FORECAST SENSITIVITY WITH DROPWINDSONDE DATA AND TARGETED OBSERVATIONS

While forecast models and analysis schemes used in numerical weather p rediction have become generally very successful, there is an increasin g research interest toward improving forecast skill by adding extra ob servations either into data sparse areas, or into regions where the ve rifying 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 US Air Force launched dropwi ndsondes over the relatively data sparse Northeast Pacific Ocean durin g 1-10 February 1995. The focus of this study is the forecast sensitiv ity to initial analysis differences, forced by these observations by u sing both the adjoint method (ADJM) and quasi-inverse linear method (Q ILM), which are both useful for determining the targeting area where t he observations are most needed. We discuss several factors that may a ffect the results, such as the radius of the mask for the targeted reg ion, the basic flow and the choice of initial differences at the verif ication time. There are some differences between the adjoint and quasi -inverse linear sensitivity methods. With 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 c omplementary: the 48-h quasi-inverse linear sensitivity is reliable in pinpointing the region of origin of a forecast difference. This is pa rticularly useful for cases in which the ensemble forecast spread indi cates a region of large uncertainty, or when a specific region require s careful forecasts. This region can be isolated with a mask and forec ast differences traced back reliably. Another important application fo r the QILM is to trace back observed 48-h forecast errors. The 48-h ad joint sensitivity, on the other hand, is useful in pointing out ar:as that have maximum impact on the region of interest, but not necessaril y the regions that actually led to observed differences, which are ind icated more clearly by QILM. At 72 h, the linear assumption made in bo th methods breaks down, nevertheless the backward integrations are sti ll very useful for pinning down all the areas that would produce chang es in the regions of interest (QILM) and the areas that will produce m aximum sensitivity (ADJM). Bo:h methods can be useful for adaptive obs ervation systems.