Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial pertur...Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.展开更多
The singular vector(SV)initial perturbation method can capture the fastest-growing initial perturbation in a tangent linear model(TLM).Based on the global tangent linear and adjoint model of GRAPES-GEPS(Global/Regiona...The singular vector(SV)initial perturbation method can capture the fastest-growing initial perturbation in a tangent linear model(TLM).Based on the global tangent linear and adjoint model of GRAPES-GEPS(Global/Regional Assimilation and Prediction System-Global Ensemble Prediction System),some experiments were carried out to analyze the structure of the moist SVs from the perspectives of the energy norm,energy spectrum,and vertical structure.The conclusions are as follows:The evolution of the SVs is synchronous with that of the atmospheric circulation,which is flowdependent.The moist and dry SVs are located in unstable regions at mid-to-high latitudes,but the moist SVs are wider,can contain more small-and medium-scale information,and have more energy than the dry SVs.From the energy spectrum analysis,the energy growth caused by the moist SVs is reflected in the relatively small-scale weather system.In addition,moist SVs can generate perturbations associated with large-scale condensation and precipitation,which is not true for dry SVs.For the ensemble forecasts,the average anomaly correlation coefficient of large-scale circulation is better for the forecast based on moist SVs in the Northern Hemisphere,and the low-level variables forecasted by the moist SVs are also improved,especially in the first 72 h.In addition,the moist SVs respond better to short-term precipitation according to statistical precipitation scores based on 10 cases.The inclusion of the large-scale condensation process in the calculation of SVs can improve the short-term weather prediction effectively.展开更多
为了体现次网格尺度能量升尺度转换过程中存在的不确定性,文中将随机动能补偿(Stochastic Kinetic Energy Backscatter,SKEB)方案应用于GRAPES(Global/Regional Assimilation and Prediction System)全球集合预报系统(GRAPES-GEPS),以...为了体现次网格尺度能量升尺度转换过程中存在的不确定性,文中将随机动能补偿(Stochastic Kinetic Energy Backscatter,SKEB)方案应用于GRAPES(Global/Regional Assimilation and Prediction System)全球集合预报系统(GRAPES-GEPS),以更好地表征模式误差并且增大集合离散度。使用的SKEB方案基于具有一定时、空相关特征的随机型以及由数值扩散导致的局地动能耗散率来构造随机流函数强迫。并根据流函数与水平风速旋转分量的关系,将SKEB方案中的流函数强迫转化为适用于GRAPES全球模式的水平风速扰动。结果表明,SKEB方案的使用一方面能够提高GRAPES对大气动能谱的模拟能力;另一方面能够改善GRAPES-GEPS的集合离散度与集合平均误差的关系,增加了集合离散度,并在一定程度上减小了集合平均误差,尤其是在热带地区这种改进更为显著。而且该方案使得热带地区连续分级概率评分(CRPS评分)显著减小。就降水预报而言,从Brier评分与相对作用特征面积(AROC,Area under the Relative Operating Characteristics)的结果来看,SKEB方案有助于改善中国地区小雨[0.1 mm,10 mm)、中雨[10 mm,25 mm)与大雨[25 mm,50 mm)量级降水的概率预报技巧,而对暴雨[50 mm,∞)量级降水预报技巧影响很小(24 h降水量)。总体上,模式扰动随机动能补偿方案提高了GRAPES-GEPS的概率预报技巧。展开更多
The Fourth Assessment Report (AR4) of the Intergovernmental Panel of Climate Change (IPCC) concluded that the climate projection using climate models that took account of both human and natural factors provided credib...The Fourth Assessment Report (AR4) of the Intergovernmental Panel of Climate Change (IPCC) concluded that the climate projection using climate models that took account of both human and natural factors provided credible quantitative estimates of future climate change; however, the mismatches between the IPCC AR4 model ensembles and the observations, especially the multi-decadal variability (MDV), have cast shadows on the confidence of the model-based decadal projections of future cli mate. This paper reports an evaluation of many individual runs of AR4 models in the simulation of past global mean tempera ture. We find that most of the individual model runs fail to reproduce the MDV of past climate, which may have led to the overestimation of the projection of global warming for the next 40 years or so. Based on such an evaluation, we propose an al ternative approach, in which the MDV signal is taken into account, to project the global mean temperature for the next 40 years and obtain that the global warming during 2011–2050 could be much smaller than the AR4 projection.展开更多
Tropical cyclone(TC)genesis prediction is a major scientific challenge to the TC operation and research community.This report surveys the current status of TC genesis forecasts by a number of major operational centers...Tropical cyclone(TC)genesis prediction is a major scientific challenge to the TC operation and research community.This report surveys the current status of TC genesis forecasts by a number of major operational centers covering the key ocean basins across both hemispheres.Since IWTC-9,we see an emergence of probabilistic TC genesis forecast products by operational centers,typically supported by the statistical processing of a combination of ensemble prediction and satellite analysis,covering time periods of couple of days to weeks ahead.The prevalence of multi-center grand ensemble approach highlights the uncertainties involved and the forecast challenges in quantitative genesis prediction.While operational practice might differ across agencies,verification efforts generally report a steady or slightly improving skill level in terms of reliability,which likely results from the continual improvement in global numerical weather prediction capability.展开更多
基金supported by the Joint Funds of the Chinese National Natural Science Foundation (NSFC)(Grant No.U2242213)the National Key Research and Development (R&D)Program of the Ministry of Science and Technology of China(Grant No. 2021YFC3000902)the National Science Foundation for Young Scholars (Grant No. 42205166)。
文摘Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.
基金the National Key R&D Program of China(Grant Nos.2017YFC1502102 and 2017YFC1501803).
文摘The singular vector(SV)initial perturbation method can capture the fastest-growing initial perturbation in a tangent linear model(TLM).Based on the global tangent linear and adjoint model of GRAPES-GEPS(Global/Regional Assimilation and Prediction System-Global Ensemble Prediction System),some experiments were carried out to analyze the structure of the moist SVs from the perspectives of the energy norm,energy spectrum,and vertical structure.The conclusions are as follows:The evolution of the SVs is synchronous with that of the atmospheric circulation,which is flowdependent.The moist and dry SVs are located in unstable regions at mid-to-high latitudes,but the moist SVs are wider,can contain more small-and medium-scale information,and have more energy than the dry SVs.From the energy spectrum analysis,the energy growth caused by the moist SVs is reflected in the relatively small-scale weather system.In addition,moist SVs can generate perturbations associated with large-scale condensation and precipitation,which is not true for dry SVs.For the ensemble forecasts,the average anomaly correlation coefficient of large-scale circulation is better for the forecast based on moist SVs in the Northern Hemisphere,and the low-level variables forecasted by the moist SVs are also improved,especially in the first 72 h.In addition,the moist SVs respond better to short-term precipitation according to statistical precipitation scores based on 10 cases.The inclusion of the large-scale condensation process in the calculation of SVs can improve the short-term weather prediction effectively.
基金supported by the National Basic Research Program of Chi-na (Grant No. 2011CB952000)the National Natural Science Founda-tion of China (Grant No. 40810059003)+1 种基金Qian Cheng was partly supported by the "Strategic Priority Research Program" of the Chinese Academy of Sciences (Grant No. XDA05090103)Wu Zhaohua was supported by the Natural Science Foundation of USA (Grant No. ATM-0917743)
文摘The Fourth Assessment Report (AR4) of the Intergovernmental Panel of Climate Change (IPCC) concluded that the climate projection using climate models that took account of both human and natural factors provided credible quantitative estimates of future climate change; however, the mismatches between the IPCC AR4 model ensembles and the observations, especially the multi-decadal variability (MDV), have cast shadows on the confidence of the model-based decadal projections of future cli mate. This paper reports an evaluation of many individual runs of AR4 models in the simulation of past global mean tempera ture. We find that most of the individual model runs fail to reproduce the MDV of past climate, which may have led to the overestimation of the projection of global warming for the next 40 years or so. Based on such an evaluation, we propose an al ternative approach, in which the MDV signal is taken into account, to project the global mean temperature for the next 40 years and obtain that the global warming during 2011–2050 could be much smaller than the AR4 projection.
文摘Tropical cyclone(TC)genesis prediction is a major scientific challenge to the TC operation and research community.This report surveys the current status of TC genesis forecasts by a number of major operational centers covering the key ocean basins across both hemispheres.Since IWTC-9,we see an emergence of probabilistic TC genesis forecast products by operational centers,typically supported by the statistical processing of a combination of ensemble prediction and satellite analysis,covering time periods of couple of days to weeks ahead.The prevalence of multi-center grand ensemble approach highlights the uncertainties involved and the forecast challenges in quantitative genesis prediction.While operational practice might differ across agencies,verification efforts generally report a steady or slightly improving skill level in terms of reliability,which likely results from the continual improvement in global numerical weather prediction capability.
