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Shu-Wen Zhang, Chong-Jian Qiu, and Qin Xu
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Shu-Wen Zhang, Chong-Jian Qiu, and Qin Xu

Abstract

A simple soil heat transfer model is used together with an adaptive Kalman filter to estimate the daily averaged soil volumetric water contents from diurnal variations of the soil temperatures measured at different depths. In this method, the soil water contents are estimated as control variables that regulate the variations of soil temperatures at different depths and make the model nonbiased, while the model system noise covariance matrix is estimated by the covariance-matching technique. The method is tested with soil temperature data collected during 1–31 July 2000 from the Soil Water and Temperature System (SWATS) within the Oklahoma Atmospheric Radiation Measurement (ARM) central facilities at Lamont. The estimated soil water contents are verified against the observed values, and the rms differences are found to be small. Sensitivity tests are performed, showing that the method is reliable and stable.

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Shun Liu, Chongjian Qiu, Qin Xu, and Pengfei Zhang

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A temporal interpolation is required for three-dimensional Doppler wind analysis when the precise measurement time is counted for each radar beam position. The time interpolation is traditionally done by a linear scheme either in the measurement space or in the analysis space. Because a volume scan often takes 5–10 min, the linear time interpolation is not accurate enough to capture the rapidly changing winds associated with a fast-moving and fast-growing storm. Performing the linear interpolation in a frame moving with the storm can reduce the error, but the analyzed wind field is traditionally assumed to be stationary in the moving frame. The stationary assumption simplifies the computation but ignores the time variation of the true wind field in the moving frame. By incorporating a linear time interpolation into the moving frame wind analysis, an improved scheme is developed. The merits of the new scheme are demonstrated by idealized examples and numerical experiments with simulated radar observations. The new scheme is also applied to real radar data for a supercell storm.

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Arun Kumar, Qin Zhang, Peitao Peng, and Bhaskar Jha

Abstract

From ensembles of 80 AGCM simulations for every December–January–February (DJF) seasonal mean in the 1980–2000 period, interannual variability in atmospheric response to interannual variations in observed sea surface temperature (SST) is analyzed. A unique facet of this study is the use of large ensemble size that allows identification of the atmospheric response to SSTs for each DJF in the analysis period. The motivation of this study was to explore what atmospheric response patterns beyond the canonical response to El Niño–Southern Oscillation (ENSO) SST anomalies exist, and to which SST forcing such patterns may be related. A practical motivation for this study was to seek sources of atmospheric predictability that may lead to improvements in seasonal predictability efforts.

This analysis was based on the EOF technique applied to the ensemble mean 200-mb height response. The dominant mode of the atmospheric response was indeed the canonical atmospheric response to ENSO; however, this mode only explained 53% of interannual variability of the ensemble means (often referred to as the external variability). The second mode, explaining 19% of external variability, was related to a general increase (decrease) in the 200-mb heights related to a Tropicwide warming (cooling) in SSTs. The third dominant mode, explaining 12% of external variability, was similar to the mode identified as the “nonlinear” response to ENSO in earlier studies.

The realism of different atmospheric response patterns was also assessed from a comparison of anomaly correlations computed between different renditions of AGCM-simulated atmospheric responses and the observed 200-mb height anomalies. For example, the anomaly correlation between the atmospheric response reconstructed from the first mode alone and the observations was compared with the anomaly correlation when the atmospheric response was reconstructed including modes 2 and 3. If the higher-order atmospheric response patterns obtained from the AGCM simulations had observational counterparts, their inclusion in the reconstructed atmospheric response should lead to higher anomaly correlations. Indeed, at some geographical regions, an increase in anomaly correlation with the inclusion of higher modes was found, and it is concluded that the higher-order atmospheric response patterns found in this study may be realistic and may represent additional sources of atmospheric seasonal predictability.

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Emily Becker, Huug van den Dool, and Qin Zhang

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Forecast skill and potential predictability of 2-m temperature, precipitation rate, and sea surface temperature are assessed using 29 yr of hindcast data from models included in phase 1 of the North American Multimodel Ensemble (NMME) project. Forecast skill is examined using the anomaly correlation (AC); skill of the bias-corrected ensemble means (EMs) of the individual models and of the NMME 7-model EM are verified against the observed value. Forecast skill is also assessed using the root-mean-square error. The models’ representation of the size of forecast anomalies is also studied. Predictability was considered from two angles: homogeneous, where one model is verified against a single member from its own ensemble, and heterogeneous, where a model’s EM is compared to a single member from another model. This study provides insight both into the physical predictability of the three fields and into the NMME and its contributing models.

Most of the models in the NMME have fairly realistic spread, as represented by the interannual variability. The NMME 7-model forecast skill, verified against observations, is equal to or higher than the individual models’ forecast ACs. Two-meter temperature (T2m) skill matches the highest single-model skill, while precipitation rate and sea surface temperature NMME EM skill is higher than for any single model. Homogeneous predictability is higher than reported skill in all fields, suggesting there may be room for some improvement in model prediction, although there are many regional and seasonal variations. The estimate of potential predictability is not overly sensitive to the choice of model. In general, models with higher homogeneous predictability show higher forecast skill.

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Xu Qin, Jiang-she Zhang, and Xiao-dong Yan

Abstract

In this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution [MWbl(2, 2)] model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution [MWbl(2, 3)] model. The other improved model is the two-component mixture three-parameter Weibull distribution [MWbl(3, 3)] model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice.

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Arun Kumar, Bhaskar Jha, Qin Zhang, and Lahouari Bounoua

Abstract

Predictability limits for seasonal atmospheric climate variability depend on the fraction of variability that is due to factors external to the atmosphere (e.g., boundary conditions) and the fraction that is internal. From the analysis of observed data alone, however, separation of the total seasonal atmospheric variance into its external and internal components remains a difficult and controversial issue. In this paper a simple procedure for estimating atmospheric internal variability is outlined. This procedure is based on the expected value of the mean square error between the observed and the general circulation model simulated (or predicted) seasonal mean anomaly. The end result is a spatial map for the estimate of the observed seasonal atmospheric internal (or unpredictable) variability. As improved general circulation models become available, mean square error estimated from the new generation of general circulation models can be easily included in the procedure proposed herein, bringing the estimate for the internal variability closer to its true estimate.

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Ping Liu, Qin Zhang, Chidong Zhang, Yuejian Zhu, Marat Khairoutdinov, Hye-Mi Kim, Courtney Schumacher, and Minghua Zhang

Abstract

This study investigates why OLR plays a small role in the Real-time Multivariate (Madden–Julian oscillation) MJO (RMM) index and how to improve it. The RMM index consists of the first two leading principal components (PCs) of a covariance matrix, which is constructed by combined daily anomalies of OLR and zonal winds at 850 (U850) and 200 hPa (U200) in the tropics after being normalized with their globally averaged standard deviations of 15.3 W m−2, 1.8 m s−1, and 4.9 m s−1, respectively. This covariance matrix is reasoned mathematically close to a correlation matrix. Both matrices substantially suppress the overall contribution of OLR and make the index more dynamical and nearly transparent to the convective initiation of the MJO. A covariance matrix that does not use normalized anomalies leads to the other extreme where OLR plays a dominant role while U850 and U200 are minor. Numerous tests indicate that a simple scaling of the anomalies (i.e., 2 W m−2, 1 m s−1, and 1 m s−1) can better balance the roles of OLR and winds. The revised PCs substantially enhance OLR over the eastern Indian and western Pacific Oceans and change it less notably in other locations, while they reduce U850 and U200 only slightly. Comparisons with the original RMM in spatial structure, power spectra, and standard deviation demonstrate improvements of the revised RMM index.

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

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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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