Weather and climate monitoring
Monitoring the weather and climate of the Colorado River Basin is made possible by an extensive network of regular observations of weather variables such as temperature, precipitation, humidity, and winds. This "meta-network" actually comprises many individual weather station networks that have been separately established and maintained, usually to serve particular monitoring purposes (e.g., agriculture, water supply). Data from the vast majority of these weather stations can be accessed in near-real-time (with lags from 5 minutes to one day) from online portals (see Data and tools below); archived data from previous months and years are also typically available.
The data from these weather stations are also gathered and processed into widely used gridded climate products. These gridded climate products are designed to alleviate inadequacies of the station data in two important ways:
- Spatial distribution: Stations are not evenly distributed across the landscape; gridded products interpolate between station observations to provide continuous spatial coverage.
- Temporal consistency: Individual stations may only be active for a few decades or less, and over time a station may experience changes in location, instrumentation, or time of observation; gridded products adjust for these "inhomogeneities" to provide consistent data over time.
Most of the gridded climate products share at least some baseline observational data from weather station networks and use similar processing, so they are not independent of each other.
In the Colorado River Basin, as elsewhere in the West, high-elevation weather stations were extremely sparse before the establishment of the SNOTEL network starting in the late 1970s. So the gridded climate data for mountain watersheds critical to water supply is much less reliable prior to 1980.
Observations from weather station networks are directly consulted and used throughout the basin for many weather and climate monitoring applications, such as agriculture, water supply, wildfire management, aviation, road safety, and drought monitoring. One key water-supply application is NOAA CBRFC's use of real-time precipitation and temperature observations from SNOTEL and COOP stations to initialize their streamflow forecasting system with the current moisture conditions for each catchment. Once real-time weather station observations are processed into gridded climate products, those products are widely used for near-real-time to seasonal monitoring applications, and also for historical analyses.
Data and tools
Weather station data
This site created by the University of Utah provides map-based access to 1000s of observations from the NWS and FAA (ASOS/AWOS) automated networks, RAWS, SNOTEL, APRSWXNET/CWOP (citizen weather stations), and many other networks. Very useful for real-time monitoring of temperature, winds, humidity, and recent precipitation; users can also access historical observations.
These maps, generated from weather station observations from the NWS COOP network and updated daily, are very helpful for monitoring conditions from weekly to annual timescales. Note that the "shaded" maps are created using a very simple interpolation, unlike that used for gridded climate products.
Gridded climate products
This versatile tool can be used to generate many types of charts, maps, and analyses from NOAA’s official nClimGrid gridded climate dataset, updated monthly.
This toolset, developed by researchers at the U. of California-Merced and partners, generates many different types of charts and analyses from the gridMET gridded (4 km) climate dataset, updated daily.
These maps display the PRISM gridded climate product, updated monthly, for climate (temperature, precipitation) variables as well as drought indices (PDSI, SPI, SPEI). The "Percentile" maps show how unusual recent conditions are relative to the historical record.
State of the Science Report
Chapter 4 of the State of the Science report describes weather station data and networks, and gridded climate products, in much greater detail.