Spatial control of rain gauge
precipitations using radar data
(Contribution to WP1)
F Mounier, P Lassègues, A-L Gibelin, J-P Céron, J-M Veysseire
Meteo-France DCLIM / CNRM-GAME
Main topic
Construct a 2007-2010 reference estimate of spatialized
precipitation for rain gauges control and validation at fine scale.
MAIN PROBLEMS of rain gauges:
density, quality, instrument types
•Real-time Meteo-France (~1500-1800)
•volunteers (~2800)
PROPOSED SOLUTION
Use radar network in the
spatialization process of the
precipitation estimate
Radar data need
also to be qualified
Diagnostic of feasibility
Daily data over 2007-2010
•
Average of station based correlation (rain gauge / radar) data over
France  in average above 0.8
•
The Tschuprow coef. per quantile classes of rainfall intensity 
always above 0.35 that implies a strong link between rain gauge and
radar estimate data for the selected stations
Using radar data to control rain gauge precipitations is
relevant to construct a frame of reference to better
spatialize precipitations.
Methodology overview
First half to calibrate
radar data
RAIN GAUGE Precipitations
•Real-time (~1500-1800)
•volunteers (~2800)
divided into two roughly equal lots by
carrying out a totally random draw
Production of an independent
estimate of rainfall from rain gauges
of First & Second halves (except the
one controlled) using calibrated radar
data via spatialisation method
 Rainfall Estimates
Controlled rainfall observation process
Second half to
be controlled
 Observations
4 spatialization methods / 2 used
 TPS: Thin Plate Spline in a 3D space
use a smoothing coef. adjusted to minimize the RMSE and the radar
data as a third dimension to estimate rain gauge value
 KED: Kriging of rain gauge with radar oriented external drift
It is the radar data that define the trend part of the model to guide the
estimation of the primary variable (rainfall) at the rain gauge.
Have been also explored but not retained:
 Neural network
 Optimal interpolation
Rain data filtering control
 Period of study: 2007 to 2010
 Only daily results are presented
 Rain data should be above 0.6 mm
 Only radar or rain gauge data with a good quality parameter are
taken (84) into account
 Only sample with a minimum of 100 radar/rain gauge couple of
data per station are employed.
Results 2007-2010
KED
Not differences easily readable!!
TPS
Cross-method (bootstrap + student test) comparison
Estimation 1
Estimation 2
Results 2007-2010: cross-method comparison
t-values mapping
Mapping of the
student t-value
(data within +/-1.96
are in white)
&
KED better
Kernal density
plot to view the
distribution of
the three scores
TPS better
TPS better
KED better
(data within +/-1.96
are set to zero)
RMSE
CORR
BIAS
-60
0
60
Results 2007-2010 by season I
Winter
Summer
TPS better
Krig better
TPS better
Krig better
RMSE
CORR
BIAS
RMSE
CORR
BIAS
-60
0
60
-60
0
60
Results 2007-2010 by season II
Autumn
TPS better
Spring
Krig better
TPS better
Krig better
RMSE
RMSE
CORR
CORR
BIAS
-60
0
BIAS
60
-60
0
60
Possible explanations for the results




orography (not significant)
Rain intensity (not significant)
Radar type C or S (not significant)
Rain type convective/non-convective (significant)
Two tools to classify rain type:
 The instantaneous Cape (Convective Available Potential Energy) from
Aladin model: An air parcel need sufficient potential energy for convection,
above 20j/Kg of Cape value the rain gauge is associated with a convective
situation.
 Antilope convective index: Generated from the Antilope radar product of
Meteo-France, convective index is based on radar reflectivity gradients in
the immediate vicinity of the pixel associated with controlled rain gauge;
Above a 0 value the rain gauge is associated with a convective situation.
Classification following Rain type convective/non-convective
Non-convective situations
TPS better
Convective situations
TPS better
Krig better
RMSE
Krig better
Aladin cape values
CORR
RMSE
BIAS
CORR
BIAS
Antilope
convective index
control of daily precipitation using radar data - I
For each rain gauge:
rain gauge observation O
Estimate of rainfall E
RMSE and Bias
standard deviation
Sd  RMSE2  bias2
Map of the % of
doubtful observations
The largest circles are for
the 10% of stations that
have the worst
performance
If |O – E| < 3Sd
 Observation plausible
If |O – E|  3Sd
 Doubtful observation
control of daily precipitation using radar data - II
number of rainfall
Tot rainfall
doubtful using
observations tested
KED
values
TPS
(0,076% of tot)
6 356 775
4866
doubtful using doubtful common to
both methods
(0,098% of tot)
6242
3 479
Number of rainfall observations available during the control process, with the
number of doubtful ones following the method employed to obtain the
estimates.
KDE control
TPS control
control of daily precipitation using radar data - III
KDE control
TPS control
Conclusions & Perspectives I
•TPS and kriging perform well to produce estimate of rain gauge
data using radar data.
•TPS tends to perform better for non-convective situations while
Kriging better for convective ones.
Type
Case 1
Case 2
of situation
Convective
Non-convective
of season
Summer
Winter
Kriging
TPS
Spatialization method to
be favored during control
 The operational development of this WP1 contribution should
be taken into account in the “best practice selection
instructions”.
Conclusions & Perspectives II
 Further analysis of the control method results & proceed to a human
expertise of the controlled data.
 Evaluate the possibility to apply this control method outside of
France following the establishment of a critical study of network
density of rain gauges and treatments related to radar data
(collaboration possible).
 Construction of a control method for situations of rain / no-rain and
establishment of special treatment for the snow situations.
 Further work on hourly data who faces various problems such as a
sparse network of hourly rain gauges data (automatic station only)
and also rainy data rarest and with a greater variability.
 Continue collaboration with MeteoSwiss on the intercomparison of
spatialization methods on specific areas (Alps…)
Acknowledgements
The research leading to these results has received
funding from the European Union, Seventh Framework
Programme
(FP/2007-2013) under grant agreement no 242093.
Methodology
overview
control of daily precipitation using radar data
rainfall
rainfall value
observations
tested
doubtful using
doubtful using
O. Krig. without
KED
TPS
radar
(0,071% of =0)
Equal to 0
Greater than 0
Total
3 230 695
3 126 080
6 356 775
(0,074% of =0)
(0,07% of =0)
local mean
(0,06% of =0)
2312
2394
2282
2057
(0,081% of >0)
(0,123% of >0)
(0,19% of >0)
(0,24% of >0)
2554
3848
6058
7611
(0,076% of tot)
(0,098% of tot)
(0,13% of tot)
(0,15% of tot)
4866
6242
8340
9668
Number of rainfall observations available during the control process, with the
number of doubtful ones following the method employed to obtain the
estimates.