THE IMPORTANCE OF ADVANCED OPERATIONAL RELIABILITY AND READINESS MONITORING OF THE SAFIR HMS LIGHTNING LOCALIZATION SYSTEM

Transcription

1 th International Lightning Detection Conference April Tucson, Arizona, USA 1st International Lightning Meteorology Conference April Tucson, Arizona, USA THE IMPORTANCE OF ADVANCED OPERATIONAL RELIABILITY AND READINESS MONITORING OF THE SAFIR HMS LIGHTNING LOCALIZATION SYSTEM Ferenc Dombai Hungarian Meteorological Service Remote Sensing Devision H-1024, Budapest, Kitaibel INTRODUCTION Since July of 1998 the SAFIR HMS lightning localization system has been operated on national basis covering about km2 with 5 stations having its own data processing and displaying system. At the forecasters workplaces we deployed SAFIR TEX terminal for displaying only lightning data in real time. From 2000 the Hungarian Meteorological Service developed a forecaster s workstation named HAWK for displaying different kinds of meteorological information in a unified coordinate system. From that time the lightning data was integrated in to HAWK and it has become the unique displaying tools of them for forecasters. The technical staff is using SAFIR CPS Central Processing Station to follow the system workability. The experiences of forecasters gained from 2000 are showing that in several times the SAFIR data could be unreliable even for long time periods. In this periods in spite of the status bars are green on the SAFIR CPS display we found the flashes displayed on HAWK and on SAFIR TEX are frequently not real flashes and controversially there are severe thunderstorms without any flashes displayed. These cases make our forecasters uncertain and their opinions became that SAFIR data are unreliable. A question was raised, that the green is really green on status bars? As a consequence of detection errors of the SAFIR HMS technical reliability became very critical. The detection errors in 2002 caused that the annual lightning climatological data became so unreliable that it s issuing was cancelled. In 2003 the SAFIR HMS was extended with 2 additional sensor data from Slovakia which was giving more redundancy into the detection, Figure 1. In spite of this enhanced redundancy the detection efficiency of SAFIR HMS was not raised enough. The continuation the functioning of the SAFIR HMS was really questioned in Figure 1. The awaited localisation errors of the extended SAFIR HMS 1

2 2. ANALYSING DETECTION ERRORS In order to find the sources of detection errors and the way out to enhance of the reliability of SAFIR HMS we made extended post processing of archived lightning data using B$ and T$ files SAFIR archive files. For this purposes we used the SAFIRD analysing program and we wrote specific programs for processing raw sensor and localized lightning data and SAFIR status information also. In the case of all the events showed in followings the SAFIR status was green! TABLE 1. Statistic on SAFIR HMS operation in July of 2004 Date B$ size station1 station2 station3 station4 station5 station6 station7 A - station active, L- VHF sensor ok, N-VHF acquisition number OK, D LF acquisition number OK i sensor OK, n sensor NOT OK, 1-8 figures means different acquisition problems for sensors The above table, Table 1, shows statistics on SAFIR HMS + 2 operation in July The rows are fields for dates with thunderstorms only. From the table you can see that extreme B$ sizes exists for a period, that 3 VHF sensors are not providing data (2 sensors are the 40 % of the SAFIR HMS sensors!!!), that 3 LF sensors are unreliable, that 3 LF are not providing data only 1 LF from 7 is working in a reliable way. In the next table, Table 2, we show the periods with extreme B$ file sizes. 2

3 TABLE 2. Statistics on detection for extrem period of July 2004 DATE SIZE B$ file CC Flash CG Flash CG/CC % Byte /CC B$ , B$ , B$ , B$ , B$ , B$ , B$ , B$ , On Table 2, we can see that between the 20 th and the 26 th of July 2004 the sensors were providing many false localization data causing very high data size on one localized flash. 281 kbyte (!) maximum. The CG/CC flash ratio is not typical either. The figures below, Figure 2, 3, 4, are showing the typical detection errors found with SAFIRD At Bugyi station many false localization start at noon in different directions but nearly at same time At Maly Javornik station false azimuth detected nearly all time, real flashes can push it down At Varboc station many falsh localization start at 21:00 in different directions at the same time, the source is possible to be in the station itself Figure 2. VHF sensor detection errors - azimuth and detection level 3

4 Station Sarvar, VHF sensor is not providing detection, but 5 minutes self tests are OK!!! Also the LF sensor has a TOP for negative signals, causing under estimation of lightning currents! Station Maly Javornik, LF sensor usually saturated and fixed for positíve signals VHF detection looks good but falsh detection exists as it is shown on the previus figure. Figure 3. VHF and LF sensor detection errors - VHF number of aquisition and LF detection level The LF sensor aquisition number extremly high and changing with about 1 hour periods. The source of this is posible inside the station. Figure 4. LF sensor detection error for VARBOC station on 17th of April 2005 number of LF aquisition Tipical consequences of these localization errors are the caused false spatial structures on lightning localization maps which are caused by false or missing flashes It can be seen easily on daily localization maps but it is important to know that such structures can not be seen at daily work of forecasters because they are following the history of thunderstorm events with 1-3 hours long in past time. In the following picture we are showing localization map which are having false structures. On the Figure 5. we can see a daily localization map. The false structures are caused by missing flashes and false flashes also. On the Figure 6. we show a circuled region at South East Hungary with possible false flashes. They are false becouse of supposing that there are not flashes coming from the clear sky. These are not groupped into the specific structure they looks like real flashes. (In this case flashes were not reported by ground observers). To many situations of these will cause the result that forecasters oppinion about the operational use of lightning localization system will became It is unreliable. 4

5 Figure 5. Structural errors in daily localization map from 09th of July This picture was taken with SAFIRD analysing program Figure 6. Flashes localized without asssociated radar and satellite data possible false flashes This picture was compiled on HAWK meteorological workstation. 5

6 3. FLASH BACKWARD CALCULATION In searching the way of the operational and automatic quality control of the lightning localisation system we tested the Flash Backward Calculation method. Using the data on localized flashes we can calculate detection data means awaited detection time, azimuth detection level, etc. - which had to be detected at the particular station. After these calculation we are matching these calculated detection data with the realy detected data with searching pairs between flashes and their s real detection data for each station. After this matching we can compare the calculated and the detected data and we can derive different quality measures. In the Figure 7. and in the following paragraphs we are illustrating this calculation method for SAFIR HMS VHF sensors using real flash data from archived T$ files and real sensor data from real archived B$ data. Figure 7. Geographical positions of stations for the Flash Backward calculation methods In following tables, Table 3, 4, 5, we are showing the Flash Backward calculation results for real flashes from T$ file and for real sensor data from B$ file. In colums marked with M we show succesfully matched flashes and detections with numbers indicating the ordering number of flashes in the time period and vice versa such number of detections also. If M equal to 0 it indicates that the matching was unsuccesfull. 6

7 TABLE 3. Raw sensor data from B$ file with M - matching number of flashes from the T$ file Station1 Station2 Station3 station4 Staion5 No Time subs azm lev M subs azm lev M subs azm lev M subs azm lev M subs azm lev M TABLE 4. Flash data from T$ file and calculated sensor data and distances for each station for a second period with M - matching number of real detected sensor data from B$ file FLASH Station1 Station2 Station3 No Time subs x y T subs azm dist M subs azm dist M subs azm dist M TABLE 5. - Differencies between the calculated and detected sensor data with M - matching number of B$ data for each flash and station FLASH Station1 Station2 Station3 No Time subs x y T subs azm dist M subs azm dist M subs azm dist M

8 At the end of this calculation we can derive different quality measures and their ratings for each station. The important derived measures are the followings: Good detection, when calculated and detected data are matched well. In this case we can calculate azimuth errors, level problems, timing difference etc False detection, when station produces detection data without real flashes detected. Missed detection, when sensor does not provide data on real flashes but it had to be. Figure 8. Flash Backward calculation results for severe thunderstorm events on 1 st of August, 1998 In the Figure 8. we can see a thunderstorm situation. On the top left the CC flash activity is shown and on top left the station VHF detection activities are shown, on left below good detection ratio and on right below false detection ratio are shown for each station and. We can see that two station were working with good detection ratio about 80 % for all time of thunderstorm activity. For other stations this ratio was low but the distances were high for those stations. When the false detection ratio is high it shows some detection level problem or noisy detection for those stations. 4. QUALITY MEASURES OF RELAIBLE OPERATION On the base of our experiences on searching the cause of the unreliability of the SAFIR HMS when the system status was reported to be green we are proposing to introduce the following measures into the operational quality control of the lightning localization system. All of these can be calculated in real time, can be displayed and can be equipped with alarming levels which could be causing some automatic processes to avoid the long time system malfunctions. 8

9 Good station - stations with good detection ratio > threshold Good detection ratio the portion of detections associated with flashes at a station False detection ratio the portion of detections without any flashes at a station Missed detection ratio the portion of detections associated with flashes are missing at a station Detection ratio - detection number comparing to maximum possible on VHF and on LF Using the Flash Backward calculations we can derive other quality measures also in real time which could be displayed or send to the maintenance team - azimuth errors, timing problems, detection level problems, etc. We hope that the results of our work will be implemented into the operational practice soon. 5. REFERENCES Dombai F., On the Reliability of the Lightning Localization System in Hungary (in Hungarian) Proceedings on 30 th Meteorological Scientific Days p Using IDL version 5.3, Research Systems Inc 1999, 684 p User Guide to HAWK Hungarian Advanced Weather Workstation version 2.9, Hungarian Meteorological Service, 2004, 150 p. SAFIR User Manual, Dimensions,1999, 95 p, 6. CONCLUSIONS The situations mentioned in paragraphs above have proved that the reliability and data quality of an embedded observation system are very critical and that we must have paid more attention to this problem. The importance of this is even more emphasized when using so called intensive observing systems to which the lightning localization system belongs too. Divertly from the conventional observing system the information provided by intensive observing systems can not be easily proved and in real-time and detection errors can not be recognized because of the nature of data. The lightning data are not a continuous meteorological fields. The existence or the lack of such data depends on the real meteorological situation but depending on detection errors as well. The later are strongly depending on the technical reliability and the physical state of the observing system. Of course these systems have some additional controlling instruments, some of that has BITE-s or simply controlling circuits which are providing status information for users. They have some data analysing tools also. But when such systems are used as an embedded and unmanned system the status information provided by is the unique possibility for real time quality control. Post processing data analysing tool are very useful but not can not be applied into real time processes. We introduced some quality measures for following the reliability of lightning localisations systems. We hope that application of these measures in operationally way will help us to raise the level of the reliability of lightning localisation system. 9