- Big Data and Business Intelligence
- Photovoltaic Systems and Sustainability
- Electric Power System Optimization
- Smart Grid and Power Systems
- Solar Radiation and Photovoltaics
North China Electric Power University
2014-2025
Loudi Central Hospital
2024
State Grid Corporation of China (China)
2023-2024
Earth System Science Interdisciplinary Center
2015-2023
University of Maryland, College Park
2015-2023
NOAA Center for Satellite Applications and Research
2014-2023
NOAA National Environmental Satellite Data and Information Service
2011-2023
Cooperative Institute for Climate and Satellites
2020-2023
Changshu Institute of Technology
2022
University Research Co (United States)
2018-2019
The goal of NASA's land long term data record (LTDR) project is to produce a consistent set from the AVHRR and MODIS instruments for climate studies. will create daily surface reflectance normalized difference vegetation index (NDVI) products at resolution 0.05deg, which identical modeling grid (CMG) used EOS Terra Aqua. Higher order such as burned area, temperature, albedo, bidirectional distribution function (BRDF) correction, leaf area (LAI), fraction photosynthetically active radiation...
Abstract. This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016, using Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (WRF-Hydro). Six-hourly forecasted forcing records at 0.5∘ spatial resolution, obtained from National Center for Environmental Prediction (NCEP) Global Forecast System (GFS), are used to drive three nested downscaling domains of both standalone WRF...
The carbon neutrality target challenges the reliability of integrated energy system (IES) and therefore it is necessary to assess comprehensive risk IES projects for investors managers. This work proposes a hesitant fuzzy multi-criteria decision-making framework IES. In this framework, 16 factors are firstly identified from economy, technology, politics, society management. Then, HF-DEMATEL-CPT approach proposed by combining sets (HFS), trial evaluation laboratory (DEMATEL) cumulative...
Integrating coal-to-hydrogen production with Carbon Capture, Utilization, and Storage (CCUS) is essential for reducing greenhouse gas emissions facilitating a shift towards more sustainable energy paradigm. This paper explores the diffusion of CCUS technology within sector against dynamic backdrop carbon trading market. An evolutionary game-theoretic approach utilized small-world network framework to analyze spread among enterprises. The simulation reveals that current market dynamics, along...
Land surface soil moisture is a critical component in understanding the interactions between water cycle, vegetation, and climate systems. It plays an important role agricultural productivity, hydrological modeling, predictions. The Advanced Scatterometer (ASCAT) onboard MetOp series of satellites, provides consistent global dataset with high spatial temporal resolutions. However, validation satellite products remains challenge, particularly across diverse land cover types, where...
Soil Moisture is a vital state variable influencing land surface dynamics across hydrological, meteorological, and climatological contexts. The Operational Product System (SMOPS), developed by the National Environmental Satellite, Data, Information Service (NESDIS) of Oceanic Atmospheric Administration (NOAA), has been operationally providing satellite soil moisture observational data products for scientific studies numerical weather water predictions. However, lack high-quality long-term...
We establish the quality of MODIS BRDF/albedo retrieval process through use Root Mean Squared Error (RMSE) and Weight Determination (WoD). These measures are constructed during inversion to quantify uncertainties. RMSE provides a deviation indicator model‐fits while WoD evaluates confidence from given angular samplings. From statistical analysis retrievals over range surface types, we an upper thresholds (0.071, 0.097) for high RMSE, (1.431, 1.848) Nadir Reflectance (assuming 45° solar...
Spectral surface albedo, a boundary condition which needs to be accurately known for aerosol remote sensing, forcing, and radiative transfer calculations, also strongly affects Earth's radiation balance. The difficulty in deriving albedo from space aircraft observations lies mainly the atmospheric correction, especially aerosol‐burdened regions. Because of different scales, comparing satellite retrievals with airborne or ground‐based is not straightforward. We use Solar Flux Radiometer...
Abstract The ensemble Kalman filter (EnKF) has been extensively applied in sequential soil moisture data assimilation to improve the land surface model performance and turn weather forecast capability. Usually, size of EnKF is determined with limited sensitivity experiments. Thus, optimal may have never reached. In this work, based on a series mathematical derivations, we demonstrate that maximum efficiency for assimilating observations into models could be reached when set 12. Simulation...
Data assimilation is the application of Bayes' theorem to condition states a dynamical systems model on observations. Any real-world approximate, and therefore we cannot expect that data will preserve all information available from models We outline framework for measuring in models, observations, evaluation way allows us quantify loss during (necessarily imperfect) assimilation. This facilitates quantitative analysis tradeoffs between improving (usually expensive) remote sensing observing...
Abstract Many studies that have assimilated remotely sensed soil moisture into land surface models generally focused on retrievals from a single satellite sensor. However, few evaluated the merits of assimilating ensemble products are merged several different sensors. In this study, assimilation Soil Moisture Operational Products System (SMOPS) blended (SBSM) product, which is combination WindSat, Advanced Scatterometer (ASCAT), and Ocean Salinity (SMOS) sensors examined. Using Kalman filter...
Abstract A global Soil Moisture Operational Product System (SMOPS) has been developed to process satellite soil moisture observational data at the NOAA National Environmental Satellite, Data, and Information Service for improving numerical weather prediction (NWP) models Weather (NWS). few studies have shown benefits of assimilating in land surface (LSMs), which are components most NWP models. In this study, synthetic experiments conducted determine how quality control may impact benefit...
Global soil moisture is one of the critical land surface initial conditions for numerical weather, climate, and hydrological predictions. Since it not practical to provide global maps using ground measurements, remote sensing has been a hot research topic in last several decades. As result, number products have produced from different satellite sensors with spatial temporal coverage quality. To make effective use all available products, Soil Moisture Operational Product System (SMOPS)...
It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite retrieval data products have also been available for applications. However, these observational not employed in any operational or climate In this study, preliminary test assimilating from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into NOAA-NCEP Global Forecast (GFS) conducted. Using ensemble Kalman filter (EnKF)...
Abstract In 2011, the National Oceanic and Atmospheric Administration (NOAA) began a cooperative initiative with academic community to help address vexing issue that has long been known as disconnection between operational research realms for weather forecasting data assimilation. The is gap, more exotically referred “valley of death,” efforts within broader NOAA’s activities, which are heavily driven by constraints. With stated goals leveraging benefit mission offering path operations...
Abstract Soil moisture (SM) data from Moisture Operational Product System (SMOPS) have been available and used by users since 2013, the latest version (3.0) has operationally released 2017. The 3.0 provides a combination of currently all daily global microwave SM retrievals including observations ASCATA, ASCATB, SMAP, SMOS, AMSR2 1 April 2015 to present. This study intercompares Noah model skills with benefits assimilating SMOPS blended (hereafter, SMOPS) five individual satellite...