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

Abstract

Direct assimilation using 1-dimensional variational method in the vertical (1D-VAR) was developed to incorporate vertically polarized brightness temperatures (TB's), and rain flag data (index of existence of precipitation) from the special sensor microwave imager (SSM/I), into a mesoscale numerical weather prediction (NWP) model. We used a radiative transfer model (RTM) developed by Liu (1998) to calculate TB's from NWP model variables. Observational residuals of TB's are assumed to be nonlinear functions of precipitation rates in rainy areas, and of other thermodynamic variables in rain-free areas. Quasiequilibrium assumptions on humidity and convective instability in model precipitation areas are used to assimilate TB's in rainy areas and rain flag data, which are functions of precipitation. TB's in rain-free areas are assimilated into total water content (cloud water content + humidity). Smith's method (1990) is used to calculate cloud water content and humidity from total water content. To simplify 1D-VAR, the following approximations are introduced: 1) In the tropics, variations in temperature are much smaller than variations in humidity.2) Variations in divergence dominates the rainy areas, compared to vorticity and mass variables. Based on the above approximation, we assumed that the background error covariance of relative vorticity and the unbalanced component of mass variables is negligible, so we divided 1D-VAR into that for total water content and that for divergence. Newtonian iteration is used to solve these 1D-VAR problems.To study the assimilated variables, assimilation was applied to cases during 19-20 Dec. 1992 over the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) domain. Results indicate: 1) Precipitable water content (PWC) obtained by assimilation agreed well with PWC calculated from radiosonde data and PWC retrieved from TB's using Shibata's algorithm (1994).2) Consistency between humidity and cloud water content profiles was attained by Smith's method (1990), although no significant improvement was seen in humidity profile accuracy by assimilation. 3) Assimilation of rain flag and TB's in rainy areas successfully produced model precipitation areas agreeing with radar observation data by Short et al. (1997).To test the impact of assimilation on NWP forecasts, experiments were conducted assimilating TB data for 19 Dec. 1992. Results indicate: 1) Assimilation modified large-scale model humidity distribution, improving large-scale humidity and precipitation forecasts for 48 hours.

Variational Assimilation of TMI Rain Type and Precipitation Retrievals into Global Numerical Weather Prediction

“…Figure 1a shows a scatter diagram between the precipitation retrieved by the algorithm, and TRMM precipitation (PR) rain intensity at 2-km height averaged over GSM grid boxes around the western part of Japan during JuneJuly of 1998. This indicates that the precipitation retrievals agree well with the PR data within the range of 1-25 mm h À1 (the detail is described in Aonashi and Liu (2000)). They also noted that root-mean-square (RMS) error of the precipitation retrievals was about half the precipitation in this range, but that the RMS error degraded for precipitation weaker than 1 mm h À1 .…”

“…They also noted that root-mean-square (RMS) error of the precipitation retrievals was about half the precipitation in this range, but that the RMS error degraded for precipitation weaker than 1 mm h À1 . For the RTM calculation, Aonashi and Liu (2000) estimated horizontal precipitation inhomogeneity from TMI TBs, assuming the log normal distribution of precipitation. The estimated standard deviation of logarithm of precipitation ðs LP Þ has good correlation with that calculated from PR rain intensity, as shown in Fig.…”

“…The data were averaged over GSM grid boxes around the western part of Japan during JuneJuly of 1998. Aonashi and Liu (2000) also estimated precipitation top levels from polarization-corrected temperature at 85.5 GHz (PCT85), on the basis of statistical comparison between PCT85 and PR observations, as shown in Fig. 1c.…”

“…As for logðqwÞ, we expressed the vertical modes with empirical orthogonal functions (EOFs) defined from the RH forecast error covariance matrix of Aonashi and Liu (1999).…”

“…To diagonalize B qw , we adopted the EOF modes defined from the RH forecast error covariance of Aonashi and Liu (1999). Thus, we employed the variances of these EOF modes as the diagonal components of B qw .…”