Precipitation Nowcasting with Three-Dimensional Space–Time Extrapolation of Dense and Frequent Phased-Array Weather Radar Observations

Otsuka, Shigenori; Tuerhong, Gulanbaier; Kikuchi, Ryota; Kitano, Yoshikazu; Taniguchi, Yusuke; Ruiz, Juan; Satoh, Shinsuke; Ushio, Tomoo; Miyoshi, Takemasa

doi:10.1175/waf-d-15-0063.1

Cited by 49 publications

(40 citation statements)

References 14 publications

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“…Thus, this result is encouraging for the accurate spatial prediction of heavy rainfall areas for MγExHR at the grid scale in the very short time range (∼10 min). It is worth pointing out that the use of 3D extrapolation‐based nowcast with phased array radar data can improve the forecast accuracy in this very short range (Otsuka et al ., ). The result from neighbourhood verification using FSS (ii) indicates that a user who needs a longer lead‐time can obtain useful forecasts of heavy rainfall areas of ≥20 mm h −1 up to ∼30 min by tolerating ∼10 km displacement errors of the heavy rainfall areas for MγExHR.…”

Section: Summary and Discussionmentioning

confidence: 99%

Predictability of meso‐γ‐scale, localized, extreme heavy rainfall during the warm season in Japan using high‐resolution precipitation nowcasts

Kato

Shimizu

Shimose

et al. 2017

Quart J Royal Meteoro Soc

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The development and verification of a forecast method for localized, meso‐γ‐scale (2–20 km), extreme heavy rainfall (MγExHR) is important, because it can cause urban flash flooding and inundation with accompanying damage and potential loss of life. Although previous studies have examined the predictability of precipitation at a very short range (≤1 h) using extrapolation‐based nowcasts, they did not specifically focus on MγExHR. In this study, we examine the predictability of 23 selected events of MγExHR (1 h rainfall accumulation ≥50 mm) that occurred during the warm season of 2014 in Japan using High‐Resolution Precipitation Nowcasts (HRPNs) provided by the Japan Meteorological Agency, which are extrapolation‐based nowcasts. Traditional grid‐scale verification using the equitable threat score shows that the HRPNs usefully predict the heavy rainfall areas of ≥20 mm h−1 for at least 12 min at the grid scale of 1 km. Neighbourhood verification using fractions skill scores shows that HRPNs usefully predict the areas of ≥20 mm h−1 up to 29 min by tolerating 11 km displacement errors. After 30 min, a useful forecast cannot be obtained, even if the 11 km displacement errors are tolerated for the ≥20 mm h−1 areas. This result suggests that a numerical weather prediction (NWP) model, whose accuracy is useful after ∼30 min, is necessary to seamlessly provide useful forecasts for heavy rainfall areas of ≥20 mm h−1 for MγExHR with ∼10 km displacement errors, by blending the extrapolation‐based nowcast with NWP.

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Section: Summary and Discussionmentioning

confidence: 99%

Predictability of meso‐γ‐scale, localized, extreme heavy rainfall during the warm season in Japan using high‐resolution precipitation nowcasts

Kato

Shimizu

Shimose

et al. 2017

Quart J Royal Meteoro Soc

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“…In this study, we take advantage of an existing spacetime extrapolation system developed by Otsuka et al (2016). The motion vector is computed by the Tracking Radar Echoes by Correlation (TREC; Rinehart and Garvey 1978) with the fractional motion vector technique of Otsuka et al (2016).…”

Section: A Space-time Extrapolationmentioning

confidence: 99%

“…The motion vector is computed by the Tracking Radar Echoes by Correlation (TREC; Rinehart and Garvey 1978) with the fractional motion vector technique of Otsuka et al (2016). The precipitation field is advected by the fifth-order weighted essentially nonoscillatory scheme (WENO; Liu et al 1994), and the motion vector field is advected by the first-order upwind scheme.…”

Section: A Space-time Extrapolationmentioning

confidence: 99%

Nowcasting with Data Assimilation: A Case of Global Satellite Mapping of Precipitation

Otsuka

Kotsuki

Miyoshi

2016

Weather and Forecasting

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Space-time extrapolation is a key technique in precipitation nowcasting. Motions of patterns are estimated using two or more consecutive images, and the patterns are extrapolated in space and time to obtain their future patterns. Applying space-time extrapolation to satellite-based global precipitation data will provide valuable information for regions where ground-based precipitation nowcasts are not available. However, this technique is sensitive to the accuracy of the motion vectors, and over the past few decades, previous studies have investigated methods for obtaining reliable motion vectors such as variational techniques. In this paper, an alternative approach applying data assimilation to precipitation nowcasting is proposed. A prototype extrapolation system is implemented with the local ensemble transform Kalman filter and is tested with the Japan Aerospace Exploration Agency's Global Satellite Mapping of Precipitation (GSMaP) product. Data assimilation successfully improved the global precipitation nowcasting with the real-case GSMaP data.

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“…At present, the extrapolation forecast based on radar echoes is the mainstay of precipitation nowcast [3,4], more accurate and efficient predicted radar echos are crucial for improving the accuracy of short-term precipitation nowcast. The purpose of the radar echo extrapolation is to predict the future position and intensity of the radar echo based on the current radar observations [5]. The key to radar echo extrapolation is to obtain a reliable extrapolated echo image.…”

Section: Introductionmentioning

confidence: 99%

“…The essence of radar echo extrapolation is based on the current and historical moments of radar echo images to predict next, unseen one. Existing methods for radar echoes extrapolation can roughly be categorized into two classes, centroid tracking methods and TREC (Tracking Radar Echoes by Correlation) methods [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks Applied on Weather Radar Echo Extrapolation

Shi¹,

Qian²,

Gu³

et al. 2018

dtcse

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Extrapolation technique of weather radar echo possesses a widely application prospects in short-term nowcast. The traditional methods of radar echo extrapolation are difficult to obtain long limitation period and lacking in utilization rate of radar data. To solve this problem, this paper proposes a method of weather radar echo extrapolation based on convolutional neural networks (CNNs). In order to adapt the strong correlation between weather radar echo images of contiguous time, on the basis of traditional convolutional neural networks, this method present a new CNNs model, namely, Recurrent Dynamic Convolutional Neural Networks (RDCNN). RDCNN consists of recurrent dynamic sub-network and probability prediction layer, and constructs a cyclic structure in the convolution layer, which improve the ability of RDCNN to process time-related images. In the experiments of radar data from Nanjing, Hangzhuo and Xiamen, compared with traditional methods, this method has achieved higher accurate of extrapolation and extended the limitation period effectively, which meets the requirement for application.

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Precipitation Nowcasting with Three-Dimensional Space–Time Extrapolation of Dense and Frequent Phased-Array Weather Radar Observations

Cited by 49 publications

References 14 publications

Predictability of meso‐γ‐scale, localized, extreme heavy rainfall during the warm season in Japan using high‐resolution precipitation nowcasts

Predictability of meso‐γ‐scale, localized, extreme heavy rainfall during the warm season in Japan using high‐resolution precipitation nowcasts

Nowcasting with Data Assimilation: A Case of Global Satellite Mapping of Precipitation

Convolutional Neural Networks Applied on Weather Radar Echo Extrapolation

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