PCA is a global leader in weather and climate information services, revolutionizing the quality and optimization of weather, water, and climate data since its founding
Bringing the results of decades of the highest quality climate research to global markets and to people in need
The Early Years
PCA is a company born from decades of research by Professor Eric Wood and his group at Princeton University. Over the past 40 years many people contributed to this research—over 30 Ph.D. students, 30 postdocs and research staff, and many collaborators.
In the early 1980s, he 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.
In early 2017, Princeton Executive-in-Residence Joe Studholme met Eric, and after determining that there was a strong market need for improved climate data products, they founded the company with Professor Justin Sheffield. They worked with Princeton University’s Office of Technology and Licensing to define, patent, and license the University’s IP for the exclusive commercial use of Princeton Climate Analytics, Inc. Once the license was in place in late 2017, key data science and technology team joined the company and began rapid commercial development and deployment of the Princeton Hydrological Engine (PHE). PCA’s first customers included World Bank, InterAmerican Development Bank, EU/Copernicus Project and others as the company delivered the first in a series of high-quality climate data and analysis products.
The company supports ongoing research via its PCA Labs division, which continues to produce the world’s most advanced, scientifically validated hydrology data and analysis products. Additionally, the company continues its commitment to serving undeveloped communities by providing pro-bono data analysis, tools, monitoring and support to agricultural users in Africa via its ongoing UNESCO partnership.