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1.
Accurate wind speed forecasting is of great societal importance. In this study, the short-term wind speed forecasting bias at automatic meteorological stations in Hangzhou, Zhejiang Province, China, was corrected using an XGBoost machine learning model called WSFBC-XGB. The products of the local NWP (numerical weather prediction) system were used as the inputs of WSFBC-XGB. The WSFBC-XGB-corrected results were compared with those corrected using the traditional MOS (model output statistics) method. Results showed that WSFBC-XGB performed better than MOS, with the root-mean-square errors (RMSEs)/accuracy rates of the wind speed forecasting (ACCs) of WSFBC-XGB being reduced/ promoted by 26.1% and 7.64%/35.6% and 7.02% relative to NWP and MOS, respectively. The RMSEs/ACCs of WSFBC-XGB were smaller/higher than those of MOS at 90% stations. In addition, the mean decrease in impurity method was used to analyze the interpretability of WSFBC-XGB to help users gain trust in the model. Results showed that the four most important features were the wind speed at 10 m (47.35%), meridional component of wind at 10 m (12.73%), diurnal cycle (9.97%), and meridional component of wind at 1000 hPa (7.45%). The WSFBC-XGB model will help improve the accuracy of short-term wind speed forecasting and provide support for large-scale outdoor activities.摘要准确的风速预报具有重要的社会意义. 在本研究中, 使用名为WSFBC-XGB的XGBoost机器学习模型对中国浙江省杭州市自动气象站的短期风速预报误差进行校正. WSFBC-XGB使用本地数值天气预报系统的产品作为输入. 将WSFBC-XGB校正的结果与传统MOS(模型输出统计)方法校正的结果进行了比较. 结果表明: WSFBC-XGB预报风速的均方根误差(RMSE)/准确率(ACC)分别比NWP和MOS降低/提高了26.1%和7.64%/35.6%和7.02%; 对于90%的站点WSFBC-XGB的RMSE/ACC均小于/高于MOS. 此外, 采用平均杂质减少法对WSFBC-XGB的可解释性进行分析, 以帮助用户增加对模型的信任. 结果表明: 10米风速(47.35%), 10米风的经向分量(12.73%), 日循环(9.97%)和1000百帕风的经向分量(7.45%)是前4个最重要的特征. WSFBC-XGB模型将有助于提高短期风速预报的准确性, 为大型户外活动提供支持.  相似文献   

2.
A deep-learning method named U-Net was applied to improve the skill in forecasting summer (June–August) precipitation for at a one-month lead during the period 1981–2020 in China. The variables of geopotential height, soil moisture, sea level pressure, sea surface temperature, ocean salinity, and snow were considered as the model input to revise the seasonal prediction of the Climate Forecast System, version 2 (CFSv2). Results showed that on average U-Net reduced the root-mean-square error of the original CFSv2 prediction by 49.7% and 42.7% for the validation and testing set, respectively. The most improved areas were Northwest, Southwest, and Southeast China. The anomaly same sign percentages and temporal and spatial correlation coefficients did not present significant improvement but maintained the comparable performances of CFSv2. Sensitivity experiments showed that soil moisture is the most crucial factor in predicting summer rainfall in China, followed by geopotential height. Due to its advantages in handling small training dataset sizes, U-Net is a promising deep-learning method for seasonal rainfall prediction.摘要本研究应用了名为U-Net的深度学习方法来提高中国夏季 (6–8月) 降水的预报技能, 预报时段为1981–2020年, 预报提前期为一个月. 将位势高度场, 土壤湿度, 海平面气压, 海表面温度, 海洋盐度和青藏高原积雪等变量作为模型输入, 本文对美国NCAR气候预报系统第2版 (CFSv2) 的季节性预报结果进行了修正. 结果显示, 在验证集和测试集上, U-Net平均将原CFSv2预测的均方根误差分别减少了49.7%和42.7%. 预报结果改善最大的地区是中国的西北,西南和东南地区. 然而, 同号率和时空相关系数没有得到明显改善, 但仍与CFSv2的预测技巧持平. 敏感性实验表明, 土壤湿度是预测中国夏季降雨的最关键因素, 其次是位势高度场. 本研究显示了U-Net模型在训练小样本数据集方面的优势, 为我国汛期季节性降雨预测提供了一种有效的深度学习方法.  相似文献   

3.
A novel multivariable prediction system based on a deep learning (DL) algorithm, i.e., the residual neural network and pure observations, was developed to improve the prediction of the El Niño–Southern Oscillation (ENSO). Optimal predictors are automatically determined using the maximal information for spatial filtering and the Taylor diagram criteria, enabling the best prediction skills at lead times of eight months compared with most operational prediction models. The hindcast skill for the most challenging decade (2011–18) outperforms the multi-model ensemble operational forecasts. At the six-month lead, the correlation (COEF) skill of the DL model reaches 0.82 with a normalized root-mean-square error (RMSE) of 0.58 °C, which is significantly better than the average multi-model performance (COEF = 0.70 and RMSE = 0.73°C). DL prediction can effectively alleviate the long-standing spring predictability barrier problem. The automatically selected optimal precursors can explain well the typical ENSO evolution driven by both tropical dynamics and extratropical impacts.摘要本文基于残差神经网络和观测数据构建了一套深度学习多因子预报测模型, 以改进厄尔尼诺-南方涛动(ENSO)的预报. 该模型基于最大信息系数进行因子时空特征提取, 并根据泰勒图的评估标准可自动确定关键预报因子进行预报. 该模型在超前8个月以内的预报性能要优于当前传统的业务预报模式. 2011–2018年间, 该模型的预报性能优于多模式集成预报的结果. 在超前6个月预报时效上, 模型预报相关性可达0.82, 标准化后的均方根误差仅为0.58°C, 多模式集成预报的相关性和标准化后的均方根误差分别为0.70和0.73°C. 该模型春季预报障碍问题有所缓解, 并且自动选取的关键预报因子可用于解释热带和副热带热动力过程对于ENSO变化的影响.  相似文献   

4.
高质量和高分辨率的降水产品在天气预报,数值模式模拟和气象防灾减灾方面起着重要的作用.本文利用四川地区高密度的地面降水传感器观测数据,比较CMPAS四种不同时空尺度的降水实况分析产品,评估CMPAS的融合准确性与在四川地区的适用性.研究表明:四种CMPAS降水产品都在四川盆地内精度较高,攀西地区和川西高原次之.随着降水量...  相似文献   

5.
Background error covariance (BEC) plays an essential role in variational data assimilation. Most variational data assimilation systems still use static BEC. Actually, the characteristics of BEC vary with season, day, and even hour of the background. National Meteorological Center–based diurnally varying BECs had been proposed, but the diurnal variation characteristics were gained by climatic samples. Ensemble methods can obtain the background error characteristics that suit the samples in the current moment. Therefore, to gain more reasonable diurnally varying BECs, in this study, ensemble-based diurnally varying BECs are generated and the diurnal variation characteristics are discussed. Their impacts are then evaluated by cycling data assimilation and forecasting experiments for a week based on the operational China Meteorological Administration-Beijing system. Clear diurnal variation in the standard deviation of ensemble forecasts and ensemble-based BECs can be identified, consistent with the diurnal variation characteristics of the atmosphere. The results of one-week cycling data assimilation and forecasting show that the application of diurnally varying BECs reduces the RMSEs in the analysis and 6-h forecast. Detailed analysis of a convective rainfall case shows that the distribution of the accumulated precipitation forecast using the diurnally varying BECs is closer to the observation than using the static BEC. Besides, the cycle-averaged precipitation scores in all magnitudes are improved, especially for the heavy precipitation, indicating the potential of using diurnally varying BEC in operational applications.摘要背景场误差协方差在资料同化系统中具有非常重要的作用, 目前业务变分同化系统中常采用静态背景场误差协方差, 未考虑其具体的日变化特征. 为构建更为合理且便于业务系统应用的日变化背景误差协方差, 本文构建了高分辨率集合预报样本的日变化背景场误差协方差, 揭示了其日变化特征, 并应用到了CMA-BJ业务系统中, 开展了基于业务框架的批量循环同化预报试验. 结果表明, 背景场误差存在明显的日变化特征, 采用集合日变化背景场误差协方差能够改进模式的预报效果.  相似文献   

6.
The stratospheric polar vortex (SPV), which is an important factor in subseasonal-to-seasonal climate variability and climateprediction, exhibited a remarkable transition from weak in early winter to strong in late winter in 1987/88 (most significant on the interannual timescale during 1979–2019). Therefore, in this study, the subseasonal predictability of this transition SPV case in 1987/88 was investigated using the hindcasts from a selected model (that of the Japan Meteorological Agency) in the Subseasonal-to-Seasonal Prediction project database. Results indicated that the predictability of both weak and strong SPV stages in winter 1987/88, especially near their peak dates, exhibited large sensitivity to the initial condition, which derived mainly from the sensitivity in capturing the 100-hPa eddy heat flux anomalies. Meanwhile, the key tropospheric precursory systems with respect to the occurrence and predictability of this transition SPV case were investigated. The Eurasian teleconnection wave trains might have been a key precursor for the weak SPV stage, while significant tropospheric precursors for the strong SPV stage were not found in this study. In addition, positive correlation (r = 0.41) existed between the forecast biases of the SPV and the NAO in winter 1987/88, which indicates that reducing the forecast biases of the SPV might help to improve the forecasting of the NAO and tropospheric weather.摘要平流层极涡作为冬季次季节尺度上一个重要的可预测性来源, 其强度在1987/88年冬季表现为1979–2019年最显著的转折, 即在前 (后) 冬极端偏弱 (强). 因此在本文中选取这一个例研究了该年冬季平流层极涡在次季节尺度上的可预测性. 结果表明弱极涡和强极涡事件的预测与模式能否准确预测上传行星波的强度紧密相关. 同时, 发现前期对流层欧亚遥相关波列可能是弱极涡事件发生的关键预兆信号. 此外, 模式对平流层极涡强度和北大西洋涛动预测误差之间存在显著正相关关系, 表明模式减少平流层极涡的预测误差可能可以提高北大西洋涛动及相关对流层气候预测.  相似文献   

7.
过去几十年,气候变化和极端气候事件造成的经济损失和灾害显著增加.虽然全球的科学家在理解和预测气候变异方面做出了巨大的努力,但当前在气候预测领域仍然存在几个重大难题.2020年,依托于国家自然科学基金基础科学中心项目的气候系统预测研究中心(CCSP)成立了,该中心旨在应对和处理气候预测领域的三大科学难题:厄尔尼诺-南方涛动(ENSO)预测,延伸期天气预报,年际-年代际气候预测,并为更加准确的气候预测和更加有效的灾害防御提供科学依据.因此,本文介绍了CCSP的主要目标和面对的科学挑战,回顾了CCSP在季风动力过程,陆-气相互作用和模式开发,ENSO变率,季节内振荡,气候预测等方面已取得的重要研究成果.未来CCSP将继续致力于解决上述领域的关键科学问题.  相似文献   

8.
In 2020, the COVID-19 pandemic spreads rapidly around the world. To accurately predict the number of daily new cases in each country, Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic (GPCP). In this article, the authors use the ensemble empirical mode decomposition (EEMD) model and autoregressive moving average (ARMA) model to improve the prediction results of GPCP. In addition, the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease, whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model. Judging from the results, the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP. For countries such as El Salvador with a small number of cases, the absolute values of the relative errors of prediction become smaller. Therefore, this article concludes that this method is more effective for improving prediction results and direct prediction.摘要2020年, 新型冠状病毒肺炎 (COVID-19) 在世界范围内迅速传播.为准确预测各国每日新增发病人数, 兰州大学开发了 COVID-19 流行病全球预测系统 (GPCP). 在本文的研究中, 我们使用集合经验模态分解 (EEMD) 模型和自回归-移动平均 (ARMA) 模型对 GPCP 的预测结果进行改进, 并对发病人数较少或处于发病初期, 不完全符合传染病规律, GPCP 模型无法预测的国家进行直接预测.从结果来看, 使用该方法修正预测结果, 古巴等国家预测误差均大幅下降, 且预测趋势更接近真实情况.对于萨尔瓦多等发病人数较少的国家直接进行预测, 相对误差较小, 预测结果较为准确.该方法对于改进预测结果和直接预测均较为有效.  相似文献   

9.
Previous studies show that temporal irreversibility (TI), as an important indicator of the nonlinearity of time series, is almost uniformly overestimated in the daily air temperature anomaly series over China in NCEP reanalysis data, as compared with station observations. Apart from this highly overestimated TI in the NCEP reanalysis, some other important atmospheric metrics, such as predictability and extreme events, might also be overestimated since there are close relations between nonlinearity and predictability/extreme events. In this study, these issues are fully addressed, i.e., intrinsic predictability, prediction skill, and the number of extreme events. The results show that intrinsic predictability, prediction skill, and the occurrence number of extreme events are also almost uniformly overestimated in the NCEP reanalysis daily minimum and maximum air temperature anomaly series over China. Furthermore, these overestimations of intrinsic predictability, prediction skill, and the number of extreme events are only weakly correlated with the overestimated TI, which indicates that the quality of the NCEP reanalysis should be carefully considered when conclusions on both predictability and extreme events are derived.摘要作为时间序列非线性的一个重要指标, 从NCEP再分析得到日气温异常的时间不可逆性 (TI) 与观测站的相比几乎一致地被高估了.因为非线性与可预报性/极端事件之间有着密切的关系, 除了高估的TI外, 这些大气测度也可能被高估.本文结果表明:NCEP再分析的日最低和最高气温异常序列的内在可预报性,预报技巧和极端事件发生次数也几乎一致被高估.而且, 这些高估的测度与高估的TI只存在微弱的相关性, 这表明利用NCEP再分析研究可预测性和极端事件时, 需要仔细考虑其质量对结论的可能影响.  相似文献   

10.
本研究基于新一代FGOALS-f2动力集合预测系统35年(1981-2015年)的热带气旋历史回报试验对南海台风季(7-11月)热带气旋活动超前10天的月预测技巧进行评估,并对2020年南海台风季热带气旋活动进行了实时月预测尝试.结果表明:FGOALS-f2能较好地预测南海热带气旋路径密度演变特征,预测的热带气旋生成个...  相似文献   

11.
Many coupled models are unable to accurately depict the multi-year La Niña conditions in the tropical Pacific during 2020–22, which poses a new challenge for real-time El Niño–Southern Oscillation (ENSO) predictions. Yet, the corresponding processes responsible for the multi-year coolings are still not understood well. In this paper, reanalysis products are analyzed to examine the ocean–atmosphere interactions in the tropical Pacific that have led to the evolution of sea surface temperature (SST) in the central-eastern equatorial Pacific, including the strong anomalous southeasterly winds over the southeastern tropical Pacific and the related subsurface thermal anomalies. Meanwhile, a divided temporal and spatial (TS) 3D convolution neural network (CNN) model, named TS-3DCNN, was developed to make predictions of the 2020/21 La Niña conditions; results from this novel data-driven model are compared with those from a physics-based intermediate coupled model (ICM). The prediction results made using the TS-3DCNN model for the 2020–22 La Niña indicate that this deep learning–based model can capture the two-year La Niña event to some extent, and is comparable to the IOCAS ICM; the latter dynamical model yields a successful real-time prediction of the Niño3.4 SST anomaly in late 2021 when it is initiated from early 2021. For physical interpretability, sensitivity experiments were designed and carried out to confirm the dominant roles played by the anomalous southeasterly wind and subsurface temperature fields in sustaining the second-year cooling in late 2021. As a potential approach to improving predictions for diversities of ENSO events, additional studies on effectively combining neural networks with dynamical processes and mechanisms are expected to significantly enhance the ENSO prediction capability.摘要2020–22年间热带太平洋经历了持续性多年的拉尼娜事件, 多数耦合模式都难以准确预测其演变过程, 这为厄尔尼诺-南方涛动(ENSO)的实时预测带来了很大的挑战. 同时, 目前学术界对此次持续性双拉尼娜事件的发展仍缺乏合理的物理解释, 其所涉及的物理过程和机制有待于进一步分析. 本研究利用再分析数据产品分析了热带东南太平洋东南风异常及其引起的次表层海温异常在此次热带太平洋海表温度(SST)异常演变中的作用, 并构建了一个时空分离(Time-Space)的三维(3D)卷积神经网络模型(TS-3DCNN)对此次双拉尼娜事件进行实时预测和过程分析. 通过将TS-3DCNN与中国科学院海洋研究所(IOCAS)中等复杂程度海气耦合模式(IOCAS ICM)的预测结果对比, 表明TS-3DCNN模型对2020–22年双重拉尼娜现象的预测能力与IOCAS ICM相当, 二者均能够从2021年初的初始场开始较好地预测2021年末 El Niño3.4区SST的演变. 此外, 基于TS-3DCNN和IOCAS ICM的敏感性试验也验证了赤道外风场异常和次表层海温异常在2021年末赤道中东太平洋海表二次变冷过程中的关键作用. 未来将神经网络与动力 模式模式间的有效结合, 进一步发展神经网络与物理过程相结合的混合建模是进一步提高ENSO事件预测能力的有效途径.  相似文献   

12.
North China May precipitation (NCMP) accounts for a relatively small percentage of annual total precipitation in North China, but its climate variability is large and it has an important impact on the regional climate and agricultural production in North China. Based on observed and reanalysis data from 1979 to 2021, a significant relationship between NCMP and both the April Indian Ocean sea surface temperature (IOSST) and Northwest Pacific Dipole (NWPD) was found, indicating that there may be a link between them. This link, and the possible physical mechanisms by which the IOSST and NWPD in April affect NCMP anomalies, are discussed. Results show that positive (negative) IOSST and NWPD anomalies in April can enhance (weaken) the water vapor transport from the Indian Ocean and Northwest Pacific to North China by influencing the related atmospheric circulation, and thus enhance (weaken) the May precipitation in North China. Accordingly, an NCMP prediction model based on April IOSST and NWPD is established. The model can predict the annual NCMP anomalies effectively, indicating it has the potential to be applied in operational climate prediction.摘要尽管华北区域五月降水 (NCMP) 占华北区域年总降水量的比率较少, 但是其气候变率较大, 对华北区域气候和农业生产等具有重要影响. 基于观测和再分析资料, 发现NCMP与前期四月的印度洋海温 (IOSST) 和西北太平洋偶极子 (NWPD) 具有显著关系, NCMP可能受到IOSST和NWPD的协同影响. 进一步分析表明, 前期四月暖 (冷) 的IOSST和正 (负) 位相的NWPD能够分别通过调节印度洋和西北太平洋区域的局地环流增强 (减弱) 从印度洋和西北太平洋向华北区域输送的水汽, 进而增强 (减弱) NCMP. 最后基于四月IOSST和NWPD构建了NCMP异常的预测模型, 后报检验显示该模型对NCMP异常具有较好的预测能力.  相似文献   

13.
China has been frequently suffering from haze pollution in the past several decades. As one of the most emission-intensive regions, the North China Plain (NCP) features severe haze pollution with multiscale variations. Using more than 30 years of visibility measurements and PM2.5 observations, a subseasonal seesaw phenomenon of haze in autumn and early winter over the NCP is revealed in this study. It is found that when September and October are less (more) polluted than the climatology, haze tends to be enhanced (reduced) in November and December. The abrupt turn of anomalous haze is found to be associated with the circulation reversal of regional and large-scale atmospheric circulations. Months with poor air quality exhibit higher relative humidity, lower boundary layer height, lower near-surface wind speed, and southerly anomalies of low-level winds, which are all unfavorable for the vertical and horizontal dispersion and transport of air pollutants, thus leading to enhanced haze pollution over the NCP region on the subseasonal scale. Further exploration indicates that the reversal of circulation patterns is closely connected to the propagation of midlatitude wave trains active on the subseasonal time scale, which is plausibly associated with the East Atlantic/West Russia teleconnection synchronizing with the transition of the North Atlantic SST. The seesaw relation discussed in this paper provides greater insight into the prediction of the multiscale variability of haze, as well as the possibility of efficient short-term mitigation of haze to meet annual air quality targets in North China.摘要中国近几十年来频受雾霾污染问题困扰, 其中华北平原作为排放最密集的区域之一, 常遭遇不同尺度的严重雾霾污染. 本文利用30余年的能见度和颗粒物 (PM2.5) 观测数据, 发现了华北平原地区在秋季和早冬时雾霾污染在次季节尺度上“跷跷板式”反向变化的关系. 研究发现, 当9–10月污染较轻 (重) 时, 11–12月的污染倾向于加重 (减轻) . 这种突然的变化与局地和大尺度环流的反向变化有关. 污染较重的月份常伴随有更高的相对湿度, 更低的边界层高度和近地面风速以及低层的南风异常, 均不利于污染的垂直和水平扩散和传输, 从而导致了次季节尺度上霾污染的加重. 进一步的研究发现环流场的突然转向与在次季节尺度上活跃的中纬度波列的传播密切相关, 而此波列可能主要与大西洋海温转变及引起的EA/WR遥相关型有关. 这一次季节反向变化为霾污染多尺度变率预测提供了新的理解, 同时为华北地区年度空气质量达标的短期目标提供了具有可行性的参考方法.  相似文献   

14.
The midwinter suppression of North Pacific storm tracks (NPSTs) reflects that the linear relationship between the NPST and baroclinicity breaks in winter. Based on the reanalysis data during the cold seasons of 1979–2019 and a tracking algorithm, this study analyzes the eddy growth process and shows that an enhanced upper-tropospheric jet favors the generation of upper-level eddies on the northeast side of the Pacific jet, but increasingly suppresses the generation of those in the Northwest Pacific. The upper-level eddies generated upstream of the jet core are unable to grow sufficiently throughout the whole cold season, and only those generated downstream of the jet core can grow normally and constitute the main body of the upper-level NPST. By contrast, the main lower-level eddy genesis area and growth area coincide with the baroclinic zone, with the genesis number and local growth rate increasing with the baroclinicity.摘要北太平洋风暴轴的深冬抑制表明风暴轴强度与斜压性之间的线性关系在冬季破裂. 本研究基于1979–2019年冷季的再分析数据和拉格朗日跟踪算法, 对比分析了高低层扰动的具体生长过程. 结果表明太平洋急流的增强有利于高层扰动在急流核东北侧产生, 但却抑制其在西北太平洋的生成. 在急流核上游产生的高层扰动在整个冷季都无法充分发展, 只有在急流核下游产生的高层扰动才能正常生长且它们是构成高层太平洋风暴轴的主体. 相比之下, 低层扰动的生成区和生长区都与斜压区重合, 并且它们的生成数量和局部增长率随着斜压性的增强而增强.  相似文献   

15.
Previous studies have indicated that the stratospheric quasi-biennial oscillation (QBO) has a global impact on winter weather, but relatively less attention has been paid to its effect in summer. Using ERA5 data, this study reports that the QBO has a significant impact on the tropospheric circulation and surface air temperature (SAT) in the extratropics in Northeast Asia and the North Pacific in early summer. Specifically, a QBO-induced mean meridional circulation prevails from Northeast Asia to the North Pacific in the westerly QBO years, exhibiting westerly anomalies in 20°–35°N and easterly anomalies in 35°–65°N from the lower stratosphere to troposphere. This meridional pattern of zonal wind anomalies can excite positive vorticity and thus lead to anomalous low pressure and cyclonic circulation from Northeast Asia to the North Pacific, which in turn cause northerly wind anomalies and decreased SAT in Northeast Asia in June. Conversely, in the easterly QBO years, the QBO-related circulation and SAT anomalies are generally in an opposite polarity to those in the westerly QBO years. These findings provide new evidence of the impact of the QBO on the extratropical climate, and may benefit the prediction of SAT in Northeast Asia in early summer.摘要本文研究了平流层准两年振荡 (QBO) 对东北亚-北太平洋地区初夏对流层环流和地表气温的影响. 在QBO西风位相年, 东北亚至北太平洋地区存在一支由QBO引发的平均经向环流异常, 该经向环流异常可在东北亚至北太平洋地区激发正涡度, 并形成异常气旋式环流. 气旋左侧出现的异常偏北风导致6月东北亚地表气温下降. QBO东风位相年的结果与西风位相年大致相反. 这些结果为QBO对热带外地区天气,气候的影响提供了新的证据, 并为东北亚初夏地表气温的预测提供了新的线索.  相似文献   

16.
SST–precipitation feedback plays an important role in ENSO evolution over the tropical Pacific and thus it is critically important to realistically represent precipitation-induced feedback for accurate simulations and predictions of ENSO. Typically, in hybrid coupled modeling for ENSO predictions, statistical atmospheric models are adopted to determine linear precipitation responses to interannual SST anomalies. However, in current coupled climate models, the observed precipitation–SST relationship is not well represented. In this study, a data-driven deep learning-based U-Net model was used to construct a nonlinear response model of interannual precipitation variability to SST anomalies. It was found that the U-Net model outperformed the traditional EOF-based method in calculating the precipitation variability. Particularly over the western-central tropical Pacific, the mean-square error (MSE) of the precipitation estimates in the U-Net model was smaller than that in the EOF model. The performance of the U-Net model was further improved when additional tendency information on SST and precipitation variability was also introduced as input variables, leading to a pronounced MSE reduction over the ITCZ.摘要SST–降水反馈过程在热带太平洋ENSO演变过程中起着重要作用, 能否真实地在数值模式中表征SST–降水年际异常之间的关系及相关反馈过程, 对于准确模拟和预测ENSO至关重要. 例如, 在一些模拟ENSO的混合型耦合模式中, 通常采用大气统计模型 (如经验正交函数; EOF) 来表征降水 (海气界面淡水通量的一个重要分量) 对SST年际异常的线性响应. 然而在当前的耦合模式中, 真实观测到的降水–SST统计关系还不能被很好地再现出来, 从而引起 ENSO模拟误差和不确定性. 在本研究中, 使用基于深度学习的U-Net模型来构建热带太平洋降水异常场对SST年际异常的非线性响应模型. 研究发现: U-Net模型的性能优于传统的基于EOF方法的模型. 特别是在热带西太平洋海区, U-Net模型估算的降水误差远小于EOF模型的模拟. 此外, 当SST和降水异常的趋势信息作为输入变量也被同时引入以进一步约束模式训练时, U-Net模型的性能可以进一步提高, 如能使热带辐合带区域的误差显著降低.  相似文献   

17.
Soil moisture drought (SMD) directly affects agricultural yield and land water resources. Understanding and predicting the occurrences and evolution of SMD are of great importance for a largely agricultural country such as China. Compared to other drought categories, SMD receives less attention due to the lack of long-term soil moisture datasets. In recent decades, SMD research has been greatly developed in China, benefiting from increased ground and satellite measurements along with state-of-the-art land surface models. Here, the authors provide a brief overview of the recent progress in SMD research in China, focus on historical drought identification and its prediction, and then raise some future perspectives. Based on historical SMD studies, drought frequency has increased overall and drought duration has been prolonged since the 1950s for China as a whole, but they both show substantial temporal variations at the regional scale. Research on SMD prediction has mainly relied on the statistical relationship between soil moisture and climate variables. Few studies based on the dynamical approach in seasonal drought prediction have highlighted the importance of initial conditions and atmospheric forcing datasets. Given the importance of SMD in agricultural practice and water resource management in China, it is necessary to emphasize the following: 1) conducting research on multiple time scales (e.g., from days to the centurial time scale) and cross-regional drought identification research; and 2) developing a SMD prediction system that takes advantage of climate prediction systems, land surface models, and multisource soil moisture datasets.摘要论文回顾了中国土壤湿度干旱 (SMD) 历史重建和季节预测研究进展, 并对未来研究进行了展望. 自1950s年代以来, 全国整体干旱频率增加, 持续时间延长, 且有明显区域特征. SMD预测多是利用土壤湿度与气候变量之间的统计关系, 而少量基于动力学方法的干旱预测研究强调了初始条件和大气强迫数据对季节尺度干旱预测的重要性. 本论文提出: 1) 加强多时间尺度, 跨区域的SMD研究; 2)联合气候预测系统, 陆面模式和多源土壤湿度数据研制SMD预测系统.  相似文献   

18.
Observational data from satellite altimetry were used to quantify the performance of CMIP6 models in simulating the climatological mean and interannual variance of the dynamic sea level (DSL) over 40°S–40°N. In terms of the mean state, the models generally agree well with observations, and high consistency is apparent across different models. The largest bias and model discrepancy is located in the subtropical North Atlantic. As for simulation of the interannual variance, good agreement can be seen across different models, yet the models present a relatively low agreement with observations. The simulations show much weaker variance than observed, and bias is apparent over the subtropics in association with strong western boundary currents. This nearshore bias is reduced considerably in HighResMIP models. The underestimation of DSL interannual variance is at least partially due to the misrepresentation of ocean processes in the CMIP6 historical simulation with its relatively low resolution. The results identify directions for future model development towards a better understanding of the mean and interannual variability of DSL.摘要本研究采用卫星测高数据与第六次国际耦合模式比较计划 (CMIP6) 海平面动力进行对比, 重点针对40°S–40°N地区的动力海平面 (DSL) , 评估了模式对其平均态与年际变率的综合模拟能力. 结果表明, 对于DSL平均态的模拟, 模式与观测结果非常吻合, 模式之间的差异较小. 其中, 副热带北大西洋是模拟偏差和模式间差异较为显著的区域. 对于DSL年际变率的模拟, 模式之间保持较高的一致性, 但是, 模式与观测结果存在明显差异, 模式普遍低估了DSL的年际方差; 其中, 误差大值区域出现在副热带西边界流附近. 模式分辨率会影响CMIP6对中小尺度海洋过程的重现能力, 这可能是导致CMIP6历史模拟出现误差的原因之一.  相似文献   

19.
Extending the atmospheric model top to high altitude is important for simulation of upper atmospheric phenomena, such as the stratospheric quasi-biennial oscillation. The high-top version of the Institute of Atmospheric Physics Atmospheric General Circulation Model with 91 vertical layers (IAP-AGCML91) extends to the mesopause at about 0.01 hPa (~80 km). The high-top model with a fully resolved stratosphere is found to simulate a warmer stratosphere than the low-top version, except near the South Pole, thus reducing its overall cold bias in the stratosphere, and significantly in the upper stratosphere. This sensitivity is shown to be consistent with two separate mechanisms: larger shortwave heating and larger poleward stratospheric meridional eddy heat flux in the high-top model than in the low-top model. Results indicate a significant influence of vertical resolution and model top on climate simulations in IAP-AGCM.摘要提高大气环流模式的模式顶层高度对中高层大气 (如平流层准两年振荡) 的准确模拟至关重要. 本研究将IAP大气环流模型 (IAP-AGCM) 延伸至中层大气顶 (~0.01 hPa, ~80 km) 并提高垂直方向分辨率 (91层) , 发展了一个中高层大气环流模型 (IAP-AGCML91) . 结果表明, 与低层模式相比, 该中高层大气模式在整体上显著减小了平流层尤其是上平流层的冷偏差.研究发现这种改善与两种机制有关:与低层模式相比, 高层模式模拟的短波加热更大, 极区平流层附近的经向涡动热通量更大.上述结果表明, 垂直分辨率和模式顶层高度对IAP-AGCML91的气候模拟有重要影响.  相似文献   

20.
This paper examines truncation and round-off errors in the numerical solution of the 1D advection equation with the Lax–Friedrichs scheme, and accumulation of the errors as they are propagated to high temporal layers. The authors obtain a new theoretical approximation formula for the upper bound of the total error of the numerical solution, as well as theoretical formulae for the optimal grid size and time step. The reliability of the obtained formulae is demonstrated with numerical experimental examples. Next, the ratio of the optimal time steps under two different machine precisions is found to satisfy a universal relation that depends only on the machine precision involved. Finally, theoretical verification suggests that this problem satisfies the computational uncertainty principle when the grid ratio is fixed, demonstrating the inevitable existence of an optimal time step size under a finite machine precision.摘要本文对于应用Lax- Friedrichs 格式数值求解一维平流方程, 研究数值求解过程中产生的截断误差与舍入误差, 以及两种误差逐层向高时间层传播的累积, 得到新的数值解总误差上界的理论近似公式, 以及最优格距和最优时间步长的理论公式. 通过数值算例验证了所得公式的可靠性. 然后, 发现了两种不同机器精度下最优时间步长之比满足的一个仅与机器精度有关的普适关系. 最后, 理论验证了在网格比固定的情况下, 此问题满足数值计算的不确定性原理, 以及在机器有限精度下最优时间步长的必然存在.  相似文献   

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