The weather in Nagano Prefecture, Japan, can be roughly classified into four types according to principal component analysis and k-means clustering. We predicted the extreme values of the maximum daily and hourly prec...The weather in Nagano Prefecture, Japan, can be roughly classified into four types according to principal component analysis and k-means clustering. We predicted the extreme values of the maximum daily and hourly precipitation in Nagano Prefecture using the extreme value theory. For the maximum daily precipitation, the vales of ξ in Matsumoto, Karuizawa, Sugadaira, and Saku were positive;therefore, it has no upper bound and tends to take large values. Therefore, it is dangerous and caution is required. The values of ξ in Nagano, Kisofukushima, and Minamishinano were determined to be zero, therefore, there was no upper limit, the probability of obtaining a large value was low, and caution was required. We predicted the maximum return levels for return periods of 10, 20, 50, and 100 years along with respective 95% confidence intervals in Nagano, Matsumoto, Karuizawa, Sugadaira, Saku, Kisofukushima, and Minamishinano. In Matsumoto, the 100-year return level was 182 mm, with a 95% CI [129, 236]. In Minamishinano, the 100-year return level was 285 mm, with a 95% CI [173, 398]. The 100-year return levels for the maximum daily rainfall were 285, 271, and 271 mm in Minamishinano, Saku, and Karuizawa, respectively, where the changes in the daily maximum rainfall were larger than those at other points. Because these values are large, caution is required during heavy rainfall. The 100-year return levels for the maximum daily and hourly precipitation were similar in Karuizawa and Saku. In Sugadaira, the 100-year return level for a maximum hourly rainfall of 107.2 mm was larger than the maximum daily rainfall. Hence, it is necessary to be careful about short-term rainfall events.展开更多
Understanding the responses of mean and extreme precipitation to climate change is of great importance.Previous studies have mainly focused on the responses to prescribed sea surface warming or warming due to increase...Understanding the responses of mean and extreme precipitation to climate change is of great importance.Previous studies have mainly focused on the responses to prescribed sea surface warming or warming due to increases of CO2.This study uses a cloud-resolving model under the idealization of radiative-convective equilibrium to examine the responses of mean and extreme precipitation to a variety of climate forcings,including changes in prescribed sea surface temperature,CO2,solar insolation,surface albedo,stratospheric volcanic aerosols,and several tropospheric aerosols.The different responses of mean precipitation are understood by examining the changes in the surface energy budget.It is found that the cancellation between shortwave scattering and longwave radiation leads to a small dependence of the mean precipitation response on forcings.The responses of extreme precipitation are decomposed into three components(thermodynamic,dynamic,and precipitation efficiency).The thermodynamic components for all climate forcings are similar.The dynamic components and the precipitation-efficiency components,which have large spreads among the cases,are negatively correlated,leading to a small dependence of the extreme precipitation response on the forcings.展开更多
By the utilization of monthly precipitation data from all stations in the Northern Hemisphere annexed to the 'World Survey of climatology, Vol. 1-15', the distributions of the maximum precipitation months (MPM...By the utilization of monthly precipitation data from all stations in the Northern Hemisphere annexed to the 'World Survey of climatology, Vol. 1-15', the distributions of the maximum precipitation months (MPM), the annual relative precipitation (ARP) and the monthly relative precipitation (percent of annual) in January and July are respectively mapped. Moreover the distributions of intermonthly relative precipitation variabilities from January to December are plotted as well. From these figures, the precipitation in the Northern Hemisphere may be classified into three types(continental, oceanic and transitional types) and 17 regions. The precipitation regime may also be divided into two patterns, the global and regional patterns. The global pattern consists of planetary front system and ITCZ and its inter-monthly variation shows the north-and-south shift of the rain belt; the regional pattern consists of the sea-land monsoon and plateau monsoon regime, in which the inter-monthly variation of rain belt shows a east-and-wcst shift.展开更多
Precipitation is a significant index to measure the degree of drought and flood in a region,which directly reflects the local natural changes and ecological environment.It is very important to grasp the change charact...Precipitation is a significant index to measure the degree of drought and flood in a region,which directly reflects the local natural changes and ecological environment.It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy.In order to accurately predict precipitation,a new precipitation prediction model based on extreme learning machine ensemble(ELME)is proposed.The integrated model is based on the extreme learning machine(ELM)with different kernel functions and supporting parameters,and the submodel with the minimum root mean square error(RMSE)is found to fit the test data.Due to the complex mechanism and factors affecting precipitation change,the data have strong uncertainty and significant nonlinear variation characteristics.The mean generating function(MGF)is used to generate the continuation factor matrix,and the principal component analysis technique is employed to reduce the dimension of the continuation matrix,and the effective data features are extracted.Finally,the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June,July and August,and a comparative experiment is carried out by using ELM,long-term and short-term memory neural network(LSTM)and back propagation neural network based on genetic algorithm(GA-BP).The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models,and it has high stability and reliability,which provides a reliable method for precipitation prediction.展开更多
The variations of regional mean daily precipitation extreme （RMDPE） events in central China and associated circulation anomalies during June, July, and August （JJA） of 1961-2010 are investigated by using daily in-...The variations of regional mean daily precipitation extreme （RMDPE） events in central China and associated circulation anomalies during June, July, and August （JJA） of 1961-2010 are investigated by using daily in-situ precipitation observations and the NCEP/NCAR reanalysis data. The precipitation data were collected at 239 state-level stations distributed throughout the provinces of Henan, Hubei, and Hunan. During 1961-2010, the 99th percentile threshold for RMDPE is 23.585 mm day-1. The number of RMDPE events varies on both interannual and interdecadal timescales, and increases significantly after the mid 1980s. The RMDPE events happen most frequently between late June and mid July, and are generally associated with anomalous baroclinic tropospheric circulations. The supply of moisture to the southern part of central China comes in a stepping way from the outer-region of an abnormal anticyclone over the Bay of Bengal and the South China Sea. Fluxes of wave activity generated over the northeastern Tibetan Plateau converge over central China, which favors the genesis and maintenance of wave disturbances over the region. RMDPE events typically occur in tandem with a strong heating gradient formed by net heating in central China and the large-scale net cooling in the surrounding area. The occurrence of RMDPE events over central China is tied to anomalous local cyclonic circulations, topographic forcing over the northeast Tibetan Plateau, and anomalous gradients of diabatic heating between central China and the surrounding areas.展开更多
A weighting scheme jointly considering model performance and independence(PI-based weighting scheme) is employed to deal with multi-model ensemble prediction of precipitation over China from 17 global climate models. ...A weighting scheme jointly considering model performance and independence(PI-based weighting scheme) is employed to deal with multi-model ensemble prediction of precipitation over China from 17 global climate models. Four precipitation metrics on mean and extremes are used to evaluate the model performance and independence. The PIbased scheme is also compared with a rank-based weighting scheme and the simple arithmetic mean(AM) scheme. It is shown that the PI-based scheme achieves notable improvements in western China, with biases decreasing for all parameters. However, improvements are small and almost insignificant in eastern China. After calibration and validation, the scheme is used for future precipitation projection under the 1.5 and 2℃ global warming targets(above preindustrial level). There is a general tendency to wetness for most regions in China, especially in terms of extreme precipitation. The PI scheme shows larger inhomogeneity in spatial distribution. For the total precipitation PRCPTOT(95 th percentile extreme precipitation R95 P), the land fraction for a change larger than 10%(20%) is 22.8%(53.4%)in PI, while 13.3%(36.8%) in AM, under 2℃ global warming. Most noticeable increase exists in central and east parts of western China.展开更多
文摘The weather in Nagano Prefecture, Japan, can be roughly classified into four types according to principal component analysis and k-means clustering. We predicted the extreme values of the maximum daily and hourly precipitation in Nagano Prefecture using the extreme value theory. For the maximum daily precipitation, the vales of ξ in Matsumoto, Karuizawa, Sugadaira, and Saku were positive;therefore, it has no upper bound and tends to take large values. Therefore, it is dangerous and caution is required. The values of ξ in Nagano, Kisofukushima, and Minamishinano were determined to be zero, therefore, there was no upper limit, the probability of obtaining a large value was low, and caution was required. We predicted the maximum return levels for return periods of 10, 20, 50, and 100 years along with respective 95% confidence intervals in Nagano, Matsumoto, Karuizawa, Sugadaira, Saku, Kisofukushima, and Minamishinano. In Matsumoto, the 100-year return level was 182 mm, with a 95% CI [129, 236]. In Minamishinano, the 100-year return level was 285 mm, with a 95% CI [173, 398]. The 100-year return levels for the maximum daily rainfall were 285, 271, and 271 mm in Minamishinano, Saku, and Karuizawa, respectively, where the changes in the daily maximum rainfall were larger than those at other points. Because these values are large, caution is required during heavy rainfall. The 100-year return levels for the maximum daily and hourly precipitation were similar in Karuizawa and Saku. In Sugadaira, the 100-year return level for a maximum hourly rainfall of 107.2 mm was larger than the maximum daily rainfall. Hence, it is necessary to be careful about short-term rainfall events.
基金supported by the National Natural Science Foundation of China (Grant No. 41875050)
文摘Understanding the responses of mean and extreme precipitation to climate change is of great importance.Previous studies have mainly focused on the responses to prescribed sea surface warming or warming due to increases of CO2.This study uses a cloud-resolving model under the idealization of radiative-convective equilibrium to examine the responses of mean and extreme precipitation to a variety of climate forcings,including changes in prescribed sea surface temperature,CO2,solar insolation,surface albedo,stratospheric volcanic aerosols,and several tropospheric aerosols.The different responses of mean precipitation are understood by examining the changes in the surface energy budget.It is found that the cancellation between shortwave scattering and longwave radiation leads to a small dependence of the mean precipitation response on forcings.The responses of extreme precipitation are decomposed into three components(thermodynamic,dynamic,and precipitation efficiency).The thermodynamic components for all climate forcings are similar.The dynamic components and the precipitation-efficiency components,which have large spreads among the cases,are negatively correlated,leading to a small dependence of the extreme precipitation response on the forcings.
文摘By the utilization of monthly precipitation data from all stations in the Northern Hemisphere annexed to the 'World Survey of climatology, Vol. 1-15', the distributions of the maximum precipitation months (MPM), the annual relative precipitation (ARP) and the monthly relative precipitation (percent of annual) in January and July are respectively mapped. Moreover the distributions of intermonthly relative precipitation variabilities from January to December are plotted as well. From these figures, the precipitation in the Northern Hemisphere may be classified into three types(continental, oceanic and transitional types) and 17 regions. The precipitation regime may also be divided into two patterns, the global and regional patterns. The global pattern consists of planetary front system and ITCZ and its inter-monthly variation shows the north-and-south shift of the rain belt; the regional pattern consists of the sea-land monsoon and plateau monsoon regime, in which the inter-monthly variation of rain belt shows a east-and-wcst shift.
基金funded by Scientific Research Project of Guangxi Normal University of Science and Technology,grant number GXKS2022QN024.
文摘Precipitation is a significant index to measure the degree of drought and flood in a region,which directly reflects the local natural changes and ecological environment.It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy.In order to accurately predict precipitation,a new precipitation prediction model based on extreme learning machine ensemble(ELME)is proposed.The integrated model is based on the extreme learning machine(ELM)with different kernel functions and supporting parameters,and the submodel with the minimum root mean square error(RMSE)is found to fit the test data.Due to the complex mechanism and factors affecting precipitation change,the data have strong uncertainty and significant nonlinear variation characteristics.The mean generating function(MGF)is used to generate the continuation factor matrix,and the principal component analysis technique is employed to reduce the dimension of the continuation matrix,and the effective data features are extracted.Finally,the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June,July and August,and a comparative experiment is carried out by using ELM,long-term and short-term memory neural network(LSTM)and back propagation neural network based on genetic algorithm(GA-BP).The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models,and it has high stability and reliability,which provides a reliable method for precipitation prediction.
基金Supported by the National Natural Science Foundation of China(41330425)China Meteorological Administration Special Public Welfare Research Fund(GYHY201406024)
文摘The variations of regional mean daily precipitation extreme （RMDPE） events in central China and associated circulation anomalies during June, July, and August （JJA） of 1961-2010 are investigated by using daily in-situ precipitation observations and the NCEP/NCAR reanalysis data. The precipitation data were collected at 239 state-level stations distributed throughout the provinces of Henan, Hubei, and Hunan. During 1961-2010, the 99th percentile threshold for RMDPE is 23.585 mm day-1. The number of RMDPE events varies on both interannual and interdecadal timescales, and increases significantly after the mid 1980s. The RMDPE events happen most frequently between late June and mid July, and are generally associated with anomalous baroclinic tropospheric circulations. The supply of moisture to the southern part of central China comes in a stepping way from the outer-region of an abnormal anticyclone over the Bay of Bengal and the South China Sea. Fluxes of wave activity generated over the northeastern Tibetan Plateau converge over central China, which favors the genesis and maintenance of wave disturbances over the region. RMDPE events typically occur in tandem with a strong heating gradient formed by net heating in central China and the large-scale net cooling in the surrounding area. The occurrence of RMDPE events over central China is tied to anomalous local cyclonic circulations, topographic forcing over the northeast Tibetan Plateau, and anomalous gradients of diabatic heating between central China and the surrounding areas.
基金Supported by the National Key Research and Development Program of China (2017YFA0603804, 2016YFA0600402, and 2018YFC1507704)。
文摘A weighting scheme jointly considering model performance and independence(PI-based weighting scheme) is employed to deal with multi-model ensemble prediction of precipitation over China from 17 global climate models. Four precipitation metrics on mean and extremes are used to evaluate the model performance and independence. The PIbased scheme is also compared with a rank-based weighting scheme and the simple arithmetic mean(AM) scheme. It is shown that the PI-based scheme achieves notable improvements in western China, with biases decreasing for all parameters. However, improvements are small and almost insignificant in eastern China. After calibration and validation, the scheme is used for future precipitation projection under the 1.5 and 2℃ global warming targets(above preindustrial level). There is a general tendency to wetness for most regions in China, especially in terms of extreme precipitation. The PI scheme shows larger inhomogeneity in spatial distribution. For the total precipitation PRCPTOT(95 th percentile extreme precipitation R95 P), the land fraction for a change larger than 10%(20%) is 22.8%(53.4%)in PI, while 13.3%(36.8%) in AM, under 2℃ global warming. Most noticeable increase exists in central and east parts of western China.
