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Sijing Ren, Lili Lei, Zhe-Min Tan, and Yi Zhang

Abstract

Ensemble sensitivity is often a diagonal approximation to the multivariate regression, leading to a simple univariate regression. Comparatively, the multivariate ensemble sensitivity retains the full covariance matrix when computing the multivariate regression. The performances of both univariate and multivariate ensemble sensitivities in multiscale flows have not been thoroughly examined, and the demonstration of the latter in realistic applications has been sparse. A high-resolution ensemble forecast of Typhoon Haiyan (2013) is used to examine the performances of the two ensemble sensitivities. Compared to the multivariate sensitivity, the univariate sensitivity overestimates the forecast metric, especially at higher levels. To increase the predicted Haiyan’s intensity, multivariate ensemble sensitivity gives initial perturbations characterized by a warming area around the center of the storm, an increased moisture area around the eyewall, a stronger primary circulation around the radius of maximum wind, and stronger inflow at low levels and stronger outflow at high levels. Perturbed initial condition experiments verify that the predicted response from the multivariate sensitivity is more accurate than that from the univariate sensitivity. Therefore, the ability of multivariate sensitivity to provide more accurate predicted responses than the univariate sensitivity has been demonstrated in a realistic multiscale flow application.

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Nedjeljka Žagar, Jeffrey Anderson, Nancy Collins, Timothy Hoar, Kevin Raeder, Lili Lei, and Joseph Tribbia

Abstract

Global data assimilation systems for numerical weather prediction (NWP) are characterized by significant uncertainties in tropical analysis fields. Furthermore, the largest spread of global ensemble forecasts in the short range on all scales is in the tropics. The presented results suggest that these properties hold even in the perfect-model framework and the ensemble Kalman filter data assimilation with a globally homogeneous network of wind and temperature profiles. The reasons for this are discussed by using the modal analysis, which provides information about the scale dependency of analysis and forecast uncertainties and information about the efficiency of data assimilation to reduce the prior uncertainties in the balanced and inertio-gravity dynamics.

The scale-dependent representation of variance reduction of the prior ensemble by the data assimilation shows that the peak efficiency of data assimilation is on the synoptic scales in the midlatitudes that are associated with quasigeostrophic dynamics. In contrast, the variance associated with the inertia–gravity modes is less successfully reduced on all scales. A smaller information content of observations on planetary scales with respect to the synoptic scales is discussed in relation to the large-scale tropical uncertainties that current data assimilation methodologies do not address successfully. In addition, it is shown that a smaller reduction of the large-scale uncertainties in the prior state for NWP in the tropics than in the midlatitudes is influenced by the applied radius for the covariance localization.

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Lei Yang, Dongxiao Wang, Jian Huang, Xin Wang, Lili Zeng, Rui Shi, Yunkai He, Qiang Xie, Shengan Wang, Rongyu Chen, Jinnan Yuan, Qiang Wang, Ju Chen, Tingting Zu, Jian Li, Dandan Sui, and Shiqiu Peng

Abstract

Air–sea interaction in the South China Sea (SCS) has direct impacts on the weather and climate of its surrounding areas at various spatiotemporal scales. In situ observation plays a vital role in exploring the dynamic characteristics of the regional circulation and air–sea interaction. Remote sensing and regional modeling are expected to provide high-resolution data for studies of air–sea coupling; however, careful validation and calibration using in situ observations is necessary to ensure the quality of these data. Through a decade of effort, a marine observation network in the SCS has begun to be established, yielding a regional observatory for the air–sea synoptic system.

Earlier observations in the SCS were scarce and narrowly focused. Since 2004, an annual series of scientific open cruises during late summer in the SCS has been organized by the South China Sea Institute of Oceanology (SCSIO), carefully designed based on the dynamic characteristics of the oceanic circulation and air–sea interaction in the SCS region. Since 2006, the cruise carried a radiometer and radiosondes on board, marking a new era of marine meteorological observation in the SCS. Fixed stations have been established for long-term and sustained records. Observations obtained through the network have been used to study regional ocean circulation and processes in the marine atmospheric boundary layer. In the future, a great number of multi-institutional, collaborative scientific cruises and observations at fixed stations will be carried out to establish a mesoscale hydrological and marine meteorological observation network in the SCS.

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