Mitigating Spatial Discontinuity of Multi-Radar QPE Based on GPM/KuPR

Chu, Zhaowei; Ma, Yingzhao; Zhang, Guifu; Wang, Zhenhui; Han, Jing; Kou, Leilei; Li, Nan

doi:10.3390/hydrology5030048

Cited by 12 publications

(10 citation statements)

References 48 publications

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“…The back-scattered electromagnetic waves are detected by the radar antenna and their reflectivity (Z) is registered, which can be converted to rain rate (R) by means of specific Z-R relationships for each rain type and region [34]. However, to estimate accurately precipitation amounts and their distribution (quantitative precipitation estimations; QPE), radar data calibration is necessary [35,36], especially in high mountains where obstacles (clutter), such as mountain peaks, obstruct the emitted pulses and do not leave valid information for these sites [22,31,37]. For correcting this shortcoming, different calibration methods based on robust algorithms exist, which generally also include limited information from surface rain gauges (e.g., [35,38,39]).…”

Section: Introductionmentioning

confidence: 99%

River Discharge Simulation in the High Andes of Southern Ecuador Using High-Resolution Radar Observations and Meteorological Station Data

et al. 2019

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The prediction of river discharge using hydrological models (HMs) is of utmost importance, especially in basins that provide drinking water or serve as recreation areas, to mitigate damage to civil structures and to prevent the loss of human lives. Therefore, different HMs must be tested to determine their accuracy and usefulness as early warning tools, especially for extreme precipitation events. This study simulated the river discharge in an Andean watershed, for which the distributed HM Runoff Prediction Model (RPM) and the semi-distributed HM Hydrologic Modelling System (HEC-HMS) were applied. As precipitation input data for the RPM model, high-resolution radar observations were used, whereas the HEC-HMS model used the available meteorological station data. The obtained simulations were compared to measured discharges at the outlet of the watershed. The results highlighted the advantages of distributed HM (RPM) in combination with high-resolution radar images, which estimated accurately the discharges in magnitude and time. The statistical analysis showed good to very good accordance between observed and simulated discharge for the RPM model (R2: 0.85–0.92; NSE: 0.77–0.82), whereas for the HEC-HMS model accuracies were lower (R2: 0.68–0.86; NSE: 0.26–0.78). This was not only due to the application of means values for the watershed (HEC-HMS), but also to limited rain gauge information. Generally, station network density in tropical mountain regions is poor, for which reason the high spatiotemporal precipitation variability cannot be detected. For hydrological simulation and forecasting flash floods, as well as for environmental investigations and water resource management, meteorological radars are the better choice. The greater availability of cost-effective systems at the present time also reduces implementation and maintenance costs of dense meteorological station networks.

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Section: Introductionmentioning

confidence: 99%

River Discharge Simulation in the High Andes of Southern Ecuador Using High-Resolution Radar Observations and Meteorological Station Data

et al. 2019

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“…For channels 13-15 and channels 5-7, both the biases and also the standard deviations increased with precipitation intensities. However, for channels 11 and 4, standard deviations of O−B in the moderate (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) and intense precipitation (>35 dBZ) categories were even smaller than those under light precipitation (5-20 dBZ) and precipitation-free (0-5 dBZ) conditions. It is abnormal that standard deviations decreased with precipitation intensities for channels 11 and 4, which is contradictory with the results from Kulie et al [40].…”

Section: Discussionmentioning

confidence: 86%

“…The key technologies for the combined application of radar and satellites are radar data quality control, generation of mosaic reflectivity for multiple radars, and spatial matching. In order to eliminate noise, biological echoes, clutter/anomalous propagation echoes and reflectivity biases, the radar dataset was subjected to a median filter, a fuzzy logic clutter filter (https://www.weather.gov/code88d/) and reflectivity bias correction [30,31]. The Severe Weather Nowcast System (SWAN) [32] developed by CMA was used to generate mosaic reflectivity for eight radars.…”

Section: Combined Satellite and Radar Datasetmentioning

confidence: 99%

Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

et al. 2019

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Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over land using a co-located ground-based radar network is described. This method is intended to characterize the O−B biases for the microwave humidity sounder -2 (MWHS-2) under four categories of precipitation: precipitation-free (0–5 dBZ), light precipitation (5–20 dBZ), moderate precipitation (20–35 dBZ), and intense precipitation (>35 dBZ). Additionally, O represents the observed brightness temperature (TB) of the satellite and B is the simulated TB from the model background field using the radiative transfer model. Thresholds for the brightness temperature differences between channels, as well as the order relation between the differences, exhibited a good estimation of precipitation. It is demonstrated that differences between observations and simulations were predominantly due to the cases in which radar reflectivity was above 15 dBZ. For most channels, the biases and standard deviations of O−B increased with precipitation intensity. Specifically, it is noted that for channel 11 (183.31 ± 1 GHz), the standard deviations of O−B under moderate and intense precipitation were even smaller than those under light precipitation and precipitation-free conditions. Likewise, abnormal results can also be seen for channel 4 (118.75 ± 0.3 GHz).

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“…In this study, we select eight S-band weather radars located in the East China from the China new-generation weather radar S-band A-type (CINRAD-SA), with an effective distance of ~230 km (Figure 1). The quality control of radar data includes fuzzy logic clutter filter (http://www.weather.gov/code88d/), median filter, and reflectivity bias correction [18]- [19]. The two-dimensional (2-D) composite reflectivity is generated by using the Severe Weather Automatic Nowcast System (SWAN) [20], which is developed by the China Meteorological Administration (CMA).…”

Section: Datamentioning

confidence: 99%

CDL: A Cloud Detection Algorithm Over Land for MWHS-2 Based on the Gradient Boosting Decision Tree

Liu

Yin

Chu

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper presents a stand-alone cloud detection algorithm over land (CDL) for Microwave Humidity Sounder-2 (MWHS-2), which is characterized by the first operational satellite sensor measuring 118.75 GHz. The CDL is based on the advanced machine learning (ML) algorithm Gradient Boosting Decision Tree (GBDT), which achieves the state-of-the-art performance on tabular data, with high accuracy, fast training speed, great generalization ability, and weight factor ranking of predictors (or features). Given that the new generation weather radar of China (CINRAD) provides improved cloud information with extensive temporal-spatial coverage, the observations from CINRAD are used to train the algorithm in this study. There are four groups of radiometric information employed to evaluate the CDL: all frequency ranges from MWHS-2 (all-algorithm), the humidity channels near 183.31 GHz (hum-algorithm), the temperature channels near 118.75 GHz (tem-algorithm), and the window channels at 89 and 150 GHz (win-algorithm). It is revealed that the tem-algorithm (around 118.75 GHz) has a superior performance for CDL along with the optimal values of most evaluation metrics. Although the all-algorithm uses all available frequencies, it shows inferior ability for CDL. Followed are the win-algorithm and humalgorithm, and the win-algorithm performs better. The analysis also indicates that the latitude, zenith angle, and the azimuth are the top ranking features for all four algorithms. The presented algorithm CDL can be applied in the quality control processes of assimilating microwave (MW) radiances or in the retrieval of atmospheric and surface parameters for cloud filtering.

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Mitigating Spatial Discontinuity of Multi-Radar QPE Based on GPM/KuPR

Cited by 12 publications

References 48 publications

River Discharge Simulation in the High Andes of Southern Ecuador Using High-Resolution Radar Observations and Meteorological Station Data

River Discharge Simulation in the High Andes of Southern Ecuador Using High-Resolution Radar Observations and Meteorological Station Data

Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

CDL: A Cloud Detection Algorithm Over Land for MWHS-2 Based on the Gradient Boosting Decision Tree

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