Improving real time observation and nowcasting RDT. E de Coning, M Gijben, B Maseko and L van Hemert Nowcasting and Very Short Range Forecasting

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1 Improving real time observation and nowcasting RDT E de Coning, M Gijben, B Maseko and L van Hemert Nowcasting and Very Short Range Forecasting

2 Introduction Satellite Application Facilities (SAFs) are centres for processing satellite data The Nowcasting SAF started in February 1997 aiming to produce the software to deal with the Nowcasting and Very Short Range Forecasting using the characteristics of the MSG SEVIRI data and the NOAA and EPS AVHRR data (EUMETSAT Satellites).

3 Nowcasting SAF products website Cloud Mask Cloud type Cloud top temperature and height Precipitation Clouds Convective Rainfall Rate Precipitation with microphysics TPW Layer PW RDT Stability indices High Resolution wind Satellite Image interpretation 3

4 Nowcasting SAF products in SA These products are operationally available in Europe, but have not been operationally implemented and/or extensively tested over regions in Africa. WRC Funded project in SA: Using MSG and the local version of the UK Met Off Unified Model data as NWP input to the algorithms Project started in 2013 and will end in

5 Aim of the WRC project To provide forecasters, aviation meteorologists and hydrologists information about the development, life cycle and dissipation of convection in regions where radar systems do not provide coverage (in between radars over South Africa) or no radars systems are available (most of South Africa s neighbouring countries).

6 Rapidly Developing Thunderstorms - background The Rapid Developing Thunderstorm (RDT) combines a cloud tracker and an algorithm to discriminate convective and non-convective cloud objects. The cloud objects defined by the RDT are cloud towers with a significant vertical extension (namely at least 6 C colder than the warmest pixels in its surroundings) The major benefit of an automatic tool like the RDT is the object and tracking approach. Improved identification of convective cloud by the RDT product Y. Guillou, F. Autones, S. Sénési 6

7 The objectives of RDT are twofold: The identification, monitoring and tracking of intense convective system clouds The detection of rapidly developing convective cells There are 3 stages in the process: Detection Tracking Discrimination

8 RDT= Detection + Tracking Phase 1: detection towers identification, based upon 10.8 m channel Adaptative threshold ( reflectivity!) Tracking is done using consecutive images Slide courtesy: Jean-Marc Moisselin Météo-France, Toulouse

9 Phase 2: The discrimination method makes use of discrimination parameters calculated from MSG channels: IR 10.8, IR8.7, IR 12.0, WV 6.2 and WV 7.3 with NWP Two kinds of such discrimination parameters are considered: spatial characteristics temporal characteristics Discrimination is done using cloud top cooling rate and expansion rate The discrimination scheme is a mix between empirical rules and statistical models tuned on a learning database 9

10 Phase of development is determined by: History (last few time steps) Temperature trend (cooling/warming) Vertical extent Expansion Convective or Non-Convective storm activity Mature: top temperature < -40 C for at least 45min Mature transition: crossing top temperature 40 C Cold transition: crossing top temperature 35 C or base of cloud tower 25 C Warm2 transition: crossing top temperature 25 C or base of cloud tower 15 C Warm1 transition: crossing top temperature 15 C or base of cloud tower 5 C Warm : top temperature > -15 and base of cloud tower > 5 C, preceding Warm1 crossing 10

11 Discrimination of convective systems The picture above displays all RDT detected cells. This picture points out the detection and tracking efficiency of RDT. The next image displays convective objects only. The ratio between no convective and convective objects is about 100. Convective mask (from NWP) identifies stable, neutral/unclear and unstable areas to remove stable regions from being processed, thus reducing processing time and reducing false alarms. Slide courtesy: Jean-Marc Moisselin Météo-France, Toulouse

12 RDT uses. Mainly (and non-optional) satellite channel is IR10.8 μm (used for detection, tracking and discrimination). Additionally WV6.2, WV7.3, IR8.7 and IR12.0 μm channels can be used for convective discrimination. Other SAF-NWC products allow to establish a cloud mask (to operate RDT detection only on cloudy areas) and to describe RDT attributes (pressure and temperature at the cloud top, cloud type, Convective Rain Rate) NWP data can be used as instability masks, improving the detection of warm systems by RDT. Lightning data, if available in real time, greatly contribute to the discrimination of convective systems. RAPID DEVELOPMENT THUNDERSTORM (RDT) J.-M. Moisselin1, P. Brovelli1, F. Autonès1 Météo-France, Nowcasting Department

13 Rapidly Developing Thunderstorms Product v2012 Cloud Products SAF/NWC Foudre NWP indices RDT PGE 11 Detection Brightness temperature + Discrimination BUFR/HDF5 Slide courtesy: Jean-Marc Moisselin Météo-France, Toulouse

14 RDT: Examples from case studies comparing to radar data

15 Case 1: 09 November UTC 1130 UTC Slide courtesy Bathobile Maseko

16 Case 2: 17 October UTC 1615 UTC Slide courtesy Bathobile Maseko

17 RDT: Examples from case studies comparing to TRMM rainfall data

18 Case 3: 28 December ZIMBABWE Slide courtesy Bathobile Maseko

19 Case 3: 28 December Slide courtesy Bathobile Maseko

20 RDT: Recent (v2013) examples

21 Case 1: 10 October 2014 Slide courtesy Bathobile Maseko

22 Case 2: 11 October 2014 Slide courtesy Bathobile Maseko

23 Case 3: 5 June

24 Case 3: Compared to radar at 1630 UTC 24

25 Case 4: 20 September 2014 Afternoon thunderstorms and hail in JhB 25

26 Case 5: 10 August 2014 from 1200 to 1445 UTC

27 Case 6: 28 Sep 2014 Radar 1045 UTC 27

28 Case 7: 6 Oct UTC - Namibia 28

29 29 Case 8: Yesterday

30 30 Hail/tornado?

31 Validation of RDT by the developers (in France) RDT provides an accurate depiction of convective phenomena, from triggering phase to mature stage. The RDT object allows pointing out some areas of interest of a satellite image. It provides relevant information on triggering and development clouds and on mature systems. The subjective evaluation confirmed the usefulness of the RDT with moderate lightning activity. Thanks to these good results the status of RDT has been set up to operational by EUMETSAT in

32 Validation of RDT by Hungarian Weather Service (v2009) It detects the majority of the mature phase convective clouds. The small and/or warm cells are often missed Better performance in pure convective situation than in frontal situation. Sometimes a huge part of a front is detected as convective. We have verified RDT without the optional lightning input. If we used lightning as input, we would get better results. 32

33 Validation of RDT in SA 24 Lightning detection sensors over SA domain which measures CLOUD-GROUND Advantages: Complete coverage of SA 90% or more of Cloud-to-ground (CG) lightning strokes can be detected. Disadvantages: Only CG lightning detected Intra-cloud lightning (IC) not detected. This can negatively impact on the evaluations since some lightning is not observed. Lightning occurs in all thunderstorms, not just rapidly developing thunderstorms. This can negatively impact the statistics unless one distinguishes between the intensities of the strokes. Slide courtesy Morne Gijben 33

34 Validation methodology Used Mature and growing phase storms (Most CG lightning) Considered lightning intensities (similar to developers of RDT) Lightning intensities defined as: Low: >3 strokes (1 flash x 3) Moderate: >15 strokes (5 flashes x 3) Severe: >60 strokes (15 flashes x 3) Slide courtesy Morne Gijben

35 Results for 10 cases over SA Slide courtesy Morne Gijben

36 Slide courtesy Morne Gijben 36

37 Slide courtesy Morne Gijben 37

38 Hanssen-Kuipers Discriminant - Average of 10 Cases TIME (UTC) 3 Strokes 15 Strokes 60 Strokes 38

39 Validation results Statistics show: Fair amount of lightning occurs inside the RDT polygons. POD, POFD and HKD are good FAR are too high due to grid-box-based validation methods. Object orientated methodology is better to use. This is future work. Visual and statistical evaluations show that RDT polygons correctly identify the storms which produce lightning. Slide courtesy Morne Gijben 39

40 Validation future plans We are looking into improving our evaluation methodology by using an object-orientated evaluation technique on both the RDT polygons and lightning. Include lightning data as input into RDT Test the 2013 version of NowSAF software (operational) The NOWSAF products are also updated regularly, so we can look forward to even better products in the future. Slide courtesy Morne Gijben 40

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42 Back to 16 October 2014 WIND ARROWS INDICATE DIRECION AND SPEED OF MOVEMENT NOWCASTING! 42

43 Tornado time to 1000 UTC 43

44 Conclusion For the initial work, the v2012 of the software was used, the latest version of the software (v2013) is now operational in SA. Improvements in the software algorithms will possibly have even better validation results RDT showed very promising results to be used in addition to other observations such as radar and lightning detection in SA, where these are available In regions without radar systems and/or lightning detection networks, these satellite and NWP products will certainly benefit nowcasting procedures. RDT - Images for SADC and SA regions on RSMC for past 2 hours

45 RDT Summary and Future work The RDT can provide useful information on the development and phase of the intense parts of thunderstorms over data sparse regions such as Africa. If we upgrade to UM on 4 or 1.5 km we will also see improvement to RDT (Sfc Press/925 hpa included) Nowcasting applications in South Africa to complement the radar data (where available) Nowcasting applications in southern Africa where very few radar systems are operationally available Validation against lightning data over SA domain showed promising results. Methodology will be improved with time. Nowcasting on direction/speed of identified storms! 45