Fig. 1. Distribution of positions collocated sonde and aircraft temperature observations at 0000 UTC on June 22, 2017. Black and blue dots mean positions of sonde temperature observations at analysis time and aircraft temperature observations within ± 30 minutes from analysis time respectively. Red dots mean positions of sonde and aircraft observations collocated out of black and blue dots.
Fig. 2. Vertical profile of differences (black dots) between sonde and aircraft temperature (sonde minus aircraft), former altitude average (blue line with dots) and altitude average (red line with dots) of background innovation differences between sonde and aircraft temperature at 0000 UTC on June 22, 2017.
Fig. 3. Vertical profile of wind speeds of sonde (blue dots) and aircraft (red dots), former altitude average of sonde (blue line) and aircraft (red line) wind speeds at 0000 UTC on June 22, 2017.
Fig. 4. Distribution of positions about sonde and aircraft observations for a one month at 0000 UTC in June 2017. Black and blue dots mean positions of sonde temperature observations at analysis time and aircraft temperature observations within ± 30 minutes from analysis time respectively. Red boxes mean coverage of analysis data.
Fig. 5. Vertical profiles of background innovation average of sonde and aircraft temperature for a 1 month at 0000 and 1200 UTC in June 2017 with respect to Global (GL), North America (NA), Europe (EU), East Asia (EA) and Australia (AU) (K).
Fig. 6. Horizontal distributions of differences about background innovation average between aircraft and sonde temperature that means the aircraft temperature bias for a 1 month at 0000 UTC in June 2017 with respect to 200~300 hPa, 300~500 hPa, 500~850 hPa and 850 hPa~Surface (K). Each value is the minimum, maximum, mean and standard deviation of global aircraft temperature bias.
Fig. 7. Same as Fig. 6 but for 1200 UTC (K).
Fig. 8. Vertical profiles of average for aircraft temperature bias before and after bias correction at 0000 and 1200 UTC in June 2017 (K).
Table 1. The number of aircraft and sonde temperature observations for one-month at 0000 and 1200 UTC in June 2017 with respect to Global (GL), North America (NA), Europe (EU), East Asia (EA) and Australia (AU).
Table 2. Vertical average of background innovation (O-B) and bias-corrected background innovation (C-B) about aircraft temperature for a 1 month at 0000 and 1200 UTC in June 2017 with respect to Global (GL), North America (NA), Europe (EU), East Asia (EA) and Australia (AU) (K).
References
- Ballish, B. and V. K. Kumar, 2006: Comparision of aircraft and radiosonde temperature biases at NCEP. Preprints, 10th Symp. On Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), Atlanta, GA, Amer. Meteor. Soc., 3.5 [Available online at http://ams.confex.com/ams/pdfpapers/103076.pdf].
- Benjamin, S. G., B. E. Schwartz, and R. E. Cole, 1999: Accuracy of ACARS wind and temperature observations determined by collocation. Wea. Forecasting, 14, 1032-1038. https://doi.org/10.1175/1520-0434(1999)014<1032:AOAWAT>2.0.CO;2
- Buehner, M., and Coauthors, 2015: Implementation of deterministic weather forecasting systems based on Ensemble-Variational data assimilation at Environment Canada. Part I: The global system. Mon. Wea. Rev., 143, 2532-2559, doi:10.1175/MWR-D-14-00354.1.
- Cardinali, C., L. Isaksen, and E. Andersson, 2003: Use and impact of automated aircraft data in a global 4DVAR data assimilation system. Mon. Wea. Rev., 131, 1865-1877. https://doi.org/10.1175//2569.1
- Collins, W. G., 1999: Determination of new adjustment tables in order to bring radiosonde temperature and height measurements from different sonde types into relative agreement. EMC/NCEP/NOAA [Available online at http://www.emc.ncep.noaa.gov/mmb/papers/collins/new_tables/new_tables.html].
- Ha, J.-H., I.-H. Kwon, J.-H. Kwon, J.-H. Kang, and H.-W. Chun, 2015: Use and impact of sonde, aircraft and satellite observations in the KIM-3DVAR system. Proceedings, The spring meeting of the Korean Meteorological Society, Seoul, Korea, KMS, 151-152 (in Korean).
- Hong, S.-Y., and Coauthors, 2018: The Korean Integrated Model (KIM) system for global weather forecasting. Asia-Pacific J. Atmos. Sci., 54, 267-292, doi:10.1007/s13143-018-0028-9.
- Kang, J.-H., and Coauthors, 2018: Development of an observation processing package for data assimilation in KIAPS. Asia-Pacific J. Atmos. Sci., 54, 303-318, doi:10.1007/s13143-018-0030-2.
- Painting, D. J., 2003: AMDAR reference manual. WMO, 84 pp [Available online at https://library.wmo.int/pmb_ged/wmo_958_en.pdf].
- Park, O.-R., and Y.-S. Kim, 2002: A study on the verification and sensitivity test for the ACARS data. Asia-Pacific J. Atmos. Sci., 38, 333-342.
- Petersen, R. A., 2016: On the impact and benefits of AMDAR observations in operational forecasting-Part I: A review of the impact of automated aircraft wind and temperature reports. Bull. Amer. Meteor. Soc., 97, 585-602, doi:10.1175/BAMS-D-14-00055.1.
- Sako, H., 2010: Assimilation of Aircraft Temperature Data in the JMA Global 4D-Var Data Assimilation System. In J. Cote, Ed., Research Activities in Atmospheric and Oceanic Modelling. WMO, S1 33-34 [Available online at http://bluebook.meteoinfo.ru/uploads/2010/individual-articles/01_Sako_Hiroshi_aircraft_temp.pdf].
- Schwartz, B., and S. G. Benjamin, 1995: A comparison of temperature and wind measurements from ACARSequipped aircraft and rawinsondes. Wea. Forecasting, 10, 528-544. https://doi.org/10.1175/1520-0434(1995)010<0528:ACOTAW>2.0.CO;2
- WMO, 2017: Guide to Aircraft-based Observations. World Meteorological Organization, 1200, 132 pp.