Radars, Hydrology and Uncertainty › polopoly_fs › 1.711855! › file › ...2017/05/30 ·...
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Radars, Hydrology and Uncertainty Francesca Cecinati University of Bristol, Department of Civil Engineering [email protected] Supervisor: Miguel A. Rico-Ramirez
Transcript of Radars, Hydrology and Uncertainty › polopoly_fs › 1.711855! › file › ...2017/05/30 ·...
Radars, Hydrology and Uncertainty
Francesca Cecinati
University of Bristol, Department of Civil Engineering
Supervisor: Miguel A. Rico-Ramirez
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)
Weather radars from the beginning
Royal Air Force Radar, 1939-1945
Hurricane Abby approaching the
coast of British Honduras, 1960
• 1940: radars first used for military purpose
… 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
The source of this material is the COMET® Website at http://meted.ucar.edu/ 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). ©1997-2015 University Corporation for Atmospheric Research. All Rights Reserved.
S-band 2 – 4 GHz
C-band 4 – 8 GHz
X-band 8 – 12 GHz
5 minutes 1 kilometre
Radar Errors a
b
c d e
f
g
h i l
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)
• Evaporation (i)
• Orographic lifting (l)
Many of these errors can be partially corrected, but a residual uncertainty remains
mm
/h
dB
Clutter
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
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
Example: Air Temperature
Ordinary Kriging Kriging with External Drift (Elevation)
Rainfall estimation using a non-stationary geostatistical model and uncertain measurements
F. Cecinati*1, A. Wadoux2, M. A. Rico-Ramirez1 , G.B.M. Heuvelink2
1 University of Bristol, Department of Civil Engineering 2 Wageningen University, Environmental Sciences
Case study presented at: Weather Radar and Hydrology Symposium (WRaH)
Seoul, 12th April 2017
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 non-stationary variance
Image credit: Australian Bureau of Meteorology
mm
/day
Case Study
UK Environment Office provided rain gauge data UK Met Office provided radar data
Event Start End Duration
(h) Mean
(mm/h) Max
(mm/h) Max Acc.
(mm) Type
1 06/01/2016
23:00 07/01/2016
17:00 19 2.2 8 31
Frontal with orographic enhancement (Desmond storm)
2 27/03/2016
23:00 29/03/2016
11:00 13 2.0 16 65 Frontal
3 07/06/2016
10:00 08/06/2016
00:00 15 1.5 50 46
Highly convective (caused flash floods)
4 29/07/2016
02:00 29/07/2016
22:00 21 0.5 30 41 Frontal
5 13/09/2016
12:00 13/09/2016
22:00 11 3.0 3 37 Frontal with orographic enhancement
KED with non-stationary variance
met
ers
elevation
met
ers
distance from the radar
dB
clutter
%
beam blockage
mm
/h
rain intensity
mm
/h
average error
𝑃𝑟𝑜𝑐𝑒𝑠𝑠 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 = 𝑎1 ∙ 𝑥1 + 𝑎2 ∙ 𝑥2 + ⋯ + 𝑏
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)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Beamblockage
Clutter DEM Distance Average Error RadarIntensity
Linear coefficients
event1 event2 event3 event4 event5
Selection of covariates Improve estimation reducing parameters: Which covariates are more important?
6 covariates 4 covariates 2 covariates
Results
mm
/h
mm
/h
mm
2/h
2
mm
2/h
2
Quantitative Evaluation
• Model based (likelihood)
• Deterministic validation
– Bias
– RMSE
– Other
• Probabilistic validation
Results: estimation skills
Akaike information criterion (AIC) = measure of relative quality of statistical models for a given set of data
Results: deterministic validation
0
0.2
0.4
0.6
0.8
1
Event1 Event2 Event3 Event4 Event5
Hanssen-Kuiper Skill Score [ - ]
Static Non-stat6 Non-stat4 Non-stat2
-0.15
-0.1
-0.05
0
0.05
0.1
Event1 Event2 Event3 Event4 Event5
Bias [mm/h]
Static Non-stat6 Non-stat4 Non-stat2
0
0.1
0.2
0.3
0.4
0.5
Event1 Event2 Event3 Event4 Event5
Mean Root Transformed Error [mm/h]
Static Non-stat6 Non-stat4 Non-stat2
0
0.5
1
1.5
2
Event1 Event2 Event3 Event4 Event5
Root Mean Square Error [mm/h]
Static Non-stat6 Non-stat4 Non-stat2
Results: probabilistic validation The set of observation percentiles should be
independent and uniformly distributes
-0.1
0
0.1
0.2
0.3
0.4
Event1 Event2 Event3 Event4 Event5
Kendall Tau Test of independence
Static Non-stat6 Non-stat4 Non-stat2
Stationary
Non-stationary 2
Non-stationary 4
Non-stationary 6
0
0.2
0.4
0.6
0.8
1
Event1 Event2 Event3 Event4 Event5
R2 Uniformity Test
Static Non-stat6 Non-stat4 Non-stat2
Conclusions
• More representative time intervals (3 months)
• Algorithm improvement
• Better optimization methods and validation techniques
• …
• Planned to submit journal paper by the end of July
Work in progress
• The method shows potential, but needs some improvements
• Balance between more information and parameter identifiability
References: Brus D.J., Heuvelink G.B.M. 2007. Optimization of sample patterns for universal kriging of environmental variables. Geoderma
2007;138(1):86–95
Laio, F., Tamea, S., 2006. Verification tools for probabilistic forecasts of continuous hydrological variables. Hydrol. Earth Syst. Sci. Discuss. 3, 2145–2173.
Mazzetti, C., Todini, E., 2009. Combining Weather Radar and Raingauge Data for Hydrologic Applications, in: Flood Risk Management: Research and Practice. Taylor & Francis Group, London.
Met Office, 2012. 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 non-stationary geostatistical model. Under review. Advances in Water Resources
Thank you!!!
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 607000. 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.
Acknowledgements:
