Research Projects Past and Present


The Texas Tech real-time ensemble and deterministic prediction systems - These two systems currently support a number of public and private real-time forecasting applications as well as commercialization efforts. The systems currently run on roughly a 1000-core linux cluster, and provide forecast products through the web for several initializations daily. The system hardware was purchased through the State of Texas Emerging Technology Fund (ETF).

The development of smart home utility systems - This project explores a potentially transformative and decentralized utility model that uses residential onsite generation and storage of renewable power (through solar panels and batteries) and water resources (through rain harvest and storage tanks). A key component is to test human residents in the home to understand how they can conserve and sustain these resources with the minimal amount of equipment such that the system is both affordable and provides an acceptable quality of life. Weather forecast information is a key input into a utility management system with which home residents will interact. Ultimately we aim to infiltrate the mainstream market with these types of homes, providing significant environmental and societal benefits.
Funded by: Texas Tech University

The Big Weather Web - This collaborative project with several other universities and NCAR aims to establish a computing infrastructure that can support university big data research and education. The specific work being conducted at TTU involves the examination of whether an ensemble system can be adapted in time to utilize a physics configuration that is best for the current flow regime.
Funded by: The National Science Foundation (NSF)

The Wind Forecast Improvement Project 2 - This large collaborative project involving several private and university organizations focuses on improving WRF model physics to increase the skill of boundary-layer wind forecasts. Uncertainty quantification of how different physics parameters influence wind forecasts assessed about different equally-likely ensemble members is a key goal.
Funded by: The Department of Energy (DOE)

Understanding non-local inadvertent weather modification - This project funded through a National Science Foundation (NSF) CAREER award aims to understand the extent to which irrigation, urban heat islands, and wind farms affect the weather non-locally. The project includes the creation of an interactive weather exhibit at the Museum of Texas Tech University, and involves the maintenance of educational resource kits for local schools and weather-themed summer camps at the museum.
Funded by: The National Science Foundation (NSF)

Integrating ensemble-based sensitivity into the National Weather Service (NWS) forecasting process to improve the prediction of high-impact weather - This project funded through successive Naitonal Oceanic and Atmospheric Administration's (NOAA) Collaborative Science, Technology, and Applied Research (CSTAR) program grants focuses on developing operational ensemble tools that improve forecasts of severe convection, flooding, and winter storm events. A key part of this research is to use ensemble-based sensitivity to choose ensemble subsets that are better than the full ensemble for these types of high-impact events, although other best-member techniques and general probabilistic forecast tools are also explored.
Funded by: The NOAA CSTAR program


Operational wind power prediction at Shell wind farms - This project funded by Shell is focused on developing an optimal real-time probabilistic wind power prediction system at existing Shell wind farms. A number of verification and tuning techniques are involved in this work, as well as an examination into the best observing network toward best forecasting wind ramps at Shell wind farms.
Funded by: Shell Wind Energy

The Weather Improvement Forecast Project (WFIP) toward improved wind power prediction- This project examines the strengths and weaknesses of fine-scale ensemble and variational data assimilation techniques with regard to wind power prediction. It also examines the value of additional project profiler/sodar and routine mesonet observations within both approaches.
Funded by: The Department of Energy (DOE)

Predictability of land-falling North American cyclones - This project focuses on examining the predictability characteristics of midlatitude cylcones that made landfall on the west coast of North America over a 2-year period, and is a collaboration with scientists from the U. of Washington and the Naval Research Laboratory. Ensemble sensitivity and ensemble spread are used to determine weather a large intrinsic potential for error growth or slower growing regions of uncertainty with larger initial magnitude are more responsible for the predictability of different types of cyclones. This project also aims to develop a climatology of land-falling cyclone predictability.
Funded by: The Naval Research Laboratory (NRL)

Comparison of an ensemble Kalman filter (EnKF) to the National Weather Service (NWS) Real Time Mesoscale Analysis (RTMA) system - This collaboration with scientsits from the National Weather Service Seattle office and the U. of Washington compares fine-scale surface wind and temperature analyses from the flow-dependent EnKF to that of the RTMA. The primary goal is to understand which approach is best for both producing a long-term, high-resolution analysis-of-record and providing the best operational situational awareness to NWS forecasters.
Funded by: The UCAR COMET Program