ASRC Research

Weather data is big data, and to harness the scientific possibilities from these data require the identification of, training for, and utilization of innovative analytical techniques. The Atmospheric Sciences Research Center recognizes the need to add techniques in Artificial Intelligence (AI), such as Machine Learning (ML), to its already robust modeling toolbox. 

ASRC is on the vanguard of developing weather and climate-centric capabilities. This includes the establishment of the ASRC ExTreme Collaboration, Innovation, and Technology (xCITE) Lab. With growing expertise in data analytics, software development, and importantly AI/ML, the xCITE lab serves as a virtual and physical infrastructure for innovative computing at ASRC. Outfitted with state-of-the-art high-end scientific visualization and GPU/CPU high-performance computing, the laboratory serves as a multi-disciplinary collaboration space for research and development across University groups as well as with public and private partners.

ML is useful in situations in which manually producing rules for label distinction is laborious. In particular, feature selection and pattern recognition output from a machine is most useful when considering the abundance of correlations that can be glossed over by a human. ML algorithms provide the capability to quickly and efficiently operate on mass data and identify features otherwise unnoticed by even a trained-eye, and perhaps ignored given the abundance of data, which would nearly be impossible to personally analyze. To function appropriately, accurate uses of ML require large quantities of data of the same type. Weather in its ever-reliable persistence provides the atmospheric science community with an unprecedented data playground in measured and modeled forms.

The atmospheric chemistry and aerosols group deals with the measurements and modeling of atmospheric trace gases and aerosols, air quality and aerosol modeling, cloud formation and chemistry, and chemical processes leading to formation and growth of new particles and cloud condensation nuclei. These activities help scientists understand our atmospheric environment, the health effects of polluted air, and the connection of chemistry and aerosols to the broader topic of climate change. Many of these activities focus on air pollution, air quality and particles within New York State. However, these issues are global in nature and ASRC researchers frequently collaborate with colleagues throughout the nation and the world.

Measurements of atmospheric chemistry and air quality are taken at a variety of fixed surface locations in New York State and from a mobile laboratory. The fixed sites allow for long term measurements to help track progress in air quality and changes in the emissions and concentrations of trace gases and aerosols. Generous support for these measurements have been supplied by the New York State Energy Research and Development Authority (NYSERDA). These measurements and the resulting data repository are a rich resource that is used by ASRC scientists, and by colleagues from many other organizations.

ASRC maintains year-round research at Whiteface Mountain Observatory in the Adirondacks, which has been active for over 50 years. We measure trace gases, aerosols, meteorology, and precipitation routinely at the summit observatory and the affiliated lodge site halfway up the mountain. Whiteface Mountain is often enveloped in clouds and is a prime location for cloud-related studies. Cloud water chemistry is measured during the summer months. Ongoing research seeks to track the changes in chemical composition of atmospheric gases, aerosols, clouds and precipitation; as well as the aerosol-cloud-climate interactions at this high-altitude location.

Air quality and aerosol modeling at ASRC seeks to integrate our understanding of atmospheric chemistry and meteorology to provide forecasts and research tools for scientists and the public. Air quality models are used to investigate how pollutant distributions are affected by local weather patterns, conduct retrospective analysis to interpret polluted events, characterize pollutant transport and evolution, and quantify the contributions from local versus non-local emission sources. Numerical simulations, in conjunction with multi-platform observations, could improve process-level understanding of the pollution problem across New York State.

Atmospheric particles are important for both climate and human health. They have a direct role in radiative forcing because they scatter and absorb visible and infrared radiation in the atmosphere. They also act as cloud condensation nuclei which modify cloud properties and precipitation and thus indirectly impact the hydrological cycle and climate. They are important to human health in that exposure to particles in the air can affect your lungs, your heart, and even your brain. Numerous scientific studies have linked particle pollution exposure to a variety of health problems. The health and climate impacts of atmospheric particles depend on their chemical and microphysical properties, including concentration, size, composition and mixing state. Various research activities have been carried out here at ASRC to understand these properties and the underlying physical and chemical processes determining these properties, through in-situ measurements, laboratory experiments, remote sensing, theoretical studies and numerical simulations.

The lowest 1- 3 km of the atmosphere above the surface of the earth is termed the atmospheric boundary layer. In this region, the atmosphere and surface are coupled via the exchange, or fluxes, of various quantities such as momentum, heat, water vapor, and trace gases including carbon dioxide. These fluxes are typically driven by turbulent motions and can have major impacts on the composition, structure and dynamics of the atmosphere.

Atmospheric boundary layer research aims toward increasing our fundamental understanding of surface-atmosphere interactions, and quantifying these relationships in mathematical expressions that can be incorporated into weather and climate models to address questions about current and future biogeochemical cycles and climate.

ASRC has a rich history of field-oriented boundary layer research, involving deployment of scientific instruments throughout the world in a variety of (often remote) terrestrial ecosystems (e.g., forests, agricultural fields), rivers, lakes and the ocean, using platforms such as towers, aircraft, boats, buoys and large research vessels.

Researchers: Richard Perez, Qilong Min, Jeff Freedman

The ASRC Renewable Energy Group conducts research exploring better knowledge and characterization of the wind and solar energy resources, and how atmosphere-land-sea-surface exchange processes may influence the performance of wind and solar energy applications. Resource assessment, technological design, resource forecasting, and socioeconomics, are subjects that depend directly or indirectly upon this knowledge. Currently, several parallel tracks of research are conducted at the ASRC:

1. Solar resource characterization is studied using remote sensing by satellites. Several of the models developed at ASRC are incorporated into well-known solar energy/building system simulation programs (e.g., PVLib, PVSyst, DOE-2) and national data sets (e.g., SolarAnywhere, Natural Resource Canada);
2. Wind resource characterization (land-based and offshore) is studied using a variety of land-based direct measurement platforms and remote sensing instruments (e.g., wind profiling lidars);
3. Improvements to wind and solar energy resource assessment and power forecasting is explored through high resolution computational approaches that merge numerical weather prediction, land-based and satellite-based measurements, and machine learning approaches;
4. Meeting electrical demand 24/365 using photovoltaics (PV) is researched. PV – a modular technology that is well suited for dispersed production (e.g., roofs, farms, rights of way, etc) – is also a natural complement to wind power. Their combination will foster the transition to 100% renewable energy, not only for electricity generation, but also for transportation and buildings’ energy demand.
5. Energy demand is investigated at city scales using urbanized weather models (such as uWRF) that account for 2-way energy and mass exchanges between buildings and the environment enabling a new field of energy demand forecasting for demand response and energy sustainability in cities;
6. The development of sophisticated outage prediction models integrating advanced NWP with machine learning techniques to reduce service disruptions and economic costs associated with power outages; and
7. The effects of climate change on renewable energy resources are explored on a spectrum of temporal and spatial scales. Recent work includes using dynamical downscaling of several Earth System Models to examine potential impacts to New York’s renewable energy resources, and an ensemble approach using 40 CMIP5 models to determine global trends in solar and wind energy.