Presentation on theme: "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."— Presentation transcript:
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 (~ ) volunteers (~2800) Construct a reference estimate of spatialized precipitation for rain gauges control and validation at fine scale. 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 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 process RAIN GAUGE Precipitations Real-time (~ ) 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 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 Not differences easily readable!! KED TPS
Results : cross-method comparison t-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 TPS betterKED better RMSE CORR BIAS
Results by season I WinterSummer RMSE CORR BIAS RMSE CORR BIAS TPS better Krig better
Results by season II AutumnSpring RMSE CORR BIAS RMSE CORR BIAS TPS better Krig better
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 situationsConvective situations Aladin cape values Antilope convective index TPS better Krig better RMSE CORR BIAS RMSE CORR BIAS
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 |O – E| < 3Sd If |O – E| < 3Sd Observation plausible |O – E| 3Sd If |O – E| 3Sd Doubtful observation Map of the % of doubtful observations The largest circles are for the 10% of stations that have the worst performance
control of daily precipitation using radar data - II 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 controlTPS 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. 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/ ) under grant agreement n o
control of daily precipitation using radar data Number of rainfall observations available during the control process, with the number of doubtful ones following the method employed to obtain the estimates.