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Convective Rainfall Rate Algorithm Evolution

Explore the evolution of a convective rainfall rate algorithm utilizing cloud top microphysical properties. The algorithm leverages 3D matrices during the day and 2D matrices at night to accurately detect precipitation patterns. Calibration and validation were performed using datasets from Spain and Hungary, showcasing improved detection probabilities and accurate rain rate estimations. Comparisons between 3D matrices and CTMP function demonstrate enhanced precipitation localization and pattern accuracy. Future work includes alternative calibrations, extended validation methods, and optimizing detection probabilities with different functions.

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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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