Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

Ivanov, S. V.; Michaelides, Silas; Ruban, Igor

doi:10.3390/rs10091453

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…New sources of observation data such as LIDAR and floating LIDAR have the potential to provide wind data at multiple heights for less cost than traditional met masts (Gottschall, Gribben, Stein, & Würth, ), while data assimilation of radar reflectivities has been shown to improve the representation of clouds and precipitation in NWP (Ivanov, Michaelides, & Ruban, ).…”

Section: The Future Of Forecasting Renewable Energymentioning

confidence: 99%

The future of forecasting for renewable energy

Sweeney

Bessa

Browell

et al. 2019

WIREs Energy & Environment

149

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Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state‐of‐the‐art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure

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Section: The Future Of Forecasting Renewable Energymentioning

confidence: 99%

The future of forecasting for renewable energy

Sweeney

Bessa

Browell

et al. 2019

WIREs Energy & Environment

149

View full text Add to dashboard Cite

show abstract

“…Ivanov et al [9] present 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 3-D regular grid in which a horizontal size of meshes coincides with the horizontal model resolution.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Editorial for Special Issue “Remote Sensing of Precipitation”

Michaelides

2019

Remote Sensing

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This Special Issue hosts papers on all aspects of remote sensing of precipitation, including applications that embrace the use of remote-sensing techniques of precipitation in tackling issues, such as precipitation estimations and retrievals, along with their methodologies and corresponding error assessment; precipitation modelling including validation, instrument comparison, and calibration; understanding of cloud and precipitation microphysical properties; precipitation downscaling; precipitation droplet size distribution; assimilation of remotely sensed precipitation into numerical weather prediction models; and measurement of precipitable water vapor.

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“…Numerous operational nowcasting systems use the relationship between static radar reflectivity (Z) and the rain rate (R), such as the commonly used Marshall-Palmer [18] formula of Z = 200 R 1.6 , to convert radar echo into rainfall estimates. Radar-and gauge-based rainfall estimates are typically consistent, particularly when the rainfall intensity is high [19][20][21][22][23][24][25]. Researchers have studied the QPE of tropical cyclones.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Hourly Rainfall during Typhoons Using Radar Mosaic-Based Convolutional Neural Networks

Wei

Hsieh

2020

Remote Sensing

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Taiwan is located at the junction of the tropical and subtropical climate zones adjacent to the Eurasian continent and Pacific Ocean. The island frequently experiences typhoons that engender severe natural disasters and damage. Therefore, efficiently estimating typhoon rainfall in Taiwan is essential. This study examined the efficacy of typhoon rainfall estimation. Radar images released by the Central Weather Bureau were used to estimate instantaneous rainfall. Additionally, two proposed neural network-based architectures, namely a radar mosaic-based convolutional neural network (RMCNN) and a radar mosaic-based multilayer perceptron (RMMLP), were used to estimate typhoon rainfall, and the commonly applied Marshall–Palmer Z-R relationship (Z-R_MP) and a reformulated Z-R relationship at each site (Z-R_station) were adopted to construct benchmark models. Monitoring stations in Hualien, Sun Moon Lake, and Taichung were selected as the experimental stations in Eastern, Central, and Western Taiwan, respectively. This study compared the performance of the models in predicting rainfall at the three stations, and the results are outlined as follows: at the Hualien station, the estimations of the RMCNN, RMMLP, Z-R_MP, and Z-R_station models were mostly identical to the observed rainfall, and all models estimated an increase during peak rainfall on the hyetographs, but the peak values were underestimated. At the Sun Moon Lake and Taichung stations, however, the estimations of the four models were considerably inconsistent in terms of overall rainfall rates, peak rainfall, and peak rainfall arrival times on the hyetographs. The relative root mean squared error for overall rainfall rates of all stations was smallest when computed using RMCNN (0.713), followed by those computed using RMMLP (0.848), Z-R_MP (1.030), and Z-R_station (1.392). Moreover, RMCNN yielded the smallest relative error for peak rainfall (0.316), followed by RMMLP (0.379), Z-R_MP (0.402), and Z-R_station (0.688). RMCNN computed the smallest relative error for the peak rainfall arrival time (1.507 h), followed by RMMLP (2.673 h), Z-R_MP (2.917 h), and Z-R_station (3.250 h). The results revealed that the RMCNN model in combination with radar images could efficiently estimate typhoon rainfall.

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Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

Cited by 6 publications

References 23 publications

The future of forecasting for renewable energy

The future of forecasting for renewable energy

Editorial for Special Issue “Remote Sensing of Precipitation”

Estimation of Hourly Rainfall during Typhoons Using Radar Mosaic-Based Convolutional Neural Networks

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