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Convection Products NWC SAF 2015 Users’ Workshop 24-26 February 2015 - Madrid AEMET HQ, Madrid (Spain) Jean-Marc Moisselin, Frédéric Autones Météo-France.

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Presentation on theme: "Convection Products NWC SAF 2015 Users’ Workshop 24-26 February 2015 - Madrid AEMET HQ, Madrid (Spain) Jean-Marc Moisselin, Frédéric Autones Météo-France."— Presentation transcript:

1 Convection Products NWC SAF 2015 Users’ Workshop February Madrid AEMET HQ, Madrid (Spain) Jean-Marc Moisselin, Frédéric Autones Météo-France –Nowcasting Department 42, av. Gaspard Coriolis Toulouse France

2 Convection Products at a glance Two convection products: CI and RDT CI = Convection Initiation = probability for a cloudy pixel to become a thunderstorm RDT = Rapidly Developing Thunderstorm = detection, tracking, description and forecast of thunderstorms in object mode

3 Overview 1. RDT 2. CI 3.Links between Products 4. MTG Context

4 RDT: data fusion for description of convection INPUT DATA: MULTISOURCE MSG data (5 IR channels + VIS) Lightning Data NWP data Other NWCSAF products OUTPUT DATA: MULTILEVEL DESCRIPTION OF CONVECTION PGE11 RDT Main description of cell: Yes/No convection diagnosis, cell-development phase, position, surface, T, gap to tropopause, cloud type and phase, cloud top pressure. Displacement Relevant trends are calculated Overshooting Tops, Lightning Activity, Convective Index, Rainfall Activity

5 5 3-steps algorithm of RDT STEP1: Detection (in order to detect cells) - Using vertical profile of 10.8µm BT - Cells (towers) are detected at each slot - Vertical extension: at least 6°C STEP2: Tracking (in order to recognize each cell in the previous slot) - Analysis of cloud cells overlap: each cell of the previous slot is advected - Merges and splits are taken into account - Trends of various parameters are calculated    STEP3: Discrimination (in order to identify convective cells). Statistical process - Made complex by the unbalanced populations, the wide variety of scales and evolution-phases of systems - Highly improved by the use of a set of 5 IR-channels as predictors, by the use of NWP data - Very highly improved by the use of lightning data STEP4: Forecast 4

6 6 Visualization of RDT through SYNERGIE (Météo-France forecasters’ workstation) Development-phases: Yellow: First detection of convective system Red: Developing system Purple: Mature system Blue: Decaying system Orange: After a split of systems Tracking: - motion vector - trajectory Attributes:

7 Evolution of RDT product  Since IOP ( )  Pursued in CDOP, CDOP2, proposal for CDOP3  Recent evolutions – v2011: use of NWP data – v2012: main cloud phase of the cell, highest convective rain rate inside the cell, second vertical level description – v2013: overshooting tops – v2016: advection scheme + change in NWCSAF Library + new output format + CTRAJ  CDOP3: CDOP2 continuation GEO imager for MSG, MTG and a set of other meteorological satellites

8 v2011: impact of NWP data How: CONVECTIVE INDEX calculated for each pixel - A mask is built at the beginning of RDT process. Allows to focus on areas of interest - Provides an additional predictor EXAMPLE 25 May 2009, 12h15 UTC. v2011 benefits from a better tuning in warmer categories, with higher early detection (cells over Italy diagnosed 30 min when v2011 and v2010 releases are compared) v2010: without NWP data v2011: with NWP Nata Consequences: - New attribute - Strong reduction of the false alarms during intermediate and winter seasons - Improvement of early detection RECOMMENDATION: USE NWP DATA!

9 When cell-extension is too large, it is interesting to have the depiction of another level additionally to « Base of Tower » level. An outline related to the « Top of Tower » has been added main contour: general attributes, including tracking attributes 2 nd contour: specific attributes v2012: 2 nd level description

10 v2013: OTD (Overshooting Tops Detection)  OTD Inside each RDT cell  Temperature of coldest pixel, BTD WV6.2-IR10.8, WBTD WV6.2-WV7.3, reflectance VIS0.6, gap to NWP tropopause.  Morphologic criteria to confirm a spot of cold temperatures and to determine the pixels that belong to an OT  HRV for tuning/validation

11 RDT: validation  Subjective validation by Météo-France various case studies, use of topical case for each release.  Objective validation by Météo-France Results fulfil the target accuracy requirements over a large domain and for an extended period: detection is superior to 70% and 25% of convective systems are diagnosed before lightning activity.  Validation by users any feedback is welcome

12 RDT: v2016 and following releases  v2016: advection scheme + change in NWCSAF Library + new output format + CTRAJ + use of CMIC  v2017: ovelapping CDOP2 and CDOP3. Improved links between other products (Ci as predictor). Himawari : impact of new channels and higher resolution. New tuning.  CDOP3: CDOP2 continuation – v2017, then version ready for MTG Day1, then preparation of a Day2 version GEO imager for MSG, MTG and a set of other meteorological satellites 20:10 21:10 WNW displacement of “A” WSW displacement of cell “B” “A” cell has disappeared. Bad forecast (False Detection) B A “B” at the expected location. Even if change in morphology is not forecast v2016

13 Overview 1. RDT 2. CI 3.Links between Products 4. MTG Context

14 14 Convection Initiation (CI) New NWC SAF product released in v2016 Low probability to develop into a thunderstorm High probability to develop into a thunderstorm The convection probability for each pixel is based on:  BT or BTD values or trends, e.g. BDT µm. Some relevant Parameters of Interest in « Best Practice Document For EUMETSAT Convection Working Group » Editors J. Mecikalski, K. Bedka, M. Marianne König  NWCSAF products: Clear Air Products, Cloud Products, Wind Products  NWP data  Past positions and characteristics of pixel

15 CI – Main principles  The CI product will be elaborated in two main steps.  The first step concerns the selection of pixels of interest. We propose to exclude the pixels that are too cold or are already thunderstorm (using RDT tracks): “too much” mask. We also exclude cloud-free pixels (using NWCSAF cloud products): “not enough” mask. Masks are combined Then cloud tower are identified having at least 3°C of vertical extension and a surface lower than km²  The second step concerns the probability calculation. Accordingly to literature on the subject three categories of predictors will be considered: –Vertical extension of the cloud, –Ice presence, –Cloud growing rate  In order to compute the trends we take into account cloud-displacement and calculate the past position of the cloud. For that purpose the HRW (High Resolution Wind) product of NWCSAF will be use additionally for an object- based movement detection.  Verification/tuning: RDT / radar / lightning data

16 Pixels of Interest selection – Preliminary results

17 Overview 1. RDT 2. CI 3.Links between Products 4. MTG Context

18 Links between products: RDT case  RDT product: –Cloud products: To operate RDT on cloudy areas Use of CMIC for v2016 –For the advection scheme. Complementary or additionally to cell speed estimate HRW/AMV can provide useful information: for new cells without speed estimate, in case of uncertainty in speed estimate, in case of merge or split. Foreseen for v2016 –RDT diagnosis: to validate RDT forecast. Foreseen for v2016 –Lightning data: to validate RDT diagnosis (operated in full configuration but without lightning data). –RDT diagnosis (motion vector, cooling rate): to contribute to RDT forecast –CRR: an attribute for RDT, a possibility to set the convection diagnosis to Yes if CRR above a threshold. Since v2012 –CI: predictor for RDT discrimination scheme. If a pixel has a high probability to become a thunderstorm it increase the chance of the corresponding cell to be defined as convective. Foreseen for v2017

19 Links between products: CI case  CI product –CI [0-60’] to be coherent with CI [0-30’] (p0-90’>p0-60’>p0-30’) –Cloud products: to operate CI on cloudy areas (mask) –RDT (diagnosis): to operate CI on non-thunderstorm areas (mask) –RDT (diagnosis - full configuration but without CI): to validate CI (additionally to radar and lightning data) –Some predictors of this new pixel-based product are based on trends. These trends have to be calculated following the cloud track. For that we will use the HRW/AMV in the low layers of the atmosphere.

20 Overview 1. RDT 2. CI 3.Links between Products 4. MTG Context

21 MTG Context LI is eagerly expected LI instrument is eagerly expected to improve many components of RDT:  Statistical scheme,  Real time mode,  Enhancement of characteristics for a more complete description of convection,  Monitoring. image credit: ESA

22 MTG Context FCI is eagerly expected Number of channels: Resolution: Better estimate of morphological parameters and small scale phenomena Spectral accuracy: better estimate of BT input data of RDT RSS Challenge The lack of channels in RSS would mean for RDT a lack of predictors and a lower quality FCI MSG SEVIRI Meteosat7 In previous years, RDT algorithm has always taken a lot of advantages from the increase of the number of channels e.g. for FCI: 0.91µm (total column precipitable water) RDT Cell with OT

23 Conclusion CI and RDT product –Algorithm: RDT algorithm has been developed before CI algorithm –Meteorology / conceptual model: CI occurs before. –Two products different in terms of presentation CI: image mode RDT: object mode –Strong links between these two products: input data, exclusion mask, validation –Strong links with other NWCSAF products: cloud products, CRRR, HRW/AMV –Together they offer a complete description of convective systems in various phases

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