Fiona Hilton

Hyperspectral Earth Observation from IASI: Five Years of Accomplishments

The Infrared Atmospheric Sounding Interferometer (IASI) forms the main infrared sounding component of the European Organisation for the Exploitation of Meteorological Satellitess (EUMETSATs) Meteorological Operation (MetOp)-A satellite (Klaes et al. 2007), which was launched in October 2006. This article presents the results of the first 4 yr of the operational IASI mission. The performance of the instrument is shown to be exceptional in terms of calibration and stability. The quality of the data has allowed the rapid use of the observations in operational numerical weather prediction (NWP) and the development of new products for atmospheric chemistry and climate studies, some of which were unexpected before launch. The assimilation of IASI observations in NWP models provides a significant forecast impact; in most cases the impact has been shown to be at least as large as for any previous instrument. In atmospheric chemistry, global distributions of gases, such as ozone and carbon monoxide, can be produ...

The Assimilation of SSMIS Radiances in Numerical Weather Prediction Models

The measurement uncertainty requirements imposed by numerical weather prediction (NWP) data assimilation applications for temperature sounding radiances are very demanding. For an ensemble of observations collected during an orbit, (postbias correction) measurement uncertainties of ~ 0.2 K (at 1 sigma) or better are required in tropospheric sounding channels to improve analyses, and hence forecasts, from current NWP models. A significant fraction of F-16 Special Sensor Microwave Imager/Sounder (SSMIS) observations are affected by calibration errors caused by solar intrusions into the warm calibration load and by thermal emission from the main reflector. The magnitude of these effects is as large as 1.5 K for the lower atmospheric temperature sounding channels. This paper describes the approach to correct for these effects, which involves data averaging, flagging solar intrusions, and modeling reflector emission. The resulting quality of the radiances is improved by a factor of three to four for mid-tropospheric temperature sounding channels. Observation minus background field differences are reduced from 0.5-0.8 K (at one standard deviation) for uncorrected data to 0.2 K for corrected data. Although localized biases remain in the corrected data, assimilation experiments using SSMIS data at four operational NWP centers (Met Office, ECMWF, NCEP, and NRL) show a neutral-to-positive impact on forecast quality in the Southern Hemisphere with, for example, mean sea-level pressure forecast errors at days 1-4 reduced by 0.5%-2.5%. Impacts in the Northern Hemisphere are neutral in most assimilation experiments.

The Radiometric Sensitivity Requirements for Satellite Microwave Temperature Sounding Instruments for Numerical Weather Prediction

Abstract The sensitivity of NWP forecast accuracy with respect to the radiometric performance of microwave sounders is assessed through a series of observing system experiments at the Met Office and ECMWF. The observing system experiments compare the impact of normal data from a single Advanced Microwave Sounding Unit (AMSU) with that from an AMSU where synthetic noise has been added. The results show a measurable reduction in forecast improvement in the Southern Hemisphere, with improvements reduced by 11% for relatively small increases in radiometric noise [noise-equivalent brightness temperature (NEΔT) increased from 0.1 to 0.2 K for remapped data]. The impact of microwave sounding data is shown to be significantly less than was the case prior to the use of advanced infrared sounder data [Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI)], with microwave sounding data now reducing Southern Hemisphere forecast errors by approximately 10% compared to 40% in the p...

An Initial Evaluation of SSMIS Radiances for Radiance Assimilation Applications

The first Special Sensor Microwave Imager/Sounder (SSMIS) was launched on 18th October 2003 and combines the surface viewing channels of its predecessor (SSMI) with a range of temperature and moisture sounding channels. The accuracy requirements set by numerical weather prediction data assimilation applications for temperature sounding channels are demanding (~0.2 K). The post-launch Cal/Val program highlighted two small, but significant, sources of bias in the SSMIS radiance data associated with solar intrusions into the warm calibration load and with thermal emission from the main reflector. These effects have been studied and initial software mitigation measures are now in place. Data assimilation experiments at the Met Office have shown that the inclusion of SSMIS data reduces short range (day 1-3) forecast errors in southern hemisphere PMSL by 1-3%. Assimilation experiments at ECMWF show that the inclusion of SSMIS radiances from the lower atmospheric temperature sounding channels produces more than half of the impact of NOAA-15 AMSU. Further improvements are expected as the correction algorithms are refined in future

Variational analysis of total water using AMSU

Vertically integrated cloud liquid water has been successfully analysed from both the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU). However microwave radiometers provide very little information on the vertical cloud liquid water profile. When considering the assimilation of microwave sounding channels, such as those on the AMSU or the Special Sensor Microwave Imager Sounder (SSMIS) the sensitivity of the measurements to cloud liquid water is dependent on the altitude of the cloud. Cloud well below the peak of the weighting function will tend to increase the measured brightness temperatures whereas cloud at or above the weighting function peak will tend to lower the measured brightness temperatures. Therefore if temperature and humidity sounding data are to be assimilated where cloud is present it will be necessary to analyse simultaneously the temperature, humidity and cloud profiles. One approach is to use a one dimensional variational analysis (1D-var) with a single moisture control variable, total water, where total water is the sum of water vapour, cloud liquid water, cloud ice water, rain and snow. For simplicity in this study rain and snow are neglected and ice cloud is assumed to be transparent. The analysis of cloud from the total water approach is compared with a variational analysis with separate cloud and water vapour control variables. The suitability of a total water control variable for variational analysis of cloudy AMSU, SSM/I or SSMIS data is then discussed.