Search

Information regarding the uncertainty associated with weather forecasts, particularly when they are related to a localized area at convective scales, can certainly play a crucial role in enhancing decision-making. In this study, we discuss and evaluate a short-range forecast (0–75 h) from of a regional ensemble prediction system (NEPS-R) running operationally at the National Centre for Medium Range Weather Forecasting (NCMRWF). NEPS-R operates at a convective scale (~ 4 km) with 11 perturbed ensemble members and a control run. We assess the performance of the NEPS-R in comparison to its coarser-resolution global counterpart (NEPS-G), which is also operational. NEPS-R relies on initial and boundary conditions provided by NEPS-G. The NEPS-G produces valuable forecast products and is capable of predicting weather patterns and events at a spatial resolution of 12 km. The objective of this study is to investigate areas where NEPS-R forecasts could add value to the short-range forecasts of NEPS-G. Verification is conducted for the period from 1st August to 30th September 2019, covering the summer monsoon over a domain encompassing India and its neighboring regions, using the same ensemble size (11 members). In addition to standard verification metrics, fraction skill scores, and potential economic values are used as the evaluation measures for the ensemble prediction systems (EPSs). Near-surface variables such as precipitation and zonal wind at 850 hPa (U850) are considered in this study. The results suggest that, in some cases, such as extreme precipitation, there is a benefit in using regional EPS forecast. State-of-the-art probabilistic measures indicate that the regional EPS has reduced under-dispersion in the case of precipitation compared to the global EPS. The global EPS tends to provide higher skill scores for U850 forecasts, whereas the regional EPS outperforms the global EPS for heavy precipitation events (> 65 mm/day). There are instances when the regional EPS can provide a useful forecast for cases, including moderate rainfall, and can add more value to the global EPS forecast products. The investigation of diurnal variations in precipitation forecasts reveals that although both models struggle to predict the correct timing, the time phase and peaks in precipitation in the convection-permitting regional model are closer to the observations. [ABSTRACT FROM AUTHOR]

Global numerical weather prediction (NWP) models, including ensemble prediction systems (EPS), are routinely used by the forecasters to predict tropical cyclone (TC) tracks and intensity. However, due to computational restrictions, the EPSs are usually of coarse resolution, which results in poor prediction of TC intensity. The bias in the model predicting maximum sustained winds (MSW) and central pressure (CP) is large when TCs are intense. This article describes the suitability of machine learning (ML) techniques to reduce errors in TC intensity forecasts obtained from the National Centre for Medium Range Weather Forecasting (NCMRWF) Ensemble Prediction System (NEPS) over the North Indian Ocean (NIO). Four different ML techniques, namely, Multivariate Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boost (XGB), have been used for the bias correction (BC) of mean MSW and CP, while the spread of ensemble members has been retained. The study is based on 20 TC cases formed over the NIO during 2018–21. The best track (BT) information from the India Meteorological Department (IMD) has been used for training the ML models and verifying them. The results show that XGB (for CP) and RF (for MSW) ML techniques are superior to MLR and SVR methods. The statistically significant reduction (at 99% CI) in mean absolute error (MAE) (root mean square error (RMSE) of mean MSW and CP while using the best algorithm are 30 (33)% and 63 (55)%, respectively. The correlation coefficient for CP increases from 0.58 to 0.93 and that for MSW from 0.60 to 0.75. The probabilistic verification of bias-corrected ensemble members also suggests that the RF- and XGB-based models have better reliability and ROC compared to the raw and other models for MSW and CP, respectively. [ABSTRACT FROM AUTHOR]

Forecasting rainfall in the tropics is a challenging task further hampered by the uncertainty in the numerical weather prediction models. Ensemble prediction systems (EPSs) provide an efficient way of handling the inherent uncertainty of these models. Verification of forecasts obtained from an EPS is a necessity, to build confidence in using these forecasts. This study deals with the verification of the probabilistic rainfall forecast obtained from the National Centre for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Forecast system (NGEFS) for three monsoon seasons, i.e., JJAS 2012, 2013 and 2014. Verification is done based on the Brier Score (BS) and its components (reliability, resolution and uncertainty), Brier Skill Score (BSS), reliability diagram, relative operating characteristic (ROC) curve and area under the ROC (A) curve. Three observation data sets are used (namely, NMSG, CPC-RFE2.0 and TRMM) for verification of forecasts and the statistics are compared. BS values for verification of NGEFS forecasts using NMSG data are the lowest, indicating that the forecasts have a better match with these observations as compared to both TRMM and CPC-RFE2.0. This is further strengthened by lower reliability, higher resolution and BSS values for verification against this data set. The ROC curve shows that lower rainfall amounts have a higher hit rate, which implies that the model has better skill in predicting these rainfall amounts. Th e reliability plots show that the events with lower probabilities were under forecasted and those with higher probabilities were over forecasted. From the current study it can be concluded that even though NGEFS is a coarse resolution EPS, the probabilistic forecast has good skill. This in turn leads to an increased confidence in issuing operational probabilistic forecasts based on NGEFS. [ABSTRACT FROM AUTHOR]