Evaluation of cloud properties in the NOAA/NCEP global forecast system using multiple satellite products

Knowledge of cloud properties and their vertical structure is important for meteorological studies due to their impact on both the Earth’s radiation budget and adiabatic heating within the atmosphere. The objective of this study is to evaluate bulk cloud properties and vertical distribution simulated by the US National Oceanic and Atmospheric Administration National Centers for Environmental Prediction Global Forecast System (GFS) using three global satellite products. Cloud variables evaluated include the occurrence and fraction of clouds in up to three layers, cloud optical depth, liquid water path, and ice water path. Cloud vertical structure data are retrieved from both active (CloudSat/CALIPSO) and passive sensors and are subsequently compared with GFS model results. In general, the GFS model captures the spatial patterns of hydrometeors reasonably well and follows the general features seen in satellite measurements, but large discrepancies exist in low-level cloud properties. More boundary layer clouds over the interior continents were generated by the GFS model whereas satellite retrievals showed more low-level clouds over oceans. Although the frequencies of global multi-layer clouds from observations are similar to those from the model, latitudinal variations show discrepancies in terms of structure and pattern. The modeled cloud optical depth over storm track region and subtropical region is less than that from the passive sensor and is overestimated for deep convective clouds. The distributions of ice water path (IWP) agree better with satellite observations than do liquid water path (LWP) distributions. Discrepancies in LWP/IWP distributions between observations and the model are attributed to differences in cloud water mixing ratio and mean relative humidity fields, which are major control variables determining the formation of clouds.     

The forecast of hail based on the atmospheric global model (T254 NCEP) output data

A technique is described of the perfect prognosis of hail on the basis of the statistical interpretation of output data of the atmospheric global model (T254, NCEP) for the area of Caucasus Mineral Waters with the lead time of 18–24 h. An estimation is carried out of the accuracy of hail forecasts based on the proposed technique as compared with the traditional approaches.     

Impact of different convective cloud schemes on the simulation of the tropical seasonal cycle in a coupled ocean–atmosphere model

The simulation of the mean seasonal cycle of sea surface temperature (SST) remains a challenge for coupled ocean–atmosphere general circulation models (OAGCMs). Here we investigate how the numerical representation of clouds and convection affects the simulation of the seasonal variations of tropical SST. For this purpose, we compare simulations performed with two versions of the same OAGCM differing only by their convection and cloud schemes. Most of the atmospheric temperature and precipitation differences between the two simulations reflect differences found in atmosphere-alone simulations. They affect the ocean interior down to 1,000 m. Substantial differences are found between the two coupled simulations in the seasonal march of the Intertropical Convergence Zone in the eastern part of the Pacific and Atlantic basins, where the equatorial upwelling develops. The results confirm that the distribution of atmospheric convection between ocean and land during the American and African boreal summer monsoons plays a key role in maintaining a cross equatorial flow and a strong windstress along the equator, and thereby the equatorial upwelling. Feedbacks between convection, large-scale circulation, SST and clouds are highlighted from the differences between the two simulations. In one case, these feedbacks maintain the ITCZ in a quite realistic position, whereas in the other case the ITCZ is located too far south close to the equator.     

Prediction of Indian summer monsoon in short to medium range time scale with high resolution global forecast system (GFS) T574 and T382

Performance of national centers for environmental prediction based global forecast system (GFS) T574/L64 and GFS T382/L64 over Indian region has been evaluated for the summer monsoon season of 2011. The real-time model outputs are generated daily at India Meteorological Department, New Delhi for the forecasts up to 7 days. Verification of rainfall forecasts has been carried out against observed rainfall analysis. Performance of the model is also examined in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water content. Case study of a monsoon depression is also illustrated. Results obtained show that, in general, both the GFS T382 and T574 forecasts are skillful to capture climatologically heavy rainfall regions. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The verification results, at the spatial scale of 50 km resolution, in a regional spatial scale and country as a whole, in terms of continuous skill score, time series and categorical statistics, have demonstrated superiority of GFS T574 against T382 over Indian region. Both the model shows bias of lower tropospheric drying and upper tropospheric moistening. A bias of anti-cyclonic circulation in the lower tropospheric level lay over the central India, where rainfall as well as precipitable water content shows negative bias. Considerable differences between GFS T574 and T382 are noticed in the structure of model bias in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water contents. The magnitude of error for these parameters increases with forecast lead time in both GFS T574 and T382. The results documented are expected to be useful to the forecasters, monsoon researchers and modeling community.     

利用海-气-浪耦合模式(COAWST)对北印度洋一次强热带风暴\

相对中国其他海域,三面环陆的黄渤海区域海洋与中纬度大气间的相互作用有其独特性,在该区域开展海气耦合模式对气象要素的影响研究十分必要。本文基于大气和海洋的全球预报和再分析资料,利用高分辨率区域海气耦合模式（简称耦合模式）和非耦合模式,开展数值模拟试验。并根据国家地面常规气象观测站和埕北石油A平台实况观测资料,对比分析了2020年6月两种模式对近地层大气温湿要素预报结果,并结合海温、海气热通量变化和风场调整情况,分析了海气双向耦合过程对近地层温湿要素预报影响的原因。结果表明：耦合模式对近地层的湿度要素预报改进较大,以增湿效应为主;海气双向耦合对温湿要素的影响范围可扩展至黄渤海周边省市全域,可提升耦合模式对黄渤海周边区域近地层湿度和白天温度的预报效果;湿度对海气双向耦合的响应速度明显快于温度,说明耦合模式先改进了近地层湿度预报效果;耦合模式海表向上的潜热通量增加是近地层温湿要素预报改进的主要原因。

     

Simulation of salinity variability and the related freshwater flux forcing in the tropical Pacific: An evaluation using the Beijing normal university earth system model (BNU-ESM)

The climatology and interannual variability of sea surface salinity(SSS) and freshwater flux(FWF) in the equatorial Pacific are analyzed and evaluated using simulations from the Beijing Normal University Earth System Model(BNU-ESM).The simulated annual climatology and interannual variations of SSS, FWF, mixed layer depth(MLD), and buoyancy flux agree with those observed in the equatorial Pacific. The relationships among the interannual anomaly fields simulated by BNU-ESM are analyzed to illustrate the climate feedbacks induced by FWF in the tropical Pacific. The largest interannual variations of SSS and FWF are located in the western-central equatorial Pacific. A positive FWF feedback effect on sea surface temperature(SST) in the equatorial Pacific is identified. As a response to El Ni ?no–Southern Oscillation(ENSO),the interannual variation of FWF induces ocean processes which, in turn, enhance ENSO. During El Ni ?no, a positive FWF anomaly in the western-central Pacific(an indication of increased precipitation rates) acts to enhance a negative salinity anomaly and a negative surface ocean density anomaly, leading to stable stratification in the upper ocean. Hence, the vertical mixing and entrainment of subsurface water into the mixed layer are reduced, and the associated El Ni ?no is enhanced. Related to this positive feedback, the simulated FWF bias is clearly reflected in SSS and SST simulations, with a positive FWF perturbation into the ocean corresponding to a low SSS and a small surface ocean density in the western-central equatorial Pacific warm pool.     

基于集合经验模态分解和自回归-移动平均模型的COVID-19流行病全球预测系统预测结果改进

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...