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Convective Rainfall Rate Algorithm Evolution

Convective Rainfall Rate Algorithm Evolution. Convection Working Group Meeting 27 – 30 March 2012, Prague Cecilia Marcos Antonio Rodríguez. CRR version 2013. Day time algorithm : 3D matrices (use of SEVIRI radiances/reflectances directly)

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Convective Rainfall Rate Algorithm Evolution

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  1. Convective Rainfall Rate Algorithm Evolution Convection Working Group Meeting 27 – 30 March 2012, Prague Cecilia Marcos Antonio Rodríguez

  2. CRR version 2013 Day time algorithm: 3D matrices (use of SEVIRI radiances/reflectances directly) Function based on Cloud Top Microphysical Properties (computed internally by NWCSAF software package) Night time algorithm: 2D matrix 2D function

  3. Day time algorithm This algorithm is based on the one developed by Roebeling and Holleman* Cloud Top Microphysical Properties used by this algorithm: • Phase (Ph) • Effective radius (Reff) • Cloud water path (CWP) Two steps: 1.- Delimitation of the precipitation area 2.- Assignment of rain rates (*) Roebeling, R. A. and I. Holleman, 2009: SEVIRI rainfall retrieval and validation using weather radar observations. J. Geophys. Res., VOL. 114, D21202. Liquid water path (LWP) Ice water path (IWP)

  4. Calibration: Precipitation area Radar (PPI) • Precipitation area: • Ph = water: • Reff 14 μm • CWP  200 gm-2 • Ph = ice: • CWP  200 gm-2 Datasets: Spain: 40 storms, May-September 2009, 12:00 UTC Hungary: 18 storms, May-September 2009, 10:00-12:00 UTC

  5. Calibration: Rain Rates Datasets: Spain: 40 storms, May-September 2009, 12:00 UTC Hungary: 18 storms, May-September 2009, 10:00-12:00 UTC Cloud Top Microphysical Properties Function:

  6. Comparison: 3D Matrices vs CTMP function Dataset: Spain: 46 days, May-September 2008, 10:00 – 14:00 UTC every 30 min RMS error decreases with CTMP function because precipitation maxima are better located and precipitation pattern is more accurate. False alarms increases 1% while probability of detection increases 24% for this dataset.

  7. Dataset: Spain: 46 storms, May-September 2008, 10:00 – 14:00 UTC every 30 min Comparison: 3D Matrices vs CTMP function

  8. Examples day timeover Spain 11th June 2008 at 12:00 UTC CRR (3D Matrices) CRR (CTMP function) Radar (PPI)

  9. Examples day timeover Spain 12th July 2008 at 13:30 UTC CRR (3D Matrices) CRR (CTMP function) Radar (PPI)

  10. Examples day time over Spain 22th August 2008 at 14:00 UTC CRR (3D Matrices) CRR (CTMP function) Radar (PPI)

  11. Examples day time over Hungary 25th June 2009 at 12:00 UTC CRR (3D Matrices) CRR (CTMP function) Radar (Maximun of refletivity in the vertical)

  12. Comparison 2D Matrix vs 2D Function Dataset: Spain: 46 days, May-September 2008, 10:00 – 14:00 UTC every 30 min 2D matrix understimates rain rates while function overestimates them. RMS error increases because 2D function gets higher rain rates. False alarms have increased 4% while probability of detection increases 16% with the function.

  13. Dataset: Spain: 46 storms, May-September 2008, 10:00 – 14:00 UTC every 30 min Comparison 2D Matrix vs 2D Function

  14. Example night time 11th June 2008 at 12:00 UTC CRR (2D matrix) CRR (2D function) Radar (PPI)

  15. Future work Conclusions • Alternative calibration for “winter” time • Solution for the day/night time transition • Extended validation • New validation method • CTMP function: • provides a precipitation pattern and rain rates more similar to the radar ones • is capable of catching smaller storms • is able to see precipitation for “warm top” rainy clouds • improves the probability of detection • 2D function: • provides rain rates closer to the radar ones • improves the probability of detection

  16. Thanks for your attention

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