Details of the Calculation of Upper Tropospheric Humidity from the GOES Water Vapor Channel

The calculation of upper tropospheric humdity (UTH) from satellite measurements is useful to monitor changes in water vapor in the upper troposphere on a global and regional scale. The water vapor transport index (WVTI) requires the calculation of specific humidity to quantitatively represent moisture variations in the upper layers of the atmosphere. Several methods have been developed for retrieving upper tropospheric humidity (UTH) from water vapor channel satellite measurements. Schmetz and Turpeinen (1988) describe the operational algorithm in use by the European Space Operations Centre (ESOC) for deriving UTH from METEOSAT 6.3-µm water vapor channel data. Their physical approach employs radiative transfer calculations with European Centre for Medium range Weather Forecast (ECMWF) temperature profile data to generate look up tables to convert the water vapor radiances to UTH. A modification to the approach (Schmetz et al. 1995a) has been used for synoptic analysis of METEOSAT moisture data. Soden and Bretherton (1993, 1996) also developed a technique for converting water vapor brightness temperatures to UTH. Simplified radiative transfer theory is used to arrive at their logrithmic relationship between layer averaged humidity and the satellite brightness temperature. This approach was originally developed for GOES-7 VAS water vapor measurements and has since been applied to TIROS Operational Vertical Sounder (TOVS) (e.g., Soden and Bretherton 1996; Stephens et al. 1996) and Special Sensor Microwave Temperature (SSM/T2) (Spencer and Braswell 1997) water vapor data. The approach used here is fundamentally the same as SB96 but applied to GOES data with regression coefficients derived on a monthly basis to account for temperature and humidity variations not properly represented in a single set of regression coefficients. In addition, the satellite zenith angle is explicitly included in the equation derive the coefficients. For much of our work, the NCEP monthly mean reanalysis (Kalnay et al. 1996) was used to obtain P_o and q, and saturation vapor pressure over ice was used to get relative humidity. These P_o values are different from SB96 where climatological estimates were used. Since the reanalysis model pressure level moisture data extends only up to 300 hPa, relative humidity was decayed exponentially above this level to satisfy the "weighting" criteria in the SB96 technique. The model thermodynamic data serves as input for the forward radiative transfer calculations [transmittances calculated as in Weinreb (1981) with the GOES-7 6.7-mircometer channel spectral response function values] to generate simulated channel brightness temperatures at each model grid point. In the radiative transfer calculations, the satellite zenith angle (based on a 0°N and 75°W satellite subpoint) for each reanalysis model grid point (within the GOES-7 view area of 60°N - 60°S and 0°-150°W) was used to calculate a simulated VAS TB under non-nadir conditions. Simulated brightness temperatures and the expression on the left side of the humidity versus brightness temperature regression equation were used to investigate the relationship between the natural logarithm of layer relative humidity and the water vapor brightness temperature developed by SB96. The results of this analysis are presented in figure. For relatively warm brightness temperatures (TB > 235 K) the relationship is highly linear with minimal scatter. Significant departure from a linear fit is observed for zenith angles greater than 75° (red points). This seems to be consistent with the other studies (Soden and Bretherton 1993, 1996; Schmetz et al 1995a; Spencer and Braswell 1997) although specifics are lacking and consistent with Soden (1998b).

The relative humidity slope and intercept coefficients derived from the monthly mean NCEP reanalysis data (2.5 x 2.5°) for 1988 with satellite zenith angles less than 75° using this procedure are presented in the Table. The linear correlation coefficients between TB and the left side of (4) are greater than about 0.95 for all months. Although it may appear that an increase in the intercept coefficient might be compensated for by a decrease in the slope coefficient (as in the Boreal summer months), the retrieved humidity is not the same in each case. To illustrate this point, two sets of regression coefficients (January and July 1988) were applied to a single day in June (June 14, 1988) and January (January 5, 1988) to demonstrate the effect of varying coefficients on the humidity retrievals. The results are shown in the figure. A reversal in the bias associated with using the wrong monthly coefficient is evident for humidity below about 60%. For example, when using July coefficients on January data (top chart in figure) in dry regions (< 60% RH), the retrieved humidity is less moist (up to 10% absolute difference). The opposite holds true when January coefficients are applied to summer data (bottom chart). For relatively moist conditions (> 60% RH) there is less of a bias but more scatter. These figures therefore illustrate that summer coefficients applied to winter data will enhance moisture gradients while winter coefficients applied to summer data will tend to slightly weaken the humidity gradients. This variation is undesirable. Therefore in our current work coefficient scorresoponding to the current month are generated and used to derive UTH. This approach will may reduce retrieved relative humidity bias due to the use of a single set of coefficients.

Last updated on: November 2, 1999