Effect of observation error variance adjustment on numerical weather prediction using forecast sensitivity to error covariance parameters

Sung-Min Kim; Hyun Mee Kim

doi:10.1080/16000870.2018.1492839

Effect of observation error variance adjustment on numerical weather prediction using forecast sensitivity to error covariance parameters

Tellus

Volume 70 (2018): Issue 1

By: Sung-Min Kim and Hyun Mee Kim

Open Access

|Jan 2018

Baker, N. L. and Daley, R. 2000. Observation and background adjoint sensitivity in the adaptive observation-targeting problem. Qjr. Meteorol. Soc. 126, 1431–1454. doi: 10.1002/qj.49712656511.
Open DOI Search in Google Scholar Back to article
Bormann, N. and Bauer, P. 2010. Estimates of spatial and interchannel observation-error characteristics for numerical weather prediction. I: Methods and application to ATOVS data. Qjr. Meteorol. Soc. 136, 1036–1050. doi: 10.1002/qj.616.
Open DOI Search in Google Scholar Back to article
Bormann, N., Collard, A. and Bauer, P. 2010. Estimates of spatial and interchannel observation-error characteristics for numerical weather prediction. II: Application to AIRS and IASI data. Qjr. Meteorol. Soc. 136, 1051–1063. doi: 10.1002/qj.615.
Open DOI Search in Google Scholar Back to article
Bormann, N., Bonavita, M., Dragani, R., Eresmaa, R., Matricardi, M. and co-authors. 2016. Enhancing the impact of IASI observations through an updated observation-error covariance matrix. Qjr. Meteorol. Soc. 142, 1767–1780. doi: 10.1002/qj.2774.
Open DOI Search in Google Scholar Back to article
Cardinali, C. 2009. Monitoring the observation impact on the short-range forecast. Qjr. Meteorol. Soc. 135, 239–250. doi: 10.1002/qj.366.
Open DOI Search in Google Scholar Back to article
Chatterjee, S. and Hadi, A. S. 1986. Influential observations, high leverage points, and outliers in linear regression. Stat. Sci. 1, 379–393. doi: 10.1214/ss/1177013622.
Open DOI Search in Google Scholar Back to article
Clayton, A. M. 2004. Var Scientific Documentation 60. UK Met Office, Exeter, UK, pp. 1–12.
Search in Google Scholar Back to article
Clayton, A. M., Lorenc, A. C. and Barker, D. M. 2013. Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office. Qjr. Meteorol. Soc. 139, 1445–1461. doi: 10.1002/qj.2054.
Open DOI Search in Google Scholar Back to article
Cordoba, M., Dance, S. L., Kelly, G. A., Nichols, N. K. and Waller, J. A. 2017. Diagnosing atmospheric motion vector observation errors for an operational high-resolution data assimilation system. Qjr. Meteorol. Soc. 143, 333–341. doi: 10.1002/qj.2925.
Open DOI Search in Google Scholar Back to article
Courtier, P., Thépaut, J.-N. and Hollingsworth, A. 1994. A strategy of operational implementation of 4D-Var using an incremental approach. Qjr. Meteorol. Soc. 120, 1367–1387. doi: 10.1002/qj.49712051912.
Open DOI Search in Google Scholar Back to article
Daescu, D. N. and Todling, R. 2010. Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters. Qjr. Meteorol. Soc. 136, 2000–2012. doi: 10.1002/qj.693.
Open DOI Search in Google Scholar Back to article
Daescu, D. N. and Langland, R. H. 2013. Error covariance sensitivity and impact estimation with adjoint 4D-Var: theoretical aspects and first applications to NAVDAS-AR. Qjr. Meteorol. Soc. 139, 226–241. doi: 10.1002/qj.1943.
Open DOI Search in Google Scholar Back to article
Desroziers, G., Berre, L., Chapnik, B. and Poli, P. 2005. Diagnosis of observation, background and analysis-error statistics in observation space. Qjr. Meteorol. Soc. 131, 3385–3396. doi: 10.1256/qj.05.108.
Open DOI Search in Google Scholar Back to article
Desroziers, G., Berre, L., Chabot, V. and Chapnik, B. 2009. A posteriori diagnostics in an ensemble of perturbed analyses. Mon. Wea. Rev. 137, 3420–3436. doi: 10.1175/2009MWR2778.1.
Open DOI Search in Google Scholar Back to article
Edwards, J. M. and Slingo, A. 1996. Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Qjr. Meteorol. Soc. 122, 689–719. doi: 10.1002/qj.49712253107.
Open DOI Search in Google Scholar Back to article
Essery, R., Best, M. and Cox, P. 2001. MOSES 2.2 Technical Documentation. Hadley Centre Technical Note 30. UK Met Office, Exeter, UK, pp. 1–30.
Search in Google Scholar Back to article
Gelaro, R., Zhu, Y. and Errico, R. M. 2007. Examination of various-order adjoint-based approximations of observation impact. Metz. 16, 685–692. doi: 10.1127/0941-2948/2007/0248.
Open DOI Search in Google Scholar Back to article
Gelaro, R. and Zhu, Y. 2009. Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models. Tellus A. 61, 179–193. doi: 10.1111/j.1600-0870.2008.00388.x.
Open DOI Search in Google Scholar Back to article
Gelaro, R., Langland, R. H., Pellerin, S. and Todling, R. 2010. The THORPEX observation impact intercomparison experiment. Mon. Wea. Rev. 138, 4009–4025. doi: 10.1175/2010MWR3393.1.
Open DOI Search in Google Scholar Back to article
Gregory, D., Kershaw, R. and Inness, P. M. 1997. Parametrization of momentum transport by convection. II: Tests in single-column and general circulation models. Qjr. Meteorol. Soc. 123, 1153–1183. doi: 10.1002/qj.49712354103.
Open DOI Search in Google Scholar Back to article
Joo, S., Lorenc, A. C. and Marriott, R. 2012. Diagnosis of exaggerated impacts on adjoint-based sensitivity studies, Met Office Technical Report 564. UK Met Office, Exeter, UK, pp. 1–18.
Search in Google Scholar Back to article
Joo, S., Eyre, J. and Marriott, R. 2013. The impact of Metop and other satellite data within the Met Office global NWP system using an adjoint-based sensitivity method. Mon. Wea. Rev. 141, 3331–3342. doi: 10.1175/MWR-D-12-00232.1.
Open DOI Search in Google Scholar Back to article
Jung, B. J., Kim, H. M., Auligné, T., Zhang, X., Zhang, X. and co-authors. 2013. Adjoint-derived observation impact using WRF in the western North Pacific. Mon. Wea. Rev. 141, 4080–4097. doi: 10.1175/MWR-D-12-00197.1.
Open DOI Search in Google Scholar Back to article
Kershaw, R. and Gregory, D. 1997. Parametrization of momentum transport by convection. I: Theory and cloud modeling results. Qjr. Meteorol. Soc. 123, 1133–1151. doi: 10.1002/qj.49712354102.
Open DOI Search in Google Scholar Back to article
Kim, H. M., Kim, S.-M., Joo, S. and Kim, E.-J. 2014. Estimation of satellite observation impact on numerical weather forecast using adjoint-based method. 26 March – 1 April 2014. In: 19th International TOVS Study Conference (ITSC-19), Jeju, Republic of Korea.
Search in Google Scholar Back to article
Kim, S., Kim, H. M., Kim, E.-J. and Shin, H.-C. 2013. Forecast sensitivity to observations for high-impact weather events in the Korean Peninsula. Atmosphere. 23, 171–186. doi: 10.14191/Atmos.2013.23.2.171 (in Korean with English abstract).
Open DOI Search in Google Scholar Back to article
Kim, S.-M. and Kim, H. M. 2013. Observation impact estimation using a Forecast Sensitivity to Observation (FSO) method in the Global and East Asia Regions. 7–12 April 2013. In: EGU General Assembly 2013, Vienna, Austria.
Search in Google Scholar Back to article
Kim, S.-M. and Kim, H. M. 2014. Sampling error of observation impact statistics. Tellus A. 66, 25435. doi: 10.3402/tellusa.v66.25435.
Open DOI Search in Google Scholar Back to article
Kim, S.-M. and Kim, H. M. 2017. Adjoint-based observation impact of Advanced Microwave Sounding Unit-A (AMSU-A) on the short-range forecast in East Asia. Atmosphere. 27, 93–104. doi: 10.14191/Atmos.2017.27.1.093 (in Korean with English abstract).
Open DOI Search in Google Scholar Back to article
Kim, M., Kim, H. M., Kim, J., Kim, S.-M., Velden, C. and Hoover, B. 2017. Effect of enhanced satellite-derived atmospheric motion vectors on numerical weather prediction in East Asia using an adjoint-based observation impact method. Wea. Forecasting. 32, 579–594. doi: 10.1175/WAF-D-16-0061.1.
Open DOI Search in Google Scholar Back to article
Langland, R. H. and Baker, N. L. 2004. Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus A. 56, 189–201. doi: 10.1111/j.1600-0870.2004.00056.x.
Open DOI Search in Google Scholar Back to article
Lorenc, A. C. and Marriott, R. 2014. Forecast sensitivity to observations in the Met Office Global numerical weather prediction system. Qjr. Meteorol. Soc. 140, 209–224. doi: 10.1002/qj.2122.
Open DOI Search in Google Scholar Back to article
Lorenc, A. C. and Payne, T. 2007. 4D-Var and the butterfly effect: statistical four-dimensional data assimilation for a wide range of scales. Qjr. Meteorol. Soc. 133, 607–614. doi: 10.1002/qj.36.
Open DOI Search in Google Scholar Back to article
Lock, A. R., Brown, A. R., Bush, M. R., Martin, G. M. and Smith, R. N. B. 2000. A new boundary-layer mixing scheme. Part 1: Scheme description and single-column model tests. Mon. Wea. Rev. 128, 3187–3199. doi: 10.1175/1520-0493(2000)128<;3187:ANBLMS>2.0.CO;2.
Open DOI Search in Google Scholar Back to article
Lupu, C., Cardinali, C. and McNally, A. P. 2015. Adjoint‐based forecast sensitivity applied to observation‐error variance tuning. Qjr. Meteorol. Soc. 141, 3157–3165. doi: 10.1002/qj.2599.
Open DOI Search in Google Scholar Back to article
Waller, J. A., Simonin, D., Dance, S. L., Nichols, N. K. and Ballard, S. P. 2016a. Diagnosing observation error correlations for Doppler radar radial winds in the Met Office UKV model using observation-minus-background and observation-minus-analysis statistics. Mon. Wea. Rev. 144, 3533–3551. doi: 10.1175/MWR-D-15-0340.1.
Open DOI Search in Google Scholar Back to article
Waller, J. A., Ballard, S. P., Dance, S. L., Kelly, G. A., and Nichols, N. K. 2016b. Diagnosing horizontal and inter-channel observation error correlation for SEVIRI observations using observation-minus-background and observation-minus-analysis statistics. Remote Sens. 8, 581. doi: 10.3390/rs8070581.
Open DOI Search in Google Scholar Back to article
Webster, S. A., Brown, A. R., Cameron, D. R., and Jones, C. P., 2003. Improvements to the representation of orography in the Met Office Unified Model. Qjr. Meteorol. Soc. 129, 1989–2010. doi: 10.1256/qj.02.133.
Open DOI Search in Google Scholar Back to article
Weston, P. P., Bell, W. and Eyre, J. R. 2014. Accounting for correlated error in the assimilation of high resolution sounder data. Qjr. Meteorol. Soc. 140, 2420–2429. doi: 10.1002/qj.2306.
Open DOI Search in Google Scholar Back to article
Wilks, D. S. 2006. Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, Burlington, MA, pp. 627.
Search in Google Scholar Back to article
Wilson, D. R. and Ballard, S. P. 1999. A microphysically based precipitation scheme for the UK Meteorological Office unified model. Qjr. Meteorol. Soc. 125, 1607–1636. doi: 10.1002/qj.49712555707.
Open DOI Search in Google Scholar Back to article