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Qingyun Zhao, Fuqing Zhang, Teddy Holt, Craig H. Bishop, and Qin Xu

Abstract

An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel algorithm was also developed to increase the system’s computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedure was developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-denying experiments). The procedure was employed in this study to examine the impacts of ensemble size and localization on data assimilation and the results reveal a very interesting relationship between the ensemble size and the localization length scale. All the tests conducted in this study demonstrate the capabilities of the EnKF as a research tool for mesoscale and storm-scale data assimilation with potential operational applications.

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Jun Qin, Ailin Shi, Guoyu Ren, Zhenghong Chen, Yuda Yang, Xukai Zou, and Panfeng Zhang

Abstract

The White Crane Ridge (WCR) Rock Fish, now submerged under the backwater of the Three Gorges Reservoir in the Yangtze River, are affirmed as one of the earliest hydrologic observations ever made in any large river in the world. The usually in-water monument provides highly valuable historical records of severe droughts in the upper Yangtze over the last 1,200 years. This article updated the historical drought chronology previously developed based on the WCR inscriptions, which can be applied in assessment of extreme climatic and hydrological risks, and also made a preliminary analysis of changes of the severe drought frequency during the last thousand years in the upper Yangtze. The analysis shows that the severe droughts occurred more frequently during the Medieval Climate Anomaly (MCA), relatively less so during the Little Ice Age (LIA), and once again more often under the background of modern global warming. It was suggested that a generally warmer Euro-Asian continent during the MCA was in favor of the stronger East Asian summer monsoon, and the resulting less precipitation and more severe droughts of the Yangtze and the lower water level at the Three Gorges area on the centennial scale, and vice versa for the period of the LIA. The results would help in understanding the causes and mechanisms of the regional climate change and variability, and also in taking measures in the fields of the watershed management to cope with the long-term change in climatic and hydrologic droughts.

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Jun Qin, Ailin Shi, Guoyu Ren, Zhenghong Chen, Yuda Yang, Xukai Zou, and Panfeng Zhang
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Wenmin Qin, Lunche Wang, Ming Zhang, Zigeng Niu, Ming Luo, Aiwen Lin, and Bo Hu

Abstract

Photosynthetically active radiation (PAR) is a key factor for vegetation growth and climate change. Different types of PAR models, including four physically based models and eight artificial intelligence (AI) models, were proposed for predicting daily PAR. Multiyear daily meteorological parameters observed at 29 Chinese Ecosystem Research Network (CERN) stations and 2474 Chinese Meteorological Administration (CMA) stations across China were used for testing, validating, and comparing the above models. The optimized back propagation (BP) neural network based on the mind evolutionary algorithm (MEA-BP) was the model with highest accuracy and strongest robustness. The correlation coefficient R, mean absolute bias error (MAE), and RMSE for MEA-BP were 0.986, 0.302 MJ m−2 day−1 and 0.393 MJ m−2 day−1, respectively. Then, a high-density PAR dataset was constructed for the first time using the MEA-BP model at 2474 CMA stations of China. A quality control process and homogenization test (using RHtestsV4) for the PAR dataset were further conducted. This high-density PAR dataset would benefit many climate and ecological studies.

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Song Yang, Yundi Jiang, Dawei Zheng, R. Wayne Higgins, Qin Zhang, Vernon E. Kousky, and Min Wen

Abstract

Variations of U.S. regional precipitation in both observations and free-run experiments with the NCEP Climate Forecast System (CFS) are investigated. The seasonality of precipitation over the continental United States and the time–frequency characteristics of precipitation over the Southwest (SW) are the focus. The differences in precipitation variation among different model resolutions are also analyzed.

The spatial distribution of U.S. precipitation is characterized by high values over the East and the West Coasts, especially over the Gulf Coast and southeast states, and low values elsewhere except over the SW in summer. A large annual cycle of precipitation occurs over the SW, northern plains, and the West Coast. Overall, the CFS captures the above features reasonably well, except for the SW. However, it overestimates the precipitation over the western United States, except the SW in summer, and underestimates the precipitation over the central South, except in springtime. It also overestimates (underestimates) the precipitation seasonality over the intermountain area and Gulf Coast states (SW, West Coast, and northern Midwest). The model using T126 resolution captures the observed features more realistically than at the lower T62 resolution over a large part of the United States.

The variability of observed SW precipitation is characterized by a large annual cycle, followed by a semiannual cycle, and the oscillating signals on annual, semiannual, and interannual time scales account for 41% of the total precipitation variability. However, the CFS, at both T62 and T126 resolution, fails in capturing the above feature. The variability of SW precipitation in the CFS is much less periodic. The annual oscillation of model precipitation is much weaker than that observed and it is even much weaker than the simulated semiannual oscillation. The weakly simulated annual cycle is attributed by the unrealistic precipitation simulations of all seasons, especially spring and summer. On the annual time scale, the CFS fails in simulating the relationship between the SW precipitation and the basinwide sea surface temperature (SST) and the overlying atmospheric circulation. On the semiannual time scale, the model exaggerates the response of the regional precipitation to the variations of SST and atmospheric circulation over the tropics and western Atlantic, including the Gulf of Mexico. This study also demonstrates a challenge for the next-generation CFS, at T126 resolution, to predict the variability of North American monsoon climate.

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Arun Kumar, Qin Zhang, J-K. E. Schemm, Michelle L’Heureux, and K-H. Seo

Abstract

For the uncoupled atmospheric general circulation model (AGCM) simulations, the quantification of errors due to the lack of coupled ocean–atmospheric evolution on the characteristics of the atmospheric interannual variability is important for various reasons including the following: 1) AGCM simulations forced with specified SSTs continue to be used for understanding atmospheric interannual variability and 2) there is a vast knowledge base quantifying the global atmospheric influence of tropical SSTs that traditionally has relied on the analysis of AGCM-alone simulations. To put such results and analysis in a proper context, it is essential to document errors that may result from the lack of a coupled ocean–atmosphere evolution in the AGCM-alone integrations.

Analysis is based on comparison of tier-two (or uncoupled) and coupled hindcasts for the 1982–2005 period, and interannual variability for the December–February (DJF) seasonal mean is analyzed. Results indicate that for the seasonal mean variability, and for the DJF seasonal mean, atmospheric interannual variability between coupled and uncoupled simulations is similar. This conclusion is drawn from the analysis of interannual variability of rainfall and 200-mb heights and includes analysis of SST-forced interannual variability, analysis of El Niño and La Niña composites, and a comparison of hindcast skill between tier-two and coupled hindcasts.

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Qin Xu, Kang Nai, Li Wei, Pengfei Zhang, Shun Liu, and David Parrish

Abstract

This paper describes a new velocity–azimuth display (VAD)-based dealiasing method developed for automated radar radial velocity data quality control to satisfy the high-quality standard and efficiency required by operational radar data assimilation. The method is built on an alias-robust velocity–azimuth display (AR-VAD) analysis. It upgrades and simplifies the previous three-step dealiasing method in three major aspects. First, the AR-VAD is used with sufficiently stringent threshold conditions in place of the original modified VAD for the preliminary reference check to produce alias-free seed data in the first step. Second, the AR-VAD is more accurate than the traditional VAD for the refined reference check in the original second step, so the original second step becomes unnecessary and is removed. Third, a block-to-point continuity check procedure is developed, in place of the point-to-point continuity check in the original third step, which serves to enhance the use of the available seed data in a properly enlarged block area around each flagged data point that is being checked with multiple threshold conditions to avoid false dealiasing. The new method has been tested extensively with aliased radial velocity data collected under various weather conditions, including hurricane high-wind conditions. The robustness of the new method is exemplified by the results tested with three cases. The limitations of the new method and possible improvements are discussed.

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Huug van den Dool, Emily Becker, Li-Chuan Chen, and Qin Zhang

Abstract

An ordinary regression of predicted versus observed probabilities is presented as a direct and simple procedure for minimizing the Brier score (BS) and improving the attributes diagram. The main example applies to seasonal prediction of extratropical sea surface temperature by a global coupled numerical model. In connection with this calibration procedure, the probability anomaly correlation (PAC) is developed. This emphasizes the exact analogy of PAC and minimizing BS to the widely used anomaly correlation (AC) and minimizing mean squared error in physical units.

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Chang-Jiang Zhang, Jin-Fang Qian, Lei-Ming Ma, and Xiao-Qin Lu

Abstract

An objective technique is presented to estimate tropical cyclone intensity using the relevance vector machine (RVM) and deviation angle distribution inhomogeneity (DADI) based on infrared satellite images of the northwest Pacific Ocean basin from China’s FY-2C geostationary satellite. Using this technique, structures of a deviation-angle gradient co-occurrence matrix, which include 15 statistical parameters nonlinearly related to tropical cyclone intensity, were derived from infrared satellite imagery. RVM was then used to relate these statistical parameters to tropical cyclone intensity. Twenty-two tropical cyclones occurred in the northwest Pacific during 2005–09 and were selected to verify the performance of the proposed technique. The results show that, in comparison with the traditional linear regression method, the proposed technique can significantly improve the accuracy of tropical cyclone intensity estimation. The average absolute error of intensity estimation using the linear regression method is between 15 and 29 m s−1. Compared to the linear regression method, the average absolute error of the intensity estimation using RVM is between 3 and 10 m s−1.

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Li-Chuan Chen, Huug van den Dool, Emily Becker, and Qin Zhang

Abstract

In this study, precipitation and temperature forecasts during El Niño–Southern Oscillation (ENSO) events are examined in six models in the North American Multimodel Ensemble (NMME), including the CFSv2, CanCM3, CanCM4, the Forecast-Oriented Low Ocean Resolution (FLOR) version of GFDL CM2.5, GEOS-5, and CCSM4 models, by comparing the model-based ENSO composites to the observed. The composite analysis is conducted using the 1982–2010 hindcasts for each of the six models with selected ENSO episodes based on the seasonal oceanic Niño index just prior to the date the forecasts were initiated. Two types of composites are constructed over the North American continent: one based on mean precipitation and temperature anomalies and the other based on their probability of occurrence in a tercile-based system. The composites apply to monthly mean conditions in November, December, January, February, and March as well as to the 5-month aggregates representing the winter conditions. For anomaly composites, the anomaly correlation coefficient and root-mean-square error against the observed composites are used for the evaluation. For probability composites, a new probability anomaly correlation measure and a root-mean probability score are developed for the assessment. All NMME models predict ENSO precipitation patterns well during wintertime; however, some models have large discrepancies between the model temperature composites and the observed. The fidelity is greater for the multimodel ensemble as well as for the 5-month aggregates. February tends to have higher scores than other winter months. For anomaly composites, most models perform slightly better in predicting El Niño patterns than La Niña patterns. For probability composites, all models have superior performance in predicting ENSO precipitation patterns than temperature patterns.

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