China Meteorological Administration Research Articles

Evaluation of Daily and Hourly Performance of Multi-Source Satellite Precipitation Products in China’s Nine Water Resource Regions

Satellite precipitation products (SPPs) are of great significance for water resource management and utilization in China; however, they suffer from considerable uncertainty. While numerous researchers have evaluated the accuracy of various SPPs, further investigation is needed to assess their performance across China’s nine major water resource regions. This study used the latest precipitation dataset of the China Meteorological Administration’s Land Surface Data Assimilation System (CLDAS-V2.0) as the benchmark and evaluated the performance of six SPPs—GSMaP, PERSIANN, CMORPH, CHIRPS, GPM IMERG, and TRMM—using six indices: correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI), at both daily and hourly scales across China’s nine water resource regions. The conclusions of this study are as follows: (1) The performance of the six SPPs was generally weaker in the west than in the east, with the Continental Basin (CB) exhibiting the poorest performance, followed by the Southwest Basin (SB). (2) At the hourly scale, the performance of the six SPPs was weaker compared to the daily scale, particularly in the high-altitude CB and the high-latitude Songhua and Liaohe River Basin (SLRB), where observing light precipitation and snowfall presents significant challenges. (3) GSMaP, CMORPH, and GPM IMERG demonstrated superior overall performance compared to CHIRPS, PERISANN, and TRMM. (4) CMORPH was found to be better suited for application in drought-prone areas, showcasing optimal performance in the CB and SB. GSMaP excelled in humid regions, displaying the best overall performance in the remaining seven basins. GPM IMERG serves as a complementary precipitation data source for the first two.

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.

An intercomparison of empirical schemes for partitioning precipitation phase

Study regionPrecipitation phase in China has changed with global warming, and accurate partitioning of the precipitation phase is crucial for understanding the hydrological processes and energy balance. Study focusBased on a 36-year daily meteorological dataset from the China Meteorological Administration, this study conducted a comprehensive evaluation of six empirical methods in estimating the precipitation phase nationwide and explored the applicability and distinction across different meteorological, topographic and geographic categories. These methods utilized inputs of air temperature, with two variants incorporating relative humidity, and encompassed a range of combined non-linear air temperature and hydrometeor temperature methods to more simple air temperature threshold methods. New hydrological insights for the regionMethods implementing non-linear functions without air temperature thresholds performed better than those relying solely on air temperature thresholds. The exponential functions with air temperature and relative humidity exhibited the best performance, while those relying only on air temperature thresholds exhibited the worst overall performance. Optimal methods were identified and recommended under different scenarios (i.e., multivariate meteorological and geographic parameters, different geographic regions). The rain–precipitation ratio exhibited a significant increasing trend, and the meteorological stations showing an increase were primarily concentrated in the three major snow-covered areas, the west and vicinity of the “Hu Huanyong Line”. This study can be applied to other regions and offers valuable insights for analyzing precipitation data and developing hydrological models.

Synoptic Analysis and Subseasonal Predictability of an Early Heatwave in the Eastern Mediterranean

Greece and the surrounding areas experienced an early warm spell with characteristics of a typical summer Mediterranean heatwave in mid-May 2020. The maximum 2 m temperature at Kalamata (southern Greece) reached 40 °C on 16 May and at Aydin (Turkey), it was 42.6 °C on 17 May. There was a 10-standard deviation positive temperature anomaly (relative to the 1975–2005 climatology) at 850 hPa, with a southwesterly flow and warm advection over Greece and western Turkey from 11 to 20 May. At 500 hPa, a ridge was located over the Eastern Mediterranean, resulting in subsidence. The aims of this study were (a) to investigate the prevailing synoptic conditions during this event in order to document its occurrence and (b) to assess whether this out-of-season heatwave was predictable on subseasonal timescales. The subseasonal predictability is not a well-researched scientific topic in the Eastern Mediterranean Sea. The ensemble global forecasts from six international meteorological centres (European Centre for Medium-Range Weather Forecasts—ECMWF, United Kingdom Met Office—UKMO, China Meteorological Administration—CMA, Korea Meteorological Administration—KMA, National Centers for Environmental Prediction—NCEP and Hydrometeorological Centre of Russia—HMCR) and limited area forecasts using the Weather Research and Forecasting model with the Advanced Research dynamic solver (WRF) forced by Climate Forecast System version 2 (CFSv.2; NCEP) forecasts were evaluated for lead times ranging from two to six weeks using statistical scores. WRF was integrated using two telescoping nests covering Europe, the Mediterranean basin and large part of the Atlantic Ocean, with a grid spacing of 25 km, and Greece–western Turkey at 5 km. The results showed that there were some accurate forecasts initiated two weeks before the event’s onset. There was no systematic benefit from the increase of the WRF model’s resolution from 25 km to 5 km for forecasting the 850 hPa temperature, but regarding the prediction of maximum air temperature near the surface, the high resolution (5 km) nest of WRF produced a marginally better performance than the coarser resolution domain (25 km).

Effects of Intraseasonal Oscillation on Timing and Subseasonal Predictability of Mei-yu Onset over the Yangtze River Basin

Abstract The time of rainy season onset is crucial information for policymakers, especially in densely populated regions such as the Yangtze River basin (YRB) in China. In this study, we proposed a new grid-based index to objectively detect mei-yu onset timing using reanalysis data and model predictions, and then we identified the key processes via which intraseasonal oscillation (ISO) affects the YRB mei-yu onset and its subseasonal predictability based on scale-decomposed moisture analysis. Climatologically, propagation of an ISO anticyclonic anomaly toward East China supports the moisture convergence required for rainy season onset over the YRB via interaction with the seasonal-mean moisture component. In the years of early mei-yu onset, the ISO was enhanced earlier in May and favored the moisture convergence anomaly in late May–early June, when the mei-yu started. In contrast, the enhanced ISO and associated moistening processes were observed later in June–early July in the years with delayed onset. The European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction models show skillful prediction of mei-yu onset at forecast lead times of 5–6 pentads, whereas the China Meteorological Administration model has limited skill of 3 pentads. The differences in model prediction skill are related to the accuracy of predicted moisture convergence anomalies induced by the ISO. The prediction bias in mei-yu onset timing (early or delayed) is also connected to bias in the occurrence timing of enhanced intraseasonal perturbations, suggesting the vital role of ISO in YRB mei-yu onset on the subseasonal time scale.

Comparative Studies Of Residual Water Level In Manado And Melonguane Coastal Area During Tropical Cyclone In 2021

North Sulawesi Province, directly bordering the Pacific Ocean, is located in an area with the highest level of tropical cyclone (TC) activity in the world. As a result, the province is vulnerable to the impacts caused by cyclones, including storm surges. The increase in water levels due to this event has the potential to cause coastal flooding. Previous studies in Manado have identified that sea level rise can be detected through residual water level (RWL), making studying the characteristics of RWL in North Sulawesi important. This research focuses on Manado and Melonguane, allowing for a comparison of characteristics. The data used includes tropical cyclone data from the China Meteorological Administration (CMA) and tidal data from the Geospatial Information Agency. The Unified Tidal Analysis and Prediction (UTide) method is used to identify RWL. The analysis was carried out by using a t-test to compare data at the two locations. The results showed that RWL at those locations had significant differences with Melonguane having the higher value between them. Generally, the increase in RWL in Melonguane occurs shortly after the cyclone period, while the RWL in Manado maximum increases 86 hours after TC's first occurrence. Keywords: tropical cyclone; residual water level; UTide.

On the Size Discrepancies between Datasets from China Meteorological Administration and Joint Typhoon Warning Center for the Northwestern Pacific Tropical Cyclones

This study analyzes the Northwestern Pacific tropical cyclone (TC) size difference between the China Meteorological Administration (CMA) dataset and the Joint Typhoon Warning Center (JTWC) dataset. The TC size is defined by the near-surface 34-knot wind radius (R34). Although there is a high correlation (correlation coefficient of 0.71) between CMA and JTWC R34 values, significant discrepancies are still found between them. The JTWC tends to report larger R34 values than the CMA for large-sized TCs, while the trend is reversed for compact TCs. Despite spatial distribution discrepancies, both datasets exhibit significant similarity (spatial correlation coefficient of 0.61), particularly in latitudinal distribution; higher R34 values are observed near 25° N. An investigation of key parameters affecting R34 estimations shows that the discrepancies in R34 values between the two agencies’ estimates of TC size are primarily influenced by the size itself and latitude. There is a high correlation between R34 difference and R34 values, with a high correlation of up to 0.58 with the JTWC’s R34 values. There is also a significant correlation between R34 difference and latitude, with a correlation coefficient of 0.26 in both the CMA and JTWC datasets. Case studies of Typhoons “Danas” and “Maysak” confirm distinct characteristics in R34 estimations during different development stages, with the JTWC capturing TC intensification better, while the CMA underestimates TC size during rapid growth phases. During the weakening stage of the TC, both agencies accurately estimate the R34 values. These findings contribute valuable insights into the discrepancies and characteristics of R34 datasets, informing the selection and utilization of data for typhoon research and forecasting.

An incremental analysis update in the framework of the four‐dimensional variational data assimilation: Description and preliminary tests in the operational China Meteorological Administration Global Forecast System

AbstractInitialization and the spin‐up effect are critical to model performance in the four‐dimensional variational data assimilation (4DVar) system. The incremental analysis update (IAU) technique is able to combat these problems. This study introduces the IAU technique into the 4DVar framework (IAU‐4DVar) of the operational China Meteorological Administration Global Forecast System (CMA‐GFS) and evaluates the performance of the IAU‐4DVar. In this IAU‐4DVar framework, the initial increments optimally estimated are split up into equally weighted forcing terms and integrated into the tangent model and its adjoint model. Then, the optimal estimation of the analysis increment is gradually introduced into the model integration during the assimilation window. A set of two‐month assimilation and forecast experiments were established and executed in this study, and the preliminary results show that the IAU technique can reduce the spin‐up effect of the initial analysis increment from the operational 4DVar system with digital filter initialization (DFI‐4DVar) by retaining the balance in dynamics and thermodynamics. In the optimal minimization process, the IAU‐4DVar converges faster than the DFI‐4DVar with a reduction of the computational cost of up to ca 40%. In addition, the introduction of IAU‐4DVar into the CMA‐GFS model could have a positive impact on the analysis and forecast skills relative to the operational DFI‐4DVar.

Integrated Evaluation and error decomposition of four gridded precipitation products using dense rain gauge observations over the Yunnan-Kweichow Plateau, China

ABSTRACT Evaluating the precision and applicability of high-quality precipitation products in the distinctive terrain and intricate climate of the Yunnan-Kweichow Plateau (YKP) is pivotal for climate research. This study comprehensively assesses four gridded precipitation datasets (AERA5-Asia, AIMERG, ERA5-Land, and IMERG-Final) using the China Meteorological Administration’s surface precipitation data. It employs eight statistical indicators and error decomposition methods at various spatiotemporal scales. The main findings are as follows: (1) AERA5-Asia, AIMERG, and IMERG-Final show similar precipitation patterns, with ERA5-Land overestimating. While all display minor seasonal variations, AERA5-Asia underestimates summer rain. ERA5-Land tends to overstate, whereas AIMERG and IMERG-Final are generally accurate but slightly undervalued in southern YKP. (2) Hourly analysis reveals AERA5-Asia leads in performance metrics (CC: 0.23, MAE: 0.49 mm/hour, RMSE: 0.18 mm/hour, CSI: 0.27). In contrast, ERA5-Land lags, marked by the lowest BIAS (35.39%), FAR (0.74), and FBI (2.85). AIMERG and IMERG-Final display comparable results but underperform in CC (0.16, 0.13), POD (0.31, 0.30), and CSI (0.19, 0.18). (3) False bias significantly contributes to the total bias of precipitation products. AERA5-Asia and AIMERG mitigate total bias and enhance false precipitation situations through calibration algorithms, albeit introducing missed bias in the central region of YKP. The study findings offer valuable insights into YKP precipitation, informing the development of grid-based fusion algorithms in the region’s complex terrain.

Causes of the severe drought in Southwest China during the summer of 2022

In summer 2022, Southwest China (SWC) suffered its worst drought since 1980, with a rainfall deficit and an extremely high temperature anomaly. This had a severely harmful effect on agricultural production, water resources, and hydroelectric supply over SWC. Both a less-than-normal water vapor supply and warmer-than-normal temperature contribute to the precipitation decrease associated with drought. Using the observed station data from the China Meteorological Administration and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) data, our results show that the 2022 summer drought over SWC was caused by both the less-than-normal water vapor supply and warmer-than-normal temperature, and the warmer temperature played a more important role, unlike in previous droughts. The warmer air temperature for the 2022 summer induced record-breaking potential evapotranspiration (PET) over SWC, reducing surface water and drying out soils. Meanwhile, the warmer air temperature made it difficult for water vapor to reach saturation, resulting in a decrease in precipitation over the whole of SWC. We found that such anomalous water vapor supply and temperature were closely related to the South Asian high (SAH) during summer 2022. The SAH was stronger and covered a larger area than during other summer SWC droughts since 1980. The anomalous 2022 summer SAH inhibited the water vapor transport from the tropical Indian Ocean to SWC and further caused a significant warming temperature anomaly by regulating the thickness of the layer between isobaric surfaces, which contributed to the record-breaking summer drought over SWC. Our study highlights the vital role played by warmer temperatures in the 2022 summer drought. Considering that the warming air temperature under greenhouse-gas forcing may cause more droughts like that in 2022 in the future, the 2022 drought may yield useful information that will provide insight into future droughts and raise awareness of the impact of temperature on extreme climate events.

Deep learning‐based postprocessing for hourly temperature forecasting

AbstractIn this article, a prediction model based on spatiotemporal stacked ResNet (Res‐STS) for hourly temperature prediction is designed. On the timescale, the Res‐STS removes the gate structure of the long short‐term memory (LSTM) model, and the data of multiple consecutive time nodes are stacked together to preserve all temporal characteristics of the data. A point‐to‐point data mapping relationship is developed on the spatial scale to maximize the impact of large‐scale environmental background field characteristics on a single grid point. Based on the historical gridded data from the China Meteorological Administration land data assimilation system (CLDAS) and the optimal factor dataset of the European Centre for Medium‐Range Weather Forecasts Integrated Forecasting System (ECMWF‐IFS) from 2017 to 2020, hourly temperature prediction models based on convolutional long short‐term memory (ConvLSTM) and Res‐STS model are developed, respectively. Furthermore, the prediction results of the two models in 2021 are compared with the ECMWF‐IFS. The results show that the root mean square error (RMSE) of the prediction results by ConvLSTM and Res‐STS models are both smaller than that of ECMWF‐IFS. Specially, the Res‐STS model performs best: it reduces the RMSE by 20.8% (24.5%) compared with the ConvLSTM (ECMWF‐IFS). Specifically, the RMSE peaks in the afternoon when the daily maximum temperature occurs, while it is relatively smaller at night. Res‐STS demonstrates a significant improvement in forecast performance compared with ECMWF‐IFS, while ConvLSTM's correction during the period of maximum temperature occurrence has been enhanced. Moreover, the forecast performance of the Res‐STS model is least affected by terrain compared with those of the ConvLSTM and ECMWF‐IFS. For the regions with terrain height greater than 1 km, the model Res‐STS evidently improves the RMSE.

The Relationship between the Typhoons Affecting South China and the Pacific Decadal Oscillation

Using typhoon data from the Shanghai Typhoon Institute of the China Meteorological Administration, the Japan Meteorological Agency’s annual Pacific decadal oscillation (PDO) index, and NCEP/NCAR reanalysis data from 1951 to 2021, correlation and composite analyses were carried out to study the relationship between the variability among tropical cyclones of different magnitudes affecting South China and the PDO. The results show that there is an obvious out-of-phase relationship between the proportion of tropical cyclones reaching a typhoon-level intensity or above in South China and the PDO index. When the PDO is in a cold (warm) phase, the sea surface temperature in the eastern and central equatorial Pacific is cold (warm), similar to the eastern Pacific La Niña (El Niño) phenomenon, and the SST in the eastern and western tropical Pacific Ocean shows a negative (positive) gradient; the subtropical high in the western Pacific Ocean is weaker (stronger) than normal, with the western ridge point to the east (west), and the 500 hPa geopotential height in the South China Sea and the area east of the Philippines is weaker (stronger), which is conducive to (unfavorable to) the formation of a monsoon trough; and the westerly (easterly) winds at high altitudes and the southwesterly (northeasterly) winds at low altitudes from the South China Sea to the Philippines are abnormally strong, and a positive (negative) vorticity at low altitudes, a low (high) sea level pressure, and strong (weak) convection are shown. These conditions are favorable (unfavorable) for the intensification of typhoons affecting South China, and as a result, the number of tropical cyclones reaching the level of typhoons or above account for a greater (smaller) proportion of those affecting South China.

Seasonal Prediction of Regional Arctic Sea Ice Using the High‐Resolution Climate Prediction System CMA‐CPSv3

AbstractSea ice is a central part of the Arctic climate system, and its changes have a significant impact on the Earth's climate. Yet, its state, especially in summer, is not fully understood and correctly predicted in dynamical forecast systems. In this study, the seasonal prediction skill of Arctic sea ice is investigated in a high‐resolution dynamical forecast system, the China Meteorological Administration Climate Prediction System (CMA‐CPSv3). A 7‐month‐long retrospective forecast is made every other month from 2001 to 2021. Employing the anomaly correlation coefficient as the metric of the prediction skill, we show that CMA‐CPSv3 can predict regional Arctic sea ice extent and sea ice thickness up to 7 lead months. The Bering Sea exhibits the highest prediction skill among the 14 Arctic subregions. CMA‐CPSv3 outperforms the anomaly persistence forecast in the Bering Sea, Sea of Okhotsk, Laptev Sea, and East Siberian Sea. The sources of the sea ice prediction skill partly come from the good performance of upper ocean temperature in CMA‐CPSv3. This holds true not only for winter sea ice in the Arctic marginal seas but also for summer sea ice in several Arctic central seas. Furthermore, CMA‐CPSv3 exhibits a strong relationship between the variability of sea ice and surface heat fluxes. This underscores the importance of enhancing the representation of air‐sea heat exchanges in dynamical forecast systems to improve the prediction skill of sea ice.

Spatial and Temporal Variability of Soil Moisture and Its Driving Factors in the Northern Agricultural Regions of China

Soil moisture, as an important variable affecting water–heat exchange between land and atmosphere, is an important feedback to climate change. Soil moisture is of great concern in Northern China, where arable land is extensive, but water resources are distributed unevenly and extremely sensitive to climate change. Using measured soil moisture data collected by the China Meteorological Administration from 164 stations during 1980–2021, we explored the drivers of soil moisture variation by analyzing its spatiotemporal variability using linear regression, partial correlation analysis, and geostatistical methods. The results indicated that (1) soil moisture increased from northwest to southeast in Northern China, with the lowest soil moisture in the IM; (2) the overall trend of soil moisture in most regions decreased, especially in the arid northwest and northeast China. However, soil moisture in some regions began to increase gradually in recent years, such as in northwestern Xinjiang and the central-eastern part of IM; and (3) soil moisture in the whole region was negatively correlated with temperature and sunshine duration and positively correlated with precipitation and relative humidity. The results of the study can provide valuable guidance for timely agricultural irrigation and the adjustment of cropping structures, thereby ensuring agricultural production and food security.

The Contribution of United States Aircraft Reconnaissance Data to the China Meteorological Administration Tropical Cyclone Intensity Data: An Evaluation of Homogeneity

The Contribution of United States Aircraft Reconnaissance Data to the China Meteorological Administration Tropical Cyclone Intensity Data: An Evaluation of Homogeneity

Development and Calibration of 532 nm Standard Aerosol Lidar with Low Blind Area

To better calibrate the aerosol lidar network constructed by the China Meteorological Administration, and ensure the data quality observed by the network, the Meteorological Observation Center (China Meteorological Administration) and the University of Naples (Italy) jointly developed a “high quality 532 nm Raman aerosol lidar” (REAL lidar) in 2018. The ability to detect Raman–Mie scattering signals was improved through signal detection in a large dynamic range. This study compared the REAL lidar with the reference lidar (European ACTRIS aerosol lidar network) considering three wavelengths and eight channels. The results show that both the original signals and data products of the two radars exhibited good consistency. In the calibration application of China’s domestic lidar network, after REAL calibration, the relative average and standard deviations of the backscattering coefficient of the in-station lidar decreased from 55.4% to 7.9% and from 64% to 9.9%, respectively. The effect was significant, which indicates that REAL is an aerosol lidar with a high-performance index. The results satisfy the demand for calibration of the aerosol lidar network, and the REAL was successfully applied to the calibration of the aerosol lidar network.

Bias Correction of Tropical Cyclone Intensity for Ensemble Forecasts Using the XGBoost Method

Abstract In this study, the extreme gradient boosting (XGBoost) algorithm is used to correct tropical cyclone (TC) intensity in ensemble forecast data from the Typhoon Ensemble Data Assimilation and Prediction System (TEDAPS) at the Shanghai Typhoon Institute (STI), China Meteorological Administration (CMA). Results show that the forecast accuracy of TC intensity may be improved substantially using the XGBoost algorithm, especially when compared with a simple ensemble average of all members in the ensemble forecast [as depicted by the ensemble average (EnsAve) algorithm in this study]. The forecast errors for maximum wind speed (MWS) and minimum sea level pressure (MSLP) have been reduced by a significant margin, ranging from 6.3% to 18.4% for MWS and from 4% to 14.9% for MSLP, respectively. The performance of the XGBoost algorithm is overall better than that of the EnsAve algorithm, although there are a few samples when it is worse. The bias analysis shows that TEDAPS underpredicts the MWS and overpredicts the MSLP, meaning that the TEDAPS underestimates TC intensity. However, the XGBoost algorithm can reduce the bias to improve the forecast accuracy of TC intensity. Specifically, it achieves a reduction of over 20% in forecast errors for both the MWS and MSLP of typhoons compared to the EnsAve algorithm, indicating the XGBoost algorithm’s particular advantage in forecasting intense TCs. These results indicate that the TC intensity forecast can be substantially improved using the XGBoost algorithm, relative to the EnsAve algorithm.

A Diagnostic Study on a Southward Southwest Vortex Track Forecasted by CMA-GFS: The Role of Initial Field

Abstract It is known that the southwest vortex (SWV) is an important weather system that may induce severe weather. The southward deviation of an SWV track forecasted by the Global Assimilation and Prediction System of the China Meteorological Administration (CMA-GFS) is systematically diagnosed in this study. The southward shift of the SWV is directly attributed to the deviation of the steering flow caused by the weak forecast of the upper-level trough. According to the diagnosis of potential tendency, the underestimation of the initial vorticity advection forecasted by CMA-GFS dominates the weak development of the upper-level trough. The underestimation of the vorticity advection is eventually sourced to the weak geostrophic wind caused by the weak initial meridional and zonal gradients of the midlevel height in front of the trough. The assimilation process on the initial field of the CMA-GFS acts a negative effect on forecasting this SWV track. It weakens the π field at midmodel level, resulting in the weak midlevel height gradient in front of the trough. A verified numerical experiment initialized by a more reasonable field is carried out and the southward shift of the SWV is obviously modified. This study suggests that a reasonable analysis field is crucial for the accurate forecast of the SWV track. Significance Statement The important impact of initial field deviation in key regions on the forecast in the late period is highlighted. A systematic diagnosis process for identifying and addressing forecast issues on SWV track is proposed. This research provides a comprehensive approach for diagnosing the forecast deviation associated with SWV track.

Dominant circulation pattern and moving path of the Mongolian Cyclone for the severe sand and dust storm in China

Severe sand and dust storm (SDS) is one of the major meteorological disasters in northern China. During 2000–2023, a total of 37 severe SDS events were monitored by the China Meteorological Administration and 34 of them occurred in the boreal spring (March to May). In this study, the 34 cases are analyzed to reveal their spatio-temporal features. The results indicate that there are two high-frequency centers. One is located in the Tarim Basin in northwestern China and the other is located in the central-western part of Inner Mongolia in northern China. In the 21st century, the duration of the severe SDS shows a clear interannual variation with an average of 3.5 days in 2000–2006, 2.7 days in 2007–2015 and 4.6 days in 2016–2023, respectively. Moreover, it occurs mainly in early spring in the third period, whereas it can occur throughout spring in the first and second periods. The composition of the general circulations for these 34 cases indicates that the simultaneous dominant pattern is a “positive-negative-positive” wave structure in the mid-high latitudes of Asia, with a cyclonic circulation (a deep trough) over the southern part of Russia to Mongolia, a major anticyclonic circulation (a ridge) over the eastern Europe to the west of Central Asia and a secondary anticyclonic center over northeastern China to the Korea Peninsula and Japan. The “positive-negative-positive” pattern enhanced the Mongolian Cyclone by increasing the zonal pressure gradients to both its west and east sides. Both the composition and typical case studies indicate that the eastward movement of the Mongolian Cyclone plays a dominant role in causing the severe SDS.

The non-uniformity characteristics of the evaporation duct in the South China Sea based on CLDAS data

The non-uniformity of the evaporation duct has a significant impact on the propagation of electromagnetic waves. Based on the near real-time dataset from the China Meteorological Administration Land Data Assimilation System (CLDAS) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) datasets, the non-uniformity characteristics of evaporation duct height (EDH) in the South China Sea (SCS) region at different distances of electromagnetic propagation and the main influencing factors of EDH non-uniformity are analyzed. The findings are given as follows. First, the SCS monsoon has a significant impact on the spatial and temporal distribution of EDH, particularly in the western coastal areas of Luzon Island and the eastern coastal areas of the Indochina Peninsula. Secondly, the non-uniformity of EDH is more pronounced in the western part of Luzon Island, the eastern coast of the Indochina Peninsula, and the sea-land boundary area of the Beibu Gulf. Besides, the non-uniformity of EDH in the western part of Luzon Island and the eastern part of the Indochinese Peninsula is greatly influenced by air-sea temperature difference and relative humidity. Finally, it is advisable to consider the influence of the non-uniformity of EDH when the distance of electromagnetic propagation in coastal areas exceeds 50 km. However, it can be disregarded within a range of 100 km on the broad ocean.