Subpixel‐scale variability of rainfall and its application to mitigate the beam‐filling problem

Abstract

[1] Surface rain radar data from areas surrounding Japan and in the tropics are used to study the variability of rainfall over an area similar to a satellite microwave radiometric instantaneous field of view (FOV). The results of such variability are applied to radiative transfer simulations to modify the brightness temperature versus rain rate relationship. First, the surface radar data from different geographical regions are used to develop a relationship between fractional rain cover and average rain rate over microwave radiometric FOV-sized areas. This resultant relationship is also compared with spaceborne Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations. It is found that the rainfall type (convective or stratiform) accounts for the most of variations in this relationship. The sub-FOV rainfall variability is then investigated using the same surface radar data sets. The conditional probability distribution function of precipitation within the FOV-sized area is parameterized in terms of average rain of the area. The form of the model function does not depend upon the characteristics of the two radars and their geographical locations and can be parameterized by a combination of two lognormal distributions. By analyzing collocated TRMM Microwave Imager (TMI) and PR data, it is shown that the averaged relationship between observed brightness temperatures and rain rates at 19.3 and 37 GHz shows a significant disagreement with that simulated by a plane-parallel radiative transfer model for convective rain events, although it shows a better agreement for stratiform rain events. Presumably, this disagreement is mainly caused by variability of the rain field within satellite instantaneous FOV. With the application of the fractional rain area and the probability distribution function derived from this study to the radiative transfer simulations, we are able to bring results of the radiative transfer simulations much closer to the observations.

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

“…5. The conditional probability distributions of the sub-window rain rates are found to follow lognormal distribution, which is in agreement with past studies (e.g., Varma et al 2004;Varma et al 2006). The abscissa of 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).…”

Intercalibrated Passive Microwave Rain Products from the Unified Microwave Ocean Retrieval Algorithm (UMORA)

“…When the attenuation is large, greater than roughly 0.6, the beamfilling correction is small and does not depend as strongly on the 19-37-GHz ratio. This behavior has also been observed by Varma et al (2004), and is much different than assuming a pure gamma distribution for P(AЈ) (i.e., the WS98 assumption). Figure 4 shows AMSR-E rain rates for a particular storm using both the WS98 and UMORA beamfilling correction.…”

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

“…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.…”