Microclimate Analysis of the High-Impact Weather for the Power Grid Operation in the Jibei Region of China

Abstract

High-impact weather affects the safety and economic operation of power systems. In this study, to provide regional microclimate of high-impact weather for the local power grid system in the northern Heibei province (known as the Jibei region in China), ERA5-Land global reanalysis data during 1981–2020 with a 0.1° grid size (about 9 km) are adopted to analyze the climate statistics and changes of the disastrous weather that affects the power grids. The results show that there have been significant climate changes in the region, including a temperature increase of about 1 °C, evident humidity and precipitation reductions, for the Jibei region and the six sub-regions that concentrated with wind and solar energy development in the 40 years. Due to the differences in terrain, the climate changes differ significantly among the six renewable energy development regions. The main types of high-impact weather that affect the power grid in the region are heavy fog and icing events, followed by cold waves, snowstorms, and rainstorms. In general, with climate changes in the last several decades, the weather disasters in Jibei region have become more frequent. Since most high-impact weather events have a small scale, it is necessary to simulate the weather processes with high-resolution models to accurately quantify the characteristics of the weather processes that affect the power grid. Therefore, a refined regional meteorological model (with grid size of 2 km) based on four-dimensional data assimilation (JB-FDDA) is established for the Jibei region. With one year of model reanalysis data, we found that JB-FDDA can significantly improve the accuracy of the local meteorological fields, and properly depicted the details of severe weather that affect the power grid operation. This study provide an analytical approach for regional electricity meteorological disasters by using reanalysis data.

1. Introduction

With the acceleration of global economic integration and modernization, energy production and consumption continues to rise in China over the last 40 years. Consequently, large-scale mineral energy development has produced a series of environmental pollution problems and the greenhouse effect. Renewable energy has little impact on the atmosphere and ecological environment [1] and has gained significant development. However, high-impact weather events have become an important factor for the safety and economic production of the modern power system [2], and have attracted great attention in the recent years.

A common method for analyzing the climatic characteristics of renewable resources and hazardous weather is to use long-term meteorological observation data [3]. Wang et al. [2] studied weather disasters that threaten the operation of the power grid system and demonstrated the feasibility of the observation-based method. However, observation sites are usually very sparse and unevenly distributed in the Jibei region, and thus cannot well represent local small and meso scale weather process [4]. In fact, many studies turned to the application of numerical interpolation methods to generate area climatic characteristics based on multiple surface observation stations [4], but their results still lack the thermodynamic and dynamic consistency and detailed information over the complex terrain in the Jibei region.

With the development of numerical weather prediction models based on the geophysical dynamic equations [5], various ground, and sounding observation data can be assimilated by the model and generate a continuous, high spatial, and temporal resolution of gridded atmospheric historical reanalysis data [6]. Reanalysis data integrate the weather observation data and numerical model, extending the heavily uneven distribution of the observation data onto model grids at given resolutions. Such reanalysis data can describe the change characteristics of meteorological elements in various spatial and temporal scales [7,8]. Therefore, several studies have used long-term reanalysis data generated by the world’s leading weather centers, such as ECMWF, NOAA/NCEP, and NASA, to analyze the meteorological characteristics and weather disasters. In fact, many studies found that the overall regional climate characteristics are well depicted by the reanalysis data in the Jibei region [4] and found evident relationships between the regional climate and the large-scale general atmospheric circulation [9,10]. However, the spatial resolutions of the reanalysis data that they used are still too coarse, with grid sizes of 50–200 km, to resolve details of the meteorological conditions and processes that pose great threats to the power grid operation over the complex terrain of the Jibei region.

The electric power system of the Jibei Electric Power Company in the Jibei region is greatly affected by adverse meteorological factors, especially those concerned with the development of renewable energy. Furthermore, large-scale wind and solar power farms have been built over the complex terrain areas in the Jibei region of Northern China, where extreme weather poses great challenges to the economy, efficiency, and security in the operation of the power generation farms and power transmission facilities. Therefore, both the development and operation of wind and solar farms as well as the power transmission rely on weather conditions. Therefore, it is highly desired to analyze the high-resolution climatology of severe weather for supporting decision-making when high-impact weather events occur.

In this study, 40 years of the ERA5-Land reanalysis data during 1981–2020 at 0.1° × 0.1° resolutions [11] are analyzed statistically to determine the spatial distribution characteristics of several types of meteorological elements and high-impact weather processes in the Jibei region. The rest of this paper is organized as follows. In Section 2, reanalysis data, the WRF-FDDA model, and statistical analysis methods are described. In Section 3, several types of severe weather processes that have high impacts on the electric power production in the region is identified and analyzed. Moreover, a four-dimensional data assimilation WRF-FDDA technology is employed to establish a high-resolution (2 km grid sizes) regional power meteorological analysis and forecast model system and generate one year of microclimate analysis. By comparing the WRF-FDDA with the ERA5-land reanalysis, we discuss the potential application of long-time high resolution of regional power meteorological reanalysis in the Jibei region. In Section 4, the summary and conclusions are provided. This paper provides detailed climatic information for high-impact weather that concerned regional electric power operations in the Jibei region, and then revealed the necessity potential of a more refined regional reanalysis dataset for electric power system.

2. Data and Methods

2.1. ERA5-Land Reanalysis Data

In this paper, a 40-year (1981–2020) ERA5-Land data are used to study the statistical features of the surface meteorological elements and the weather processes that have high impact on the power system in the Jibei region. The ERA5 dataset is the latest generation of climate reanalysis data produced by the Copernicus Climate Change Service at the European Mid-term Weather Forecast Center ECMWF (European Centre for Medium-Range Weather Forecasts). ERA5-Land data shares most of the parameters with the upper-air ERA5 data to ensure numerical weather forecasting (NWP) using state-of-the-art surface models [12] and generating surface meteorological field consistent with atmospheric dynamics. ERA5-Land has a higher spatial resolution than ERA5, with a horizontal resolution of 0.1° × 0.1° (about 9 km) and a time resolution of 1 h, which provides unprecedentedly long-time historical reanalysis for studying the regional meteorological conditions and weather disasters in the Jibei region.

2.2. WRF-FDDA for the Downscaling Reanalysis in the Jibei Region

The WRF model with four-dimensional data assimilation technology (WRF-FDDA) is employed to establish a customized refined numerical weather modeling system for microclimate reanalysis and rapid-update weather forecasts to support the electric power production in the Jibei region, operated by the Jibei electric power company. The model produced one year continuous small and meso scale weather analysis data. The study region and nesting domains are shown in Figure 1a, with a 10 km × 10 km coarse grid domain covering Hebei province (D1) and a 2 km × 2 km fine grid domain that covers the Jibei electric power production region (D2).

2.3. Statistical Analysis Methods

In this study, six meteorological variables including temperature and relative humidity at 2 m above ground (2 m temperature and 2 m relative humidity), downward short-wave radiation, horizontal wind speed at 10 m above ground (10 m wind speed), annual cumulative rainfall, and snowfall, are derived or diagnosed from ERA5-Land dataset, and then are analyzed for regional climatical characteristic in the Jibei region during 1981–2020. Here, 2 m temperature is calculated by interpolating between the air temperature at the lowest model level and the Earth’s surface, taking account of the atmospheric conditions. 2 m relative humidity is diagnosed from 2 m temperature and 2 m dew point temperature from ERA5-land dataset. Moreover, 10 m wind speed is diagnosed by the u-component and v-component of the wind field at 10 m above the ground.

The high-impact weather processes, including heavy rain, snowstorms, cold waves, strong wind, fog, and icing events, are identified with the six variables derived or diagnosed from ERA5-Land reanalysis data. The criteria for identifying the six types of high-impact weather events are shown in

Table 1. Criteria defining high-impact meteorological events.

High-Impact Weather	Identification Indicators
Rainstorm	Rainstorm with a 24-h precipitation of more than 50 mm
Snowstorm	24-h snowfall (melting into water) ≥ 10 mm
Cold wave	After cold air passes over an area, the temperature drops by more than 8 °C within 24 h, and the minimum temperature drops below 4 °C; Or the temperature drops by more than 10 °C within 48 h, and the minimum temperature drops below 4 °C; or the temperature drops continuously more than 12 °C within 72 h, and the minimum temperature is below 4 °C
Strong Wind	Wind speed greater than or equal to10.8 m s−1
Fog	Near-surface relative humidity greater than 97% and liquid water content greater than 0.05 g m−3
Icing event	Sunshine duration is less than or equal to 2 h, minimum temperature is between −10 °C and 1 °C, and the daily average relative humidity is greater than or equal to 80%

. First, if the meteorological condition during some time of a day met the criteria of certain type of high-impact weather listed in Table 1, the day is considered as a high-impact weather affected. And then, for each type of high-impact weather, the numbers of their affected days are counted and averaged at different temporal or spatial scales, the result of which represent the occurrence of each high-impact weather. It should be noted that since ERA5-Land includes the rainfall and snowfall data rainstorms and snowstorms can be identified separately.

With the ERA5-Land reanalysis, statistical analyses of meteorological conditions and high-impact weather processes are conducted for the Jibei region and the six renewable power generation regions, including Northern Zhangjiakou (NZ), Eastern Zhangjiakou (EZ), Northern Chengde (NC), Southern Tangshan (ST), Central South of Zhangjiakou (CSZ), and Central South of Chengde (CSC), as shown in Figure 1b. The specific information on these regions was given in

Table 2. Information on six renewable energy development regions in the Jibei region in 2021.

Renewable Energy Development Regions	Number of Wind Farm	Number of Photovoltaic Farm	Region Boundary
Northern Zhangjiakou (NZ)	36	12	114.0° E~115.1° E 40.8° N~42.1° N
Eastern Zhangjiakou (EZ)	16	2	115.2° E~116.4° E 41.0° N~41.9° N
Northern Chengde (NC)	24	2	117.0° E~118.0° E 42.0° N~42.7° N
Southern Tangshan (ST)	7	0	118.7° E~119.5° E 39.1° N~39.9° N
Central South of Zhangjiakou (CSZ)	3	17	114.5° E~115.7° E 39.9° N~40.6° N
Central South of Chengde (CSC)	1	11	116.4° E~119.0° E 40.8° N~41.8° N

. The statistics computed herein are averaged value for all the grid point (at 10 km grid intervals) within each region. Therefore, the result represents the overall properties of the meteorological elements or high-impact meteorological events within each renewable energy development region.

3. Results

3.1. Statistics of Surface Meteorological Elements

In this section, statistics of surface meteorological elements are presented and analyzed to establish a climate background for the further analysis of the high-impact weather. The Jibei region is to the east of the Bohai Sea. The area is featured by complex terrain, with high mountains in the northwest and low in the southeast (Figure 1b). Such complex terrain can form climate diversity across the Jibei region. Figure 2 shows the 40-year statistical result of 2 m temperature and relative humidity, downward short-wave radiation, 10 m wind speed, annual cumulative rainfall, and snowfall in the region.

There is a large warm area in the North China Plain, the temperature transit from warm to cold crossing the mountainous region of Yanshan and Taihang Mountain. The coldest area is over the northwest and northern part of the study region (Figure 2a). There is a clear correlation between terrain height (Figure 1b) and surface temperature, which results from the adiabatic decline of temperate in the lower atmosphere. Among the renewable energy development areas, the NZ, EZ, and CN regions show cold climates since they are mainly located within high-altitude areas, of which NZ has the coldest climate (about 0~2 °C near its center). The CSZ and CSC regions are located in the temperature transition zone and have a large temperature span, ranging from 4 °C to 8 °C. The ST region, which is located at the edge of the high-temperature area of the North China Plain, has a relatively warm climate, with 2 m temperature ranging from 10 °C to 12 °C.

The statistical result of 2 m relative humidity (Figure 2b) shows a different scenario. First, the spatial distribution of 2 m relative humidity show less correlation with topography than 2 m temperature. Second, the 2 m relative humidity presents an uneven climatic pastern in the North China Plain. The NC region is affected by a high humidity center (60~64%), and the CSZ region is with significantly low humidity (46~50%).

The statistical result of 10 m wind speed also has a topographically related climatic distribution (Figure 2c). However, the distribution of 10 m wind speed over coastal and inland regions show very different patterns. The northwest of the study area displays a high wind speed which ranged from 4.0 m/s to 4.8 m/s. The eastern North China Plain also shows a high wind belt near the coastline, which is influenced by sea wind, ranging from 3 m/s to 3.5 m/s. The CSZ and CSC regions are characterized by low wind speed, while the ST region has higher wind speed as it is a coastal region. The other regions have large fluctuations in wind speeds due to the complex terrain.

The downward shortwave radiation flux at the surface level in Hebei ranged from 170 W/m² to 182 W/m² (Figure 2d). Its climate distribution shows a gradual decrease trend from northwest to southeast and most of the Jibei region has low values. The NZ, EZ, and CSZ regions, where high terrain exists, have overall better solar energy, with shortwave radiation flux of 172~182 W/m²). On the other hand, the CSC and ST regions, located in the low-latitude and low-altitude coastal areas in the southeast, are characterized by less solar power resource, with relatively low downward shortwave radiation flux (less than 172 W/m²). In general, the surface downward shortwave radiation is affected by many factors including clouds, aerosol effects, and gases. Since the extinction effect caused by aerosols is not considered in the ERA5-Land data, cloud and precipitation processes are the main process that affect the shortwave radiation reaching the surface level in the ERA5-Land data. Therefore, the area of lower shortwave radiation flux in the Jibei region indicates where cloud and precipitation activities are more frequent.

The annual cumulative rainfall also has similar climate characteristics to the downward shortwave (Figure 2e). There is a high-value area with 600 mm/y~800 mm/y in the Jibei region. However, the total annual snowfall shows a different pattern. A low-value of snowfall is found in the low altitude and latitude area of southeast region, while a significant high-value of snowfall area is found in the northern Jibei region (60 mm/y~120 mm/y). There is a high-value center with annual snowfall ranging from 100 mm/y to 120 mm/y in the NC region.

In summary, the climatic characteristics of meteorological elements in the Jibei region show a strong topographical correlation. Due to the complex terrain over Taihang and Yanshan mountains, the NZ, CSZ, and CSC regions had more rainfall or snowfall activities.

Figure 3 shows the evolution of 2 m temperature, 2 m relative humidity, downward shortwave radiation, 10 m wind speed, annual cumulative rainfall, and annual cumulative snowfall during 1981~2020 in six renewable energy development areas. The colored lines in Figure 3 represent mean value of each six renewable energy development areas, the black line represents the average of all the six renewable energy development areas, and the black dashed line is for the linear fit of the average.

The interannual variation of the average 2 m temperature in the six regions show (Figure 3a) similar trends, but with significant overall differences. The average temperature in the ST region is the highest (except for 1992), ranging from 10 °C to 12 °C, and the average temperature in the NC region is the lowest, about 0 °C to 3 °C. In 1985, 1992, 2009, and 2012, the temperature shows cooling fluctuation, and 1992 is the coldest year with a temperature over 7 °C decreased from the year before. According to the high-impact weather event records by China Meteorological Disaster Yearbook,1992, this is mainly due to an extreme cold wave in 1992 in northern China. Nevertheless, the overall climate change of temperature in the region, as indicated by the linear fitting of the regional average, shows a slow warming trend during the past 40 years (1981~2020), and the regional average temperature is increased from about 4 °C in 1981 to above 5 °C in 2020.

The interannual variations of 2 m relative humidity (Figure 3b) show no systematic differences between the concerned regions. The NZ and SZ regions were the driest (45~60%), followed by the CSC region (55~65%), and the others are slightly moister. The 2 m relative humidity also shows significant annual fluctuations. The relative humidity of 2 m in all regions increased sharply by about 6% (in absolute value) in 2003, but there was no significant cooling in the same period of time. This indicates that the fluctuations of 2 m relative humidity in these regions are caused mainly by the change in specific humidity. The fitted line of regional average 2 m relative humidity in the region showed a decreasing trend, from 61% in 1981 to 57% in 2020.

The interannual variations of 10 m wind speed shows (Figure 3c) that the CSZ and CSC regions have the lowest wind speed (1.7 m/s~2.1 m/s), followed by the ST, CN, and EZ regions (2.7 m/s~3.2 m/s), and the highest wind speed area is in the NZ region (3.4 m/s~3.8 m/s). However, the climate change of wind speed over the past 40 years was not significant in all six renewable energy development regions.

The climate change of downward shortwave radiation (Figure 3d) is similar to the temperature and humidity characteristics of 2 m. There are evident differences among renewable energy development regions and fluctuations during some time periods. The differences are mainly caused by the different latitude and altitude of those regions. While the interannual fluctuations are likely caused by frequent weather events at different scales. The fitting results showed an overall increasing trend, about +2 w/m² in 40 years, indicating that the overall precipitation activity in the regions has a decreasing trend.

Correspondingly, the annual cumulative snowfall and rainfall show consistent results (Figure 3e,f) of decreasing trends. The rainfall from most regions shows an overall decrease trend over 40 years, by about 50 mm, and the snowfall decreased by about 5 mm. This reflects a weakening of the precipitation activities and their intensity in the Jibei region due to climate change. Among the different renewable energy development regions, the Chengde region has the highest precipitation intensity and fluctuations over 40 years. The CSC region has the highest rainfall (480 mm~800 mm), while the NC region has the highest snowfall (30 mm~160 mm). This is because the Chengde region is a coastal zone and it often has moist conditions and favors topographical air uplifting, providing great conditions for the formation of cloud and precipitation. The weakest in precipitation intensity is found in the ST region. Although the ST region is located in the coastal zone, its plain topography and relatively uniform underlying surface properties are less conducive to the formation and development of convective activity.

Figure 4 shows the monthly variation of the surface meteorological elements averaged over the past 40 years. The parameters include 2 m temperature, 2 m relative humidity, downward shortwave radiation, 10 m wind speed, annual cumulative rainfall, and snowfall.

In general, the climate in the regions is characterized by four distinct seasons. Significant seasonal variations of the meteorological elements were found in all six renewable energy development regions. The highest 2 m temperature and relative humidity are in summer (July and August). The peak of downward shortwave radiation occurred in May, with a maximum average daily radiation flux exceeding 225 w/m². The minimum downward shortwave radiation occurs in December and January with an average daily radiation flux of less than 125 W/m. One noteworthy feature is that solar radiation has a larger regional variation in summer (June, July, and August).

In Figure 4c, the wind speed from all six renewable energy development regions shows bimodal seasonal variation characteristics, with higher wind speeds in April and December, and lower wind speeds in July and August, respectively. The seasonal variations of rainfall and snowfall seasons are particularly opposite. The rainfall occurs in uni-modal patterns, with peaks in July (over 120 mm) but the snowfall is bimodal with two peaks in November and March, respectively.

In addition, significant differences were found for meteorological elements among the renewable energy regions, which agreed well with Figure 3. Furthermore, these regional differences change seasonally. For example, the 2 m temperature during winter (December to February) has larger regional differences than that in other months. Downward radiation, rainfall, and snowfall have larger regional differences in summer (June~August) than other months. This is mainly caused by the local terrain and synoptic weather forcing in the six renewable energy regions. For instance, the coastal ST region often encounters obvious seasonal changes due to the contrast in thermal properties between sea and land, which can lead to annual fluctuations in temperature.

3.2. Statistics of High-Impact Weather Events with High Impact on Electricity

Surrounded by Bohai Bay, Hebei Province is a disaster-weather-prone area with a complex topography and diverse land uses, and varying climate forcing. High-impact weather events account for more than 70% of all kinds of natural disasters [13]. In the last two decades, China has constructed massive electric power transmission lines. The high-impact weather events are of great concern to the Jibei Power company as meteorological conditions are closely related to power grid security [14,15], which threaten the safety of power grid operations [16]. High-impact weather events such as lightning, strong wind, and icing cause damage to electric power transmission lines, towers, and electric equipment, causing interruptions of transmission network, even broken wires, tower collapse, and damage to substation equipment [17,18,19].

In this section, the ERA5-Land data are used to identify high-impact weather events, and then analyze the temporal and spatial distribution of their occurrences. Figure 5 shows the horizontal distribution of the 40-year average annual occurrence of six types of high-impact weather events to the electric power grids. Note that these results only depict the overall occurrence frequencies of the high-impact weather on the grid, but not the extreme weather elements.

Previous observation-based statistical studies found that heavy rain and flooding are one of the main severe weather phenomena in Hebei Province [13]. Figure 5a shows the statistical distribution of the annual average rainstorm number. It can be seen that rainstorms mainly occur in the south of the Jibei region and North China Plain with frequences about 0.8~2 times/year. The high-impact area of the rainstorms is located in the central and western regions of the Jibei region with a high value greater than 1.8 times/year. Comparing the terrain height in Figure 1b, we found that the rainstorm mainly occurs in the North China Plain, where water vapor carried by the westerly wind belt was often gathered here due to the blocking effect of the Taihang Mountain in the northeast. This provides favorable conditions for the formation of strong convective weather. There are a large number of snowstorms reported in the Jibei region every year, causing electric power equipment damage and failures of electric power transmission lines and towers. Figure 5b shows that snowstorms mainly occur in the northern Jibei region and its surrounding area where the annual average number of snowstorms is between 0.4 times/year and 1.2 times/year, and the highest incidence of snowstorms is found in the northernmost part of the Jibei region, over 1.5 times/year.

In general, cold waves bring cold air mass from high latitudes to the middle and low latitudes and cause widespread severe cooling, strong wind, rain, and snow. The cold waves affecting China were mainly from the Arctic, Siberia in Russia, and Mongolia. Figure 5c shows that in the Jibei region, cold waves affect NZ, EZ, and NC more than other areas. The number of cold wave incidents in these regions can reach about 1~5 times/year, which is much more than other area of North China (0~2 times).

Strong wind events are very harmful to the power transmission line and cause instability of wind power generation. The ERA5-Land statistics show a small frequency of strong wind (Figure 5c) in the region. However, previous observation-based study [20] has reported that there are strong wind disasters in the Jibei region interferencing the power generation and transmission. Thus, the frequency of strong wind events in the Jibei region is underestimated by ERA5-Land data, indicating that the spatial-temporal resolution of the dataset may be too coarse to resolve the local wind extremes.

Icing event may cause failures in transmission lines and large-scale power outages. Figure 5e shows that the icing events are mainly concentrated in the southwest coastal area of the study area because there is an abundance of moisture near the coast. Occurrences of icing events gradually weaken from the coastline to the inland. The coastal areas in the Jibei region are affected by icing events over 7 times per year. There are about 7~9 icing occurrences per year in the ST region, 4~7 times/year in the CSZ and CSC regions, and less for the other regions.

Heavy fog can “pollute” the power equipment and cause short circuits, tripping, and other failures. This phenomenon is called fog flashover. Advection fog in coastal areas contains a large amount of salt. When encountering insulated porcelain bottles on transmission lines, accumulated salt can lead to fog flashover and cause power failure accidents. A sustained heavy fog also impairs photovoltaic power generation by blocking solar radiation from reaching the surface. Figure 6e shows that fog in North China mainly affects the coastal areas. When the near-surface atmosphere is dominated by northwest wind, these regions are often filled with warm and humid air mass from the sea, which is favorable for the formation of fog after radiation cooling at night. This process of advection radiation fog often affects coastal areas and causes many losses [21]. The ERA5 statistical results (Figure 5f) show that the areas mainly affected by fog in the Jibei region are the southern coastal areas with more than 20 times/year. The ST regions are affected by the most often fog incidents (about 15~20 times/year), followed by CSZ and CSC which are typically partially affected by fog due to the complex terrain. The NZ region is generally fog-free due to its dry climate (Figure 2b).

Figure 6 shows the climate trend of the high-impact weather events over 40 years. The frequency of the high-impact weather events (units: times/year) in each main renewable development region is obtained by averaging the occurrence days in the target area for every decade. In the six renewable energy development regions, fog and icing (0~24 times/year) are more frequent than cold wave, rainstorm, snowstorm, and strong wind (0~1.6 times/year). Of all the high-impact weather events, fog occurs most frequently, followed by icing events. However, all regions appear to have more high-impact weather events in recent years. The occurrence of fog, snowstorms, and icing events have become significantly more frequent since 2001 than the earlier years. It is also interesting to see that although the overall amounts of rainfall and snowfall gradually decrease in the last 40 years, (Figure 3e,f), rainstorms and snowstorms actually increase (Figure 6).

The main types of the high-impact weather in the NZ region are fog and cold waves (34.8% and 34.7%), followed by icing events (24.4%) and in the EZ region they are fog (49.7%), cold waves (20.9%), and icing events (23.7%). The frequency of rainstorms in this region has increased significantly over the past 40 years. The high-impact weather characteristics in the NC region are similar to the EZ region, dominated by fog, followed by cold waves and icing events.

The EZ region is found to have the highest frequency of snowstorms among the six renewable energy development regions. The frequency of snowstorms in the EZ region was more than 1.5 times/year during 1991~2000 and 0.8~1.0 times/year in the other time periods. The main type of the high-impact weather in the ST region was also fog (70.9%) and icing event (25.6%). The highest occurrence of fog in this region was found from 2001~2010 at 24 times/year, and more than 16 times/year during the other time periods. This is mainly because the ST region is a coastal area characterized by a moist climate. The high-impact weather situation in the CSZ is similar to the ST region, also dominated by fog (58.9%) and icing events (30.8%). However, it can be seen that the rainstorm, snowstorms, and icing events in the region have significantly increased over the past 40 years, indicating a clear climate change in its local atmospheric circulation, temperature, and humidity conditions. Finally, the CSC region is with fog accounting for 58.9% and icing events for 27.9%, but with a more complex local climate change for 40 years.

Figure 7 shows the statistical results of the monthly variation of the electric-power high-impact weather events over the past 40 years. It is obtained by averaging the number of occurrence days of each high-impact weather type in the different months of the year.

Rainstorm mainly occurs in summer, with the highest incidence in July and August. The ST and CSC regions are affected most often. Fewer occurrences of rainstorms are found from October to April. Cold waves mainly occur from October to May in the second year. Cold waves occur mostly in November, with 0.3 times/year averaged over the six regions and 0.8 times/year in the CSZ region.

The strong wind events captured by ERA5-Land occur most often in the NZ region from April to May, at about 0.06 times/year. In the ST region in August, they are at about 0.02 times per year. The occurrences of snowstorms show a bimodal distribution, with the peaks at around March to May and October to November. The NC region is affected by strong wind in April, up to 0.4 times/year. Icing events mainly occur from October to January and the highest incidence of icing events occurs during October and November, at 1~2 times/year. In the ST region, the icing was found most frequently in December at about 3~4 times/year. The monthly variation of fog (Figure 7f) is correlated with the seasonal changes in humidity conditions (Figure 4b). The months in which fog occurs most are summer and autumn, especially in the ST region.

3.3. An Evaluation of JB-FDDA

Although the statistical analysis of the ERA5-Land dataset exposed many important climatic characteristics of the high-influence weather in the Jibei region, it significantly underestimates the local-scale intensity of severe weather due to its relative coarse horizontal resolution (0.1° × 0.1°), especially in the areas with complex-terrain. All high-impact weather types studied here have important small and meso scale structures. The Four-Dimensional Data Assimilation scheme with the Weather Research and Forecasting (WRF-FDDA) by the National Center for Atmospheric Research (NCAR) is adopted to conduct refined modeling in the region. WRF-FDDA can assimilate multi-source observations to initiate real-time weather forecasting, and for producing historical weather reanalysis to generate high-resolution climate datasets as well [22,23]. In this paper, aiming at the high-impact weather processes in the Jibei region, A WRF-FDDA system, established with a 2 km grid resolution over the Jibei region, is evaluated for supporting the Jibei electric power production. The system is briefly named as JB-FDDA. The historical observation data in 2020 are assimilated to generate a downscaled counterpart ERA5-Land for the Jibei region. The reanalysis datasets from ERA5-Land and JB-FDDA in 2020 are compared to identify the electric-power operation need of refined weather modeling for the Jibei region.

Figure 8 shows the annual average 2 m temperature during 2020–2021 based on the JB-FDDA and the ERA5-Land datasets. The overall spatial distribution of the 2 m temperature of the two reanalysis data are consistent and both have high-temperature areas in the North China Plain. However, JB-FDDA (Figure 8a) presents much more details than ERA5-Land (Figure 8b). The temperature distribution pattern is closely consistent with the high-resolution terrain height features. Similar results can be seen in the surface water vapor and wind speed analyses (figures not shown). This demonstrates that JB-FDDA can depict the desired details of the local meteorological features and provides more valuable products for the power-grid design and operation for the Jibei region.

Furthermore, unlike ERA5-Land which does not resolve cloud explicitly, JB-FDDA provides the mass and number concentration of cloud water, cloud ice, hail, graupel, snow, and rain, which supports a more reasonable diagnosis of fog, icing events, snowstorms, and rainstorms.

An important feature of JB-FDDA is its ability to assimilate weather observations along the model forward integration. A cold wave event that occurred on 1 November 2022, is taken to illustrate the data assimilation benefit. In this case, two simulation experiments with JB-FDDA were evaluated using surface observations, one is with data assimilation and the other is not. Figure 9a shows the result without data assimilation. It can be seen that there are many stations with high positive bias in the 2 m temperature. When the data assimilation is enabled (Figure 9b), the 2 m temperature is obviously in good agreement with the surface observations. Similar conclusions are found for the model humidity, wind speed, etc. (figures not shown). This result shows that the JB-FDDA data assimilation system presents a significant value in weather forecasting in the Jibei region. The model system should be run to extend the simulation for all 40 years to supplement the climate reanalysis for the Jibei region to achieve more accurate and informative high-impact weather climatology.

4. Summary and Discussion

In this study, ERA5-Land reanalysis data during 1981–2020 are adopted to analyze the climate characteristics of the meteorological elements and high-impact weather events concerned by the electric power grid in the Jibei region. An advanced four-dimensional data assimilation system (JB-FDDA) is also established to generate refined regional reanalysis data for the area. One year of the JB-FDDA reanalysis are compared with the ERA5-Land products to illustrate a need to use high-resolution models to support electric power operation.

The statistics based on the ERA5-Land data show that there have been significant climate changes in the Jibei region in the past 40 years. The 2 m temperature is increased by about 1 °C, the relative humidity by about 4%, and the rainfall and snowfall are decreased. The Jibei region is characterized by large geographical and topographic differences, resulting in quite different climatical characteristics in the six renewable energy development regions. Overall, the Jibei region are with a transition from high mountains to the lower plain and thus present gradient distributions in meteorological elements. The northern Jibei is featured with low temperature, high humidity, and high snowfall, while the south is with high humidity and high rainfall. Furthermore, there are significant differences in the seasonal variations of the meteorological conditions between the six renewable energy development regions, with larger fluctuations in temperature and relative humidity and smaller fluctuations in precipitation in the southern Tangshan region than in the other regions.

The Jibei region is affected by cold waves, fog, icing events, rainstorms, and snowstorms. Among them, cold waves and fog are more frequent. These severe weather processes have a large impact on the North China Plain and the southern coast of the Jibei region. The central part of the Jibei region is strongly affected by rainstorms, while the northern Jibei region is more frequently affected by snowstorms in winter. Strong wind events are relatively fewer. The six renewable energy development regions are affected by more fog and icing events than cold waves, rainstorms, snowstorms, and strong wind. Among the six types of the high-impact weather, fog occurs most frequently, followed by icing event. All types of high-impact weather show seasonal changes. Overall, in response to global warming, high-impact weather events in the Jibei region tend to become more frequent, especially for fog, snowstorms, and icing events, which occurs more often in 2011~2020. It should be noted that, when over complex terrain, unevenly distributed meteorology parameters can affect process of the small scale weather, and need to be resolved by high-resolution reanalysis data. Therefore, the result yield from the statistical and analytical approaches we used in this work are sensitive to the refinement of reanalysis dataset adopted when the study area is over complex terrain.

Although ERA5-land data with 0.1° × 0.1° provide much more detailed climate information than the previous generation of global reanalysis data such as the NCEP, CFSR, and MERRA2. This study also shows that further refined weather reanalysis data are highly desired to resolve the small-scale extreme weather phenomena that threaten regional power production. It is shown that the reanalysis data generated by a 2-km grid JB-FDDA model provides more detailed and more accurate high-impact weather features in the Jibei region than the 0.1° × 0.1° ERA5-Land data. Our next step study will be completing the 40-year microclimate reanalysis dataset with the 2-km grid JB-FDDA and extending the model to other regions.

This work achieved exploration and estimation of using high-spatial-resolution reanalysis data to generate refined regional climatical weather hazard information for the electric power production in the Jibei region. The result not only provides detailed climatic characteristics of meteorological parameters and high-impact weather in Jibei regions, but also shows merit and, eventually, the necessity of developing reanalysis data for regional power production with higher spatial resolution. Furthermore, this study provide an analytical approach for regional electricity meteorological disasters by using reanalysis data, which can be applied for other electric-power-concerned regions around the world.