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The range error caused by ionospheric delay in Global Positioning System (GPS) signals is currently the main factor that affected the positioning accuracy and navigation determination. Ionospheric modeling and optimization of its range error is a practical approach to improve the GPS positioning accuracy. The global ionospheric models are mostly unable to predict ionospheric delay corrections over the India region due to the Equatorial Ionization Anomaly (EIA)/fountain effect and lack of equatorial/low latitude GPS stations data. This paper proposes a method to facilitates regional ionospheric delay corrections based on the Adjusted Spherical Harmonic Function (ASHF) model by considering fewer coefficients into the algorithm. The ionospheric correction is driven with low-resolution harmonics coefficients with order ≤2 (≤9 coefficients), and coefficients are estimated with the Modified Gram-Schmidt (MGS) approach. The performance of the proposed model (ASHF) is compared with the Spherical Harmonic Function and Klobuchar models. Preliminary results reveal that the low-resolution ASHF model improved the ionospheric time delay corrections by 12.98% relative to the Klobuchar model. The results could be useful in the context of ionospheric time delay modeling for regional navigation satellite systems. • Estimation of ionospheric delays is proposed based on Orthogonal Dimensionality Reduction. • Model outcomes are assessed by comparing with the Weighted Least Square estimations. • The SVD-SHF method improves the accuracy of the model results in geomagnetic storm conditions. • The SVD-SHF method resulted in dimensional reduction and low computational load. • The outcomes suggest that the model implementation is used for GNSS and surveying applications. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the ability of the operational global ensemble prediction system (EPS) at the National Centre for Medium Range Weather Forecasting (NCMRWF, NEPS in predicting the probabilities of the maximum 2 m temperature (TMAX). The mean of the TMAX forecasts from NEPS are bias corrected (1st moment) using the technique of Decaying Average. This method does not lead to any correction in the spread of the EPS. Therefore the method of Variation Inflation is used to correct the spread (2nd moment) of NEPS. The forecasts of TMAX from the raw and the bias corrected data are then compared for March to May 2019 using standard verification metrics for probabilistic forecasts like Brier Score (BS), Brier Skill Score (BSS), ROC and Reliability Diagrams as well as the Value score. A case by case comparison of several heat wave cases during March to May 2019 is also performed in order to assess the day-to-day performance of the model. The forecasts after bias correction in the mean (1st moment) show a large improvement in terms of reduced false alarms and increased hit rate. However these forecasts show a reduced reliability in terms of under forecasting events with low probabilities and over forecasting events with high probabilities. The analysis of the forecast reliability after the correction of both the mean and the spread (1st and 2nd moments) shows an improvement in reliability. The forecasts with correction in the spread show a slight decrease in the ROC (i.e., increased false alarms) as compared to forecasts with just the corrected mean but the improvement in the reliability is appreciable. Thus the study demonstrates improved skill in probabilistic forecasts of TMAX over India during MAM 2019. • Prediction of heat waves over India using and ensemble prediction system • Improving forecast quality and skill by bias correction • Correction in the ensemble mean and spread. • Calculation of Relative Economic Value for heat wave forecasts [ABSTRACT FROM AUTHOR]

Present study has assessed different sources of uncertainties in multi-model precipitation projection using Global Climate Models (GCMs) from coupled model inter-comparison project phase five (CMIP5) experiment over seven homogeneous rainfall zones of India namely North Mountainous India (NMI), Northwest India (NWI), North Central India (NCI), Northeast India (NEI), West Peninsular India (WPI), East Peninsular India (EPI), and South Peninsular India (SPI). A relatively new method has been employed to separate out internal variability and climate change signal from precipitation time series before quantifying the uncertainties. In our method, signal has been defined as dynamic trend instead of considering a fixed trend line. Three different types of weighting namely equal weighting, independence based weighting and performance based weighting have been employed to assess the uncertainties of GCM projection over different zones. It has been found that ensemble with performance based weighting has produced smaller inter-model uncertainty but the patterns of temporal evolution of uncertainties have been quite irregular compared to other two ensembles. On the other hand, it has been noticed that bias correction using quantile mapping can effectively reduce the range of uncertainty in a systematic way. It has been observed that inter-model uncertainties over NEI has been relatively lower compared to other zones indicating more robust projection over this zone. A dynamic threshold on signal-to-internal variability ratio (S/I) has been used for estimating time of emergence (TOE) at 95% confidence level over each zone. TOE would be earlier in case of NEI and late in NMI. However no zone may experience TOE in first half of the present century. [ABSTRACT FROM AUTHOR]