Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China

Gou, Yabin; Chen, Haonan; Zheng, Jiafeng

doi:10.3390/rs11202335

Cited by 9 publications

(8 citation statements)

References 42 publications

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“…Furthermore, the type of the radar and settings have an impact. It is known that polarimetric radars provide more options and advantages for QPE than single-polarization radar (e.g., [3,23]).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, modelling of spatial representation of precipitation may lead to the necessity of including not only input feature vectors, but 2D input information. In that case, it is assumed that Convolutional Neural Networks are the most suitable model class, as they are able to respect for neighbourhood dependencies within the input feature maps (e.g., [3,14,[17][18][19]).…”

Section: Discussionmentioning

confidence: 99%

“…Since its discovery, the empirical Z-R relation has been continuously improved. After the development of dual-polarization radars, different types of hydrometeors could be distinguished and hydrometeor-based Z-R relationships were estimated, leading to a more sophisticated QPE [2,3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

Vogl

Laux

Bialas³

et al. 2022

Water

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Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

Vogl

Laux

Bialas³

et al. 2022

Water

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“…Recent update of MRMS incorporated the specific attenuation (AH) and KDP to enhance the ZH-based algorithm (Wang et al, 2019) and such an update can benefit from (i) the insensitivity of AH to raindrop size distribution (DSD) variability ( Ryzhkov et al, 2014); (ii) KDP is a better indicator of rain rate and liquid water content (LWC, g‧m -3 ) than ZH since KDP is more tightly connected to the precipitation particle size distribution; (iii) R(KDP) and R(AH) inherit the immunity of ФDP to miscalibration, attenuation, partial beam blockage, and wet radome effects (Park et al, 2005;Ryzhkov et al 2014), which is hard to address when using ZH for QPE, especially at higher frequencies such as C-and X-bands (Park et al, 2005;Matrosov.2010;Frasier et al, 2013). Multi-parameter radar QPE algorithms further integrates ZDR with ZH, KDP or AH to infer more information about raindrop shape, such as the double-measurement algorithm R(ZH, ZDR), R(KDP, ZDR), R(AH, ZDR) and the triple-measurement radar QPE algorithm as R(ZH, ZDR, KDP) (Matrosov,2010;Gosset et al, 2010;Schneebeli andBerne, 2012, Keenan et al, 2001;Chen et al, 2017;Gou et al,2019), but these algorithms usually assume that ZDR measurements are well calibrated and attenuation-corrected (Ryzhkov et al, 2005;Bringi et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Microphysical Characteristics of Super Typhoon Lekima (2019) and Its Impacts on Polarimetric Radar Remote Sensing of Precipitation

Gou

Chen²,

Xue³

2022

Preprint

Self Cite

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Abstract. The complex precipitation microphysics associated with super typhoon Lekima (2019) and its potential impacts on the consistency of multi-source datasets and radar quantitative precipitation estimation has been disentangled using a suite of in situ and remote sensing observations around the disastrous waterlogging area near the groove windward slope (GWS) of Yan Dang Mountain and Kuo Cang Mountain, China. The dynamic microphysical processes, in which breakup overwhelms over coalescence as the main outcome of the collision of precipitation particles, noticeably changes the self-consistency between radar reflectivity (ZH), differential reflectivity (ZDR) and the specific differential phase (KDP), and their consistency with theoretical counterparts simulated with surface disdrometer measurements, which are quality-controlled in terms of fall velocity characteristics of different hydrometeors to remove wind effects, hailstones and graupels. The overwhelming breakup accounts for phenomenon that high concentration rather than size contributes more to large ZH of the storm and the large deviation of attenuation-corrected ZDR from its expected values (ẐDR). As a result, although a comparable performance of ZH-based rainfall estimates R() and KDP-based rainfall estimates R(KDP) can be achieved, R(ZH, ZDR) tends to overestimate precipitation. R(ZH, ẐDR) performs the best among all rainfall estimators and it is less sensitive to the potential microphysical processes, owning to the improved self-consistency between radar-measured ZH, ZDR and KDP and their consistency with surface counterparts.

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“…Roversi et al: CMLs for operational rainfall monitoring (Figueras i Ventura et al, 2012;Gou et al, 2019). Satellite estimates have received a renewed boost in the last decade from the full exploitation of the Global Precipitation Measurement (GPM) mission (Skofronick-Jackson et al, 2017) that has operationally released a new suite of precipitation products with a high temporal and spatial resolution (Mugnai et al, 2013;Grecu et al, 2016). Despite the undoubted potential of satellite products to provide estimates over open oceans and regions not equipped with ground instruments, their accuracy is difficult to assess at high spatial and temporal scales (Tang et al, 2020), and their latency hinders a real-time use.…”

Section: Introductionmentioning

confidence: 99%

Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy

Roversi

Alberoni²,

Fornasiero³

et al. 2020

Atmos. Meas. Tech.

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Abstract. There is a growing interest in emerging opportunistic sensors for precipitation, motivated by the need to improve its quantitative estimates at the ground. The scope of this work is to present a preliminary assessment of the accuracy of commercial microwave link (CML) retrieved rainfall rates in Northern Italy. The CML product, obtained by the open-source RAINLINK software package, is evaluated on different scales (single link, 5 km×5 km grid, river basin) against the precipitation products operationally used at Arpae-SIMC, the regional weather service of Emilia-Romagna, in Northern Italy. The results of the 15 min single-link validation with nearby rain gauges show high variability, which can be caused by the complex physiography and precipitation patterns. Known sources of errors (e.g. the attenuation caused by the wetting of the antennas or random fluctuations in the baseline) are particularly hard to mitigate in these conditions without a specific calibration, which has not been implemented. However, hourly cumulated spatially interpolated CML rainfall maps, validated with respect to the established regional gauge-based reference, show similar performance (R2 of 0.46 and coefficient of variation, CV, of 0.78) to adjusted radar-based precipitation gridded products and better performance than satellite-based ones. Performance improves when basin-scale total precipitation amounts are considered (R2 of 0.83 and CV of 0.48). Avoiding regional-specific calibration therefore does not preclude the algorithm from working but has some limitations in probability of detection (POD) and accuracy. A widespread underestimation is evident at both the grid box scale (mean error of −0.26) and the basin scale (multiplicative bias of 0.7), while the number of false alarms is generally low and becomes even lower as link coverage increases. Also taking into account delays in the availability of the data (latency of 0.33 h for CML against 1 h for the adjusted radar and 24 h for the quality-controlled rain gauges), CML appears as a valuable data source in particular from a local operational framework perspective. Finally, results show complementary strengths for CMLs and radars, encouraging joint exploitation.

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Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China

Cited by 9 publications

References 42 publications

Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

Microphysical Characteristics of Super Typhoon Lekima (2019) and Its Impacts on Polarimetric Radar Remote Sensing of Precipitation

Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy

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