BPS04

N. E. Bowler, C. E. Pierce, and A. W. Seed. STEPS: a probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Forecasting Research Technical Report 433, UK Met Office, Wallingford, United Kingdom, 2004.

BPS06

N. E. Bowler, C. E. Pierce, and A. W. Seed. STEPS: a probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Quarterly Journal of the Royal Meteorological Society, 132(620):2127–2155, 2006. doi:10.1256/qj.04.100.

BrockerS07

J. Bröcker and L. A. Smith. Increasing the reliability of reliability diagrams. Weather and Forecasting, 22(3):651–661, 2007. doi:10.1175/WAF993.1.

CRS04

B. Casati, G. Ross, and D. B. Stephenson. A new intensity-scale approach for the verification of spatial precipitation forecasts. Meteorological Applications, 11(2):141––154, 2004. doi:10.1017/S1350482704001239.

CP02

A. Clothier and G. Pegram. Space-time modelling of rainfall using the string of beads model: integration of radar and raingauge data. WRC Report No. 1010/1/02. Water Research Commission, Durban, South Africa, 2002.

EWW+13

E. Ebert, L. Wilson, A. Weigel, M. Mittermaier, P. Nurmi, P. Gill, M. Göber, S. Joslyn, B. Brown, T. Fowler, and A. Watkins. Progress and challenges in forecast verification. Meteorological Applications, 20(2):130–139, 2013. doi:10.1002/met.1392.

FGGB21

M. Feldmann, U. Germann, M. Gabella, and A. Berne. A characterisation of alpine mesocyclone occurrence. Weather and Climate Dynamics Discussions, pages 1–26, 2021. URL: https://wcd.copernicus.org/preprints/wcd-2021-53/, doi:10.5194/wcd-2021-53.

FSN+19

L. Foresti, I.V. Sideris, D. Nerini, L. Beusch, and U. Germann. Using a 10-year radar archive for nowcasting precipitation growth and decay: a probabilistic machine learning approach. Weather and Forecasting, 34:1547–1569, 2019. doi:10.1175/WAF-D-18-0206.1.

FW05

N. I. Fox and C. K. Wikle. A bayesian quantitative precipitation nowcast scheme. Weather and Forecasting, 20(3):264–275, 2005.

FNP+20

G. Franch, D. Nerini, M. Pendesini, L. Coviello, G. Jurman, and C. Furlanello. Precipitation nowcasting with orographic enhanced stacked generalization: improving deep learning predictions on extreme events. Atmosphere, 11(3):267, 2020. doi:10.3390/atmos11030267.

GZ02

U. Germann and I. Zawadzki. Scale-dependence of the predictability of precipitation from continental radar images. Part I: description of the methodology. Monthly Weather Review, 130(12):2859–2873, 2002. doi:10.1175/1520-0493(2002).

GZ04

U. Germann and I. Zawadzki. Scale-dependence of the predictability of precipitation from continental radar images. Part II: probability forecasts. Journal of Applied Meteorology, 43(1):74–89, 2004. doi:10.1175/1520-0450(2004).

HMG+04

A. M. Hering, C. Morel, G. Galli, P. Ambrosetti, and M. Boscacci. Nowcasting thunderstorms in the alpine region using a radar based adaptive thresholding scheme. Proceedings of ERAD Conference 2004, pages 206–211, 2004.

Her00

H. Hersbach. Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting, 15(5):559–570, 2000. doi:10.1175/1520-0434(2000).

HCLK15

Yunsung Hwang, Adam J Clark, Valliappa Lakshmanan, and Steven E Koch. Improved nowcasts by blending extrapolation and model forecasts. Weather and Forecasting, 30(5):1201–1217, 2015. doi:10.1175/WAF-D-15-0057.1.

NBS+17

D. Nerini, N. Besic, I. Sideris, U. Germann, and L. Foresti. A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform. Hydrology and Earth System Sciences, 21(6):2777–2797, 2017. doi:10.5194/hess-21-2777-2017.

PvGPO94

M. Proesmans, L. van Gool, E. Pauwels, and A. Oosterlinck. Determination of optical flow and its discontinuities using non-linear diffusion. In J.-O. Eklundh, editor, Computer Vision — ECCV '94, volume 801 of Lecture Notes in Computer Science, pages 294–304. Springer Berlin Heidelberg, 1994.

PCH18a

S. Pulkkinen, V. Chandrasekar, and A.-M. Harri. Fully spectral method for radar-based precipitation nowcasting. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(5):1369–1382, 2018.

PCH18b

S. Pulkkinen, V. Chandrasekar, and A.-M. Harri. Nowcasting of precipitation in the high-resolution Dallas-Fort Worth (DFW) urban radar remote sensing network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8):2773–2787, 2018. doi:10.1109/JSTARS.2018.2840491.

PCH19

S. Pulkkinen, V. Chandrasekar, and A.-M. Harri. Stochastic spectral method for radar-based probabilistic precipitation nowcasting. Journal of Atmospheric and Oceanic Technology, 36(6):971–985, 2019.

PCN21

S. Pulkkinen, V. Chandrasekar, and T. Niemi. Lagrangian integro-difference equation model for precipitation nowcasting. Journal of Atmospheric and Oceanic Technology, 2021. submitted.

PCvLH20

S. Pulkkinen, V. Chandrasekar, A. von Lerber, and A.-M. Harri. Nowcasting of convective rainfall using volumetric radar observations. IEEE Transactions on Geoscience and Remote Sensing, pages 1–15, 2020. doi:10.1109/TGRS.2020.2984594.

RLW+11

Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, Rachel Prudden, Amol Mandhane, Aidan Clark, Andrew Brock, Karen Simonyan, Raia Hadsell, Niall Robinson, Ellen Clancy, Alberto Arribas, and Shakir Mohamed. Skilful precipitation nowcasting using deep generative models of radar. Nature, 597(7878):672–677, 2011. doi:10.1038/s41586-021-03854-z.

RFvHP06

N. Rebora, L. Ferraris, J. von Hardenberg, and A. Provenzale. Rainfarm: rainfall downscaling by a filtered autoregressive model. Journal of Hydrometeorology, 7(4):724–738, 2006. doi:10.1175/JHM517.1.

RL08

N. M. Roberts and H. W. Lean. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Monthly Weather Review, 136(1):78–97, 2008. doi:10.1175/2007MWR2123.1.

RC11

E. Ruzanski and V. Chandrasekar. Scale filtering for improved nowcasting performance in a high-resolution X-band radar network. IEEE Transactions on Geoscience and Remote Sensing, 49(6):2296–2307, June 2011.

RCW11

E. Ruzanski, V. Chandrasekar, and Y. Wang. The CASA nowcasting system. Journal of Atmospheric and Oceanic Technology, 28(5):640–655, 2011. doi:10.1175/2011JTECHA1496.1.

See03

A. W. Seed. A dynamic and spatial scaling approach to advection forecasting. Journal of Applied Meteorology, 42(3):381–388, 2003. doi:10.1175/1520-0450(2003).

SPN13

A. W. Seed, C. E. Pierce, and K. Norman. Formulation and evaluation of a scale decomposition-based stochastic precipitation nowcast scheme. Water Resources Research, 49(10):6624–6641, 2013. doi:10.1002/wrcr.20536.

WHZ09

Heini Wernli, Christiane Hofmann, and Matthias Zimmer. Spatial forecast verification methods intercomparison project: application of the sal technique. Weather and Forecasting, 24(6):1472 – 14847, 2009.

WPHF08

Heini Wernli, Marcus Paulat, Martin Hagen, and Christoph Frei. Sal—a novel quality measure for the verification of quantitative precipitation forecasts. Monthly Weather Review, 136(11):4470 – 4487, 2008.

XWF05

K. Xu, C. K Wikle, and N. I. Fox. A kernel-based spatio-temporal dynamical model for nowcasting weather radar reflectivities. Journal of the American Statistical Association, 100(472):1133–1144, 2005.

ZR09

P. Zacharov and D. Rezacova. Using the fractions skill score to assess the relationship between an ensemble QPF spread and skill. Atmospheric Research, 94(4):684–693, 2009. doi:10.1016/j.atmosres.2009.03.004.