Rainfall Reflectivity Relationship For Rainfall Nowcasting In Northern Region Of Peninsular Malaysia

Short term rainfall forecasting is one of the most crucial forecasting tasks in meteorology. The success of rainfall forecasting depend upon many factors such as, experience of forecaster, the capability of radar hardware and the parameter conversion between reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z=ARb). Using the optimization technique, new Z-R relationship parameter for Alor Star Radar was derived. Sensitivity analysis was conducted to ease the calibration process. Calibration and validation process were performed between rainfall radar and gauge data to get the best parameter of A and exponential b. Short term rainfall forecasting was conducted using cross correlation technique to find the speed and direction of rainfall. Then, persistence forecast using linear extrapolation applied to forecast the next storm with the assumption there are no growth and decay of rainfall. The new Z-R relationship parameter for Alor Star Radar was determined to be Z = 40R1.6. Four statistical analyses were performed and it was found that the Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²) value within the acceptable statistical indicators with the values of 1.77, 2.19, 3.11, and 0.90 respectively. Short term rainfall forecasting shows an acceptable result for 10 and 30 minutes lead time according to Nash and Sutcliffe Coefficient of Efficiency with the value is 0.86 and 0.48 respectively. As a conclusion, persistence forecast is suitable to forecast short term rainfall while Z-R relationship parameter value is an important input to the successfulness of rainfall forecasting by radar

Progression approach for image denoising

Removing noise from the image by retaining the details and features of this treated image remains a standing challenge for the researchers in this field. Therefore, this study is carried out to propose and implement a new denoising technique for removing impulse noise from the digital image, using a new way. This technique permits the narrowing of the gap between the original and the restored images, visually and quantitatively by adopting the mathematical concept ''arithmetic progression''. Through this paper, this concept is integrated into the image denoising, due to its ability in modelling the variation of pixels’ intensity in the image. The principle of the proposed denoising technique relies on the precision, where it keeps the uncorrupted pixels by using effective noise detection and converts the corrupted pixels by replacing them with other closest pixels from the original image at lower cost and with more simplicity

An overview of the fundamental approaches that yield several image denoising techniques

Digital image is considered as a powerful tool to carry and transmit information between people. Thus, it attracts the attention of large number of researchers, among them those interested in preserving the image features from any factors that may reduce the image quality. One of these factors is the noise which affects the visual aspect of the image and makes others image processing more difficult. Thus far, solving this noise problem remains a challenge for the researchers in this field. A lot of image denoising techniques have been introduced in order to remove the noise by taking care of the image features; in other words, getting the best similarity to the original image from the noisy one. However, the findings are still inconclusive. Beside the enormous amount of researches and studies which adopt several mathematical concepts (statistics, probabilities, modeling, PDEs, wavelet, fuzzy logic, etc.), there is also the scarcity of review papers which carry an important role in the development and progress of research. Thus, this review paper intorduce an overview of the different fundamental approaches that yield the several image-denoising techniques, presented with a new classification. Furthermore, the paper presents the different evaluation tools needed on the comparison between these techniques in order to facilitate the processing of this noise problem, among a great diversity of techniques and concepts