Abstract

The latent heat represented by atmospheric water vapor is extremely important to the energetics of the Earth system. Future satellite (NOAA and DMSP) will carry microwave radiometers designed to measure the profile of water vapor globally. The problem of retrieving water vapor from the measurements is highly nonlinear even in clear atmospheres and the addition of clouds only makes it more so. In this paper, an algorithm with several novel features, which will retrieve water vapor profiles from microwave radiometric measurements even in the presence of clouds, is developed. Simulations with this algorithm show a vertical resolution on the order of 3 km and that clouds are well handled in many, but not all, circumstances. The most surprising result is that clouds can actually improve the vertical resolution of the retrieval.

Abstract

At 37 GHz, the frequency at which the Nimbus 6 Electrically Scanning Microwave Radiometer (ESMR 6) measures upwelling radiance, it has been shown theoretically that the atmospheric scattering and the relative independence on electromagnetic polarization of the radiances emerging from hydrometeors make it possible to monitor remotely active rainfall over land. In order to verify experimentally these theoretical findings and to develop an algorithm to monitor rainfall over land, the digitized ESMR 6 measurements were examined statistically.

Horizontally and vertically polarized brightness temperature pairs (T_H,T_V ) from ESMR 6 were sampled for areas of rainfall over land as determined from the rain recording stations and the WSR 57 radar, and areas of wet and dry ground (whose thermodynamic temperatures were greater than 5°C) over the southeastern United States. These three categories of brightness temperatures were found to be significantly different in the sense that the chances that the mean vectors of any two populations coincided were less than 1 in 100. Since these categories were significantly different, classification algorithms were then developed. Three decision rules were examined: the Fisher linear classifier, the Bayesian quadratic classifier, and a non-parametric linear classifier. The Bayesian algorithm was found to perform best, particularly at a higher confidence level. An independent test case analysis showed that a rainfall area delineated by the Bayesian classifier coincided well with the synoptic-scale rainfall area mapped by ground recording rain data and radar echoes.

Abstract

The Tropical Rainfall Measuring Mission (TRMM) Kwajalein Experiment (KWAJEX) was designed to obtain an empirical physical characterization of precipitating convective clouds over the tropical ocean. Coordinated datasets were collected by three aircraft, one ship, five upper-air sounding sites, and a variety of continuously recording remote and in situ surface-based sensors, including scanning Doppler radars, profilers, disdrometers, and rain gauges. This paper describes the physical characterization of the Kwajalein cloud population that has emerged from analyses of datasets that were obtained during KWAJEX and combined with long-term TRMM ground validation site observations encompassing three rainy seasons. The spatial and temporal dimensions of the precipitation entities exhibit a lognormal probability distribution, as has been observed over other parts of the tropical ocean. The diurnal cycle of the convection is also generally similar to that seen over other tropical oceans. The largest precipitating cloud elements—those with rain areas exceeding 14 000 km²—have the most pronounced diurnal cycle, with a maximum frequency of occurrence before dawn; the smallest rain areas are most frequent in the afternoon. The large systems exhibited stratiform rain areas juxtaposed with convective regions. Frequency distributions of dual-Doppler radar data showed narrow versus broad spectra of divergence in the stratiform and convective regions, respectively, as expected because strong up- and downdrafts are absent in the stratiform regions. The dual-Doppler profiles consistently showed low-level convergence and upper-level divergence in convective regions and midlevel convergence sandwiched between lower- and upper-level divergence in stratiform regions. However, the magnitudes of divergence are sensitive to assumptions made in classifying the radar echoes as convective or stratiform. This sensitivity implies that heating profiles derived from satellite radar data will be sensitive to the details of the scheme used to separate convective and stratiform rain areas. Comparison of airborne passive microwave data with ground-based radar data indicates that the pattern of scattering of 85-GHz radiance by ice particles in the upper portions of KWAJEX precipitating clouds is poorly correlated with the precipitation pattern at lower levels while the emission channels (10 and 19 GHz) have brightness temperature patterns that closely correspond to the lower-level precipitation structure. In situ ice particle imagery obtained by aircraft at upper levels (∼11 km) shows that the concentrations of ice particles of all densities are greater in the upper portions of active convective rain regions and lower in the upper portions of stratiform regions, probably because the active updrafts convey the particles to upper levels, whereas in the stratiform regions sedimentation removes the larger ice particles over time. Low-level aircraft flying in the rain layer show similar total drop concentrations in and out of convective cells, but they also show a sudden jump in the concentration of larger raindrops at the boundaries of the cells, indicating a discontinuity in growth processes such as coalescence at the cell boundary.