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1.
Accurate forecasting of ocean waves is of great importance to the safety of marine transportation. Despite wave forecasts having been improved, the current level of prediction skill is still far from satisfactory. Here, the authors propose a new physically informed deep learning model, named Double-stage ConvLSTM (D-ConvLSTM), to improve wave forecasts in the Atlantic Ocean. The waves in the next three consecutive days are predicted by feeding the deep learning model with the observed wave conditions in the preceding two days and the simultaneous ECMWF Reanalysis v5 (ERA5) wind forcing during the forecast period. The prediction skill of the d-ConvLSTM model was compared with that of two other forecasting methods—namely, the wave persistence forecast and the original ConvLSTM model. The results showed an increasing prediction error with the forecast lead time when the forecasts were evaluated using ERA5 reanalysis data. The d-ConvLSTM model outperformed the other two models in terms of wave prediction accuracy, with a root-mean-square error of lower than 0.4 m and an anomaly correlation coefficient skill of ∼0.80 at lead times of up to three days. In addition, a similar prediction was generated when the wind forcing was replaced by the IFS forecasted wind, suggesting that the d-ConvLSTM model is comparable to the Wave Model of European Centre for Medium-Range Weather Forecasts (ECMWF-WAM), but more economical and time-saving.摘要海浪预报对海上运输安全至关重要. 本研究提出了一种涵盖物理信息的深度学习模型Double-stage ConvLSTM (D-ConvLSTM) 以改进大西洋的海浪预报. 将D-ConvLSTM模型与海浪持续性预测和原始ConvLSTM模型的预测技巧进行对比. 结果表明, 预测误差随着预测时长的增加而增加. D-ConvLSTM模型在预测准确度方面优于前二者, 且第三天预测的均方根误差低于0.4 m, 距平相关系数约在0.8. 此外, 当使用IFS预测风替代再分析风时, 能够产生相似的预测效果. 这表明D-ConvLSTM模型的预测能力能够与ECMWF-WAM模式相当, 且更节省计算资源和时间.  相似文献   

2.
Tropical cyclones (TCs) seriously endanger human life and the safety of property. Real-time monitoring of TCs has been one of the focal points in meteorological studies. With the development of space technology and sensor technology, satellite remote sensing has become the main means of monitoring TCs. Furthermore, with its superior data mining capability, deep learning has shown advantages over traditional physical or statistical-based algorithms in the geosciences. As a result, more deep-learning algorithms are being developed and applied to extract TC information. This paper systematically reviews the deep-learning frameworks used for TC information extraction and then gives two typical applications of deep-learning models for TC intensity and wind radius estimation. In addition, the authors present an outlook on the future perspectives of deep learning in TC information extraction.摘要热带气旋 (TC) 严重危害人类生命和财产安全, TC的实时监测一直是研究热点, 随着空间和传感器技术的发展, 卫星遥感已成为监测TC的主要手段. 此外, 深度学习具有卓越的数据挖掘能力, 在地球科学中的表现优于基于物理或统计的算法, 越来越多的深度学习算法被开发和应用于TC信息的提取, 本文系统地回顾了深度学习在TC信息提取中的应用, 并给出了深度学习模型在TC强度和风圈半径提取中的应用. 此外, 本文还展望了深度学习在TC信息提取中的应用前景.  相似文献   

3.
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   

4.
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   

5.
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   

6.
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   

7.
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   

8.
Based on data observed from 1979 to 2017, the influence of Arctic sea ice in the previous spring on the first mode of interannual variation in summer drought in the middle and high latitudes of Asia (MHA) is analyzed in this paper, and the possible associated physical mechanism is discussed. The results show that when there is more sea ice near the Svalbard Islands in spring while the sea ice in the Barents–Kara Sea decreases, the drought distribution in the MHA shows a north–south dipole pattern in late summer, and drought weakens in the northern MHA region and strengthens in the southern MHA region. By analyzing the main physical process affecting these changes, the change in sea ice in spring is found to lead to the Polar–Eurasian teleconnection pattern, resulting in more precipitation, thicker snow depths, higher temperatures, and higher soil moisture in the northern MHA region in spring and less precipitation, smaller snow depths, and lower soil moisture in the southern MHA region. Such soil conditions last until summer, affect summer precipitation and temperature conditions through soil moisture–atmosphere feedbacks, and ultimately modulate changes in summer drought in the MHA.摘要本文分析了亚洲中高纬度地区 (MHA) 年际尺度夏季干旱的主模态时空变化特征, 以及影响第一模态的主要影响因子和可能的物理过程. 结果显示该区域夏季干旱第一模态主要呈现一个南北偶极性的分布. 而影响MHA夏季干旱的主要影响因子为前春北极海冰. 当春季斯瓦尔巴群岛附近海冰偏多, 而巴伦支海-喀拉海海冰减少时, 通过冰-气相互作用, 使得MHA北部春季降水增加, 雪深加厚, 土壤湿度偏高, 而南部则相反. 然后这样的土壤湿度条件从春季持续到夏季, 通过土壤湿度-大气反馈影响夏季MHA降水和温度变化, 最终对夏季干旱主模态产生影响.  相似文献   

9.
A machine-learning (ML) model, the light gradient boosting machine (LightGBM), was constructed to simulate the variation in the summer (June–July–August) heatwave frequency (HWF) over eastern Europe (HWF_EUR) and to analyze the contributions of various lower-boundary climate factors to the HWF_EUR variation. The examined lower-boundary climate factors were those that may contribute to the HWF_EUR variation—namely, the sea surface temperature, soil moisture, snow-cover extent, and sea-ice concentration from the simultaneous summer, preceding spring, and winter. These selected climate factors were significantly correlated to the summer HWF_EUR variation and were used to construct the ML model. Both the hindcast simulation of HWF_EUR for the period 1981–2020 and its real-time simulation for the period 2011–2020, which used the constructed ML model, were investigated. To evaluate the contributions of the climate factors, various model experiments using different combinations of the climate factors were examined and compared. The results indicated that the LightGBM model had comparatively good performance in simulating the HWF_EUR variation. The sea surface temperature made more contributions to the ML model simulation than the other climate factors. Further examination showed that the best ML simulation was that which used the climate factors in the preceding winter, suggesting that the lower-boundary conditions in the preceding winter may be critical in forecasting the summer HWF_EUR variation.摘要本文使用LightGBM机器学习模型模拟了欧洲东部夏季热浪频率的变化, 并分析了多个底边界层气候因子的贡献. 所选取的气候因子包括前期冬季, 前期春季以及同期夏季的下垫面海温, 土壤湿度, 积雪以及海冰. 分析结果说明LightGBM模型能够较好的模拟出欧洲东部夏季热浪频率的变化, 其中海温因子对模拟的贡献最大. 进一步的分析研究显示, 使用前期冬季的气候因子进行的模拟可以获得最佳模拟结果, 意味着前期冬季的下垫面气候因子可能对夏季欧洲东部热浪频率变化的预报能起到关键作用.  相似文献   

10.
西伯利亚地区异常的升温可能会给生态系统带来灾难性的影响.本文从气候角度分析西伯利亚地区初夏升温的特征以及北极海冰减小的可能贡献.观测和再分析资料表明,1979-2020年间西伯利亚地区6月地表气温有很强的升温趋势(0.9℃/10年),明显高于同纬度地区平均的升温趋势(0.46℃/10年).升温从地表延伸至300hPa左...  相似文献   

11.
Topography as well as its attributes are fundamental factors during precipitation generation. Various models with different complexity have been established to interpret the topography–precipitation relationship. In this study, the topography–precipitation relationships simulated by two dynamical downscaling models (DDMs) at the kilometer-scale and traditional quarter-degree resolution in eastern China are evaluated by utilizing multi-scale geographically weighted regression with station precipitation observations as reference. The precipitation simulated by the kilometer-scale DDM had a higher agreement with observations than the quarter-degree simulation. For the effects of topography on precipitation, observations revealed a dominant role played by the topographical relief in the precipitation distribution at most stations in the study region. The kilometer-scale DDM generally reflected this dominant role of topographical relief. However, the quarter-degree DDM showed an excessive dependency of the precipitation distribution on the topographical elevation. This research highlights the key role of underground sub-grid variations on the precipitation in eastern China, which implies a potential way forward for precipitation simulation improvements.摘要与传统的1/4度 (≈25-30 km) 动力降尺度模拟相比, 公里尺度模拟的降水空间分布与观测结果更为接近. 为了研究这一差异原因, 本研究以华东地区为例, 探究了地形因子在观测和模拟的降水中的作用. 为了更好地体现地形因子对降水分布非均匀性的影响, 以及不同地形因子作用的尺度差异, 本研究采用多尺度地理加权回归模型, 对五个主要地形因子与公里尺度和1/4度分辨率模拟的降水的关系进行了评估. 基于观测数据的研究结果显示地形起伏度, 地形高程和离海岸线距离对华东地区降水分布的非均匀性都有重要影响, 其中地形起伏度在研究区大部分站点降水分布中起主导作用; 公里尺度模拟结果基本反映了地形起伏度的主导作用; 而1 / 4度模拟结果表现出降水对地形高程的过度依赖. 本研究揭示了公里尺度地形分布对中国东部降水的非均匀分布的关键作用, 研究结果可以为改进降水模拟提供新的思路.  相似文献   

12.
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   

13.
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   

14.
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   

15.
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   

16.
The global planetary boundary layer height (PBLH) estimated from 11 years (2007–17) of Integrated Global Radiosonde Archive (IGRA) data, Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) soundings, and European Center for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data, are compared in this study. In general, the spatial distribution of global PBLH derived from ERA-Interim is consistent with the one from IGRA, both at 1200 UTC and 0000 UTC. High PBLH occurs at noon local time, because of strong radiation energy and convective activity. There are larger differences between the results of COSMIC and the other two datasets. PBLHs derived from COSMIC are much higher than those from radiosonde and reanalysis data. However, PBLHs derived from the three datasets all exhibit higher values in the low latitudes and lower ones in the high latitudes. The latitudinal difference between IGRA and COSMIC ranges from −1700 m to −500 m, while it ranges from −500 m to 250 m for IGRA and ERA-Interim. It is found that the differences among the three datasets are larger in winter and smaller in summer for most studied latitudes.摘要用11年的全球无线电掩星数据 (COSMIC) , 无线电探空数据 (IGRA) 以及欧洲中心再分析资料 (ERA-Interim) 对全球大气边界层高度 (PBLH) 进行估算比较. 结果表明: (1) 在1200 UTC和0000 UTC, 由ERA-Interim和IGRA数据估算得到的全球PBLH空间分布较为一致, 相关性较好, 在白天正午时候太阳辐射能力较强, 对流活动频繁, 估算得到的大气边界层高度较高. (2) 由COSMIC掩星数据估算得到的边界层高度比探空数据和再分析数据估算结果整体偏大. (3) COSMIC掩星数据, IGRA 探空数据以及 ERA-Interim 再分析资料估算结果都表明边界层高度在低纬度地区偏大, 高纬度地区偏小. (4) 分析不同数据估算边界层高度纬向季节性差异表明, IGRA探空数据和COSMIC数据间差异为-1700m至-500m, IGRA与ERA-Interim之间的差异为-500m至250m.此外, 对于大多数纬度而言, 三个数据集之间的差异在冬季较大, 在夏季较小.  相似文献   

17.
Coordinated numerical ensemble experiments with six different state-of-the-art atmosphere models were used to evaluate and quantify the impact of global SST (from reanalysis data) on the early winter Arctic warming during 1982–2014. Two sets of experiments were designed: in the first set (EXP1), OISSTv2 daily sea-ice concentration and SST variations were used as the lower boundary forcing, while in the second set (EXP2) the SST data were replaced by the daily SST climatology. In the results, the multi-model ensemble mean of EXP1 showed a near-surface (~850 hPa) warming trend of 0.4 °C/10 yr, which was 80% of the warming trend in the reanalysis. The simulated warming trend was robust across the six models, with a magnitude of 0.36–0.50 °C/10 yr. The global SST could explain most of the simulated warming trend in EXP1 in the mid and low troposphere over the Arctic, and accounted for 58% of the simulated near-surface warming. The results also suggest that the upper-tropospheric warming (~200 hPa) over the Arctic in the reanalysis is likely not a forced signal; rather, it is caused by natural climate variability. The source regions that can potentially impact the early winter Arctic warming are explored and the limitations of the study are discussed.摘要本文使用六个不同的最新大气模式进行了协调数值集合实验, 评估和量化了全球海表面温度 (SST) 对1982–2014年冬季早期北极变暖的影响.本研究设计了两组实验:在第一组 (EXP1) 中, 将OISSTv2逐日变化的海冰密集度和SST数据作为下边界强迫场;在第二组 (EXP2) 中, 将逐日变化的SST数据替换为逐日气候态.结果表明: (1) EXP1的多模式集合总体平均值显示0.4 °C/10年的近地表 (约850 hPa) 升温趋势, 为再分析数据结果中升温趋势的80%. (2) 在这六个模式中, 模拟的变暖趋势均很强, 幅度为0.36–0.50 °C/10年. (3) 全球海表温度可以解释北极对流层中低层EXP1的大部分模拟的变暖趋势, 占再分析数据结果的58%. (4) 再分析数据结果中, 北极上空的对流层上层变暖 (约200 hPa) 不是由强迫信号而可能是由自然气候变率引起的.本文还探索了影响北极初冬变暖的可能源区, 并讨论了该研究的局限性.  相似文献   

18.
In this study, the impact of environmental factors on tropical cyclone (TC) outer-core size was investigated for both migrating and local TCs in the South China Sea during the period 2001–2019. Among all the thermodynamic and dynamic factors, the low-level environmental helicity showed the strongest positive correlation with TC outer-core size. Large helicity favors the development and organization of convection in TCs, and the corresponding strong inflow and large angular momentum fluxes into the system is beneficial for the maintenance and enlargement of TC outer-core size. Besides, the asymmetric distribution of helicity may account for the asymmetry of TC outer-core size. Therefore, the environmental helicity, as an integrated dynamic factor, can provide an alternative view on TC outer-core size.摘要本文利用2001–2019年间的ERA5再分析数据集和热带气旋 (TC) 最佳路径观测, 分析了中国南海TC的外围尺度与环境变量之间的关系. 研究发现, 低层环境螺旋度与TC外围尺度具有显著的正相关. 大的环境螺旋度有利于TC外围对流的增强和组织化, 与之对应的径向入流和角动量输送有利于TC外围尺度的维持或扩张. 此外, 螺旋度的非对称分布与TC外围尺度的非对称性结构也密切相关. 环境螺旋度作为一个包含TC外围对流强度和组织化程度等信息的综合动力因子, 为理解TC外围尺度变化提供了不同的视角.  相似文献   

19.
To evaluate the downscaling ability with respect to tropical cyclones (TCs) near China and its sensitivity to the model physics representation, the authors performed a multi-physics ensemble simulation with the regional Climate–Weather Research and Forecasting (CWRF) model at a 30 km resolution driven by ERA-Interim reanalysis data. The ensemble consisted of 28 integrations during 1979–2016 with varying CWRF physics configurations. Both CWRF and ERA-Interim can generally capture the seasonal cycle and interannual variation of the TC number near China, but evidently underestimate them. The CWRF downscaling and its multi-physics ensemble can notably reduce the underestimation and significantly improve the simulation of the TC occurrences. The skill enhancement is especially large in terms of the interannual variation, which is most sensitive to the cumulus scheme, followed by the boundary layer, surface and radiation schemes, but weakly sensitive to the cloud and microphysics schemes. Generally, the Noah surface scheme, CAML(CAM radiation scheme as implemented by Liang together with the diagnostic cloud cover scheme of Xu and Randall(1996)) radiation scheme, prognostic cloud scheme, and Thompson microphysics scheme stand out for their better performance in simulating the interannual variation of TC number. However, the Emanuel cumulus and MYNN boundary layer schemes produce severe interannual biases. Our study provides a valuable reference for CWRF application to improve the understanding and prediction of TC activity.摘要为评估CWRF模式的降尺度能力和其热带气旋模拟对物理参数化方案的敏感性, 本文利用ERI再分析资料驱动CWRF在30km网格上对1982-2016年中国近海热带气旋开展了一次集合模拟.结果表明:CWRF与ERI均能模拟出热带气旋的季节变化和年际变化形势且均存在低估, 但相较ERI, CWRF的降尺度技术和集合模拟可以再现更多的热带气旋, 显著减少低估.年际变化结果提升最为明显, 它对积云方案最为敏感, 其次是边界层, 陆面和辐射方案, 对云和微物理方案较弱.该研究为应用CWRF理解和预报热带气旋提供了参考.  相似文献   

20.
This paper investigates the distribution of spatial modes of cloud-to-ground (CG) lightning activity across China's land areas during the period 2010–20 and their possible causes based on the CG lightning dataset of the China National Lightning Detection Network. It is found that the first empirical orthogonal function mode (EOF1) occupies 32.86% of the total variance of the summer CG lightning anomaly variation. Also, it exhibits a negative–positive–negative meridional seesaw pattern from north to south. When the SST of the East Pacific and Indian Ocean warms abnormally and the SST of the Northwest Pacific becomes abnormally cold, a cyclonic circulation is stimulated in the Yellow Sea, East China Sea, and tropical West Pacific region of China. As the water vapor continues to move southwards, it converges with the water vapor deriving from the Bay of Bengal in South China, and ascending motion strengthens here, thus enhancing the CG lightning activity of this area. Affected by the abnormal high pressure, the corresponding CG lightning activities in North China and Northeast China are relatively weak. The ENSO phenomenon is the climate driver for the CG lightning activity occurring in land areas of China.摘要本文利用中国气象局国家雷电监测网 (CNLDN) 的地闪观测数据集, 分析了2010–2020年中国陆地区域地闪空间模态分布特征及其可能的气候成因. 研究发现, 夏季地闪第一模态的方差贡献率为32.86%, 其分布从北到南呈现出“−+−”的经向跷跷板模式. 当东太平洋和印度洋的海温异常增暖, 西北太平洋的海温异常变冷时, 在中国黄海, 东海及热带西太平洋地区激发出气旋性环流. 随着水汽南下至华南地区, 与来自孟加拉湾的水汽汇合, 上升运动在此加强, 从而使得该地区的雷电活动增强. 表明厄尔尼诺-南方涛动 (ENSO) 现象, 是发生在中国陆地区域的地闪活动的气候驱动因子.  相似文献   

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