DEVELOPMENT OF COMMON NOWCASTING TOOLS IN CENTRAL EUROPEan NATIONAL WEATHER SERVICES

1. DEVELOPMENT OF COMMON NOWCASTING TOOLS IN CENTRAL EUROPEan NATIONAL WEATHER SERVICES V. Zwatz-Meise and A. Jann et. al. 1 , N. Strelec-Mahović and D. Drvar 2, A. Horvath et. al. 3 , M. Jurasek and J. Kanak et. al. 4, A. Poredoš et. al. 5 presentors: ales.poredos@gov.si drvar@cirus.dhz.hr 1 Zentralanstalt fur Meteorogie und Geodynamik, Austria (ZAMG) 2 Meteorological and Hydrological Service of Croatia (MHS) 3 Hungarian Meteorological Service (HMS) 4 Slovak Hydrometeorological Institute (SHMI) 5 Environmental Agency of the Republic of Slovenia (EARS)

2. Content • INTRODUCTION - BACKGROUND OF COMMON PROJECTS • PROJECTS 2.1 2002-2004 • Remote sensing based nowcasting techniques(satellite, radar): • Convective Cell Detection • Displacement Vectors • Forecast Images • Validation and comparison of methods • Conceptual models of convective cell life cycle • Training • Operationalization of methods 2.2 2004-2006 • Adaptation to MSG • Nowcasting methods (above) to MSG • Comparison of some methods with NWCSAF • Fog and low clouds detection module • Integrated high resolution precipitation analysis • RESULTS

3. Instruction to reader • Only outline/keywords and images are presented in slides • For more text please refer also to "notes" • View / Notes

4. Introduction - background of common projects • missing nowc.tools at the beginning of the projects • Austria • all radar-based nowcasting modules • Slovenia and Slovakia • all satellite-based nowcasting modules • Croatia • all remote-sensing nowcasting modules • Hungary • some satellite-based Nowcasting modules • joining limited resources of central european NWS (Austria, Croatia, Hungary, Slovakia, Slovenia) • sharing know-how • establishing common software depository • exchanging software modules • modules then locally adapted & tuned

5. Projects • 2002-2004 ("CEI Nowcasting System") CEI = Central European Inititative • remote sensing, pattern recognition, extrapolation • 2004-2006 ("CONEX II") COoperation and Networking for EXcellence • adaptation to MSG, NWC-SAF, fog & low clouds, prec.analysis resources of partner institutes + additionally funded by the Austrian Federal Ministry for Education, Science and Culture.

6. Projects / 2002-2004 / modules • Nowcasting of satellite / radar images (SatRad) • automatic Convective Cell Detection (CCD) • CC (Convective Cell) • FCC (Find Convective Cell) • TITAN (Ts Identification, Tracking, Analysis and Nowcasting) • Displacement Vectors (DV) • AMV (Atmospheric Motion Vectors) • RMV (Radar Motion Vectors) • TRACK (tracking) • TITAN • Forecast Images (FI) • FCI (based on AMV) • dBcast (based on RMV) • TITAN • Validation & comparison of methods • Conceptual models of convective cell life cycle (CM of CC) • Training • Operationalization of methods

7. Projects / 2002-2004 / modules / SatRad / CCD • automatic Convective Cell Detection (sat ^ radar) • CC (newly coded) • Typical patterns : CC reach high levels  local IR T min; CC have a circular or oval shape; diameter varies during the life cycle. • Detection procedure: find the local minimum; investigate pixels on 4 concentric circles; a sufficiently large difference (center vs. surroundings) • FCC ("Find CC" method) • Detection procedure: Detection of nodal points; Transfer of nodal points into cells/gravity centers; Cells’ distance and cells’ size tests; Output – Cells’coordinates • TITAN method (newly coded following parts of Dixon and Wiener, 1993) • Detection procedure: data evaluated & adapted to ellipses = "cells"

8. Time t + T (10 min,...) Time t Projects / 2002-2004 / modules / SatRad / DV / AMV • Nowcasting of satellite images • AMV (Atmospheric Motion Vectors) • two succesive images • compressed with Gaussian pyramids technique • standard cross-correlation technique for rectangular targets • backward tracking of features

9. Projects / 2002-2004 / modules / SatRad / DV / RMV • Nowcasting of radar images • RMV (Radar Motion Vectors) • two succesive images --> vectors (cross--correlation method TREC) • average vector of entire image --> blank areas • COTREC algorithm (variational techn. & shallow continuity equat.) • smoothing of vectors ("COntinuity of TREC vectors") An example of RMV field obtained by TREC method (left), smoothed field using COTREC method (right).

10. Input data: - Time sequence of images: T(-n) T(-3) T(-2) T(-1) T(0) T(+1) T(+2) … Tracking of cells’ centers in time sequence of images Time extrapolation of cell centers Location of cell centers Projects 2002-2004 / modules / SatRad / DV / TRACK • Nowcasting of radar ^ sat images • TRACK • input=cells’coordinates (from FCC module) and time sequence of images • cells’parrents detection • cells’trajectory construction • cells’trajectory time extrapolation

11. new 10' TITANcast (above) missed cells in an actual radar image (below) Projects 2002-2004 / modules / SatRad / DV / TITAN • Nowcasting of radar images • TITANcoded following Dixon and Wiener, 1993 • method for radar tracking and echo forecast • cell detection at t0 and t1 • normalized eigenvectors, centroid position, parameters like area of the storm, etc. • cell pairing (parents --> no merging, splitting !) • cell forecast • extrapolation of DVs and trends of ellipses' parameters • validation on case studies • "diagnostic" mode OK • for e.g. climatology of convective objects • poor forecasts --> "prognostic" mode abandoned

12. Actual sat image and corresponding AMV field (left) forecast sat images (right). Projects / 2002-2004 / modules / SatRad / FI / FCI • Nowcasting of sat ^ radar images • FCI (ForeCast Image) • What • computes forecast images from AMV field and the second involved image • for any kind of imagery • under the assumption of a motion field remaining unchanged with time • How • Computes for each pixel the trajectory • repeatedly applying the displacement given in the AMV file • hence, forecasts for lead times = multiples of the AMV interval • considerable smoothing of the trajectory field at every time step • determination of pixel values of the forecast image: • weighted mean of all pixels which are forecast to overlap • filling of gaps with averages of adjacent pixel values

13. Projects / 2002-2004 / modules / SatRad / FI / dBcast • Nowcasting of radar images • dBcast (forecast radar image) • What • computes forecast images from RMV field and the second involved image • for any kind of imagery • under the assumption of a motion field remaining unchanged with time • How • extrapolated echo patterns <-- backward-time integration of trajectories • ... similar to FCI (see above) ... Originating radar image and corresponding AMV field (left),30' forecast radar image (middle), actual radar image (right). Error of dBcast (from bottom to top): stratiform case, frontal convection, organized convection, air--mass convection

14. Projects / 2002-2004 / modules / validation & comparison • nowcast of satellite image • nowcast: +30,+60,+120 min; period: Sep-Jan 03, number of cases: > 4000 • evaluation method: • MeteoSat "B" image divided into squares; filled with 0/1 when > threshold • for every square: contingency tables • evaluated method: FCI method (using AMV fields);e.g. +120 min: • average / best / worst : POD % ~ 88 / 94 /67; FAR % ~ 11 / 6 / 30 • nowcast of sat Conv.Cell position • nowcast: +60 min; period: summer 2003 (nov&dec/2003), number of cases: 44 (600) • evaluation method: • a Central European sector of the sat image divided into squares; • for every square: contingency tables • compared 4 methods: FCC (or CC) + TRACK (or AMV);quite similar resuts: • POD ~ 30%; FAR ~ 65% (allowing app. 30km of CC distances) • POD ~ 45%; FAR ~ 45% (allowing app. 45km of CC distances) • POD ~ 65%; FAR ~ 20% (allowing app. 100km of CC distances) • nowcast of radar Conv.Cell position • nowcast: +30 min; period: summer 2002&2003, number of cases: > 800 • evaluation method: • radar image divided into squares; • for every square: contingency tables • evaluated method: FCC + TRACK • POD ~ 37%; FAR ~ 39% (allowing app. 5km of CC distances) • POD ~ 62%; FAR ~ 20% (allowing app. 15km of CC distances)

15. Projects / 2002-2004 / modules / validation & comparison 2 • nowcast of precipitation areas • diagnosed QUALITATIVELY (yes/no) • interpolation (weather reports, IR sat > threshold, radar) to sat_grid • then extrapolated with FCI method (using AMV fields) • nowcast: +30,+60,+120 min; period: Jan-Sep 03, 3 hourly, • every pixel within Austria • all weather situations/ all pixels : no signal - comparable to persistence • "nowcast" situations/ "interesting" pixel = starts/stops/keeps raining • % of correct pixels = app. 70% (30') to 60 % (120') --- 10% better than persistence • "nowcast" situations/ "changing" pixel = starts/stops raining • % of correct pixels = app. 30% (30') to 40 % (120') • precipitation areas (manually selected, automatic counting of pixels) • % of correct pixels = app. 50% (120') for MCS to 70 % (120') for fronts

16. Projects / 2002-2004 / modules / validation & comparison 3 • PODs, FARs ... and what now? • Q: are such methods really useful? • A: a useful nowcasting product must: • lead to better results than persistence : • some of the above comparisons indeed confirmed that methods (e.g. nowcast of convective cells) can be better than persistence up to three times, • assist at subjective nowcasting : • some of the investigations among operational forecasters showed that methods are accepted reasonably well in the subjective operational practice: • example answers to the investigation (G: general fcast, A: airport) Which products do you use in the operational process? How useful you find each product? Q: In which situations are sat. nowcasting products valuable? A: “nice advection”; frontal zone with intensive Cb; stratiform process (WF better, CF "also good"); time of frontal passage; determination of high/low clouds; tracking of storm cells; The satellite based nowcasting products give better overview of atmospheric evolution (agreed by 8).

17. Projects / 2002-2004 / modules / CM of CC • Conceptual models of life cycles of convective cells • using time series of remote sensing data • using LAM models (Aladin - MM5) --> no firm results

18. Projects / 2002-2004 / modules / training • brochure, posters, etc. • Computer Aided Learning (CAL) - Training CD • Joined Training Workshop (sponsored by EUMETSAT)

19. Projects / 2002-2004 / modules / operationalization • commonly developed methods --> operational use • examples of "national" operational visualization of "common" nowcast products:

20. Projects / 2004-2006 / modules • new data source - MSG • adaptation of the developed CEI nowcasting methods to MSG • comparison of some CEI methods with NWC SAF products • fog and low clouds detection module • integrated high resolution precipitation analysis

21. Projects / 2004-2006 / modules / MSG / AMV, FCI, RGB • Adaptation to MSG • AMV, FCI • common RGB composites • detection/dyagnosis of certain features • combinations of channels (e.g. 139, 321, etc.) • difference of channels (e.g. 4-9 for fog, etc.) Example of 120 min forecast out of 15 min interval between two succssive IR 10.9 images; grey shades for current image, vectors for AMVs, isolines for some values of nowcast image MSG-1, 5 June 2003, 11:30 UTC, RGB 01-03-09

22. Projects / 2004-2006 / modules / MSG / CEI vs. NWCSAF • Adaptation to MSG • Conceptual models of convective cells • comparison of conv.cell detection algorithms CEI (CC, FCC) vs. "Rapidly Developing Thunderstorm" (Nowcasting-SAF) • inclusion of MSG channels for cloud phase and life cycle • e.g. 3 (1.6 µm), 4 (3.9 µm), HRVIS • eventually improved "cell detection" algorithm • e.g. ch10 - ch9 filter to distinguish non-convective phenomena (such as lee clouds) from actual convection.

23. Projects / 2004-2006 / modules / MSG / fog & low clouds • Adaptation to MSG • fog and low cloud detection (more info) --> P 4.30: A. Wirth, ZAMG, Austria. Day/night-time low cloud detection using different spectral bands from meteosat second generation • Qualitative analysis of low clouds • thresholds --> binarized image (yes/no) • day- & night-time algorithm • difference (e.g. ch4-ch9) • combination (e.g. ch9, ch4, diff.) • dawn/dusk problems Example of "fog and low clouds" product. Red areas indicate appearance of this phenomena (according to the algorithm). Overlayed is MSG IR 10.9 image and ww from SYNOPs. Area left of the tilted line (purple) illustrates the dawn/dusk sensitivity of the algorithm.

24. Projects / 2004-2006 / modules / MSG / fog & low clouds 2 • Adaptation to MSG • fog and low cloud detection (more info) --> P 4.30: A. Wirth, ZAMG, Austria. Day/night-time low cloud detection using different spectral bands from meteosat second generation • Qualitative analysis + surface.temperature --> lower fog boundary • model (or objective analysis) T vs. cloud top T (ch 10.8) • in certain range -- > high probability for fog reaching the ground • Q: ? to use NWCSAF Cloud Type product instead ? • classes „low“ and „very low“ • although often confusing low clouds and snow ... • ... it is still developing !

25. Projects / 2004-2006 / modules / MSG / Precip. analysis • Integrated high-resolution precipitation analysis • Quantitative analysis of precipitation • Production of (national) integrated prec.fields • from surface observations, radar data, NWC-SAF products • Synthesis of the (national) prec.fields --> supranational • exchanging national products • with common software • Semiquantitative analysis • where the quantitative analyses are missing --> beyond the common region • including e.g. foreign countries / sea / mountains • intensity in e.g. 3 categories (light/medium/strong) • from an available combination of NWC-SAF products/radar data/conventional precipitation measurements

26. Projects / 2004-2006 / modules / MSG / NWP • Adaptation of NWP output • in : local observations, remote sensing data, high resolution topography • between: objective analysis, adaptation of DMO, assimilation • out: analysis & nowcasting fields; frequent update ... • operational national examples • MEANDER (H) (more info) --> O 6.14: A. Horvath Nowcasting system of the hungarian meteorological service • INCA (A) (more info) --> O 2.13: T. Haiden Prediction of convective cell initiation in mountainous terrain using a high-resolution analysis system • exchange of know-how, collaboration, ...., --> common orientation • remote-sensing & NWP & operational nowcasting specialists

27. Results • nothing revolutionary ... • way of work -- common for projects • scientifically -- well-known, relatively simple methods • ... • ... but noteworthy anyway • operationally: • improved or even first operational nowc.applications in partner NWServices • resources were "optimized": • used for adaptation and local tuning instead for re-coding of algorithms • "... spirit of international collaboration is enhanced ..." • operational exchange of nowc.data became more possible • and so, as such: " ... an example of possible fruitful cross-border meteo-collaboration ..." :-)