|Title||The value of targeted observations - Part II: The value of observations taken in singular vectors-based target areas.|
|Year of Publication||2007|
|Authors||Buizza, R, Cardinali, C, Kelly, GA, Thepaut, J-J|
|Secondary Title||Technical Memorandum|
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 forecast error reduction in downstream areas, specifically a North-American region for targeted observations taken in the Pacific Ocean, and a European region for targeted observations taken in the Atlantic Ocean. Overall, results have indicated (i) that observations taken in SV-target areas are more valuable than observations taken in randomly selected areas, (ii) that it is important that the daily set of singular vectors are used to compute the target areas, and (iii) 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.