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.

The Tropical Rainfall Measuring Mission (TRMM) is a United States-Japan joint project to measure rainfall from space. The first spaceborne rain radar is aboard the TRMM satellite. Rain/no-rain discrimination for the TRMM provides useful information for on-line data processing, storage, and post-processing analysis. In this paper, rain/no-rain discrimination for the TRMM has been validated through simulation and theory for the no-rain condition and by comparison with the ground-based radar data for rain conditions.

An algorithm that detects the surface echo peak position in a radar echo range profile has been developed for the TRMM Precipitation Radar (PR). The purpose of the surface echo peak detection is to determine the range window in which "over-sample" data are collected. The surface echo position in the range profile is variable due to the systematic change of satellite geodetic altitude and surface topography. The dynamic control of the over-sample range window using the surface detection algorithm contributes significantly to the reduction of PR data rate that should be sent to the ground station. The algorithm employs an α-β tracking filter and has three functions; surface tracking, lock-off detection and tracking loop initialization. After the launch of the TRMM satellite, a series of initial check-out of the PR was conducted. The performance of the algorithm was evaluated through the initial check-out and two-years operation of the PR. The results indicate that the algorithm is working as expected and basically meets the specification; however, it is found that some functions such as the tracking loop initialization algorithm need to be improved.

A radio propagation experiment at the Okinawa Radio Observatory of the Communications Research Laboratory is investigating the feasibility of calibrating the spaceborne precipitation radar onboard the Tropical Rainfall Measuring Mission by using the path-averaged rainfall rate estimated from rain attenuation. Because this estimated rainfall rate has errors due to the spatial inhomogeneity of rainfall rate and the variability of raindrop size distribution, we used distrometer data to evaluate both of these errors by computer simulation.

We have compared the various raindrop-size distributions (DSD) with the recent experimental data collected by the distrometer. It is shown that the Weibull distribution is the best fit to the experimental data for drizzle, widespread and thunderstorm rain cases. By using this Weibull DSD, we obtained a new expression of the radar reflectivity factor (Z) and the rainfall rate (R) relation, that is Z=285R1.48, which gives few errors comparing to some measurements in TRMM frequency of 14GHZ.

A new airborne rain radar named CAMPR (CRL Airborne Multiparameter precipitation Radar) has been developed for the major purpose of calibrating PR (Precipitation Radar) onboard TRMM (Tropical Rainfall Measuring Mission; scheduled to be launched in 1997) in orbit by observing the same rain with both CAMPR and TRMM satellite. CAMPR operates as a coherent radar at 13.8 GHz, the same frequency as TRMM-PR, and has polarimetric and Doppler capabilities. It is installed on a relatively small aircraft and can scan the antenna over a wide angle range, from the nadir to the near-horizon. These functions have been verified to work well and it is shown that the radar system is accurately calibrated. Examples of measurement data show CAMPR's high capability to extract various quantities relating to precipitation and cloud physics. Before the TRMM launch, CAMPR is being used to obtain TRMM-PR simulation data to help its algorithm development as well as to obtain data concerning precipitation and cloud physics.

The MU radar of Japan is one of important candidates for providing accurate ground truth for the TRMM precipitation radar. It can provide the dropsize distribution data together with the background atmospheric wind data with high accuracy and high spatial resolution. Special observation scheme developed for TRMM validation using the MU radar is described, and preliminary results from its test experiment are shown. The high-resolution MU radar data are also used in numerical simulations to validate the rain retrieval algorithm for the TRMM PR data analysis. Among known sources of errors in the rain retrieval, the vertical variability of the dropsize distribution and the partial beam-filling effect are examined in terms of their significance with numerical simulations based on the MU radar data. It is shown that these factors may seriously affect the accuracy of the TRMM rain retrieval, and that it is necessary to establish statistical means for compensation. However, suggested means to improve the conventional α-adjustment method require careful treatment so that they do not introduce new sources of errors.