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

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

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: http://www.nwcsaf.org

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)

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

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.

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)

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

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

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

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

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

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

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

Visualization Examples 3D Calibration: R = f (IR, IR-WV, VIS) Instantaneous rates 12-07-2008 18:00Z version 2008 version 2009

Visualization Examples 2D Calibration: R = f (IR, IR-WV) Instantaneous rates 09-09-2008 19:00Z version 2008 version 2009

Visualization Examples 3D Calibration: R = f (IR, IR-WV, VIS) Hourly accumulations 27-08-2008 18:00Z version 2008 version 2009

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)

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)

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)

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

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

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

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

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

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

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

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

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

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

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

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. 1996 Meteorological Scientific User's Conference, Vienna, Austria, EUM P19, pp.89-96. 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, pp433-441. 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. 1883-1898. 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. 1999 Eumetsat Meteorological Satellite Data User's Conferences, EUM P26, pp. 161-168.

Thanks Comments and suggestions: cmarcos@inm.es tea@inm.es