An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar

Dutta, Devajyoti; Sharma, Sanjeev; Sen, Goutam Kumar; Kannan, B. A. M.; Venketswarlu, S.; Gairola, R. M.; Das, Jayanta Kumar; Viswanathan, G. M.

doi:10.1016/j.asr.2011.02.002

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…Doppler shift has been applied in technology for instrumentation in many fields [ 4 ]. For example, Doppler radar for weather has been widely used for real-time monitoring of precipitation and severe weather, including thunderstorms and tornadoes [ 5 , 6 , 7 , 8 , 9 ]. The U.S. is now equipped with ~159 Next-Generation Radars (NEXRAD [ 10 , 11 ]) for weather service.…”

Section: Introductionmentioning

confidence: 99%

Estimating Water Transport from Short-Term Vessel-Based and Long-Term Bottom-Mounted Acoustic Doppler Current Profiler Measurements in an Arctic Lagoon Connected to the Beaufort Sea

Boswell

2021

Sensors

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Acoustic Doppler current profilers (ADCP) are quasi-remote sensing instruments widely used in oceanography to measure velocity profiles continuously. One of the applications is the quantification of land–ocean exchange, which plays a key role in the global cycling of water, heat, and materials. This exchange mostly occurs through estuaries, lagoons, and bays. Studies on the subject thus require that observations of total volume or mass transport can be achieved. Alternatively, numerical modeling is needed for the computation of transport, which, however, also requires that the model is validated properly. Since flows across an estuary, lagoon, or bay are usually non-uniform and point measurements will not be sufficient, continuous measurements across a transect are desired but cannot be performed in the long run due to budget constraints. In this paper, we use a combination of short-term transect-based measurements from a vessel-mounted ADCP and relatively long-term point measurements from a moored ADCP at the bottom to obtain regression coefficients between the transport from the vessel-based observations and the depth-averaged velocity from the bottom-based observations. The method is applied to an Arctic lagoon by using an ADCP mounted on a buoyant platform towed by a small inflatable vessel and another ADCP mounted on a bottom deployed metal frame. The vessel-based measurements were performed continuously for nearly 5 h, which was sufficient to derive a linear regression between the datasets with an R2-value of 0.89. The regression coefficients were in turn applied to the entire time for the moored instrument measurements, which are used in the interpretation of the subtidal transport variations.

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Section: Introductionmentioning

confidence: 99%

Estimating Water Transport from Short-Term Vessel-Based and Long-Term Bottom-Mounted Acoustic Doppler Current Profiler Measurements in an Arctic Lagoon Connected to the Beaufort Sea

Boswell

2021

Sensors

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“…A commonly used quantity to describe the propagation behavior of electromagnetic wave is the index of refraction n. Variations of the atmosphere refractive index control the propagation conditions of the radar beam [33][34][35][36]. A decrease of vertical refractivity gradient generates an effect of bending the beam faster than normal.…”

Section: Introductionmentioning

confidence: 99%

Pso Algorithm of Retrieving Surface Ducts by Doppler Weather Radar Echoes

Li¹,

Wang²,

et al. 2016

PIER B

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Doppler weather radar is an effective tool for monitoring mesoscale and small scale weather systems, quantitatively estimating precipitation and guarding against severe convective weather. The quality of the data obtained by Doppler weather radar will be seriously affected by the anomalous propagation of electromagnetic wave in tropospheric ducts. A novel method is introduced in this paper to retrieve the surface ducts, and it is based on the Principal Component Analysis (PCA) method for modeling M profile and Parabolic Equation (PE) propagation model which is a well-established technique for efficiently solving the equations for beam propagation in an inhomogeneous atmosphere. The inversion echo powers and equivalent reflectivity factor are in accordance with the measured data, which indicates that the surface ducts can be effectively retrieved by this method.

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“…Over the ocean, relatively few rain gauges are available at buoy platforms. Due to its nature of point observation and limited access, rainfall measurement from rain gauges is supplemented by radars [1], satellites [2], rain parameterization schemes of physical models [3], and empirical statistical models [4]. Although the merged rainfall product is a new trend where precipitation estimation is based on the combined use of satellite, rain gauge, and the numerical weather prediction (NWP) model rain output, the fundamental issue of accuracy of rain product of each component is still a topic of active research.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, artificial neural networks (ANNs), under nonparametric category, have found their ways to solve many problems related to rainfall such as rainfall forecasting [13][14][15][16][17][18][19][20], rainfall-runoff model [21][22][23], rainfall estimation by radars [1,24,25] and satellites [26][27][28][29][30][31], and temporal and spatial rainfall disaggregation [32,33]. e advantage of an ANN approach is that it can be used to develop a functional relationship, including a nonlinear relationship, amongst the various parameters of the process under study even in the absence of full understanding of its mathematical model [34].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

Misra

Deshamukhya

Sharma³

et al. 2018

Advances in Meteorology

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In the present work, daily rainfall is simulated over the core monsoon region of India by using a feedforward multilayer perceptron (MLP) model. Daily rainfall is found to be optimally dependent on four concurrent meteorological parameters, namely, geopotential height, specific humidity, zonal, and meridional wind at 1000 mb, 925 mb, 850 mb, and 700 mb pressure levels during 00, 06, 12, and 18 Greenwich Mean Time (GMT). e architecture of the optimized feedforward MLP model consists of 64 nodes in the input layer, 10 nodes in the hidden layer, and 1 node in the output layer. e results from the model are compared with the 3B42 (version 7) rainfall product. In terms of root mean square error (rmse) and correlation coefficient (cc), the model is performing better compared to the satellite-derived 3B42 rainfall product, whereas in terms of bias, the performance of the 3B42 product is better compared to the model. e weight matrices of the feedforward MLP model are estimated at a particular location (22.5°N, 82.5°E). ese weight matrices are able to simulate daily rainfall at neighbourhood locations also with reasonably good accuracy with cc in the range of 0.41 to 0.55. e performance of the model improves in case of an aerial average of daily rainfall with significantly enhanced cc (0.72). e model is able to capture monthly and intraseasonal variation of rainfall with reasonably good accuracy, with cc of 0.88 and 0.68, respectively. e simulation model has a limitation that it is not able to simulate extreme high rainfall events (>60 mm/day). Overall, the developed model is performing reasonably well. is approach has a potential to be used as a rain parameterization scheme in the dynamical atmospheric and coupled models to simulate daily rainfall. Nevertheless, the present approach can also be used for multistep prediction of rainfall.

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An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar

Cited by 13 publications

References 21 publications

Estimating Water Transport from Short-Term Vessel-Based and Long-Term Bottom-Mounted Acoustic Doppler Current Profiler Measurements in an Arctic Lagoon Connected to the Beaufort Sea

Estimating Water Transport from Short-Term Vessel-Based and Long-Term Bottom-Mounted Acoustic Doppler Current Profiler Measurements in an Arctic Lagoon Connected to the Beaufort Sea

Pso Algorithm of Retrieving Surface Ducts by Doppler Weather Radar Echoes

Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

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