Articles | Volume 18, issue 13
https://doi.org/10.5194/gmd-18-4075-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-4075-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
Juan Zhao
China Meteorological Administration Training Centre, Beijing, 100081, China
Jianping Guo
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Xiaohui Zheng
China Meteorological Administration Training Centre, Beijing, 100081, China
Related authors
No articles found.
Deli Meng, Jianping Guo, Juan Chen, Xiaoran Guo, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Hui Xu, Tianmeng Chen, Rongfang Yang, and Jiajia Hua
Earth Syst. Sci. Data, 17, 4023–4037, https://doi.org/10.5194/essd-17-4023-2025, https://doi.org/10.5194/essd-17-4023-2025, 2025
Short summary
Short summary
This study provides a high-resolution dataset of low-level atmospheric turbulence across China, using radar and weather balloon observations. It reveals regional and seasonal variations in turbulence, with stronger activity in spring and summer. The dataset supports weather forecasting, aviation safety, and low-altitude flight planning, aiding China's growing low-altitude economy, and is accessible at https://doi.org/10.5281/zenodo.14959025.
Xiaoran Guo, Jianping Guo, Deli Meng, Yuping Sun, Zhen Zhang, Hui Xu, Liping Zeng, Juan Chen, Ning Li, and Tianmeng Chen
Earth Syst. Sci. Data, 17, 3541–3552, https://doi.org/10.5194/essd-17-3541-2025, https://doi.org/10.5194/essd-17-3541-2025, 2025
Short summary
Short summary
Optimal atmospheric dynamic conditions are essential for convective storms. This study generates a dataset of high-resolution divergence and vorticity profiles using the measurements of a radar wind profiler mesonet in Beijing. The negative divergence and positive vorticity are present ahead of rainfall events. This suggests that this dataset can help improve our understanding of the pre-storm environment and has the potential to be applied in weather forecasting.
Xiaozhong Cao, Qiyun Guo, Haowen Luo, Rongkang Yang, Peng Zhang, Jianping Guo, Jincheng Wang, Die Xiao, Jianping Du, Zhongliang Sun, Shijun Liu, Sijie Chen, and Anfan Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2012, https://doi.org/10.5194/egusphere-2025-2012, 2025
Short summary
Short summary
This study aims to introduce in-situ profiling techniques and cost-effective technology for upper-air observation—the Round-trip Drifting Sounding System (RDSS)—which reduces costs relative to intensive sounding and achieves three sounding phases: Ascent-Drift-Descent (ADD). The RDSS not only provides additional data for weather analysis and numerical prediction models but also makes substantial contributions to targeted observations.
Seoung Soo Lee, Chang Hoon Jung, Jinho Choi, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, Sang-Keun Song, and Kyung-Ja Ha
Atmos. Chem. Phys., 25, 705–726, https://doi.org/10.5194/acp-25-705-2025, https://doi.org/10.5194/acp-25-705-2025, 2025
Short summary
Short summary
This study attempts to test a general factor that explains differences in the properties of different mixed-phase clouds using a modeling tool. Although this attempt is not to identify a factor that can perfectly explain and represent the properties of different mixed-phase clouds, we believe that this attempt acts as a valuable stepping stone towards a more complete, general way of using climate models to better predict climate change.
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data, 16, 5753–5766, https://doi.org/10.5194/essd-16-5753-2024, https://doi.org/10.5194/essd-16-5753-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we generate a global long-term TC size and intensity reconstruction dataset, covering a time period from 1959 to 2022, with a 3 h temporal resolution, using machine learning models. These can be valuable for filling observational data gaps and advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
Deli Meng, Jianping Guo, Xiaoran Guo, Yinjun Wang, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Haoran Li, Fan Zhang, Bing Tong, Hui Xu, and Tianmeng Chen
Atmos. Chem. Phys., 24, 8703–8720, https://doi.org/10.5194/acp-24-8703-2024, https://doi.org/10.5194/acp-24-8703-2024, 2024
Short summary
Short summary
The turbulence in the planetary boundary layer (PBL) over the Tibetan Plateau (TP) remains unclear. Here we elucidate the vertical profile of and temporal variation in the turbulence dissipation rate in the PBL over the TP based on a radar wind profiler (RWP) network. To the best of our knowledge, this is the first time that the turbulence profile over the whole TP has been revealed. Furthermore, the possible mechanisms of clouds acting on the PBL turbulence structure are investigated.
Xiaoran Guo, Jianping Guo, Tianmeng Chen, Ning Li, Fan Zhang, and Yuping Sun
Atmos. Chem. Phys., 24, 8067–8083, https://doi.org/10.5194/acp-24-8067-2024, https://doi.org/10.5194/acp-24-8067-2024, 2024
Short summary
Short summary
The prediction of downhill thunderstorms (DSs) remains elusive. We propose an objective method to identify DSs, based on which enhanced and dissipated DSs are discriminated. A radar wind profiler (RWP) mesonet is used to derive divergence and vertical velocity. The mid-troposphere divergence and prevailing westerlies enhance the intensity of DSs, whereas low-level divergence is observed when the DS dissipates. The findings highlight the key role that an RWP mesonet plays in the evolution of DSs.
Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, https://doi.org/10.5194/essd-16-2425-2024, 2024
Short summary
Short summary
A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide spatially contiguous AOD and PM2.5 concentration maps with daily 1 km resolution from 2000 to 2021. This gap-free dataset has good data accuracies compared to ground-based AOD and PM2.5 concentration observations, which is a reliable database to advance aerosol-related studies and trigger multidisciplinary applications for environmental management, health risk assessment, and climate change analysis.
Boming Liu, Xin Ma, Jianping Guo, Renqiang Wen, Hui Li, Shikuan Jin, Yingying Ma, Xiaoran Guo, and Wei Gong
Atmos. Chem. Phys., 24, 4047–4063, https://doi.org/10.5194/acp-24-4047-2024, https://doi.org/10.5194/acp-24-4047-2024, 2024
Short summary
Short summary
Accurate wind profile estimation, especially for the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. We propose a novel method that combines the power-law method with the random forest algorithm to extend wind profiles beyond the surface layer. Compared with the traditional algorithm, this method has better stability and spatial applicability and can be used to obtain the wind profiles on different land cover types.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
Short summary
Short summary
A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Hui Xu, Jianping Guo, Bing Tong, Jinqiang Zhang, Tianmeng Chen, Xiaoran Guo, Jian Zhang, and Wenqing Chen
Atmos. Chem. Phys., 23, 15011–15038, https://doi.org/10.5194/acp-23-15011-2023, https://doi.org/10.5194/acp-23-15011-2023, 2023
Short summary
Short summary
The radiative effect of cloud remains one of the largest uncertain factors in climate change, largely due to the lack of cloud vertical structure (CVS) observations. The study presents the first near-global CVS climatology using high-vertical-resolution soundings. Single-layer cloud mainly occurs over arid regions. As the number of cloud layers increases, clouds tend to have lower bases and thinner layer thicknesses. The occurrence frequency of cloud exhibits a pronounced seasonal diurnal cycle.
Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong
Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023, https://doi.org/10.5194/acp-23-3181-2023, 2023
Short summary
Short summary
Wind energy is one of the most essential clean and renewable forms of energy in today’s world. However, the traditional power law method generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speeds. This inevitably leads to significant uncertainties in estimating the wind speed profile. To minimize the uncertainties, we here use a machine learning algorithm known as random forest to estimate the wind speed at hub height.
Seoung Soo Lee, Junshik Um, Won Jun Choi, Kyung-Ja Ha, Chang Hoon Jung, Jianping Guo, and Youtong Zheng
Atmos. Chem. Phys., 23, 273–286, https://doi.org/10.5194/acp-23-273-2023, https://doi.org/10.5194/acp-23-273-2023, 2023
Short summary
Short summary
This paper elaborates on process-level mechanisms regarding how the interception of radiation by aerosols interacts with the surface heat fluxes and atmospheric instability in warm cumulus clouds. This paper elucidates how these mechanisms vary with the location or altitude of an aerosol layer. This elucidation indicates that the location of aerosol layers should be taken into account for parameterizations of aerosol–cloud interactions.
Seoung Soo Lee, Jinho Choi, Goun Kim, Kyung-Ja Ha, Kyong-Hwan Seo, Chang Hoon Jung, Junshik Um, Youtong Zheng, Jianping Guo, Sang-Keun Song, Yun Gon Lee, and Nobuyuki Utsumi
Atmos. Chem. Phys., 22, 9059–9081, https://doi.org/10.5194/acp-22-9059-2022, https://doi.org/10.5194/acp-22-9059-2022, 2022
Short summary
Short summary
This study investigates how aerosols affect clouds and precipitation and how the aerosol effects vary with varying types of clouds that are characterized by cloud depth in two metropolitan areas in East Asia. As cloud depth increases, the enhancement of precipitation amount transitions to no changes in precipitation amount with increasing aerosol concentrations. This indicates that cloud depth needs to be considered for a comprehensive understanding of aerosol-cloud interactions.
Peilin Song, Yongqiang Zhang, Jianping Guo, Jiancheng Shi, Tianjie Zhao, and Bing Tong
Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, https://doi.org/10.5194/essd-14-2613-2022, 2022
Short summary
Short summary
Soil moisture information is crucial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatiotemporal resolutions or in ensuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has the potential to promote the investigation of earth hydrology and be extended to the global scale.
Kaixu Bai, Ke Li, Mingliang Ma, Kaitao Li, Zhengqiang Li, Jianping Guo, Ni-Bin Chang, Zhuo Tan, and Di Han
Earth Syst. Sci. Data, 14, 907–927, https://doi.org/10.5194/essd-14-907-2022, https://doi.org/10.5194/essd-14-907-2022, 2022
Short summary
Short summary
The Long-term Gap-free High-resolution Air Pollutant concentration dataset, providing gap-free aerosol optical depth (AOD) and PM2.5 and PM10 concentration with a daily 1 km resolution for 2000–2020 in China, is generated and made publicly available. This is the first long-term gap-free high-resolution aerosol dataset in China and has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environment management.
Linye Song, Shangfeng Chen, Wen Chen, Jianping Guo, Conglan Cheng, and Yong Wang
Atmos. Chem. Phys., 22, 1669–1688, https://doi.org/10.5194/acp-22-1669-2022, https://doi.org/10.5194/acp-22-1669-2022, 2022
Short summary
Short summary
This study shows that in most years when haze pollution (HP) over the North China Plain (NCP) is more (less) serious in winter, air conditions in the following spring are also worse (better) than normal. Conversely, there are some years when HP in the following spring is opposed to that in winter. It is found that North Atlantic sea surface temperature (SST) anomalies play important roles in HP evolution over the NCP. Thus North Atlantic SST is an important preceding signal for NCP HP evolution.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-26, https://doi.org/10.5194/amt-2022-26, 2022
Publication in AMT not foreseen
Short summary
Short summary
Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is the comparison of wind speed on a large scale between the Aeolus, ERA5 and RS , shedding important light on the data application of Aeolus wind products.
Jianping Guo, Jian Zhang, Kun Yang, Hong Liao, Shaodong Zhang, Kaiming Huang, Yanmin Lv, Jia Shao, Tao Yu, Bing Tong, Jian Li, Tianning Su, Steve H. L. Yim, Ad Stoffelen, Panmao Zhai, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, https://doi.org/10.5194/acp-21-17079-2021, 2021
Short summary
Short summary
The planetary boundary layer (PBL) is the lowest part of the troposphere, and boundary layer height (BLH) is the depth of the PBL and is of critical importance to the dispersion of air pollution. The study presents the first near-global BLH climatology by using high-resolution (5-10 m) radiosonde measurements. The variations in BLH exhibit large spatial and temporal dependence, with a peak at 17:00 local solar time. The most promising reanalysis product is ERA-5 in terms of modeling BLH.
Seoung Soo Lee, Kyung-Ja Ha, Manguttathil Gopalakrishnan Manoj, Mohammad Kamruzzaman, Hyungjun Kim, Nobuyuki Utsumi, Youtong Zheng, Byung-Gon Kim, Chang Hoon Jung, Junshik Um, Jianping Guo, Kyoung Ock Choi, and Go-Un Kim
Atmos. Chem. Phys., 21, 16843–16868, https://doi.org/10.5194/acp-21-16843-2021, https://doi.org/10.5194/acp-21-16843-2021, 2021
Short summary
Short summary
Using a modeling framework, a midlatitude stratocumulus cloud system is simulated. It is found that cloud mass in the system becomes very low due to interactions between ice and liquid particles compared to that in the absence of ice particles. It is also found that interactions between cloud mass and aerosols lead to a reduction in cloud mass in the system, and this is contrary to an aerosol-induced increase in cloud mass in the absence of ice particles.
Ifeanyichukwu C. Nduka, Chi-Yung Tam, Jianping Guo, and Steve Hung Lam Yim
Atmos. Chem. Phys., 21, 13443–13454, https://doi.org/10.5194/acp-21-13443-2021, https://doi.org/10.5194/acp-21-13443-2021, 2021
Short summary
Short summary
This study analyzed the nature, mechanisms and drivers for hot-and-polluted episodes (HPEs) in the Pearl River Delta, China. A total of eight HPEs were identified and can be grouped into three clusters of HPEs that were respectively driven (1) by weak subsidence and convection induced by approaching tropical cyclones, (2) by calm conditions with low wind speed in the lower atmosphere and (3) by the combination of both aforementioned conditions.
Tianmeng Chen, Zhanqing Li, Ralph A. Kahn, Chuanfeng Zhao, Daniel Rosenfeld, Jianping Guo, Wenchao Han, and Dandan Chen
Atmos. Chem. Phys., 21, 6199–6220, https://doi.org/10.5194/acp-21-6199-2021, https://doi.org/10.5194/acp-21-6199-2021, 2021
Short summary
Short summary
A convective cloud identification process is developed using geostationary satellite data from Himawari-8.
Convective cloud fraction is generally larger before noon and smaller in the afternoon under polluted conditions, but megacities and complex topography can influence the pattern.
A robust relationship between convective cloud and aerosol loading is found. This pattern varies with terrain height and is modulated by varying thermodynamic, dynamical, and humidity conditions during the day.
Jianping Guo, Boming Liu, Wei Gong, Lijuan Shi, Yong Zhang, Yingying Ma, Jian Zhang, Tianmeng Chen, Kaixu Bai, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 2945–2958, https://doi.org/10.5194/acp-21-2945-2021, https://doi.org/10.5194/acp-21-2945-2021, 2021
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China have thus far not been evaluated by in situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future research and applications.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-41, https://doi.org/10.5194/acp-2021-41, 2021
Revised manuscript not accepted
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future researches and applications.
Kaixu Bai, Ke Li, Chengbo Wu, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 12, 3067–3080, https://doi.org/10.5194/essd-12-3067-2020, https://doi.org/10.5194/essd-12-3067-2020, 2020
Short summary
Short summary
PM2.5 data from the national air quality monitoring network in China suffered from significant inconsistency and inhomogeneity issues. To create a coherent PM2.5 concentration dataset to advance our understanding of haze pollution and its impact on weather and climate, we homogenized this PM2.5 dataset between 2015 and 2019 after filling in the data gaps. The homogenized PM2.5 data is found to better characterize the variation of aerosol in space and time compared to the original dataset.
Yang Yang, Min Chen, Xiujuan Zhao, Dan Chen, Shuiyong Fan, Jianping Guo, and Shaukat Ali
Atmos. Chem. Phys., 20, 12527–12547, https://doi.org/10.5194/acp-20-12527-2020, https://doi.org/10.5194/acp-20-12527-2020, 2020
Short summary
Short summary
This study analyzed the impacts of aerosol–radiation interaction on radiation and meteorological forecasts using the offline coupling of WRF and high-frequency updated AOD simulated by WRF-Chem. The results revealed that aerosol–radiation interaction had a positive influence on the improvement of predictive accuracy, including 2 m temperature (~ 73.9 %) and horizontal wind speed (~ 7.8 %), showing potential prospects for its application in regional numerical weather prediction in northern China.
Ruqian Miao, Qi Chen, Yan Zheng, Xi Cheng, Yele Sun, Paul I. Palmer, Manish Shrivastava, Jianping Guo, Qiang Zhang, Yuhan Liu, Zhaofeng Tan, Xuefei Ma, Shiyi Chen, Limin Zeng, Keding Lu, and Yuanhang Zhang
Atmos. Chem. Phys., 20, 12265–12284, https://doi.org/10.5194/acp-20-12265-2020, https://doi.org/10.5194/acp-20-12265-2020, 2020
Short summary
Short summary
In this study we evaluated the model performances for simulating secondary inorganic aerosol (SIA) and organic aerosol (OA) in PM2.5 in China against comprehensive datasets. The potential biases from factors related to meteorology, emission, chemistry, and atmospheric removal are systematically investigated. This study provides a comprehensive understanding of modeling PM2.5, which is important for studies on the effectiveness of emission control strategies.
Cited articles
Amante, C. and Eakins, B. W.: ETOPO1 arc-minute global relief model: procedures, data sources and analysis, http://www.ngdc.noaa.gov/mgg/global/relief/ETOPO1/data/bedrock/cell_registered/netcdf/ (last access: August 2024), 2009.
Benjamin, S. G., Grell, G. A., Brown, J. M., Smirnova, T. G., and Bleck, R.: Mesoscale Weather Prediction with the RUC Hybrid Isentropic–Terrain-Following Coordinate Model, Mon. Weather Rev., 132, 473–494, https://doi.org/10.1175/1520-0493(2004)132<0473:MWPWTR>2.0.CO;2, 2004a.
Benjamin, S. G., Schwartz, B. E., Szoke, E. J., and Koch, S. E.: The Value of Wind Profiler Data in U.S. Weather Forecasting, B. Am. Meteorol. Soc., 85, 1871–1886, https://doi.org/10.1175/BAMS-85-12-1871, 2004b.
Bouttier, F.: The use of profiler data at ECMWF, Meteorol. Z., 10, 497–510, https://doi.org/10.1127/0941-2948/2001/0010-0497, 2001.
Bucci, L. R., Majumdar, S. J., Atlas, R., Emmitt, G. D., and Greco, S.: Understanding the response of tropical cyclone structure to the assimilation of synthetic wind profiles, Mon. Weather Rev., 149, 2031–2047, https://doi.org/10.1175/MWR-D-20-0153.1, 2021.
Clark, A. J., Gallus, W. A., and Weisman, M. L.: Neighborhood-Based Verification of Precipitation Forecasts from Convection-Allowing NCAR WRF Model Simulations and the Operational NAM, Weather Forecast., 25, 1495–1509, https://doi.org/10.1175/2010WAF2222404.1, 2010.
Dudhia, J.: Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model, J. Atmos. Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2, 1989.
Dunn, L.: An Example of Subjective Interpretation of Network Profiler Data in Real-Time Forecasting, Weather Forecast., 1, 219–225, https://doi.org/10.1175/1520-0434(1986)001<0219:AEOSIO>2.0.CO;2, 1986.
Fierro, A. O., Wang, Y., Gao, J., and Mansell, E. R.: Variational Assimilation of Radar Data and GLM Lightning-Derived Water Vapor for the Short-Term Forecasts of High-Impact Convective Events, Mon. Weather Rev., 147, 4045–4069, https://doi.org/10.1175/MWR-D-18-0421.1, 2019.
Gao, J. and Stensrud, D. J.: Assimilation of Reflectivity Data in a Convective-Scale, Cycled 3DVAR Framework with Hydrometeor Classification, J. Atmos. Sci., 69, 1054–1065, https://doi.org/10.1175/JAS-D-11-0162.1, 2012.
Gao, J. and Stensrud, D. J.: Some Observing System Simulation Experiments with a Hybrid 3DEnVAR System for Storm-Scale Radar Data Assimilation, Mon. Weather Rev., 142, 3326–3346, https://doi.org/10.1175/MWR-D-14-00025.1, 2014.
Gao, J., Xue, M., Brewster, K., and Droegemeier, K. K.: A Three-Dimensional Variational Data Analysis Method with Recursive Filter for Doppler Radars, J. Atmos. Ocean. Tech., 21, 457–469, https://doi.org/10.1175/1520-0426(2004)021<0457:ATVDAM>2.0.CO;2, 2004.
Gao, J., Smith, T. M., Stensrud, D. J., Fu, C., Calhoun, K., Manross, K. L., Brogden, J., Lakshmanan, V., Wang, Y., Thomas, K. W., Brewster, K., and Xue, M.: A Real-Time Weather-Adaptive 3DVAR Analysis System for Severe Weather Detections and Warnings, Weather Forecast., 28, 727–745, https://doi.org/10.1175/WAF-D-12-00093.1, 2013.
Gao, J., Fu, C., Stensrud, D. J., and Kain, J. S.: OSSEs for an Ensemble 3DVAR Data Assimilation System with Radar Observations of Convective Storms, J. Atmos. Sci., 73, 2403–2426, https://doi.org/10.1175/JAS-D-15-0311.1, 2016.
Gao, J., Heinselman, L. P., Xue, M., Wicker, L. J., Yussouf, N., Stensrud, D. J., and Droegemeier, K. K.: The Numerical Prediction of Severe Convective Storms: Advances in Research and Applications, Remaining Challenges, and Outlook for the Future, in: Encyclopedia of Atmospheric Sciences, Elsevier, https://doi.org/10.1016/B978-0-323-96026-7.00127-2, 2024.
Ge, G., Gao, J., and Xue, M.: Diagnostic Pressure Equation as a Weak Constraint in a Storm-Scale Three-Dimensional Variational Radar Data Assimilation System, J. Atmos. Ocean. Tech., 29, 1075–1092, https://doi.org/10.1175/JTECH-D-11-00201.1, 2012.
Guo, X., Guo, J., Zhang, D., and Yun, Y.: Vertical divergence profiles as detected by two wind-profiler mesonets over East China: Implications for nowcasting convective storms, Q. J. Roy. Meteorol. Soc., 149, 1629–1649, https://doi.org/10.1002/qj.4474, 2023.
Heinselman, P. L., Burke, P. C., Wicker, L. J., Clark, A. J., Kain, J. S., Gao, J., Yussouf, N., Jones, T. A., Skinner, P. S., Potvin, C. K., Wilson, K. A., Gallo, B. T., Flora, M. L., Martin, J., Creager, G., Knopfmeier, K. H., Wang, Y., Matilla, B. C., Dowell, D. C., Mansell, E. R., Roberts, B., Hoogewind, K. A., Stratman, D. R., Guerra, J., Reinhart, A. E., Kerr, C. A., and Miller, W.: Warn-on-Forecast System: From Vision to Reality, Weather Forecast., 39, 75–95, https://doi.org/10.1175/WAF-D-23-0147.1, 2024.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., De Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hoffman, R. N. and Atlas, R.: Future Observing System Simulation Experiments, B. Am. Meteorol. Soc., 97, 1601–1616, https://doi.org/10.1175/BAMS-D-15-00200.1, 2016.
Hoffmann, L., Günther, G., Li, D., Stein, O., Wu, X., Griessbach, S., Heng, Y., Konopka, P., Müller, R., Vogel, B., and Wright, J. S.: From ERA-Interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations, Atmos. Chem. Phys., 19, 3097–3124, https://doi.org/10.5194/acp-19-3097-2019, 2019.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Hu, H., Sun, J., and Zhang, Q.: Assessing the Impact of Surface and Wind Profiler Data on Fog Forecasting Using WRF 3DVAR: An OSSE Study on a Dense Fog Event over North China, J. Appl. Meteorol. Clim., 56, 1059–1081, https://doi.org/10.1175/JAMC-D-16-0246.1, 2017.
Hu, J., Fierro, A. O., Wang, Y., Gao, J., and Mansell, E. R.: Exploring the Assimilation of GLM-Derived Water Vapor Mass in a Cycled 3DVAR Framework for the Short-Term Forecasts of High-Impact Convective Events, Mon. Weather Rev., 148, 1005–1028, https://doi.org/10.1175/MWR-D-19-0198.1, 2020.
Hu, J., Gao, J., Wang, Y., Pan, S., Fierro, A. O., Skinner, P. S., Knopfmeier, K., Mansell, E. R., and Heinselman, P. L.: Evaluation of an experimental Warn-on-Forecast 3DVAR analysis and forecast system on quasi-real-time short-term forecasts of high-impact weather events, Q. J. Roy. Meteorol. Soc., 147, 4063–4082, https://doi.org/10.1002/qj.4168, 2021.
Huang, Y., Wang, X., Kerr, C., Mahre, A., Yu, T.-Y., and Bodine, D.: Impact of Assimilating Future Clear-Air Radial Velocity Observations from Phased-Array Radar on a Supercell Thunderstorm Forecast: An Observing System Simulation Experiment Study, Mon. Weather Rev., 148, 3825–3845, https://doi.org/10.1175/MWR-D-19-0391.1, 2020.
Huang, Y., Wang, X., Mahre, A., Yu, T.-Y., and Bodine, D.: Impacts of assimilating future clear-air radial velocity observations from phased array radar on convection initiation forecasts: An observing system simulation experiment study, Mon. Weather Rev., 150, 1563–1583, https://doi.org/10.1175/MWR-D-21-0199.1, 2022.
Huo, Z., Liu, Y., Shi, Y., Chen, B., Fan, H., and Li, Y.: An Investigation on Joint Data Assimilation of a Radar Network and Ground-Based Profiling Platforms for Forecasting Convective Storms, Mon. Weather Rev., 151, 2049–2064, https://doi.org/10.1175/MWR-D-22-0332.1, 2023.
Ishihara, M., Kato, Y., Abo, T., Kobayashi, K., and Izumikawa, Y.: Characteristics and Performance of the Operational Wind Profiler Network of the Japan Meteorological Agency, J. Meteorol. Soc. Jpn., 84, 1085–1096, https://doi.org/10.2151/jmsj.84.1085, 2006.
Jones, T. A., Wang, X., Skinner, P., Johnson, A., and Wang, Y.: Assimilation of GOES-13 Imager Clear-Sky Water Vapor (6.5 µm) Radiances into a Warn-on-Forecast System, Mon. Weather Rev. 146, 1077–1107, https://doi.org/10.1175/MWR-D-17-0280.1, 2018.
Li, N., Guo, J., Wu, M., Zhang, F., Guo, X., Sun, Y., Zhang, Z., Liang, H., and Chen, T.: Low-Level Jet and Its Effect on the Onset of Summertime Nocturnal Rainfall in Beijing, Geophys. Res. Lett., 51, e2024GL110840, https://doi.org/10.1029/2024GL110840, 2024.
Liu, B., Guo, J., Gong, W., Shi, L., Zhang, Y., and Ma, Y.: Characteristics and performance of wind profiles as observed by the radar wind profiler network of China, Atmos. Meas. Tech., 13, 4589–4600, https://doi.org/10.5194/amt-13-4589-2020, 2020.
Liu, D., Huang, C., and Feng, J.: Influence of Assimilating Wind Profiling Radar Observations in Distinct Dynamic Instability Regions on the Analysis and Forecast of an Extreme Rainstorm Event in Southern China, Remote Sens., 14, 3478, https://doi.org/10.3390/rs14143478, 2022.
Liu, S., Zheng, Y., and Tao, Z.: The analysis of the relationship between pulse of LLJ and heavy rain using wind profiler data, J. Trop. Meteorol., 9, 158–163, 2003.
Mallick, S. and Jones, T. A.: Assimilation of GOES-16 satellite derived winds into the warn-on-forecast system, Atmos. Res., 245, 105131, https://doi.org/10.1016/j.atmosres.2020.105131, 2020.
Mansell, E. R. and Ziegler, C. L.: Aerosol Effects on Simulated Storm Electrification and Precipitation in a Two-Moment Bulk Microphysics Model, J. Atmos. Sci., 70, 2032–2050, https://doi.org/10.1175/JAS-D-12-0264.1, 2013.
Mansell, E. R., Ziegler, C. L., and Bruning, E. C.: Simulated Electrification of a Small Thunderstorm with Two-Moment Bulk Microphysics, J. Atmos. Sci., 67, 171–194, https://doi.org/10.1175/2009JAS2965.1, 2010.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
National Centers for Environmental Prediction, National Weather Service, NOAA, and US Department of Commerce: NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D65D8PWK, 2015.
Pan, S., Gao, J., Stensrud, D. J., Wang, X., and Jones, T. A.: Assimilation of Radar Radial Velocity and Reflectivity, Satellite Cloud Water Path, and Total Precipitable Water for Convective-Scale NWP in OSSEs, J. Atmos. Ocean. Tech., 35, 67–89, https://doi.org/10.1175/JTECH-D-17-0081.1, 2018.
Pan, S., Gao, J., Jones, T. A., Wang, Y., Wang, X., and Li, J.: The Impact of Assimilating Satellite-Derived Layered Precipitable Water, Cloud Water Path, and Radar Data on Short-Range Thunderstorm Forecasts, Mon. Weather Rev., 149, 1359–1380, https://doi.org/10.1175/MWR-D-20-0040.1, 2021.
Purser, R. J., Wu, W.-S., Parrish, D. F., and Roberts, N. M.: Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part I: Spatially Homogeneous and Isotropic Gaussian Covariances, Mon. Weather Rev., 131, 1524–1535, https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2, 2003a.
Purser, R. J., Wu, W. S., Parrish, D. F., and Roberts, N. M.: Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part II: Spatially Inhomogeneous and Anisotropic General Covariances, Mon. Weather Rev., 131, 1536–1548, 2003b.
Sheng, J., Zheng, Y., and Shen, X.: Climatology and environmental conditions of two types of quasi-linear convective systems with extremely intense weather in North China, Acta Meteorol. Sin., 78, 877–898, 2020.
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Wang, W., Huang, X.-Y., and Duda, M.: A Description of the Advanced Research WRF Version 3, UCAR/NCAR, https://doi.org/10.5065/D68S4MVH, 2008.
St-James, J. S. and Laroche, S.: Assimilation of Wind Profiler Data in the Canadian Meteorological Centre's Analysis Systems, J. Atmos. Ocean. Tech., 22, 1181–1194, https://doi.org/10.1175/JTECH1765.1, 2005.
Wang, C., Chen, Y., Chen, M., and Shen, J.: Data assimilation of a dense wind profiler network and its impact on convective forecasting, Atmos. Res., 238, 104880, https://doi.org/10.1016/j.atmosres.2020.104880, 2020.
Wang, C., Chen, M., and Chen, Y.: Impact of Combined Assimilation of Wind Profiler and Doppler Radar Data on a Convective-Scale Cycling Forecasting System, Mon. Weather Rev., 150, 431–450, https://doi.org/10.1175/MWR-D-20-0383.1, 2022.
Wang, C., Chen, Y., Chen, M., and Huang, X.-Y.: Evaluation of two observation operator schemes for wind profiler radar data assimilation and its impacts on short-term forecasting, Atmos. Res., 283, 106549, https://doi.org/10.1016/j.atmosres.2022.106549, 2023a.
Wang, S., Guo, J., Xian, T., Li, N., Meng, D., Li, H., and Cheng, W.: Investigation of low-level supergeostrophic wind and Ekman spiral as observed by a radar wind profiler in Beijing, Front. Environ. Sci., 11, 1195750, https://doi.org/10.3389/fenvs.2023.1195750, 2023b.
Wang, Y., Gao, J., Skinner, P. S., Knopfmeier, K., Jones, T., Creager, G., Heiselman, P. L., and Wicker, L. J.: Test of a Weather-Adaptive Dual-Resolution Hybrid Warn-on-Forecast Analysis and Forecast System for Several Severe Weather Events, Weather Forecast., 34, 1807–1827, https://doi.org/10.1175/WAF-D-19-0071.1, 2019.
Zhang, L. and Pu, Z.: An Observing System Simulation Experiment (OSSE) to Assess the Impact of Doppler Wind Lidar (DWL) Measurements on the Numerical Simulation of a Tropical Cyclone, Adv. Meteorol., 2010, 743863, https://doi.org/10.1155/2010/743863, 2010.
Zhang, X., Luo, Y., Wan, Q., Ding, W., and Sun, J.: Impact of Assimilating Wind Profiling Radar Observations on Convection-Permitting Quantitative Precipitation Forecasts during SCMREX, Weather Forecast., 31, 1271–1292, https://doi.org/10.1175/WAF-D-15-0156.1, 2016.
Zhang, Y., Chen, M., and Zhong, J.: A Quality Control Method for Wind Profiler Observations toward Assimilation Applications, J. Atmos. Ocean. Tech., 34, 1591–1606, https://doi.org/10.1175/JTECH-D-16-0161.1, 2017.
Zhao, J.: WRF model and the input ERA5 reanalysis and GFS forecast data, Zenodo [data set], https://doi.org/10.5281/zenodo.14321805, 2024a.
Zhao, J.: Namelist files for WRF and the assimialtion system, Zenodo [code], https://doi.org/10.5281/zenodo.14241597, 2024b.
Zhao, J., Gao, J., Jones, T. A., and Hu, J.: Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts: 1. Observing System Simulation Experiment (OSSE), J. Adv. Model. Earth Syst., 13, e2021MS002484, https://doi.org/10.1029/2021MS002484, 2021a.
Zhao, J., Gao, J., Jones, T. A., and Hu, J.: Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts: 2. Assimilation Experiments of GOES-16 Satellite Derived Winds, J. Adv. Model. Earth Syst., 13, e2021MS002486, https://doi.org/10.1029/2021MS002486, 2021b.
Zhao, J., Gao, J., Jones, T., and Hu, J.: Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity, J. Adv. Model. Earth Syst., 14, e2022MS003246, https://doi.org/10.1029/2022MS003246, 2022.
Zhao, N., Yue, T., Li, H., Zhang, L., Yin, X., and Liu, Y.: Spatio-temporal changes in precipitation over Beijing–Tianjin–Hebei region, China, Atmos. Res., 202, 156–168, https://doi.org/10.1016/j.atmosres.2017.11.029, 2018.
Zhong, S., Fast, J. D., and Bian, X.: A Case Study of the Great Plains Low-Level Jet Using Wind Profiler Network Data and a High-Resolution Mesoscale Model, Mon. Weather Rev., 124, 785–806, https://doi.org/10.1175/1520-0493(1996)124<0785:ACSOTG>2.0.CO;2, 1996.
Zhou, S., Yang, J., Wang, W., Gong, D., Shi, P., and Gao, M.: Shift of daily rainfall peaks over the Beijing–Tianjin–Hebei region: An indication of pollutant effects?, Int. J. Climatol., 38, 5010–5019, https://doi.org/10.1002/joc.5700, 2018.
Zhuang, Z., Yussouf, N., and Gao, J.: Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble, Adv. Atmos. Sci., 33, 544–558, https://doi.org/10.1007/s00376-015-5072-0, 2016.
Ziegler, C. L.: Retrieval of Thermal and Microphysical Variables in Observed Convective Storms. Part 1: Model Development and Preliminary Testing, J. Atmos. Sci., 42, 1487–1509, https://doi.org/10.1175/1520-0469(1985)042<1487:ROTAMV>2.0.CO;2, 1985.
Short summary
A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
A series of observing system simulation experiments are conducted to assess the impact of...