Juan Zhao

China Meteorological Administration

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Junjun Hu

Guizhou University

Jidong Gao

Cancer Hospital of Chinese Academy of Medical Sciences

Thomas A. Jones

NOAA National Severe Storms Laboratory

Yanben Han

National Astronomical Observatories

Yuanshu Jing

Nanjing University of Information Science and Technology

Fuzhong Weng

China Meteorological Administration

Li Wang

Chinese Academy of Sciences

Zhian Li

South China Botanical Garden

Wenjiang Huang

Chinese Academy of Sciences

Haibo Lin

Beijing Normal University

Cooperative Institutions

National Oceanic and Atmospheric Administration

Goddard Space Flight Center

Chinese Academy of Sciences

Centre National de la Recherche Scientifique

University of Colorado Boulder

Cooperative Institute for Research in Environmental Sciences

NOAA National Environmental Satellite Data and Information Service

University of Alaska Fairbanks

Scripps Institution of Oceanography

University of Maryland, College Park

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Model Space Exploration for Determining Landslide Source History from Long-Period Seismic Data

Pure and Applied Geophysics (2014)

Juan Zhao L. Moretti A. Mangeney É. Stutzmann Hiroo Kanamori

10.1007/s00024-014-0852-5

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Citations (35)

Seismic signals analysis of the June 2017 Maoxian Landslide

Japan Geoscience Union (2018)

Juan Zhao

Source

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Citations (1)

Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 2. Assimilation Experiments of GOES‐16 Satellite Derived Winds

Journal of Advances in Modeling Earth Systems (2021)

Juan Zhao Jidong Gao Thomas A. Jones Junjun Hu

Abstract Building on the results from the observing system simulation experiments in Part I, this study investigates the impact of assimilating Geostationary Operational Environmental Satellite‐16 (GOES‐16) derived atmospheric motion vector (AMV) data on the convective scale numerical weather prediction (NWP) by using the National Severe Storms Laboratory (NSSL) three‐dimensional variational (3DVAR) data assimilation (DA) system. The benefit of the AMV DA for short‐term severe weather forecast is assessed with three high‐impact weather events that occurred in spring 2018 and 2019 over the Great Plains of the United States. The results show that the wind and equivalent potential temperature fields associated with the storm environment and the nearby ongoing convection are improved by the AMV DA, which yields better simulation of the boundaries and the subsequent forecasts of storm evolution. For the quasi‐linear or mesoscale convective system, the assimilation of AMVs has a positive impact on the 0–3 h forecasts of composite reflectivity and accumulated precipitation in terms of the shape, location, and magnitude. However, the AMV DA has difficulty in capturing the sharp moisture gradient associated with the dryline and mostly underpredicts the associated scattered storms.

Convective storm detection

Mesoscale convective system

10.1029/2021ms002486

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Citations (7)

The Relationship Between the Interannual Variation of Earth’s Rotation and El Niño Events

Pure and Applied Geophysics (2008)

Juan Zhao Yanben Han

Variation (astronomy)

Anomaly (physics)

Earth surface

10.1007/s00024-008-0358-0

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Citations (3)

The comparison of different spectrum analysis methods for LOD time series

Applied Geophysics (2018)

Haibo Lin Juan Zhao

Interpolation

Data series

10.1007/s11770-018-0671-z

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Citations (0)

Forecast of annual precipitation in Beijing area for 2003

Ziran zaihai xuebao (2003)

Juan Zhao

Considering the relationship between solar activity,El Nino and annual precipitation in Beijing area since 1870, a mathematic model is proposed to forecast the annual precipitation in the area. Based on the forecast for former five years,450±50mm of annual precipitation in Beijing area is presented for 2003.

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A new vegetation index for cooperative inversion of winter wheat covered surface soil moisture based on sentinel-1/2 remote sensing data

IET conference proceedings. (2021)

Baozhong Zhang Juan Zhao Ningning Li Zhengfei Guo

Winter wheat

10.1049/icp.2021.0636

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Detection of Radio-Frequency Interference Signal Over Land From FY-3B Microwave Radiation Imager (MWRI)

IEEE Transactions on Geoscience and Remote Sensing (2012)

Xiaolei Zou Juan Zhao Fuzhong Weng Zhengkun Qin

The MicroWave Radiation Imager (MWRI) onboard the FengYun (FY)-3B satellite has five frequencies at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz, each having dual channels at vertical and horizontal polarization states, respectively. It is found that radio-frequency interference (RFI) is present in MWRI data over land. The RFI signals are, in general, detectable from a spectral difference method and a principal component analysis (PCA) method. In particular, the PCA method is applied to derive RFI signals from natural radiations by using the characteristics of natural radiation measurements having all-channel correlations. In the area where data have a higher projection onto the first principle component (PC) mode, RFI is, in general, present. However, both the spectral and PCA methods cannot detect RFI reliably over frozen grounds and scattering surfaces, where the brightness temperature difference between 10.65 and 18.7 GHz is large. Thus, detection is improved through the use of normalized PCA. The new RFI detection algorithm is now working reliably for MWRI applications. It is found that RFI at 10.65 GHz distributes widely over Europe and Japan, and is less popular over the United States and China.

Scatterometer

Radio spectrum

10.1109/tgrs.2012.2191792

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Citations (41)

Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity

Journal of Advances in Modeling Earth Systems (2022)

Juan Zhao Jidong Gao Thomas A. Jones Junjun Hu

Abstract Based on the idealized supercell and real case studies in Part I and II, the purpose of this subsequent study is to further investigate the impact of assimilating Geostationary Operational Environmental Satellites‐16 (GOES‐16) derived atmospheric motion vectors (AMVs) in addition to WSR_88D Doppler radar observations on convective scale numerical weather prediction. Five high‐impact weather events that occurred in spring 2018 and 2019 are analyzed using the National Severe Storms Laboratory three‐dimensional variational data assimilation (DA) system. Four types of experiments are implemented and compared: (a) the control experiment (NoDA) without assimilating any observation, (b) the radar DA experiment (RAD), (c) the GOES‐16 AMV DA experiment (AMV), and (d) the experiment assimilating AMVs together with radar data (AMV_RAD). Score metrics aggregated over all cases indicate that AMV_RAD performs slightly better than RAD in 0–3 hr reflectivity and precipitation forecasts especially at higher thresholds, suggesting the added value of GOES‐16 AMVs on radar data. Detailed case examinations also show that AMV_RAD generally exhibits slightly more skillful storm forecast in terms of the areal coverage, storm mode, and storm orientations, owing to improvements in the analysis of boundary locations and localized enhanced divergence signatures. In spite of encouraging objective and subjective evaluation results, AMV_RAD has difficulty in adjusting the moisture gradient associated with dryline and tends to underpredict the associated weak discrete storms.

Supercell

Convective storm detection

Weather radar

10.1029/2022ms003246

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Citations (1)

Explore the relationship between sunspot area and sunspot number with a new method

Acta Geodynamica et Geomaterialia (2024)

Juan Zhao

Sunspot

Sunspot number

10.13168/agg.2024.0003

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Citations (0)