ASSIMILATION EXPERIMENTS WITH DATA FROM THREE CONICALLY SCANNING MICROWAVE INSTRUMENTS (SSMIS, AMSR-E, TMI) IN THE ECMWF SYSTEM

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1 ASSIMILATION EXPERIMENTS WITH DATA FROM THREE CONICALLY SCANNING MICROWAVE INSTRUMENTS (SSMIS, AMSR-E, TMI) IN THE ECMWF SYSTEM Niels Bormann 1, Graeme Kelly 1, Peter Bauer 1, and Bill Bell 2 1 ECMWF, Shinfield Park, Reading, RG2 9AX, United Kingdom 2 The Met.Office, FitzRoy Road, Exeter, EX1 3PB, United Kingdom Abstract The status of monitoring and assimilation experiments with clear-sky radiances from SSMIS, AMSR-E, and TMI is presented. SSMIS combines channels similar to AMSU-A, AMSU-B, and SSMI, with a three-channel mesospheric sounder in one conically scanning instrument, whereas AMSR-E and TMI are microwave imagers similar to SSMI. The quality of the data is assessed by comparisons against model equivalents. For SSMIS, the pre-processed dataset prepared by the Met.Office is used. Window channels on SSMIS, AMSR-E, and TMI primarily provide information on total column water vapour, and monitoring of the radiance data indicates a quality that is comparable to that of SSMI. Assimilation trials with data from these instruments show a positive forecast impact for lower tropospheric humidity and surface wind in the tropics. Monitoring of the temperature-sounding channels of the F-16 SSMIS shows that noise characteristics for the pre-processed data approach those of AMSU-A, but considerable local biases remain. Assimilation trials show a neutral impact of the SSMIS temperature-sounding channels from adding these channels to the full operational set of observations. However, when added to a system that otherwise makes limited use of satellite data, SSMIS can lead to improved forecasts. INTRODUCTION This contribution reports on the assimilation of clear-sky microwave radiances from three conically scanning instruments in the ECMWF system. Data from imaging channels from TMI, AMSR-E, and SSMIS are used to improve the analysis of total column water vapour and surface wind speed over sea. Data from temperature sounding channels from SSMIS are used to investigate their potential to improve temperature analyses. DATA The three instruments are: the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) on the TRMM satellite (e.g., Kummerow et al. 1998), the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite (e.g., Kawanishi et al. 2003), and the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite (DMSP) F-16 (e.g., Bell et al. 2007). All three instruments provide imaging channels similar to those on SSMI (Table 1), which has long been an established ingredient to the ECMWF assimilation system. The new instruments greatly improve the coverage for this kind of observation (Fig. 1).

2 SSM/I TMI AMSR-E SSMIS Frequ. [GHz] & polarisation Frequ. [GHz] & polarisation Frequ. [GHz] & polarisation Frequ. [GHz] & polarisation 1, V, H 1, V, H 3, V, H 1, V, H 3, V, H 5, V, H 12, H, V V V 7, V, H V 4, V, H 6, V, H 9, V, H 15, H, V 6, V, H 8, V, H 11, V, H 17, ± 0.9 V, H Table 1: Microwave imaging channels available from SSM/I, TMI, AMSR-E, and SSMIS (only SSM/I-like channels for SSMIS). Figure 1: 6-hour coverage of SSMI-like imager data. SSMI in blue, SSMIS in cyan, AMSR-E in orange, and TMI in green. In addition, SSMIS also offers AMSU-A/B-like temperature and humidity sounding channels and some channels sensitive to mesospheric temperature. For SSMIS, the pre-processed dataset prepared by the Met.Office is used (e.g., Bell et al. 2007). This dataset also addresses various instrument anomalies established during the Cal/Val period for F-16: Data affected by solar intrusions into the calibration warm load are flagged (~40% of the data). A correction for the thermal emissions from the main reflector is applied. Spatial averaging (~120 km) and remapping is performed to reduce the noise and to collocate the 24 channels. EXPERIMENTS WITH SSMI-LIKE IMAGING CHANNELS Experiments were performed with clear-sky data from the SSMI-like imaging channels of the three instruments (i.e., channels 3-9 for TMI, 5-10 for AMSR-E, and for SSMIS). Monitoring Passive monitoring shows good noise characteristics for all three instruments (Fig. 2). Standard deviations of First Guess departures are similar to equivalent SSMI channels, or, in the case of SSMIS, even smaller, most likely due to the averaging employed in the data used in this study.

3 Figure 2: Statistics from passive monitoring of AMSR-E (a), TMI (b), and SSMIS (c) imager channels (black lines, respectively), compared to statistics for the equivalent SSMI channels (red lines). Standard deviations of First Guess departures [K] are shown in solid lines, and analysis departures in dotted lines. Note that SSMI data were used, whereas the new sensors were passive, explaining the smaller analysis departures for SSMI. Data are taken over the tropics, 1-20 November Assimilation experiments Two sets of assimilation experiments were performed to investigate the influence of the new observations in ECMWF's 12-hour 4DVAR system (Table 2). Both consisted of a control experiment without the additional data, and a trial experiment in which the new data was added to the control setup. First set Second set Period 10 Oct - 30 Nov Dec January 2007 Model resolution T511 (~40 km), 91 levels up to 0.01 hpa T799 (~25 km), 91 levels up to 0.01 hpa Analysis resolution T159 (~125 km), 91 levels T255 (~80 km), 91 levels Other observations As operations at the time As operations at the time, plus ATOVS data from METOP-A Table 2: Assimilation experiments with additional microwave imager data. Results Assimilation of the additional MW imager data considerably improves the fit of the First Guess to SSMI data already assimilated in the system (Fig. 3). This demonstrates an improved First Guess, as a result of the improvements made to the analysis through the assimilation of the extra data. Figure 3: Standard deviations of First Guess (solid) and analysis (dotted) departures for used F-13 SSMI data for the experiment with (black) and without (red) the additional microwave imager data. Statistics were calculated for the Tropics for the Decemer 2006 experiment. Forecasts with the extra data show improvements for tropical lower tropospheric humidity and surface wind (Fig. 4). The results were very similar for the two sets of experiments.

4 Figure 4: Left: Normalised difference [%] in the root mean square forecast error for 850 hpa humidity over the tropics between the experiment with and the one without the additional data as a function of forecast range. Negative values show a reduction in forecast error. Error bars indicate 90 % confidence intervals. Statistics are shown for the October/November experiment (52 cases). Right: As on the left, but for the 1000 hpa vector wind in the tropics. EXPERIMENTS WITH TEMPERATURE SOUNDING CHANNELS FROM SSMIS Data from the SSMIS temperature sounding channels (2-7, 23-24, Fig. 5) were investigated for assimilation. These data are most affected by the instrument anomalies mentioned above. Figure 5: Weighting functions for the SSMIS temperature sounding channels used in the experiments as a function of height [km]. Monitoring Passive monitoring shows good noise characteristics for the temperature sounding channels (Fig. 6). The averaging performed as part of the Met.Office pre-processor successfully brings the noise to levels comparable to AMSU-A, albeit at much poorer effective resolution (~120 km vs km). Figure 6: Root mean square (RMS) First Guess departures after bias correction for SSMIS (black) for May 2007 over the Southern Hemisphere. Also shown are the RMS for First Guess departures for equivalent channel for METOP AMSU-A in red.

5 However, considerable local biases still remain in the pre-processed data, most likely due to uncorrected instrument anomalies. There tend to be different biases in FG departures for the ascending/descending nodes for most channels, and channel 6 and 7 also show additional bias features (Fig. 7). Such biases can not be found in equivalent AMSU-A channels which use the same approach for bias correction in the assimilation. This suggests an instrument rather than a FG problem. Some residual local biases for SSMIS reach levels comparable to the standard deviations. Figure 7: a) First Guess departures for SSMIS channel 6 (57.29 GHz), after bias correction, quality control and thinning, for the period 9-21 Z on 15 April Gaps in the orbit are due to intrusion flagging. b) As a), but for AMSU-A channel 9 from METOP-A, peaking at a similar altitude.

6 Assimilation experiments Despite these residual anomalies, assimilation trials have been performed. Added to a system with the full set of observations used operationally, the SSMIS temperature sounding channels resulted in a neutral forecast impact. Observations in the full system included ATOVS data from 5 satellites and AIRS radiances. It appears that the other observational coverage is robust enough that the biases in SSMIS data mentioned earlier are not detrimental. Another experiment was performed in which the temperature sounding channels were added to a system with reduced use of satellite data as follows: Reference: System that uses conventional data and Atmospheric Motion Vectors from geostationary and polar satellites. SSMIS: As BASELINE, but SSMIS temperature sounding channels were added. AMSU-A: As BASELINE, but data from AMSU-A on NOAA-15 were added. These experiments covered the period 12 December January 2006, and used ECMWF's 12h 4DVAR at T159 (~125 km) resolution. Results Adding SSMIS temperature sounding channels to a system with limited use of other satellite data results in a clear positive impact, especially over the Southern Hemisphere (Fig. 8). The impact is about 60 % of that observed from adding a single AMSU-A instrument to the reduced system. In the reference system effectively no temperature sounding information is available over the oceans. Therefore, adding data with some biases nevertheless provides useful additional information. Figure 8: a) Anomaly correlation for the 500 hpa geopotential forecast against forecast range over the Northern Hemisphere for the four experiments indicated (31 cases). b) As a), but for the Southern Hemisphere. CONCLUSIONS Using additional clear-sky SSMI-like data from TMI, AMSR-E, and SSMIS results in improved forecasts of lower topospheric humidity and surface winds over the tropics. Operational assimilation of the data will start in autumn Temperature sounding channels from SSMIS exhibit residual anomalies, even after corrections for known instrument anomalies have been applied. Further work is required to reduce these anomalies

7 or develop bias correction strategies. Nevertheless, added to a system with limited use of other satellite data, the SSMIS temperature sounding channels can improve forecasts. REFERENCES Bell, B., S.J. English, B. Candy, N. Atkinson, F. Hilton, N. Bormann, G. Kelly, W.F. Campbell, S.D. Swadley, and M. Kazumori (2007): The assimilation of SSMIS radiances in Numerical Weather Prediction Models. IEEE Trans. Geosc. Remote Sens., 45, in press. Kawanishi, T., T. Sezai, Y. Ito, K. Imaoka, T. Taksehima, Y. Ishido, A. Shibata, M. Miura, H. Inahata, and R.W. Spencer (2003): The Advanced Microwave Scanning Radiometer for the Earth Observaing System (AMSR-E), NASA's contribution to the EOS for global energy and water cycle studies. IEEE Trans. Geosc. Remote Sens., 41, pp Kummerow, C., W. Barnes, T. Kozu, J. Shiue, J. Simpson (1998): The Tropical Rainfall Measuring Mission (TRMM) Sensor Package. J. Atmos.Oceanic Tech. 15, pp