Performance of a Radar Mosaic Quantitative Precipitation Estimation Algorithm Based on a New Data Quality Index for the Chinese Polarimetric Radars
Abstract
:1. Introduction
2. Materials and Methods
2.1. Polarimetric Radar Data Analysis
2.1.1. Beam Blockage Situation of Polarimetric Radars
2.1.2. Data and Quality Control Method
2.1.3. Analysis of Polarimetric Radars Data Consistency
2.2. The Key Methods in Polarimetric Radar Mosaic QPE Algorithm
2.2.1. BB Correction
2.2.2. Radar Data Quality Index and Polarimetric Radar Mosaic Algorithm
2.2.3. Polarimetric Radar Mosaic QPE Algorithm
3. Results
3.1. Performances of BB Correction
3.2. Results of the Polarimetric Radar Data Mosaic
3.3. Results of Polarimetric Radar Mosaic QPE
3.4. Sensitivity Tests about the Precipitation Types
4. Discussion
4.1. AVPD under BB
4.2. The Relationship between QPE Accuracy and RQI
5. Conclusions
- After BB correction, the values of ZH, ZDR, and KDP in BB become closer to those under BB than before. However, the BB correction performance of KDP is not as good as that of ZH and ZDR. Only the corrected ZH and ZDR are used to estimate precipitation in the BBA. Precipitation is overestimated even when using polarimetric parameters in the BBA prior to BB correction. BB correction in this new radar mosaic QPE algorithm obviously mitigates the overestimation of rainfall in the BBA.
- The new polarimetric radar mosaic QPE algorithm based on RQI can combine the different radars’ advantages to improve QPE performances in the blocked area and the area far from the radars, thereby obtaining more accurate and wider range of mosaic data and QPE products. The new algorithm also performs better than the single radar QPE algorithm in the area close to the two radars. Within 180 km of the radars, the RMSE and NE decrease by at least 5.29% and 5.59%, respectively. The near real-time statistics of evaluated indicators show that there is a near real-time improvement when the radar mosaic QPE algorithm is applied. It is important for the operational application of this new algorithm.
- The sensitivity tests with the changing percentage of stratiform and convective precipitation show that NE and NB are basically stable when this percentage changes. The new polarimetric radar mosaic QPE algorithm can perform well and stably for any type of precipitation occurred in warm seasons.
- There is good correlation between QPE accuracy and the RQI of ZH (ZDR and KDP). An effective QPE area can be defined with an RQI of ZH lager than 0.9, resulting in a small bias (NB = −2.84%) for rainfall events in this study.
Author Contributions
Funding
Conflicts of Interest
References
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Parameter Type | Setting |
---|---|
The antenna diameter (m) | 8.54 |
The antenna gain (dB) | 45.31 |
The beam width (°) | <0.98 |
The first side lobe (dB) | <−30 |
The wave length (cm) | 10.3 |
The operating mode | simultaneous horizontal and |
vertical transmission and reception | |
The minimum detectable power (dBm) | −117.8 |
The volume scan mode | VCP21 (9 tilts) |
The range resolution (km) | 0.25 |
# | Date (UTC) | Total Time (h) | No. of Valued Gauges | Mean Gauge Accumulation (mm) | Max Gauge Accumulation (mm) | Precipitation Type |
---|---|---|---|---|---|---|
1 | 6 May 2016 | 12 | 730 | 19.04 | 118.5 | squall line |
2 | 9–10 May 2016 | 34 | 905 | 34.68 | 222.8 | convective |
3 | 15 May 2016 | 10 | 963 | 14.19 | 67.1 | squall line |
4 | 19–21 May 2016 | 43 | 1001 | 55.77 | 416.6 | stratocumulus |
5 | 27–28 May 2016 | 24 | 991 | 30.48 | 177.2 | squall line |
6 | 4–5 June 2016 | 24 | 935 | 29.20 | 109.4 | stratocumulus |
7 | 9 June 2016 | 8 | 269 | 9.56 | 80.2 | stratocumulus |
8 | 11–14 June 2016 | 86 | 1007 | 44.57 | 211.4 | stratocumulus |
9 | 15 June 2016 | 6 | 719 | 11.99 | 79 | squall line |
Method | Based on Raw Data in BB | Based on Corrected Data in BB | ||||
---|---|---|---|---|---|---|
CC | NE (%) | NB (%) | CC | NE (%) | NB (%) | |
R1(ZH) | 0.39 | 41.66 | 37.55 | 0.80 | 11.79 | −1.24 |
R2(ZH) | 0.39 | 40.42 | 36.42 | 0.80 | 11.50 | −1.22 |
R1(KDP) | 0.15 | 100.2 | 73.30 | 0.26 | 60.28 | 16.96 |
R2(KDP) | 0.15 | 97.52 | 71.14 | 0.26 | 59.02 | 16.41 |
R(ZH,ZDR) | 0.52 | 38.38 | 31.06 | 0.87 | 13.81 | 1.53 |
R(KDP,ZDR) | 0.16 | 93.39 | 66.61 | 0.26 | 59.30 | 16.17 |
Method | CC | RMSE (mm) | NE (%) | NB (%) |
---|---|---|---|---|
Radar mosaic QPE | 0.86 | 3.76 | 39.72 | −5.89 |
Guangzhou Radar QPE | 0.85 | 3.97 | 42.07 | −2.06 |
Yangjiang Radar QPE | 0.65 | 5.88 | 64.26 | −29.30 |
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Zhang, Y.; Liu, L.; Wen, H. Performance of a Radar Mosaic Quantitative Precipitation Estimation Algorithm Based on a New Data Quality Index for the Chinese Polarimetric Radars. Remote Sens. 2020, 12, 3557. https://doi.org/10.3390/rs12213557
Zhang Y, Liu L, Wen H. Performance of a Radar Mosaic Quantitative Precipitation Estimation Algorithm Based on a New Data Quality Index for the Chinese Polarimetric Radars. Remote Sensing. 2020; 12(21):3557. https://doi.org/10.3390/rs12213557
Chicago/Turabian StyleZhang, Yang, Liping Liu, and Hao Wen. 2020. "Performance of a Radar Mosaic Quantitative Precipitation Estimation Algorithm Based on a New Data Quality Index for the Chinese Polarimetric Radars" Remote Sensing 12, no. 21: 3557. https://doi.org/10.3390/rs12213557