Utilization of satellite data and regional scale numerical models in short range weather forecasting

Overwhelming evidence was developed in a number of studies of satellite data impact on numerical weather prediction that it is unrealistic to expect satellite temperature soundings to improve detailed regional numerical weather prediction. It is likely that satellite data over the United States would substantially impact mesoscale dynamical predictions if the effort were made to develop a composite moisture analysis system. The horizontal variability of moisture, most clearly depicited in images from satellite water vapor channels, would not be determined from conventional rawinsondes even if that network were increased by a doubling of both the number of sites and the time frequency

Statistical, quantitative probability and nowcasting forecasting methods of severe convective storms

This paper deals with the comparison of the statistical, quantitative and nowcasting method of prediction of convective precipitation and the risk of flood floods, which are the main outputs calculated by the Algorithm of Storm Prediction. The evaluation of the success of these outputs was carried out on the basis of verified 63 thunderstorms and three floods that affected the Zlín Region between 2015 and 2017. The first part of the article focuses on the description and evaluation of the predictive outputs of the quantitative prediction of the probability of the occurrence and the intensity of convective precipitation computed from NWP models. At the same time, these outcomes are compared with the outputs of the statistical and nowcasting predictions of convective precipitation. The statistical prediction of convective precipitation is calculated on the selection of the predicted and historical situation from the statistics database. The nowcasting prediction works with the outputs of the MMR50 X-band meteorological radar of the Zlín Region. The second part explores the use of track storms for statistical prediction, which is intended as an indicative and complementary forecast for the method of quantitative prediction of precipitation. The conclusion of the two chapters is a comparison of the success of the predicted outputs of methods, which can be used and put into practice in particular for the prediction of convective precipitation and the risk of floods for purposes of warning and meteorological services and crisis management. © 2018, World Scientific and Engineering Academy and Society. All rights reserved

Quantitative Precipitation Nowcasting: A Lagrangian Pixel-Based Approach

Short-term high-resolution precipitation forecasting has important implications for navigation, flood forecasting, and other hydrological and meteorological concerns. This article introduces a pixel-based algorithm for Short-term Quantitative Precipitation Forecasting (SQPF) using radar-based rainfall data. The proposed algorithm called Pixel- Based Nowcasting (PBN) tracks severe storms with a hierarchical mesh-tracking algorithm to capture storm advection in space and time at high resolution from radar imagers. The extracted advection field is then extended to nowcast the rainfall field in the next 3. hr based on a pixel-based Lagrangian dynamic model. The proposed algorithm is compared with two other nowcasting algorithms (WCN: Watershed-Clustering Nowcasting and PER: PERsistency) for ten thunderstorm events over the conterminous United States. Object-based verification metric and traditional statistics have been used to evaluate the performance of the proposed algorithm. It is shown that the proposed algorithm is superior over comparison algorithms and is effective in tracking and predicting severe storm events for the next few hours. © 2012 Elsevier B.V

WRF model sensitivity to choice of parameterization: a study of the 'York Flood 1999'

© 2014, Springer-Verlag Wien. Numerical weather modelling has gained considerable attention in the field of hydrology especially in un-gauged catchments and in conjunction with distributed models. As a consequence, the accuracy with which these models represent precipitation, sub-grid-scale processes and exceptional events has become of considerable concern to the hydrological community. This paper presents sensitivity analyses for the Weather Research Forecast (WRF) model with respect to the choice of physical parameterization schemes (both cumulus parameterisation (CPSs) and microphysics parameterization schemes (MPSs)) used to represent the ‘1999 York Flood’ event, which occurred over North Yorkshire, UK, 1 st –14 th March 1999. The study assessed four CPSs (Kain–Fritsch (KF2), Betts–Miller–Janjic (BMJ), Grell–Devenyi ensemble (GD) and the old Kain–Fritsch (KF1)) and four MPSs (Kessler, Lin et al., WRF single-moment 3-class (WSM3) and WRF single-moment 5-class (WSM5)] with respect to their influence on modelled rainfall. The study suggests that the BMJ scheme may be a better cumulus parameterization choice for the study region, giving a consistently better performance than other three CPSs, though there are suggestions of underestimation. The WSM3 was identified as the best MPSs and a combined WSM3/BMJ model setup produced realistic estimates of precipitation quantities for this exceptional flood event. This study analysed spatial variability in WRF performance through categorical indices, including POD, FBI, FAR and CSI during York Flood 1999 under various model settings. Moreover, the WRF model was good at predicting high-intensity rare events over the Yorkshire region, suggesting it has potential for operational use