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Hong Guan, Bo Cui, and Yuejian Zhu

Abstract

The National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) generated a multidecadal (from 1985 to present) ensemble reforecast database for the 2012 version of the Global Ensemble Forecast System (GEFS). This dataset includes 11-member reforecasts initialized once per day at 0000 UTC. This GEFS version has a strong cold bias for winter and warm bias for summer in the Northern Hemisphere. Although the operational decaying average bias-correction approach performs well in winter and summer, it sometimes fails during the spring and fall transition seasons at long lead times (>~5 days). In this paper, 24- (1985–2008) and 25-yr (1985–2009) reforecast biases are used to calibrate 2-m temperature forecasts in 2009 and 2010, respectively. The reforecast-calibrated forecasts for both years are more accurate than those adjusted by the decaying average method during transition seasons. A long training period (>5 yr) is necessary to help avoid a large impact on bias correction from an extreme year case and keep a broader diversity of weather scenarios. The improvement from using the full 25-yr, 31-day window, weekly training dataset is almost equivalent to that from using daily training samples. This provides an option to reduce computational expenses while maintaining a desired accuracy. To provide the potential to improve forecast accuracy for transition seasons, reforecast information is added into the current operational bias-correction method. The relative contribution of the two methods is determined by the correlation between the ensemble mean and analysis. This method improves the forecast accuracy for most of the year with a maximum benefit during April–June.

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Bo Cui, Zoltan Toth, Yuejian Zhu, and Dingchen Hou

Abstract

The main task of this study is to introduce a statistical postprocessing algorithm to reduce the bias in the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble forecasts before they are merged to form a joint ensemble within the North American Ensemble Forecast System (NAEFS). This statistical postprocessing method applies a Kalman filter type algorithm to accumulate the decaying averaging bias and produces bias-corrected ensembles for 35 variables. NCEP implemented this bias-correction technique in 2006. NAEFS is a joint operational multimodel ensemble forecast system that combines NCEP and MSC ensemble forecasts after bias correction. According to operational statistical verification, both the NCEP and MSC bias-corrected ensemble forecast products are enhanced significantly. In addition to the operational calibration technique, three other experiments were designed to assess and mitigate ensemble biases on the model grid: a decaying averaging bias calibration method with short samples, a climate mean bias calibration method, and a bias calibration method using dependent data. Preliminary results show that the decaying averaging method works well for the first few days. After removing the decaying averaging bias, the calibrated NCEP operational ensemble has improved probabilistic performance for all measures until day 5. The reforecast ensembles from the Earth System Research Laboratory’s Physical Sciences Division with and without the climate mean bias correction were also examined. A comparison between the operational and the bias-corrected reforecast ensembles shows that the climate mean bias correction can add value, especially for week-2 probability forecasts.

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Shou-Jun Chen, Ying-Hwa Kuo, Wei Wang, Zu-Yu Tao, and Bo Cui

Abstract

On 12–13 June 1991, a series of convective rainstorms (defined as mesoscale precipitation systems with rainfall rates exceeding 10 mm h−1) developed successively along the Mei-Yu front. During this event, new rainstorms formed to the east of preceding storms at an interval of approximately 300–400 km. The successive development and eastward propagation of these rainstorms produced heavy rainfall over the Jiang-Huai Basin in eastern China, with a maximum 24-h accumulation of 234 mm. This study presents the results of a numerical simulation of this heavy rainfall event using the Penn State–NCAR Mesoscale Model Version 5 (MM5) with a horizontal resolution of 54 km.

Despite the relatively coarse horizontal resolution, the MM5, using a moist physics package comprising an explicit scheme and the Grell cumulus parameterization, simulated the successive development of the rainstorms. The simulated rainstorms compared favorably with the observed systems in terms of size and intensity. An additional sensitivity experiment showed that latent heat release is crucial for the development of the rainstorms, the mesoscale low-level jet, the mesolow, the rapid spinup of vorticity, and the Mei-Yu frontogenesis. Without latent heat release, the maximum vertical motion associated with the rainstorm is reduced from 70 to 6 cm s−1.

Additional model sensitivity experiments using the Kain–Fritsch cumulus parameterization with grid sizes of 54 and 18 km produced results very similar to the 54-km control experiment with the Grell scheme. This suggests that the simulation of Mei-Yu rainstorms, the mesoscale low-level jet, and the mesolow is not highly sensitive to convective parameterization and grid resolution. In all the full-physics experiments, the model rainfall was dominated by the resolvable-scale precipitation. This is attributed to the high relative humidity and low convective available potential energy environment in the vicinity of the Mei-Yu front.

The modeling results suggest that there is strong interaction and positive feedback between the convective rainstorms embedded within the Mei-Yu front and the Mei-Yu front itself. The front provides a favorable environment for such rainstorms to develop, and the rainstorms intensify the Mei-Yu front.

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Bo-Wen Shen, Roger A. Pielke Sr., Xubin Zeng, Jong-Jin Baik, Sara Faghih-Naini, Jialin Cui, and Robert Atlas

Abstract

Over 50 years since Lorenz’s 1963 study and a follow-up presentation in 1972, the statement “weather is chaotic” has been well accepted. Such a view turns our attention from regularity associated with Laplace’s view of determinism to irregularity associated with chaos. In contrast to single-type chaotic solutions, recent studies using a generalized Lorenz model (GLM) have focused on the coexistence of chaotic and regular solutions that appear within the same model using the same modeling configurations but different initial conditions. The results, with attractor coexistence, suggest that the entirety of weather possesses a dual nature of chaos and order with distinct predictability. In this study, based on the GLM, we illustrate the following two mechanisms that may enable or modulate two kinds of attractor coexistence and, thus, contribute to distinct predictability: 1) the aggregated negative feedback of small-scale convective processes that can produce stable nontrivial equilibrium points and, thus, enable the appearance of stable steady-state solutions and their coexistence with chaotic or nonlinear oscillatory solutions, referred to as the first and second kinds of attractor coexistence; and 2) the modulation of large-scale time-varying forcing (heating) that can determine (or modulate) the alternative appearance of two kinds of attractor coexistence. Based on our results, we then discuss new opportunities and challenges in predictability research with the aim of improving predictions at extended-range time scales, as well as subseasonal to seasonal time scales.

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Dingchen Hou, Mike charles, Yan Luo, Zoltan Toth, Yuejian Zhu, Roman Krzysztofowicz, Ying Lin, Pingping Xie, Dong-Jun Seo, Malaquias Pena, and Bo Cui

Abstract

Two widely used precipitation analyses are the Climate Prediction Center (CPC) unified global daily gauge analysis and Stage IV analysis based on quantitative precipitation estimate with multisensor observations. The former is based on gauge records with a uniform quality control across the entire domain and thus bears more confidence, but provides only 24-h accumulation at ⅛° resolution. The Stage IV dataset, on the other hand, has higher spatial and temporal resolution, but is subject to different methods of quality control and adjustments by different River Forecasting Centers. This article describes a methodology used to generate a new dataset by adjusting the Stage IV 6-h accumulations based on available joint samples of the two analyses to take advantage of both datasets. A simple linear regression model is applied to the archived historical Stage IV and the CPC datasets after the former is aggregated to the CPC grid and daily accumulation. The aggregated Stage IV analysis is then adjusted based on this linear model and then downscaled back to its original resolution. The new dataset, named Climatology-Calibrated Precipitation Analysis (CCPA), retains the spatial and temporal patterns of the Stage IV analysis while having its long-term average and climate probability distribution closer to that of the CPC analysis. The limitation of the methodology at some locations is mainly associated with heavy to extreme precipitation events, which the Stage IV dataset tends to underestimate. CCPA cannot effectively correct this because of the linear regression model and the relative scarcity of heavy precipitation in the training data sample.

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