Abstract
The adjoints of the numerical weather prediction (NWP) model and data assimilation system may be used together to objectively determine the observation impact – or whether a given observation platform or observing system improves or degrades the subsequent NWP forecast.
The observation impact is a very specific measure of forecast impact, as it depends upon the choice of forecast metric, the suite of observations assimilated, the data assimilation system, and the NWP forecast model. This chapter presents an overview of data assimilation adjoint theory, the observation impact calculation, and the appropriate choices for the forecast metric. Several applications of the observation adjoint technique are presented to illustrate its usefulness to help identify systematic problems with the observing network, to quantify the value of different observing platforms, to monitor the quality of the observing network, and for channel selection for satellite radiometers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baker NL (2000) Observation adjoint sensitivity and the adaptive observation-targeting problem. Ph.D. dissertation, Naval Postgraduate School, 265 pp. Available from the Naval Research Laboratory, Monterey, CA 93943.
Baker NL, Daley R (2000) Observation and background adjoint sensitivity in the adaptive observation-targeting problem. Q J R Meteorol Soc 126: 1431–1454.
Bergot T (1999) Adaptive observations during FASTEX: A systematic survey of upstream flights. Q J R Meteorol Soc 125: 3271–3298.
Bergot T, Doerenbecher A (2002) A study of the optimization of the deployment of targeted observations using adjoint-based Methods. Q J R Meteorol Soc 128: 1689–1712.
Buizza R, Montani A (1999) Targeting observations using singular vectors. J Atmos Sci 56: 2965–2985.
Daley R (1991) Atmospheric data assimilation, Cambridge University Press, 457 pp.
Daley R, Barker E (2001) NAVDAS: Formulation and Diagnostics. Mon Wea Rev 129: 869–883
Doerenbecher A, Bergot T (2001) Sensitivity to observations applied to FASTEX cases. Nonlinear Proc Geophys 8: 467–481.
Errico R (2007) Interpretation of ad adjoint-derived observational impact measure. Tellus 59A: 273–276.
Fourrié NA, Rabier F (2004) Cloud characteristics and channel selection for IASI radiances in meteorologically sensitive areas. Q J R Meteorol Soc 130: 1839–1856.
Gelaro R, Langland RH, Rohaly GD, Rosmond TE (1999) An assessment of the singular vector approach to targeted observing using the FASTEX data set. Q J R Meteorol Soc 125: 3299–3327.
Gelaro R, Zhu Y, Errico R (2007) Examination of various-order adjoint-based approximations of observation impact. Meteorologische Zeitschrift, 16: 685–692.
Gelb A (1974) Applied optimal estimation. MIT Press, 374 pp.
Hogan TF, Rosmond TE, Pauley RL (1999) The navy operational global atmospheric prediction system: Recent changes and testing of gravity wave and cumulus parameterizations. Preprints, 13th Conf Numerical Weather Prediction, Denver, CO, Amer Meteorol Soc, pp 60–65.
Ide K, Courtier P, Ghil M, Lorenc AC (1997) Unified notation for data assimilation: operational, sequential and variational. J Meteorol Soc Japan, 751B: 181–189.
Joly A, Jorgensen D, Shapiro MA, Thorpe A, Bessemoulin P, Browning KA, Chalon J-P, Clough SA, Emanuel KA, Eymard L, Gall R, Hildebrand PH, Langland RH, Lemaitre Y, Lynch P, Moore JA, Persson POG, Snyder C, Wakimoto R (1997) The Fronts and Atlantic Storm-track Experiment (FASTEX): Scientific objectives and experimental design. Bull Amer Meteorol Soc, 78: 1917–1940.
Langland RH, Rohaly GD (1996) Adjoint-based targeting of observations for FASTEX cyclones. Preprints, 7th Conf Mesoscale Processes, Reading, UK, Amer Meteorol Soc, pp 369–371.
Langland RH, Baker NL (2004a) Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, 56A: 189–201.
Langland, RH, Baker NL (2004b) A technical description of the NAVDAS adjoint system. NRL/MR/7530-04-8746. Available from the Naval Research Laboratory, Monterey, CA, 93943, 62 pp.
Morneau J, Pellerin S, Laroche S, Tanquay M (2006) Estimation of the adjoint sensitivity gradients in observation space using the dual (PSAS) formulation of the Environment Canada operational 4D-Var. Proceedings, 2nd THORPEX Intl Science Symp, Landshut, Germany, 4–8 December 2006, pp 162–163.
Palmer TN, Gelaro R, Barkmeijer J, Buizza R (1998) Singular vectors, metrics and adaptive observations. J Atmos Sci 55: 633–653.
Rabier F, Klinker E, Courtier P, Hollingsworth A (1996) Sensitivity of forecast errors to initial conditions. Q J R Meteorol Soc, 122: 121–150.
Rodgers, C. D. (1996) Information content and optimization of high spectral resolution measurements. Optical Spectroscopic Techniques and Instrumentation for Atmospheric and Space Research II, SPIE Vol. 2830, 136–147. Published by the International Society for Optical Engineering, PO Box 10, Bellingham, Washington, 98227-0010, USA, 2830.
Rosmond TE (1997) A technical description of the NRL adjoint modeling system, NRL/MR/7532/97/7230 Available from the Naval Research Laboratory, Monterey, CA 93943, 55 pp.
Rosmond T, Xu L (2006) Development of NAVDAS-AR: non-linear formulation and outer loop tests. Tellus, 58A, 45–58.
Ruston B, Blankenship C, Campbell W, Langland R, Baker N (2006) Assimilation of AIRS data at NRL. 15th Intl TOVS Study Conf, Maratea, Italy, 4–10 October 2006.
Xu L, Rosmond R (2005) Development of NAVDAS-AR: formulation and initial tests of the linear problem. Tellus, 57A, 546–559.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Baker, N.L., Langland, R.H. (2009). Diagnostics for Evaluating the Impact of Satellite Observations. In: Park, S.K., Xu, L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71056-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-71056-1_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71055-4
Online ISBN: 978-3-540-71056-1
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)