Small-Scale Horizontal Rain-Rate Variability Observed by Satellite

Abstract

The horizontal distribution of rain rates within an area comparable to the pixel size of satellite microwave radiometers and the grid size of numerical weather prediction models has been studied over the global Tropics using three years of the Tropical Rainfall Measuring Mission satellite precipitation radar (PR) data. The global distribution of rain-rate standard deviation derived from the PR data suggests that the horizontal variability of rain rates is largely influenced by two factors: surface type (land or ocean) and latitudinal location (tropical or extratropical). Except for light stratiform rain, the land–ocean contrast seems to be the dominant feature for the differences in conditional probability density functions (PDFs) of rain rate. That is, oceanic rain-rate distribution is narrower when the rain rate is low, but becomes broader when the rain rate is high. For light stratiform rain, there is no clear difference among the rain-rate PDFs for rain events over land and ocean. The latitudinal variation of rain-rate PDFs seems to be greater for heavy rain than for light rain. In particular, there is no measurable difference in overland convective rain-rate PDFs between the Tropics and extratropics. Based on three years of observational data, two attributes, fractional rain cover and conditional rain-rate PDFs, are parameterized as a function of 0.25° × 0.25° areal rain rate. These parameterizations are particularly useful in satellite microwave rainfall retrieval and assimilation of satellite microwave radiance data in numerical weather prediction models.

Development of Nonuniform Beamfilling Correction Method in Rainfall Retrievals for Passive Microwave Radiometers over Ocean Using TRMM Observations

“…This threshold is used because a certain number of rain pixels is necessary to establish the lognormal assumption. For small rain area cases, parameterization based upon PR data can be effective, as demonstrated by Varma and Liu (2006).…”

“…By analyzing collo cated TMI and PR data, Varma et al (2004) demon strated that a simulation by a plane-parallel RTM significantly underestimates in convective rainfall events with reference to the averaged relationship be ween ob served Tbs and rain rates at 19 and 37 GHz. Varma and Liu (2006) suggested the parameterization of fractional rain cover (FRC) and conditional rain-rate probability density functions (PDFs), based upon three years of PR data. Their approach is an applica tion of statistical parameterization to observed Tbs, while the approach of the KG94 method is an esti mation of the NUBF effect from instantaneous satellite observations.…”

An Analysis of Rainfall Measurements over Different Spatio-Temporal Scales and Potential Implications for Uncertainty in Satellite Data Validation

“…The plot for autocorrelation of rain for 4 km pixel for cruise 1 shows a steeper drop in the autocorrelation function than that for cruise 2, which is not so obviously manifested in similar plots for 8 km pixel possibly because of its larger spatial area. A plausible reason may be that cruise 1 had highest number of convective rainfall (80%) and the highest percentage of convective occurrences (Short et al 1997) which may have resulted in higher spatial variability (Houze 1997;Varma and Liu 2006) which is manifested in the plot for 4 km pixel area. Like Fig.…”

“…Despite fairly good understanding of radiative transfer through the atmosphere, the rain measurement from passive microwave measurements still remains a challenging task. One of challenges in the passive microwave retrieval of rain arises due to uncertainty in the horizontal and vertical distribution of rainfall within its large instantaneous field of view (IFOV), which is referred as the beam filling problem (Varma et al 2004;Varma and Liu 2006). The horizontal rain variability within IFOV results in severe underestimation of precipitation, whereas vertical variability results in deviations from mean brightness temperature Tb versus rain rate relationship (Liu 2003).…”

“…Several researchers (e.g., Wilheit 1986;Chiu et al 1990;Varma et al 2004;Varma and Liu 2006) described and also suggested a possible stochastic solution to the beam filling problem that leads to severe underestimation of the precipitation at microwave frequencies. A number of researchers have examined the precipitation variability on a small scale and reported its spatial distribution as a lognormal distribution (e.g., Lopez 1976;Kedem and Slud 1994).…”