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CONUS404 Monthly Precipitation Climatology 1986-2020. Credit: NCAR and UCAR
An amazing new dataset of more than 40 years of high-resolution weather simulations over the continental United States is now available to the Earth system science community.
This unprecedented resource (which took nearly a year’s worth of supercomputing time to create and is nearly a petabyte in size) will help, among many other applications, as weather patterns change as the climate warms. It provides rich opportunities for scientists and stakeholders interested in how things are already changing. For example, scientists are already using data to improve long-term forecasts, plan water resource allocation, and develop new techniques to better understand the causes and effects of extreme and rare weather events. I’m digging.
The dataset, known as CONUS404, is the result of a collaboration between the National Center for Atmospheric Research (NCAR) and the United States Geological Survey (USGS).
“To study the rare and extreme weather events that we’re really interested in, we need decades of data, and that data has to be of high resolution,” said the lead author of the project. said Roy Rasmussen, a senior scientist at NCAR. “CONUS404 allows us to study both long-term events that span many years, such as droughts, and rare events that do not last long but occur rarely, such as extreme floods.”
fill the water manager gap
Weather in the United States is relatively well observed by local weather stations, flow meters, snow sensors, radar, weather balloons, and satellites. But these observations alone don’t always provide a clear picture of how weather patterns change over time. This is because data is often regionally concentrated, and information about conditions in remote areas or rough terrain is sparse and unreliable. For example, the accuracy of temperature, humidity, wind, and other important weather data can be affected by equipment performance and local conditions. Additionally, measurements across different observation platforms do not always match.
Because of these factors, scientists combine observations and modeling to create datasets that provide internally consistent weather information at fixed points on grids across regions or across the globe, known as meteorological “reanalyses.” depends on. These reanalysis products are important tools for scientists. For example, it can be used to test how well climate models simulate past conditions. This is an important test to determine how well future simulations will perform. Scientists use these products to initialize or “kick-start” model simulations in real-world conditions.
Despite the importance of reanalysis products, they generally have low resolution, with distances between grid points of approximately 30 kilometers (19 miles) or more. This interval is too coarse to capture relatively fine-scale weather phenomena, such as summer thunderstorms, and the local topography that influences them. An event like a mountain range. They are also too coarse to provide meaningful data on precipitation in individual watersheds, important information for water managers. This last point was particularly frustrating for the USGS, which is responsible for collecting and distributing information about water resources, such as river flow and groundwater data, across the country.
To address this gap, the USGS partnered with NCAR to “downscale” one of the most widely used global reanalysis datasets, called ERA5, and to update NCAR’s climate research and forecasting models. was used to create a high-resolution dataset for the continental United States (CONUS) (WRF).
The resulting dataset covers over 40 years (1980-2021) with a grid spacing of 4 kilometers, hence the name CONUS404.
Weather simulations that cover such a wide area and for such a long period of time with such high resolution have never before been possible. However, several factors have converged over the past decade to make this enterprise a reality, including advances in both supercomputing and weather modeling capabilities. Despite advances in computing, it took him more than 11 months to complete the simulation on the USGS Denali Supercomputing System.
Improvements in WRF over the past few years have briefly fixed some issues encountered in previous attempts to run models at high resolution on CONUS. One problem was that the WRF tended to make the central United States too hot and dry, thereby impacting the model’s ability to accurately simulate thunderstorms in the region. However, the updated version of WRF includes a groundwater module that cools and moistens the area, allowing for more realistic simulations. The updated version also improves the simulation of snowpack in the West, which affects river runoff and surface temperatures, and corrects the model’s tendency for winter temperatures to be too cold in snow-covered regions.
“We can now capture the main factors driving real-world weather,” Rasmussen said. “We’re not perfect and we’re always learning, but this model does a great job of accurately reproducing historical weather.”
dig into the data
A paper introducing this dataset was published earlier this summer. Bulletin of the American Weather Society, But many scientists are already poring over data to answer research questions. For example, the CONUS404 dataset has helped researchers uncover patterns during historical droughts and is now being used to improve seasonal drought predictions in Western countries.
Scientists are also looking for subtle evidence of changes in weather patterns over the past few decades, including one study that identified a shift in precipitation from drizzles and drizzles to downpours. Climate models have long predicted this change should occur, but the existing low-resolution reanalysis datasets weren’t detailed enough to pinpoint the change.
Researchers are also analyzing harmful local wind changes that can accompany storms. Other scientists are looking at extreme conditions in river flows and whether CONUS404 data can be used as input to crop models to simulate water use and food production.
Although the new dataset is still in its infancy, the NCAR and USGS collaboration is already working on the second part of the project. It’s a weather simulation across the United States, this time in the future, spanning more than 40 years. Scientists use the same methodology, but rather than reanalyzing what happened in the past, scientists use data from NCAR’s Community Earth System Model version 2 (CESM2) to predict future conditions. It is planned. Like in the future. Together, the two datasets provide more than 80 years of simulated data, giving an unprecedented picture of how the weather will continue to change as the climate warms.
“It is very important to link the two parts of NCAR, climate and weather modeling,” said NCAR scientist Andreas Plein, co-author of the study. “To bring climate data to a useful scale, we must think wisely about how we use it.”
CONUS404 data are freely accessible from NCAR’s Research Data Archive.
For more information:
RM Rasmussen et al., CONUS404: NCAR-USGS 4 km long-term regional hydroclimatic reanalysis over CONUS; Bulletin of the American Weather Society (2023). DOI: 10.1175/BAMS-D-21-0326.1
Magazine information:
Bulletin of the American Weather Society