spatial control of rain gauge precipitations using radar data (contribution to wp1)
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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
MAIN PROBLEMS of rain gauges: density, quality, instrument types
•Real-time Meteo-France (~1500-1800)
•volunteers (~2800)
Construct a 2007-2010 reference estimate of spatialized precipitation for rain gauges control and validation at fine scale.
PROPOSED SOLUTIONUse radar network in the
spatialization process of the precipitation estimate
Radar data need Radar data need also to be qualifiedalso 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.
First half to calibrate radar data
Methodology overview
Controlled rainfall observation processControlled rainfall observation process
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
Second half to be controlled
Observations
4 spatialization methods / 2 used
TPS: Thin Plate Spline in a 3D spaceuse 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 driftIt 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: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
Not differences easily readable!!
KED TPS
Cross-method (bootstrap + student test) comparison
Estimation 1
Estimation 2
Results 2007-2010: cross-method comparisont-values mapping
Mapping of the student t-value(data within +/-1.96 are in white)
&
Kernal density plot to view the distribution of the three scores (data within +/-1.96 are set to zero)
TPS better
KED better
-60 0 60
TPS better KED better
RMSE
CORR
BIAS
Results 2007-2010 by season I
Winter Summer
RMSE
CORR
BIAS RMSE
CORR
BIAS
TPS betterTPS better
Krig betterKrig better
-60 0 60 -60 0 60
Results 2007-2010 by season II
Autumn Spring
RMSE
CORR
BIAS
RMSE
CORR
BIAS
TPS better TPS betterKrig better Krig better
-60 0 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 Convective situations
Aladin cape values
Antilope convective index
TPS better TPS betterKrig better Krig better
RMSE
CORR
BIAS
RMSE
CORR
BIAS
control of daily precipitation using radar data - I
For each rain gauge:For each rain gauge:rain gauge observation O
Estimate of rainfall E
RMSE and Bias
standard deviation
22 biasRMSESd
If |O – E| < 3Sd|O – E| < 3Sd
Observation plausible
If |O – E| |O – E| 3Sd 3Sd
Doubtful observation
Map of the % of doubtful observationsThe largest circles are for the 10% of stations that have the worst performance
control of daily precipitation using radar data - IITot rainfall
observations testeddoubtful using
KEDdoubtful using
TPSdoubtful common to
both methods
6 356 775 (0,076% of tot)
4866(0,098% of tot)
6242 3 479
number of rainfall values
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 2of situation Convective Non-convective
of season Summer Winter
Spatialization method to be favored during control Kriging TPS
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 valuerainfall
observations tested
doubtful using KED
doubtful using TPS
O. Krig. without radar local mean
Equal to 0 3 230 695(0,071% of =0)
2312(0,074% of =0)
2394(0,07% of =0)
2282(0,06% of =0)
2057
Greater than 0 3 126 080(0,081% of >0)
2554(0,123% of >0)
3848(0,19% of >0)
6058(0,24% of >0)
7611
Total 6 356 775 (0,076% of tot)
4866(0,098% of tot)
6242(0,13% of tot)
8340(0,15% of tot)
9668
Number of rainfall observations available during the control process, with the number of doubtful ones following the method employed to obtain the
estimates.