Abstract. The goals of this study are the evaluation of current fast radiative transfer models (RTMs) and line-by-line (LBL) models. The intercomparison focuses on the modeling of 11 representative sounding channels routinely used at numerical weather prediction centers: 7 HIRS (High-resolution Infrared Sounder) and 4 AMSU (advanced microwave sounding unit) channels. Interest in this topic was evident by the participation of 24 scientists from 16 institutions. An ensemble of 42 diverse atmospheres was used and results compiled for 19 infrared models and 10 microwave models, including several LBL RTMs. For the first time, not only radiances but also Jacobians (of temperature, water vapor, and ozone) were compared to various LBL models for many channels. In the infrared, LBL models typically agree to within 0.05-0.15 K (standard deviation) in terms of top-of-the-atmosphere brightness temperature (BT). Individual differences up to 0.5 K still exist, systematic in some channels, and linked to the type of atmosphere in others.The best fast models emulate LBL BTs to within 0.25 K, but no model achieves this desirable level of success for all channels. The ozone modeling is particularly challenging. In the microwave, fast models generally do quite well against the LBL model to which they were tuned. However, significant differences were noted among LBL models. Extending the intercomparison to the Jacobians proved very useful in detecting subtle or more obvious modeling errors. In addition, total and single gas optical depths were calculated, which provided additional insight on the nature of differences.

A Canadian Land Data Assimilation System (CaLDAS) for the analysis of land surface prognostic variables is designed and implemented at the Meteorological Service of Canada for the initialization of numerical weather prediction and climate models. The assimilation of different data sources for the production of daily soil moisture and temperature analyses is investigated in a set of observing system simulation experiments over North America. A simplified variational technique is adapted to accommodate different observation types at their appropriate time in a 24-h time window. The screen-level observations of temperature and relative humidity, from conventional synoptic surface observations (SYNOP)/aviation routine weather report (METAR)/surface aviation observation (SA) reports, are considered together with presently available satellite observations provided by the Aqua satellite (microwave C-band), Geostationary Operational Environmental Satellite (GOES) [infrared (IR)], and observations available in the future by the Soil Moisture and Ocean Salinity (SMOS) satellite mission (microwave L-band). The aim of these experiments is to assess the information content brought by each observation type in the land surface analysis. The observation systems are simulated according to their spatial coverage, temporal availability, and nominal or expected errors. The results show that the observable with the largest dynamical response to perturbations of the control variable carries the greatest information content into the analysis. The observational error and the observation frequency counterbalance this feature in the analysis. If one considers a single observation both for soil moisture and soil temperature analysis, then satellite measurements (L-band, C-band, and IR in decreasing order of importance) are the primary source of information. When observation availability is considered and the highest temporal frequency of screen-level observations is used (1 h), a large amount of information is extracted from SYNOP-like reports. The screen-level observations are shown to provide valuable soil moisture information mainly during the daytime, while during nighttime these observations (and particularly screen-level temperature) are mostly useful for the soil temperature analysis. The results are presented with perspectives for future operational developments and preliminary assimilation experiments are performed with hourly screen-level observations.

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Precipitable water (PW) derived from the GPS zenith tropospheric delay (ZTD) is evaluated (as a first step toward variational data assimilation) through comparison with that of collocated radiosondes (RS_PW), operational analyses, and 6-h forecasts (from the Canadian Global Environmental Multiscale model) of the Canadian Meteorological Centre. Two sources of ZTD data are considered: 1) final ZTD (over Canada), computed by the Geodetic Survey Division (GSD) of Natural Resources Canada, and 2) final ZTD (distributed globally), obtained from the International GPS Service (IGS). The mean GSD GPS–derived PW (GPS_PW) is 14.9 mm (reflecting the relatively cold Canadian climate), whereas that of the IGS dataset is 20.8 mm. Intercomparison statistics [correlation, standard deviation (SD), and bias] between GPS_PW and RS_PW are, respectively, 0.97, 2.04 mm, and 1.35 mm for the GSD data and 0.98, 2.6 mm, and 0.67 mm for the IGS data. Comparisons of GPS_PW with 6-h forecast PW (TRIAL_PW) show slightly lower correlations and a higher SD. The increase in SD is greater for the IGS data, which is not surprising, because in regions such as the Tropics and subtropics, moisture forecasts are of a lower quality and the RS observation network is sparse. From a three-way intercomparison (IGS GPS_PW, RS_PW, and TRIAL_PW) of the SD statistics, it is found that GPS_PW has the lowest estimated PW error (≈1 mm) for PW in the 5–30-mm range. For PW greater than 30 mm, the RS_PW estimated error is ≈2 mm, and that of GPS_PW is ≈2.5 mm. The TRIAL_PW estimated error increases with PW, reaching 5.5 mm in the 40–55-mm PW range. These intercomparison results indicate that GPS_PW should be a useful source of humidity information for NWP applications.

Half-hourly GPS zenith tropospheric delay (ZTD) and collocated surface weather observations of pressure, temperature, and relative humidity are available in near-real time from the NOAA Global Systems Division (GSD) research GPS receiver network. These observations, located primarily over the continental United States, are assimilated in a research version of the Environment Canada (EC) regional (North America) analysis and forecast system. The impact of the assimilation on regional analyses and 0-48-h forecasts is evaluated for two periods: summer 2004 and winter 2004/05. Forecasts are verified against radiosonde, rain gauge, and NOAA GPS network observations.The impacts of GPS ZTD and collocated surface weather observations for the summer period are generally positive, and include reductions in forecast errors for precipitable water, surface pressure, and geopotential height. It is shown that the ZTD data are primarily responsible for these forecast error reductions. The impact on precipitation forecasts is mixed, but more positive than negative, especially for the central U.S. region and for forecasts of larger precipitation amounts. Assimilation of the collocated surface weather data with ZTD contributes to the positive impact on precipitation forecasts for the central U.S. region. The NOAA GPS network data also have a positive impact on tropical storm system forecasts over the southeast United States, in terms of both storm track and precipitation. Impacts for the winter case are generally smaller because of the lower precipitable water (PW) over North America, but some positive impacts are observed for precipitation forecasts. The greatest regional impacts in the winter case are observed for the southeast U.S. (the Gulf) region where average PW is highest.