Researchers across the University of Maryland are collaborating on initiatives to discover opportunities, analyze trends, and make recommendations for the future.
Ranked 8th among all public institutions in Research and Development spending by the National Science Foundation’s Higher Education Research and Development survey for fiscal year 2019, the University of Maryland is a world-class research enterprise.
Recent Publications
Managing Model Risk in Financial Climate Risk Assessment
Working Paper
Authors: Michael Gerst, Tim Canty, Clifford Rossi
The global change science community and the financial sector are both characterized by significant reliance on quantitative models to make predictions and understand risk. Matching scientific model capabilities with user needs is a challenging process and will be exacerbated in the financial sector because of model risk regulatory requirements designed to mitigate the risk of systemic improper model use. The authors elaborate on this challenge, inform the science and financial communities of current model risk practices and requirements, and sketch model risk potential solutions.
Do Fund Managers Misestimate Climatic Disaster Risk
The Review of Financial Studies
Author: Russell R. Wemers
After examining whether professional money managers overreact to large climatic disasters, the author finds managers within a major disaster region underweight disaster zone stocks to a much greater degree than distant managers. This aversion to disaster zone stocks is related to a salience bias that decreases over time and distance from the disaster, rather than to superior.
Regional Climate-Weather Research and Forecasting Model
Bulletin of the American Meteorological Society, Author: Xin-Zhong Liang et al.
The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction.