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Forecast sensitivity with dropwindsonde data and targeted observations
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  • Description:
    While forecast models and analysis schemes used in numerical weather prediction have become generally very successful, there is an increasing research interest toward improving forecast skill by adding extra observations either into data sparse areas, or into regions where the verifying forecast is most sensitive to changes in the initial analysis. The latter approach is referred to as "targeting" observations. In a pioneering experiment of this type, the U.S. Air Force launched dropwindsondes over the relatively data sparse Northeast Pacific Ocean during 1-10 February 1995. The focus of this study is the forecast sensitivity to initial analysis differences, forced by these observations by using both the adjoint and quasi-inverse linear methods (Pu et al.1997 a and b), which are both useful for determining the targeting area where the observations are most needed. We discuss several factors that may affect the results, such as the radius of the mask for the targeted region, the basic flow and the choice of initial differences at the verification time. There are some differences between the adjoint and quasi-inverse linear sensitivity methods. Using both sensitivity methods it is possible to find areas where changes in initial conditions lead to changes in the forecast. We find that these two methods are somewhat complementary: the 48-hr linear sensitivity is reliable in pinpointing the region of origin of a forecast difference. This is particularly useful for cases in which the ensemble forecast spread indicates a region of large uncertainty, or when a specific region requires careful forecasts. This region can be isolated with a mask and forecast differences traced back reliably. It can also be used to trace back observed 48 hr forecast errors. The 48-hr adjoint sensitivity, on the other hand, is useful in pointing out areas that have maximum impact on the region of interest, but not necessarily the regions that actually led to observed differences, which are indicated more clearly by the quasi-inverse linear method (QILM). At 72 hrs the linear assumption made in both methods breaks down, nevertheless the backward integrations are still very useful for pinning down all the areas that would produce changes in the regions of interest (QILM) and the areas that will produce maximum sensitivity (adjoint method). Both methods could be useful for adaptive observation systems.

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