This National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) Project Grant, awarded on February 15, 2024 for $275,000.00, supports the development of a weather forecasting and climate prediction tool for subseasonal forecasting, extreme weather events, and long-term climatological changes. The project aims to establish the feasibility of utilizing physics-informed machine learning to create probabilistic models of crucial climatological parameters and extreme...
This $202,000 Project Grant awarded by the National Science Foundation's Geosciences Program (CFDA 47.050) supports the development of data-driven methods for predicting extreme weather events under climate change. The grant aims to create neural network models capable of capturing the statistics of extreme events, such as heat waves and droughts, in future climate projections. The work builds on previous research on modeling chaotic systems and addressing the spectral bias in neural networks....
This Project Grant award from the National Oceanic and Atmospheric Administration (NOAA) Small Business Innovation Research (SBIR) Program, CFDA 11.021, provides $1,298,986 to Climate Forecast Applications Network, LLC to develop a high-impact innovation for weather forecasting. The innovation integrates global ensemble weather forecasts with an AI-driven post-processing model of extreme weather indices (XTREMECAST). This will enable skillful probabilistic forecasts of compound extreme weather...
This Project Grant award of $322,555.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of statistical and machine learning methods to study weather and climate extremes. The key products and services to be delivered under this three-year project include: Validating climate models in their ability to mimic real climate extremes, which involves comparing modeled and observed spatial extremes and addressing...
This Project Grant award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) provides $275,000 to Sunairio Inc. to develop a high-fidelity climate simulation engine powered by generative adversarial networks. The goal is to create a broad (1,000 outcomes), hyperlocal (less than 3 km) climate simulation archive that can be used by power grid planners and energy investors to better understand forward-looking risks to grid...
The National Oceanic and Atmospheric Administration (NOAA) awarded a $3,249,675 Project Grant under the NOAA Small Business Innovation Research (SBIR) Program (CFDA 11.021) to Salient Predictions Inc., a for-profit technology company based in Falmouth, MA. The grant supports the development of advanced machine learning models for improved subseasonal to seasonal (S2S) water availability forecasts. Specifically, Salient Predictions aims to integrate its S2S weather forecasting technology with the...
The National Science Foundation (NSF) awarded a $300,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Massachusetts (UMass) to investigate distributed machine learning for climate modeling and weather forecasting. Through this EAGER (EArly-concept Grants for Exploratory Research) award, UMass will leverage existing testbeds, including the CASA radar network, to support the development of a National Discovery Cloud for Climate...
The National Science Foundation (NSF) Division of Social and Economic Science awarded a $243,767 Project Grant to The Trustees of Columbia University in the City of New York (Columbia University) for a 3-year collaborative research project (Award Date: Jul 15, 2023, Completion Date: Jun 30, 2026). The research examines how weather forecast accuracy impacts economic behavior and human wellbeing, including the differential effects across different demographic groups. Specifically, the project...
This $301,719 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will fund research to develop flexible regression methods that can better measure the economic impacts of climate change. The research will focus on improving statistical techniques to accurately capture the effects of extreme temperature exposure on economic outcomes, which is critical for informing effective climate policy. The work will include...
This $284,443 Project Grant award, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), will support the development of advanced AI emulator tools for improved estimation of rare and extreme climate events, such as heat waves and cold snaps. The award, granted to the University of Chicago, will focus on creating mathematical tools to leverage AI methods to enhance the analysis of rare climate phenomena. Specifically,...