[1] The heaviest rainfall in 6 decades fell in Beijing on 21 July 2012 with a record-breaking amount of 460 mm in 18 h and hourly rainfall rates exceeding 85 mm. This extreme rainfall event appeared to be reasonably well predicted by current operational models, albeit with notable timing and location errors. However, our analysis reveals that the model-predicted rainfall results mainly from topographical lifting and the passage of a cold front, whereas the observed rainfall was mostly generated by convective cells that were triggered by local topography and then propagated along a quasi-stationary linear convective system into Beijing. In particular, most of the extreme rainfall occurred in the warm sector far ahead of the cold front. Evidence from a cloud-permitting simulation indicates the importance of using high-resolution cloud-permitting models to reproduce the above-mentioned rainfall-production mechanisms in order to more accurately predict the timing, distribution, and intensity of such an extreme event.

A pattern projection downscaling method is applied to predict summer precipitation at 60 stations over Korea. The predictors are multiple variables from the output of six operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction was made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of six model downscaled precipitation forecasts using the best predictors and will be referred to as ''DMME.'' It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse-resolution predictions of general circulation models. Although Korea's precipitation is strongly influenced by local mountainous terrain, DMME performs well at 59 stations with correlation skill significant at the 95% confidence level. The improvement of the prediction skill is attributed to three steps: coupled pattern selection, optimal predictor selection, and the multimodel downscaled precipitation ensemble. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected, can be used to make skillful predictions of the local precipitation by using appropriate statistical downscaling methods.

[1] This study assesses how well the East Asian monsoon index (EAMI), developed on the basis of zonal and meridional land -sea thermal contrasts over the AsiaPacific region, can represent the seasonal and interannual variations of the East Asian summer and winter monsoons (EASM and EAWM). It suggests that the EAMI can be used to estimate the timing of the onset and the relative intensity of the EASM, characterized by dominant meridional circulation and rainfall patterns over the Asia-Pacific region, as well as represent the EAWM, which is dominated by a nearly zonal dipole structure composed of Siberian high and Aleutian low prevailing in the middle and high latitudes. The EAMI is therefore of benefit in understanding the seasonal evolution of the East Asian monsoon circulation and interannual variation of the individual monsoons both in summer and in winter. Citation: Zhu, C., W.-S. Lee, H. Kang, and C.-K. Park (2005), A proper monsoon index for seasonal and interannual variations of the East Asian monsoon, Geophys. Res. Lett., 32, L02811,

[1] This study investigates the potential of predicting local precipitation over northern Taiwan using statistical downscaling of large-scale circulation variables from global climate models (GCMs). Historical hindcast data of 500 hPa geopotential height (Z500) and sea level pressure (SLP) from six different GCMs, with the target season of being that of June, July, and August (JJA), are used as predictors for downscaling. Singular value decomposition analysis (SVDA) using observational data reveals that the rainfall over northern Taiwan is strongly coupled with a prominent tripole pattern of Z500 (SLP) field over the western North Pacific/East Asian coast. SVDA using model SLP or height field and station rainfall as input also gives similar results, indicating that most models can capture this mode of covariability. SLP and Z500 from models are then used for local rainfall prediction based on their relationship, which is drawn from the SVDA. For every station considered in this study, downscaled prediction shows considerable improvement when compared with model output. In particular, downscaling is able to correct the erroneous sign of model rainfall prediction. However, a few models show very low skill in their downscaled precipitation. For these models, the correlation between observed rainfall and simulated Z500 (SLP) leading SVD patterns is found to be weak. The performance based on the average of downscaled prediction using Z500 and SLP is also evaluated. In general, the average prediction is more stable and skillful when compared with results based on one predictor. Overall, this study demonstrates that useful regional climate information can be obtained from downscaling using large-scale variables from coarse-resolution GCM products.

[1] Six dynamical seasonal model outputs, which are currently used in the APEC Climate Center Multimodel Ensemble (MME) prediction system, are employed for statistical downscaling prediction of station-scale precipitation in the Philippines and Thailand. Correlation analysis and Singular Value Decomposition Analysis are used to reveal atmosphere dynamic linkage based on the observed data other than model data. The observed linkage provides a robust basis for the choice of predictor and its range in predicted fields. In order to avoid spatial shift of predicted field away from observed climate, a movable window is set to select the most sensible area within the range of predictor for downscaling. The downscaled MME prediction is verified against observed station precipitation in a cross-validation manner, and the prediction skill is apparently improved compared with the simple composite of raw model predictions for most of the stations. Citation:

This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

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Text logo design heavily relies on the creativity and expertise of professional designers, in which arranging element layouts is one of the most important procedures. However, few attention has been paid to this task which needs to take many factors (e.g., fonts, linguistics, topics, etc.) into consideration. In this paper, we propose a contentaware layout generation network which takes glyph images and their corresponding text as input and synthesizes aesthetic layouts for them automatically. Specifically, we develop a dual-discriminator module, including a sequence discriminator and an image discriminator, to evaluate both the character placing trajectories and rendered shapes of synthesized text logos, respectively. Furthermore, we fuse the information of linguistics from texts and visual semantics from glyphs to guide layout prediction, which both play important roles in professional layout design. To train and evaluate our approach, we construct a dataset named as TextLogo3K, consisting of about 3,500 text logo images and their pixel-level annotations. Experimental studies on this dataset demonstrate the effectiveness of our approach for synthesizing visually-pleasing text logos and verify its superiority against the state of the art.