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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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Hyun Mee Kim
,
Michael C. Morgan
, and
Rebecca E. Morss

Abstract

The structure and evolution of analysis error and adjoint-based sensitivities [potential enstrophy initial singular vectors (SVs) and gradient sensitivities of the forecast error to initial conditions] are compared following a cyclone development in a three-dimensional quasigeostrophic channel model. The results show that the projection of the evolved SV onto the forecast error increases during the evolution.

Based on the similarities of the evolved SV to the forecast error, use of the evolved SV is suggested as an adaptive observation strategy. The use of the evolved SV strategy for adaptive observations is evaluated by performing observation system simulation experiments using a three-dimensional variational data assimilation scheme under the perfect model assumption. Adaptive strategies using the actual forecast error, gradient sensitivity, and initial SV are also tested. The observation system simulation experiments are implemented for five simulated synoptic cases with two different observation spacings and three different configurations of adaptive observation location densities (sparse, dense, and mixed), and the impact of the adaptive strategies is compared with that of the nonadaptive, fixed observations.

The impact of adaptive strategies varies with the observation density. For a small number of observations, several of the adaptive strategies tested reduce forecast error more than the nonadaptive strategy. For a large number of observations, it is more difficult to reduce forecast errors using adaptive observations. The evolved SV strategy performs as well as or better than the adjoint-based strategies for both observation densities. The impact of using the evolved SVs rather than the adjoint-based sensitivities for adaptive observation purposes is larger in the situation of a large number of observation stations for which the forecast error reduction by adjoint- based adaptive strategies is difficult.

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Eun-Gyeong Yang
,
Hyun Mee Kim
,
JinWoong Kim
, and
Jun Kyung Kay

Abstract

To improve the prediction of Asian dust events on the Korean Peninsula, meteorological fields must be accurately predicted because dust transport models require them as input. Accurate meteorological forecasts could be obtained by integrating accurate initial conditions obtained from data assimilation processes in numerical weather prediction. In data assimilation, selecting the appropriate observation location is important to ensure that the initial conditions represent the surrounding meteorological flow. To investigate the effect of observation network configuration on meteorological forecasts during Asian dust events on the Korean Peninsula, observing system simulation experiments using several simulated and real observation networks were tested with the Weather Research and Forecasting modeling system for 11 Asian dust events affecting the Korean Peninsula during a recent 6-yr period. First, the characteristics of randomly fixed and adaptively selected observation networks were investigated with various observation densities. The adaptive observation strategy could reduce forecast errors more efficiently than the fixed observation strategy. For both the fixed and adaptive observation strategies, the mean forecast error reduction rates increased as the number of assimilated observations and the distance between observation sites increased up to 300 km. Second, the effects of redistributing the real observation sites and adding observation sites to the real observation network based on the adaptive observation strategy were investigated. Adding adaptive observation sites to the real observation network in statistically sensitive regions improved the forecast performance more than redistributing real observation sites did. The strategy of adding adaptive observation sites is used to suggest the optimal meteorological observation network for meteorological forecasts of Asian dust transport events on the Korean Peninsula.

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Byoung-Joo Jung
,
Hyun Mee Kim
,
Thomas Auligné
,
Xin Zhang
,
Xiaoyan Zhang
, and
Xiang-Yu Huang

Abstract

An increasing number of observations have contributed to the performance of numerical weather prediction systems. Accordingly, it is important to evaluate the impact of these observations on forecast accuracy. While the observing system experiment (OSE) requires considerable computational resources, the adjoint-derived method can evaluate the impact of all observational components at a lower cost. In this study, the effect of observations on forecasts is evaluated by the adjoint-derived method using the Weather Research and Forecasting Model, its adjoint model, and a corresponding three-dimensional variational data assimilation system in East Asia and the western North Pacific for the 2008 typhoon season. Radiance observations had the greatest total impact on forecasts, but conventional wind observations had the greatest impact per observation. For each observation type, the total impact was greatest for radiosonde and each Advanced Microwave Sounding Unit (AMSU)-A satellite, followed by surface synoptic observation from a land station (SYNOP), Quick Scatterometer (QuikSCAT), atmospheric motion vector (AMV) wind from a geostationary satellite (GEOAMV), and aviation routine weather reports (METARs). The fraction of beneficial observations was approximately 60%–70%, which is higher than that reported in previous studies. For several analyses of Typhoons Sinlaku (200813) and Jangmi (200815), dropsonde soundings taken near the typhoon had similar or greater observation impacts than routine radiosonde soundings. The sensitivity to the error covariance parameter indicates that reducing (increasing) observation (background) error covariance helps to reduce forecast error in the current analysis framework. The observation impact from OSEs is qualitatively similar to that from the adjoint method for major observation types. This study confirms that radiosonde observations provide primary information on the atmospheric state as in situ observations and that satellite radiances are an essential component of atmospheric observation systems.

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Toru Terao
,
Shinjiro Kanae
,
Hatsuki Fujinami
,
Someshwar Das
,
A. P. Dimri
,
Subashisa Dutta
,
Koji Fujita
,
Azusa Fukushima
,
Kyung-Ja Ha
,
Masafumi Hirose
,
Jinkyu Hong
,
Hideyuki Kamimera
,
Rijan Bhakta Kayastha
,
Masashi Kiguchi
,
Kazuyoshi Kikuchi
,
Hyun Mee Kim
,
Akio Kitoh
,
Hisayuki Kubota
,
Weiqiang Ma
,
Yaoming Ma
,
Milind Mujumdar
,
Masato I. Nodzu
,
Tomonori Sato
,
Z. Su
,
Shiori Sugimoto
,
Hiroshi G. Takahashi
,
Yuhei Takaya
,
Shuyu Wang
,
Kun Yang
,
Satoru Yokoi
,
Peter van Oevelen,
, and
Jun Matsumoto

Abstract

The Asian Precipitation Experiment (AsiaPEX) was initiated in 2019 to understand terrestrial precipitation over diverse hydroclimatological conditions for improved predictions, disaster reduction, and sustainable development across Asia under the framework of the Global Hydroclimatology Panel (GHP)/Global Energy and Water Exchanges (GEWEX). AsiaPEX is the successor to GEWEX Asian Monsoon Experiment (GAME; 1995–2005) and Monsoon Asian Hydro-Atmosphere Scientific Research and Prediction Initiative (MAHASRI; 2006–16). While retaining the key objectives of the aforementioned projects, the scientific targets of AsiaPEX focus on land–atmosphere coupling and improvements to the predictability of the Asian hydroclimatological system. AsiaPEX was designed for both fine-scale hydroclimatological processes occurring at the land surface and the integrated Asian hydroclimatological system characterized by multiscale interactions. We adopt six approaches including observation, process studies, scale interactions, high-resolution hydrological modeling, field campaigns, and climate projection, which bridge gaps in research activities conducted in different regions. Collaboration with mesoscale and global modeling researchers is one of the core methods in AsiaPEX. We review these strategies based on the literature and our initial outcomes. These include the estimation and validation of high-resolution satellite precipitation, investigations of extreme rainfall mechanisms, field campaigns over the Maritime Continent and Tibetan Plateau, areas of significant impact on the entire AsiaPEX region, process studies on diurnal- to interdecadal-scale interactions, and evaluation of the predictabilities of climate models for long-term variabilities. We will conduct integrated observational and modeling initiative, the Asian Monsoon Year (AMY)-II around 2025–28, whose strategies are the subregional observation platforms and integrated global analysis.

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