• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.473-482
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• 2015

Doppler weather radar is an important tool for meteorological research. Through several decades of development, Doppler weather radar has enormous progress in understanding, detection and warning of meso and micro scale weather system. It makes a significant contribution to weather forecast and weather disaster warning. But the large amount of data process limits the application of Doppler weather radar. This paper proposed for fast weather radar data processing based on CUDA. CDUA is a powerful platform for highly parallel programming developed by NVIDIA. Through running plenty of threads, radar data can be calculated at same time. In experiment, CUDA parallel program can significantly improve weather data processing time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.7B
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• pp.951-956
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• 2001

A weather radar usually extracts the necessary information from the return Doppler spectrum moment estimates. Phase stability is a very important factor in obtaining accurate and reliable information in a Doppler weather radar system since the system phase noise may seriously degrade the weather spectrum moment estimation quality. These spectrum moment estimates are commonly obtained using the pulse pair method which is simple to implement and fast enough to process an enormous amount of weather radar data in real time. Therefore, an analytical method is developed in this paper to analyze and quantify the phase noise effects on the pulse pair spectrum moment estimates in terms of the phase noise power and broadness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.561-564
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• 2007

Recently, The detection of weather conditions and weather related hazards with a weather radar are being actively investigated based on the echo intensity and the Doppler spectrum analysis. For this purpose, many types of simulated weather signals are needed for investigation. Therefore, this paper analyzed the method to simulate the many weather radar signals.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.853-856
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• 2007

A weather radar extracts the weather information from the return echoes which consist of scattered electromagnetic wave signals from rain, cloud and dust particles, etc. The acquisition of accurate weather information depends on the operation environment which include the Doppler weather signal and ground clutter characteristics. Since the conventional symmetric weather Doppler model does not represent the measurements in real situations, the improved model is suggested to describe the skewness in the Doppler spectrum model. Using the suggested model, many various weather signals can be simulated to verify the accuracy of signal processing algorithms and the reliability of the extracted weather information

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.205-210
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• 2002

In a Doppler weather radar, high resolution windspeed profile measurements are needed to provide the reliable detection of hazardous weather conditions. For this purpose, the pulse pair method is generally considered to be the most efficient estimator, However, this estimator has some bias errors due to asymmetric spectra and may yield meaningless results in the case of a multimodal return spectrum. Although the poly-pulse pair method can reduce the bias errors of skewed weather spectra, the modes of spectrum may provide more reliable information than the statistical mean for the case of a multimodal or seriously skewed spectrum. Therefore, the idea of relatively simple mode estimator for a weather radar is developed in this paper, Performance simulations show promising results in the detection of hazardous weather conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1492-1499
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• 2020

A weather radar system generally shows the weather phenomena related with rainfall and wind velocity. These systems are usually very helpful to monitor the relatively high altitude weather situation for the wide and long range area. However, since the weather hazards due to the strong hail and heavy rainfall occurring locally are observed frequently in recent days, it is important to detect these wether phenomena. For this purpose, it is necessary to detect the fast varying low altitude weather conditions. In this environment, the effect of surface clutter is more evident and the antenna dwell time is much shorter. Therefore, the conventional Doppler spectrum estimation method may cause serious problems. In this paper, the AR(autoregressive) Doppler spectrum estimation methods were applied to solve these problems and the results were analyzed. Applied methods show that improved Doppler spectra can be obtained comparing with the conventional FFT(Fast Fourier Transform) method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.1999-2005
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• 2015

A wind velocity is measured in a weather radar as well as the strength of return echoes from rain clouds. These wind velocities are obtained through estimation of Doppler frequencies in return signals. This kind of Doppler frequency estimation method is called as a correlation method. It is widely used in most weather radars because of less computation time. However, it may cause serious errors if a spectrum is not symmetric. Therefore, in this paper, it is shown that the improved method using 3^rd order phase estimation model yields the more accurate estimation of the average Doppler frequency using various simulated weather data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.452-458
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• 2007

Since there are very strong clutter returns in an airborne weather radar used for the detection of low altitude weather hazards, the reliable weather data cannot be extracted from the weak Doppler weather signal without cancellation of these strong clutter returns. However, the system phase noise spreads both the clutter and Doppler signal and causes the serious problems in the efficient clutter cancellation. Therefore, in this paper, the phase noise effects on the clutter and Doppler weather signal were analyzed. The system phase noise model was suggested and the effects were derived and explained using this phase noise model. It can be shown that there exists the limit in the clutter cancellation capability to improve the signal-to-clutter ratio (SCR) due to the system phase noise. It may be prominent especially in the low SCR situations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1641-1648
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• 2016

A weather radar estimates Doppler frequency and width of Doppler spectrum from the received weather signal which represents the return echoes from rain or dust particles in a corresponding area. These estimates are very important parameters since they are directly related to precipitation, wind velocity and degree of turbulence. Therefore, these estimated values should be highly reliable to obtain accurate weather information. However, the echoes of a weather radar include both the weather signal and the clutter which occurred from ground reflection or moving objects, etc. The existence of the clutter in the echoes may cause serious errors in the estimation of weather-related parameters. Therefore, in this paper, models are developed to represent the weather signal and the clutter for the purpose of analyzing estimation errors caused by the strong clutter echoes. Using these models, various return echoes according to the weather signal and clutter power are simulated to analyze the effects of the clutter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1737-1743
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• 2008

A weather radar extracts the weather information from the return echoes which consist of scattered electromagnetic wave signals from rain, cloud and dust particles, etc. The characteristics of Doppler weather signal and ground clutter should be analyzed to extract the accurate weather information. However, the conventional symmetric weather Doppler model is somewhat inappropriate in representing various weather situations. Therefore, the improved model is suggested to describe the skewness in the Doppler spectrum model. Using the suggested model, many various weather signals can be simulated efficiently in time and spectral domain according to weather situations, operation environment and system characteristics. This simulation method may be very helpful in verifying the accuracy of the weather information extraction algorithms and developing the new system for further performance improvement.