Wide-band noise modulation is added to the adaptive scan technique for spaceborne rain radar. The performance of this technique is studied by simulation using one month of TRMM (Tropical Rainfall Measuring Mission) Precipitation Radar (PR) data from the viewpoints of improving the sensitivity and reducing power consumption. The results show that the adaptive scan technique with wide-band noise modulation uses about 25% less energy than the conventional scanning technique. The adaptive scan using wide-band noise modulation is more effective than that using a normal pulse for localized rainy areas. Surface data as well as rainfall data can be obtained by using the adaptive scan using wide-band noise modulation.

The performance of the adaptive scan for spaceborne rain radar, which uses a quick scan for rain search followed by a normal or concentrated scan only for rainy areas, are studied through a simulation using TRMM (Tropical Rainfall Measuring Mission) Precipitation Radar (PR) data. Trade-off studies are performed to find an optimum quick-scan and rain search method to minimize rain missing and false alarm of rain area. Using the optimum method thus determined, consecutive 8-day TRMM PR data are used to statistically evaluate the performance of the adaptive scan in terms of sensitivity improvement and power consumption saving. It is shown that more than 3-dB improvement in effective signal-to-noise ratio (SNe) can be achieved for 40% of the total observations. Alternatively, about 26% power saving can be achieved if the SNe is kept the same.

A method to correct the path-integrated attenuation derived from spaceborne radar measurement for the non-uniform beam filling (NUBF) effect is studied . A preliminary test using the data obtained from shipborne and ground-based radars is performed. It is found that the relation between the coarse-scale variability (radar-measurable quantity, σL) and the fine-scale variability (a quantity necessary for the NUBF correction, σH) of rain depends somewhat upon the rain cases studied and there still remains some underestimation in the corrected results. Nevertheless, the test result demonstrates the potential of utilizing the "local" statistical properties of rain in order to decrease the bias error in rain rate estimation caused by the NUBF.