1 / 14

McGill University, Montreal Canada

Bias and random errors in radar measurements of precipitation and their scale dependence. Aldo Bellon, Isztar Zawadzki, and GyuWon Lee. McGill University, Montreal Canada. Each source of error in precipitation estimates by radar is evaluated separately. Radar calibration. AP+GE removal.

aiden
Télécharger la présentation

McGill University, Montreal Canada

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. Bias and random errors in radar measurements of precipitation and their scale dependence.Aldo Bellon, Isztar Zawadzki, and GyuWon Lee McGill University, Montreal Canada

  2. Each source of error in precipitation estimates by radar is evaluated separately Radar calibration AP+GE removal Wet-radome Attenuation Beam broadening Height increase How to get Z at ground ! Strong attenuation by precipitation at C-band Z  R Optimum Surface Precipitation (OSP)

  3. High Resolution Sector:15-35 km & 120-320 az.

  4. 40 80 120 160 200 Projection of near data into far ranges 1.5 h=1.1 3.1 1.9

  5. ERRORS DUE TO EXTRAPOLATION FROM HEIGHT OF MEASUREMENT TO GROUND When observed at a farther range 2.0 1.7 0.5 BW=0.2 To determine the errors, values at the measurement height are compared with the low level ground truth

  6. Projection of near range VPRs into far ranges 250 hr of data, 21 events

  7. Height of lowest elv. angle Errors in non-corrected Errors in corrected data

  8. Climatological Vertical Profiles

  9. Errors before Correction with Correction with correction inner VPR climatological VPR * * *

  10. Total Error Summary: Stratified by BB height(Data from 0.50 elevation for r>100km and from 1.5 km CAPPI for r<100km)

  11. Spatial-scale dependence of errors range Normalized RMS error after VPR correction as a function of the verification area. (For 1-hr accumulations at the “lowest default height”).

  12. Time-scale dependence of errors Normalized RMS error at the lowest default height after correction by the VPR as a function of accumulation time at 10-km resolution.

  13. Representativeness of VPR Increased NRMS errors for 1-hr accumulations generated with VPR correction factors that are appropriate for a different time interval (in order to simulate a different VPR at farther ranges). The uncorrected NRMS and that from the climatological correction are provided as reference. (At the “lowest default height” and at 10-km resolution).

  14. Conclusions Errors depend: Height of BB, Verification area, Length of accumulation, Range Errors < 20 % if: BB height > 2.5 km, Area > 100 km2 Acc. Time > 45 min. Range < 130 km The non-homogeneity of the VPR adds may substantially increase the error. Errors due to Ground clutter, DSD variability (R-Z uncertainty) Calibration, Attenuation (C-band) must be added to the VPR errors. Essential to differentiate between Stratiform/Convection, particularly at far ranges (> 150 km)

More Related