Liming Zhou, Ph.D. - US grants

This INSPIRE award is partially funded by Physical and Dynamic Meteorology program in the Division of Atmospheric and Geospace Sciences in the Directorate for Geosciences, Climate and Large-scale Dynamics program in the Division of Atmospheric and Geospace Sciences in the Directorate for Geosciences, and Environmental Sustainability program in the Division of Chemical, Bioengineering, Environmental, and Transport Systems in the Directorate for Engineering.

The global wind industry has experienced a remarkably rapid expansion of capacity in recent years and this fast growth is expected to continue in the future. While converting wind's kinetic energy into electricity, wind turbines modify surface-atmosphere exchanges of energy, momentum, mass and moisture. Given the current installed capacity and the projected installation worldwide, wind farms (WFs) are likely becoming a major driver of manmade land use change on Earth. Hence, understanding WF-atmosphere-environment interactions and assessing potential environmental impacts are of significant societal importance. However, recent studies of WF impacts on meteorology have been primarily in the modeling domain using simplified wind turbine parameterizations (WTPs) due to the lack of observations. Given the availability of high resolution radar and remote sensing data, we believe it is time to begin systematically assessing WF impacts in the U.S.

The project will conduct a process-based observational and modeling study to investigate possible impacts on weather, climate and environments due to the rapid development of wind farms (WF) in the Great Plains and Midwest. Specifically, the study will involve analyzing a variety of observational data (near-surface meteorological variables, radar and remote sensed), detecting, quantifying and attributing such impacts over the 17 biggest U.S. WFs, and then performing a series of high resolution mesoscale simulations to evaluate wind turbine parameterizations (WTPs). The investigations will unveil how operational WFs influence the diurnal, seasonal and interannual variations of atmospheric boundary layer (ABL) structures and phenomena, near-surface hydrometeorology, and crop/vegetation growth, and how these changes vary under various meteorological and surface conditions and WF configurations (e.g., elevation, land cover, wind patterns, local climate).

Intellectual merit:
This research can potentially bridge scientific knowledge gap of (a) atmospheric boundary layer dynamics and thermodynamics within and downwind of operational wind farms and (b) physical processes and mechanisms of wind farm impacts on environment. It will also generate knowledge about the performance of the wind turbine parameterizations and identify model refinements required to simulate wind farms in mesoscale models. It is potentially transformative by providing a comprehensive picture of wind farm footprint on weather and climate, a fundamental step toward projecting the future impacts at large scales, and by challenging conventional wisdom with far more complicated wind farm-atmosphere-environment interactions.

Broader Impacts:
Wind power supports environmental sustainability and is likely to be part of the solution to the climate change, air pollution and energy security problem. Assessing potential WF impacts is critical for developing efficient adaptation and management strategies to ensure long-term sustainability of wind power. The study will lead to improvements in numerical weather prediction and projection of WF impacts on weather, climate, environments, water cycle and agricultural practices. The obtained knowledge and approaches can be generalized to other studies. In addition, this project has important education and outreach components and will provide learning and training experience for undergraduate and graduate students.

Central African forests, the driest and second largest on Earth, have experienced a long-term drying trend. A recent study detected a widespread decline in forest photosynthetic capacity over the Congo Basin and attributed this large-scale decline, at least partially, to this drying trend. Persistent drought in the region could alter the Congolese forest composition and structure in a manner that might favor the spread of drought tolerant species and thus impact biodiversity, carbon storage, and the global water cycle. Understanding the nature and cause of this drought and assessing its impacts on the forests are of significant societal, environmental, and economic importance. Understanding of climatic variability in the Congo basin is greatly hindered by a lack of observations and resources due to political, economic and historical factors.

Under this project, a combination of observational and modeling studies will be undertaken to investigate the nature and cause of the Congo rainfall variability in the 20th century. The role of global sea surface temperatures will be examined. For the observational component, observations and reanalysis data will be utilized to identify major patterns and drivers of the variability. For the modeling component, the Coupled Model Intercomparison Project (CMIP5) simulations and idealized climate model simulations will be examined to investigate the physical mechanisms for the Congo drought. The relative contribution of anthropogenic versus natural forcing on the observed rainfall decadal variability over the Congo Basin will be investigated.

This award permits University at Albany, SUNY, to purchase an unmanned aircraft system. Researchers led by Dr. Alexander Buyantuev will use the system to conduct a multidisciplinary study focused on monitoring fast ecological and geophysical processes and advance the understanding of environmental changes at scales of individual trees to whole landscapes. Several projects that utilize near surface remote sensing made possible by the cutting edge drone technology and ground measurements will examine effects of climate warming and local anthropogenic impacts, such as soil acidification, wind power generation, and urbanization, on vegetation growth. Gaining insight into these effects is of great importance to predict more accurately the state of ecosystems in the future and to develop sound mitigation strategies. Researchers will also be committed to look closely into the use of the technology in identifying best practices and developing protocols for post-storm area analysis and future emergency management applications. Overall, these studies are expected to benefit a large number of students and the public by either their direct involvement in the work or through exposure to research during classwork or public presentations.

Dr. Buyantuev with colleagues will use a professional grade unmanned aircraft system (UAS) outfitted with visual, multi (hyper-) spectral, thermal, and laser sensors to collect geospatial data at forested field sites and near developed areas. Such very high resolution data will provide the missing link in scaling between ground sampling and airborne and spaceborne remote sensing, primarily data from widely used Landsat and MODIS satellites. It will allow researchers to assess how climate warming and associated changes in timing of tree growth affect the dynamics of nonstructural carbon in spruce trees and understand better the effects of soil acidification and warming on growing season arrival and length for sugar maple stands in Upstate New York, USA. The team is also particularly interested in how wind turbines, being the sources of alternative and environmentally friendly power production, and the process of urbanization characterized by the expansion of built-up land, modify local climates and vegetation/crop growth. These effects will be studied by quantifying inter-annual growing seasons and biomass changes in the vicinity of an existing wind farm and along the gradient of land development in a suburban site. The research will generate valuable datasets and methods for processing high resolution imagery into important scientific information.