TY - JOUR
T1 - Integration, Quality Assurance, and Usage of Global Aircraft Observations in CRA
AU - Liao, Jie
AU - Wang, Huiying
AU - Zhou, Zijiang
AU - Liu, Zhiquan
AU - Jiang, Lipeng
AU - Yuan, Fang
N1 - Publisher Copyright:
© 2021, The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg.
PY - 2021/2
Y1 - 2021/2
N2 - This paper presents a detailed description of integration, quality assurance procedure, and usage of global aircraft observations for China’s first generation global atmospheric reanalysis (CRA) product (1979–2018). An integration method named “classified integration” is developed. Aircraft observations from nine different sources are integrated into the Integrated Global Meteorological Observation Archive from Aircraft (IGMOAA), a new dataset from the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA). IGMOAA consists of global aircraft temperature, wind, and humidity data from the surface to 100 hPa, extending from 1973 to the present. Compared with observations assimilated in the Climate Forecast System Reanalysis (CFSR) of NCEP, the observation number of IGMOAA increased by 12.9% between 2010 and 2014, mainly as a result of adding more Chinese Aircraft Meteorological Data Relay (AMDAR) data. Complex quality control procedures for aircraft observations of NCEP are applied to detect data errors. Observations are compared with ERA-Interim reanalysis from 1979 to 2018 to investigate data quality of different types and aircraft, and subsequently to develop the blacklists for CRA. IGMOAA data have been assimilated in CRA in 2018 and are real-time updated at the CMA Data-as-a-Service (CMADaaS) platform. For CRA, the fits to observations improve over time. From 1994 to 2018, root-mean-square error (RMSE) of observations relative to CRA background decreases from 1.8 to 1.0°C for temperature above 300 hPa, and from 4.5 to 3 m s−1 for zonal wind. The RMSE for humidity appears to exhibit an apparent seasonal variation with larger errors in summer and smaller ones in winter.
AB - This paper presents a detailed description of integration, quality assurance procedure, and usage of global aircraft observations for China’s first generation global atmospheric reanalysis (CRA) product (1979–2018). An integration method named “classified integration” is developed. Aircraft observations from nine different sources are integrated into the Integrated Global Meteorological Observation Archive from Aircraft (IGMOAA), a new dataset from the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA). IGMOAA consists of global aircraft temperature, wind, and humidity data from the surface to 100 hPa, extending from 1973 to the present. Compared with observations assimilated in the Climate Forecast System Reanalysis (CFSR) of NCEP, the observation number of IGMOAA increased by 12.9% between 2010 and 2014, mainly as a result of adding more Chinese Aircraft Meteorological Data Relay (AMDAR) data. Complex quality control procedures for aircraft observations of NCEP are applied to detect data errors. Observations are compared with ERA-Interim reanalysis from 1979 to 2018 to investigate data quality of different types and aircraft, and subsequently to develop the blacklists for CRA. IGMOAA data have been assimilated in CRA in 2018 and are real-time updated at the CMA Data-as-a-Service (CMADaaS) platform. For CRA, the fits to observations improve over time. From 1994 to 2018, root-mean-square error (RMSE) of observations relative to CRA background decreases from 1.8 to 1.0°C for temperature above 300 hPa, and from 4.5 to 3 m s−1 for zonal wind. The RMSE for humidity appears to exhibit an apparent seasonal variation with larger errors in summer and smaller ones in winter.
KW - China’s global atmospheric reanalysis (CRA) product
KW - aircraft observation
KW - blacklist
KW - integration
KW - quality assurance
UR - https://www.scopus.com/pages/publications/85101716640
U2 - 10.1007/s13351-021-0093-3
DO - 10.1007/s13351-021-0093-3
M3 - Article
AN - SCOPUS:85101716640
SN - 2095-6037
VL - 35
JO - Journal of Meteorological Research
JF - Journal of Meteorological Research
IS - 1
ER -