1 / 21

Spatial control of rain gauge precipitations using radar data (Contribution to WP1)

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.

emmet
Download Presentation

Spatial control of rain gauge precipitations using radar data (Contribution to WP1)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Main topic Construct a 2007-2010 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 • MAIN PROBLEMS of rain gauges: density, quality, instrument types • Real-time Meteo-France (~1500-1800) • volunteers (~2800)

  3. 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 betterspatialize precipitations.

  4. 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 Second half to be controlled  Observations Production of an independent estimate of rainfall from rain gauges of First& Second halves (except the one controlled) using calibrated radar datavia spatialisation method  Rainfall Estimates Controlled rainfall observation process

  5. 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

  6. 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.

  7. Results 2007-2010 KED TPS Not differences easily readable!!

  8. Cross-method (bootstrap + student test) comparison Estimation 1 Estimation 2

  9. 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) KED better TPS better TPS better KED better RMSE CORR BIAS -60 0 60

  10. 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

  11. Results 2007-2010 by season II Autumn Spring TPS better Krig better TPS better Krig better RMSE CORR BIAS RMSE CORR BIAS -60 0 60 -60 0 60

  12. 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.

  13. Classification following Rain type convective/non-convective Non-convective situations Convective situations TPS better Krig better TPS better Krig better Aladin cape values RMSE CORR BIAS RMSE CORR BIAS Antilope convective index

  14. 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 If |O – E| < 3Sd  Observation plausible 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

  15. 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

  16. control of daily precipitation using radar data - III KDE control TPS control

  17. 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”.

  18. 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 datawho 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…)

  19. 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.

  20. Methodology overview

  21. 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.

More Related