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Godelieve Deblonde

Abstract

The direct assimilation of brightness temperatures (Tb’s) from the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Water Vapor Profiler (SSM/T-2) in a one-dimensional variational (1D-Var) assimilation system is studied over the oceans. The control variables of the 1D-Var are the natural logarithm of specific humidity (lnq), near-surface wind speed (SWS), and cloud liquid water (CLW) path.

Synthetic Tb’s, with and without noise added, were simulated and used to estimate the strength of the 1D-Var assimilation (weight given to the observations) and the information content of the Tb. In clear skies, it is shown that except for very dry profiles (TPW < 5–6 kg m−2) SSM/I Tb’s are superior to the SSM/T-2 Tb’s for the determination of total precipitable water (TPW). In the presence of clouds, the SSM/T-2 TPW retrievals are underestimated and the underestimation increases with CLW. Cloudy profiles should be filtered out.

It is also shown that the SSM/I 1D-Var retrievals for low wind speeds are erroneous (<2–3 m s−1) and so are retrievals of low CLW (<0.1 kg m−2). Otherwise, SSM/I Tb retrievals of TPW, SWS, and CLW perform well. 1D-Var analyses using bias-corrected observed Tb’s were also computed for the same case as that using synthetic Tb’s and were compared with retrievals obtained using published regression equations. When both SSM/I and SSM/T-2 Tb’s were assimilated simultaneously (in collocation) in the 1D-Var system, the retrievals of TPW, SWS, and CLW were very similar to those when only SSM/I Tb’s were assimilated. However, the SSM/T-2 Tb also provided specific humidity information in the upper troposphere.

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Godelieve Deblonde

Abstract

The impact of assimilating Special Sensor Microwave/Imager (SSM/I) total precipitable water (TPW) on the Canadian Meteorological Centre (CMC) operational analyses and forecasts is evaluated. Assimilation cycles were performed for the months of July 1996 and December 1996. The agreement between the SSM/I TPW climatology and the analyzed TPW for the control case (for which only conventional observations were assimilated) was quite good (root-mean-square difference of 2.8 kg m−2), which showed that the humidity analysis for the control case was already good. As a result of assimilating SSM/I TPW and depending on the month studied, collocations with radiosondes over the oceans showed that both the analyses and the 6-h forecasts of humidity were improved in the Tropics and to a lesser degree in the Southern Hemisphere extratropics. The geopotential anomaly correlations that were computed only for the July 1996 case showed an increase of 1%–2% starting with the day 3 forecast in the Tropics and the day 4 forecast in the Southern Hemisphere extratropics.

Comparison with precipitation climatological observations indicated that the CMC spectral finite element (SEF) global forecast model (which has a considerable precipitation spinup) has a hydrological cycle that is too active. The precipitation that occurs in the intertropical convergence zone (ITCZ) and South Pacific convergence zone covers an area that is too wide and the size of the areas where large-scale subsidence occurs are greatly underestimated. The net impact of assimilating SSM/I TPW was to slightly reduce the globally averaged precipitation rate and thus bring the 6-h forecasts closer to the observations. The precipitation rates were mainly decreased in the Southern Hemisphere (where the SEF model is shown to have a wet bias) and increased where the maximum ITCZ precipitation occurs. The precipitation in the large-scale subsidence zones was also reduced in agreement with the observations. The residence time of the impact of the SSM/I TPW was fairly short: about 24 h in the midlatitudes and 48 h in the Tropics. Although the humidity analysis was univariate, assimilating SSM/I TPW accelerated the Hadley cell and increased the meridional transport of humidity in the Tropics.

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Godelieve Deblonde and Stephen English

Abstract

Retrievals using synthetic background fields and observations for the Special Sensor Microwave Imager Sounder (SSMIS) instrument are performed using a one-dimensional variational data assimilation (1DVAR) scheme for clear and cloudy nonprecipitating skies over open oceans. Two retrieval techniques are implemented in the 1DVAR and are extensively tested. Profiles of temperature, marine surface wind speed, and skin temperature are retrieved with both techniques. In addition, with technique A, profiles of the natural logarithm of specific humidity and liquid water path are also retrieved. With technique B, the natural logarithm of total water content (sum of specific humidity and liquid cloud water content) is retrieved instead of the natural logarithm of humidity and liquid water path. A function specifies how total water content is split among its two components. In essence, excess water vapor oversaturation leads to cloud formation. Retrievals in clear and cloudy conditions for a variety of experiments thoroughly demonstrate how technique A works. The choice of humidity control variable, the presence of biases in the moisture retrievals, and the impact of applying a supersaturation constraint are also discussed. Furthermore, in the presence of clouds, it is shown that little temperature information can be extracted with this technique if the a priori cloud vertical distribution is not known well. With technique B, however, temperature information can be extracted from the observations even in the presence of clouds. Because of its more physically based parameterization, it has some skill at positioning the cloud in the vertical direction.

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Josep M. Aparicio and Godelieve Deblonde

Abstract

The data assimilation system of Environment Canada (EC) is adapted to accept GPS radio occultation (GPSRO) data. Observations of this type are available with extensive coverage from several satellites. In this study, experiments are performed to compare the skill of EC’s three-dimensional variational data assimilation (3DVAR) system (including all data normally assimilated operationally), with and without the addition of radio occultation refractivity data from the Challenging Minisatellite Payload for Geophysical Research (CHAMP). These data were not available at the time studied as near-real-time (NRT) observations. However, data from this and other radio occultation missions are now available as NRT data, and the conditions (latency, reliability) are improving. It is expected that NRT GPSRO data from a number of satellite missions will continue to be available through the following years. The results of the assimilation tests are evaluated against the following three data types: radiosondes (temperature and dewpoint depression), satellite brightness temperatures (from the Advanced Microwave Sounding Unit-A), and GPS radio occultation refractivity profiles. For the 6-h forecasts, the differences between GPSRO observations and forecasts (OF) are significantly reduced in the experiment that assimilates the GPSRO data. This reduction increases as the experiment proceeds in time, and stabilizes after a transient period of approximately 2 weeks, suggesting that the addition of GPSRO data to the assimilation system has a beneficial, persisting, and cumulative effect. This effect is more pronounced in the stratosphere than in the troposphere. In the stratosphere, the standard deviation of GPSRO (OF) of the experiment that assimilates GPSRO decreases after the initial transient period by approximately 10%. This improvement can best be observed in the southern stratosphere where reductions of the order of 30% are common. This shows that, as a globally distributed and vertically well-resolved source of data, the GPSRO observations are not only useful for assimilation, but also as a tool to quantify the forecast skill of the assimilation system. Comparisons with radiometer and radiosonde data confirm the positive impact in these geographical areas. Longer-range forecasts (up to 6 days) also show a positive impact with similar geographical and altitude distribution.

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Godelieve Deblonde, Louis Garand, Pierre Gauthier, and Christopher Grassotti

Abstract

Total precipitable water (TPW) retrieved from Special Sensor Microwave/lmager (SSM/I) brightness temperatures and specific humidity retrieved from Geostationary Operational Environmental Satellite (GOES) radiances are assimilated using a one-dimensional (ID) variational analysis technique. The study is divided into two parts. First, collocations with radiosondes are performed to arm the quality of the satellite water vapor retrievals. Collocations are also performed with 6-h forecast Acids. Second, SSM/I TPW and GOES specific humidity are assimilated using a ID variational analysis technique that minimizes the error variance of the analyzed field.

A global collocation study over the oceans for SSM/I TPW retrievals and 6-h forecasts of TPW shows that the rmse (with respect to radiosondes) are, respectively, 4.7 and 5.0 kg m−2. A separate collocation study over both the oceans and land for GOES retrieved TPW and 6-h forecasts of TPW yields rmse of 4.6 and 4.4 kg m−2, respectively, in the midlatitudes and 6.8 and 5.9 kg m−2 in the Tropics.

The reduction of the 6-h forecast rmse when assimilating SSM/I TPW is 1 kg m−2, which is a reduction of 20% in the rmse. When GOES retrievals of specific humidity are assimilated, the elective reduction is 0.6 kg m−2. It is shown that in the upper levels of the troposphere (above 600 mb), the error reduction of specific humidity is largely due to the GOES retrievals, whereas in the lower troposphere (850 and 700 mb), the reduction is mostly due to the SSM/I TPW. This emphasizes the complementarity of the information contained at different wavelengths and the advantage of using multisensor retrievals in data analysis.

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Godelieve Deblonde, Stephen Macpherson, Yves Mireault, and Pierre Héroux

Abstract

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.

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