EUMETSAT Precipitation Week Nowcasting SAF: Precipitating Clouds (PC) and Convective Rainfall Rate (CRR) products EUMETSAT Precipitation Week 4 - 8 February 2013 Cecilia Marcos Antonio Rodríguez

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Overview I. Introduction to EUMETSAT Nowcasting SAF II. Precipitating Clouds Product Algorithm description Applications, limitations and visual examples III. Convective Rainfall Rate Product Algorithm description (Matrices) Algorithm description (Analytical functions) IV. Precipitation products through microphysical cloud top information (in development) Precipitating Clouds Product Convective Rainfall Rate Product Limitations, improvements and visual examples V. Comparison of precipitation products: visual examples

Introduction to EUMETSAT NWCSAF The Nowcasting Satellite Application Facility was established in 1996 between Eumetsat and former INM (Instituto Nacional de Meteorología). Consortium: Objectives: Development of Nowcasting products derived from both GEO and PPS satellite systems To be delivered to users as SW Packages Responsible for Development and maintenance of the NWC products Development and maintenance of the SW Packages User's support tasks made through dedicated Help Desk

Introduction to EUMETSAT NWCSAF Products are generated in the users’ premises Features of the products: Near Real Time (NRT) Full resolution 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 NWCSAF

Introduction to EUMETSAT NWCSAF MSG Precipitation Products

Precipitating Clouds Product for MSG v2012 INTRODUCTION: The objetive is the delineation of non-precipitating and precipitating clouds. PC serves as a geneal tool for Nowcasting of precipitation, especially for areas where no surface data is available.

Precipitating Clouds Product for MSG v2012 SEVIRI

Precipitating Clouds Product for MSG v2012 ALGORITHM DESCRIPTION: A Precipitation Index (PI) has been built through a linear combination of those spectral features which have the highest correlation with precipitation: Two different calibrations: European Synops Precipitation rates from rain gauge over France (using the Cloud Type product) Different algorithms for day and night situations The choice of the algorithm to be used is done through the Configuration File PI=a0 +a1*TSurf +a2*T10.8+a3*(T10.8-T12.0)+a4*abs(a5-R0.6/R1.6)+ a6*R0.6+a7*R1.6+a8*T6.2+a9*T7.3+a10*T3.9)

Precipitating Clouds Product for MSG v2012 OUTPUTS: The PC product shall consist of a numerical value for the likelihood The following probability classes will be used:       0% (= 0-5%)      10% (= 5-15%)      20% (= 15-25%)      30% (= 25-35%)      40% (= 35-45%)      50% (= 45-55%)      60% (= 55-65%)      70% (= 65-75%)      80% (= 75-85%)      90% (= 85-95%)      100% (= 95-100%) FLAG: information about the processing conditions

Precipitating Clouds Product for MSG v2012 Applications: Provide information about precipitation ocurrence likelihood over extensive areas, out of the Radar coverage or as a Radar complement. RADAR PC Example: 9th September 2012 at 15:00 UTC

Precipitating Clouds Product for MSG v2012 Limitations: 16th January 2013 at 10:30 UTC RADAR PC IR 10,8 - Similar to the cloud tops - Too big estimated precipitation area Different result for day and night 12th April 2012 at 06:30 UTC PC

Precipitating Clouds Product for MSG v2012 Precipitating Clouds Loop:

Convective Rainfall Rate Product v2012 INTRODUCTION: The CRR goal is to estimate rainfall rates from convective systems, using IR, WV and VIS MSG SEVIRI channels and lightning information (as optional input).

Convective Rainfall Rate Product v2012

Convective Rainfall Rate Product v2012 CALIBRATION MATRICES: Calibration matrices have been built through a statistic method using: SEVIRI data Composite radar data: (2Km spatial resolution) Reflectivities Echotop (only to select convective areas) Two different calibrations: R = f (IR, IR-WV, VIS), for 3-D calibration (day time) R = f (IR, IR-WV), for 2-D calibration (night time) For each type of calibration there are two regional matrices: Spanish matrices Nordic matrices The CRR retrieval can be latitude dependant (difference matrices) PROCESSING: Basic CRR mm/h value for each pixel obtained from the calibration matrices. Filtering process Corrections: Moisture Correction, Evolution Correction (Cloud-top Temperature Gradient Correction), Parallax correction and Orographic correction Lightning algorithm

Convective Rainfall Rate Product v2012 Automatic station data provided by IM (Portuguese Meteorological Service) Madeira 20th February 2010 – 12:00 UTC Moisture Correction : (PWRH) Depends on total precipitable water (surface- 500 hPa) and relative humidity No corrections applied Only Moisture correction applied

Convective Rainfall Rate Product v2012 Evolution Correction / Gradient Correction : No Evolution Correction Radar Evolution Correction: Rain rate decreases if the analysed pixel becomes warmer in the second image Cloud-top Temperature Gradient Correction: Rain rate decreases if the analysed pixel has a temperature maximum, which indicates that this pixel is warmer than its surroundings Evolution Correction Gradient Correction

Convective Rainfall Rate Product v2012 Parallax correction: A spatial shift is applied to every pixel with precipitation according the basic CRR value Only Moisture correction applied Moisture + Evolution corrections applied Moisture + Evolution + Parallax corrections applied

Convective Rainfall Rate Product v2012 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. Moisture + Evolution + Parallax corrections applied Moisture + Evolution + Parallax + Orographic corrections applied

Convective Rainfall Rate Product v2012 LIGHTNING ALGORITHM: The Lightning algorithm assumes that the higher are the spatial and temporal density of lightning, the higher are the probability and the intensity of convective precipitation. The rain rates assigned to every lightning takes into account: - the time distance between the lightning event and scanning time of the processing region centre. - the location of the lightning. - the spatial density of lightning in a time interval. Only Cloud-to-Ground lightning flashes are used by this algorithm. When the lightning precipitation pattern has been computed, it is compared to the CRR precipitation one in order to obtain the final product, that will contain the highest rain rates of the two. Lightning algorithm CRR without lightning CRR with lightning

Convective Rainfall Rate Product v2012 OUTPUTS: CRR rainfall rates expressed in classes CRR rainfall rates expressed in mm/h (only for developing purposes) CRR Hourly Accumulations CRR-QUALITY CRR-DATAFLAG Where: Ai: hourly accumulation, in mm, corresponding to the time i. T: time interval between scenes in hours (T= 0.25) Ф: part of T that corresponds to the time that takes the satellite to reach the centre of the region. Ii: Instantaneous rainfall rate for each scene in mm/h

Convective Rainfall Rate Product v2012 Applications: Estimation of rain rates for convective events over extensive areas, out of the radar coverage or as a radar complement. RDT CRR RADAR + Lightning info

Courtesy of Jean-Marc Moisselin Links between SAF/NWC products Use of CRR in RDT (Rapid Development Thunderstorm)–Implemented in v2012 RDT takes advantage of CRR data with different options: CRR can be used to identify maximum convective rain rate under cloud cell High CRR values can help to qualify a cloud cell as significant and thus to encode this cell in BUFR output (only for the last BUFR version) Very high CRR values can be used to set diagnosis of convection to « YES » (only for the last BUFR version) RDT main outlines Attributes PGE11 can take advantage of CRR data (PGE05) as optional input or for additional attribute : o CRR data are used to identify maximum convective rain rate over cloud cell extension o High CRR data may be used additionally to qualify a cloud cell as significant and to encode this cell in BUFR output (for the last BUFR version only) The default threshold for that purpose is 10mm/h. It can be customized through parameter “–crr” of the model configuration file o Very High CRR data may be used on request to force convective diagnostic in last BUFR output version. The default threshold is 50mm/h. This option is activated only when the parameter “–crrdiscri” is set to 1 in the model configuration file. For a given detected cloud system, the corresponding maximum convective rain rate is defined as the 99th percentile of processed CRR intensity over the cell’s horizontal Attributes coming from other NWC SAF products : Cloud Top Pressure, Cloud Type, Cloud Phase, Convective Rain Rate

Convective Rainfall Rate Product v2012 Problems: 26th July 2012 - 22:00 UTC CRR IR 10.8 RADAR - Similar to the cloud tops - Too big estimated precipitation area and lower rain rates than Radar Different result for day and night CRR 31th August 2012 - 06:00 UTC

Convective Rainfall Rate Product v2013 From matrices to analitical functions Matrices Analitical functions + recalibration

Convective Rainfall Rate Product v2013 Visual examples 22th August 2008 14:00 UTC RADAR (PPI) CRR 2D MATRIX CRR 3D MATRIX CRR 2D FUNCTION CRR 3D FUNCTION

Precipitation products through microphysical cloud top information 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 optical thickness (COT) Rain rate (*) Roebeling, R. A. and I. Holleman, 2009: SEVIRI rainfall retrieval and validation using weather radar observations. J. Geophys. Res., VOL. 114, D21202.

Precipitation products through microphysical cloud top information Precipitating clouds: likelihood of precipitation Comparison reprojected Radar with CWP CWP Thresholds Precipitation Probability

Precipitation products through microphysical cloud top information Limitations: Only day time Only for estimated phase Some dependance on illumination conditions – better VIS normalization needed Improvements: More confidence on the assignment of the precipitation likelihood PC (Currently available) PC (CTMP) Radar 8th September 2012 - 15:30 UTC

Precipitation products through microphysical cloud top information Datasets: Spain: 40 storms, May-September 2009, 12:00 UTC Hungary: 18 storms, May-September 2009, 10:00-12:00 UTC CRR Calibration: Precipitation area Radar (PPI) Precipitation area: Ph = water: Reff  14 μm CWP  200 gm-2 Ph = ice: CRR Calibration: Rain Rates Cloud Top Microphysical Properties Function

Precipitation products through microphysical cloud top information Limitations: Only day time Only for estimated phase High dependance on illumination conditions – better VIS normalization needed 9th September 2012 12:00 UTC Illumination Quality flag Radar CRR CTMP Illumination Quality Flag

Precipitation products through microphysical cloud top information Limitations: Only day time Only for estimated phase High dependance on illumination conditions – better VIS normalization needed 9th September 2012 13:00 UTC Illumination Quality flag Radar CRR CTMP Illumination Quality Flag

Precipitation products through microphysical cloud top information Limitations: Only day time Only for estimated phase High dependance on illumination conditions – better VIS normalization needed 9th September 2012 14:00 UTC Illumination Quality flag Radar CRR CTMP Illumination Quality Flag

Precipitation products through microphysical cloud top information Limitations: Only day time Only for estimated phase High dependance on illumination conditions – better VIS normalization needed 9th September 2012 15:00 UTC Illumination Quality flag Radar CRR CTMP Illumination Quality Flag

Precipitation products through microphysical cloud top information Limitations: Only day time Only for estimated phase High dependance on illumination conditions – better VIS normalization needed 9th September 2012 16:00 UTC Illumination Quality flag Radar CRR CTMP Illumination Quality Flag

Precipitation products through microphysical cloud top information CRR matrices Improvements: Precipitation patterns closer to the radar ones 28 September 2008 12:00 UTC CRR CTMP Radar

Precipitation products through microphysical cloud top information CRR matrices Improvements: Precipitation intensities closer to the radar ones 5th August 2008 13:00 UTC CRR CTMP Radar

Precipitation products through microphysical cloud top information CRR matrices IR 10.8 Improvements: No Cold Rings and detection of smaller precipitation nuclei CRR CTMP Radar 9th September 2008 13:00 UTC

Precipitation products through microphysical cloud top information CRR matrices IR 10.8 Improvements: Detection of precipitation for warm top clouds CRR CTMP Radar 11th August 2012 14:00 UTC

Comparison of precipitation products PC (Currently available) Visual example 12th July 2008 at 13:00 UTC PC (CTMP) Radar

12th July 2008 13:00 UTC CRR 2D MATRIX Version 2012 CRR 2D FUNCTION Version 2013 CRR 3D MATRIX Version 2012 CRR 3D FUNCTION Version 2013 CRR CTMP Version 2013 RADAR (PPI)

Quiz Which CRR correction allows a better location of the raining clouds? Orographic correction Parallax correction No CRR correction tries to adjust the position of the raining clouds Why the estimation of precipitation with GEO satellite data gives better results during daytime? There is no difference on the results between day and night time Because during night time only IR SEVIRI channels give information and during day time only solar SEVIRI channels give information Because solar channels, which provide microphysical information on the cloud tops, are useful only during day time.

This image shows the CRR rain rates over Europe at dawn or at dusk, can you indicate where is the transition between 2D and 3D algorithms and put an star at the day time side?

Solution CRR Rain rates CRR-QUALITY DAY TIME NIGHT TIME DAY TIME

Thank you for your attention! Question time