[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.
[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,
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 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.
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