利用折射指数推算大气纬圈平均风场方法

鉴于地转风、梯度风和平衡风各自计算风场的特点，该文利用COSMIC掩星折射指数资料，根据大气折射指数与大气密度、风场之间的关系，选用梯度风方程，建立了推算20~60 km中层大气纬圈平均风场的方法，分别与ECMWF提供的ERA-interim及NASA/GMAO提供的MERRA再分析风场资料对比验证。选用2007年1，4，7月和10月的COSMIC掩星折射指数数据，按照构建的方法计算了大气纬圈平均风场，并简单分析了大气纬圈平均纬向风随纬度、高度的变化特征及规律。计算风场与ECMWF及MERRA再分析风场资料变化规律基本一致，符合效果很好, 能够正确反映出纬向平均风场特性。1月及7月不同高度标准偏差、最大偏差随高度增加而增大，标准偏差最大约为6 m/s，最大偏差不超过11 m/s，沿纬度方向相关系数有减小的趋势，但不低于0.98，4月及10月偏差稍小，最大偏差不超过8 m/s。结果表明:利用COSMIC掩星折射指数资料通过梯度风方程计算风场，是获取中层大气20~60 km纬圈平均风场的一种有效方法。

Deriving Atmospheric Zonal Mean Winds from Refractive Index Data

There are few effective ways to explore the middle atmospheric wind field directly at the altitude range of 20—60 km, and the direct sounding methods have some limitations, but the wind field could be derived from atmospheric refractive index and pressure data. From the bending angles, a large number of profiles of atmospheric refractivity, pressure and temperature are obtained with the newly launched Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC)/Formosa Satellite 3(FORMOSAT-3) System. Taking full advantage of these data has a positive impact on the research of the global middle atmospheric wind field. The approach for calculating middle atmosphere zonal mean winds at the altitude range of 20—60 km is constructed according to gradient wind equations from atmospheric refractive index data, considering the characteristics and calculation methods of geostrophic wind, gradient wind and balance wind respectively, and the relationships among atmospheric refractive index, density and wind field. Following the method constructed above, the middle atmosphere zonal mean winds are calculated by the gridded refractive index data in January, April, July and October of 2007 and the latitude-height distributions of zonal mean winds are discussed. The gridded data is derived through the inverse distance weighted interpolation method. The data is compared with monthly average wind data of European Centre for Medium Range Weather Forecasts Reanalysis-interim (ERA-interim) and the Modern Era Retrospective-analysis for Research and Applications (MERRA) data sets for validation. The comparisons reveal excellent agreement, and the characteristics of calculated winds are similar with that of the reanalyzed winds. In January and July, easterly winds prevail in summer hemisphere zonal mean zona1 winds and it increase as the height increases, while in winter westerly winds prevail hemisphere zone-mean zona1 winds. The zonal wind first increases and then decreases from the high-latitude to the low-latitude regions of winter hemisphere, with the maximum in the middle-latitude regions of winter hemisphere. The root mean square deviation and the largest deviation at different heights are larger and larger along the heights, while the correlation coefficients along latitude get smaller, but it is still greater than 0.98. The root mean square deviation is about 6 m·s-1, and the largest deviation is less than 11 m·s-1in January and July. Spring and autumn are the transition periods, when westerly winds prevail in global, but decrease versus increasing heights in the high-latitude regions of northern hemisphere and even reverse near the top in April; westerly winds prevail in the high-latitude regions, while for some altitudes in the low-latitude regions easterly winds are dominant. The differences are not very large in April and October, with the largest deviation no more than 8 m·s-1, indicating that deriving wind fields from the COSMIC refractive index data through gradient wind equations is an effective way.