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Jun Kyung Kay
and
Hyun Mee Kim

Abstract

In this study, the initial ensemble perturbation characteristics of the new Korea Meteorological Administration (KMA) ensemble prediction system (EPS), a version of the Met Office Global and Regional Ensemble Prediction System, were analyzed over two periods: from 1 June to 31 August 2011, and from 1 December 2011 to 29 February 2012. The KMA EPS generated the initial perturbations using the ensemble transform Kalman filter (ETKF). The observation effect was reflected in both the transform matrix and the inflation factor of the ETKF; it reduced (increased) uncertainties in the initial perturbations in regions with dense observations via the transform matrix (inflation factor). The reduction in uncertainties is generally governed by the transform matrix but locally modulated by the inflation factor. The sea ice significantly affects the initial perturbations near the lower boundary layer. The large perturbation energy in the lower stratosphere of the tropics was related to the dominant zonal wind, whereas the perturbation energy in the upper stratosphere of the winter hemispheres was related to the dominant polar night jet. In the early time-integration stage, the initial perturbations decayed in the lower troposphere but grew rapidly in the mid- to upper troposphere. In the meridional direction, the initial perturbations grew greatest in the northern polar region and smallest in the tropics. The initial perturbations maintained a hydrostatic balance, especially during the summer in both hemispheres and during both the summer and winter in the tropics, associated with the smallest growth rates of the initial perturbations. The initial perturbations in the KMA EPS appropriately describe the uncertainties associated with atmospheric features.

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Hyun Mee Kim
,
Sung-Min Kim
, and
Byoung-Joo Jung

Abstract

In this study, structures of real-time adaptive observation guidance provided by Yonsei University (YSU) in South Korea during The Observing System Research and Predictability Experiment (THORPEX)-Pacific Asian Regional Campaign (T-PARC) are presented and compared with those of no-lead-time adaptive observation guidance recalculated as well as other adaptive observation guidance for a tropical cyclone (Jangmi 200815). During the T-PARC period, real-time dry total energy (TE) singular vectors (SVs) based on the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the corresponding tangent linear and adjoint models with a Lanczos algorithm are provided by YSU to help determine sensitive regions for targeted observations. While YSU provided the real-time TESV guidance based on a mesoscale model, other institutes provided real-time TESV guidance based on global models. The overall features of the real-time MM5 TESVs were similar to those generated from global models, showing influences from tropical cyclones, midlatitude troughs, and subtropical ridges. TESV structures are very sensitive to verification region and forecast lead time. If a more accurate basic-state trajectory with no lead time is used, more accurate TESVs, which yield more accurate determinations of sensitive regions for targeted observations, may be calculated. The results of this study may imply that reducing forecast lead time is an important component to obtaining better sensitivity guidance for real-time targeted observation operations.

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Myunghwan Kim
,
Hyun Mee Kim
,
JinWoong Kim
,
Sung-Min Kim
,
Christopher Velden
, and
Brett Hoover

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.

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