Sensitivity of Surface Analyses over the Western United States to RAWS Observations

Abstract

Federal, state, and other wildland resource management agencies contribute to the collection of weather observations from over 1000 Remote Automated Weather Stations (RAWS) in the western United States. The impact of RAWS observations on surface objective analyses during the 2003/04 winter season was assessed using the Advanced Regional Prediction System (ARPS) Data Assimilation System (ADAS). A set of control analyses was created each day at 0000 and 1200 UTC using the Rapid Update Cycle (RUC) analyses as the background fields and assimilating approximately 3000 surface observations from Meso-West. Another set of analyses was generated by withholding all of the RAWS observations available at each time while 10 additional sets of analyses were created by randomly withholding comparable numbers of observations obtained from all sources.Random withholding of observations from the analyses provides a baseline estimate of the analysis quality. Relative to this baseline, removing the RAWS observations degrades temperature (wind speed) analyses by an additional 0.5°C (0.9 m s Ϫ1 ) when evaluated in terms of rmse over the entire season. RAWS temperature observations adjust the RUC background the most during the early morning hours and during winter season cold pool events in the western United States while wind speed observations have a greater impact during active weather periods. The average analysis area influenced by at least 1.0°C (2.5°C) by withholding each RAWS observation is on the order of 600 km 2 (100 km 2 ). The spatial influence of randomly withheld observations is much less.

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).…”

“…1 follows assumptions for those covariances (e.g., equal error variance for observations and background) discussed later and results in an IDI value of 0.5 near an isolated observation (i.e., the observation and background value receive equal weight there). Observation impacts have often been evaluated through cross-validation experiments, in which a control analysis using all observations is compared to an analysis in which observations of interest are excluded from the assimilation (Seaman and Hutchinson 1985;Zapotocny et al 2000;Myrick and Horel 2008;Benjamin et al 2010;Tyndall et al 2010;Horel and Dong 2010). 1 with many stations and enhanced data coverage (few observations) have IDI values much larger (smaller) than 0.5.…”

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

“…Figures 3 and 6 provide a gross indication of some of the largest data voids. Myrick and Horel's (2008) results for winter temperature over the western United States only, the removal of RAWS on average has a larger detrimental impact on analyses than removal of NWS stations (Table 6). Hence, the -value‖ of RAWS as examined here from the standpoint of analysis impact is higher than the value of NWS stations.…”

“…The land management agencies through their support of the RAWS network will play a critical role in any future national -network of networks‖ because those agencies are particularly interested in the atmospheric state in typically undersampled remote locations, as well as within increasingly important urban-wildland interface regions. Myrick and Horel (2008) evaluated the impact of RAWS observations on winter temperature and wind analyses in the western United States. That study demonstrated the the impact of removing RAWS observations on such analyses relative to the impact of removing Automated Surface Observing System (ASOS) and Automated Weather Observing System (AWOS) observations from primarily airport locations that are disseminated by the National Weather Service (NWS).…”

Error estimation of objective analysis of surface observations

“…The RTMA process (De Pondeca et al 2011) includes an estimate of the analysis error-the method being specific to the analysis process. Myrick and Horel (2008) have studied the sensitivity of surface analyses to a particular type of observation.…”