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Carpe Diem WP7: FMI progress report

Carpe Diem WP7: FMI progress report. Jarmo Koistinen, Heikki Pohjola Finnish Meteorological Institute. Area 1/WP3: Development of a variational assimilation scheme for doppler winds (FMI + SMHI, responsible persons at FMI Heikki Järvinen, Kirsti Salonen)

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Carpe Diem WP7: FMI progress report

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  1. Carpe DiemWP7: FMI progress report Jarmo Koistinen, Heikki Pohjola Finnish Meteorological Institute

  2. Area 1/WP3: Development of a variational assimilation scheme for doppler winds (FMI +SMHI, responsible persons at FMI Heikki Järvinen, Kirsti Salonen) • Area 2/WP7: Advanced surface precipitation estimate from radars and a NWP model (HIRLAM) (Jarmo Koistinen, Heikki Pohjola) • Objective: Improve the accuracy and quality of operational real time precipitation measurements

  3. End Users • 1. Finnish Road Authority • Applies observed and nowcast distributions of rain, sleet and snow (EUMETSAT Atmospheric Motion Vector software for dBZ- fields implemented for operational use at FMI) and • Accumulated snow • Average snow clearing and road salting costs ~100 million euros/winter => The end user is very motivated to require continuous improvements in the accuracy of radar products

  4. 2. Kemijoki hydroelectric power company • Applies instantaneous precipitation images from radar to river control • Applies accumulated rain to a flow model (~80 000 km2 , grid 1 · 1 km2 ) together with Finnish Environment Institute (involved in COST 717) • Applies accumulated snow from radar to estimate spring flood due to melting snow • Applies existing radar products and improved ones from CARPE DIEM • 3. FMI (we are working in the Operative Services) • Continuous and urgent need to improve radar products.

  5. WP 7.1: Attenuation correction based on 3D water phase diagnosis from NWP model quantities • Deliverables: • Large attenuation statistics for rain-only (assumed in most existing radar systems) and variable water phase statistics • Improvement in surface precipitation • Limitation: Applies single polarization radar data => overestimation of attenuation possible in cases of hail

  6. WP 7.2 Elimination of overhanging precipitation (OP) from surface estimates Altostratus: 13 % of all VPR in Finland in 2001

  7. WP 7.3 Vertical reflectivityprofile correction applying radars and NWP • Deliverables: • Automatic classification of VPR above each radar based on radar, soundings, NWP: • Precipitation • OP • Clutter • Clear air echo • Bright Band: • Amplitude • Thickness • Height

  8. The original WP order has been rearranged • New order: 7.3 begins first and continues during the whole 36 months. WPs 7.1-7.2 start later and feed 7.3. • Reasons for change: 1. End users, including FMI, continuously demand a working VPR correction. 2. HIRLAM-data difficult to get at the moment as the group is very busy due to version change and platform change in autumn 2002. Practical result: We have developed a VPR correction for a network based on radar VPRs and radio soundings. It will be semi-operational in July 2002.

  9. VPR correction at FMI 3D polar volume IRIS • measured VPR • 7 radars • every 15 minutes • layer thickness 200 m • range 2 - 40 km • max bin count 5000 / layer 1(2)

  10. 1(2)

  11. 1(2)

  12. Classification and QC of each VPR • representativity • rain • bright band • snow • clutter • clear air echo • overhanging precipitation • unphysical gradient Statistics Freezing level (FL) from radio soundings (later: from HIRLAM too) Climatological profile adjusted to freezing level Reference dBZ at ground level Calculation of the correction 1(2)

  13. Profile diagnostics tree 1(2)

  14. Profile diagnostics tree 1(2)

  15. 1(2)

  16. 1(2)

  17. OP and evaporation cases 1(2)

  18. Clear air echo and clutter Rain and bright band 1(2)

  19. Snow Bright band at ground 1(2)

  20. Climatological profile 1(2)

  21. VPR CORRECTION IN A NETWORK, PRINCIPLES • dBZ at 500 m PsCAPPI surface is corrected to ground level . • Applies measured VPRs and climatological VPRs and time-space interpolated freezing level heights from 3 radio sounding stations. • The magnitude of the correction is: 10*log (the ground level reference Z from the profile/ convolution (Z-profile*beam)) at each range (height). Upper threshold for the correction is 30 dB.

  22. VPR CORRECTION IN A NETWORK, MAIN STEPS • Calculation of the single radar correction factor at each range based on the measured and classified VPR from each polar volume. • Calculation of the single radar correction factor at each range based on the climatological, temperature-adjusted VPR for each polar volume. • Weighted average of factors 1 and 2 is derived based on the quality of factor 1. • Space smoothing is applied: Actual correction at each composite pixel is a distance-weighted average of factor 3, from all radars closer than 300 km from the pixel. • Time smoothing is applied: Factor 4 is linearly averaged during the past 6 hours. • Post-correction tresholding is applied: The corrected dBZ may not exceed preselected values.

  23. Profile correction for 500 m PsCAPPI Snow 1(2)

  24. Snow and clutter Profile correction 1(2)

  25. Rain Profile correction 1(2)

  26. Rain Profile correction 1(2)

  27. STEP 1: SINGLE RADAR CORRECTION FACTOR • The reference dBZ at ground level must be carefully selected to avoid spurious corrections: • The reference dBZ at ground level (green dot in the previous example cases) is usually the measured dBZ at the lowest level (100 m) of the profile. • Reference dBZ at ground is extrapolated from upper levels in two automatically classified cases: • - bright band is diagnosed at ground level • - clutter is diagnosed at ground level • In the following images examples are shown.

  28. Rain Profile correction 1(2)

  29. Rain Profile correction (no cutter) 1(2)

  30. Bright band at ground Profile correction 1(2)

  31. Profile correction (no extrapol.) Bright band at ground 1(2)

  32. STEPS 2-3: MIXING OF MEASURED AND CLIMATOLOGICAL VPR • Each instantaneous, single radar VPR correction factor is a weighted mean of two factors, based on two simultaneous profiles: • - The climatological VPR, adjusted to the actual freezing level, obtained from the time-space interpolated temperature soundings, weight=0.2. • - The measured VPR, weight = 0-1 according to the quality of the profile (quality ~ height scaled precipitation volume).

  33. Climatological profile Profile correction 1(2)

  34. STEP 4: SPATIAL AVERAGING OF THE CORRECTION FACTOR • Instantaneous correction factor at each composite pixel applies profiles from several surrounding radars: • A composite pixel in a network is selected from the radar, whose measurement is closest to the ground (height h). • Each instantaneous VPR correction factor at a composite pixel is a weighted mean of the simultaneous factors at each radar, within the range 300 km from the pixel; weight ~ distance to each radar squared/300**2. • The factor from each radar is taken from the same height h. In this way we “borrow” the neighbouring profiles to the radar from which the actual pixel value is taken. • Important effect: The correction does not introduce reflectivity steps along the seams of composites.

  35. STEP 5: TIME AVERAGING • So far we have only derived correction factors for each composite pixel at each time moment (time series). Small scale VPR variability is eliminated applying time-averaging: • Each instantaneous VPR correction factor at a composite pixel is a linearly weighted average of the 24 instantaneous corrections during the last 6 hours; the older the correction the less is the weight. • The correction factors derived in step 5 are the ones we actually apply for the 500 m PseudoCAPPI composite. The factors in steps 1-4 are only used for derivation of the final correction factor.

  36. STEP 6: TRESHOLDING OF TOO LARGE CORRECTIONS • In case that the resulting corrected dBZ is too large, e.g. embedded convection occurs at longer distances whereas the measured VPR represents shallow precipitation close to the radar, the final dBZ is not allowed to exceed preselected values. Treshold value at each pixel depends on the actual hydrometeor water phase analysis (rain, sleet, snow) at ground level. The phase analysis is based on linearly extrapolated 3-hourly analysis of T and RH at the height of 2 m.

  37. FUTURE OF WP 7 • WP 7.3 will continue 36 months (inclusion of HIRLAM) and apply all useful results from WPs 7.1 and 7.2 • Deliverables from WP 7.3 at +13 months: • - Statistics of VPRs and single radar VPR corrections in Finland, a large climatological data set (1 year, 7 radars, every 15 minutes). • - VPR correction in a network, the method (tuning of the parameters still required) • - validation of the method (applies radar pairs and gauges) • - end user experience • - 2 ERAD papers, textbook in radar meteorology, (peer reviewed paper around +18 months)

  38. Original deliverables: • VPR correction at a single radar applying time-averaged (~6h) VPR corrections (TA) based on reflectivity profiles derived from the measured polar volumes every 15 minutes • VPR correction at a single radar applying TA-corrections and climatological VPR based on the actual freezing level height obtained from meteorological soundings and climatological profile shape statistics measured with the radars (TAC) • VPR correction in the radar network based on space-averaged TAC-corrections (TSAC) • VPR correction in the network based on TSAC-correction and VPRs estimated from a NWP model in co-operation with SMHI (Gunther Haase).

  39. Validation of the deliverables from WP 7.1-7.3 • Improvement in gauge-radar comparisons of 12 h accumulations • Improvement of long range dBZ from radar A, compared to the short range dBZ from radar B in the same overlapping region • Improved long range POD and FAR of precipitation at ground from radar compared to AWS/SYNOP (ON/OFF) • Improved POD and FAR in the precipitation nowcasts • Improved skill in the End User applications

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