Articles | Volume 28, issue 20
https://doi.org/10.5194/hess-28-4539-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-4539-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Oct 2024
Research article |
| 23 Oct 2024
| 23 Oct 2024
Assessing rainfall radar errors with an inverse stochastic modelling framework
Amy C. Green
CORRESPONDING AUTHOR
School of Engineering, Newcastle University, Cassie Building, Newcastle upon Tyne, Tyne and Wear, NE1 7RU, United Kingdom
Chris Kilsby
School of Engineering, Newcastle University, Cassie Building, Newcastle upon Tyne, Tyne and Wear, NE1 7RU, United Kingdom
András Bárdossy
Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, 70569 Stuttgart, Germany
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Cited articles
AghaKouchak, A., Bárdossy, A., and Habib, E.: Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula, Adv. Water Resour., 33, 624–634, https://doi.org/10.1016/j.advwatres.2010.02.010, 2010a. a
AghaKouchak, A., Habib, E., and Bárdossy, A.: A comparison of three remotely sensed rainfall ensemble generators, Atmos. Res., 98, 387–399, https://doi.org/10.1016/j.atmosres.2010.07.016, 2010b. a
Atlas, D. and Banks, H. C.: The Interpretation of Microwave Reflections From Rainfall, J. Meteorol., 8, 271–282, https://doi.org/10.1175/1520-0469(1951)008<0271:tiomrf>2.0.co;2, 1951. a
Battan, L. J. and Theiss, J. B.: Wind Gradients and Variance of Doppler Spectra in Showers Viewed Horizontally, J. Appl. Meteorol., 12, 688–693, https://doi.org/10.1175/1520-0450(1973)012<0688:wgavod>2.0.co;2, 1973. a
Berne, A. and Uijlenhoet, R.: Quantitative analysis of X-band weather radar attenuation correction accuracy, Nat. Hazards Earth Syst. Sci., 6, 419–425, https://doi.org/10.5194/nhess-6-419-2006, 2006. a
Berne, A., Delrieu, G., Creutin, J. D., and Obled, C.: Temporal and spatial resolution of rainfall measurements required for urban hydrology, J. Hydrol., 299, 166–179, https://doi.org/10.1016/j.jhydrol.2004.08.002, 2004. a
Berne, A., Delrieu, G., and Andrieu, H.: Estimating the vertical structure of intense Mediterranean precipitation using two X-band weather radar systems, J. Atmos. Ocean. Tech., 22, 1656–1675, https://doi.org/10.1175/JTECH1802.1, 2005. a
Cecinati, F., Rico-Ramirez, M. A., Heuvelink, G. B., and Han, D.: Representing radar rainfall uncertainty with ensembles based on a time-variant geostatistical error modelling approach, J. Hydrol., 548, 391–405, https://doi.org/10.1016/j.jhydrol.2017.02.053, 2017. a
Ciach, G. J. and Gebremichael, M.: Empirical Distribution of Conditional Errors in Radar Rainfall Products, Geophys. Res. Lett., 47, 1–8, https://doi.org/10.1029/2020GL090237, 2020. a
Ciach, G. J., Krajewski, W. F., and Villarini, G.: Product-error-driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data, J. Hydrometeorol., 8, 1325–1347, https://doi.org/10.1175/2007JHM814.1, 2007. a
Crane, R. K.: Automatic Cell Detection and Tracking, IEEE T. Geosci. Elect., 17, 250–262, https://doi.org/10.1109/TGE.1979.294654, 1979. a
De Vos, L., Leijnse, H., Overeem, A., and Uijlenhoet, R.: The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam, Hydrol. Earth Syst. Sci., 21, 765–777, https://doi.org/10.5194/hess-21-765-2017, 2017. a
Gabella, M. and Notarpietro, R.: ERAD 2002 Ground clutter characterization and elimination in mountainous terrain, Proceedings of ERAD, 305–311, https://www.copernicus.org/erad/online/erad-305.pdf (last access: 16 October 2024), 2002. a
Gires, A., Onof, C., Maksimovic, C., Schertzer, D., Tchiguirinskaia, I., and Simoes, N.: Quantifying the impact of small scale unmeasured rainfall variability on urban runoff through multifractal downscaling: A case study, J. Hydrol., 442–443, 117–128, https://doi.org/10.1016/j.jhydrol.2012.04.005, 2012. a
Green, A. C.: Radar rainfall errors, Zenodo [data set], https://doi.org/10.5281/zenodo.8029394, 2023. a
Green, A. C.: RadErr, GitHub [code], https://github.com/amyycb/raderr) (last access: 16 October 2024), 2024. a
Green, A. C., Kilsby, C., and Bardossy, A.: A framework for space-time modelling of rainfall events for hydrological applications of weather radar, J. Hydrol., 630, 130630, https://doi.org/10.1016/j.jhydrol.2024.130630, 2024. a, b, c, d
Grundmann, J., Hörning, S., and Bárdossy, A.: Stochastic reconstruction of spatio-Temporal rainfall patterns by inverse hydrologic modelling, Hydrol. Earth Syst. Sci., 23, 225–237, https://doi.org/10.5194/hess-23-225-2019, 2019. a
Hall, W., Rico-Ramirez, M. A., and Krämer, S.: Classification and correction of the bright band using an operational C-band polarimetric radar, J. Hydrol., 531, 248–258, https://doi.org/10.1016/j.jhydrol.2015.06.011, 2015. a
Harrison, D., Norman, K., Darlington, T., Adams, D., Husnoo, N., and Sandford, C.: The Evolution Of The Met Office Radar Data Quality Control And Product Generation System: RADARNET, in: 37th Conference on Radar Meteorology, 14–18- September 2015, Embassy Suites Conference Center, Norman, Oklahoma, p. 14B.2, https://ams.confex.com/ams/37RADAR/webprogram/Manuscript/Paper275684/RadarnetNextGeneration_AMS_2015.pdf (last access: 16 October 2024), 2017. a
Hasan, M. M., Sharma, A., Johnson, F., Mariethoz, G., and Seed, A.: Correcting bias in radar Z–R relationships due to uncertainty in point rain gauge networks, J. Hydrol., 519, 1668–1676, https://doi.org/10.1016/j.jhydrol.2014.09.060, 2014. a
Hasan, M. M., Sharma, A., Mariethoz, G., Johnson, F., and Seed, A.: Improving radar rainfall estimation by merging point rainfall measurements within a model combination framework, Adv. Water Resour., 97, 205–218, https://doi.org/10.1016/j.advwatres.2016.09.011, 2016. a
Hooper, J. E. N. and Kippax, A. A.: The bright band – a phenomenon associated with radar echoes from falling rain, Q. J. Roy. Meteorol. Soc., 76, 125–132, https://doi.org/10.1002/qj.49707632803, 1950. a
Kitchen, M., Brown, R., and Davies, A. G.: Real‐time correction of weather radar data for the effects of bright band, range and orographic growth in widespread precipitation, Q. J. Roy. Meteorol. Soc., 120, 1231–1254, https://doi.org/10.1002/qj.49712051906, 1994. a
Krämer, S.: Quantitative Radardatenaufbereitung für die Niederschalgsvorhersage und die Siedlungsentwässerung, Leibniz Universität Hannover, Mitteilungen des Instituts für Wasserwirtschaft, Hydrologie und landwirtschaftlichen Wasserbau, Heft 92, p. 392, 2008. a
Krämer, S. and Verworn, H.-R.: Improved C-band radar data processing for real time control of urban drainage systems, in: 11th International Conferenc on Urban Drainage, 23–26- September 2018, 1–10, https://doi.org/10.2166/wst.2009.282, 2008. a
Lee, G. W., Seed, A. W., and Zawadzki, I.: Modeling the variability of drop size distributions in space and time, J. Appl. Meteorol. Clim., 46, 742–756, https://doi.org/10.1175/JAM2505.1, 2007. a
Li, Y., Zhang, G., Doviak, R. J., Lei, L., and Cao, Q.: A new approach to detect ground clutter mixed with weather signals, IEEE T. Geosci. Remote, 51, 2373–2387, https://doi.org/10.1109/TGRS.2012.2209658, 2013. a
Libertino, A., Allamano, P., Claps, P., Cremonini, R., and Laio, F.: Radar estimation of intense rainfall rates through adaptive calibration of the Z–R relation, Atmosphere, 6, 1559–1577, https://doi.org/10.3390/atmos6101559, 2015. a, b
Marshall, J. S. and Palmer, W. M. K.: The Distribution of Raindrops With Size, J. Meteorol., 5, 165–166, https://doi.org/10.1175/1520-0469(1948)005<0165:tdorws>2.0.co;2, 1948. a
Meischner, P.: Weather Radar – Principles and Advanced Applications, https://books.google.com/books/about/Weather_Radar.html?id=pnNNi9gD1CIC (last access: 16 October 2024), 2005. a
Michelson, D., Einfalt, T., Holleman, I., Gjertsen, U., Friedrich, K., Haase, G., Lindskog, M., and Jurczyk, A.: Weather radar data quality in Europe – quality control and characterization, Publications Office of the European Union Weather radar data quality in Europe – Publications Office of the EU, ISBN 92-898-0018-6, 2005. a
Nicol, J. C. and Austin, G. L.: Attenuation correction constraint for single-polarisation weather radar, Meteorol. Appl., 10, 345–354, https://doi.org/10.1017/S1350482703001051, 2003. a
Ochoa-Rodriguez, S., Wang, L. P., Gires, A., Pina, R. D., Reinoso-Rondinel, R., Bruni, G., Ichiba, A., Gaitan, S., Cristiano, E., Assel, J. V., Kroll, S., Murlà-Tuyls, D., Tisserand, B., Schertzer, D., Tchiguirinskaia, I., Onof, C., Willems, P., Veldhuis, M. C. T., Van Assel, J., Kroll, S., Murlà-Tuyls, D., Tisserand, B., Schertzer, D., Tchiguirinskaia, I., Onof, C., Willems, P., and Ten Veldhuis, M. C.: Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation, J. Hydrol., 531, 389–407, https://doi.org/10.1016/j.jhydrol.2015.05.035, 2015. a
Ośródka, K., Szturc, J., and Jurczyk, A.: Chain of data quality algorithms for 3-D single-polarization radar reflectivity (RADVOL-QC system), Meteorol. Appl., 21, 256–270, https://doi.org/10.1002/met.1323, 2014. a
Pegram, G. G. and Clothier, A. N.: Downscaling rainfields in space and time, using the String of Beads model in time series mode, Hydrol. Earth Syst. Sci., 5, 175–186, https://doi.org/10.5194/hess-5-175-2001, 2001. a
Rico-Ramirez, M. A., Liguori, S., and Schellart, A. N. A.: Quantifying radar-rainfall uncertainties in urban drainage flow modelling, J. Hydrol., 528, 17–28, https://doi.org/10.1016/j.jhydrol.2015.05.057, 2015. a
Savina, M., Schäppi, B., Molnar, P., Burlando, P., and Sevruk, B.: Comparison of a tipping-bucket and electronic weighing precipitation gage for snowfall, in: Rainfall in the Urban Context: Forecasting, Risk and Climate Change, vol. 103, Elsevier B.V., 45–51, https://doi.org/10.1016/j.atmosres.2011.06.010, 2012. a
Schleiss, M., Olsson, J., Berg, P., Niemi, T., Kokkonen, T., Thorndahl, S., Nielsen, R., Nielsen, J. E., Bozhinova, D., Pulkkinen, S., Ellerbæk Nielsen, J., Bozhinova, D., and Pulkkinen, S.: The accuracy of weather radar in heavy rain: A comparative study for Denmark, the Netherlands, Finland and Sweden, Hydrol. Earth Syst. Sci., 24, 3157–3188, https://doi.org/10.5194/hess-24-3157-2020, 2020. a
Seo, B. C., Krajewski, W. F., Quintero, F., ElSaadani, M., Goska, R., Cunha, L. K., Dolan, B., Wolff, D. B., Smith, J. A., Rutledge, S. A., and Petersen, W. A.: Comprehensive evaluation of the IFloodS Radar rainfall products for hydrologic applications, J. Hydrometeorol., 19, 1793–1813, https://doi.org/10.1175/JHM-D-18-0080.1, 2018. a
Shehu, B. and Haberlandt, U.: Relevance of merging radar and rainfall gauge data for rainfall nowcasting in urban hydrology, J. Hydrol., 594, 125931, https://doi.org/10.1016/j.jhydrol.2020.125931, 2021. a
Smith, J. A. and Krajewski, W. F.: A modeling study of rainfall rate‐reflectivity relationships, Water Resour. Res., 29, 2505–2514, https://doi.org/10.1029/93WR00962, 1993. a
Thorndahl, S., Einfalt, T., Willems, P., Nielsen, J. E., Veldhuis, M. C. T., Arnbjerg-Nielsen, K., Rasmussen, M. R., Molnar, P., Ellerbæk Nielsen, J., Ten Veldhuis, M. C., Arnbjerg-Nielsen, K., Rasmussen, M. R., and Molnar, P.: Weather radar rainfall data in urban hydrology, Hydrol. Earth Syst. Sci., 21, 1359–1380, https://doi.org/10.5194/hess-21-1359-2017, 2017. a, b
Uijlenhoet, R. and Berne, A.: Stochastic simulation experiment to assess radar rainfall retrieval uncertainties associated with attenuation and its correction, Hydrol. Earth Syst. Sci., 12, 587–601, https://doi.org/10.5194/hess-12-587-2008, 2008. a
Ventura, J. F. I. and Tabary, P.: The new French operational polarimetric radar rainfall rate product, J. Appl. Meteorol. Clim., 52, 1817–1835, https://doi.org/10.1175/jamc-d-12-0179.1, 2013. a
Villarini, G. and Krajewski, W. F.: Review of the different sources of uncertainty in single polarization radar-based estimates of rainfall, Surv. Geophys., 31, 107–129, https://doi.org/10.1007/s10712-009-9079-x, 2010. a, b
Villarini, G., Seo, B.-C., Serinaldi, F., and Krajewski, W. F.: Spatial and temporal modeling of radar rainfall uncertainties, Atmos. Res., 135, 91–101, 2014. a
Yan, J., Li, F., Bárdossy, A., and Tao, T.: Conditional simulation of spatial rainfall fields using random mixing: A study that implements full control over the stochastic process, Hydrol. Earth Syst.Sci., 25, 3819–3835, https://doi.org/10.5194/hess-25-3819-2021, 2021. a
Short summary
Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject...
