By using the data in 169 sounding stations over the world,NCEP/NCAR reanalysis data were tested,and the distribution characteristics of standard errors of geopotential height,temperature and wind speed field from the ...By using the data in 169 sounding stations over the world,NCEP/NCAR reanalysis data were tested,and the distribution characteristics of standard errors of geopotential height,temperature and wind speed field from the upper troposphere to the lower stratosphere over the world(most were the land zone) were analyzed.The results showed that the standard error distribution of reanalysis wind speed field data was mainly affected by the jet stream zone.There existed the obvious difference between the jet stream zone and the actual wind field.The distribution of standard error in the wind speed field had the obvious seasonal difference in winter,summer,and the average deviation was larger near the coastline.The high value zones of standard errors of reanalysis geopotential height and temperature field mainly concentrated in the low-latitude region in the Eastern Hemisphere(Indian Ocean coast).The distribution of standard error was basically consistent with average error.Therefore,the standard error could be explained well by the average error.The standard errors of reanalysis temperature and geopotential height data in the inland zone were lower.The high value zone mainly distributed along the coastline,and the average error of wind speed field was bigger near the coastline.It closely related to the quality of data in the sounding stations,the regional difference and the fact that the land observation stations were dense,and the ocean observation stations were fewer.展开更多
Due to the difficult logistics in the extreme high elevation regions over the Himalayas and Tibetan Plateau, the observational meteorological data are very few. In 2003, an automatic weather station was deployed at th...Due to the difficult logistics in the extreme high elevation regions over the Himalayas and Tibetan Plateau, the observational meteorological data are very few. In 2003, an automatic weather station was deployed at the northeastern saddle of Mt. Nyainqentanglha （NQ） （30°24＇44.3″N, 90°34＇13.1″E, 5850 m a.s.l.）, the southern Tibetan Plateau. In 2005, another station was operated at the East Rongbuk GlacierCol （28°01 ＇0.95″N, 86°57＇48.4″E, 6523 m a.s.l.） of Mt. Qomolangma. Observational data from the two sites have been compared with the reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research （NCEP/NCAR）, reliability of NCEP/NCAR reanalysis data has been investigated in the Himalayas/Tibetan Plateau region. The reanalysis data can capture much of the synoptic-scale variability in temperature and pressure, although the reanalysis values are systematically lower than the observation. Furthermore, most of the variability magnitude is, to some degree, underestimated. In addition, the weather event extracted from the NCEP/NCAR reanalyzed pressure and temperature prominently appears one day ahead of the observational data on Mt. Qomolangma, while on Mt. NQ it occurs basically in the same day.展开更多
Mt.Everest (27°54' N,86°54' E),the highest peak,is often referred to as the earth's 'third' pole,at an elevation of 8844.43 m. Due to the difficult logistics in the extreme high elevation...Mt.Everest (27°54' N,86°54' E),the highest peak,is often referred to as the earth's 'third' pole,at an elevation of 8844.43 m. Due to the difficult logistics in the extreme high elevation regions over the Himalayas,observational meteorological data are very few on Mt. Everest. In 2005,an automatic weather station was operated at the East Rongbuk glacier Col of Mt. Everest over the Himalayas. The observational data have been compared with the reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR),and the reliability of NCEP/NCAR reanalysis data has been investigated in the Himalayan region,after the reanalyzed data were interpolated in the horizontal to the location of Mt. Everest and in the vertical to the height of the observed sites. The reanalysis data can capture much of the synoptic-scale variability in temperature and pressure,although the reanalysis values are systematically lower than the observation. Furthermore,most of the variability magnitude is,to some degree,underestimated. In addition,the variation extracted from the NCEP/NCAR reanalyzed pressure and temperature prominently appears one-day lead to that from the observational data,which is more important from the standpoint of improving the safety of climbers who attempt to climb Mt. Everest peak.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
Due to long-term time series and many elements, reanalysis data of National Centers for Environmental Prediction （NCEP） and European Center for MediumRange Weather Forecasts （ECMWF） are widely used in present clim...Due to long-term time series and many elements, reanalysis data of National Centers for Environmental Prediction （NCEP） and European Center for MediumRange Weather Forecasts （ECMWF） are widely used in present climate studies. Even so, there are discrepancies between NCEP and ECMWF reanalysis. Some climate fields may be better reproduced by NCEP than by ECMWF. On the other hand, ECMWF may describe some climate characteristics more realistically than NCEP. Xu et al.pointed out that NCEP data are of uncertainty when used for studying long-term trends of climate change. By comparing temperatures and pressures from NCEP and observation, it can be seen that NCEP data show higher reliability in the east and lower-latitudes of China than in its west and higher latitudes, NCEP temperature is of more reality than pressure and NCEP data after 1979 are closer to the observations than before. Yang et al.also revealed some serious problems of NCEP data in the north of subtropical Asia. Regional differences of NCEP data in representation are also explored by other studiest. As for seasonal variability, NCEP simulates relatively real conditions of Chinese summer and annual mean but winter data are relatively bad, as in comparisons of NCEP data wity China surface station observations by Zhao et al.Moreover, Trenberth and Stepaniak showed that ECMWF data had better energy budgets than NCEP data for pure pressure coordinates are adopted by ECMWF. Renfrew et al. compared NCF, P data to ECMWF data in terms of surface fluxes and the results indicate that the time series of surface sensible and latent heating fluxes from ECMWF are 13% and 10% larger than the observations and those from NCEP would be 51% and 27% larger than the observations, respectively. So, Renfrew et al. suggested that it be more appropriate to drive ocean models by ECMWF data. Based on comparisons of multiple elements by some scientists, it seems that ECMWF data are better than NCEP data on global, hemispheric and regional scales. Whereas, reanalysis have big errors in some regions in contrast to observations, especially the variables related to humidity. Since that, researchers should compare the two sets of data and select a better one according to specific problems.展开更多
基金Supported by The National Key Basic Research Development Plan(2010CB428602)
文摘By using the data in 169 sounding stations over the world,NCEP/NCAR reanalysis data were tested,and the distribution characteristics of standard errors of geopotential height,temperature and wind speed field from the upper troposphere to the lower stratosphere over the world(most were the land zone) were analyzed.The results showed that the standard error distribution of reanalysis wind speed field data was mainly affected by the jet stream zone.There existed the obvious difference between the jet stream zone and the actual wind field.The distribution of standard error in the wind speed field had the obvious seasonal difference in winter,summer,and the average deviation was larger near the coastline.The high value zones of standard errors of reanalysis geopotential height and temperature field mainly concentrated in the low-latitude region in the Eastern Hemisphere(Indian Ocean coast).The distribution of standard error was basically consistent with average error.Therefore,the standard error could be explained well by the average error.The standard errors of reanalysis temperature and geopotential height data in the inland zone were lower.The high value zone mainly distributed along the coastline,and the average error of wind speed field was bigger near the coastline.It closely related to the quality of data in the sounding stations,the regional difference and the fact that the land observation stations were dense,and the ocean observation stations were fewer.
基金National Natural Science Foundation of China, No.40501015 No. 90411003+1 种基金 Innovation Program of CAS, No.KZCX3-SW-344 No.KZCX3-SW-354
文摘Due to the difficult logistics in the extreme high elevation regions over the Himalayas and Tibetan Plateau, the observational meteorological data are very few. In 2003, an automatic weather station was deployed at the northeastern saddle of Mt. Nyainqentanglha （NQ） （30°24＇44.3″N, 90°34＇13.1″E, 5850 m a.s.l.）, the southern Tibetan Plateau. In 2005, another station was operated at the East Rongbuk GlacierCol （28°01 ＇0.95″N, 86°57＇48.4″E, 6523 m a.s.l.） of Mt. Qomolangma. Observational data from the two sites have been compared with the reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research （NCEP/NCAR）, reliability of NCEP/NCAR reanalysis data has been investigated in the Himalayas/Tibetan Plateau region. The reanalysis data can capture much of the synoptic-scale variability in temperature and pressure, although the reanalysis values are systematically lower than the observation. Furthermore, most of the variability magnitude is, to some degree, underestimated. In addition, the weather event extracted from the NCEP/NCAR reanalyzed pressure and temperature prominently appears one day ahead of the observational data on Mt. Qomolangma, while on Mt. NQ it occurs basically in the same day.
基金the Strategic Study Foundation of Chinese Polar Science （Grant No. 2007228） the National Nature Science Foundation of China （Grant No. 40501015） the Chinese Academy of Science （Grant No. KZCX3-SW-354 and KZCX3-SW-344）.
文摘Mt.Everest (27°54' N,86°54' E),the highest peak,is often referred to as the earth's 'third' pole,at an elevation of 8844.43 m. Due to the difficult logistics in the extreme high elevation regions over the Himalayas,observational meteorological data are very few on Mt. Everest. In 2005,an automatic weather station was operated at the East Rongbuk glacier Col of Mt. Everest over the Himalayas. The observational data have been compared with the reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR),and the reliability of NCEP/NCAR reanalysis data has been investigated in the Himalayan region,after the reanalyzed data were interpolated in the horizontal to the location of Mt. Everest and in the vertical to the height of the observed sites. The reanalysis data can capture much of the synoptic-scale variability in temperature and pressure,although the reanalysis values are systematically lower than the observation. Furthermore,most of the variability magnitude is,to some degree,underestimated. In addition,the variation extracted from the NCEP/NCAR reanalyzed pressure and temperature prominently appears one-day lead to that from the observational data,which is more important from the standpoint of improving the safety of climbers who attempt to climb Mt. Everest peak.
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.
基金Natural Science Foundation of China (40505019) Natural Science Foundation of GuangdongProvince (5300001) Open Foundation of Guangzhou Institute of Tropical and Marine Meteorology,CMA
文摘Due to long-term time series and many elements, reanalysis data of National Centers for Environmental Prediction （NCEP） and European Center for MediumRange Weather Forecasts （ECMWF） are widely used in present climate studies. Even so, there are discrepancies between NCEP and ECMWF reanalysis. Some climate fields may be better reproduced by NCEP than by ECMWF. On the other hand, ECMWF may describe some climate characteristics more realistically than NCEP. Xu et al.pointed out that NCEP data are of uncertainty when used for studying long-term trends of climate change. By comparing temperatures and pressures from NCEP and observation, it can be seen that NCEP data show higher reliability in the east and lower-latitudes of China than in its west and higher latitudes, NCEP temperature is of more reality than pressure and NCEP data after 1979 are closer to the observations than before. Yang et al.also revealed some serious problems of NCEP data in the north of subtropical Asia. Regional differences of NCEP data in representation are also explored by other studiest. As for seasonal variability, NCEP simulates relatively real conditions of Chinese summer and annual mean but winter data are relatively bad, as in comparisons of NCEP data wity China surface station observations by Zhao et al.Moreover, Trenberth and Stepaniak showed that ECMWF data had better energy budgets than NCEP data for pure pressure coordinates are adopted by ECMWF. Renfrew et al. compared NCF, P data to ECMWF data in terms of surface fluxes and the results indicate that the time series of surface sensible and latent heating fluxes from ECMWF are 13% and 10% larger than the observations and those from NCEP would be 51% and 27% larger than the observations, respectively. So, Renfrew et al. suggested that it be more appropriate to drive ocean models by ECMWF data. Based on comparisons of multiple elements by some scientists, it seems that ECMWF data are better than NCEP data on global, hemispheric and regional scales. Whereas, reanalysis have big errors in some regions in contrast to observations, especially the variables related to humidity. Since that, researchers should compare the two sets of data and select a better one according to specific problems.
