ST-CORA with KDE storm segmentation

Spatiotemporal Object-based Rainfall Analysis

• 3D and 4D object-based storm analysis
• 4D analysis using a Multivariate kernel density estimation (KDE)
• multicore analysis

Dependencies

• numpy==1.16.2
• pandas==0.25.0
• scikit-image==0.14.2
• statsmodels==0.10.1
• pickleshare==0.7.5
• datetime == 4.0.1
• skimage == 0.17.2
• scipy
• cc3d (https://github.com/seung-lab/connected-components-3d)
• matplotlib
• joblib
• multiprocessing

@author: M. Laverde-Barajas @email: [email protected] [email protected]

@company SERVIR- MEKONG program ; IHE Delft Institute for Water Education; Delft University of Technology

Paramaters

• T = # wet values

• T2 = # delineation

• Minsize = # noise value

• kernel = 1= kernel segmentation 4D 0= 3D

• Psize = # Min Object size

• pixel_value = # resolution

• StartTime = datetime(YYY, M, D, h, 0, 0) # (GMT)

• EndTime = datetime(YYY, M, D, h, 0, 0)

• MATRIX = 3D rainfall matrix

• boundary = [Xmin_lbm,Xmax_lbm,Ymin_lbm,Ymax_lbm]

REFERENCES

ST-CORA Laverde-Barajas, M., Corzo, G., Bhattacharya, B., Uijlenhoet, R., & Solomatine, D. P. (2019). Spatiotemporal analysis of extreme rainfall events using an object-based approach. In Spatiotemporal Analysis of Extreme Hydrological Events (pp. 95-112). Elsevier.

ST-CORA with Multivariate kernel density estimation (KDE) Laverde-Barajas, M., Corzo, G. A., Poortinga, A., Chishtie, F., Meechaiya, C., Jayasinghe, S., ... & Solomatine, D. P. (2020a). St-corabico: A spatiotemporal object-based bias correction method for storm prediction detected by satellite. Remote Sensing, 12(21), 3538.