ABSTRACT Weather radars measure the amount of mean power reflected from precipitation targets. To convert reflectivity (Z) data into rainfall (R) data, an empirical power relationship, Z=aR , is usually used. However, the parameters a and b vary with time and space. These means these parameters need to be estimated for different locations. In this thesis, new Z-R relationships are derived for Lake Tana Basin (LTB) precipitation by applying linear regression and bulk adjustment methods using reflectivity data from Shawra C-band weather radar and rainfall data from Bahir Dar metrological station. By linear regression method, we found that Z=277R 1.5 . And, using bulk adjustment method the relations, Z=270R 1.6 , Z=315R 1.5 and Z=390R 1.4 were found. These relationships are comparable with previously derived relationships for different areas of the world in estimating the amount of rainfall rate from reflectivity data. A correction factor between precipitation rates measured by radar (Rr) and gauge (Rg) is also found by linear regression of these two data. The relationship between the two precipitation rates was found to be, Rr = 1.06 Rg + 0.75. By using this relationship, we have shown that the radar measurement captures the daily variation of rainfall rate estimated by gauges. The mean error of rainfall estimated by the calibrated radar compared to gauges’ measurement was 1.65 mm. This is better compared to mean errors 7.74 mm and 7.98 mm of the new model derived by linear regression and Marshall Palmer, respectively. Further studies about altitude profile of reflectivity and spatiotemporal pattern of precipitation over LTB in June and July 2016 are also included. We have also presented detailed discussions and models on weather radar signal processing including transmission, reception and filtering. b