Princeton Climate Analytics is a company born from decades of research by Professor Eric Wood and his colleagues at Princeton University

Our research division PCA Labs continues to push the frontiers of climate and hydrological research to develop and deliver state-of-the-art data products

PCA Labs Research Pipeline

Early Years of Research

In the early 1980s, Eric was encouraged to think about process-based hydrologic processes, including the effects of landscape variability that led to his “Representative Elementary Area” concept. Understanding the impact of landscape variability on water and energy fluxes had been an unresolved research problem, but the work of his students indicated that ignoring such variability leads to biased surface fluxes. Including spatial variability in land surface models led to a long-term collaboration in the development of the Variable Infiltration Capacity model. About twenty-five years later, Eric proposed the development of hyper-resolution land surface modeling (LSM)—30 to 100 meters at continental scales—to capture this variability, which led to the development by his student of HydroBlocks, which we’ve run at 30 meters across the contiguous United States.

In the mid-1990s a strategy was developed for using VIC and remote sensing ranging from small-scale airborne sensing and modeling (focusing on processes) to assimilate NASA’s Earth Observation System into continental- to global-scale modeling focus of the global water cycle.  He developed the first continental-scale, long-term forcing data set for land surface modeling as part of the North American Land Data Assimilation System (NLDAS), a signature project that included Princeton, U. Washington, NASA, NOAA and Rutgers University. This historical data set was used by Justin Sheffield to develop a VIC-based objective drought index and by Ming Pan to develop assimilation systems with remote sensing data. These approaches have become the standard methods in our fields.

Within the NLDAS project, he also used NOAA’s Climate Forecast System seasonal forecasting model to develop seasonal hydrological forecasting and, recently, a multi-model (NMME: North American Multi-model Ensemble) forecast system. The experience over the United States allowed them to expand to a global domain, where it was possible to now run historical and real-time flood and drought monitors as a climate service to help users improve their decisions and risk management.

Building the Princeton Hydrological Engine

In 2008, UNESCO approached Wood to discuss the possibility of developing a drought monitoring and forecasting system for Africa.  Building on his work for the Continental United States (CONUS), he and Justin Sheffield developed a system for Africa that included a 0.25km daily resolution monitoring of floods and droughts, short term forecasting using NOAA’s Global Forecast System (GFS) and NMME seasonal forecasts.  Over the last 8 years, the systems were installed at AGRHYMET (in Niger, Niamey), ICPAC (in Nairobi, Kenya) and WaterNet (in Zimbabwe), including training under the auspices of UNESCO. The system was adapted for Latin America and the Caribbean, and a high resolution (3km, hourly) over the continental United States.  This work confirmed their belief that groups struggled to obtain data needed to do risk studies related to global flood and drought.

All during this period, fundamental research supported the monitors, with papers appearing in the best journals including Nature, Bulletin of the American Meteorological Society, J. Climate, J. Hydrometeorology, J. Geophysical Research, Hydrology and Earth System Science among others.

Building upon Research and Experience

Over the last 12 years, the developers and researchers who make up PCA’s Engineering and Development team have been awarded over $33 million in research funding from NASA, NOAA, NSF, the European Commission, the European Space Agency (ESA), and more. Through these projects, the team has solidified their experience and expertise delivering innovative technologies to the market. Some of these projects include developing consistent earth system data records for the global terrestrial water cycle, developing and diagnostically analyzing a multi-decadal global evaporation product, advancing a refined ASMR-E Soil Moisture Data Product, multi-sensor enhancement of real-time satellite precipitation retrievals for improved flood and drought monitoring, developing pre-SWOT ESDRs for global surface water shortage dynamics, developing consistent global long-term records of atmospheric evaporative demand, and much more.

PCA Labs Today

PCA’s VP of Data Science, Ming Pan, is the Director of PCA Labs, and curates PCA’s research to our commercial pipeline.

PCA has developed industry advantages through the work of over 40 years of research experience by the preeminent terrestrial hydrology research group in the field. With recognition from the National Academy of Engineering, the American Geophysical Union, the European Geosciences Union, and the American Meteorological Society, PCA Labs’ team is recognized as a world-renowned group in hydrological modeling, hydroclimatology, and remote sensing. 

Through the use of PCA’s considerable IP and our continuing work with Princeton University, PCA Labs continues to push the bounds of climate science, increasing the quality and accuracy of flood, drought, NRT, and seasonal forecasting.