The THORPEX Observation Impact Intercomparison Experiment

Abstract

An experiment is being conducted to directly compare the impact of all assimilated observations on shortrange forecast errors in different forecast systems using an adjoint-based technique. The technique allows detailed comparison of observation impacts in terms of data type, location, satellite sounding channel, or other relevant attributes. This paper describes results for a ''baseline'' set of observations assimilated by three forecast systems for the month of January 2007. Despite differences in the assimilation algorithms and forecast models, the impacts of the major observation types are similar in each forecast system in a global sense. However, regional details and other aspects of the results can differ substantially. Large forecast error reductions are provided by satellite radiances, geostationary satellite winds, radiosondes, and commercial aircraft. Other observation types provide smaller impacts individually, but their combined impact is significant. Only a small majority of the total number of observations assimilated actually improves the forecast, and most of the improvement comes from a large number of observations that have relatively small individual impacts. Accounting for this behavior may be especially important when considering strategies for deploying adaptive (or ''targeted'') components of the observing system.

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

“…Radiances have a substantial contribution to the forecast-error reduction whereas, when taking into account the data volume, it is noticed that measurements associated with other atmospheric parameters such as u-and v-wind speed, temperature, humidity, and pressure have a larger impact per observation. A detailed study on OBSI assessment at NWP centres is provided by Gelaro et al (2010). In the following, we focus on the forecast R-and B-sensitivity guidance to covariance weight adjustments which may further reduce the forecast errors.…”

“…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.…”

Assessing the influence of the model trajectory in the adaptive observation Kalman Filter Sensitivity method

“…A recent study (Gelaro et al, 2010) showed that relatively few observations (around 53%) bring useful information to the assimilation process. Such results are taken from experiments considering the whole observation network and no specific meteorological phenomena, without any data targeting concern.…”

“…Recent studies have been carried out on observation sensitivity and observation impact, and have presented methods to verify the effectiveness of the observations used in the assimilation process (Daescu, 2008;Trémolet, 2008;Gelaro and Zhu, 2009;Cardinali, 2009;Gelaro et al, 2010). The Data Targeting System (DTS) experiment, currently hosted by the European Centre for Medium-Range Weather Forecasts (ECMWF), aims to add routine observations at non-standard times to help study high-impact weather events over Europe.…”

Optimal placement of mobile sensors for data assimilations

“…The following is a list of works related to the variational data assimilation algorithms: Cardinali et al (2004); Xu et al (2006); Daescu (2008); Tremolet (2008); Baker and Langland (2009);Cardinali (2009) ;Daescu andTodling (2009, 2010); Gelaro and Zhu (2009) ;Gelaro et al (2010); Cardinali and Prates (2011). Similar studies were also conducted for ensemble data assimilation methods, such as Liu and Kalnay (2008), Liu et al (2009), andLi et al (2009).…”