Barbara Scherllin-Pirscher

A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility

[1] Global Navigation Satellite System (GNSS)-based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth’s atmosphere for weather and climate applications. Above about 30km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium-range Weather Forecasts (ECMWF) short-range forecast and analysis fields, together with averaged-observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method’s performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrievedrefractivityprofilesandatmospheric(temperature)profilesbenefit by improvederror characteristics,especiallyabove about 30km. Based on theseencouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.

The vertical and spatial structure of ENSO in the upper troposphere and lower stratosphere from GPS radio occultation measurements

[1] The vertical and spatial structure of the atmospheric El Nino-Southern Oscillation (ENSO) signal is investigated using radio occultation (RO) data from August 2006 to December 2010. Due to their high vertical resolution and global coverage, RO data are well suited to describe the full 3-dimensional ENSO structure in the troposphere and lower stratosphere. We find that interannual temperature anomalies in the equatorial region show a natural decomposition into zonal-mean and eddy (deviations from the zonal-mean) components that are both related to ENSO. Consistent with previous studies, we find that during the warm phase of ENSO, zonal-mean temperatures increase in the tropical troposphere and decrease in the tropical stratosphere. Maximum warming occurs above 8 km, and the transition between warming and cooling occurs near the tropopause. This zonal-mean response lags sea surface temperature anomalies in the eastern equatorial Pacific by 3 months. The atmospheric eddy component, in contrast, responds rapidly (within 1 month) to ENSO forcing. This signal features a low-latitude dipole between the Indian and Pacific Oceans, with off-equatorial maxima centered around 20 to 30 latitude in both hemispheres. The eddy response pattern attains maximum amplitude in the upper troposphere near 11 km and (with opposite polarity) in a shallow layer near the tropopause at approximately 17 km. The eddy ENSO signal tends to be out-of-phase between low and middle latitudes in both the troposphere and lower stratosphere. Citation: Scherllin-Pirscher, B., C. Deser, S.-P. Ho, C. Chou, W. Randel, and Y.-H. Kuo (2012), The vertical and spatial structure of ENSO in the upper troposphere and lower stratosphere from GPS radio occultation measurements, Geophys. Res. Lett., 39, L20801, doi:10.1029/2012GL053071.

The power of vertical geolocation of atmospheric profiles from GNSS radio occultation

Abstract High‐resolution measurements from Global Navigation Satellite System (GNSS) radio occultation (RO) provide atmospheric profiles with independent information on altitude and pressure. This unique property is of crucial advantage when analyzing atmospheric characteristics that require joint knowledge of altitude and pressure or other thermodynamic atmospheric variables. Here we introduce and demonstrate the utility of this independent information from RO and discuss the computation, uncertainty, and use of RO atmospheric profiles on isohypsic coordinates—mean sea level altitude and geopotential height—as well as on thermodynamic coordinates (pressure and potential temperature). Using geopotential height as vertical grid, we give information on errors of RO‐derived temperature, pressure, and potential temperature profiles and provide an empirical error model which accounts for seasonal and latitudinal variations. The observational uncertainty of individual temperature/pressure/potential temperature profiles is about 0.7 K/0.15%/1.4 K in the tropopause region. It gradually increases into the stratosphere and decreases toward the lower troposphere. This decrease is due to the increasing influence of background information. The total climatological error of mean atmospheric fields is, in general, dominated by the systematic error component. We use sampling error‐corrected climatological fields to demonstrate the power of having different and accurate vertical coordinates available. As examples we analyze characteristics of the location of the tropopause for geopotential height, pressure, and potential temperature coordinates as well as seasonal variations of the midlatitude jet stream core. This highlights the broad applicability of RO and the utility of its versatile vertical geolocation for investigating the vertical structure of the troposphere and stratosphere.

Deriving dynamics from GPS radio occultation: Three‐dimensional wind fields for monitoring the climate

Global Positioning System (GPS) radio occultation (RO) measurements are proven highly useful for observing the thermal structure of the troposphere and stratosphere. Here we use RO data for the first time to derive climatological wind fields from sampling error-corrected geopotential height fields on isobaric surfaces from about 800 hPa to 3 hPa. We find monthly mean RO geostrophic wind and gradient wind fields (2007 to 2012, about 500 km horizontal resolution, outside tropics) to clearly capture all main wind features, with differences to atmospheric analysis winds being, in general, smaller than 2 m/s. Larger differences (up to 10 m/s) occur close to the subtropical jet where RO winds underestimate actual winds. Such biases are caused by the geostrophic and gradient wind approximations, while RO retrieval errors introduce negligible effect. These results demonstrate that RO wind fields are of high quality and can provide new information on troposphere-stratosphere dynamics, for the benefit of monitoring the climate from weekly to decadal scales.