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Fine tuning of Radar Rainfall Estimates based on Bias and Standard Deviations Adjustments

Fine tuning of Radar Rainfall Estimates based on Bias and Standard Deviations Adjustments. Angel Luque, Alberto Martín, Romualdo Romero and Sergio Alonso Balearic Islands University, Spain angel.luque@uib.es This presentation can be downloaded from: ftp://eady.uib.es/pub/Angel/. Introduction.

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Fine tuning of Radar Rainfall Estimates based on Bias and Standard Deviations Adjustments

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  1. Fine tuning of Radar Rainfall Estimates based on Bias and Standard Deviations Adjustments Angel Luque, Alberto Martín, Romualdo Romero and Sergio Alonso Balearic Islands University, Spain angel.luque@uib.es This presentation can be downloaded from: ftp://eady.uib.es/pub/Angel/

  2. Introduction WESTERN MEDITERRANEAN REGION BASINS OF CATALONIA A detailed synoptic and numerical analysis of this case of study can be found in the article of Martin et al (2006). Authors have deduced that the atmospheric instability over Catalonia was caused by an active cold front.

  3. Spanish Provinces. • Catalonian internal basins. • 126 ACA rain gauges and kriging area. • Barcelona radar spatial coverage.

  4. Radar maximum temporal resolution is 10 minutes. • ACA maximum temporal resolution is 5 minutes.

  5. HMT CDF(dBZ) DCM MPS HMT MPC FR(dBZ) dBZ=10·log Z(mm6/m3) dBR=10 ·log R(mm/h) CDF(R) MPC HMT MPS DCM FR(R)

  6. CORR RMS SDD BIAS BIAS = 0

  7. ACA rain rates versus radar reflectivities and rain curves (Luque et al. 2006) R(mm/h) = 0.0485·Z2 – 0.7099·Z +4.8289 r2=0.997

  8. Direct Calibration Method (DCM) Z= A ·RBdBZ= B·dBR+10·log(A) DZDF (Marshall & Palmer, 1948) where dBZ=10·log(Z) and dBR=10·log(R) dBZ  y dBR  x B  slope 10·log(A)  intercept dBZ=9.4200·dBR - 50.8131 A=8.293·10-6, B= 9.42 R(dBZ) = 10 [(dBZ-10·log(A))/(10·B)]

  9. 10 30 90 270 dBZ=9.4200·dBR - 50.8131 A=8.293·10-6, B= 9.42 R(dBZ) = 10 [(dBZ-10·log(A))/(10·B)]

  10. RMS CORR BIAS SDD = 0 SDD Centre of rotation (º) c) RMS CORR SDD BIAS BIAS = 0

  11. 10 30 90 270 dBZ=2.1386·dBR – 4.8268 A=3.0386, B= 2.13869 R(dBZ) = 10 [(dBZ-10·log(A))/(10·B)]

  12. Standard Coefficients (MPS, MPC) Z= A ·RBdBZ= B·dBR+10·log(A) DZDF (Marshall & Palmer, 1948) where dBZ=10·log(Z) and dBR=10·log(R) R(dBZ) = 10 [(dBZ-10·log(A))/(10·B)] Stratiform coefficients (MPS)  A=200, B=1.6 Convective coefficients (MPC)  A=800, B=1.6 Figure Verification of the methods • Qualitative and numerical intercomparison of 3 hours rainfall accumulations. • HMT, DCM, MPS and MPC estimations versus the ACA observations

  13. L B HMT(mm/3h), day 09, 21-24h MPC(mm/3h), day 10, 00-03h T Obs(mm/3h), day 09, 21-24h DCM(mm/3h), day 09, 21-24h MPS(mm/3h), day 10, 00-03h Results

  14. L B T Obs(mm/3h), day 10, 00-03h HMT(mm/3h), day 10, 00-03h DCM(mm/3h), day 10, 00-03h MPS(mm/3h), day 09, 21-24h MPC(mm/3h), day 09, 21-24h

  15. HMT(mm/3h), day 10, 06-09h L B T Obs(mm/3h), day 10, 06-09h MPS(mm/3h), day 10, 06-09h DCM(mm/3h), day 10, 06-09h MPC(mm/3h), day 10, 06-09h

  16. Conclusions • radar and rain gauges can be combined to improve the spatial distribution of the precipitation field and to gain accuracy in rainfall amounts within an operational context. • old radar algorithms not adjusted or corrected for a specific area can produce significant errors in rainfall rates and accumulations. • the HMT tuned by the BIAS is the method that provides the best CORR and the MPC method, the worse one. • The DCM is the one that provides the best BIAS and SDD while the MPC is the method with the worst ones. • The HMT is an ATI (Area-Time Integral) method and it needs rain rate fields well distributed in time and space to be appropriately developed. The DCM curve can be performed with few rain gauges that can provide rain rates at high time frequency. • Our results in radar calibration are derived under the circumstances of a flood case and should not be applied directly to events in other areas and situations.

  17. Detailed Paper Luque A., Martin A., Romero R., and Alonso S., 2006, Assessment of Rainfall Estimates Using Standard Z-R Relationships and the Histogram Matching Technique Applied to Radar Data in a flood case in Catalonia. Submitted the 21-Jun-2006 to the International Journal of Remote Sensing, first version of the work available in ftp://eady.uib.es/pub/Angel/ in the radar-draft-submited.pdf file. A. Luque, A. Martin, R. Romero, S. Alonso Balearic Islands University, Spain angel.luque@uib.es This presentation can be downloaded from: ftp://eady.uib.es/pub/Angel/ Thank you

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