A historical record of Pacific Northwest (defined here as west of the Cascade Mountains in Washington and Oregon) heat waves is identified using the U.S. Historical Climate Network, version 2, daily data (1901–2009). Both daytime and nighttime events are examined, defining a heat wave as three consecutive days above the 99th percentile for the maximum and minimum temperature anomalies separately. Although the synoptic characteristics of the daytime and nighttime heat events are similar, they do indicate some differences between the two types of events. Most notable is a stronger influence of downslope warming over the Cascade Mountains for the daytime events versus a more important role of precipitable water content for the nighttime events, presumably through its impact on downward longwave radiative fluxes. Current research suggests that the frequency and duration of heat waves are expected to increase in much of the United States, and analysis of the heat events reveals that a significant, increasing trend in the frequency of the nighttime events is already occurring in the Pacific Northwest. A heat wave occurred in 2009 that set all-time-record maximum temperatures in many locations and ranked as the second strongest daytime event and the longest nighttime event in the record.
Station siting for environmental observing networks is usually made subjectively, which suggests that the monitoring goals for the network may not be met optimally or cost effectively. In Antarctica, where harsh weather conditions make it difficult to install and maintain stations, practical considerations have largely guided the development of the staffed and automated weather station network. The current network coverage in Antarctica is evaluated as a precursor to optimal network design. The Antarctic Mesoscale Prediction System (AMPS) 0000 UTC analysis is used for 4 years (2008-12) with 15-km horizontal grid spacing, and results show that AMPS reproduces the daily correlations in surface temperature and pressure observed between weather stations across the continent. Temperature correlation length scales are greater in East Antarctica than in West Antarctica (including the Antarctic Peninsula), implying that more stations per unit area are needed to sample weather in West Antarctica compared to East Antarctica. There is variability in the temperature correlation length scales within these regions, emphasizing the need for objective studies such as this one for determining the impact of current and new stations. Further analysis shows that large regions are not well sampled by the current network, particularly on daily time scales. Observations are particularly limited in West Antarctica. Combined with the shorter temperature correlation length scales, this implies that West Antarctica is a compelling location for implementing an objective, optimal network design approach.
As harsh weather conditions in Antarctica make it difficult to support a dense weather observing network there, it is critical to place new weather stations in locations that are optimal for a given monitoring goal. Here we demonstrate a network design algorithm that uses ensemble sensitivity to identify optimal locations for new automatic weather stations in Antarctica. We define the optimal location as one that maximizes the reduction in total variance of a given spatial field. Using WRF Model forecast output from the Antarctic Mesoscale Prediction System (AMPS), we identify the best locations for observations across the continent by considering two spatial fields: (i) the daily 0000 UTC 2-m temperature analysis field and (ii) the daily 0000 UTC 2-m air temperature 24-h forecast field. We explore the impact of spatial localization on the results, finding that a covariance length scale of 3000 km is appropriate for these metrics. We find optimal locations assuming that no stations exist on the continent (blank slate) and conditional on existing stations (CD90). In the “blank slate” scenario, the Megadunes region emerges as the most important location to both monitor temperature and reduce temperature forecast errors, with the Ronne Coast and the Siple Coast following. Results for the monitoring and forecasting metrics are similar for the CD90 subset as well, indicating that additional stations could benefit multiple performance goals. Considering the CD90 subset, Wilkes Land–Adelie Coast, Ellsworth Land, and Queen Maud Land–Interior are identified as regions to consider installing new stations for optimizing network performance.
Abstract. Station locations in existing environmental networks are typically chosen based on practical constraints such as cost and accessibility, while unintentionally overlooking the geographical and statistical properties of the information to be measured. Ideally, such considerations should not take precedence over the intended monitoring goal of the network: the focus of network design should be to adequately sample the quantity to be observed.Here we describe an optimal network design technique, based on ensemble sensitivity, that objectively locates the most valuable stations for a given field. The method is computationally inexpensive and can take practical constraints into account. We describe the method, along with the details of our implementation, and present-example results for the US Pacific Northwest, based on the goal of monitoring regional annual-mean climate. The findings indicate that optimal placement of observing stations can often be highly counterintuitive, thus emphasizing the importance of objective approaches. Although at coarse scales the results are generally consistent, sensitivity tests show important differences, especially at smaller spatial scales. These uncertainties could be reduced with improvements in datasets and improved estimates of the measurement error. We conclude that the method is best suited for identifying general areas within which observations should be focused, and suggest that the approach could serve as a valuable complement to land surveys and expert input in designing new environmental observing networks.
Networks of observations ideally provide adequate sampling of parameters to be monitored for climate and weather forecasting applications. This is a challenge for any network, but is particularly difficult in the harsh environment of the Antarctic continent. We evaluate a network design method providing objective information on station siting for optimal sampling of a variable, here taken to be surface air temperature. The method uses the concept of ensemble sensitivity to predict locations reducing the most total ensemble variance, that is, uncertainty, across the continent. The method is applied to a network of frequently-reporting stations, and validation is performed using results from assimilating station observations. A cost-efficient "offline" data assimilation framework is used to allow testing over a large sample of experiments, including a large number of randomly chosen networks that serve as a null hypothesis. Network design predictions agree well with observed error reductions from assimilation. The important role of stations on the East Antarctic Plateau in monitoring surface air temperature is evident in network design and data assimilation results, followed by stations in West Antarctica and the Ross Ice Shelf region. Antarctic coastal and Peninsula stations are found to provide the smallest information content integrated over the continent. Validation results are also robust to covariance localization, an essential factor for ensemble methods. Optimal networks outperform randomly chosen-networks in all cases, by up to nearly 50%, depending on the size of the network and the covariance localization distance.
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