An Evaluation of the Distribution of Remote Automated Weather Stations (RAWS)

Abstract

This study estimates whether surface observations of temperature, moisture, and wind at some stations in the continental United States are less critical than others for specifying weather conditions in the vicinity of those stations. Two-dimensional variational analyses of temperature, relative humidity, and wind were created for selected midday hours during summer 2008. This set of 8925 control analyses was derived from 5-km-resolution background fields and Remote Automated Weather Station (RAWS) and National Weather Service (NWS) observations within roughly 4° × 4° latitude–longitude domains. Over 570 000 cross-validation experiments were completed to assess the impact of removing each RAWS and NWS station. The presence of observational assets within relatively close proximity to one another is relatively common. The sensitivity to removing temperature, relative humidity, or wind observations varies regionally and depends on the complexity of the surrounding terrain and the representativeness of the observations. Cost savings for the national RAWS program by removing a few stations may be possible. However, nearly all regions of the country remain undersampled, especially mountainous regions of the western United States frequently affected by wildfires.

The 2017 North Bay and Southern California Fires: A Case Study

“…At Fremont Canyon RAWS, >99th percentile values for FFWI, wind gust maximum, and wind speed with RH in the 2nd percentile was observed from 16:00-18:00 UTC 7 December. Up the coast, the Montecito RAWS recorded its lowest RH ever (1.0%, although we note that RH estimates are prone to errors at very low levels of ambient humidity [47]) at 09:00 UTC 12 December with 6.7 ms −1 northerly winds, and >99th percentile values for FFWI, wind gust maximum, and wind speed from 11:00-12:00 UTC 16 December. Shortly after these observations, the Montecito RAWS was burned over by the Thomas Fire.…”

Impacts of Mesonet Observations on Meteorological Surface Analyses

“…Prior work has demonstrated that the near-surface boundary layer in the CONUS remains undersampled (Myrick and Horel 2008;Horel and Dong 2010). Figure 1 illustrates the need for additional observing capabilities, particularly in mountainous and coastal areas, in terms of an integrated data influence analysis (IDI; Uboldi et al 2008).…”

“…Mesoscale surface observations are vital data sources for applications in many different meteorological subfields, including operational forecasting, wind power management, transportation safety, wildfire management, dispersion modeling, and defense applications (Dabberdt et al 2005;Horel and Colman 2005). Two recent reports (National Academy of Sciences 2009, 2010) recommend that existing and future mesoscale observations be integrated into a network of networks.…”

“…Figure 1 illustrates the need for additional observing capabilities, particularly in mountainous and coastal areas, in terms of an integrated data influence analysis (IDI; Uboldi et al 2008). As discussed by Horel and Dong (2010), the IDI analysis is a measure of station density and depends on the assumed observation and background error covariances. As discussed by Horel and Dong (2010), the IDI analysis is a measure of station density and depends on the assumed observation and background error covariances.…”

The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development

“…To help meet its demand for high-resolution analyses, the NWS in 2004 established a multiphased program to build the Reanalysis of Record (Horel and Colman 2005). The Reanalysis of Record is an approximate 30-yr record of high spatial and temporal resolution threedimensional reanalyses paralleling the global National Centers for Environmental Prediction-National Center for Atmospheric Research 40-Year Reanalysis (NCEP-NCAR; Kalnay et al 1996) and the North American Regional Reanalysis (Mesinger et al 2006).…”