Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system

Abstract

An adjoint-based procedure for assessing the impact of observations on the short-range forecast error in numerical weather prediction is described. The method is computationally inexpensive and allows observation impact to be partitioned for any set or subset of observations, by instrument type, observed variable, geographic region, vertical level or other category. The cost function is the difference between measures of 24-h and 30-h global forecast error in the Navy Operational Global Atmospheric Prediction System (NOGAPS) during June and December 2002. Observations are assimilated at 00UTC in the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS). The largest error reductions in the Northern Hemisphere are produced by rawinsondes, satellite wind data, and aircraft observations. In the Southern Hemisphere, the largest error reductions are produced by Advanced TIROS Operational Vertical Sounder (ATOVS) temperature retrievals, satellite wind data and rawinsondes. Approximately 60% (40%) of global observation impact is attributed to observations below (above) 500 hPa. A significant correlation is found between observation impact and cloud cover at the observation location. Currently, without consideration of moisture observations and moist processes in the forecast model adjoint, the observation impact procedure accounts for about 75% of the actual reduction in 24-h forecast error.

Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters

“…has been first considered in the work of Langland and Baker (2004) and in recent adjoint-based OBSI studies (Cardinali, 2009;Gelaro and Zhu, 2009). Efficient alternatives to (11), such as a midpoint rule, are discussed by Daescu and Todling (2009).…”

“…Adjoint-DAS estimation of the observation sensitivity was considered in the work of Baker and Daley (2000) and Doerenbecher and Bergot (2001) as a tool to design observation targeting strategies. Subsequently, Langland and Baker (2004) have shown that the combined information derived from the adjoint of the forecast model and the adjoint-DAS may be used to provide a detailed (all-at-once) assessment of the observation impact on reducing the forecast errors in variational data assimilation (VDA). Adjoint-DAS tools have been developed at major NWP centres and are currently used to monitor the impact of data provided by the global observing network in reducing short-range forecasts errors, to provide data quality diagnostics and guidance for optimal satellite channel selection, and to design observation targeting strategies Fourrié et al, 2002;Langland, 2005;Zhu and Gelaro, 2008;Baker and Langland, 2009;Cardinali, 2009;Gelaro and Zhu, 2009).…”

Error covariance sensitivity and impact estimation with adjoint 4D‐Var: theoretical aspects and first applications to NAVDAS‐AR

“…Baker and Daley (2000) have shown that an all-at-once evaluation of the forecast sensitivity to observations may be performed by developing the adjoint of the data assimilation system (adjoint-DAS). The analysis of the information content of observations and the observation impact assessment through observation sensitivity and adjoint-DAS techniques are routine activities at NWP centres to monitor the observing system performance on reducing the short-range forecast errors (Langland and Baker, 2004;Trémolet, 2008;Baker and Langland, 2009;Cardinali, 2009;Daescu and Todling, 2009;Gelaro and Zhu, 2009;Gelaro et al, 2010;Cardinali and Prates, 2011;Lupu et al, 2011). The adjoint-DAS applications may be extended to incorporate the sensitivity analysis with respect to error covariance parameters and the estimation of the forecast impact from adjusting the error covariance models.…”

Predicting Air Quality: Current Status and Future Directions

“…This is called the targeted observations technique. Different techniques for the placement of obser-vations in the context of numerical weather prediction models are discussed in [9,78,66,49,77,72,7]. Studies performed during field experiments revealed the potential benefits that may be achieved using adaptive observations as well as various practical issues and shortcomings of the current targeting methodologies.…”

“…This may be achieved in a posteriori analysis and data assimilation experiments that are used to provide valuable insight on the benefits and shortcomings of various targeted observations strategies. Langland and Baker use forecasts of different lengths that verify at the same time to define J v and to assess the observation impact on the forecast error [66].…”