This study compiled a database of precipitating cloud clusters from 85-GHz data in 10 regions of the wet Tropics for a calendar year (November 1992–October 1993). The cloud clusters were grouped into four classes of basic system types, based on size (closed 250 K contour greater or less than 2000 km²) and minimum enclosed 85-GHz brightness temperature (greater or less than 225 K) to indicate the presence or absence of large areas of active, deep convection. For each cloud cluster, instantaneous volumetric rain rates (mm km² h⁻¹) were calculated using an 85-GHz ice-scattering-based rain-rate retrieval algorithm. Because the ice-scattering signature is linearly related to but does not directly measure rain rate, the methodology was appropriate for estimating relative contributions rather than quantifying tropical rainfall.

For the 3-month wet season of each study region, the rainfall contributions with respect to system type, size, and intensity were calculated. Regional differences were small among the contributions with respect to system type and to precipitating area. Although mesoscale convective systems constituted 10%–20% of the regional populations, they contributed 70%–80% of the rainfall. With respect to cloud cluster area, the top 10% of cloud cluster areas contributed more than 70% of the rainfall, and the top 1% (greater than 20 000 km²) contributed about 35% of the total rainfall. Regional differences were apparent in the distributions of rainfall contribution with respect to minimum brightness temperature. The Amazon’s distribution more closely resembled the oceanic distributions than the continental distributions. The distributions of the oceanic regions peaked at 200 K, and over half of the rain in the oceanic regions was contributed by the fewer than 20% of the cloud clusters colder than 210 K. Distributions in the continental regions peaked at 175 K. A total of 70%–80% of the rain was contributed by the 20%–30% of continental cloud clusters colder than 200 K, with nonnegligible contributions from a small number of cloud clusters colder than 120 K. Sub-Saharan Africa had the largest contribution from cloud clusters colder than 120 K.

The Parameterization for Land–Atmosphere–Cloud Exchange (PLACE), a typical surface–vegetation–atmosphere transfer (SVAT) parameterization, was used in a case study of a 2500 km² area in southwestern Oklahoma for 9–16 July 1997. The research objective was to assess PLACE’s simulation of the spatial variability and temporal evolution of soil moisture and heat fluxes without optimization for this case study. Understanding PLACE’s performance under these conditions may provide perspective on results from more complex coupled land–atmosphere simulations involving similar land surface schemes in data-poor environments. Model simulations were initialized with simple initial soil moisture and temperature profiles tied to soil type and forced by standard meteorological observations. The model equations and parameters were not adjusted or tuned to improve results.

For surface soil moisture, 5- and 10-cm soil temperature, and surface fluxes, the most accurate simulation (5% error for soil moisture and 2 K for 5- and 10-cm soil temperature) occurred during the 48 h following heavy rainfall on 11 and 15 July. The spatial pattern of simulated soil moisture was controlled more strongly by soil texture than was observed soil moisture, and the error was correlated with rainfall. The simplifications of the subsurface soil moisture, soil texture, and vegetation cover initialization schemes and the uncertainty in the rainfall data (>10%) could account for differences between modeled and observed surface fluxes that are on the order of 100 W m⁻² and differences in soil moisture that are greater than 5%. It also is likely that the soil thermal conductivity scheme in PLACE damped PLACE’s response to atmospheric demand after 13 July, resulting in reduced evapotranspiration and warmer but slower-drying soils. Under dry conditions, the authors expect that SVATs such as PLACE that use a similar simple initialization also would demonstrate a strong soil texture control on soil moisture and surface fluxes and limited spatial variability.