Abstract
Abstract When producing forecasts by integrating a numerical weather prediction model from an analysis, not all observations assimilated into the analysis improve the forecast. Therefore, the impact of particular observations on the forecast needs to be evaluated quantitatively to provide relevant information about the impact of the observing system. One way to assess the observation impact is to use an adjoint-based method that estimates the impact of each assimilated observation on reducing the error of the forecast. In this study, the Weather Research and Forecasting Model and its adjoint are used to evaluate the impact of several types of observations, including enhanced satellite-derived atmospheric motion vectors (AMVs) that were made available during observation campaigns for two typhoons: Sinlaku and Jangmi, which both formed in the western North Pacific during September 2008. Without the assimilation of enhanced AMV data, radiosonde observations and satellite radiances show the highest total observation impact on forecasts. When enhanced AMVs are included in the assimilation, the observation impact of AMVs is increased and the impact of radiances is decreased. The highest ratio of beneficial observations comes from GPS Precipitable Water (GPSPW) without the assimilation of enhanced AMVs. Most observations express a ratio of approximately 60%. Enhanced AMVs improve forecast fields when tracking the typhoon centers of Sinlaku and Jangmi. Both the model background and the analysis are improved by the continuous cycling of enhanced AMVs, with a greater reduction in forecast error along the background trajectory than the analysis trajectory. Thus, while the analysis–forecast system is improved by assimilating these observations, the total observation impact is smaller as a result of the improvement.
Highlights
In numerical weather prediction (NWP), an analysis can be produced through assimilation of observations into a ‘‘background’’ or first-guess state, often from a short forecast from the previous analysis
While there are numerous studies that show the impacts of atmospheric motion vectors (AMVs) on tropical cyclone (TC) track and intensity prediction by using observing system experiments (OSEs), there has been little research regarding the influence of AMVs on model forecast error reduction (FER) that comprehensively examines the impact of different observation types using forecast sensitivity to observations (FSO) by the adjoint method, especially for forecast periods during TC events
Forecast error reduction experiment; d is the distance between the TC centers of the best track and the experiments; Rd is the mean radius of the earth; u1 and u2 are the longitudes of the simulated and best track represented in radians, respectively; Figures 3a–c represent the time series of 24-h nonlinear FERs and their linear estimates during September 2008 for every cycle in EXP0, EXP1, and EXP2, respectively
Summary
In numerical weather prediction (NWP), an analysis can be produced through assimilation of observations into a ‘‘background’’ or first-guess state, often from a short forecast from the previous analysis. While there are numerous studies that show the impacts of AMVs on TC track and intensity prediction by using OSEs, there has been little research regarding the influence of AMVs on model forecast error reduction (FER) that comprehensively examines the impact of different observation types using FSO by the adjoint method, especially for forecast periods during TC events.
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