393
Instantaneous Precipitation and Latent Heating Estimation over Land
the calculation of 85-GHz brightness temperatures (i.e., surface emissivity, surface
temperature, relative humidity profile, and cloud water profile) are modified, and the
brightness temperatures are recalculated. The differences relative to the nominal
brightness are displayed in Figure 16.2 in red for 0.05 reduction in the surface emis6.2 in red for 0.05 reduction in the surface emis.2 in red for 0.05 reduction in the surface emissivity, blue for 4-K reduction in the surface temperature, green for 10% reduction in
the relative humidity, and orange for no cloud water. It may be noted in the bottom
panel of Figure 16.2 that parameters not describing precipitation (i.e., surface emis6.2 that parameters not describing precipitation (i.e., surface emis.2 that parameters not describing precipitation (i.e., surface emissivity, surface temperature, etc.) do not have a large impact on simulated brightness
temperatures in precipitating regions. This is an indication that 85-GHz brightness
temperatures might be used to improve the estimation of hydrometeors above the
freezing level. The relative humidity parameterization appears to have an impact
on 85-GHz simulated brightness temperatures, but uncertainties in describing the
relative humidity profiles in precipitating regions are not deemed to have as large an
impact as N w -induced changes in the hydrometeor water content. In nonprecipitating regions, it is difficult to interpret the 85-GHz brightness temperature, because
the surface conditions as well water vapor and cloud water have a notable impact
on brightness temperatures. This is why precipitation retrieval from passive-only
observations over land is extremely challenging. Nevertheless, information can be
extracted from high-frequency radiometer observations in precipitating regions,
because absorption by precipitation is effective in masking the surface. A general
methodology to make use of the high-frequency radiometer information in spaceborne radar retrievals is presented next.
16.3 COMBINED RADAR–RADIOMETER ALGORITHM
As previously mentioned, satellite radar–radiometer algorithms were developed and
applied for retrievals over oceans (e.g., Grecu and Anagnostou 2002; Masunga and
Kummerow 2005). Little consideration had been given to combined retrievals over
land because the variability of surface parameters such as emissivity and temperature had been deemed to considerably limit the informational content of radiometer
observations. Nevertheless, the analysis in the previous section suggests that, in precipitating regions, most of the signal in the 85-GHz brightness temperature is sensitive to the electromagnetic properties of hydrometeors above the freezing level. The
existing radar–radiometer algorithms do not effectively exploit this fact. For example, the algorithm of Grecu and Anagnostou (2002) employs only one parameter per
profile to describe the N w variability, while Masunaga and Kummerow (2005) used
an equivalent parameter, namely, the mean particle diameter. Both these algorithms
feature a variable related to the density of ice particles, but observational and theoretical studies (Westbrook et al. 2004) suggest that such a variable is not necessarily
an independent variable and should be parameterized as a function of the particle
size diameter rather than independently retrieved. Therefore, it is beneficial to allow
for more flexibility in the vertical variation of N w instead of exclusively attributing
discrepancies between simulated and observed 85-GHz brightness temperatures to
uncertainties in the ice-phase density parameterization. A general formulation was
developed by Grecu and Anagnostou (2002) to retrieve precipitation from satellitecombined radar–radiometer observations based on the solution of an optimization
Instantaneous Precipitation and Latent Heating Estimation over Land
the calculation of 85-GHz brightness temperatures (i.e., surface emissivity, surface
temperature, relative humidity profile, and cloud water profile) are modified, and the
brightness temperatures are recalculated. The differences relative to the nominal
brightness are displayed in Figure 16.2 in red for 0.05 reduction in the surface emis6.2 in red for 0.05 reduction in the surface emis.2 in red for 0.05 reduction in the surface emissivity, blue for 4-K reduction in the surface temperature, green for 10% reduction in
the relative humidity, and orange for no cloud water. It may be noted in the bottom
panel of Figure 16.2 that parameters not describing precipitation (i.e., surface emis6.2 that parameters not describing precipitation (i.e., surface emis.2 that parameters not describing precipitation (i.e., surface emissivity, surface temperature, etc.) do not have a large impact on simulated brightness
temperatures in precipitating regions. This is an indication that 85-GHz brightness
temperatures might be used to improve the estimation of hydrometeors above the
freezing level. The relative humidity parameterization appears to have an impact
on 85-GHz simulated brightness temperatures, but uncertainties in describing the
relative humidity profiles in precipitating regions are not deemed to have as large an
impact as N w -induced changes in the hydrometeor water content. In nonprecipitating regions, it is difficult to interpret the 85-GHz brightness temperature, because
the surface conditions as well water vapor and cloud water have a notable impact
on brightness temperatures. This is why precipitation retrieval from passive-only
observations over land is extremely challenging. Nevertheless, information can be
extracted from high-frequency radiometer observations in precipitating regions,
because absorption by precipitation is effective in masking the surface. A general
methodology to make use of the high-frequency radiometer information in spaceborne radar retrievals is presented next.
16.3 COMBINED RADAR–RADIOMETER ALGORITHM
As previously mentioned, satellite radar–radiometer algorithms were developed and
applied for retrievals over oceans (e.g., Grecu and Anagnostou 2002; Masunga and
Kummerow 2005). Little consideration had been given to combined retrievals over
land because the variability of surface parameters such as emissivity and temperature had been deemed to considerably limit the informational content of radiometer
observations. Nevertheless, the analysis in the previous section suggests that, in precipitating regions, most of the signal in the 85-GHz brightness temperature is sensitive to the electromagnetic properties of hydrometeors above the freezing level. The
existing radar–radiometer algorithms do not effectively exploit this fact. For example, the algorithm of Grecu and Anagnostou (2002) employs only one parameter per
profile to describe the N w variability, while Masunaga and Kummerow (2005) used
an equivalent parameter, namely, the mean particle diameter. Both these algorithms
feature a variable related to the density of ice particles, but observational and theoretical studies (Westbrook et al. 2004) suggest that such a variable is not necessarily
an independent variable and should be parameterized as a function of the particle
size diameter rather than independently retrieved. Therefore, it is beneficial to allow
for more flexibility in the vertical variation of N w instead of exclusively attributing
discrepancies between simulated and observed 85-GHz brightness temperatures to
uncertainties in the ice-phase density parameterization. A general formulation was
developed by Grecu and Anagnostou (2002) to retrieve precipitation from satellitecombined radar–radiometer observations based on the solution of an optimization