Merging radar and gauge information within a dynamical model combination framework for precipitation estimation in cold climates

Sivasubramaniam, Kuganesan; Sharma, Ashish; Alfredsen, Knut

doi:10.1016/j.envsoft.2019.05.013

Cited by 10 publications

(2 citation statements)

References 48 publications

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“…Methods have been developed to correct radar-based precipitation fields (Vogl et al, 2012) and to assess the associated uncertainty (Kirstetter et al, 2010;Villarini et al, 2014;Kirstetter et al, 2015). However the low quality of precipitation estimates based on radar measurements in complex terrain, even when combined with in-situ observations (Sideris et al, 2014;Sivasubramaniam et al, 2019;Champeaux et al, 2009), currently prevents their direct use to successfully force a snowpack model (Haddjeri et al, 2023). More sophisticated products combining NWP outputs, surface observations and precipitation estimates from radar measurements (CaPA, Fortin et al, 2015Khedhaouiria et al, 2022) suffer from significant biases in winter (Lespinas et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Radar based high resolution ensemble precipitation analyses over the French Alps

Vernay,

Lafaysse,

Augros

2024

Preprint

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Abstract. Reliable estimation of precipitation fields at high resolution is a key issue for snow cover modelling in mountainous areas, where the density of precipitation networks is far too low to capture their complex variability with topography. Adequate quantification of the remaining uncertainty in precipitation estimates is also necessary for further assimilation of complementary snow observations in snow models. Radar observations provide spatialised estimates of precipitation with high spatial and temporal resolution, and are often combined with rain gauge observations to improve the accuracy of the estimate. However, radar measurements suffer from significant shortcomings in mountainous areas (in particular, unrealistic spatial patterns due to ground clutter). Precipitation fields simulated by high-resolution numerical weather prediction (NWP) models provide an alternative estimate, but suffer from systematic biases and positioning errors. Even though these uncertainties can be partially described by ensemble NWP systems and systematic errors can be reduced by statistical post-processing, NWP precipitation estimates are still not reliable enough for the requirements of high resolution snow cover modelling. In this study, better precipitation estimates are obtained through a specific analysis based on a combination of all these available products. First, a pre-processing step is proposed to mitigate the main deficiencies of radar and gauges precipitation estimation products, focusing on reducing unrealistic spatial patterns. This method also provides a spatialised estimate of the associated error in mountainous areas, based on a climatological analysis of both radar and NWP-estimated precipitation. Three ensemble daily precipitation analysis methods are then proposed, first using only the modified precipitation estimates and associated errors, then combining them with ensemble NWP simulations based on the Particle Filter and Ensemble Kalman Filter data assimilation algorithms. The performance of the different precipitation analysis methods is evaluated at a local scale using independent ski resort precipitation observations. The evaluation of the pre-processing step shows its ability to remove the main spatial artefacts coming from the radar measurements and to improve the precipitation estimates at the local scale. The local scale evaluations of the ensemble analyses do not demonstrate an additional benefit of ensemble NWP forecasts, but their contrasted spatial patterns are challenging to evaluate with the available data.

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Section: Introductionmentioning

confidence: 99%

Radar based high resolution ensemble precipitation analyses over the French Alps

Vernay,

Lafaysse,

Augros

2024

Preprint

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“…The methods used to develop BPDs include Bayesian model averaging [3,5,30], conditional merging [31,32], simple scaling method [32], data assimilation [12], variation approach [20], probability density function [21], simple model averaging [29], principal component analysis [24], neural network analysis [33], and the non-parametric kernel merging method [34]. A detailed description of techniques to blend SPDs is available in references [22,[34][35][36].…”

Section: Introductionmentioning

confidence: 99%

A Regional Blended Precipitation Dataset over Pakistan Based on Regional Selection of Blending Satellite Precipitation Datasets and the Dynamic Weighted Average Least Squares Algorithm

Rahman

Shang

2020

Remote Sensing

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Substantial uncertainties are associated with satellite precipitation datasets (SPDs), which are further amplified over complex terrain and diverse climate regions. The current study develops a regional blended precipitation dataset (RBPD) over Pakistan from selected SPDs in different regions using a dynamic weighted average least squares (WALS) algorithm from 2007 to 2018 with 0.25° spatial resolution and one-day temporal resolution. Several SPDs, including Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42-v7, Precipitation Estimates from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), ERA-Interim (reanalysis dataset), SM2RAIN-CCI, and SM2RAIN-ASCAT are evaluated to select appropriate blending SPDs in different climate regions. Six statistical indices, including mean bias (MB), mean absolute error (MAE), unbiased root mean square error (ubRMSE), correlation coefficient (R), Kling–Gupta efficiency (KGE), and Theil’s U coefficient, are used to assess the WALS-RBPD performance over 102 rain gauges (RGs) in Pakistan. The results showed that WALS-RBPD had assigned higher weights to IMERG in the glacial, humid, and arid regions, while SM2RAIN-ASCAT had higher weights across the hyper-arid region. The average weights of IMERG (SM2RAIN-ASCAT) are 29.03% (23.90%), 30.12% (24.19%), 31.30% (27.84%), and 27.65% (32.02%) across glacial, humid, arid, and hyper-arid regions, respectively. IMERG dominated monsoon and pre-monsoon seasons with average weights of 34.87% and 31.70%, while SM2RAIN-ASCAT depicted high performance during post-monsoon and winter seasons with average weights of 37.03% and 38.69%, respectively. Spatial scale evaluation of WALS-RPBD resulted in relatively poorer performance at high altitudes (glacial and humid regions), whereas better performance in plain areas (arid and hyper-arid regions). Moreover, temporal scale performance assessment depicted poorer performance during intense precipitation seasons (monsoon and pre-monsoon) as compared with post-monsoon and winter seasons. Skill scores are used to quantify the improvements of WALS-RBPD against previously developed blended precipitation datasets (BPDs) based on WALS (WALS-BPD), dynamic clustered Bayesian model averaging (DCBA-BPD), and dynamic Bayesian model averaging (DBMA-BPD). On the one hand, skill scores show relatively low improvements of WALS-RBPD against WALS-BPD, where maximum improvements are observed in glacial (humid) regions with skill scores of 29.89% (28.69%) in MAE, 27.25% (23.89%) in ubRMSE, and 24.37% (28.95%) in MB. On the other hand, the highest improvements are observed against DBMA-BPD with average improvements across glacial (humid) regions of 39.74% (36.93%), 38.27% (33.06%), and 39.16% (30.47%) in MB, MAE, and ubRMSE, respectively. It is recommended that the development of RBPDs can be a potential alternative for data-scarce regions and areas with complex topography.

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Flood Estimation for SMART Control Operation Using Integrated Radar Rainfall Input with the HEC-HMS Model

Ramly

Tahir

Abdullah

et al. 2020

Water Resour Manage

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Merging radar and gauge information within a dynamical model combination framework for precipitation estimation in cold climates

Cited by 10 publications

References 48 publications

Radar based high resolution ensemble precipitation analyses over the French Alps

Radar based high resolution ensemble precipitation analyses over the French Alps

A Regional Blended Precipitation Dataset over Pakistan Based on Regional Selection of Blending Satellite Precipitation Datasets and the Dynamic Weighted Average Least Squares Algorithm

Flood Estimation for SMART Control Operation Using Integrated Radar Rainfall Input with the HEC-HMS Model

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