Bárdossy, A. and Hörning, S.: Process-Driven Direction-Dependent
Asymmetry: Identification and Quantification of Directional Dependence in
Spatial Fields, Math. Geosci., 49, 871–891, https://doi.org/10.1007/s11004-017-9682-1, 2017. a, b

Chilès, J. P. and Delfiner, P.: Geostatistics: Modeling Spatial Uncertainty, in: Series In Probability and Statistics, Wiley,
https://doi.org/10.1002/9781118136188, 2012. a, b

Collier, C.: The impact of wind drift on the utility of very high spatial
resolution radar data over urban areas, Phys. Chem. Earth Pt. B, 24, 889–893, https://doi.org/10.1016/S1464-1909(99)00099-4, 1999. a

Cristiano, E., ten Veldhuis, M.-C., and van de Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review, Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, 2017. a

Deutsch, C. V. and Journel, A. G.: The application of simulated annealing to stochastic reservoir modeling, SPE Adv. Technol. Ser., 2, 222–227, https://doi.org/10.2118/23565-PA, 1994. a

Deutsch, C. V. and Journel, A.: GSLIB – Geostatistical Software Library
and User's Guide, Technometrics, 40, 132–145, https://doi.org/10.2307/1270548, 1998. a

Deutsch, C. V.: Annealing techniques applied to reservoir modeling and the integration of geological and engineering (well test) data, PhD thesis, Stanford University, Stanford, CA, 1992. a

Doviak, R. J. and Zrnić, D. S.: Doppler radar and weather observations, 2nd Edn., Academic Press, San Diego, 523–545, 1993. a

Emmanuel, I., Andrieu, H., Leblois, E., and Flahaut, B.: Temporal and spatial
variability of rainfall at the urban hydrological scale, J. Hydrol., 430, 162–172, 2012. a

Fabry, F.: Radar meteorology: principles and practice, Cambridge University
Press, Cambridge, 2015. a, b, c

Gabella, M. and Notarpietro, R.: Ground clutter characterization and
elimination in mountainous terrain, in: vol. 305, Proceedings of ERAD, Delft, the Netherlands, 2002. a

Geman, S. and Geman, D.: Stochastic Relaxation, Gibbs Distributions, and the
Bayesian Restoration of Images, IEEE T. Patt. Anal. Mach. Intel., PAMI-6, 721–741, 1984. a

Guthke, P. and Bárdossy, A.: On the link between natural emergence and
manifestation of a fundamental non-Gaussian geostatistical property:
Asymmetry, Spat. Stat., 20, 1–29, https://doi.org/10.1016/j.spasta.2017.01.003, 2017. a

Hasan, M. M., Sharma, A., Johnson, F., Mariethoz, G., and Seed, A.: Merging
radar and in situ rainfall measurements: An assessment of different
combination algorithms, Water Resour. Res., 52, 8384–8398,
https://doi.org/10.1002/2015WR018441, 2016. a

Heistermann, M., Jacobi, S., and Pfaff, T.: Technical Note: An open source
library for processing weather radar data (wradlib), Hydrol. Earth Syst. Sci., 17, 863–871, https://doi.org/10.5194/hess-17-863-2013, 2013. a

Hörning, S. and Bárdossy, A.: Phase annealing for the conditional
simulation of spatial random fields, Comput. Geosci., 112, 101–111, 2018. a, b, c, d

Jacobi, S. and Heistermann, M.: Benchmarking attenuation correction procedures for six years of single-polarized C-band weather radar observations in South-West Germany, Geomatics, Nat. Hazards Risk, 7, 1785–1799, 2016. a

Journel, A. G.: Geostatistics for conditional simulation of ore bodies, Econ. Geol., 69, 673–687, 1974. a

Khintchine, A.: Korrelationstheorie der stationären stochastischen
Prozesse, Math. Annal., 109, 604–615, 1934. a

Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P.: Optimization by Simulated
Annealing, Science, 220, 671–680, 1983. a

Krämer, S. and Verworn, H.: Improved C-band radar data processing for real time control of urban drainage systems, in: Proceedings of the 11th International Conference on Urban Drainage, 31 August–5 September 2008, Edinburgh, Scotland, UK, 1–10, 2008. a

Kumar, P. and Foufoula-Georgiou, E.: Characterizing multiscale variability of
zero intermittency in spatial rainfall, J. Appl. Meteorol., 33, 1516–1525, 1994. a

Lantuéjoul, C.: Geostatistical Simulation: Models and Algorithms, Springer Science & Business Media, Berlin, 2013. a

Lauzon, D. and Marcotte, D.: Calibration of random fields by FFTMA-SA, Comput. Geosci., 127, 99–110, https://doi.org/10.1016/j.cageo.2019.03.003, 2019. a

Marcotte, D.: Fast variogram computation with FFT, Comput. Geosci., 22, 1175–1186, https://doi.org/10.1016/S0098-3004(96)00026-X, 1996. a

Martín, J. F. D. and Sierra, J. M. R.: A Comparison of Cooling Schedules for Simulated Annealing, in: Encyclopedia of Artificial Intelligence, edited by: Dopico, J. R. R., Dorado, J., and Pazos, A., 344–352, https://doi.org/10.4018/978-1-59904-849-9.ch053, 2020. a

Mendez Antonio, B., Magaña, V., Caetano, E., Da Silveira, R., and
Domínguez, R.: Analysis of daily precipitation based on weather radar
information in México City, Atmósfera, 22, 299–313, 2009. a

Nourani, Y. and Andresen, B.: A comparison of simulated annealing cooling
strategies, J. Phys. A, 31, 8373–8385, 1998. a

Ravalec, M. L., Noetinger, B., and Hu, L. Y.: The FFT Moving Average (FFT-MA)
Generator: An Efficient Numerical Method for Generating and Conditioning
Gaussian Simulations, Math. Geol., 32, 701–723, https://doi.org/10.1023/A:1007542406333, 2000. a

Wiener, N.: Generalized harmonic analysis, Acta Math., 55, 117–258, 1930. a

Wood, A. T.: When is a truncated covariance function on the line a covariance
function on the circle?, Stat. Probabil. Lett., 24, 157–164,
https://doi.org/10.1016/0167-7152(94)00162-2, 1995. a

Wood, A. T. and Chan, G.: Simulation of stationary Gaussian processes in [0, 1] d, J. Comput. Graph. Stat., 3, 409–432, 1994.
a

Yan, J.: Input data for conditional simulation of surface rainfall field (30 min accumulated rainfall) using phase annealing, Figshare, https://doi.org/10.6084/m9.figshare.11515395.v1, 2020. a

Yan, J. and Bárdossy, A.: Short time precipitation estimation using weather radar and surface observations: With rainfall displacement information integrated in a stochastic manner, J. Hydrol., 574, 672–682,
https://doi.org/10.1016/j.jhydrol.2019.04.061, 2019. a, b, c