Variational retrieval of humidity profile, wind speed and cloud liquid-water path with the SSM/I: Potential for numerical weather prediction

Abstract

SUMMARYSeveral regression algorithms have been proposed to retrieve geophysical parameters from the Special Sensor Microwaveflmager (SSMfl) radiances. Their performances are generally limited by a simplified handling of nonlinearities and/or by the poor quality of the a priori information. In this paper, a variational method is proposed for retrieving the atmospheric humidity profile, the wind speed and the cloud liquid-water path from SSM/I observations over ocean. This method is based on nonlinear optimal estimation theory. The first guess is derived from a European Centre for Medium-Range Weather Forecasts forecast, and the forecast-error covariance is used as a constraint. The geophysical variable space is mapped into the radiance space through a radiative-transfer model which permits an accurate representation of nonlinearities. This method has been applied to several orbits and the results for one of them are presented and discussed. It is argued that the variational approach is a simple optimal way of extracting information from SSMfl radiances, exploiting the high quality a priori information available from a numerical weather-prediction model. The retrieved humidity profiles are found to compare well with the total precipitable water estimated from a regression algorithm, while avoiding local bias in very dry and very wet conditions. It is also shown that the potential wind speed-cloud ambiguity is removed through the use of a high-quality wind speed first guess.

Direct Assimilation of Multichannel Microwave Brightness Temperatures and Impact on Mesoscale Numerical Weather Prediction over the TOGA COARE Domain

“…Results indicate that assimilation of SSM/I data with high horizontal resolution improved the horizontal distribution of the assimilated humidity. This agrees with the reports in recent papers (Filiberti et al, 1994;Phalippou, 1996). The results also suggest that this humidity adjustment leads to the improvement in large-scale precipitation forecasts as well as humidity forecasts over 48 hours.…”

“…Retrieving these variables from TB's requires combining multichannel TB's. The relationship between TB's and the physical variables is nonlinear, as Phalippou (1996) pointed out. To linearize this relation, many retrieval algorithns use TB's calculated from the first guess of atmospheric and surface situations.…”

“…Phalippou (1996) applied this idea to variational retrieval using SSM/I TB's. His method is not appropriate for incorporating TB's around meteorological disturbances into mesoscale NWP models, because information on precipitation is not derived from TB's since precipitation is ignored in this retrieval.…”

Experimental 1D + 4D‐Var assimilation of CloudSat observations

“…The method has also proven to be successful in an early implementation of clear-sky infrared radiance assimilation in the past (e.g. Eyre et al, 1993;Phalippou, 1996) and for SSM/I brightness temperatures in clear-sky areas (Gérard and Saunders, 1999). The application of the 1D + 4D-Var technique to assimilate observations from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar by Benedetti et al (2006) has shown that active sensor data can provide useful information on the moisture field, but the results depend on the product types (level 1 versus level 2) and their underlying assumptions.…”

Study of cloud liquid water path and total precipitable water from irs-p4/msmr and numerical weather prediction model output

“…Now it is also available from the present operational US satellites National Oceanic and Atmospheric Administration (NOAA) F13, F14, F15 and IRS-P4/MSMR. Numerous studies have been carried out using these data from SSM/I in relation to the numerical model outputs (Liu et al, 1992;Phalippou, 1996;Ferraro, et al, 1996;Kar et al, 2003). These studies bring out the usability of such data by the NWP community in general.…”