This project will assess the nature and strength of soil moisture feedbacks on convective precipitation in the central United States using a coupled observation-modeling framework to examine the physical processes linking the soil and boundary layer atmosphere. The objectives of the project are to: (1) evaluate whether convection occurs preferentially over wet or dry soils in the central United States, (2) quantify the influence of soil moisture on convection and precipitation using process-based metrics and high quality land and atmosphere observations, and (3) evaluate the response of convection and precipitation to changes in soil moisture conditions using the Weather Research and Forecast (WRF) model.
I am looking for a PhD student or Postdoc who is interested in working on this project.
EmPOWERment is accepting applications for the 2021-2022 academic year from current and prospective PhD students to The Ohio State University with an interest in sustainable energy. Prospective and first-year PhD students who are accepted into EmPOWERment also will be considered for a highly prestigious and competitive one-year fellowship opportunity funded by National Science Foundation that provides full funding to help support the trainee’s PhD studies and participation in the program.
EmPOWERment offers PhD students the opportunity to pursue interdisciplinary training in energy-system modeling, data science, energy policy, business, energy technologies, and legal, economic, and social-behavioral issues to address the challenges facing a sustainable energy future. EmPOWERment trainees are immersed in coursework, research, experiential-learning opportunities, a student community of like-minded individuals, and opportunities that are designed to introduce students to the unique challenges facing the advancement of sustainable energy systems. Trainees gain an in-depth knowledge of every facet of the sustainable energy landscape through a unique interdisciplinary experience that combines research, education, and communication training. Working together with faculty, industry mentors, and fellow trainees, EmPOWERment students prepare to become the next generation of innovative leaders in sustainable energy.
Prospective PhD students must apply and be enrolled in a PhD program to participate in EmPOWERment. EmPOWERment is designed to supplement a student’s PhD studies and is not a standalone PhD program.
Abstract: There are a variety of metrics that are used to monitor drought conditions, including soil moisture and drought indices. This study examines the relationship between in situ soil moisture, NLDAS-2 soil moisture, and four drought indices: the standardized precipitation index, the standardized precipitation evapotranspiration index, the crop moisture index, and the Palmer Z index. We evaluate how well drought indices and the modeled soil moisture represent the intensity, variability, and persistence of the observed soil moisture in the southern Great Plains. We also apply the drought indices to evaluate land–atmosphere interactions and compare the results with soil moisture. The results show that the SPI, SPEI, and Z index have higher correlations with 0–10-cm soil moisture, while the CMI is more strongly correlated with 0–100-cm soil moisture. All the drought indices tend to overestimate the area affected by moderate to extreme drought conditions. Significant drying trends from 2003 to 2017 are evident in SPEI, Z index, and CMI, and they agree with those in the observed soil moisture. The CMI captures the intra- and interannual variability of 0–100-cm soil moisture better than the other drought indices. The persistence of CMI is longer than that of 0–10-cm soil moisture and shorter than that of 0–100-cm soil moisture. Model-derived soil moisture does not outperform the CMI in the 0–100-cm soil layer. The Z index and CMI are better drought indices to use as a proxy for soil moisture when examining land–atmosphere interactions while the SPI is not recommended. Soil type and climate affect the relationship between drought indices and observed soil moisture.
Abstract: Soil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increase the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’ current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1,200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for: (1) inclusion in national-scale soil moisture datasets, (2) deriving in situ-informed gridded soil moisture products, and (3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1,233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. in situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.
I presented in the University of Colorado-Boulder Department of Geography Colloquium Series on Friday, September 11. My talk is available on YouTube.
Weather can cause significant damage to the electrical power system, leading to prolonged power interruptions to a large number of customers. The estimated annual cost to the U.S. economy from storm-related power outages is >$20 billion. The number of weather-related outages has increased significantly in recent years. One approach to deal with this problem is to develop predictive techniques for forecasting how storms will impact the power grid hours to days in advance. This information can help utilities, first responders, and emergency managers to better prepare for the outages and more quickly restore power. This presentation summarizes the data-driven power outage models that we have developed for the U.S. Department of Energy and a number of investor-owned electrical utilities in the United States. These models are used to support decision making for near-term events (e.g., pre-storm preparation) and longer-term planning. The development and validation of our models will be presented and our approach for quantifying uncertainty will also be discussed. The talk will also highlight the challenges and successes from recent applications for American Electric Power, FirstEnergy, Southern Company and Southern California Edison.