Lars Peter Riishojgaard

The GEOS ozone data assimilation system: Specification of error statistics

A global three-dimensional ozone data assimilation system has been developed at the Data Assimilation Office of the NASA Goddard Space Flight Center. The Total Ozone Mapping Spectrometer (TOMS) total ozone data and the Solar Backscatter Ultraviolet/2 (SBUV/2) partial ozone profile observations are assimilated. The assimilation, into an off-line ozone transport model, is done using the global Physical-space Statistical Analysis Scheme. This system became operational in December 1999. A detailed description of the statistical analysis scheme and, in particular, of the forecast- and observation-error covariance models is given. A new global anisotropic horizontal forecast-error correlation model accounts for a varying distribution of observations with latitude. Correlations are largest in the zonal direction in the tropics where data are sparse. Forecast-error variance is assumed to be proportional to the ozone field. The forecast-error covariance parameters were determined by maximum-likelihood estimation. The error covariance models are validated using χ2 statistics. The analysed ozone fields in the winter 1992 are validated against independent observations from ozone sondes and the Halogen Occultation Experiment (HALOE). The difference between the mean HALOE observations and the analysis fields is less than 10% at pressure levels between 70 and 0.2 hPa. The global root-mean-square difference between TOMS observed and forecast values is less than 4%. The global root-mean-square difference between SBUV observed and analysed ozone between 50 and 3 hPa is less than 15%.

Impact of Different Satellite Wind Lidar Telescope Configurations on NCEP GFS Forecast Skill in Observing System Simulation Experiments

AbstractThe Global Wind Observing Sounder (GWOS) concept, which has been developed as a hypothetical space-based hybrid wind lidar system by NASA in response to the 2007 National Research Council (NRC) decadal survey, is expected to provide global wind profile observations with high vertical resolution, precision, and accuracy when realized. The assimilation of Doppler wind lidar (DWL) observations anticipated from the GWOS is being conducted as a series of observing system simulation experiments (OSSEs) at the Joint Center for Satellite Data Assimilation (JCSDA). A companion paper (Riishojgaard et al.) describes the simulation of this lidar wind data and evaluates the impact on global numerical weather prediction (NWP) of the baseline GWOS using a four-telescope configuration to provide independent line-of-sight wind speeds, while this paper sets out to assess the NWP impact of GWOS equipped with alternative paired configurations of telescopes. The National Centers for Environmental Prediction (NCEP) Gri...

Analysis and evaluation of Observing System Simulation Experiments (OSSEs) forecast data for Indian summer monsoon

An attempt is made here to evaluate the skill of forecast during boreal summer monsoon regime over the Indian region using the Observation Simulation System Experiment (OSSE) with Doppler Wind LIDAR (DWL) onboard International Space Station (ISS), assimilated in the initial condition. Through various techniques such as pattern correlation, root mean square error etc, we found that there is some positive impact of assimilating the DWL data on the forecast particularly at the lower tropospheric level. Impact on lowering the RMSE is seen for wind fields in the 850 and 500 hPa over Indian domain but not much impact is seen over larger domain. The moisture field and cloud also show marginal impact due to assimilation of DWL. This indicates that possibly due to lower spatial resolution of DWL data and more data gap over Indian and surrounding oceanic region, the impact on forecast is less. However, it shows the promise that monsoon being a convectively coupled system; increase in spatial data by DWL may better resolve the low level wind and subsequently the low level shear which is important for convection trigger in boundary layer.