A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar

Tang, Jianbo; Matyas, Corene J.

doi:10.3390/atmos9050200

Cited by 14 publications

(12 citation statements)

References 65 publications

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“…While gauge-based studies have helped to understand historical TCP variability, missing observations and the irregular distribution of rain gauges limit interpretability. Efforts to address this problem often employ satellite or radar measurements of TCP (e.g., Rodgers et al 1994;Rodgers and Pierce 1995;Rodgers et al 2001;Lonfat et al 2004;Benedetti et al 2005;Shepherd et al 2007;Jiang and Zipser 2010;Jiang and Ramirez 2013;Prat and Nelson 2013a,b;Zhu and Quiring 2017;Rios Gaona et al 2018;Tang and Matyas 2018). Using satellite measurements improves data coverage.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability of Tropical Cyclone Precipitation Using a High-Resolution, Gridded (0.25° × 0.25°) Dataset for the Eastern United States, 1948–2015

et al. 2020

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Tropical cyclones (TCs) are an important source of precipitation for much of the eastern United States. However, our understanding of the spatiotemporal variability of tropical cyclone precipitation (TCP) and the connections to large-scale atmospheric circulation is limited by irregularly distributed rain gauges and short records of satellite measurements. To address this, we developed a new gridded (0.25° × 0.25°) publicly available dataset of TCP (1948–2015; Tropical Cyclone Precipitation Dataset, or TCPDat) using TC tracks to identify TCP within an existing gridded precipitation dataset. TCPDat was used to characterize total June–November TCP and percentage contribution to total June–November precipitation. TCP totals and contributions had maxima on the Louisiana, North Carolina, and Texas coasts, substantially decreasing farther inland at rates of approximately 6.2–6.7 mm km−1. Few statistically significant trends were discovered in either TCP totals or percentage contribution. TCP is positively related to an index of the position and strength of the western flank of the North Atlantic subtropical high (NASH), with the strongest correlations concentrated in the southeastern United States. Weaker inverse correlations between TCP and El Niño–Southern Oscillation are seen throughout the study site. Ultimately, spatial variations of TCP are more closely linked to variations in the NASH flank position or strength than to the ENSO index. The TCP dataset developed in this study is an important step in understanding hurricane–climate interactions and the impacts of TCs on communities, water resources, and ecosystems in the eastern United States.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability of Tropical Cyclone Precipitation Using a High-Resolution, Gridded (0.25° × 0.25°) Dataset for the Eastern United States, 1948–2015

et al. 2020

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“…Moreover, one can construct ConvRNN models as sequence-to-sequence networks with many stacked layers (Figure 1), and use the convolutional downsampling technique in image processing to build hierarchical structures. This allows ConvRNN models to "see" and predict radar images at different spatial scales, rather than ignoring scale as TREC models [15]. It also allows flexibly extending the input length or prediction lead times, as well as utilize precipitation information from all previous steps rather than using only the current situation like traditional models [16].…”

mentioning

confidence: 99%

Computer Vision in Precipitation Nowcasting: Applying Image Quality Assessment Metrics for Training Deep Neural Networks

Tran

Song

2019

Atmosphere

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This paper presents a viewpoint from computer vision to the radar echo extrapolation task in the precipitation nowcasting domain. Inspired by the success of some convolutional recurrent neural network models in this domain, including convolutional LSTM, convolutional GRU and trajectory GRU, we designed a new sequence-to-sequence neural network structure to leverage these models in a realistic data context. In this design, we decreased the numbers of channels in high abstract recurrent layers rather than increasing them. We formulated the task as a problem of encoding five radar images and predicting 10 steps ahead at the pixel level, and found that using only the common mean squared error can misguide the training and mislead the testing. Especially, the image quality of last predictions usually degraded rapidly. As a solution, we employed some visual image quality assessment techniques including Structural Similarity (SSIM) and multi-scale SSIM to train our models. Experimental results show that our structure was more tolerant to increasing uncertainty in the data, and the use of image quality metrics can significantly reduce the blurry image issue. Moreover, we found that using SSIM was very effective and a combination of SSIM with mean squared error and mean absolute error yielded the best prediction quality.

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“…Doppler weather radar [13], also called weather radar or weather surveillance radar (WSR), is a type of radar used to locate precipitation, calculate its motion, and estimate its type (rain, snow, hail etc.). In addition to the reflectivity factor, it can also obtain radial velocity information of raindrops.…”

Section: Structure Model Of Airflow Field and Template Designmentioning

confidence: 99%

An Automatic Recognition Method for Airflow Field Structures of Convective Systems Based on Single Doppler Radar Data

Wang¹,

Gu²,

Hou³

et al. 2020

Atmosphere

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Airflow structures within convective systems are important predictors of damaging convective disasters. To automatically recognize different kinds of airflow structures (the convergence, divergence, cyclonic rotation, and anticyclonic rotation) within convective systems, an airflow structure recognition method is proposed, in this paper, based on a regular hexagonal template. On the basis of single Doppler radar data, the template is designed according to the appearance model of airflows in radial velocity maps. The proposed method is able to output types and intensities of airflow structures within convective systems. In addition, the outputs of the proposed method are integrated into a projection map of the airflow field structure types and intensities (PMAFSTI), which is developed in this work to visualize three-dimensional airflow structures within convective cells. The proposed airflow structure automatic recognition method and the PMAFSTI were tested using three typical cases. Results of the tests suggest the following: (1) At different evolution stages of the convective systems, e.g., growth, split, and dissipation, the three-dimensional distribution of the airflow fields within convective systems could be clearly observed through the PMAFSTI and (2) on the basis of recognizing the structures of the airflow field, the complex airflow field, such as a squall line, could be further divided into several small parts making the analysis of convective systems more scientific and elaborate.

show abstract

A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar

Cited by 14 publications

References 65 publications

Spatiotemporal Variability of Tropical Cyclone Precipitation Using a High-Resolution, Gridded (0.25° × 0.25°) Dataset for the Eastern United States, 1948–2015

Spatiotemporal Variability of Tropical Cyclone Precipitation Using a High-Resolution, Gridded (0.25° × 0.25°) Dataset for the Eastern United States, 1948–2015

Computer Vision in Precipitation Nowcasting: Applying Image Quality Assessment Metrics for Training Deep Neural Networks

An Automatic Recognition Method for Airflow Field Structures of Convective Systems Based on Single Doppler Radar Data

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