Probabilistic seasonal precipitation forecasts using quantiles of ensemble forecasts

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: 38(5), P. 2041 - 2063

Published: Feb. 29, 2024

Abstract Seasonal precipitation forecasting is vital for weather-sensitive sectors. Global Circulation Models (GCM) routinely produce ensemble Climate Forecasts (SCFs) but suffer from issues like low forecast resolution and skills. To address these in this study, we introduce a post-processing method, Quantile Ensemble Bayesian Model Averaging (QEBMA). It utilises quantiles GCM to create pseudo-ensemble forecast. Through their reasonable linear relationships with observations, each pseudo-member connects hurdle distribution point mass at zero dry months gamma wet months. These distributions are mixed construct probability weights, proportional the quantiles’ historical performance. QEBMA applied three GCMs, including GloSea5 United Kingdom, ECMWF Europe ACCESS-S1 Australia, monthly forecasts 32 locations across four climate zones Australia. Leave-one-month-out cross-validation results illustrate that enhances skills compared raw GCMs other techniques, quantile mapping Extended Copula Post-Processing (ECPP), lead time of 0 2 months, based on five metrics. The skill improvements achieved by often statistically significant, particularly when study locations. Among models, only consistently outperforms SCF benchmark climatology, offering promising alternative improving seasonal forecasts.

Language: Английский

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Improving the accuracy of precipitation estimates in a typical inland arid region of China using a dynamic Bayesian model averaging approach

Journal of Arid Land, Journal Year: 2024, Volume and Issue: 16(3), P. 331 - 354

Published: March 1, 2024

Language: Английский

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A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Hydrology and earth system sciences, Journal Year: 2024, Volume and Issue: 28(5), P. 1147 - 1172

Published: March 7, 2024

Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge conducting over remote regions with rugged topography that weather stations are usually scarce unevenly distributed. However, open-source satellite-based precipitation products (SPPs) suitable resolution provide alternative options these data-scarce regions, which typically associated high uncertainty. To reduce the uncertainty individual satellite products, we have proposed D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs rain gauges (RGs). DVQR was employed during 2001–2017 summer monsoon seasons compared two other methods based on multivariate linear (MLQR) Bayesian averaging (BMAQ) techniques, respectively, traditional merging – simple modeling average (SMA) one-outlier-removed (OORA) using descriptive categorical statistics. Four been considered this study, namely, Tropical Applications Meteorology SATellite (TAMSAT v3.1), Climate Prediction Center MORPHing Product Data Record (CMORPH-CDR), Global Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), Estimation from Remotely Sensed Information Artificial Neural Networks (PERSIANN-CDR). bilinear (BIL) interpolation technique applied downscale coarse fine spatial (1 km). rugged-topography region upper Tekeze–Atbara Basin (UTAB) Ethiopia selected as study area. results indicate data estimates DVQR, MLQR, BMAQ models outperform downscaled SPPs. Monthly evaluations reveal all perform better July September than June August due variability. exhibit higher accuracy UTAB. substantially improved statistical metrics (CC = 0.80, NSE 0.615, KGE 0.785, MAE 1.97 mm d−1, RMSE 2.86 PBIAS 0.96 %) MLQR models. did not respect probability detection (POD) false-alarm ratio (FAR), although it had best frequency bias index (FBI) critical success (CSI) among Overall, newly approach improves quality demonstrates value such

Language: Английский

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Enhancing drought monitoring through spatial downscaling: A geographically weighted regression approach using TRMM 3B43 precipitation in the Urmia Lake Basin

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(4), P. 2995 - 3020

Published: May 15, 2024

Language: Английский

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Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(13), P. 20534 - 20555

Published: Feb. 20, 2024

Language: Английский

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