Verification and Assessment of Real-time Forecasts of Two Extreme Heavy Rain Events in Zhengzhou by Operational NWP Models

Henan Province experienced extremely heavy rainfall during 17-22 July 2021. The strongest rainfall period was during 19-20 July, which was characterized by its heavy accumulated rainfall, long duration, and prominent extreme of the rainfall. In particular, Zhengzhou was hit by unprecedented hourly rainfall with 201.9 mm during 1600-1700, 20 July, which broke the maximum hourly rainfall record of the rainfall in the Chinese mainland (198.5 mm, in Lin Zhuang, Henan Province, on August 5, 1975 ^[1]). The accumulated rainfall reached 333 mm in 3 hours and 627.4 mm in 24 hours. Due to its unique geographic characteristics, Zhengzhou is seriously affected by typhoon and heavy rainfall. The main mechanisms of the extreme rainfall might be attributed to three favorable conditions: (1) the stagnation and little movement of the low-pressure system in the Huang-Huai Region, (2) the abundant water vapor transport (WVT) by the easterly outflow of the typhoon in the Western Pacific, and (3) the convection-initialization and enhancement effects by local mountains. The impact mechanism of the extreme rainfall may also be attributed to several additional factors, for example, the influence of urbanization ^[2-5] and the impacts of climate change ^[6-9].

In terms of the formation mechanism of extreme rainfall, water vapor transport plays an important role in each extreme rainfall event. Severe weather is often associated with favorable WVT conditions. There has been a notable advance in the number of studies of the WVT, including the atmospheric rivers ^[10-17], low-level jets and monsoons ^[18-23]. In the western U.S., more than 60% of the most extreme storms are associated with the strong WVT by atmospheric rivers ^[16]. In the Yangtze River Valley of China, the strong moisture source from the Indian monsoon region and the Western Pacific Ocean accounted for the main moisture source for the extreme Meiyu flood in 2020 ^[19]. Additionally, the maintenance of the low-level jet could provide continuous water vapor transport to the extreme rainfall over Sichuan Basin in southwest China ^[21] and the organized warm-sector rainfall over south China ^[23].

Despite the important influences of the WVT on extreme rainfall, few studies focus on the predictability of WVT based on the operational numerical weather prediction (NWP) models. Currently, NWP models show generally low predictabilities on extreme rainfall ^[24-28]. The low capabilities are mainly attributed to the unsuccessful fidelity of the initial data ^[29] and the insufficient description accuracy of model physics ^[30-33]. For example, the low bias of surface temperature and moisture condition in the initial data, as well as the overestimated low-level wind speed over the coastal region, could lead to underestimated rainfall in the warm sector over south China ^[34]. The performances of the models on prediction of the heavy rainfall might be improved by using data assimilation and inclusion of physic parameterization of the sub-grid process ^[32-35].

The major purpose of this paper is to investigate the impacts of the WVT and the source of the moisture transport as well as the examination of the performances of the NWP models on the forecast of extreme rainfall. About one month later, Zhengzhou has subsequently experienced heavy rainfall on 22 August 2021. For comparative analysis, the heavy rainfall that occurred on 22 August 2021 is also discussed. In this study, we address the following research questions:

· To what extent can the NWP model predict the extreme rainfall over Zhengzhou? To answer this question, we examine the current operational NWP models from the National Meteorological Service Network (Section 4a).

·What are the mechanical differences between two heavy rainfall events? To address this question, we investigate the WVT comparisons and discuss the threedimensional structure of the WVT during the heavy rainfall periods (Section 4b).

· What are the surface dynamic and thermal characteristics of the extreme rainfall (Section 4c)? To answer this question, we examine surface winds and specific humidity of two heavy rainfalls as well as the analysis comparisons of the Convective Available Potential Energy (CAPE, Section 4c).

The Model introduction and methods are described in Section 2. Cases overview is given in Section 3. Conclusions and discussions are given in Section 5.

The model systems are evaluated based on the CMA-GD model in the Guangzhou Regional Meteorological Center, including the Tropical Regional Atmospheric Model System for the South China Sea (CMA-TRAMS ^[34], a typhoon model with a horizontal resolution of 9 km), and the Mesoscale Atmospheric Regional Model System (CMA-GD ^[30], a typhoon model with a horizontal resolution of 3km). Table 1 gives the specific parameterization scheme used in the model physics. Both CMA-TRAMS and CMA-GD have been updated by adopting a three-dimensional reference atmosphere and coupling technic between model dynamics and physics ^[35], as well as the inclusion of the Gravity Wave Drag parameterization scheme ^[32] and the improvement of cumulus parameterization ^[33]. Readers could refer to Zhong et al. ^[30] for more details of model description and performance evaluation.

In this study, the verification results are obtained from the operational NWP models from the National Meteorological Service verification platform. There are 402 stations that are used to evaluate model precipitation forecasts over Henan, including those from the national meteorological observations and automatic weather observations after quality control. The verification method includes the TS and Fraction Skill Score (FSS) ^[36].

where P_f and P₀ represent the ratio of the forcasting and observation in a fixed radium, respectively. N denotes the number of spatial windows of the neighborhood radius in the verification region.

In this study, hourly surface observations are used to examine the model performances on the simulation of surface wind and specific humidity. The archived data of the verification results are obtained from the verification platform of high-resolution NWP models from the National Meteorological Service Network. The impacts of the water vapor transport to the extreme rainfall are investigated by using the vertically integrated water vapor transport (IVT), which has been widely used in previous studies ^{[12, 15]} (e. g., to define and detect atmospheric river activities). In this study, IVT was analyzed to investigate the water vapor transport effects on extreme rainfall by employing 1-hourly model output. IVT is defined as

where Q_u (Q_v) denotes vertical integral of eastward (northward) water vapor flux (units: kg m^-1 s^-1), P is the pressure, P₀ is the surface pressure (units: Pa), q denotes specific humidity (units: kg kg^-1), g is the acceleration of gravity (units: m s^-2) and u(v) is the eastward (northward) component of wind (units: m s^-1).

In this study, the IVT of three major layers are investigated, including the layers below 700 hPa, the layers between 700-500 hPa and 500-200 hPa, and the layers below 200 hPa, which represents the vertically integrated water vapor transport layers of the planetary boundary layer including the lower troposphere, upper troposphere, and the whole layer, respectively.

Two heavy rainfall cases over Henan Province are investigated in this study, including the extreme rainfall on 20 July (hereinafter 7 · 20) and the heavy rainfall on 22 August 2021 (hereinafter 8·22). It can be seen that 7·20 showed a strong heavy rainfall center over Zhengzhou. The maximum 24-h accumulated rainfall reached 627.4 mm, and the strongest hourly rainfall was recorded at Zhengzhou city with 201.9 mm during 1600-1700, 20 July at local time (LST). For the heavy rainfall of 8 · 22, the maximum 24-h accumulated rainfall was 198.5 mm, and the strongest hourly rainfall in Henan Province was 79.4 mm during 1700-1800 LST, 22 August. In general, 7 · 20 is more extreme than 8 · 22 in terms of the intensity of the rainfall.

Figure 2 compares the large-scale environment of the two rainfall cases. For case 7 · 20, it can be seen that two typhoons located in the east of the Pacific (Typhoon In-Fa) and north of the South China Sea (Typhoon Cempaka). Henan Province was mainly affected by the southeasterly outflows from Typhoon In-Fa. In contrast, for case 8 · 22, although there was also a typhoon (Omais) over the west of the Pacific, which was much weaker than Typhoon In-Fa, Henan Province was not directly affected by the typhoon. It was mainly affected by the southwesterly wind from the Chinese mainland.

Figure 1.  The observation of 24-h accumulative rainfall of (a) 7 · 20 (from 0000 UTC 20 to 0000 UTC 21 July 2021) and (b) 8 · 22 (from 0000 UTC 22 to 0000 UTC 23 August 2021). The red rectangle denotes the region of concern in Fig. 1a.

Figure 2.  The geopotential height (units: gpm) and wind analysis at 850 hPa on (a) 0000 UTC 20 July 2021 and (b) 0000 UTC 22 August 2021.

To examine the predictabilities of the two heavy rainfall events, we first gave the comparisons of the TS and FSS (> 50 mm) of the rainfall over Henan Province from the verification platform of high-resolution NWP model from the National Meteorological Service Network. The 24h and 48h forecast of the TS (Table 2) over Henan Province were examined, including the TRAMS model (horizontal resolution of 9km, CMATRAMS), CMA-GD, Beijing regional model (9 km, CMA-BJ), Shanghai regional model (9 km, CMA-SH9), CMA-MESO model from the National Meteorological Center, European Center for Medium-Range Weather Forecasts (12.5 km, EC) and the National Centers for Environmental Prediction of the United States (25 km, NCEP). In general, these NWP models exhibited relatively high TS scores for 7 · 20 and 8 · 22. In particular, the CMA-GD and CMA-TRAMS showed generally higher scores of TS than other NWP models. For example, the 24h TS from CMA-GD and CMATRAMS reached 0.56 and 0.50, respectively. For the 48h TS, those from CMA-GD and CMA-TRAMS reached 0.64 and 0.52, respectively. The longer the forecasting time, the generally better performances of both models on the two heavy rainfalls. More details of the robust performances of the model could be found in Zhong et al. ^[34-35].

The comparisons of the FSS verifications showed that the NWP models generally exhibited high FSS scores within the coverage radium of 10-40 km (Table 3). The FSS of the heavy rainfall by all above NWP models exceeded 0.6 for all verification radium. In particular, the CMA-GD and CMA-TRAMS showed generally higher scores of FSS than other NWP models. For example, the FSS at the radium of 40km by the CMA-GD and CMA-TRAMS reached 0.96, 0.87, respectively. It can be concluded that all NWP models showed generally high performances on the forecasting of heavy rainfall.

To reveal the performances of CMA-GD and CMA-TRAMS on the spatial distribution of the extreme rainfall, Figs. 3 and 4 give the distribution of the 24h accumulative rainfall of 7 · 20 and 8 · 22 by the 24h forecasts. It can be seen that both models exhibited realistic intensity and distribution forecasts of the heavy rainfall over the north of Henan Province. For example, for 7·20, the maximum 24h accumulative rainfall by the 24h forecasts of CMA-GD reached 556 mm over Henan Province, showing that the intensity and location of the rainfall matched well with the observation shown in Fig. 1. For CMA-TRAMS, it predicted a maximum 24h accumulative rainfall with 431 mm by the 24h forecasts. Both models have successfully predicted the intensity and distribution of the heavy rainfall in case 8 · 22 (Fig. 5). In general, CMA-GD provided a more realistic forecast of the extreme rainfall than CMA-TRAMS did.

Figure 3.  The 24-h forecasting of the accumulative rainfall (units: mm) of 7·20 (started from 0000 UTC 20 July 2021) by (a) CMAGD and (b) CMA-TRAMS.

Figure 4.  The same as in Fig. 3, but for case 8·22 (started from 0000 UTC 22 August 2021).

Figure 5.  Comparisons of the hourly rainfall of 7 · 20 (0300-1500 UTC 20 July 2021, 3 hours intervals) between observations (left column, units: mm) and forecasting by CMA-GD. The second column, third column and forth column represent different model initial time from 0000 UTC 20 July, 1200 UTC 19 July and 0000 UTC 19 July 2021, respectively.

The time variation of the hourly rainfall over Zhengzhou showed that the CMA-GD had realistic forecasts of rainfall intensity compared with observation (Fig. 5). For example, both forecasts initiated from 0000 UTC and 1200 UTC 19 July as well as the forecasts initiated from 0000 UTC 20 July exhibited realistic rainfall distribution forecasting. However, CMA-GD predicted too early occurrence of the peak of the extreme rainfall for about 3 hours by the forecasts initiated from 0000 UTC 20 July. For other forecasts time, it generally underestimated the extreme rainfall over Zhengzhou, especially for the longer forecasting time.

The impacts of the IVT on the extreme rainfall are examined by using the hourly model output from the operational CMA-GD. It can be seen that the IVT of the whole layer before the occurrence of the heavy rainfall exhibited a double-eyes distribution, which was located over Henan and the typhoon eye (Cempaka) over south China, respectively. Each IVT eye showed about the same magnitude of WVT as the other one. In particular, the planetary boundary layer (< 700 hPa) showed a majority of the WVT than in the upper level. Moreover, the IVT in the upper level (> 500 hPa) showed a relatively high IVT than that in the lower troposphere (< 500 hPa), which might strengthen the extreme rainfall due to its strong IVT in the whole layer. It should be noted that the north eye of the IVT had a majority of low-level water vapor transport from Typhoon In-Fa. than the southern typhoon eye did. On one hand, the southern typhoon eye of Typhoon Cempaka carried little amount of water vapor from Typhoon In-Fa. On the other hand, it might help to accelerate the westerly transport of the water vapor of Typhoon In-Fa.

For the late stage of the heavy rainfall, it is also can be seen that the IVT of the whole layer continuously exhibited a large amount of IVT over Henan Province (Fig. 7). The water vapor transport from Typhoon In-Fa in the planetary boundary layer was also strong. However, the location of the strong IVT was more northward than that before the occurrence of the heavy rainfall. For case 8 · 22, it showed a high IVT belt over Henan Province with the strong southwesterly wind in the planetary boundary layer. Although there was also a typhoon (Omais) over the west of the Pacific, Henan was not directly affected by the weak typhoon.

Figure 6.  Comparisons of the IVT (units: kgm^-1s^-1) and wind (> 8m s^-1) on 0600 UTC 20 July 2021 (6-h forecasting by CMA-GD) in (a) planetary boundary layer (< 700 hPa, wind vectors at 925 hPa), (b) the layers between 700-500 hPa (wind vectors at 700 hPa), (c) the layers between 500-200 hPa (wind vectors at 500 hPa) and (d) the layers below 200 hPa (wind vectors at 850 hPa). The red rectangles represent the concerning region of the average IVT over Henan and Typhoon Cempaka in Fig. 9.

Figure 7.  Same as in Fig. 6, but on 1400 UTC 20 July 2021 (14-h forecasting by CMA-GD).

Figure 8.  Same as in Fig. 6, but on 0700 UTC 22 August 2021 (7-h forecasting by CMA-GD).

Figure 9.  Hourly variations of average IVT (units: kg m^-1 s^-1) of case (a, b) 7·20 and (c, d) 8·22 at different layers over (a, c) Henan and (b, d) south China.

To compare the time variation of the WVT, Fig. 9 gives the hourly variation of regional average IVT of two cases. It can be seen that case 7 · 20 exhibited a strong amount of IVT in the daytime. Furthermore, the IVT over Henan Province reached the same magnitude as Typhoon Cempaka over south China. For 8 · 22, the IVT showed a stronger IVT than in south China, as well as a strong single peak at 1500 UTC (2300 LST) in the nighttime over both regions. Therefore, the continuously strong water vapor transport from Typhoon In-Fa provided favorable water vapor conditions to the formation of the extreme rainfall in 7·20.

The impacts of the surface wind and specific humidity for both heavy rainfall events are discussed by using the dense surface observations. As shown in Fig. 10, the southeasterly wind brought strong warm moisture from the Mideast of Henan Province, which formed a wind convergent zone with the northeasterly drier wind over the north of Henan Province. The forecasting results by different initial times by CMA-GD showed that the model could generally capture the distributional structure and intensity of the surface wind. However, it generally overestimated the surface wind over the northwest of Henan, especially for the longer forecasting time. It also underestimated the water vapor from the southeast of Henan Province.

Figure 10.  Surface wind (units: m s^-1, red vectors: > 3.6 m s^-1) and specific humidity (units: g kg-1) on 0600 UTC 20 July, 2021: (a) observation (b) 6-h forecasting from 0000 UTC 20 July, (c) 18-h and (d) 30-h forecasting from 1200 UTC 19 July and 0000 UTC 19 July, respectively.

For case 8 · 22, it can be seen that CMA-GD generally captured the distributional structure and intensity of the surface wind and the specific humidity. Both the forecasts initiated from 21 and 22 August exhibited better estimations than those in case 7 · 20, especially for the specific humidity forecasting. It should be noted that both the heavy rainfall events were caused by the large-scale environment (Fig. 1 and 2). For case 7 · 20, the extreme rainfall was related to the terrain effects and the strong IVT transport by Typhoon In-Fa, as well as the possible strong urban effects on the extreme rainfall ^[7]. Nevertheless, the heavy rainfall of 8· 22 was mainly caused by large-scale convergence between the southerly wind and northerly wind over Henan Province. In general, CMA-GD exhibited robust performances on the large-scale weather system forecasting.

It also can be seen that there was a strong southerly wind surge and abundant water vapor transport before the occurrence of the heavy rainfall in both cases, which could be referred to as the early signals as the occurrence of the heavy rainfall ^{[18, 20]}. Furthermore, the continuously strong IVT in the planetary boundary layer also exhibited strong early signal characteristics of the heavy rainfall. Fig. 12 gives the evolution of the surface wind and CAPE in both cases. It can be seen that 7 · 20 showed a high CAPE and strong southeast wind from the upstream region before the occurrence of the heavy rainfall. The strong southeasterly wind was mainly influenced by Typhoon In-Fa. For case 8 · 22, it can be seen that the surface wind and CAPE were weaker than those in case 7 · 20. Therefore, the strong southeasterly wind by the IVT tunnel from Typhoon In-Fa to Henan Province provided favorable water vapor condition and dynamic condition to the formation and maintaining of the extreme rainfall of 7 · 20, as well as the favorable thermal conditions provided by the high CAPE over Henan and upstream regions, which mainly occurred in the late afternoon and might be attributed to the influence of urbanization ^{[7-9, 37]}.

Figure 11.  Surface wind (units: m s^-1, red vectors: > 3.6 m s^-1) and specific humidity (units: g kg^-1) on 0800 UTC 22 August, 2021: (a) observation, (b) 8-h forecasting from 0000 UTC 22 August, (c) 20-h and (d) 32-h forecasting from 1200 UTC 21 August and 0000 UTC 21 August, respectively.

Figure 12.  Surface wind (units: m s^-1, > 5 m s^-1) and CAPE (units: J kg^-1) by (a) 4-h, (b) 8-h, (c) 12-h and (d) 16-h forecasting from 0000 UTC 20 July 2021.

Figure 13.  Same as in Fig. 12, but from 0000 UTC 22 August.

This study investigated the performances of NWP models on the prediction of two heavy rainfalls on 20 July and 22 August 2021 over Zhengzhou, Henan Province. The impact mechanisms of the water vapor transport and surface winds between the two heavy rainfalls were also clarified, as well as the thermal impacts on the heavy rainfalls. The results showed that the CMA-GD and CMA-TRAMS showed generally high scores on the heavy rainfall over Henan. For 7 · 20, the maximum 24h accumulative rainfall by the 24h forecasts from CMA-GD reached 556 mm over Henan (maximum 24-h observation was 627.4 mm). CMA-TRAMS predicted a maximum 24h accumulative rainfall of 431 mm. The evaluations showed that the 24-h and 48-h TS of heavy rainfall by CMA-GD reached 0.56 and 0.64 over Henan, respectively. For CMA-TRAMS, the 24-h and 48-h TS reached 0.50 and 0.52, respectively. The comparisons of the FSS verifications of the heavy rainfalls further showed that both models generally exhibited high FSS scores within the coverage radium of 10-40 km. For example, the FSS at the radium of 40km by CMA-GD and CMA-TRAMS reached 0.96 and 0.87, respectively.

The analysis of the impacts of the water vapor transport on the extreme rainfalls showed that the IVT of the whole layer before the occurrence of the heavy rainfall exhibited a double-eyes distribution in case 7 · 20. The north eye over Henan reached the same magnitude as the typhoon eye (Cempaka) over south China. The IVT over the lower troposphere (< 500 hPa) showed an overwhelming magnitude than that over the upper level, especially in the planetary boundary layer (< 700 hPa). For 8 · 22, the IVT showed a stronger IVT than that in south China, as well as a strong single peak at 1500 UTC (2300 LST) in the nighttime over both regions. The continuously strong water vapor transport from Typhoon In-Fa provided favorable water vapor conditions to the formation of the extreme rainfall in 7 · 20, as well as the higher CAPE and stronger southeast wind from the upstream region than those in case 8·22.

Both CMA-GD and CMA-TRAMS models experienced rapid development in the last decade^[34-35]. In this study, it should be noted that though CMA-GD and CMA-TRAMS exhibited robust performances on these two heavy rainfalls, the model overestimated the surface wind over the northwest of Henan and underestimated the water vapor from the southeast of Henan Province. These biases might be related to the imperfection of the model physics ^{[26, 28, 32]} and initial error ^[24]. More practical technology needs to be developed to improve its performances on the forecasting of the extreme rainfall, including the parameterization scheme of the sub-grid process (e. g., sub-grid orographic effects ^[38-39] and turbulence in the planetary boundary layer ^[40-41]) and the operational application of ensemble forecasting ^[42-43], as well as improvement of the post-process technology based on machine learning ^[44]. For the trigger-mechanism of the extreme rainfall over Henan, more experiments need to be conducted to examine the effects of the specific terrain and physical schemes on the extreme rainfall.