1. The Convective Rainfall Rate in the NWCSAF
Convection week
2nd – 5th June 2009
Cecilia Marcos (AEMET)
Antonio Rodríguez (AEMET)
NWCSAF team
AEMET Lightning and Radar network team

2. Overview I. Introduction to Eumetsat Nowcasting SAF
II. Goal of Convective Rainfall Rate Product
III. Algorithm description
IV. Visualization examples
V. Limitations
VI. Usefulness and applications
VII. Future work

3. Introduction to Eumetsat Nowcasting SAF
Nowcasting SAF objectives:
To provide operational services to Enhance Nowcasting and Very short range Weather forecasting from MSG and EPS data.
The Nowcasting SAF does this by developing and maintaining software packages and supporting users on the software.
Features of the products:
Near Real Time (NRT)
Full resolution (3km x 3km at Nadir)
Frequency to be selected by the user (default every repeat cycle)
Region to be selected by the user
More information on the project is available at Nowcasting SAF Web site:

4. Introduction to Eumetsat Nowcasting SAF
Products:
From Geostationary (MSG) and Polar (NOAA & METOP) Satellites:
- Cloud Mask
- Cloud Type
- Cloud Top Temperature & Height
- Precipitating Clouds
From Geostationary (MSG) Satellites:
- Total Precipitable Water
- Layer Precipitable Water
- Stability Analysis Imagery
- Automatic Satellite Image Interpretation
- Rapidly Developing Thunderstorms
- Air Mass Analysis
- High Resolution Winds from HRVIS Channel
- Convective Rainfall Rate (CRR)

5. Goal of Convective Rainfall Rate Product
The objective of the CRR product is to estimate the precipitation rate associated to convective clouds.
Hourly accumulations
Instantaneous rates

6. Algorithm Description
INPUTS
Mandatory inputs:
10,8 IR SEVIRI brightness temperatures (Information about cloud top height (Scofield, R.A., 1987; Vicente, G.A. and R.A. Scofield, 1996))
6,2 WV SEVIRI brightness temperatures (IR-WV information about deep convective cloud with heavy rainfall (Kurino, T., 1996))
Optional inputs:
0,6 VIS SEVIRI normalized reflectances (Information about cloud thickness (Scofield, R.A., 1987; Vicente, G.A. and R.A. Scofield, 1996))
Numerical Model:
Relative humidity from 1000 to 500 hPa.
Temperature from 1000 to 500 hPa.
2 meter temperature.
Dew point temperature of 2 m.
Surface pressure.
U and V wind components in 850 hPa level.

7. Algorithm Description
CALIBRATION:
Calibration matrices have been built through a statistic method using:
SEVIRI data
Composite radar data: (2Km spatial resolution)
Rainfall Rate from PPI (Spanish matrices)
Echotop (to select convective areas only for Spanish matrices)
Rainfall Rate from 500m Psedo_CAPPI (Nordic matrices)
Two different calibrations:
R = f (IR, IR-WV, VIS), for 3-D calibration
R = f (IR, IR-WV), for 2-D calibration
For each type of calibration there are two regional matrices:
Spanish matrices
Nordic matrices
The CRR retrieval can be latitude dependant (difference matrices)

8. Algorithm Description
PROCESSING:
The basic CRR mm/h value for each pixel is obtained from the calibration matrices (updated in version 2009).
A filtering process of the convective rain is applied
The following corrections take place:
- Moisture Correction
Cloud Growth Rate Correction / Evolution Correction
Cloud-top Temperature Correction / Gradient Correction
- Parallax correction
- Orographic correction

9. Algorithm Description
Moisture Correction: multiply the rain rate of each pixel by a factor called PWRH. This factor depends on total precipitable water, PW, in the layer from the surface to 500 hPa and the relative humidity, RH.
All corrections applied
All but moisture correction applied

10. Algorithm Description
No moisture correction
Exagerated precipitation pattern
Radar (PPI)
All corrections applied
All but moisture
correction applied

11. Algorithm Description
Cloud Growth Rate / Evolution Correction: changes the magnitude of the rain rate if the analysed pixel becomes warmer in the second image
Cloud-top Temperature / Gradient Correction: the rain rate of the analysed pixel can be modified through a coefficient if it has a temperature maximum which indicates that this pixel is warmer than its surroundings
All corrections
applied (evolution correction)
Radar (PPI)
All corrections
Applied (gradient correction)
All but evolution and gradient
corrections applied

12. Algorithm Description
Parallax correction:
a spatial shift is applied to every pixel with precipitation according the basic CRR value
Radar (PPI)
All corrections applied
All but parallax
correction applied

13. Algorithm Description
Orographic correction: This correction uses the interaction between the wind vector (taken from the 850 hPa. numerical model) and the local terrain height gradient in the wind direction to create a multiplier that enhances of diminishes the previous rainfall estimate, as appropriate.
All but orographic
correction applied
All corrections applied

14. Algorithm Description
OUTPUTS:
CRR rainfall rates expressed in classes
CRR rainfall rates expressed in mm/h (only version 2009)
CRR Hourly Accumulations (only version 2009)
CRR-QUALITY
CRR-DATAFLAG

15. Visualization Examples
3D Calibration:
R = f (IR, IR-WV, VIS)
Instantaneous rates
18:00Z
version 2008
version 2009

16. Visualization Examples
2D Calibration:
R = f (IR, IR-WV)
Instantaneous rates
19:00Z
version 2008
version 2009

17. Visualization Examples
3D Calibration:
R = f (IR, IR-WV, VIS)
Hourly accumulations
18:00Z
version 2008
version 2009

18. Limitations of the product
A consequence of the use of a statistic method to calibrate the product is that CRR rates are lower than Radar rates in the same situations.
CRR calibration 2008 (CRR palette)
Radar (Radar palette)

19. Limitations of the product
A consequence of the use of a statistic method to calibrate the product is that CRR rates are lower than Radar rates in the same situations.
New calibration higher rates
CRR calibration 2009 (CRR palette)
Radar (Radar palette)

20. Limitations of the product
A consequence of the use of a statistic method to calibrate the product is that CRR rates are lower than Radar rates in the same situations.
New calibration higher rates, but not as high as Radar
CRR calibration 2009 (CRR palette)
Radar (CRR palette)

21. Limitations of the product
Higher rates more false alarms (compromise)
CRR
Radar (PPI)

22. Limitations of the product
Radar (PPI)
Quantitative validation problem: Rain pattern obtained from the Radar will never match exactly with that one obtained from the geostationary satellite.
Radar (Echotop)
CRR

23. Limitations of the product
Radar (PPI)
3D calibration (more information) gives better results than 2D calibration
3D calibration
2D calibration

24. Usefulness and applications
CRR
When or where Radar is not available
Radar (PPI)
Radar (PPI) + Lightning information

25. Usefulness and applications
When or where Radar is not available
Radar (PPI)
CRR

26. Usefulness and applications
CRR
When or where Radar is not available
Radar (PPI)
Radar (PPI) + Lightning information

27. Usefulness and applications
SEVERE STORM: 20th May 2009
Natural RGB loop from 14:00 to 19:00 (source: EUMETSAT)

28. Usefulness and applications
SEVIRI derived products: 20th May 2009
Rapid Development
Thunderstorms 17:30 UTC
Natural RGB Composition 17:30 UTC
Cloud Type 17:30 UTC
CRR Hourly Accumulations 18:00 UTC
CRR Instantaneous Rates 17:30 UTC
Precipitating Clouds 17:30 UTC

29. Usefulness and applications
Convection related products: 20th May 2009
Rapid Development
Thunderstorms
17:30 UTC
CRR Instantaneous Rates
17:30 UTC
Radar (PPI)
17:40 UTC
Lightning Information from 17:00 to 18:00 UTC

30. Usefulness and applications
Convective episode: 20th May 2009
Loop from 14:00 to 22:00 UTC
Radar (PPI)
CRR Instantaneous Rates
Lightning Information
Cloud Type

31. Future work Possibility of adding lightning information to CRR.
Radar (PPI) + Lightning information

32. References
Product User Manual for "Convective Rainfall Rate“ (CRR-PGE05 v3.0)
Algorithm Theoretical Basis Document for "Convective Rainfall Rate" (CRR-PGE05 v3.0)
Validation Report for "Convective Rainfall Rate" (CRR-PGE05 v3.0)
Vicente, G.A. and R.A. Scofield, 1996: Experimental GOES-8/9 derived rainfall estimates for flash flood and hydrological applications, Proc Meteorological Scientific User's Conference, Vienna, Austria, EUM P19, pp
Schmetz J., S. S. Tjemkes, M. Gube and L. van de Berg, 1997: Monitoring deep convection and convective overshooting with METEOSAT. Adv. Space Res., Vol. 19, pp
Vicente, G.A., Scofield, R.A. and Menzel W.P. 1998: The Operational GOES Infrared Rainfall Estimation Technique, Bull. American Meteorological Society, Vol. 79, No. 9, pp
Vicente, G.A., Davenport, J.C. and Scofield, R.A., 1999: The role of orographic and parallax corrections on real time high resolution satellite rainfall estimation, Proc Eumetsat Meteorological Satellite Data User's Conferences, EUM P26, pp

33. Thanks
Comments and suggestions: