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Guoxing Chen, Wei-Chyung Wang, Chao-Tzuen Cheng, and Huang-Hsiung Hsu

Abstract

Winter extreme snowstorm events along the coast of the northeast United States have significant impacts on social and economic activities, and their potential changes under global warming are of great concern. Here, we adopted the pseudo–global warming approach to investigate the responses of 93 events identified in our previous observational analysis. The study was conducted by contrasting two sets of WRF simulations for each event: the first set driven by the ERA-Interim reanalysis and the second set by that data superimposed with mean-climate changes simulated from HiRAM historical (1980–2004) and future (2075–99; RCP8.5) runs. Results reveal that the warming together with increased moisture tends to decrease the snowfall along the coast but increase the rainfall throughout the region. For example, the number of events having daily snow water equivalent larger than 10 mm day−1 at Boston, Massachusetts; New York City, New York; Philadelphia, Pennsylvania; and Washington, D.C., is decreased by 47%, 46%, 30%, and 33%, respectively. The compensating changes in snowfall and rainfall lead to a total-precipitation increase in the three more-southern cities but a decrease in Boston. In addition, the southwestward shift of regional precipitation distribution is coherent with the enhancement (reduction) of upward vertical motion in the south (north) and the movement of cyclone centers (westward in 58% of events and southward in 72%). Finally, perhaps more adversely, because of the northward retreat of the 0°C line and the expansion of the near-freezing zone, the number of events with mixed rain and snow and freezing precipitation in the north (especially the inland area) is increased.

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Wei Wang, Jiaping Xu, Yunqiu Gao, Ivan Bogoev, Jian Cui, Lichen Deng, Cheng Hu, Cheng Liu, Shoudong Liu, Jing Shen, Xiaomin Sun, Wei Xiao, Guofu Yuan, and Xuhui Lee

Abstract

Performance evaluation of an integrated eddy covariance (EC) instrument called the IRGASON, with a separated EC for reference, was conducted in a desert riparian Populus euphratica stand in the lower Tarim River basin in northwestern China. The separated EC consisted of an open-path gas analyzer and a sonic anemometer separated by 20 cm. The IRGASON integrates an open-path gas analyzer and a sonic anemometer into the same sensing volume, thus eliminating sensor separation in comparison to the traditional open-path EC setup. Integrating the infrared gas analyzer’s sensing head into the sensing volume of the sonic anemometer had negligible effects on wind speed and friction velocity observations of the IRGASON. Physiologically unreasonable daytime CO2 uptake was observed by both systems during the cold winter season (mean air temperature of −6.7°C), when the trees were dormant without any photosynthetic activities. The mean midday CO2 flux was −1.65 and −1.61 μmol m−2 s−1 for the IRGASON and the separated EC setup, respectively. No evidence was found for sensor self-heating as the cause of the apparent uptake CO2 flux. Instead, the uptake CO2 flux appeared to be an artifact of the spectroscopic effect of the IRGASON’s gas analyzer. After adjusting for this spectroscopic effect using a relationship with the sensible heat flux, the wintertime IRGASON CO2 flux became physiologically reasonable (mean value of −0.04 μmol m−2 s−1).

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Xuhui Lee, Shoudong Liu, Wei Xiao, Wei Wang, Zhiqiu Gao, Chang Cao, Cheng Hu, Zhenghua Hu, Shuanghe Shen, Yongwei Wang, Xuefa Wen, Qitao Xiao, Jiaping Xu, Jinbiao Yang, and Mi Zhang

Lakes are an important component of the climate system. They provide moisture for precipitation, buffer temperature variations, and contribute to regional atmospheric carbon budgets. This article describes an eddy covariance (EC) mesonet on Lake Taihu, a large (area 2400 km2) and shallow (depth 2 m) lake situated in the heavily populated Yangtze River Delta, China. The mesonet consists of five lake sites, representing different biological attributes and wind–wave patterns, and a land site near the lake shore. Common to all the sites are standard EC instruments for measurement of the momentum, sensible heat, water vapor, and CO2 flux. One site is also equipped with laser-based analyzers for precise measurement of the CO2, CH4, and H2O mixing ratios and their isotopic compositions. To the authors' best knowledge, this is the first lake eddy flux mesonet. Early results reveal evidence of biological and pollution controls on the surface–air fluxes of energy, momentum, and greenhouse gases across the lake. The data will be used to address five science questions: 1) Are lake–air parameterizations established for deep lakes applicable to shallow lakes? 2) Why are lake–land breeze circulations less prevalent in the Taihu lake basin than in lake basins in northern latitudes? 3) How do algal blooms alter the lake–atmosphere interactions? 4) Is this eutrophic lake a source or sink of atmospheric CO2? 5) Does the decay of algal and macrophyte biomass contribute significant amounts of CH4 to the atmosphere?

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Youlong Xia, David M. Mocko, Shugong Wang, Ming Pan, Sujay V. Kumar, Christa D. Peters-Lidard, Helin Wei, Dagang Wang, and Michael B. Ek

Abstract

Since the second phase of the North American Land Data Assimilation System (NLDAS-2) was operationally implemented at NOAA/NCEP as part of the production suite in August 2014, developing the next phase of NLDAS has been a key focus of the NCEP and NASA NLDAS teams. The Variable Infiltration Capacity (VIC) model is one of the four land surface models of the NLDAS system. The current operational NLDAS-2 uses version 4.0.3 (VIC403), the research NLDAS-2 used version 4.0.5 (VIC405), and the NASA Land Information System (LIS)-based NLDAS uses version 4.1.2.l (VIC412). The purpose of this study is to evaluate VIC403 and VIC412 and check if the latter version has better performance for the next phase of NLDAS. Toward this, a comprehensive evaluation was conducted, targeting multiple variables and using multiple metrics to assess the performance of different model versions. The evaluation results show large and significant improvements in VIC412 over the southeastern United States when compared with VIC403 and VIC405. In other regions, there are very limited improvements or even deterioration to some degree. This is partially due to 1) the sparseness of USGS streamflow observations for model parameter calibration and 2) a deterioration of VIC model performance in the Great Plains (GP) region after a model upgrade to a newer version. Overall, the model upgrade enhances model performance and skill scores for most parts of the continental United States; exceptions include the GP and western mountainous regions, as well as the daily soil moisture simulation skill, suggesting that VIC model development is on the right path. Further efforts are needed for scientific understanding of land surface physical processes in the GP, and a recalibration of VIC412 using reasonable reference datasets is recommended.

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Xianxin Li, Zhangjun Wang, Libin Du, Xingtao Liu, Xiufen Wang, Chao Chen, Xiangqian Meng, Hui Li, Quanfeng Zhuang, Wei Deng, Xin Pan, and Xinzhao Chu

Abstract

Observations of the atmospheric trace gases are crucial for quality assessment of the human living environment. Multiaxis differential optical absorption spectroscopy (MAX-DOAS) is the most promising candidate to meet the requirements on observations of atmospheric trace gases with high sensitivity, good stability, and a wide range of regional monitoring. The shipborne observations of tropospheric trace gases (NO2, SO2, and O3) over a coastal city, Qingdao, with MAX-DOAS were conducted by a Chinese oceanographic research vessel, XiangYangHong 08 (XYH 08). During the observational campaign, the shipborne MAX-DOAS equipment was used to make anchor measurements for 3 days, and a sailing measurement along Qingdao coast for half an hour. Measurement results are presented for both sailing and anchor point measurements in this paper. Combining geometry characteristic of the monitoring area, it can be concluded from the sailing measurements that the traffic emissions may play an important role in the boundary layer (BL) pollution of a coastal city’s atmosphere. The anchor point measurements showed that the NO2 vertical column density (VCD) mean value of Jiaozhou Bay is about 2.7 times of the value of the Qingdao offshore sea area. Likewise, the tropospheric VCDs of SO2 and O3 have an increase of 30% and 40%, respectively, on 1 September in Jiaozhou Bay, compared to the other 2 days in Qingdao offshore sea area.

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Qinglan Li, Zenglu Li, Yulong Peng, Xiaoxue Wang, Lei Li, Hongping Lan, Shengzhong Feng, Liqun Sun, Guangxin Li, and Xiaolin Wei

Abstract

This study proposes a statistical regression scheme to forecast tropical cyclone (TC) intensity at 12, 24, 36, 48, 60, and 72 h in the northwestern Pacific region. This study utilizes best track data from the Shanghai Typhoon Institute (STI), China, and the Joint Typhoon Warning Center (JTWC), United States, from 2000 to 2015. In addition to conventional factors involving climatology and persistence, this study pays close attention to the land effect on TC intensity change by considering a new factor involving the ratio of seawater area to land area (SL ratio) in the statistical regression model. TC intensity changes are investigated over the entire life-span, over the open ocean, near the coast, and after landfall. Data from 2000 to 2011 are used for model calibration, and data from 2012 to 2015 are used for model validation. The results show that the intensity change during the previous 12 h (DVMAX), the potential future intensity change (POT), and the area-averaged (200–800 km) wind shear at 1000–300 hPa (SHRD) are the most significant predictors of the intensity change for TCs over the open ocean and near the coast. Intensity forecasting for TCs near the coast and over land is improved with the addition of the SL ratio compared with that of the models that do not consider the SL ratio. As this study has considered the TC intensity change over the entire TC life-span, the proposed models are valuable and practical for forecasting TC intensity change over the open ocean, near the coast, and after landfall.

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Guoxing Chen, Wei-Chyung Wang, Lijun Tao, Huang-Hsiung Hsu, Chia-Ying Tu, and Chao-Tzuen Cheng

Abstract

This study used both observations and global climate model simulations to investigate the characteristics of winter extreme snowfall events along the coast (the Interstate 95 corridor) of the northeast United States where several mega-cities are located. Observational analyses indicate that, during 1980–2015, 110 events occurred when four coastal cities—Boston, New York City, Philadelphia, and Washington, D.C.—had either individually or collectively experienced daily snowfall exceeding the local 95th percentile thresholds. Boston had the most events, with a total of 69, followed by 40, 36, and 30 (moving southward) in the other three cities. The associated circulations at 200 and 850 hPa were categorized via K-means clustering. The resulting three composite circulations are characterized by the strength and location of the jet at 200 hPa and the coupled low pressure system at 850 hPa: a strong jet overlying the cities coupled with an inland trough, a weak and slightly southward shifted jet coupled with a cyclone at the coast, and a weak jet stream situated to the south of the cities coupled with a cyclone over the coastal oceans. Comparative analyses were also conducted using the GFDL High Resolution Atmospheric Model (HiRAM) simulation of the same period. Although the simulated extreme events do not provide one-to-one correspondence with observations, the characteristics nevertheless show consistency notably in total number of occurrences, intraseasonal and multiple-year variations, snow spatial coverage, and the associated circulation patterns. Possible future change in extreme snow events was also explored utilizing the HiRAM RCP8.5 (2075–2100) simulation. The analyses suggest that a warming global climate tends to decrease the extreme snowfall events but increase extreme rainfall events.

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Chaing Chen, Wei-Kuo Tao, Pay-Liam Lin, George S. Lai, S-F. Tseng, and Tai-Chi Chen Wang

Abstract

During the period of 21–25 June 1991, a mei-yu front, observed by the post–Taiwan Area Mesoscale Experiment, produced heavy precipitation along the western side of the Central Mountain Range of Taiwan. Several oceanic mesoscale convective systems were also generated in an area extending from Taiwan to Hong Kong. Numerical experiments using the Penn State–NCAR MM5 mesoscale model were used to understand the intensification of the low-level jet (LLJ). These processes include thermal wind adjustment and convective, inertial, and conditional symmetric instabilities.

Three particular circulations are important in the development of the mei-yu front. First, there is a northward branch of the circulation that develops across the upper-level jet and is mainly caused by the thermal wind adjustment as air parcels enter an upper-level jet streak. The upper-level divergence associated with this branch of the circulation triggers convection.

Second, the southward branch of the circulation, with its rising motion in the frontal region and equatorward sinking motion, is driven by frontal vertical deep convection. The return flow of this circulation at low levels can produce an LLJ through geostrophic adjustment. The intensification of the LLJ is sensitive to the presence of convection.

Third, there is a circulation that develops from low to middle levels that has a slantwise rising and sinking motion in the pre- and postfrontal regions, respectively. From an absolute momentum surface analysis, this slantwise circulation is maintained by conditionally symmetric instability located at low levels ahead of the front. The presence of both the LLJ and moisture is an essential ingredient in fostering this conditionally symmetric unstable environment.

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Bradfield Lyon, Michael A. Bell, Michael K. Tippett, Arun Kumar, Martin P. Hoerling, Xiao-Wei Quan, and Hui Wang

Abstract

The inherent persistence characteristics of various drought indicators are quantified to extract predictive information that can improve drought early warning. Predictive skill is evaluated as a function of the seasonal cycle for regions within North America. The study serves to establish a set of baseline probabilities for drought across multiple indicators amenable to direct comparison with drought indicator forecast probabilities obtained when incorporating dynamical climate model forecasts. The emphasis is on the standardized precipitation index (SPI), but the method can easily be applied to any other meteorological drought indicator, and some additional examples are provided. Monte Carlo resampling of observational data generates two sets of synthetic time series of monthly precipitation that include, and exclude, the annual cycle while removing serial correlation. For the case of no seasonality, the autocorrelation (AC) of the SPI (and seasonal precipitation percentiles, moving monthly averages of precipitation) decays linearly with increasing lag. It is shown that seasonality in the variance of accumulated precipitation serves to enhance or diminish the persistence characteristics (AC) of the SPI and related drought indicators, and the seasonal cycle can thereby provide an appreciable source of drought predictability at regional scales. The AC is used to obtain a parametric probability density function of the future state of the SPI that is based solely on its inherent persistence characteristics. In addition, a method is presented for determining the optimal persistence of the SPI for the case of no serial correlation in precipitation (again, the baseline case). The optimized, baseline probabilities are being incorporated into Internet-based tools for the display of current and forecast drought conditions in near–real time.

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Xiao-Wei Quan, Martin P. Hoerling, Bradfield Lyon, Arun Kumar, Michael A. Bell, Michael K. Tippett, and Hui Wang

Abstract

The prospects for U.S. seasonal drought prediction are assessed by diagnosing simulation and hindcast skill of drought indicators during 1982–2008. The 6-month standardized precipitation index is used as the primary drought indicator. The skill of unconditioned, persistence forecasts serves as the baseline against which the performance of dynamical methods is evaluated. Predictions conditioned on the state of global sea surface temperatures (SST) are assessed using atmospheric climate simulations conducted in which observed SSTs are specified. Predictions conditioned on the initial states of atmosphere, land surfaces, and oceans are next analyzed using coupled climate-model experiments. The persistence of the drought indicator yields considerable seasonal skill, with a region’s annual cycle of precipitation driving a strong seasonality in baseline skill. The unconditioned forecast skill for drought is greatest during a region’s climatological dry season and is least during a wet season. Dynamical models forced by observed global SSTs yield increased skill relative to this baseline, with improvements realized during the cold season over regions where precipitation is sensitive to El Niño–Southern Oscillation. Fully coupled initialized model hindcasts yield little additional skill relative to the uninitialized SST-forced simulations. In particular, neither of these dynamical seasonal forecasts materially increases summer skill for the drought indicator over the Great Plains, a consequence of small SST sensitivity of that region’s summer rainfall and the small impact of antecedent soil moisture conditions, on average, upon the summer rainfall. The fully initialized predictions for monthly forecasts appreciably improve on the seasonal skill, however, especially during winter and spring over the northern Great Plains.

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