Rainfall Estimation from Polarimetric S-Band Radar Measurements: Validation of a Neural Network Approach

2009 ◽  
Vol 48 (10) ◽  
pp. 2022-2036 ◽  
Author(s):  
Gianfranco Vulpiani ◽  
Scott Giangrande ◽  
Frank S. Marzano
Keyword(s):  
Neural Network ◽  
Ad Hoc ◽  
Radar Data ◽  
Retrieval Algorithm ◽  
Network Approach ◽  
Rainfall Estimation ◽  
Neural Network Approach ◽  
Raindrop Size Distribution ◽  
Hourly Rainfall ◽  
The Impact

Abstract A procedure for the estimation of rainfall rate, capitalizing on a radar-based raindrop size distribution (RSD) parameter retrieval and neural network (NN) inversion techniques, is validated using an extensive and quality-controlled archive. The RSD retrieval algorithm utilizes polarimetric variables measured by the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), through an ad hoc regularized neural network method. Evaluation of rainfall estimation from the NN-based method is accomplished using a large radar data and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign. Point estimates of hourly rainfall accumulations and instantaneous rainfall rates from NN-based and parametric polarimetric rainfall relations are compared with dense surface gauge observations. Rainfall accumulations from RSD retrieval-based methods are shown to be sensitive to the choice of a raindrop fall speed model. To minimize the impact of this choice, a new “direct” neural network approach is tested. Proposed NN-based approaches exhibit bias and root-mean-square error characteristics comparable with those obtained from parametric relations, specifically optimized for the JPOLE dataset, indicating an appealing generalization capability with respect to the climatological context. All tested polarimetric relations are shown to be sensitive to hail contamination as inferred from the results of automatic polarimetric echo classification and available storm reports.

Automatic detection of volcanic eruptions in Doppler radar observations using a neural network approach

2020 ◽  
Author(s):  
Matthias Hort ◽  
Daniel Uhle ◽  
Fabio Venegas ◽  
Lea Scharff ◽  
Jan Walda ◽  
...  
Keyword(s):  
Neural Network ◽  
Doppler Radar ◽  
Volcanic Eruptions ◽  
Radar Data ◽  
Visual Observation ◽  
Data Sets ◽  
Network Approach ◽  
Neural Network Approach ◽  
Data Set ◽  
The Impact

<p>Immediate detection of volcanic eruptions is essential when trying to mitigate the impact on the health of people living in the vicinity of a volcano or the impact on infrastructure and aviation. Eruption detection is most often done by either visual observation or the analysis of acoustic data. While visual observation is often difficult due to environmental conditions, infrasound data usually provide the onset of an event. Doppler radar data, admittedly not available for a lot of volcanoes, however, provide information on the dynamics of the eruption and the amount of material released. Eruptions can be easily detected in the data by visual analysis and here we present a neural network approach for the automatic detection of eruptions in Doppler radar data. We use data recorded at Colima volcano in Mexico in 2014/2015 and a data set recorded at Turrialba volcano between 2017 and 2019. In a first step we picked eruptions, rain and typical noise in both data sets, which were the used for training two networks (training data set) and testing the performance of the network using a separate test data set. The accuracy for classifying the different type of signals was between 95 and 98% for both data sets, which we consider quite successful. In case of the Turriabla data set eruptions were picked based on observations of OVSICORI data. When classifying the complete data set we have from Turriabla using the trained network, an additional 40 eruptions were found, which were not in the OVSICORI catalogue.</p><p>In most cases data from the instruments are transmitted to an observatory by radio, so the amount of data available is an issue. We therefore tested by what amount the data could be reduced to still be able to successfully detect an eruption. We also kept the network as small as possible to ideally run it on a small computer (e.g. a Rasberry Pi architecture) for eruption detection on site, so only the information that an eruption is detected needs to be transmitted.</p>

Validating the impact of accounting disclosures on stock market: A deep neural network approach

2021 ◽  
Vol 170 ◽  
pp. 120903
Author(s):  
Prajwal Eachempati ◽  
Praveen Ranjan Srivastava ◽  
Ajay Kumar ◽  
Kim Hua Tan ◽  
Shivam Gupta
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Deep Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Accounting Disclosures ◽  
The Impact

Spatial Diversity in Local Government Revenue Effort under Decentralization: A Neural-Network Approach

10.1068/c16r ◽  
2005 ◽  
Vol 23 (5) ◽  
pp. 657-677 ◽  
Author(s):  
Mildred E Warner ◽  
James E Pratt
Keyword(s):  
Neural Network ◽  
State Policy ◽  
Local Governments ◽  
Classification Tree ◽  
The United States ◽  
Spatial Diversity ◽  
Federal Aid ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Impact

Decentralization reflects a global trend to increase the responsiveness of state and local governments to economic forces, but it raises the challenge of how to secure redistributive goals. Theoretically, as the equalizing impact of federal aid declines under devolution, we expect subnational state-level government policy to become more important, and geographic diversity in local governments' efforts to raise revenue to increase. In this paper we explore the impact of state fiscal centralization and intergovernmental aid on local revenue effort with the aid of Census of Governments data for county areas from 1987 for the Mid-Atlantic and East North Central region of the United States, with particular attention paid to rural counties. The 1987 period was chosen because it is the first year in which state policy trends diverged from federal decentralization trends and both state aid and state centralization increased while federal aid to localities continued to decline. Using a neural-network approach, we explore the spatially differentiated impact of state policy and find complementary responses in effort among some localities and substitution responses among others. Classification-tree analysis of this diversity suggests that decentralization and the competitive government it promotes are likely to exacerbate inequality among local governments.

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