Monday, 7 January 2013
Exhibit Hall 3 (Austin Convention Center)
With over ten years of continuous GPS radio occultation (RO) data now available from CHAMP, COSMIC, and other missions, there has been an increasing interest of applying the data for climate trend detection. GPS RO data are traditionally available as retrieved vertical profiles that are quasi-randomly distributed in space and time, whereas gridded data are more useful in many applications. In this study, we describe our approach towards the generation of global monthly gridded data products that include temperature, geopotential height, and geostrophic wind in the upper troposphere and lower stratosphere (UTLS). In addition, tropopause parameters corresponding to the lapse-rate and cold-point tropopauses will be derived. To interpolate or map the irregularly sampled RO data into a 2D grid, the Bayesian method with spherical harmonic basis functions is implemented. Interpolation or mapping coefficients are calculated in such a way that selects the optimal balance between misfit of the data and over-fit of the data. We will discuss the choice of optimal interpolation parameters and the monthly averaging approach. Furthermore, we will quantify the uncertainty associated with the monthly gridded data. The dataset will be used to examine the trend and variability of the UTLS, and the results will be compared with radiosonde, reanalyses, and climate models.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner