Showing papers by "Eugenia Kalnay published in 1998"

Maturity of Operational Numerical Weather Prediction: Medium Range

Abstract: In 1939 Rossby demonstrated the usefulness of the linearized perturbation of the equations of motion for weather prediction and thus made possible the first successful numerical forecasts of the weather by Charney et al. In 1951 Charney wrote a paper on the science of numerical weather prediction (NWP), where he predicted with remarkable vision how NWP would evolve until the present. In the 1960's Lorenz discovered that the chaotic nature of the atmosphere imposes a finite limit of about two weeks to weather predictability. At that time this fundamental discovery was “only of academic interest” and not really relevant to operational weather forecasting, since at that time the accuracy of even a 2-day forecast was rather poor. Since then, however, computer-based forecasts have improved so much that Lorenz's limit of predictability is starting to become attainable in practice, especially with ensemble forecasting, and the predictabilty of longer-lasting phenomena such as El Nino is beginning to be ...

Forecast sensitivity with dropwindsonde data and targeted observations

Abstract: While forecast models and analysis schemes used in numerical weather prediction have becomegenerally very successful, there is an increasing research interest toward improving forecast skillby adding extra observations either into data sparse areas, or into regions where the verifyingforecast 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 launcheddropwindsondes over the relatively data sparse Northeast Pacific Ocean during 1–10 February1995. The focus of this study is the forecast sensitivity to initial analysis differences, forced bythese observations by using both the adjoint method (ADJM) and quasi-inverse linear method(QILM), which are both useful for determining the targeting area where the observations aremost needed. We discuss several factors that may affect the results, such as the radius of themask for the targeted region, the basic flow and the choice of initial differences at the verificationtime. There are some differences between the adjoint and quasi-inverse linear sensitivitymethods. With both sensitivity methods it is possible to find areas where changes in initialconditions lead to changes in the forecast. We find that these two methods are somewhatcomplementary: the 48-h quasi-inverse linear sensitivity is reliable in pinpointing the region oforigin of a forecast difference. This is particularly useful for cases in which the ensemble forecastspread 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. Anotherimportant application for the QILM is to trace back observed 48-h forecast errors. The 48-hadjoint sensitivity, on the other hand, is useful in pointing out areas that have maximum impacton the region of interest, but not necessarily the regions that actually led to observed differences,which are indicated more clearly by QILM. At 72 h, the linear assumption made in bothmethods breaks down, nevertheless the backward integrations are still very useful for pinningdown all the areas that would produce changes in the regions of interest (QILM) and the areasthat will produce maximum sensitivity (ADJM). Both methods can be useful for adaptiveobservation systems. DOI: 10.1034/j.1600-0870.1998.t01-3-00002.x