It is increasingly recognised that atmospheric wind profiles and vertical wind shear are crucial to better understanding the more frequent extreme rainfall events (Huuskonen et al., 2014; Nash and Oakley, 2001; Weber et al., 1990), the intensification of clear-air turbulence associated with aircraft safety (Williams and Joshi, 2013), complicated aerosol–cloud–precipitation interaction (Fan et al., 2009; Guo et al., 2016a, 2019; Lee et al., 2016) and persistent particulate-pollution episodes (Yang et al., 2019; Zhang et al., 2020). For the wind speed in the planetary boundary layer (PBL), the most striking feature is that the turning of winds with height dominates the whole PBL and beyond, which can be explained in terms of force vectors (drag, pressure gradient force, Coriolis force) at the surface and the top of the PBL (pressure gradient force and Coriolis force) (LeMone et al., 2018). Under the influence of large-scale dynamic forcing and land surface processes, wind speed and direction will dramatically vary (Michelson and Bao, 2008), which poses a great challenge for models to simulate or forecast the variation in wind very well, especially in the PBL (Constantinescu et al., 2009; Guo et al., 2016b; Liu et al., 2017).

Radar wind profilers (RWPs), which are generally a Doppler radar that operates in either the VHF (30–300 MHz) or UHF (300-1000 MHz) bands, have been widely applied to atmospheric wind field research (Dolman et al., 2018; Molod et al., 2015; Ishihara et al., 2006; Schlatter and Zbar, 1994). To date, a large spectrum of field campaigns involving the RWP-observed wind profiles, especially over the regions with intensive anthropogenic and industrialised activities, have been conducted and their archived dataset has been increasingly receiving attention (Liu et al., 2019, 2020; Kottayil et al., 2016; Singh et al., 2016; LeMone et al., 2013; Bianco et al., 2008; Le et al., 1998); most of these are based on ground-based remotely sensed measurements. The earliest space-borne wind products generally referred to the atmospheric motion vectors that are derived by tracking clouds or areas of water vapour through consecutive infrared remote-sensing images (Schmetz et al., 1993; Velden et al., 2005). Later on, the vector winds over the ocean surface were measured by the spaceborne microwave instruments such as SeaWinds onboard QuikSCAT (Bentamy et al., 1999; Draper and Long, 2002). Since 2018, new satellite-based wind observational era has set in with the launch of the European Space Agency (ESA)'s Aeolus wind satellite on which the direct-detection Doppler wind lidar ALADIN is accommodated, which provides line-of-sight winds along the satellite track (Reitebuch et al., 2009; Reitebuch, 2012). To ensure the quality of Aeolus wind products, the ALADIN team conducted several airborne wind measurement experiments for the validation of the Aeolus satellite winds product, which were based on Doppler wind lidar on research aircraft (Lux et al., 2018, 2020; Zhai et al., 2020). Meanwhile, the Aeolus experts from different organisations worked together in the Data Innovation and Science Cluster team and ultimately optimised the data processing and bias correction methods. Starting on 12 May 2020, the Aeolus data went public after the bias correction of the winds has been adequately made and are now being distributed publicly to forecasting services and scientific users within less than 3 h of the measurements being made from space (https://www.esa.int/Applications/Observing_the_Earth/Aeolus/Aeolus_goes_public, last access: 19 August 2020).

To gain a panoramic picture of regional-scale wind fields, a number of RWP networks have been set up across the world. As early as 1990s, the demonstration wind profile network is deployed and maintained by the National Oceanic Atmospheric Administration (NOAA), which is also termed NOAA profiler network (NPN) and operated at a frequency of 404 MHz (Schlatter and Zbar, 1994; van de Kamp, 1993; Weber at al., 1990). The second type of profiler is the 915 MHz boundary-layer profiler that is much smaller, transportable, commercially available but lacks height coverage compared with the 404 MHz wind profiler and thus is mainly used for NOAA research and outside agencies. Nevertheless, probably due to the fact that the RWPs reached the end of their useful lives, the NPN largely ceased to operate in 2014 and the last stations closed in 2017. As an alternative data source, the high-density airborne wind and temperature profiles from the civil-aviation industry have gradually taken over the role of the RWP since then (https://madis.ncep.noaa.gov/madis_npn.shtml, last access: 22 May 2020). To combine the best sampling attributes of the abovementioned two types of wind profiler, a third type of profiler operated at 449 MHz. Later on (in 1996), the European Cooperation in Science and Technology framework (COST) initiated the project Wind Initiative Network Demonstration in Europe (CWINDE). The European RWP network named E-PROFILE was constructed within the framework of CWINDE as part of the EUMETNET Composite Observing System (EUCOS), providing the monitoring of vertical profiles of wind across Europe (Dibbern et al., 2001; Oakley et al., 2000; Nash and Oakley, 2001). Moreover, the Japan Meteorological Agency developed the operational wind profiler network in Japan in 2011, which is a nationwide network of 33 wind profilers currently in operation. The wind data have significant influence on improving numerical weather prediction (Ishihara et al., 2006; Rennie and Isaksen, 2020). The Australian Bureau of Meteorology completed the installation of the Australian wind profiler network of 19 wind profilers in 2017 that runs in the 55 MHz frequency band, which produces wind data of sufficient accuracy for the presentation to forecasters and ingestion into global numerical weather prediction models (Dolman et al., 2018). The aforementioned networks have provided vertical profiles of wind for model assimilation through the Global Telecommunication System at a regional or national scale (e.g. Benjamin et al., 2004; Chipilski et al., 2019), which was found to significantly improve the forecast of rainfall onset and atmospheric pollution episodes (Liu et al., 2018, 2019; Singh et al., 2016; LeMone et al., 2013; Du et al., 2012; Bianco et al., 2008; Angevine et al., 1994).

Given the considerable advantages over conventional ground-based in situ or remote-sensing observations, wind profiler measurements have been applied well in a variety of applications in China, including air quality and weather forecast (Sun, 1994; Hu and Li, 2010; Dong et al., 2011; Miao et al., 2018; Zhang et al., 2020). Nevertheless, the RWP is generally deployed either in specific regions or for short time periods. Recent model simulation work by assimilating wind measurements from a regional wind profiler network in north China indicated the network observation significantly improved the convective forecasting (Wang et al., 2020). Meanwhile, extreme precipitation is continuously intensified under global warming and atmospheric pollution is increasing, especially in Eastern Asian countries such as China and India (Zhang et al., 2006; Pfahl et al., 2017; Guo et al., 2019, 2020; Li et al., 2020). However, the characteristics and performance of the nationwide profiler network in China have never been revealed, and the assessment of systematic observation performance and data accuracy is still lacking, to the best of our knowledge. This motivates us to evaluate the performance and accuracy of the RWP network of China, ultimately in an attempt to present wind profile data as a new data source for numerical weather prediction or climate-related studies. The remainder of this paper is organised as follows. The RWP network of China is briefly introduced in Sect. 2. The performance and accuracy are evaluated in Sect. 3. Section 4 will discuss the detailed application of wind profile data. A summary of results is presented in Sect. 5.

This network began to be constructed in 2008, when there were five sites having wind profiling measurements transmitted to the headquarter of the China Meteorological Administration (CMA). The number of RWP sites continuously increased to 92 by the end of 2017, all of which operate in the 405 MHz frequency band. The RWP network of China was comprised of 106 stations by March 2019 and is designed primarily for measuring winds at various altitudes. Afterwards, the working frequency band changed to the L band (1290 MHz), and the number increased to 128 in February 2020 (personal communication with Ruiyi Li from the CMA, 22 May 2020). The Meteorological Observation Center (MOC) of the CMA is responsible for the operation and maintenance of the nationwide wind profiler network. Table 1 shows the instrument information for RWPs used in this study; there are three types of RWP: high-troposphere, low-troposphere and boundary layer RWPs. It can be seen that the majority of the radars are boundary layer RWPs operating in the L band (101 sites), and a few of the sites are instrumented with tropospheric RWPs operating in the P band (five sites). Figure 1 shows the spatial distribution of the wind profiler network in China, which exhibits a large spatial domain extending from the northernmost site located at Wulumuqi to the southernmost one at Nanhai and from the westernmost site also located at Wulumuqi to the easternmost one in Shenyang. Detailed information on the RWP network of China is shown in Table S1 in the Supplement.

The MOC (CMA) is responsible for the maintenance and collection of wind measurements from the wind profiler network, as shown in Fig. 2. Specifically, the data transfer from radar sites to MOC (CMA) is mainly done using internet connections. The data centre of the CMA was established to efficiently process the data collected via the internet. There are two main types of data collected from the wind profiler network: raw data and product data. The former data include the power spectrum data files (indicated by FFT) and radial data files (indicated by RAD). The power spectrum data file is composed of file identification, basic parameters of the station, performance parameters, observation parameters and observation data. The power spectrum data file is dynamically generated in real time according to demand. The radial data files are of two kinds: one is reference information, such as the basic parameters of the station, radar performance parameters and observation parameters; the other is the observation data of each beam at each sampling height, including sample height, velocity spectrum width, signal-to-noise ratio and radial velocity. As for the product data, three main wind profile products are produced by the data centre of the CMA:

The real-time sampling data file (at 6 min intervals), mainly including the sampling height, horizontal wind direction, horizontal wind speed, vertical wind speed, horizontal credibility, vertical credibility and refractive index structure parameter (Cn2). An individual file will be produced for every 6 min detection and is marked as ROBS.

These wind profile products are generated for each observation site. The vertical resolution of wind profile data at most sites is 120 m. However, a few sites use a low-level detection mode with a high sampling rate; these provide a vertical resolution of 60 m. Examples of the wind profile product are shown in Fig. 3. Seven different heights (150, 500, 1000, 1500, 2000, 2500 and 3000 m) are selected to show the atmospheric vertical wind field (Fig. 3e). It can provide the vertical profiles of horizontal wind direction, horizontal wind speed and vertical wind speed. These products are available for official duty use and for research and education. The observation data from November 2018 to March 2019 are used to evaluate the performance of the RWP network of China. Due to the fact that the measurements from the China RWP network have to be further assessed, data sharing via the Global Telecommunications System is expected to occur in the next several years, which depends greatly on the process of data quality assessment.

The RWP network of China includes a variety of types of RWPs, including high-troposphere, low-troposphere and boundary layer RWPs. Because the algorithms and setting parameters of different instruments are inconsistent, the system performance index and data accuracy are inhomogeneous. Therefore, it is necessary to evaluate the system performance index and data accuracy of the radars in the RWP network. This is a major step forward in the harmonisation of the product generation and data quality of the RWP network of China. Three system performance indicators on data application are investigated: effective detection height, data acquisition rate and data confidence. In order to estimate the data accuracy, the wind profiles from the RWP are compared with hourly wind measurements in a 0.25∘×0.25∘ latitude–longitude grid from the fifth-generation European Centre for Medium-range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5; Hoffmann et al., 2019; Hersbach et al., 2020).

The wind profiles are of great importance to the accuracy of numerical weather prediction models, the prediction of precipitation, the diffusion of air pollution, research on regional climate change and site selection for wind power plants. For the first time, to the best of our knowledge, we reported the height-resolved winds starting from ground surface to as high as 3–10 km, based on the RWP network of China, which consists of more than 100 RWP stations. It can provide the vertical profiles of horizontal wind direction and horizontal wind speed. Then, the availability of the RWP network was investigated regarding the system performance index and data accuracy. The evaluation criteria are that the effective detection height reaches 3 km, the data acquisition rate exceeds 60 % and the data confidence is 100 %. In addition, in terms of data accuracy, the MMSD is better less than 4 m s-1 and the RMSD is less than 6 m s-1. Under this criterion, the availability of the RWP network of China is 84 %, and 89 stations are recommended and 17 stations are not recommended. Finally, the wind profile data have a wide range of applications, such as daily maximum wind detection and regional atmospheric wind field research. This RWP network would serve as a key data source on the spatiotemporal distribution of atmospheric wind field in support of scientific research related to renewable energy, severe weather, climate and climate change in the future.