Comparison of Rain Gauge Network and Weather Radar Data: Case Study in Angra dos Reis, Brazil
Abstract
:1. Introduction
2. Materials and Methods
2.1. Case Study Area
2.2. Rainfall Data
2.2.1. Rain Gauge Data
2.2.2. Radar Data
- Product 1: , Marshall–Palmer
- 2
- Product 2: , Morales
- 3
- Product 3: Pluv
- 4
- Product 4: Marshall—Palmer
- 5
- Product 5: MoralesProduct 5 was obtained through the - relationship proposed by Morales Rodriguez [68] (similar to Product 2, with parameters = 378 and = 1.34 in Equation (1)) for low rainfall intensity () and the relationship for higher rainfall intensity (). Similar to Product 4, the algorithm used by Ryzhkov et al. [53] and Wang et al. [61] was also used in this study.
- 6
- Product 6: PluvProduct 6 was also obtained through the - relationship for low rainfall intensity () and the relationship for higher rainfall intensity (). The - relationships were those used in Product 3. Similar to Product 4, the algorithm used by Ryzhkov et al. [53] and Wang et al. [61] was also used in this study.
2.2.3. Rainfall Events Studied
2.3. Statistical Methods
- The Root-Mean-Square Error between actual and predicted time series:
- The Pearson correction coefficient () estimates the strength and direction of the linear relationship between time series:
- The Nash–Sutcliffe efficiency measures how well the outputs of a model reproduce observations against a model that uses only the average of the observed data:
- The mean absolute error between actual and predicted time series:
3. Results
3.1. Rain Gauge Data vs. Estimated Rainfall Radar Dara from Reflectivity
3.2. Rain Gauge Data vs. Estimated Rainfall Radar Data from Reflectivity
4. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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ID | Station Name | Lat | Lon |
---|---|---|---|
P1 | Angra dos Reis | −22.9785 | −44.2958 |
P2 | Areal | −22.9820 | −44.2920 |
P3 | Ariró | −22.9020 | −44.3320 |
P4 | BNH | −22.9920 | −44.2410 |
P5 | Bracuí | −22.9330 | −44.3870 |
P6 | Camorim | −22.9980 | −44.2640 |
P7 | Camorim Pequeno | −23.0050 | −44.2790 |
P8 | Enseada | −22.9870 | −44.3200 |
P9 | Frade | −22.9660 | −44.4400 |
P10 | Itanema | −22.9230 | −44.3590 |
P11 | Manbucaba | −22.9510 | −44.5650 |
P12 | Mombaça | −23.0170 | −44.2910 |
P13 | Monsuaba | −23.0100 | −44.2220 |
P14 | Monsuaba 2 | −22.9870 | −44.2180 |
P15 | Parque do Belém | −22.9604 | −44.2941 |
P16 | Parque Perequê | −23.0140 | −44.5330 |
P17 | Ponta Leste | −23.0520 | −44.2430 |
P18 | Pontal | −22.9480 | −44.3290 |
P19 | Portogalo | −23.0370 | −44.1950 |
P20 | Praia Brava | −23.0050 | −44.4800 |
P21 | Praia da Chacara | −23.0000 | −44.3050 |
P22 | Praia das Goiabas | −23.0240 | −44.5120 |
P23 | Praia de Araçatiba | −23.1550 | −44.3270 |
P24 | Praia de Bananal | −23.1090 | −44.2480 |
P25 | Praia de Garatucaia | −23.0370 | −44.1770 |
P26 | Praia Sitio Forte | −23.1370 | −44.2820 |
P27 | São Bento | −23.0120 | −44.3220 |
P28 | Serra d’Água | −22.8890 | −44.2780 |
P29 | Vila do Abraão | −23.1390 | −44.1690 |
P30 | Vila Velha | −23.0240 | −44.3490 |
Parameter | Dual-Polarization S-Band Doppler Radar |
---|---|
Transmitter | 2.8 GHz |
Pulse Repetition Frequency (PRF) | 600 Hz |
Pulse width | 1μsec |
Pulse Repetition Time (PRT) | 1.67 ms |
Peak power | 750 kW |
Antenna gain, horizontal and vertical | 45 dB |
Antenna aperture | 8.5 |
Beam width horizontal and vertical | 1° |
Polarimetric mode | STSR ¹ |
Nyquist Velocity | 48.195 m/s |
Number of samples used to compute moments | 60 |
Radar Receiver Bandwidth | 1 MHz |
Radar Transmit Power Horizontal and Vertical Channel | 800 watts |
Scan mode | Plan Position Indicator (PPI) |
Radial range | 250 km |
Radar fields 2 | UH, UV, DBZH, DBZV, ZDR, RHOHV, PHIDP, NCPH, NCPV, SNRHC, SNRVC, VELH, VELV, WIDTHH, WIDTHV, CCORH, CCORV |
Event ID | Event Duration | Number of Rain Gauge Stations | Rain Gauges’ Temporal Resolution | Radar’s Temporal Resolution and Number of Time Steps |
---|---|---|---|---|
Event 1 | 14 December 2016 (00:00:00 UTC-3)–18 December 2016 (23:50:00 UTC-3) | 29 1 | 10 min | 10 min (720) |
Event 2 | 21 January 2017 (00:00:00 UTC-3)–23 January 2017 (23:50:00 UTC-3) | 27 2 | 10 min | 10 min (227) |
Event 3 | 13 March 2017 (00:00:00 UTC-3)–18 March 2017 (23:50:00 UTC-3) | 27 3 | 10 min | 10 min (720) |
Event 4 | 12 December 2017 (00:00:00 UTC-3)–13 December 2017 (23:55:00 UTC-3) | 28 4 | 10 min | 5 min (275) |
Event 5 | 3 July 2018 (00:00:00 UTC-3)–4 July 2018 (23:55:00 UTC-3) | 22 5 | 10 min | 5 min (201) |
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Silva, E.J.R.d.; Alves, C.N.; Campos, P.C.d.O.; Oliveira, R.A.A.C.e.; Marques, M.E.S.; Amorim, J.C.C.; Paz, I. Comparison of Rain Gauge Network and Weather Radar Data: Case Study in Angra dos Reis, Brazil. Water 2022, 14, 3944. https://doi.org/10.3390/w14233944
Silva EJRd, Alves CN, Campos PCdO, Oliveira RAACe, Marques MES, Amorim JCC, Paz I. Comparison of Rain Gauge Network and Weather Radar Data: Case Study in Angra dos Reis, Brazil. Water. 2022; 14(23):3944. https://doi.org/10.3390/w14233944
Chicago/Turabian StyleSilva, Elton John Robaina da, Camila Nascimento Alves, Priscila Celebrini de Oliveira Campos, Raquel Aparecida Abrahão Costa e Oliveira, Maria Esther Soares Marques, José Carlos Cesar Amorim, and Igor Paz. 2022. "Comparison of Rain Gauge Network and Weather Radar Data: Case Study in Angra dos Reis, Brazil" Water 14, no. 23: 3944. https://doi.org/10.3390/w14233944