The value of observations. II: The value of observations located in singular‐vector‐based target areas

Data-assimilation experiments have been run in seven different configurations for two seasons to assess the value of observations taken in target regions identified either using singular vectors (SVs) or randomly, and located over the Pacific or the Atlantic Oceans. The value has been measured by the relative short-range forecast error reduction in downstream areas, specifically a North American region for observations taken in the Pacific Ocean, and a European region for observations taken in the Atlantic Ocean. Overall, results have indicated (1) that observations taken in SV-target areas are on average more valuable than observations taken in randomly selected areas, (2) that it is important that the daily set of singular vectors are used to compute the target areas, and (3) that the value of targeted observations depends on the region, the season and the baseline observing system. If the baseline observing system is data-void over the ocean, then the average value of observations taken in SV-target areas is very high. Considering for example winter 2004, SV-targeted observations over the Pacific (Atlantic) reduce the day-2 forecasts error of 500 hPa geopotential height forecasts in the verification region by 27.5% (19.1%), compared to 15.7% (14.9%) for observations taken in random areas. By contrast, if the baseline observing system is data-rich over the ocean, then the average value of observations taken in SV-target areas is rather small. Considering for example winter 2004, it has been estimated that adding SV-targeted observations over the Pacific (Atlantic) would reduce, on average, the day-2 forecasts error in the verification region by 4.0% (2.0%), compared to 0.5% (1.7%) for observations in random areas. These average results have been confirmed by single-case investigations, and by a careful examination of time series of forecast errors. These results indicate that more accurate assimilation systems that can exploit the potential value of localized observations are needed to increase the average return of investments in targeting field experiments. Copyright © 2007 Royal Meteorological Society