Impacts of Future Changes in Heavy Precipitation and Extreme Drought on the Economy over South China and Indochina

Heavy precipitation and extreme drought have caused severe economic losses over South China and Indochina(INCSC)in recent decades.Given the areas with large gross domestic product(GDP)in the INCSC region are distributed along the coastline and greatly affected by global warming,understanding the possible economic impacts induced by future changes in the maximum consecutive 5-day precipitation(RX5day)and the maximum consecutive dry days(CDD)is critical for adaptation planning in this region.Based on the latest data released by phase 6 of the Coupled Model Intercomparison Project(CMIP6),future projections of precipitation extremes with bias correction and their impacts on GDP over the INCSC region under the fossil-fueled development Shared Socioeconomic Pathway(SSP5-8.5)are investigated.Results indicate that RX5day will intensify robustly throughout the INCSC region,while CDD will lengthen in most regions under global warming.The changes in climate consistently dominate the effect on GDP over the INCSC region,rather than the change of GDP.If only considering the effect of climate change on GDP,the changes in precipitation extremes bring a larger impact on the economy in the future to the provinces of Hunan,Jiangxi,Fujian,Guangdong,and Hainan in South China,as well as the Malay Peninsula and southern Cambodia in Indochina.Thus,timely regional adaptation strategies are urgent for these regions.Moreover,from the sub-regional average viewpoint,over two thirds of CMIP6 models agree that maintaining a lower global warming level will reduce the economic impacts from heavy precipitation over the INCSC region.

Spatio-Temporal Characteristics of Heavy Precipitation Forecasts from ECMWF in Eastern China

This study examines the spatio-temporal characteristics of heavy precipitation forecasts in eastern China from the European Centre for Medium-Range Weather Forecasts(ECMWF) using the time-domain version of the Method for Object-based Diagnostic Evaluation(MODE-TD). A total of 23 heavy rainfall cases occurring between 2018 and 2021 are selected for analysis. Using Typhoon “Rumbia” as a case study, the paper illustrates how the MODE-TD method assesses the overall simulation capability of models for the life history of precipitation systems. The results of multiple tests with different parameter configurations reveal that the model underestimates the number of objects’ forecasted precipitation tracks, particularly at smaller radii. Additionally, the analysis based on centroid offset and area ratio tests for different classified precipitation objects indicates that the model performs better in predicting large-area, fast-moving, and longlifespan precipitation objects. Conversely, it tends to have less accurate predictions for small-area, slow-moving, and shortlifespan precipitation objects. In terms of temporal characteristics, the model overestimates the forecasted movement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it for precipitation with fast movement. In terms of temporal characteristics, the model tends to overestimate the forecasted movement speed for precipitation objects with small-area, slow movement, or both long and short lifespans while underestimating it for precipitation with fast movement. Overall, the model provides more accurate predictions for the duration and dissipation of precipitation objects with large-area or long-lifespan(such as typhoon precipitation) while having large prediction errors for precipitation objects with small-area or short-lifespan. Furthermore, the model’s simulation results regarding the generation of precipitation objects show that it performs relatively well in simulating the generation of large-area and fast-moving precipitation objects. However, there are significant differences in the forecasted generation of small-area and slow-moving precipitation objects after 9 hours.

Analysis of Heavy Precipitation Event in Northeast China from August 1 to 5, 2023

This study delves into the multiple weather systems and their interaction mechanisms that caused the severe rainfall event in Northeast China in early August 2023. The analysis reveals that the atmospheric circulation in the mid-to-high latitudes of the Eurasian continent exhibited a significant “two troughs and two ridges” structure, with Northeast China located precisely in the peripheral region of the subtropical high, significantly influenced by its marginal airflows. Additionally, the residual circulation of Typhoon “Doksuri” interacting with the subtropical high and upper-level troughs significantly increased the rainfall intensity and duration in the region. In particular, the continuous and powerful transport of the southwest jet provided the necessary moisture and unstable conditions for the generation and development of convective systems. The rainfall event resulted in nearly 40,000 people affected and crop damage covering an area of approximately 4000 hectares, demonstrating the severity of extreme weather. The study emphasizes that strengthening meteorological monitoring and early warning systems, as well as formulating and improving emergency response mechanisms, are crucial for reducing potential disaster losses caused by heavy rainfall. Future research can further explore the interaction mechanisms among weather systems, limitations of data sources, and the connection between long-term trends of heavy rainfall events and global climate change.

Analysis of Heavy Precipitation Process in North China from August 23 to 24, 2020

In order to better understand the formation mechanism of rainstorm in China and promote disaster prevention and reduction, based on the meteorological data of National Meteorological Information Center and Japan Meteorological Agency, this paper draws the isobaric surface map of 850 hPa and 500 hPa, relative humidity and precipitation distribution map. In this study, synoptic methods were used to analyze the heavy precipitation process in North China from August 23th to 24th, 2020. The results show that 1) The formation of short-term heavy precipitation requires sufficient water vapor and very strong upward movement;2) the heavy precipitation in August 23th to 24th 2020 in North China was influenced by the upper-level trough line, cold vortex and cold front, which made the warm and cold air strongly converge over North China, resulting in strong convective weather;3) the heavy rainfall over North China was also influenced by Typhoon Bawei, which caused maximum precipitation and air humidity.

Climatic trends of different intensity heavy precipitation events concentration in China

Based on 740 stations of daily precipitation datasets in China, the precipitationconcentration degree （PCD） and precipitation-concentration period （PCP） of different intensity durative precipitation events were calculated to analyze their statistical characteristics, mainly including spatial and temporal distributions, variations and climatic trends of the two parameters of the durative heavy precipitation events in China. It is proved that these two parameters of heavy rainfall can display the temporal inhomogeneity in the precipitation field. And it is also found that there is a good positive relationship between the precipitation-concentration degree and annual rainfall amount in the Eastern and Central China. This method can be anolied in flood assessment and climate change fields.

Changes in Typhoon Regional Heavy Precipitation Events over China from 1960 to 2018

In earlier studies,objective techniques have been used to determine the contribution of tropical cyclones to precipitation(TCP)in a region,where the Tropical cyclone Precipitation Event(TPE)and the Regional Heavy Precipitation Events(RHPEs)are defined and investigated.In this study,TPE and RHPEs are combined to determine the Typhoon Regional Heavy Precipitation Events(TRHPEs),which is employed to evaluate the contribution of tropical cyclones to regional extreme precipitation events.Based on the Objective Identification Technique for Regional Extreme Events(OITREE)and the Objective Synoptic Analysis Technique(OSAT)to define TPE,temporal and spatial overlap indices are developed to identify the combined events as TRHPE.With daily precipitation data and TC best-track data over the western North Pacific from 1960 to 2018,86 TRHPEs have been identified.TRHPEs contribute as much as 20%of the RHPEs,but100%of events with extreme individual precipitation intensities.The major TRHPEs continued for approximately a week after tropical cyclone landfall,indicating a role of post landfall precipitation.The frequency and extreme intensity of TRHPEs display increasing trends,consistent with an observed positive trend in the mean intensity of TPEs as measured by the number of daily station precipitation observations exceeding 100 mm and 250 mm.More frequent landfalling Southeast and South China TCs induced more serious impacts in coastal areas in the Southeast and the South during 1990-2018 than1960-89.The roles of cyclone translation speed and"shifts"in cyclone tracks are examined as possible explanations for the temporal trends.

Diagnosis of Moist Vorticity and Moist Divergence for a Heavy Precipitation Event in Southwestern China

A regional heavy precipitation event that occurred over Sichuan Province on 8-9 September 2015 is analyzed based on hourly observed precipitation data obtained from weather stations and NCEP FNL data. Two moist dynamic parameters, i.e., moist vorticity （mζ and moist divergence （mδ）, are used to diagnose this heavy precipitation event. Results show that the topography over southwestern China has a significant impact on the ability of these two parameters to diagnose precipitation. When the impact of topography is weak （i.e., low altitude）, rn（ cannot exactly depict the location of precipitation in the initial stage of the event. Then, as the precipitation develops, its ability to depict the location improves significantly. In particular, m（ coincides best with the location of precipitation during the peak stage of the event. Besides, the evolution of the m（ center shows high consistency with the evolution of the precipitation center. For mδ, although some false-alarm regions are apparent, it reflects the location of precipitation almost entirely during the precipitation event. However, the mδ center shows inconsistency with the precipitation center. These results suggest that both m（ and mδ have a significant ability to predict the location of precipitation. Moreover, m（ has a stronger ability than mδ in terms of predicting the variability of the precipitation center. However, when the impact of topography is strong （i.e., high altitude）, both of these two moist dynamic parameters are unable to depict the location and center of precipitation during the entire precipitation event, suggesting their weak ability to predict precipitation over complex topography.

Diagnosis of a Heavy Precipitation Process in Northern Guangxi

[Objective] The aim was to analyze one strong precipitation process in Northern Guangxi from May 27 to 28 in 2010.[Method] By dint of 2.5×2.5 NCEP reanalysis data,physical quantities such as the water vapor flux,pseudo-equivalent temperature,Non-geotropic wet Q vector in one front rainstorm process in north Guangxi from May 27 to 28 in 2010 was expounded.The forecast application of Non-geotropic wet Q vector in rainstorm falling area in Guangxi during early flood period was discussed.[Result] The water vapor in Bay of Bengal transported to Guangxi and formed convergence lifting movement in north Guangxi,which provided favorable water vapor transportation condition for the generation of strong precipitation in north Guangxi.The 850 hPa pseudo-equivalent temperature front (close area) moved southward to the north part of Guangxi.North Guangxi was in pseudo-equivalent temperature area.The highly wet unstable energy of lower layer and the cold air penetrating downward from the middle layer led to potential instability in the lower level established in northern Guangxi,which thus provided certain thermal condition for the strong precipitation process;Northern Guangxi was in the overlap region of the maximum gradient region of contour Qx at 850 hPa and stronger negative areas of ▽Q,which provided favorable dynamic condition for the rainstorm process in northern Guangxi in the future.[Conclusion] The study provided reference in accordance to the forecast of rainstorm.

Convective/Large-scale Rainfall Partitions of Tropical Heavy Precipitation in CMIP6 Atmospheric Models

Convective/large-scale(C/L)precipitation partitions are crucial for achieving realistic rainfall modeling and are classified in 16 phase 6 of the Coupled Model Intercomparison Project(CMIP6)atmospheric models.Only 4 models capture the feature that convective rainfall significantly exceeds the large-scale rainfall component in the tropics while the other 12 models show 50%–100%large-scale rainfall component in heavy rainfall.Increased horizontal resolution generally increases the convective rainfall percentage,but not in all models.The former 4 models can realistically reproduce two peaks of moisture vertical distribution,respectively located in the upper and the lower troposphere.In contrast,the latter 12 models correspond to three types of moisture vertical profile biases:(1)whole mid-to-lower tropospheric wet biases(60%–80%large-scale rainfall);(2)mid-tropospheric wet peak(50%convective/large-scale rainfall);and(3)lower-tropospheric wet peak(90%–100%large-scale rainfall).And the associated vertical distribution of unique clouds potentially causes different climate feedback,suggesting accurate C/L rainfall components are necessary to reliable climate projection.

Climate Change Characteristics and Return Periods of Heavy Precipitation in the Northeast Side of Qinghai-Tibet Plateau

[ Objective] The study aimed to discuss analyze climate change characteristics and return periods of heavy precipitation in the northeast side of Qinghai-Tibet Plateau. [ Method] Based on the data of daily precipitation from 1943 to 2008 in 6 representative meteorological stations in Linxia located in the northeast side of Qinghai-Tibet Plateau, the climate change characteristics of heavy precipitation were analyzed, and the return periods of heavy precipitation were calculated by Pearson-Ill probability distribution method. [ Result] Days of heavy precipitation in Linxia region in- creased conspicuously since the 1990s. The return periods of heavy precipitation in the six stations on August 20, 2008 were consistent with the re- sults of artificial estimation. [ Conclusion] The research could provide scientific references for the reasonable utilization of climate resources, disas- ter prevention and rational arranqement of anricultural plantina svstems in Linxia reaion.

On the Improvement of the DSAEF_LTP Model to Heavy Precipitation Simulation of Landfalling Tropical Cyclones over China in 2018

In this study,the Dynamical-Statistical-Analog Ensemble Forecast model(DSAEF_LTP model)for landfalling tropical cyclone(LTC)precipitation was employed to simulate the precipitation of 10 LTCs that occurred over China in 2018.With similarity region scheme(SRS)parameter values added and TC intensity introduced to the generalized initial value(GIV),four groups of precipitation simulation experiments were designed to verify the forecasting ability of the improved model for more TC samples.Results show that the simulation ability of the DSAEF_LTP model can be optimized regardless of whether adding SRS values only,or introducing TC intensity into GIV,while the experiment with both the two improvements shows a more prominent advantage in simulating the heavier precipitation of LTCs.Compared with four NWP models(i.e.,ECMWF,GFS,GRAPES and SMS-WARMS),the overall forecasting performance of the DSAEF_LTP model achieves a better result in simulating precipitation at the thresholds over 250 mm and performs slightly better than NWP models at the thresholds over 100 mm.

Application of Weather Radar in Quantitative Forecast of Short-term Heavy Precipitation

Based on the C-band Doppler radar data and the hourly precipitation data of heavy precipitation in Ulanqab from 2018 to 2019,the Z-I relationship of local convective precipitation and the correlation between vertically integrated liquid water(VIL)and precipitation were studied.The heavy precipitation was divided into cumulus precipitation and cumulus mixed cloud precipitation,and different types of Z-I relationship was established to compare it with the traditional and unclassified overall optimal Z-I relationship.It shows that the estimation of different types of precipitation by different Z-I relationships was obviously better than the other two.Cumulus precipitation had a certain lag correlation with VIL,and the last 4 scanning VIL values within an hour had no indicative significance for precipitation;mixed precipitation was basically synchronized with VIL;the correlation decreased with the increase of the distance from radar,the corresponding degree of VIL and precipitation in stations within 30 km was significantly higher than that of other regional stations.A continuous non-zero VIL sequence close to a certain place can be used as a forecast indicator.Once a zero value appeared in the VIL time series,even it occurred only once,the two sequences before and after the zero value should be distinguished.

Analysis of a Heavy Precipitation Process Based on Wet Potential Vortex

In order to explore the relationship between heavy precipitation and the distribution characteristics of wet potential vorticity field,based on the NCEP 1°×1°6-hour reanalysis data,the wet potential vortex field in a heavy precipitation weather process in the central-southern part of Inner Mongolia was analyzed through the analysis of weather situation and calculation of vertical component(MPV_(1))and horizontal component(MPV_(2))of wet potential vortex(MPV).The results show that this heavy precipitation was a heavy precipitation weather process under the combined action of the cold air behind the trough and the southwest warm and humid airflow.The diagnostic analysis of θ_(se) shows that the south side of the area where the cold and warm air met before the precipitation was a high-temperature and high-humidity area,where a large amount of unstable energy was gathered,and the heavy precipitation occurred in the area with the most dense θ_(se) lines.This heavy precipitation process was dominated by the release of convective unstable energy in the lower troposphere,the positive pressure term of wet potential vortex MPV 1 was smaller than 0 in the lower troposphere and larger than 0 in the middle and upper layers;the positive-value wet potential vortex was transported to the lower layer,which was conducive to the release of unstable energy,so that precipitation increased.The negative-value center of wet potential vortex in the middle and low layers corresponded well to the heavy precipitation area,and the precipitation intensity and duration were consistent with the increase and enhancement time of the negative-value area of MPV_(1).The positive-value area of MPV_(1) was 6-12 h earlier than the heavy precipitation area,which has good indication significance for the prediction of the heavy precipitation area.In the lower troposphere,the configuration of MPV_(1)<0 and MPV_(2)>0 can be used as the basis for judging the location of precipitation center.

Objective Identification and Climatic Characteristics of Heavy-Precipitation Northeastern China Cold Vortexes

The northeastern China cold vortex(NCCV)plays an important role in regional rainstorms over East Asia.Using the National Centers for Environmental Prediction Final reanalysis dataset and the Global Precipitation Measurement product,an objective algorithm for identifying heavy-precipitation NCCV(HPCV)events was designed,and the climatological features of 164 HPCV events from 2001 to 2019 were investigated.The number of HPCV events showed an upward linear trend,with the highest frequency of occurrence in summer.The most active region of HPCV samples was the Northeast China Plain between 40°–55°N.Most HPCV events lasted 3–5 days and had radii ranging from 250 to 1000 km.The duration of HPCV events with larger sizes was longer.About half of the HPCV events moved into(moved out of)the definition region(35°–60°N,115°–145°E),and half initiated(dissipated)within the region.The initial position was close to the western boundary of the definition region,and the final position was mainly near the eastern boundary.The locations associated with the precipitation were mostly concentrated within 2000 km southeast of the HPCV systems,and they were farther from the center in the cold season than in the warm season.

Analysis of Characteristics of a Heavy Rainstorm Process in Nanchang City on July 7,2020

Based on the conventional observation data,dual polarization radar data and NCEP reanalysis data,the large-scale circulation background field,mesoscale conditions and formation causes of a heavy rainstorm in Nanchang on July 7,2020 were studied.It was found that this heavy rainstorm occurred under the weather background of the confrontation between the northward air flow behind the trough and the strong southwest warm and humid air flow to the northwest of the subtropical high.The divergence at the upper level,the shear in the middle and low levels,the southward movement of cold air at the low level,unusually abundant water vapor and high unstable energy caused the heavy rainstorm weather.In this process,under the influence of continuous eastward movement of several strong echo cells,an obvious"train effect"was formed in Nanchang,so that the local rainfall was continuous and intense.Moreover,the average of VIL was about 17 kg/m 2,and its variation characteristics were consistent with the variation trend of 5-min rainfall intensity,which had a certain indicator effect on short-term heavy precipitation.The topography of the Meiling Mountain in the west of Nanchang had a great influence on the formation and precipitation distribution of the heavy rain process.There was a strong rainstorm center near the mountain,and the precipitation was obviously larger than that in the plain area.

An application of the LTP＿DSEF model to heavy precipitation forecasts of landfalling tropical cyclones over China in 2018

Recently, a track-similarity-based Dynamical-Statistical Ensemble Forecast(LTP_DSEF) model has been developed in an attempt to predict heavy rainfall from Landfalling Tropical cyclones(LTCs). In this study, the LTP_DSEF model is applied to predicting heavy precipitation associated with 10 LTCs occurring over China in 2018. The best forecast scheme of the model with optimized parameters is obtained after testing 3452 different schemes for the 10 LTCs. Then, its performance is compared to that of three operational dynamical models. Results show that the LTP_DSEF model has advantages over the three dynamical models in predicting heavy precipitation accumulated after landfall, especially for rainfall amounts greater than 250 mm. The model also provides superior or slightly inferior heavy rainfall forecast performance for individual LTCs compared to the three dynamical models. In particular, the LTP_DSEF model can predict heavy rainfall with valuable threat scores associated with certain LTCs, which is not possible with the three dynamical models. Moreover, the model can reasonably capture the distribution of heavier accumulated rainfall, albeit with widespread coverage compared to observations. The preliminary results suggest that the LTP_DSEF model can provide useful forecast guidance for heavy accumulated rainfall of LTCs despite its limited variables included in the model.

The Mesoscale Predictability of a Heavy Precipitation Event

Using the mesoscale model MM5, the development of initial condition uncertainties at different scales and amplitudes and their influences on the mesoscale predictability of the ＂0185＂ Shanghai heavy precipitation event are investigated. It is found that different initial conditions obtained from different globe model analyses lead to large variations in the simulated location and strength of the heavy precipitation, and the scales and amplitudes of the initial condition perturbations significantly influence the model error growth. The power spectrum evolution of the difference total energy （DTE） between a control simulation and a sensitivity experiment indicates that the error growth saturates after 12 h, which is the predictable time limit of the heavy precipitation event. The power spectrum evolution of the accumulated precipitation difference between the control and sensitivity simulations suggests a loss of the mesoscale predictability for precipitation systems of scales smaller than 300 kin, i.e., the predictable space for the heavy precipitation event is beyond 300 km. The results also show that the initial uncertainties at larger scales and amplitudes generally result in larger forecast divergence than the uncertainties at smaller scales and amplitudes. The predictable forecasting time and space can be expanded （e.g., from 12 to 15 h, and from beyond 300 kin to beyond 200 km） under properly prescribed initial perturbations at smaller scales and amplitudes.

Low-Frequency Oscillations of the East Asia-Pacific Teleconnection Pattern and Their Impacts on Persistent Heavy Precipitation in the Yangtze-Huai River Valley

Based on the daily reanalysis data from NCEP NCAR and daily precipitation data from the China National Meteorological Information Center,an ensemble empirical mode decomposition method is employed to extract the predominant oscillation modes of the East Asia Pacific（EAP） teleconnection pattern.The influences of these low-frequency modes on persistent heavy precipitation in the Yangtze Huai River（YHR）valley are investigated.The results indicate that the EAP pattern and rainfall in YHR valley both exhibit remarkable 10 30- and 30 60-day oscillations.The impacts of the EAP pattern on the YHR persistent heavy precipitation can be found on both the 10 30- and 30 60-day timescales the 10 30-day scale for most cases.Composite analysis indicates that,on the 10 30-day timescale,formation of the EAP pattern in the lower and middle troposphere is determined by convective systems near the tropical western Pacific;whereas in the middle troposphere,the phase transition is jointly contributed by both the dispersion of zonal wave energies at higher latitudes and convective systems over the South China Sea.In the context of the10 30-day EAP pattern,the anomalously abundant moisture is transported by an anomalous subtropical anticyclone system,and strong moisture convergence results from that anomalous anticyclone system and a cyclonic system in the midlatitude East Asia.Such a combination of systems persists for at least three days,contributing to the formation of persistent heavy precipitation in the YHR valley.

A Case Study of the Error Growth and Predictability of a Meiyu Frontal Heavy Precipitation Event

The Advanced Regional Eta-coordinate Model （AREM） is used to explore the predictability of a heavy rainfall event along the Meiyu front in China during 3-4 July 2003. Based on the sensitivity of precipitation prediction to initial data sources and initial uncertainties in different variables, the evolution of error growth and the associated mechanism are described and discussed in detail in this paper. The results indicate that the smaller-amplitude initial error presents a faster growth rate and its growth is characterized by a transition from localized growth to widespread expansion error. Such modality of the error growth is closely related to the evolvement of the precipitation episode, and consequently remarkable forecast divergence is found near the rainband, indicating that the rainfall area is a sensitive region for error growth. The initial error in the rainband contributes significantly to the forecast divergence, and its amplification and propagation are largely determined by the initial moisture distribution. The moisture condition also affects the error growth on smaller scales and the subsequent upscale error cascade. In addition, the error growth defined by an energy norm reveals that large error energy collocates well with the strong latent heating, implying that the occurrence of precipitation and error growth share the same energy source-the latent heat. This may impose an intrinsic predictability limit on the prediction of heavy precipitation.

Application of an Improved Analog-Based Heavy Precipitation Forecast Model to the Yangtze–Huai River Valley and Its Performance in June–July 2020

Precipitation extremes,such as the record-breaking Meiyu characterized by frequent occurrences of rainstorms that resulted in severe flooding over the Yangtze-Huai River valley(YHRV)in June-July 2020,are always attracting considerable interest,highlighting the importance of improving the forecast accuracy at the medium-to-long range.To elevate the skill in forecasting heavy precipitation events(HPEs)with both long and short durations,the Key Influential Systems Based Analog Model(KISAM)was further improved and brought into operational application in 2020.Verification and comparison of this newly adapted analog model and ensemble mean forecasts from the ECMWF at lead times of up to 15 days were carried out for the identified 16 HPEs over the YHRV in June-July 2020.The results demonstrate that KISAM is advantageous over ECMWF ensemble mean for forecasts of heavy precipitation≥25 mm day-1 at the medium-to-long(6-15-day)lead times,based on the traditional dichotomous metrics.At short lead times,ECMWF ensemble mean outperforms KISAM due largely to the low false alarm rates(FARs)benefited from an underestimation of the frequency of heavy precipitation.However,at the medium-to-long forecast range,the large fraction of misses induced by the high degree of underforecasting overwhelms the fairly good FARs in the ECMWF ensemble mean,which partly explains its inferiority to KISAM in terms of the threat score.Further assessment on forecasts of the latitudinal location of accumulated heavy precipitation indicates that smaller displacement errors also account for a part of the better performance of KISAM at lead times of 8-12 days.