为了改善地面自动气象站观测数据在WRF-DA(the Weather Research and Forecasting Model-Data Assimilation)系统中的同化应用效果,提高数值模式的预报性能,对WRF-DA地面资料同化方案中温度和风速订正方案进行改进,形成地面资料同化更...为了改善地面自动气象站观测数据在WRF-DA(the Weather Research and Forecasting Model-Data Assimilation)系统中的同化应用效果,提高数值模式的预报性能,对WRF-DA地面资料同化方案中温度和风速订正方案进行改进,形成地面资料同化更新方案。通过考虑地形高度差异对同化效果的影响,调整同化方案中地面要素由实际地形高度订正到模式高度的同化订正方案,提升地面观测温度及风速订正值的合理性。并进一步分析了2017年7月5日个例试验结果及2017年7月整月的批量试验,发现相比于使用原方案同化后的预报效果,选取更新方案同化地面自动气象站观测资料的模式预报效果在前9 h温度和风速的预报误差明显降低,且在24 h预报时效内更新方案对于模式预报均起到改善作用。展开更多
Mt. Everest is often referred to as the earth＇s ＇third＇ pole. As such it is relatively inaccessible and little is known about its meteorology. In 2005, an automatic weather station was operated at North Col （28...Mt. Everest is often referred to as the earth＇s ＇third＇ pole. As such it is relatively inaccessible and little is known about its meteorology. In 2005, an automatic weather station was operated at North Col （28°1′ 0.95＂ N, 86°57′ 48.4＂ E, 6523 m a.s.l.） of Mt. Everest. Based on the observational data, this paper compares the reanalysis data from NCEP/NCAR （hereafter NCEP-Ⅰ） and NCEP-DOE AMIP-Ⅱ （NCEP- Ⅱ）, in order to understand which reanalysis data are more suitable for the high Himalayas with Mr. Everest region. When comparing with those from the other levels, pressure interpolated from 500 hPa level is closer to the observation and can capture more synoptic-scale variability, which may be due to the very complex topography around Mt. Everest and the intricately complicated orographic land-atmosphereocean interactions. The interpolation from both NCEP-Ⅰ and NCEP-Ⅱ daily minimum temperature and daily mean pressure can capture most synopticscale variability （r〉0.82, n=83, p〈0.001）. However, there is difference between NCEP-Ⅰ and NCEP-Ⅱ reanalysis data because of different model parameterization. Comparing with the observation, the magnitude of variability was underestimated by 34.1%, 28.5 % and 27.1% for NCEP-Ⅰ temperature and pressure, and NCEP-Ⅱ pressure, respectively, while overestimated by 44.5 % for NCEP-Ⅱ temperature. For weather events interpolated from the reanalyzed data, NCEP-Ⅰ and NCEP-Ⅱ show the same features that weather events interpolated from pressure appear at the same day as those from the observation, and some events occur one day ahead, while most weather events and NCEP-Ⅱ temperature interpolated from NCEP-Ⅰ happen one day ahead of those from the observation, which is much important for the study on meteorology and climate changes in the region, and is very valuable from the view of improving the safety of climbers who attempt to climb Mt. Everest.展开更多
文摘为了改善地面自动气象站观测数据在WRF-DA(the Weather Research and Forecasting Model-Data Assimilation)系统中的同化应用效果,提高数值模式的预报性能,对WRF-DA地面资料同化方案中温度和风速订正方案进行改进,形成地面资料同化更新方案。通过考虑地形高度差异对同化效果的影响,调整同化方案中地面要素由实际地形高度订正到模式高度的同化订正方案,提升地面观测温度及风速订正值的合理性。并进一步分析了2017年7月5日个例试验结果及2017年7月整月的批量试验,发现相比于使用原方案同化后的预报效果,选取更新方案同化地面自动气象站观测资料的模式预报效果在前9 h温度和风速的预报误差明显降低,且在24 h预报时效内更新方案对于模式预报均起到改善作用。
基金funded by the National Natural Science Foundation of China (Grant No. 40501015)the Chinese Academy of Science (Grant No. KZCX3-SW-344)
文摘Mt. Everest is often referred to as the earth＇s ＇third＇ pole. As such it is relatively inaccessible and little is known about its meteorology. In 2005, an automatic weather station was operated at North Col （28°1′ 0.95＂ N, 86°57′ 48.4＂ E, 6523 m a.s.l.） of Mt. Everest. Based on the observational data, this paper compares the reanalysis data from NCEP/NCAR （hereafter NCEP-Ⅰ） and NCEP-DOE AMIP-Ⅱ （NCEP- Ⅱ）, in order to understand which reanalysis data are more suitable for the high Himalayas with Mr. Everest region. When comparing with those from the other levels, pressure interpolated from 500 hPa level is closer to the observation and can capture more synoptic-scale variability, which may be due to the very complex topography around Mt. Everest and the intricately complicated orographic land-atmosphereocean interactions. The interpolation from both NCEP-Ⅰ and NCEP-Ⅱ daily minimum temperature and daily mean pressure can capture most synopticscale variability （r〉0.82, n=83, p〈0.001）. However, there is difference between NCEP-Ⅰ and NCEP-Ⅱ reanalysis data because of different model parameterization. Comparing with the observation, the magnitude of variability was underestimated by 34.1%, 28.5 % and 27.1% for NCEP-Ⅰ temperature and pressure, and NCEP-Ⅱ pressure, respectively, while overestimated by 44.5 % for NCEP-Ⅱ temperature. For weather events interpolated from the reanalyzed data, NCEP-Ⅰ and NCEP-Ⅱ show the same features that weather events interpolated from pressure appear at the same day as those from the observation, and some events occur one day ahead, while most weather events and NCEP-Ⅱ temperature interpolated from NCEP-Ⅰ happen one day ahead of those from the observation, which is much important for the study on meteorology and climate changes in the region, and is very valuable from the view of improving the safety of climbers who attempt to climb Mt. Everest.
