Surface Meteorological Stations Research Articles

Extreme Precipitation Events During the Wet Season of the South America Monsoon: A Historical Analysis over Three Major Brazilian Watersheds

Most of South America, particularly the region between the southern Amazon and southeastern Brazil, as well as a large part of the La Plata Basin, has its climate regulated by the South American Monsoon System. Extreme weather and climate events in these areas have significant socioeconomic impacts. The Madeira, São Francisco, and Paraná river basins, three major watersheds in Brazil, are especially vulnerable to wet and drought periods due to their importance as freshwater ecosystems and sources of water for consumption, energy generation, and agriculture. The scarcity of surface meteorological stations in these basins makes meteorological studies challenging, often using reanalysis and satellite data. This study aims to identify extreme weather (wet) and climate (wet and drought) events during the extended wet season (October to March) from 1980 to 2022 and evaluate the performance of two gridded datasets (CPC and ERA5) to determine which best captures the observed patterns in the Madeira, São Francisco, and Paraná river basins. Wet weather events were identified using the 95th percentile, and wet and drought periods were identified using the Standardized Precipitation Index (SPI) on a 6-month scale. In general, CPC data showed slightly superior performance compared to ERA5 in reproducing statistical measures. For extreme day precipitation, both datasets captured the time series pattern, but CPC better reproduced extreme values and trends. The results also indicate a decrease in wet periods and an increase in drought events. Both datasets performed well, showing they can be used in the absence of station data.

The Distribution of Climate Comfort Duration for Forest Therapy Has Temporal and Regional Heterogeneity in Xinjiang

Climatic comfortability serves as a crucial factor in tourism decision making; however, there remains a gap in evaluating the climate comfort conditions specifically for forest therapy. We developed a new index—Forest Therapy Climate Comfort Index (FTCCI)—to evaluate the climate comfort conditions for forest therapy by integrating the Temperature (T), Temperature and Humidity Index (THI), and Wind Efficiency Index (WEI). A total of 26 potential forest therapy bases were selected from the protected areas in Xinjiang and divided into five clusters: Aksu cluster, Hami cluster, Altai cluster, Ili and its surrounding cluster, and Urumqi and its surrounding cluster. Based on the monthly observation data from 25 surface meteorological stations in Xinjiang, spanning from 1994 to 2023, employing the Co-Kriging interpolation method, we explored the spatial–temporal variation in FTCCI from June to September and made clear the climate comfort duration across 26 bases in Xinjiang. The results indicated that (1) The variation in T, THI, and WEI in 26 bases demonstrated a consistent pattern of temporal variation. July emerged as the optimal month, followed closely by August, with most indices in both months falling within the comfort level. Conversely, September proved to be the least favorable month due to frigid conditions and discomfort for the human body, whereas June’s sensation was slightly more tolerable. (2) The distribution of T, THI, and WEI showed regional heterogeneity. The Urumqi and its surrounding cluster displayed the most favorable conditions for forest therapy, whereas the Aksu cluster showed the poorest performance. (3) There were differences in both FTCCI and climate comfort duration among various clusters in Xinjiang. Overall, excluding Tomur Peak and Nalati (July and August), the remaining 24 bases offered ideal climate comfort conditions for forest therapy from mid to late June through August. Notably, the bases in Urumqi and its surrounding cluster had the longest climate comfort duration, ranging from 3.5 to 4 months. Therefore, reliance on the unique climate, resource, and geographical condition of each base is crucial in creating special forest therapy products that cater to the diverse health needs of tourists.

Automatic Identification of Three-Body Scatter Spike Based on Jensen–Shannon Divergence and Support Vector Machine

Abstract The three-body scatter spike (TBSS), an echo artifact in radar imagery, manifests as a weak, linear echo extending radially from a core of high reflectivity. Adequately, though not indispensably, indicating the presence of large hail in convective storms, the automatic identification of the TBSS proves advantageous in significantly improving the effectiveness of hailstorm detection. This study introduces an algorithm that synergizes Jensen–Shannon divergence (JSD) and support vector machine (SVM) for rapid TBSS detection in two decades’ worth of single-polarization radar data across China. The algorithm, tested on data from 50 S-band China Next Generation Weather Radar (CINRAD) in central and eastern China, utilized reflectivity factor images for sample extraction. An application in Chenzhou, China, demonstrates the algorithm’s efficacy in improving hailstorm detection resolution. Significance Statement In recent years, China’s hail recordkeeping, primarily based on manual observations at national surface meteorological stations, has suffered from limited spatial and temporal detail. However, the advent of the China Next Generation Weather Radar (CINRAD) network offers a new avenue for hailstorm detection. TBSS, a secondary but significant indicator for large hail in S-band radar, presents an opportunity for enhanced hail warning capabilities. By automating TBSS detection in radar archives spanning two decades, this research significantly enhances the resolution of hailstorm climatology, contributing to more effective hail disaster mitigation and management.

Geostationary Satellite-Based Overshooting Top Detections and Their Relationship to Severe Weather over Eastern China

Overshooting tops (OTs), prominent signatures within deep convective storms, are produced by intense updrafts and are closely linked to heavy rainfall, strong winds, and other severe weather conditions. Using an OT dataset derived from multiyear observations of precipitation radar on board the Global Precipitation Measurement core observatory as a reference, the performances of two commonly used OT detection algorithms are evaluated for the Himawari-8 and Fengyun-4A satellites. The results indicate that the infrared contour-based algorithm based on Himawari-8 is the most effective for objective OT detection in eastern China. It exhibits a probability of detection (POD) of 62.1% and a false-alarm ratio (FAR) of 36.6%, outperforming others by achieving a greater POD and a lower FAR. Furthermore, based on the severe weather records from surface meteorological stations and nearby OT detections, a strong relationship is revealed between GEO-detected OTs and the occurrence of short-term heavy rainfall (e.g., ≥20 mm h−1) and extreme wind speed (e.g., ≥17.2 m s−1) events. The OT matched percentages for these events are 61.8% and 54.0%, respectively. This suggests that GEO satellite-based OT data can serve as an important objective product for forecasters to increase their understanding of severe convective storms.

Research on Frequency Matching Correction Techniques for South China Precipitation Ensemble Forecast Based on the GRAPES Model

This study focuses on the real-time precipitation forecast products of the GRAPES_MESO regional ensemble forecast model, which is developed by the Numerical Weather Prediction Center of the China Meteorological Administration and is initialized 1–3 days in advance at 12:00 UTC. Using a national-level homogenized precipitation grid dataset from surface meteorological stations as observational data, a frequency matching method (FMM) is employed to correct precipitation forecasts for different precipitation intensity levels, including light rain, moderate rain, heavy rain, and torrential rain. Case studies and statistical tests (TS scores) are conducted to compare the forecast performance before and after correction. The results indicate that the model’s Cumulative Distribution Function (CDF) curves deviate from observations, and the longer the lead time, the more significant the error. The correction coefficients (CCs) show an increasing trend with the growth of precipitation intensity, indicating that for larger precipitation amounts and longer lead times, larger CCs are needed, highlighting the necessity of correction. Analyzing two precipitation events in South China in July 2019, the FMM results in an increase in precipitation intensity and a widening of the range of heavy precipitation. The corrected precipitation magnitudes are closer to the observations. The statistical tests using TS scores reveal that the FMM has a certain correction effect on the overall precipitation forecast in the South China region, especially for longer lead times and higher precipitation intensities, where the correction effect is more significant. The necessity of frequency matching correction becomes more apparent for heavier precipitation, and the correction effect becomes more significant with longer lead times.

Validation of the CHIRPS precipitation estimate in a Brazilian Cerrado area

Measuring and understanding precipitation over space and time is essential for several human activities. Satellite remote sensing products are presented as an alternative to the low-density network of pluviometric stations. Thus, the objective of the present study was to evaluate precipitation estimates obtained by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) product, from 1981 to 2020, in the Rio Grande basin, Bahia state, Brazil. This watershed has about 75,000 km², is inserted in one of the most active agricultural frontiers in the world and has undergone significant changes in land use and occupation and changes in rainfall patterns. We compared data from 11 series of conventional (Hidroweb) and CHIRPS-derived surface stations on monthly and seasonal scales, using statistical metrics – relative bias (BIAS), correlation coefficient (R²), mean error (ME), and mean squared error (RMSE) – and categorical – correct proportion (PC), probability of detection (POD), frequency bias index (FBI), false alarm (FAR). Results showed that the CHIRPS precipitation estimates provided good responses compared to the data observed in conventional surface meteorological stations. Furthermore, CHIRPS products accurately detected rain with an excellent capacity to represent the space-time precipitation variation.

Energy Consumption Characteristics Study of Surface Meteorological Observation Station in North and South China

Surface meteorological observation is one of the most important sources of primary data for the meteorological sector and are crucial to the accuracy of weather forecasting. China’s meteorological authorities have built a dense network of surface observation stations, which require a considerable amount of electrical energy to keep the network running normally. There has been a lack of analytical studies dedicated to the energy consumption of surface meteorological observation stations domestically and internationally. This paper selects measured energy consumption data of 4 surface meteorological observation stations in two typical provinces (autonomous regions) in the north and south China over the past two years, and proposes, for the first time, to use the average power of the surface meteorological observation station, adopts the electrical energy consumption calculation method, carries out energy consumption calculation and analysis by month, sub-region and season, derives the characteristics of energy consumption changes of the surface meteorological observation station, and proposes energy saving and consumption reduction measures.

Possible impact of North Atlantic sea surface temperature on decadal variability of dust activity in Gobi Desert

Semiarid to arid regions located in north of China are known as one of the largest sources of dust aerosols in the globe. Previous studies demonstrated direct and indirect effects of atmospheric dust loading on climate. The surface and meteorological properties are known to be affecting dust entrainment process. In this study, we found decadal variations of dust occurrence in Gobi Desert with the period of ∼24 years, utilizing the data acquired at the surface meteorological stations. An analysis of reanalysis datasets shows significant correlations between February North Atlantic Sea Surface Temperature (NASST) and precipitation in Gobi Desert and Mongolia in the following summer, causing a decadal variation of dust event frequency in the subsequent spring over the region. Strong time-lagged correlation is shown to be linked to an atmospheric wave train pattern that initiated from the NASST region, affecting large-scale circulation, ultimately causing surface drought over Gobi Desert.

An Evaluation of CRA40 and ERA5 Precipitation Products over China

Precipitation datasets derived from reanalysis products play a crucial role in weather forecasting and hydrological applications. This paper aims to evaluate the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European reanalysis precipitation product (ERA5), over mainland China. The evaluation is based on continuous and categorical statistical indicators with daily-scale gridded-point rain gauge data obtained from Chinese surface meteorological stations. The findings of this study can be summarized as follows: (1) CRA40 demonstrates a clear superiority over ERA5 in terms of the 13-year daily mean precipitation and seasonal daily precipitation. CRA40 exhibits better correlation coefficients (0.97), relative biases (5.25%), root mean square errors (0.34 mm), and fractional standard errors (0.05). (2) Both reanalyzed precipitation products generally exhibit an overestimation of precipitation in mainland China. The degree of overestimation is particularly pronounced in dry climatic regions (e.g., QZ, XJ), while wet regions (e.g., CJ, HN) demonstrate relatively less overestimation. (3) ERA5 shows better performance in the detection of daily precipitation. Neither CRA40 nor ERA5 can capture heavy precipitation events well. These findings are expected to advance our understanding of the strengths and limitations of the reanalysis precipitation products, CRA40 and ERA5, over China.

AI-based estimation of precipitation in the Tibetan Plateau using multi-frame FY-4A satellite data

ABSTRACT The weather system over the Tibetan Plateau can cause disasters around the plateau and its downstream areas. Since there are few surface meteorological observation stations over the Tibetan Plateau, the precipitation here cannot be comprehensively detected. Therefore, satellite precipitation estimation products with high timeliness, accuracy, and full coverage are relied upon. This paper presents an AI-based precipitation estimation method using multi-frame FY-4A meteorological satellite data (FY-4A PRE-M). Firstly, multiple-derived features are constructed according to the brightness temperature of the satellite different channels, and the regional precipitation estimation model is then trained using the light gradient boosting machine (LGBM) algorithm. Based on the research results from precipitation retrieval using the infrared channel radiation features, this model analyzes the possible contribution of the radiation difference between channels at the same time and the radiation change in the same channel at different periods (15, 30, 45 and 60 min) to the precipitation estimation. The algorithm continuously updates the model with the history of available precipitation data. From 1 September 2021 to 31 August 2022, the precipitation estimations using the multi-frame FY-4A satellite data were evaluated through the ground rainfall gauge data based on the spatial – temporal matching. The results show that the multi-frame FY-4A satellite data have advantages in precipitation estimation. The heavy rainfall caused by a Tibetan Plateau shear line is evaluated using this algorithm. The results show that the precipitation estimations of FY-4A PRE-M and IMERG-L are close to the values of rain gauges. The FY-4A PRE-M can well describe the diurnal variations and the distribution of 24 h cumulative precipitation over the Tibetan Plateau, and the heavy rainfall belts in the central and northern Tibetan Plateau are also well monitored.

Regional‐Scale Heavy Precipitation Over Fujian‐Jiangxi Region in the Late Spring‐Early Summer of 2006 and Its Connection With Baroclinic Wave Packets in the Upper Troposphere

AbstractThe most serious heavy precipitation in the past 43 years occurred over Fujian‐Jiangxi region from late May to early June of 2006, causing significant economic losses. Using the ERA5 reanalysis database and daily precipitation collected at 2,479 surface meteorological stations in China, the present study investigates the relationship between the heavy precipitation over Fujian‐Jiangxi region in late spring‐early summer of 2006 and baroclinic wave packets in the upper troposphere. Information flow between the two systems has been diagnosed. Results indicate that different from the disturbance source for Meiyu in the middle and lower reaches of the Yangzi River, the disturbance source for this heavy precipitation originated from areas near the Syrian Desert to the north of the Arabian Peninsula and propagated along the northwest‐southeast direction, reaching Fujian‐Jiangxi region 4 days later. This kind of baroclinic wave packets provide the necessary energy for the occurrence and persistence of heavy precipitation. Analysis of wave activity flux vectors indicates that during the heavy precipitation period, disturbance energy was transported from the upstream westerly belt to Fujian‐Jiangxi region almost every day. Obviously, there existed information transfer between the two regions, re‐confirming that the upstream Rossby wave packets affect the Fujian‐Jiangxi precipitation. The above results provide helpful hints for a better understanding of the mechanisms for heavy precipitation in this region and will be helpful for its effective prediction.

Comparative study among solar radiation data estimated by satellite images and measured by meteorological stations in the Paraíba state

Solar energy availability can be determined by different methodologies, the most used being those estimated by satellites or by using irradiation data obtained from a chain of surface meteorological stations. The aim of the present study is to compare the Glocal Horizontal Irradiaton (GHI) data obtained by Automated Meteorological Stations, provided by National Institute of Meteorology (INMET) with estimated data from satellite images, offered by the National Renewable Energy Laboratory (NREL) in the National Solar Radiation Database (NSRDB), for cities of Paraíba State, in the northeastern region of Brasil. To execute the comparisons, statistical methods were used such as Pearson's correlation coefficient, the bias, the root of mean squared error and hypothesis test. The results presented a good correlation between the databases. It was verified that the estimated data by satellite images are slightly higher than those observed in soil. While applying test t, it was observed that only one of the localities presented evidences of difference between the averages. The present study assisted to a better understanding of the differences between GHI data, estimated by satellite images and observed in surface meteorological stations, for the state of Paraíba.

Comparative study among solar radiation data estimated by satellite images and measured by meteorological stations in the paraíba state

Solar energy availability can be determined by different methodologies, the most used being those estimated by satellites or by using irradiation data obtained from a chain of surface meteorological stations. The aim of the present study is to compare the Glocal Horizontal Irradiaton (GHI) data obtained by Automated Meteorological Stations, provided by National Institute of Meteorology (INMET) with estimated data from satellite images, offered by the National Renewable Energy Laboratory (NREL) in the National Solar Radiation Database (NSRDB), for cities of Paraíba State, in the northeastern region of Brasil. To execute the comparisons, statistical methods were used such as Pearson's correlation coefficient, the bias, the root of mean squared error and hypothesis test. The results presented a good correlation between the databases. It was verified that the estimated data by satellite images are slightly higher than those observed in soil. While applying test t, it was observed that only one of the localities presented evidences of difference between the averages. The present study assisted to a better understanding of the differences between GHI data, estimated by satellite images and observed in surface meteorological stations, for the state of Paraíba.

Characteristics of Dust Weather in the Tarim Basin from 1989 to 2021 and Its Impact on the Atmospheric Environment

Dust emission is a common catastrophic weather phenomenon in Northern China. This phenomenon not only causes environmental problems, such as air pollution, but also has an important impact on the global dust cycle and climate change. On the basis of the dust weather observation data of 44 surface meteorological stations in the Tarim Basin from 1989 to 2021, combined with the dust aerosol optical depth (DAOD), dust surface mass concentration (DUSMASS) and wind speed data, this paper analyses the spatial and temporal dust weather characteristics in the Tarim Basin over the past 33 years. Results show that the frequency of dust weather in the Tarim Basin has declined in the past 33 years. Dust weather mainly consisted of floating dust, followed by blowing dust and dust storm. This weather had a significant seasonal change, with more dust in spring and summer and less in autumn and winter. The dust weather was mainly distributed along the south edge of the Tarim Basin and the desert hinterland of Tazhong. The spatial distribution of the dust intensity (DI) index was basically consistent with the dust weather days. Moreover, the DAOD was obviously affected by dust weather and had a significant positive correlation with the number of dust weather days and the DI, suggesting the vertical concentration of dust particles to a certain extent. Wind is also one of the most important factors affecting the release of dust. The frequency of strong wind weather decreases from the northeast to the southwest, which corresponds to the distribution of the DUSMASS.

Nowcasting of Wind in the Venice Lagoon Using WRF-FDDA

The Four-Dimensional Data Assimilation module (FDDA) is used in combination with the WRF model for the analysis of two case studies of high tide (on 4 April 2019 and on 12 November 2019) that affected the Venice Lagoon in the recent past. The system is implemented in the perspective of an operational use for nowcasting of 10 m wind, which will be part of a numerical system aimed at the forecast of the sea level height in the Venice Lagoon. The procedure involves the assimilation of data from meteorological surface stations distributed within the Venice Lagoon and in the open northern Adriatic Sea in front of the lagoon, as well asthe radiosonde profiles available within the simulation domain. The two cases were selected considering that the real-time forecasts missed their evolution, and the sea level height was significantly underpredicted. The comparison of the simulated wind with the observations shows a fairly good agreement over short time scales (1–2 h) in both cases; hence, the WRF-FDDA system represents a promising tool and a possibly valuable support to the decision makers in case of high tide in the Venice Lagoon.

Assimilating GNSS PWV and radiosonde meteorological profiles to improve the PWV and rainfall forecasting performance from the Weather Research and Forecasting (WRF) model over the South China

Precipitable Water Vapor (PWV) and rainfall play important roles in the meteorological processes. In this study, we have investigated the impact of assimilation of Global Navigation Satellite System (GNSS) PWV and radiosonde profiles on the performance of Weather Research and Forecasting (WRF) model in forecasting PWV and rainfall over the South China region for April 01, 2020 to May 31, 2020. PWV observations derived from 213 GNSS stations and meteorological profiles recorded by 23 radiosonde stations are assimilated into WRF model. Five different WRF schemes are adopted: WRF scheme 0: no data assimilation (DA); WRF scheme 1: assimilation of PWV from 76 GNSS stations; WRF scheme 2: assimilation of PWV from 213 GNSS stations; WRF scheme 3: assimilation of meteorological profiles from 23 radiosonde stations; WRF scheme 4: assimilation of both PWV from 213 GNSS stations and meteorological profiles from 23 radiosonde stations. PWV observations derived from 170 independent GNSS (have not been used in assimilation) and rainfall data recorded by 648 surface meteorological stations are used to evaluate WRF forecasting performance in PWV and rainfall, respectively. The results indicate that all DA schemes improve the WRF forecasting performance for both PWV and rainfall. For the first 6 h after data assimilation, WRF schemes 1 to 4 improve the PWV forecasting accuracy by 11.6%, 14.5%, 2.9%, and 14.8%, respectively. For the accumulated rainfall within the first 6 h after data assimilation, WRF scheme 2 and WRF scheme 4 have a similar performance and outperform other DA schemes while the WRF scheme 4 is the best one among all five schemes. WRF scheme 4 improves the rainfall forecast probability of detection and equitable threat score by 0.085 and 0.057, respectively.

Analysis of the variation tendency and influencing factors of reference evapotranspiration in Shandong Province, China

AbstractReference evapotranspiration (ET0) is an important part of the water and energy cycles during crop growth. The study of its changing trend is of significance for guiding efficient water utilization and reasonable regulation of agricultural production. Based on daily meteorological data, including temperature, relative humidity, wind speed and sunshine hours, from 38 surface meteorological stations in Shandong Province from 1980 to 2019, the FAO56 Penman–Monteith formula was used to calculate the ET0 and analyse trend and influencing factors, combined with the methods of climate tendency rate, Morlet wavelet, sensitivity analysis and contribution rate analysis. ET0 had 26–30 main oscillation cycles during the 1980–2019 period, and 28‐, 18‐ and 8‐year time scales corresponded to the first, second and third main cycles of ET0, respectively, which determined the characteristics of ET0 change in the past 40 years. The annual average ET0 was significantly positively correlated with average temperature (T), wind speed (WS) and sunshine daily hours (SSD) and significantly negatively correlated with average relative humidity (RH), among which WS, SSD and RH were the main factors affecting ET0. On this basis, because T was not the dominant factor affecting ET0 change, a simplified ET0 calculation algorithm based on seasonal temperature was established that had high goodness of fit R2 accuracy. This study quantitatively analysed the periodic changes and causes of ET0 from a spatio‐temporal view and provided a scientific basis for the rational regulation of agricultural production in Shandong Province.

Satellite thermographies as an essential tool for the identification of cold air pools: an example from SE Spain

ABSTRACT The processes involved in the formation of nocturnal temperature inversions (NTIs) are of great relevance throughout the year, notably influencing the surface distribution of minimum temperatures during nights of atmospheric stability. The low density of surface meteorological stations in the study area motivated the use of thermographies for the mapping and identification of cold air pools CAPs. Thermal distribution during stable nights leads to the formation of CAPs in valley areas and depressed areas, and in areas with warmer air (WAM) in orographically complex areas. The thermographies carried out with satellite products from AQUA and SUOMI-NPP (MODIS and VIIRS LST) represent the only tool capable of fully radiographing the territory under study, thus overcoming the limitations in the interpolation of minimum surface temperatures. The main objective of the research was, therefore, to value thermography as an important tool in the identification of CAPs. The products used were subjected to statistical validation with the surface temperatures recorded in meteorological observatories (R2 0.87/0.88 and Bias −1.2/-1.3) with a new objective of making thermal distribution maps in nocturnal stability processes …

Daily Spatial Distribution of Apparent Temperature Comfort Zone in China Based on Heat Index

Apparent temperature (AT) is used to evaluate human comfort and is of great importance for studies on the effects of environmental factors on human health. This study used the daytime heat index (HI) calculated by national surface meteorological stations in China as the AT dependent variable, with August 2020 employed as an example. The daytime fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) data and multi-source data extracted from the stations were used as the independent variables. Due to the presence of multicollinearity among the independent variables, we implemented a multiple stepwise regression model and developed a daily near-surface 1 km HI estimation model. The correlation analysis using the model showed that the coefficient of determination (R2) was 0.89; the mean absolute error (MAE) was 1.49 °C, and the root mean square error (RMSE) was 2.08 °C. We also used 10-fold cross-validation to calculate the error between the parameter and predicted values. The R2 of the model was 0.96; the MAE was 1.80 °C, and the RMSE was 2.40 °C. In this month, the mean daily daytime HI was 20.51 °C. According to the Universal Thermal Climate Index (UTCI), the areas with more than 20 days of heat stress for one month were largely distributed in the desert areas of northwest China and the coastal areas in southeast China, accounting for 29.98% of the total land area of China. This study improves the spatial resolution and accuracy of HI prediction, thus providing a scientific reference for studying residential environments and the urban heat island effect.

Sounding Data from Ground-Based Microwave Radiometers for a Hailstorm Case: Analyzing Spatiotemporal Differences and Initializing an Idealized Model for Prediction

Atmospheric physical sounding data from three ground-based microwave radiometers located in Xi’an were analyzed to explore the temporal and spatial differences of a hailstorm event and were initialized into an idealized Weather Research and Forecasting (WRF) model to predict the total evolution of the event, which occurred on 29 July 2019. Liquid water and relative humidity profiles revealed a consistent sequence of hailstorm intensity among observations from surface meteorological stations and the FY-4A satellite, where the precipitation and cloud top temperature intensified from north to south, corresponding to the locations of the ground-based microwave radiometers in Gaoling, Weiyang, and Chang’an. Compared with those of a similar storm without hail that occurred on 9 August 2018, the humidity profiles and heights at 0 °C and −20 °C exhibited more dramatic changes. The heights at 0 °C and −20 °C obviously increased with a low-value zone in the relative humidity profiles during the strongest stage of the hailstorm in Chang’an and Weiyang. Later, the heights sharply dropped in Chang’an when strong, downward ice-phased hydrometers occurred with hail production in the storm. A time-saving, idealized WRF simulation, initialized with pre-3-h sounding data from ground-based microwave radiometers, was designed to qualitatively predict this hailstorm. The simulations consistently showed a strong-to-weak intensity of storms from Chang’an to Weiyang to Gaoling. Although the first attempt at this model has uncertainties in both the observations and the model, it provides a potential new method for single-point fine hailstorm prediction.