Assimilation experiments with ground-based GPS observations in the Environment Canada Global and Regional Deterministic Prediction Systems

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Wednesday, 5 February 2014
Hall C3 (The Georgia World Congress Center )
Stephen R. Macpherson, EC, Dorval, QC, Canada; and S. Laroche and J. Aparicio

Handout (647.7 kB)

The NOAA Global Systems Division (GSD) GPS-IPW network consists of over 600 sites equipped with high-precision dual-frequency GPS receivers, located mainly in the North America region. The primary ground-based GPS (GB-GPS) observation for meteorological applications is the Zenith Tropospheric Delay (ZTD), a measure of delay in GPS satellite signal reception at the ground due to the retarding effect of atmospheric refractivity. The ZTD can be related to total air weight (surface pressure) and integrated atmospheric water vapour (IWV) above the GPS antenna. Collocated or nearby surface weather observations of pressure, temperature and relative humidity, available at most of the GPS-IPW network sites, allow IWV to be retrieved from ZTD. Data from the network are available every 30 minutes and the quality of the observations is largely unaffected by clouds and precipitation.

This paper presents results of recent data assimilation experiments involving addition of NOAA/GSD GPS-IPW network ZTD observations to new experimental versions of the EC global and regional deterministic prediction systems (GDPS and RDPS respectively). In the new GDPS and RDPS, analyses that provide initial conditions for forecasts are obtained with a hybrid Ensemble-Variational (EnVar) approach, as opposed to the current operational 4D-Variational (4D-Var) method. In EnVar, a variational analysis is done using background error covariances that are, in part, derived from a 4-dimensional ensemble of model forecasts produced by the EC Ensemble Kalman Filter. As ZTD is an integrated quantity, background error covariances are critical in distributing the humidity analysis increments from ZTD data assimilation optimally in the vertical. The flow-dependent background error covariances for humidity in the new EnVar data assimilation systems (DAS) are better suited for this purpose than the static “NMC-method” variances applied in the operational 4D-Var systems. In addition, a higher analysis increment grid resolution in the new DAS compared to the operational 4D-Var systems is better able to capture the smaller scale variations in humidity sampled by the GB-GPS ZTD data.

The impact of GB-GPS data assimilation in the experimental EnVar-based versions of the EC GDPS and RDPS is evaluated through verifications of forecasts for a two month period in the summer of 2011 using radiosonde observations, GB-GPS observations, rain gauge data and analyses from the European Centre for Medium-range Weather Forecasts (ECMWF).