Abstract
The rainfall amount observed at a given location mostly depend on the cloud density, which can be quantified with the reflectivity values observed by meteorology weather radars. In this study, we aim to estimate the rainfall amount using a Kalman filter with radar reflectivity measurements. We first assume that the amount of rainfall observed at automatic weather observation stations (AWOSs) are elements of an unknown state vector and consider the Kalman filter process model as the true rainfall amounts observed at these AWOSs over time. For the measurement model of the Kalman filter, we use the radar reflectivity values observed at each AWOS location. For the execution of the Kalman filter, a number of rainfall amount and radar reflectivity value pairs are first required to learn the process and measurement models of the Kalman filter. The estimation performance of the proposed Kalman filter is then compared with empirical reflectivity (Z) - rainfall (R) relationships. Numerical results show that when the Kalman filter is executed with radar reflectivity measurements observed around a large number of AWOS locations, the mean squared errors of the Kalman filter rainfall estimates are smaller than the ones obtained with empirical ZR relationships.
Highlights
Weather forecasting predicts the level of precipitation, cloudiness, temperature, wind speed, and direction for a given time period in the near future
Within the range of the Çatalca radar, there are a number of automatic weather observation stations (AWOS), where an AWOS is equipped with sensors that can measure meteorological parameters such as precipitation, temperature, wind speed, and wind direction
We review the multivariate least squares method, which is used for estimating the process and measurement models of the Kalman filter
Summary
Weather forecasting predicts the level of precipitation, cloudiness, temperature, wind speed, and direction for a given time period in the near future. In this work, our aim is to directly predict the amount of rainfall at each automatic weather observation station (AWOS) based on the radar reflectivity measurements. A logarithmic bias model between the predicted and the true rainfall amount was presented in [8, 9], where the authors employed a Kalman filter [10] to estimate the bias term over time. The authors of [12] first determined the optimal parameters of the ZR relationship for given weather data and compared their precipitation estimates with the Kalman filter used for bias correction. The parameters of the process and measurement models of the Kalman filter are determined by using multivariate least squares, which requires some initial training data consisting of radar reflectivity measurements and rainfall amount data pairs.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call