Seasonal Prediction of Indian Summer Monsoon Using WRF: A Dynamical Downscaling Perspective

Seasonal forecasting of the Indian summer monsoon by dynamically downscaling the CFSv2 output using a high resolution WRF model over the hindcast period of 1982-2008 has been performed in this study. The April start ensemble mean of the CFSv2 has been used to provide the initial and lateral boundary conditions for driving the WRF. The WRF model is integrated from 1st May through 1st October for each monsoon season. The analysis suggests that the WRF exhibits potential skill in improving the rainfall skill as well as the seasonal pattern and minimizes the meteorological errors as compared to the parent CFSv2 model. The rainfall pattern is simulated quite closer to the observation (IMD) in the WRF model over CFSv2 especially over the significant rainfall regions of India such as the Western Ghats and the central India. Probability distributions of the rainfall show that the rainfall is improved with the WRF. However, the WRF simulates copious amounts of rainfall over the eastern coast of India. Surface and upper air meteorological parameters show that the WRF model improves the simulation of the lower level and upper-level winds, MSLP, CAPE and PBL height. The specific humidity profiles show substantial improvement along the vertical column of the atmosphere which can be directly related to the net precipitable water. The CFSv2 underestimates the specific humidity along the vertical which is corrected by the WRF model. Over the Bay of Bengal, the WRF model overestimates the CAPE and specific humidity which may be attributed to the copious amount of rainfall along the eastern coast of India. Residual heating profiles also show that the WRF improves the thermodynamics of the atmosphere over 700 hPa and 400 hPa levels which helps in improving the rainfall simulation. Improvement in the land surface fluxes is also witnessed in the WRF model.

A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).

基于GRACE/GRACE-FO数据降尺度方法反演库尔勒东区地下水储量变化

GRACE与GRACE-FO重力卫星为全球中大尺度地下水反演监测提供了新的手段,但难以提供小尺度上较高空间分辨率的地下水储量变化(GWSA)信息。本文针对新疆库尔勒东部缺资料区,采用动力降尺度方法提高GRACE/GRACE-FO反演GWSA数据的空间分辨率,分析GWSA的时空分布规律。首先,基于数据融合方法构建了库尔勒东区GWSA数值模型;然后,利用优化后的模型将GWSA反演数据的分辨率从1°降尺度至0.25°和0.05°,将反演的GWSA与水井监测的地下水位(GWL)数据进行对比验证;最后,利用0.05°GWSA数据分析研究区GWSA态势。结果表明:①与降尺度前1°GWSA数据相比,降尺度转换后的高分辨率GWSA数据在空间上更加平滑、展示了更加丰富的细节,且提高了与水井GWL监测数据的相关性,改进了反演精度和可靠性;②小尺度上,降尺度后的GWSA数据能够反映水源地地下水的季节性、年际和长期开采下的亏损等动态特征;③研究区GWSA呈现出时空分布差异性,2005—2020年区内地下水储量变化率为-1~1 mm·a-1,总体上呈南增、北减态势,南、北部山区的变幅大于中部相对平坦区域。

气候变化影响中国东北地区降水侵蚀力的高分辨率区域模式预估

土壤侵蚀是生态环境和农业生产的重要影响因子。我国东三省土地面积79.33万平方公里,占我国国土面积的9.3%,是重要的商品粮基地,同时也是世界四大黑土区之一。在气候变化背景下,我国东北地区的土壤侵蚀情况及其未来风险,目前仍不清楚。影响土壤侵蚀力的重要因素之一是降水侵蚀力(R-factor),其与强降水有关。本研究利用CN05.1和APHRODITE观测降水资料揭示了我国东北地区降水侵蚀力的观测特征。在气候平均态上,东北地区东南部降水侵蚀力最强。降水侵蚀力存在明显的年循环,以夏季为主导,占全年总侵蚀的80%以上。在观测分析基础上,对RegCM4动力降尺度模式进行了评估和订正,并预估了未来不同共享社会经济路径(SSP1-2.6和SSP5-8.5)下中国东北地区降水侵蚀力的变化。随着未来增温,到21世纪末,两种排放情景下东北地区平均的降水侵蚀力分别增加9.90%和26.70%。高排放情景下将面临更严重的降水侵蚀风险,SSP5-8.5情景下降水侵蚀力的增强幅度约为SSP1-2.6情景下变化的2.7倍,同时77.69%的区域面积上降水侵蚀力将更强。因此,采取切实有效的减排措施,走可持续发展路径,对于减缓我国东北地区黑土地的土壤侵蚀风险,进而保障粮食安全,具有重要意义。

一种改进的数值模式降水预报时间降尺度方法

数值模式降水预报时间降尺度的目的是将较低时间分辨率的模式降水预报通过统计方法降尺度到较高的时间分辨率上,以近似代替较高时间分辨率的模式降水预报。将现有的基于三次样条插值逐点拆分、位置订正、光流动态重构、频率匹配、逐点总量约束的网格降水预报时间降尺度方法(以下简称为“现有方法”)应用于未经订正的数值模式降水预报时,其中的订正步骤受制于数值模式预报的总降水量的约束,所生成的降水预报时间降尺度产品在强度上存在明显的不合理波动。因此,改进设计了一种适用于数值模式降水预报的时间降尺度方法(以下简称为“改进方法”),包含分段三次Hermite插值逐点拆分、金字塔LK光流动态重构、均衡的逐点总量约束三个步骤,不依赖于降水实况订正,且保留了原始模式的系统偏差。利用2020年4—10月中国气象局华东区域中心模式逐1h降水预报场和其累加生成的逐3h降水预报场,分别将其作为数值模式降水预报时间降尺度方法的验证场和输入场,评估对比了单纯的逐点拆分、“现有方法”和“改进方法”的时间降尺度效果,并使用匀速运动的圆形雨带理想模型和真实个例分析分别加以验证。结果表明:(1)“改进方法”所生成的逐1h降水预报在强度分布上更加均衡合理,短时强降水的平均TS评分和BIAS偏差的波动幅度仅为“现有方法”的27%和9%。(2)使用“改进方法”所生成的逐1h降水预报与真实的数值模式生成的逐1h降水预报场更为相似,平均TS评分和BIAS偏差幅度分别提升13%和下降31%以上,且在理想模型中均方根误差相比于“现有方法”总体下降10%。改进的数值模式降水预报时间降尺度方法同时满足运动合理、强度波动合理和降水总量约束的基本要求,可通用于任意网格降水预报的时间降尺度。

基于CALMET-WRF耦合的复杂地形下导线覆冰的精细化数值模拟研究

利用耦合了WRF模式的CALMET模型,对2022年1月5—10日山西省南部中条山区导线覆冰事件的天气背景场进行了数值模拟,在评估了WRF和CALMET对气象要素的模拟效果的基础上,分别利用WRF和CALMET模拟的气象场驱动Makkonen覆冰模型,对本次导线覆冰过程进行了数值模拟,得到了如下结论:1)相比于WRF模式的模拟结果,CALMET降尺度后的气象场能更符合实际地形影响下近地面温度场和风场的分布规律,其模拟的近地面低温区(气温<0℃)范围较WRF模拟范围更大。2)CALMET的气温均方根误差整体较WRF模式减小0.5~1℃,相关系数由0.5~0.8提升至0.6~0.85;风速的均方根误差较WRF减少了1 m/s,相关系数较WRF提升0.2,说明WRF模式结合CALMET模拟气象场更加接近真实观测结果。3)利用CALMET降尺度场驱动覆冰模型能较好地反映微尺度地形下电线积冰的时空分布特征,各杆塔模拟的覆冰厚度偏差较WRF显著减小了2 mm,且降低了模式对覆冰启动的滞后时间。

青藏铁路沿线气候特征精细化模拟研究

青藏铁路格拉段(以下简称青藏铁路),特别是从西大滩至安多穿越550 km多年冻土区的路段,沿线有着复杂的地形地貌和冻土环境。近年来随着气候变暖和多年冻土退化,对于建造在多年冻土之上的青藏铁路的维护需求剧增,为了能充分捕捉青藏铁路沿线地区复杂的地形地貌对局地气候变化的影响,以期对青藏铁路的运行维护提供理论支持。本文利用铁路沿线站点观测数据,并以ERA5数据驱动WRF模式进行网格距离为10 km×10 km的动力降尺度模拟。结果显示,青藏铁路沿线的六个站点自1998-2020年普遍呈现增温趋势,增温率最低的站点为0.27℃·(10a)^(-1),最高为0.56℃·(10a)^(-1);WRF模式对表面气温的模拟在年平均气温结果与观测数据略有差异,在夏季和秋季的模拟结果较好,夏季相关系数达到0.95以上,秋季在0.80以上,春季和冬季较差;WRF降水模拟的结果显示,Nudging方法有效改进了青藏高原夏季降水的湿偏差,青藏铁路北段至铁路南段降水逐渐增加,且在铁路中部降水出现极大值。WRF模式在青藏高原的温度和降水模拟上仍存在着一定的冷偏差和湿偏差,寻找新的方法或利用高质量驱动数据来驱动模式将可能对青藏高原地区的降尺度模拟结果有更进一步的改善。

A high-resolution Asia-Pacific regional coupled prediction system with dynamically downscaling coupled data assimilation

A regional coupled prediction system for the Asia-Pacific(AP-RCP)(38°E-180°,20°S-60°N) area has been established.The AP-RCP system consists of WRF-ROMS(Weather Research and Forecast,and Regional Ocean Model System) coupled models combined with local observational information through dynamically downscaling coupled data assimilation(CDA).The system generates 18-day forecasts for the atmosphere and ocean environment on a daily quasi-operational schedule at Pilot National Laboratory for Marine Science and Technology(Qingdao)(QNLM),consisting of 2 different-resolution coupled models:27 km WRF coupled with 9 km ROMS,9 km WRF coupled with 3 km ROMS,while a version of 3 km WRF coupled with 3 km ROMS is in a test mode.This study is a first step to evaluate the impact of high-resolution coupled model with dynamically downscaling CDA on the extended-range predictions,focusing on forecasts of typhoon onset,improved precipitation and typhoon intensity forecasts as well as simulation of the Kuroshio current variability associated with mesoscale oceanic activities.The results show that for realizing the extended-range predictability of atmospheric and oceanic environment characterized by statistics of mesoscale activities,a fine resolution coupled model resolving local mesoscale phenomena with balanced and coherent coupled initialization is a necessary first step.The next challenges include improving the planetary boundary physics and the representation of air-sea and air-land interactions to enable the model to resolve kilometer or sub-kilometer processes.

A High-Resolution Modeling Strategy to Assess Impacts of Climate Change for Mesoamerica and the Caribbean

Mesoamerica and the Caribbean are low-latitude regions at risk for the effects of climate change. Global climate models provide large-scale assessment of climate drivers, but, at a horizontal resolution of 100 km, cannot resolve the effects of topography and land use as they impact the local temperature and precipitation that are keys to climate impacts. We developed a robust dynamical downscaling strategy that used the WRF regional climate model to downscale at 4 - 12 km resolution GCM results. Model verification demonstrates the need for such resolution of topography in order to properly simulate temperatures. Precipitation is more difficult to evaluate, being highly variable in time and space. Overall, a 36 km resolution is inadequate;12 km appears reasonable, especially in regions of low topography, but the 4 km resolution provides the best match with observations. This represents a tradeoff between model resolution and the computational effort needed to make simulations. A key goal is to provide climate change specialists in each country with the information they need to evaluate possible future climate change impacts.

黑河流域降水统计——动力降尺度问题研究

依据区域气候模式RIEMS2.0输出的3 km高分辨率数据和站点降水记录分析了中国西北黑河流域降水的动力降尺度和统计—动力降尺度问题,检验了多种因子组合下多元线性回归(MLR)和贝叶斯模式平均(BMA)降尺度模型,评估了降尺度降水的均方根误差、相关系数、方差百分率及“负降水”偏差率等方面的统计特征。结果表明,动力降尺度降水相关系数最高,误差也最大,降水方差达到观测值的1.5~2倍;除相关系数外,统计—动力降尺度模型的几个统计特征均最优,纯统计模型次之。检验表明,仅用700 hPa位势高度场、经向风和比湿等构建的统计降尺度模型估计的站点降水相关系数较低,均方根误差也较大。当在统计降尺度模型中引入模式降水因子后站点降水的估计得到明显改善,其中MLR类模型的降水相关系数和方差百分率均明显高于BMA类模型,均方根误差二者相当,但前者“负降水”出现频次明显大于后者,“负降水”偏差主要出现在降水稀少的冬半年及黑河中、下游干旱或极端干旱区,上游出现频率较低,其中MLR类模型“负降水”出现频次明显高于BMA类模型,后者仅出现在黑河中、下游地区。包含模式降水因子的统计—动力降尺度模型能减少“负降水”出现的频次。此外,降尺度模型估计降水的统计特征随季节变化,其中7种降尺度模型估计的站点降水误差与站点气候降水量成比例,但相对误差与之相反。这些评估结果表明,即使用高分辨率动力降尺度估计干旱区站点降水也存在明显偏差,需要结合统计降尺度模型进一步降低站点降水估计的不确定性。

基于统计与动力相结合的南极夏季海冰跨季节预测

选取美国国家环境预测中心气候预测系统(CFS)第二版预测的南大洋(40°S—80°S)夏季海温、海洋性大陆地区(100°E—130°E,10°N—15°S)春季海温以及南半球热带外(20°S—90°S)春季海平面气压3个预测因子,利用奇异值分析的方法提取相关信息,采用回归模型对南极夏季海冰场进行预测。从该模式对1983—2018年南极夏季海冰密集度异常值的回报效果看,模型的预测技巧较CFS原始预测有显著的提高。对单格点上预测与实测的时间相关系数提高到0.76;从交叉检验的结果来看,预测技巧较CFS同样有显著提高;从对南极海冰范围的预测来看,该模型的预测效果最好,明显优于CFS原始预测和持续性预测。这些结果表明,该模型具有较高的预测性能。

Dynamic downscaling of near-surface air temperature at the basin scale using WRF-a case study in the Heihe River Basin, China

The spatial resolution of general circulation models （GCMs） is too coarse to represent regional climate variations at the regional, basin, and local scales required for many environmental modeling and impact assessments. Weather research and forecasting model （WRF） is a nextgeneration, fully compressible, Euler non-hydrostatic mesoscale forecast model with a runtime hydrostatic option. This model is useful for downscaling weather and climate at the scales from one kilometer to thousands of kilometers, and is useful for deriving meteorological parameters required for hydrological simulation too. The objective of this paper is to validate WRF simulating 5 km/ 1 h air temperatures by daily observed data of China Meteorological Administration （CMA） stations, and by hourly in-situ data of the Watershed Allied Telemetry Experimental Research Project. The daily validation shows that the WRF simulation has good agreement with the observed data; the R2 between the WRF simulation and each station is more than 0.93; the absolute of meanbias error （MBE） for each station is less than 2℃; and the MBEs of Ejina, Mazongshan and Alxa stations are near zero, with R2 is more than 0.98, which can be taken as an unbiased estimation. The hourly validation shows that the WRF simulation can capture the basic trend of observed data, the MBE of each site is approximately 2℃, the R2 of each site is more than 0.80, with the highest at 0.95, and the computed and observed surface air temperature series show a significantly similar trend.

中国北方未来沙尘起沙通量变化的数值模拟研究

本研究利用WRF-Chem(Weather Research and Forecasting model with online coupled Chemistry)模式对未来中国北方沙尘起沙过程变化进行了模拟预测。为了提高预测结果的准确度,研究综合考虑了气溶胶、温室气体和植被覆盖率等因素对天气、气候和起沙过程的影响。预测结果显示,2016~2029年西北部沙尘源地起沙量高于北部沙尘源地,地形和气候的差异是导致两地起沙过程及其季节变化差异的主要原因。两个沙尘源地四季起沙通量呈总体减少而部分季节增加的趋势,西北部沙尘源地起沙通量在春季总体呈上升趋势,在夏、秋和冬季呈下降趋势;北部沙尘源地起沙通量在春、夏和冬季呈下降趋势,在秋季呈微弱上升趋势。两个沙尘源地各季起沙通量的变化趋势由近地面风速主导,植被覆盖率、降水和地面温度等因素对起沙通量的年际波动有着重要影响。

Statistical Downscaling of Pattern Projection Using Multi-Model Output Variables as Predictors

A pattern projection downscaling method is employed to predict monthly station precipitation. The predictand is the monthly precipitation at 1 station in China, 60 stations in Korea, and 8 stations in Thailand. The predictors are multiple variables from the output of operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction is made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of the model downscaled precipitation forecasts using the best predictors and is referred to as DMME. It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse resolution predictions of general circulation models. The correlation coefficient between the prediction of DMME and the observation in Beijing of China reaches 0.71; the skill is improved to 0.75 for Korea and 0.61 for Thailand. The improvement of the prediction skills for the first two cases is attributed to three steps： coupled pattern selection, optimal predictor selection, and multi-model downscaled precipitation ensemble. For Thailand, we use the single-predictor prediction, which results in a lower prediction skill than the other two cases. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected well, can be used to make skillful predictions of local precipitation by means of appropriate statistical downscaling.

基于WRF模式的城市尺度极端降雨空间格局特征研究——以珠海市为例

利用区域气象模型WRF(Weather Research and Forecasting Model)对欧洲气象中心大气再分析资料(ERA-Interim)进行动力学降尺度至4 km水平分辨率,反演模拟珠海市长期的逐年最大日降雨事件过程;基于广义极值理论(GEV)计算不同降雨历时(1 h和24 h)、不同重现期(2 a、5 a、10 a、20 a、50 a和100 a)下珠海市4 km格网的暴雨强度空间格局,以期为当地城市防灾减灾规划和洪涝风险评估提供科学支撑和建议。

统计降尺度法对未来区域气候变化情景预估的研究进展

由于迄今为止大部分的海气耦合气候模式 (AOGCM)的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测,降尺度法已广泛用于弥补AOGCM在这方面的不足。简要介绍了 3种常用的降尺度法:动力降尺度法、统计降尺度法和统计与动力相结合的降尺度法;系统论述了统计降尺度方法的理论和应用的研究进展,其中包括:统计降尺度法的基本假设,统计降尺度法的优缺点,以及常用的 3种统计降尺度法;还论述了用统计降尺度法预估未来气候情景的一般步骤,以及方差放大技术在统计降尺度中的应用;同时还强调了统计降尺度方法和动力降尺度方法比较研究在统计降尺度研究中的重要性;最后指出统计与动力相结合的降尺度方法将成为降尺度技术的重要发展方向。

多模式集合预报及其降尺度技术在东亚夏季降水预测中的应用

利用动力季节模式输出的匹配域投影技术和多模式集合预报技术对多个国家和城市的站点月平均降水进行预报。预报变量是北京1个站、韩国60个站和曼谷地区8个站点的月平均降水,预报因子是从多个业务动力季节预报模式输出的多个大尺度变量。模式回报数据和站点观测降水数据时段是1983—2003年。降尺度预报降水的技巧是在交叉验证的框架下进行的。匹配域投影方法是设定一个可以活动的窗口在全球范围内大尺度场上进行扫描,寻求与目标站点降水最优化的因子和最相关的区域,目标站点的降水变率就是由该匹配域上大尺度环流场信息决定的。最终预报是用多个降尺度模式预报结果的集合预报(DMME)。多个降尺度模式预报结果的集合预报能显著地提高站点降水的预报技巧。北京站,多个降尺度模式预报结果的集合预报的预报和观测降水的相关系数可以提高到0.71;韩国地区,多个降尺度模式预报结果的集合预报平均技巧提高到0.75;泰国,多个降尺度模式预报结果的集合预报技巧是0.61。

区域风能资源评价分析的动力降尺度研究

不同于以往的统计风能评估方法,本研究将动力降尺度方法应用到江苏省的风能评价分析中。利用NCEP/NCAR再分析资料与江苏省65个地面气象观测站1971～2000年的观测资料,建立了动力降尺度区域气候模式MM5V3的初始场和边界条件,用较高分辨率(水平分辨率为5km)评估了江苏省60m高度的风能分布,分析了动力降尺度方法在区域风能资源评估中的有效应用。结果表明,江苏省风能资源由东部沿海向西部内陆递减,以西连岛为代表的东部沿海风能资源最丰富,其次为长江三角洲地区,太湖、洪泽湖及高邮湖地区的风能资源也比较丰富,徐州市与南京市的风能资源最贫乏。分析表明,动力降尺度方法能够用较高分辨率模拟局地环流和地面风的主要分布特征,可以作为区域风能资源评价分析的有效手段。

降尺度方法在东亚气候预测中的应用

东亚气候变异十分复杂,全球动力预测系统对该地气候异常的预测能力偏低,如何进一步提高东亚地区气候异常的预测水平是一个非常重要的科学和现实需求问题。为此,近些年一系列的动力和统计降尺度方法得以发展。本文主要回顾了这些降尺度方法在东亚气候预测研究和实时预测中的应用。首先,文中简要介绍了我国目前应用于实时预测的全球动力预测系统及其性能,这是开展降尺度的科学和技术基础;在此基础上,从区域模式物理过程参数化方案的评估与遴选、区域模式在东亚气候预测中的应用两个方面,对于动力降尺度方法的发展和应用做了回顾;在统计降尺度的综述中,本文主要关注了东亚夏季汛期和冬季气候异常的预测,特别是针对东亚冬季气候异常,本文中提出了新的高效的统计与动力相结合的预测方法。最后,展望了短期气候预测需要进一步深入研究的科学和技术问题。

基于DERF的SD方法预测月降水和极端降水日数

针对动力气候模式对区域或更小空间尺度内的日降水预测技巧偏低的问题,应用最优子集回归(OSR)方法对国家气候中心业务化的月动力气候模式(DERF)输出的高度场、风场和海平面气压场进行降尺度处理用于降水预测,旨在提高预测准确率。1982—2006年交叉检验结果表明:OSR方法能显著提高降水预测技巧,其中11～40 d改善效果最为显著。在此基础上,应用一步法和两步法两种统计降尺度方法预测极端降水日数,交叉检验结果表明:两种方法均优于随机预测,冬季两步法预测技巧略高于一步法,夏季一步法略优于两步法。综合认为OSR,OSR结合随机天气发生器(WG)两种统计降尺度方法对月尺度降水或极端降水日数的预测均具有较高的技巧,可作为短期气候预测的重要参考信息。