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Wenxue Tong, Gang Li, Juanzhen Sun, Xiaowen Tang, and Ying Zhang

Abstract

This study examines two strategies for improving the analysis of an hourly update three-dimensional variational data assimilation (3DVAR) system and the subsequent quantitative precipitation forecast (QPF). The first strategy is to assimilate synoptic and radar observations in different steps. This strategy aims to extract both large-scale and convective-scale information from observations typically representing different scales. The second strategy is to add a divergence constraint to the momentum variables in the 3DVAR system. This technique aims at improving the dynamic balance and suppressing noise introduced during the assimilation process. A detailed analysis on how the new techniques impact convective-scale QPF was conducted using a severe storm case over Colorado and Kansas during 8 and 9 August 2008. First, it is demonstrated that, without the new strategies, the QPF initialized with an hourly update analysis performs worse than its 3-hourly counterpart. The implementation of the two-step assimilation and divergence constraint in the hourly update system results in improved QPF throughout most of the 12-h forecast period. The diagnoses of the analysis fields show that the two-step assimilation is able to preserve key convective-scale as well as large-scale structures that are consistent with the development of the real weather system. The divergence constraint is effective in improving the balance between the momentum control variables in the analysis, which leads to less spurious convection and improved QPF scores. The improvements of the new techniques were further verified by eight convective cases in 2014 and shown to be statistically significant.

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Lei Zhang, Gang Wang, Matthew Newman, and Weiqing Han

Abstract

The Indian Ocean has received increasing attention for its large impacts on regional and global climate. However, sea surface temperature (SST) variability arising from Indian Ocean internal processes has not been well understood particularly on decadal and longer time scales, and the external influence from the tropical Pacific has not been quantified. This paper analyzes the interannual-to-decadal SST variability in the tropical Indian Ocean in observations and explores the external influence from the Pacific versus internal processes within the Indian Ocean using a linear inverse model (LIM). Coupling between Indian Ocean and tropical Pacific SST anomalies (SSTAs) is assessed both within the LIM dynamical operator and the unpredictable stochastic noise that forces the system. Results show that the observed Indian Ocean basin (IOB)-wide SSTA pattern is largely a response to the Pacific ENSO forcing, although it in turn has a damping effect on ENSO especially on annual and decadal time scales. On the other hand, the Indian Ocean dipole (IOD) is an Indian Ocean internal mode that can actively affect ENSO; ENSO also has a returning effect on the IOD, which is rather weak on decadal time scale. The third mode is partly associated with the subtropical Indian Ocean dipole (SIOD), and it is primarily generated by Indian Ocean internal processes, although a small component of it is coupled with ENSO. Overall, the amplitude of Indian Ocean internally generated SST variability is comparable to that forced by ENSO, and the Indian Ocean tends to actively influence the tropical Pacific. These results suggest that the Indian–Pacific Ocean interaction is a two-way process.

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Pengfei Zhang, Gang Chen, Weiming Ma, Yi Ming, and Zheng Wu

Abstract

Atmospheric rivers (ARs), narrow intense moisture transport, account for much of the poleward moisture transport in midlatitudes. While studies have characterized AR features and the associated hydrological impacts in a warming climate in observations and comprehensive climate models, the fundamental dynamics for changes in AR statistics (e.g., frequency, length, width) are not well understood. Here we investigate AR response to global warming with a combination of idealized and comprehensive climate models. To that end, we developed an idealized atmospheric GCM with Earth-like global circulation and hydrological cycle, in which water vapor and clouds are modeled as passive tracers with simple cloud microphysics and precipitation processes. Despite the simplicity of model physics, it reasonably reproduces observed dynamical structures for individual ARs, statistical characteristics of ARs, and spatial distributions of AR climatology. Under climate warming, the idealized model produces robust AR changes similar to CESM large ensemble simulations under RCP8.5, including AR size expansion, intensified landfall moisture transport, and an increased AR frequency, corroborating previously reported AR changes under global warming by climate models. In addition, the latitude of AR frequency maximum shifts poleward with climate warming. Further analysis suggests the thermodynamic effect (i.e., an increase in water vapor) dominates the AR statistics and frequency changes while both the dynamic and thermodynamic effects contribute to the AR poleward shift. These results demonstrate that AR changes in a warming climate can be understood as passive water vapor and cloud tracers regulated by large-scale atmospheric circulation, whereas convection and latent heat feedback are of secondary importance.

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Weixing Zhang, Yidong Lou, Jennifer S. Haase, Rui Zhang, Gang Zheng, Jinfang Huang, Chuang Shi, and Jingnan Liu

Abstract

Global positioning system (GPS) data from over 260 ground-based permanent stations in China covering the period from 1 March 1999 to 30 April 2015 were used to estimate precipitable water (PW) above each site with an accuracy of about 0.75 mm. Four types of radiosondes (referred to as GZZ2, GTS1, GTS1-1, and GTS1-2) were used in China during this period. Instrumentation type changes in radiosonde records were identified by comparing PW calculated from GPS and radiosonde data. Systematic errors in different radiosonde types introduced significant biases to the estimated PW trends at stations where more than one radiosonde type was used. Estimating PW trends from reanalysis products (ERA-Interim), which assimilate the unadjusted radiosonde humidity data, resulted in an artificial downward PW trend at almost all stations in China. The statistically significant GPS PW trends are predominantly positive, consistent in sign with the increase in moisture expected from the Clausius–Clapeyron relation due to a global temperature increase. The standard deviations of the differences between ERA-Interim and GPS PW in the summer were 3 times larger than the observational error of GPS PW, suggesting that potentially significant improvements to the reanalysis could be achieved by assimilating denser GPS PW observations over China. This work, based on an entirely independent GPS PW dataset, confirms previously reported significant differences in radiosonde PW trends when using corrected data. Furthermore, the dense geographical coverage of the all-weather GPS PW observations, especially in remote areas in western China, provides a valuable resource for calibrating regional trends in reanalysis products.

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Chao Li, Ying Sun, Francis Zwiers, Dongqian Wang, Xuebin Zhang, Gang Chen, and Hui Wu

Abstract

On the basis of a newly developed observational dataset and a suite of climate model simulations, we evaluate changes in summer mean wet bulb globe temperature (WBGT) in China from 1961 through 2080. We show that summer mean WBGT has increased almost everywhere across China since 1961 as a result of human-induced climate change. Consequently, hot summers as measured by summer mean WBGT are becoming more frequent and more conducive to heat stress. Hot summers like the hottest on record during 1961–2015 in western or eastern China are now expected occur once every 3–4 years. These hot WBGT summers have become more than 140 times as likely in eastern China in the present decade (2010s) as in the 1961–90 baseline period and more than 1000 times as likely in western China. The substantially larger influence in western China is associated with its stronger warming signal, which is likely due to the high Bowen ratio of sensible to latent heat fluxes of dry soils and increases in absorbed solar radiation from the decline in mountain snow cover extent. Observation-constrained projections of future summer mean WBGT under the RCP8.5 emissions scenario indicate that, by the 2040s, almost every summer in China will be at least as hot as the hottest summer in the historical record, and by the 2060s it will be common (on average, every other year) for summers to be as much as 3.0°C hotter than the historical record, pointing to potentially large increases in the likelihood of human heat stress and to a massive adaption challenge.

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Hao Huang, Guifu Zhang, Kun Zhao, Su Liu, Long Wen, Gang Chen, and Zhengwei Yang

Abstract

Drop size distribution (DSD) is a fundamental parameter in rain microphysics. Retrieving DSDs from polarimetric radar measurements extends the capabilities of rain microphysics research and quantitative precipitation estimation. In this study, issues in rain DSD retrieval were studied with simulated and measured data. It was found that a three-parameter gamma distribution model was not suitable for directly retrieving DSD from polarimetric radar measurements. A statistical constraint, such as the shape–slope relation used in the constrained-gamma (C-G) distribution model, helped to reduce the uncertainties and errors in the retrieval. The inclusion of specific differential phase (K DP) measurements resulted in more accurate DSD retrieval and rain physical parameter estimation if the measurement errors were properly characterized in the error minimization analysis (EMA), which was verified using two real precipitation events. The study demonstrated the potential of using full polarimetric radar measurements to improve rain DSD retrieval.

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Xiaoye Yang, Gang Zeng, Guwei Zhang, Jingwei Li, Zhongxian Li, and Zhixin Hao

Abstract

The summer heatwaves (HWs) in Northeast China (NEC) during 1961-2016 can be classified into two types, namely wave-train HWs and blocking HWs based on the hierarchical clustering algorithm by using ERA-Interim daily reanalysis datasets. Wave-train HWs occurred accompanied by eastward-moving wave trains with a "-+-+" structure formed over Eurasia, while the blocking HWs occurred with blocking circulation anomalies over Eurasia. In general, the blocking HWs could cause the positive temperature anomalies in NEC to last longer than wave-train HWs. During the period from 1961 to 2016, the wave-train HWs experienced an interdecadal variation from less to more, while the blocking HWs experienced interdecadal variations of less-more-less. Regression analysis and information flow indicate that the interdecadal variation of the wave-train HWs is associated with Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), while the interdecadal variation of the blocking HWs is more likely associated with PDO. The positive phase of AMO (negative phase of PDO) could increase the wave-train (blocking) HWs by strengthening the zonal wave-train similar to the Silk Road pattern (the arched wave-train like the polar-Eurasian pattern). The observed results are in agreement with the numerical experiments with the NCAR Community Atmosphere Model version 5.3.

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Hao Huang, Kun Zhao, Guifu Zhang, Qing Lin, Long Wen, Gang Chen, Zhengwei Yang, Mingjun Wang, and Dongming Hu

Abstract

Quantitative precipitation estimation (QPE) with polarimetric radar measurements suffers from different sources of uncertainty. The variational approach appears to be a promising way to optimize the radar QPE statistically. In this study a variational approach is developed to quantitatively estimate the rainfall rate (R) from the differential phase (ΦDP). A spline filter is utilized in the optimization procedures to eliminate the impact of the random errors in ΦDP, which can be a major source of error in the specific differential phase (K DP)-based QPE. In addition, R estimated from the horizontal reflectivity factor (Z H) is used in the a priori with the error covariance matrix statistically determined. The approach is evaluated by an idealized case and multiple real rainfall cases observed by an operational S-band polarimetric radar in southern China. The comparative results demonstrate that with a proper range filter, the proposed variational radar QPE with the a priori included agrees well with the rain gauge measurements and proves to have better performance than the other three approaches, that is, the proposed variational approach without the a priori included, the variational approach proposed by Hogan, and the conventional power-law estimator-based approach.

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Gang Chen, Kun Zhao, Guifu Zhang, Hao Huang, Su Liu, Long Wen, Zhonglin Yang, Zhengwei Yang, Lili Xu, and Wenjian Zhu

Abstract

In this study, the capability of using a C-band polarimetric Doppler radar and a two-dimensional video disdrometer (2DVD) to estimate monsoon-influenced summer rainfall during the Observation, Prediction and Analysis of Severe Convection of China (OPACC) field campaign in 2014 and 2015 in eastern China is investigated. Three different rainfall R estimators, for reflectivity at horizontal polarization [R(Zh)], for reflectivity at horizontal polarization and differential reflectivity factor [R(Zh, Zdr)], and for specific differential phase [R(KDP)], are derived from 2-yr 2DVD observations of summer precipitation systems. The radar-estimated rainfall is compared to gauge observations from eight rainfall episodes. Results show that the two polarimetric estimators, R(Zh, Zdr) and R(KDP), perform better than the traditional ZhR relation [i.e., R(Zh)]. The KDP-based estimator [i.e., R(KDP)] produces the best rainfall accumulations. The radar rainfall estimators perform differently across the three organized convective systems (mei-yu rainband, typhoon rainband, and squall line). Estimator R(Zh) overestimates rainfall in the mei-yu rainband and squall line, and R(Zh, Zdr) mitigates the overestimation in the mei-yu rainband but has a large bias in the squall line. QPE from R(KDP) is the most accurate among the three estimators, but it possesses a relatively large bias for the squall line compared to the mei-yu case. The high variability of drop size distribution (DSD) related to the precipitation microphysics in different types of rain is largely responsible for the case-dependent QPE performance using any single radar rainfall estimator. The squall line has a distinct ice-phase process with a large mean size of raindrops, while the mei-yu rainband and typhoon rainband are composed of smaller raindrops. Based on the statistical QPE error in the ZHZDR space, a new composite rainfall estimator is constructed by combining R(Zh), R(Zh, Zdr), and R(KDP) and is proven to outperform any single rainfall estimator.

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Xiaoyan Zhang, Jianping Huang, Gang Li, Yongwei Wang, Cheng Liu, Kaihui Zhao, Xinyu Tao, Xiao-Ming Hu, and Xuhui Lee

Abstract

The Weather Research and Forecasting (WRF) Model is used in large-eddy simulation (LES) mode to investigate a lake-breeze case occurring on 12 June 2012 over the Lake Taihu region of China. Observational data from 15 locations, wind profiler radar, and the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to evaluate the WRF nested-LES performance in simulating lake breezes. Results indicate that the simulated temporal and spatial variations of the lake breeze by WRF nested LES are consistent with observations. The simulations with high-resolution grid spacing and the LES scheme have a high correlation coefficient and low mean bias when evaluated against 2-m temperature, 10-m wind, and horizontal and vertical lake-breeze circulations. The atmospheric boundary layer (ABL) remains stable over the lake throughout the lake-breeze event, and the stability becomes even stronger as the lake breeze reaches its mature stage. The improved ABL simulation with LES at a grid spacing of 150 m indicates that the non-LES planetary boundary layer parameterization scheme does not adequately represent subgrid-scale turbulent motions. Running WRF fully coupled to a lake model improves lake-surface temperature and consequently the lake-breeze simulations. Allowing for additional model spinup results in a positive impact on lake-surface temperature prediction but is a heavy computational burden. Refinement of a water-property parameter used in the Community Land Model, version 4.5, within WRF and constraining the lake-surface temperature with observational data would further improve lake-breeze representation.

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