Radars, Hydrology and Uncertainty

Transcription

1 Radars, Hydrology and Uncertainty Francesca Cecinati University of Bristol, Department of Civil Engineering Supervisor: Miguel A. Rico-Ramirez

2 Research objectives Study radar rainfall uncertainty Optimally merge radar and rain gauge rainfall Model the residual uncertainty Understand the impact of rainfall uncertainty in Integrated Catchment Models (ICMs)

3 Weather radars from the beginning 1940: radars first used for military purpose Royal Air Force Radar, but they detected noise and patches of echoes it was precipitation 1944: first weather radar networks in Panama, and then in India 1950 s: spread of weather radar operational use for meteorology 1961: first operational weather doppler radar 1983: first operational polarimetric weather radar 2017: Met Office will finish the update of the UK network to doppler polarimetric radars Hurricane Abby approaching the coast of British Honduras, 1960

4 S-band C-band X-band 2 4 GHz 4 8 GHz 8 12 GHz 5 minutes 1 kilometre The source of this material is the COMET Website at of the University Corporation for Atmospheric Research (UCAR), sponsored in part through cooperative agreement(s) with the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce (DOC) University Corporation for Atmospheric Research. All Rights Reserved.

5 db mm/h Radar Errors Some of the error sources are: Attenuation (a) Shielding and partial beam blockage (b/c) Ground clutter (d) Anomalous propagation (g) Different Z-R relationships for different types of precipitation (h) Beam overshooting (e) Bright band and vertical reflectivity profiles (f) Clutter Evaporation (i) Orographic lifting (l) d f g h e i Many of these errors can be partially corrected, but a residual uncertainty remains a l c b

6 Merging radar - rain gauges Radars offer areal high-resolution estimates x Radars are not accurate enough Rain gauges are usually more accurate x Rain gauges are available only in points Radar Merged rainfall estimate

7 Kriging with External Drift (KED) One of the best performing and most efficient methods Estimate based on kriging interpolation of rain gauges Process mean = a radar + b Kriging Variance allows to calculate uncertainty Ordinary Kriging Example: Air Temperature Kriging with External Drift (Elevation)

8 Case study presented at: Weather Radar and Hydrology Symposium (WRaH) Seoul, 12 th April 2017 Rainfall estimation using a non-stationary geostatistical model and uncertain measurements F. Cecinati* 1, A. Wadoux 2, M. A. Rico-Ramirez 1, G.B.M. Heuvelink 2 1 University of Bristol, Department of Civil Engineering 2 Wageningen University, Environmental Sciences

9 mm/day Radar Errors in KED Radar is used as a trend: Process mean = a radar + b Spatially uniform radar errors are not influent In reality radar errors are spatially variant KED with nonstationary variance Image credit: Australian Bureau of Meteorology

10 Case Study Event Start End /01/ /01/ :00 17:00 27/03/ /03/ :00 11:00 07/06/ /06/ :00 00:00 29/07/ /07/ :00 22:00 13/09/ /09/ :00 22:00 Duration (h) Mean (mm/h) Max (mm/h) Max Acc. (mm) Type Frontal with orographic enhancement (Desmond storm) Frontal Highly convective (caused flash floods) Frontal Frontal with orographic enhancement UK Environment Office provided rain gauge data UK Met Office provided radar data

11 % mm/h mm/h meters meters db KED with non-stationary variance Process standard deviation = a 1 x 1 + a 2 x b elevation distance from the radar clutter beam blockage rain intensity average error

12 Maximum Likelihood Geo-statistical model (2 parameters) Mean = linear function of the radar (2 parameters) Standard deviation = linear function of the n covariates (n+1 parameters)

13 Selection of covariates Improve estimation reducing parameters: Which covariates are more important? covariates Linear coefficients 0 Beam blockage Clutter DEM Distance Average Error Radar Intensity event1 event2 event3 event4 event5

14 mm/h mm/h Results mm 2 /h 2 mm 2 /h 2

15 Quantitative Evaluation Model based (likelihood) Deterministic validation Bias RMSE Other Probabilistic validation

16 Results: estimation skills Akaike information criterion (AIC) = measure of relative quality of statistical models for a given set of data

17 Results: deterministic validation Root Mean Square Error [mm/h] Mean Root Transformed Error [mm/h] Event1 Event2 Event3 Event4 Event5 0 Event1 Event2 Event3 Event4 Event5 Static Non-stat6 Non-stat4 Non-stat2 Static Non-stat6 Non-stat4 Non-stat2 Bias [mm/h] Hanssen-Kuiper Skill Score [ - ] Event1 Event2 Event3 Event4 Event Event1 Event2 Event3 Event4 Event5 Static Non-stat6 Non-stat4 Non-stat2 Static Non-stat6 Non-stat4 Non-stat2

18 Results: probabilistic validation The set of observation percentiles should be independent and uniformly distributes 0.4 Kendall Tau Test of independence Event1 Event2 Event3 Event4 Event5 Static Non-stat6 Non-stat4 Non-stat R 2 Uniformity Test Stationary Non-stationary 2 0 Non-stationary 4 Event1 Event2 Event3 Event4 Event5 Non-stationary 6 Static Non-stat6 Non-stat4 Non-stat2

19 Conclusions The method shows potential, but needs some improvements Balance between more information and parameter identifiability Work in progress More representative time intervals (3 months) Algorithm improvement Better optimization methods and validation techniques Planned to submit journal paper by the end of July

20 Thank you!!! Acknowledgements: This work was carried out in the framework of the Marie Skłodowska Curie Initial Training Network QUICS. The QUICS project has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration under grant agreement no The authors would like to thank the UK Met Office and the Environment Agency, which provided the radar rainfall data and the rain gauge data to develop this study, and the British Atmospheric Data Centre for providing access to the datasets. References: Brus D.J., Heuvelink G.B.M Optimization of sample patterns for universal kriging of environmental variables. Geoderma 2007;138(1):86 95 Laio, F., Tamea, S., Verification tools for probabilistic forecasts of continuous hydrological variables. Hydrol. Earth Syst. Sci. Discuss. 3, Mazzetti, C., Todini, E., Combining Weather Radar and Raingauge Data for Hydrologic Applications, in: Flood Risk Management: Research and Practice. Taylor & Francis Group, London. Met Office, Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current). NCAS Br. Atmos. Data Cent. Wadoux, A., Brus D.J., Rico-Ramirez M.A., Heuvelink G.B.M., Sampling design optimisation for rainfall prediction using a nonstationary geostatistical model. Under review. Advances in Water Resources