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Ya-Chien Feng, Hsiu-Wei Hsu, Tammy M. Weckwerth, Pay-Liam Lin, Yu-Chieng Liou, and Tai-Chi Chen Wang

Abstract

The radar-retrieved refractivity fields provide detailed depictions of the near-surface moisture distribution at the meso-gamma scale. This study represents a novel application of the refractivity fields by examining the spatiotemporal characteristics of moisture variability in a summertime coastal region in Taiwan over four weeks. The physiography in Taiwan lends itself to a variety of flow features and corresponding moisture behavior, which has not been well-studied. High-resolution of refractivity analyses demonstrate how a highly variable moisture field is related to the complex interaction between the synoptic-scale winds, diurnal local circulations, terrain, storms, and heterogeneous land-use. On average, higher refractivity (water vapor) is observed along the coastline and refractivity decreases inland toward the foothills. Under weak synoptic forcing conditions, the daytime refractivity field develops differently under local surface wind directions determined by the synoptic-scale prevailing wind and the sea breeze fronts. High moisture penetrates inland toward the foothills with southwesterly winds, but it stalls along the coastline with southerly and the northwesterly winds. The moisture distribution may further affect the occurrence of the inland afternoon storms. During the nighttime, the dry downslope wind decreases the moisture from the foothills toward the coast and forms a refractivity gradient perpendicular to the meridionally-oriented mountains. Furthermore, the refractivity fields illustrate higher resolution moisture distribution over surface station point measurements by showing the lagged daytime sea-breeze front between the urban and rural areas and the detailed nighttime heterogeneous moisture distribution related to land-use and rivers.

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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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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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Yaohui Li, Xing Yuan, Hongsheng Zhang, Runyuan Wang, Chenghai Wang, Xianhong Meng, Zhiqiang Zhang, Shanshan Wang, Yang Yang, Bo Han, Kai Zhang, Xiaoping Wang, Hong Zhao, Guangsheng Zhou, Qiang Zhang, Qing He, Ni Guo, Wei Hou, Cunjie Zhang, Guoju Xiao, Xuying Sun, Ping Yue, Sha Sha, Heling Wang, Tiejun Zhang, Jinsong Wang, and Yubi Yao

Abstract

A major experimental drought research project entitled “Mechanisms and Early Warning of Drought Disasters over Northern China” (DroughtEX_China) was launched by the Ministry of Science and Technology of China in 2015. The objective of DroughtEX_China is to investigate drought disaster mechanisms and provide early-warning information via multisource observations and multiscale modeling. Since the implementation of DroughtEX_China, a comprehensive V-shape in situ observation network has been established to integrate different observational experiment systems for different landscapes, including crops in northern China. In this article, we introduce the experimental area, observational network configuration, ground- and air-based observing/testing facilities, implementation scheme, and data management procedures and sharing policy. The preliminary observational and numerical experimental results show that the following are important processes for understanding and modeling drought disasters over arid and semiarid regions: 1) the soil water vapor–heat interactions that affect surface soil moisture variability, 2) the effect of intermittent turbulence on boundary layer energy exchange, 3) the drought–albedo feedback, and 4) the transition from stomatal to nonstomatal control of plant photosynthesis with increasing drought severity. A prototype of a drought monitoring and forecasting system developed from coupled hydroclimate prediction models and an integrated multisource drought information platform is also briefly introduced. DroughtEX_China lasted for four years (i.e., 2015–18) and its implementation now provides regional drought monitoring and forecasting, risk assessment information, and a multisource data-sharing platform for drought adaptation over northern China, contributing to the global drought information system (GDIS).

Open access
Jeffrey Beck, John Brown, Jimy Dudhia, David Gill, Tracy Hertneky, Joseph Klemp, Wei Wang, Christopher Williams, Ming Hu, Eric James, Jaymes Kenyon, Tanya Smirnova, and Jung-Hoon Kim

Abstract

A new hybrid, sigma-pressure vertical coordinate was recently added to the Weather Research and Forecasting (WRF) Model in an effort to reduce numerical noise in the model equations near complex terrain. Testing of this hybrid, terrain-following coordinate was undertaken in the WRF-based Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) models to assess impacts on retrospective and real-time simulations. Initial cold-start simulations indicated that the majority of differences between the hybrid and traditional sigma coordinate were confined to regions downstream of mountainous terrain and focused in the upper levels. Week-long retrospective simulations generally resulted in small improvements for the RAP, and a neutral impact in the HRRR when the hybrid coordinate was used. However, one possibility is that the inclusion of data assimilation in the experiments may have minimized differences between the vertical coordinates. Finally, analysis of turbulence forecasts with the new hybrid coordinate indicate a significant reduction in spurious vertical motion over the full length of the Rocky Mountains. Overall, the results indicate a potential to improve forecast metrics through implementation of the hybrid coordinate, particularly at upper levels, and downstream of complex terrain.

Free access
Chong Shen, Xiaoyang Chen, Wei Dai, Xiaohui Li, Jie Wu, Qi Fan, Xuemei Wang, Liye Zhu, Pakwai Chan, Jian Hang, Shaojia Fan, and Weibiao Li

Abstract

On urban scales, the detailed characteristics of land-use information and building properties are vital to improving the meteorological model. The WRF Model with high-spatial-resolution urban fraction (UF) and urban morphology (UM) is used to study the impacts of these urban canopy parameters (UCPs) on dynamical and thermal meteorological fields in two representative seasons in Guangzhou. The results of two seasons are similar and as follows. 1) The impacts of updated UF and UM are obvious on wind speed but minor on temperature and humidity. In the urban environment, the results with updated UF and UM are more consistent with observations compared with the default UCPs, which means the performance of the model has been improved. 2) The dynamical factors associated with wind speed are analyzed. Turbulent kinetic energy (TKE) is significantly affected by UM but little by UF. And both UF and UM are found to influence friction velocity U*. The UM and greater UF attained larger U*. 3) In addition, the thermal fields are analyzed. The UM and increased UF induce higher surface skin temperature (TSK) and ground heat flux in the daytime, indicating that more heat is transported from the surface to the soil. At night, more heat is transported from the soil to the surface, producing higher TSK. For sensible heat flux (HFX), greater UF induces larger HFX during the daytime. But the effects of UM are complex, which makes HFX decrease during the daytime and increase at night. Finally, larger UF attains lower latent heat in the daytime.

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Yang Shi, Jiahua Wei, Yan Ren, Zhen Qiao, Qiong Li, Xiaomei Zhu, Beiming Kang, Peichong Pan, Jiongwei Cao, Jun Qiu, Tiejian Li, and Guangqian Wang

Abstract

Acoustic agglomerations have increasingly attracted widespread attention as a cost-effective and environmentally friendly approach for fog removal and weather modification. In this study, research on precipitation interference and the agglomeration performance of droplet aerosols under large-scale acoustic waves was presented. In total, 49 field experiments in the source region of the Yellow River in the summer of 2019 were performed to reveal the influences of acoustic waves on precipitation, such as the radar reflectivity factor Z, rain rate R, and raindrop size distribution (DSD). A monitoring system that consisted of rain gauges and raindrop spectrometers was employed to monitor near-ground rainfall within a 5-km radius of the field site. The ground-based observations showed that acoustic waves could significantly affect the rainfall distribution and microstructure of precipitation particles. The average values of rainfall increased by 18.98%, 10.61%, and 8.74% within 2, 3, and 5 km, respectively, of the operation center with acoustic application. The changing trend of microphysical parameters of precipitation was roughly in line with variation of acoustic waves for stratiform cloud. Moreover, there was a good quadratic relationship between the spectral parameters λ and μ. Raindrop kinetic energy e K and the radar reflectivity factor Z both exhibited a power function relationship with R.

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Fan Yang, Qing He, Jianping Huang, Ali Mamtimin, Xinghua Yang, Wen Huo, Chenglong Zhou, Xinchun Liu, Wenshou Wei, Caixia Cui, Minzhong Wang, Hongjun Li, Lianmei Yang, Hongsheng Zhang, Yuzhi Liu, Xinqian Zheng, Honglin Pan, Lili Jin, Han Zou, Libo Zhou, Yongqiang Liu, Jiantao Zhang, Lu Meng, Yu Wang, Xiaolin Qin, Yongjun Yao, Houyong Liu, Fumin Xue, and Wei Zheng

Abstract

As the second-largest shifting sand desert worldwide, the Taklimakan Desert (TD) represents the typical aeolian landforms in arid regions as an important source of global dust aerosols. It directly affects the ecological environment and human health across East Asia. Thus, establishing a comprehensive environment and climate observation network for field research in the TD region is essential to improve our understanding of the desert meteorology and environment, assess its impact, mitigate potential environmental issues, and promote sustainable development. With a nearly 20-yr effort under the extremely harsh conditions of the TD, the Desert Environment and Climate Observation Network (DECON) has been established completely covering the TD region. The core of DECON is the Tazhong station in the hinterland of the TD. Moreover, the network also includes 4 satellite stations located along the edge of the TD for synergistic observations, and 18 automatic weather stations interspersed between them. Thus, DECON marks a new chapter of environmental and meteorological observation capabilities over the TD, including dust storms, dust emission and transport mechanisms, desert land–atmosphere interactions, desert boundary layer structure, ground calibration for remote sensing monitoring, and desert carbon sinks. In addition, DECON promotes cooperation and communication within the research community in the field of desert environments and climate, which promotes a better understanding of the status and role of desert ecosystems. Finally, DECON is expected to provide the basic support necessary for coordinated environmental and meteorological monitoring and mitigation, joint construction of ecologically friendly communities, and sustainable development of central Asia.

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William S. Olson, Christian D. Kummerow, Song Yang, Grant W. Petty, Wei-Kuo Tao, Thomas L. Bell, Scott A. Braun, Yansen Wang, Stephen E. Lang, Daniel E. Johnson, and Christine Chiu

Abstract

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.

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Takamichi Iguchi, Wei-Kuo Tao, Di Wu, Christa Peters-Lidard, Joseph A. Santanello, Eric Kemp, Yudong Tian, Jonathan Case, Weile Wang, Robert Ferraro, Duane Waliser, Jinwon Kim, Huikyo Lee, Bin Guan, Baijun Tian, and Paul Loikith

Abstract

This study investigates the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall fields were simulated at 24-km resolution using the NASA-Unified Weather Research and Forecasting (NU-WRF) Model for June–August 2000. Four cumulus parameterization schemes and two options for shallow cumulus components in a specific scheme were tested. The spread in the domain-mean rainfall rates across the parameterization schemes was generally consistent between the entire CONUS and most subregions. The selection of the shallow cumulus component in a specific scheme had more impact than that of the four cumulus parameterization schemes. Regional variability in the performance of each scheme was assessed by calculating optimally weighted ensembles that minimize full root-mean-square errors against reference datasets. The spatial pattern of the seasonally averaged rainfall was insensitive to the selection of cumulus parameterization over mountainous regions because of the topographical pattern constraint, so that the simulation errors were mostly attributed to the overall bias there. In contrast, the spatial patterns over the Great Plains regions as well as the temporal variation over most parts of the CONUS were relatively sensitive to cumulus parameterization selection. Overall, adopting a single simulation result was preferable to generating a better ensemble for the seasonally averaged daily rainfall simulation, as long as their overall biases had the same positive or negative sign. However, an ensemble of multiple simulation results was more effective in reducing errors in the case of also considering temporal variation.

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