Evaluating Precipitation Estimates from Eta, TRMM and CHRIPS Data in the South-Southeast Region of Minas Gerais State—Brazil

Nogueira, Sulimar Munira Caparoci; Moreira, Maurício Alves; Volpato, Margarete Marin Lordelo

doi:10.3390/rs10020313

Cited by 54 publications

(31 citation statements)

References 32 publications

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“…The good performance of TRMM is also shown in other recent studies that used TRMM datasets worldwide [59,60]. However, the good results achieved with PERSIANN grid in [8] are not found in the present study, due to various and extreme climatic conditions which characterize Spain, as in the case of complex topography in Chile shown by Zambrano-Bigiarini et al [61].…”

Section: Discussionsupporting

confidence: 69%

Using Multiple Monthly Water Balance Models to Evaluate Gridded Precipitation Products over Peninsular Spain

et al. 2018

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Abstract:The availability of precipitation data is the key driver in the application of hydrological models when simulating streamflow. Ground weather stations are regularly used to measure precipitation. However, spatial coverage is often limited in low-population areas and mountain areas. To overcome this limitation, gridded datasets from remote sensing have been widely used. This study evaluates four widely used global precipitation datasets (GPDs): The Tropical Rainfall Measuring Mission (TRMM) 3B43, the Climate Forecast System Reanalysis (CFSR), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), against point gauge and gridded dataset observations using multiple monthly water balance models (MWBMs) in four different meso-scale basins that cover the main climatic zones of Peninsular Spain. The volumes of precipitation obtained from the GPDs tend to be smaller than those from the gauged data. Results underscore the superiority of the national gridded dataset, although the TRMM provides satisfactory results in simulating streamflow, reaching similar Nash-Sutcliffe values, between 0.70 and 0.95, and an average total volume error of 12% when using the GR2M model. The performance of GPDs highly depends on the climate, so that the more humid the watershed is, the better results can be achieved. The procedures used can be applied in regions with similar case studies to more accurately assess the resources within a system in which there is scarcity of recorded data available.

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Section: Discussionsupporting

confidence: 69%

Using Multiple Monthly Water Balance Models to Evaluate Gridded Precipitation Products over Peninsular Spain

et al. 2018

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“…As expected, the value and size of these changes depend on the smoothing parameter. The larger this parameter, the smoother the impact in spatial coverage is and the smaller the deviations from the analysis are [21]. Matching the radar resolution to that of the model should minimize numerical effects while focusing on physical features.…”

Section: Results Of Radar Data Assimilationmentioning

confidence: 99%

Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

2018

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This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The proposed method creates a 3D regular grid in which a horizontal size of meshes coincides with the horizontal model resolution. This minimizes the representative error associated with the discrepancy between resolutions of informational sources. After such preprocessing, horizontal structure functions and their gradients for radar reflectivity maintain the sizes and shapes of precipitation patterns similar to those of the original data. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. It redistributes precipitable water with smoothed values over the common domain since the control runs show, among several sub-domains with increased and decreased values, correspondingly. It also reproduces the mesoscale belts and cell patterns of sizes from a few to ten kilometers in precipitation fields. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 km to 6 km with major variations at 2.5 km to 3 km. It also reproduces the mesoscale belt and cell patterns of precipitation fields.

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“…As expected, the value and size of these changes depend on the smoothing parameter. The larger this parameter, the smoother the impact in spatial coverage is and the smaller the deviations from the analysis are [17]. Matching the radar resolution to that of the model, should minimize numerical effects, while focusing on physical features.…”

Section: Results Of Radar Data Assimilationmentioning

confidence: 99%

Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

Ivanov¹,

Michaelides²,

Ruban³

2018

Preprint

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This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model.The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5-3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

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Evaluating Precipitation Estimates from Eta, TRMM and CHRIPS Data in the South-Southeast Region of Minas Gerais State—Brazil

Cited by 54 publications

References 32 publications

Using Multiple Monthly Water Balance Models to Evaluate Gridded Precipitation Products over Peninsular Spain

Using Multiple Monthly Water Balance Models to Evaluate Gridded Precipitation Products over Peninsular Spain

Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

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