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[object Object],[object Object],[object Object],[object Object],Comparing satellite derived rainfall with ground based radar for Northwestern Europe Under the supervision of Dr. Ben  Maathuis Dr. Chris  Mannaerts
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction  ,[object Object],[object Object],[object Object],[object Object]
Background ,[object Object],[object Object],[object Object],(Source: Heinemann and Kerényi, 2003)
Background ,[object Object],[object Object],[object Object],(Source: Turk et al. 2000)
Background ,[object Object],[object Object],(Source: Turk et al. 2000)
Background ,[object Object],[object Object],Geostationary stellite1(meteosat 8) High temporal-spatial resolution  Cloud infrared images Polar  MW- hourly rainfall measurement  Combination Algorithm Multi-sensor Precipitation Estimate (MPE)
Background ,[object Object],[object Object],[object Object],(source: Holleman, 2006)
Significance of the research - The primary reason of implementing meteorological satellites is avoid the coverage and time gap of conventional ground-based rainfall data for a number of applications, above all hydrology and weather forecasting. - Similar to any observational data, investigating their accuracy and limitations is crucial. This is done by verifying the satellite estimates against independent data from rain gauges and radars (Levizzani, et al. 2007). -  No study has compared the potential of METEOSAT 8 and METEOSAT 9 MPE products with ground based radar data and/or gauge in estimating rainfall in high spatial and temporal resolution.
Research Objective ,[object Object],[object Object],[object Object],[object Object],[object Object]
Source data and study area (1) ,[object Object],[object Object],[object Object],Gauge adjusted ground based radar data  (mm/h) (dBZ) Z - R Z = 200R 1.6
Source data and study area (2) EUMETSAT MPE products and blending algorithm (mm/h) (mm/h) Geostationary satellite  (MET8 and MET9) High temporal-spatial resolution  Cloud infrared images Polar-SSM/I MW- hourly rainfall measurement  Blending Algorithm Multi-sensor Precipitation  Estimate (MPE)
Rainfall events (20090514, 19:00 UTC to 20090517, 23:45 UTC)  (20090525, 0000 UTC to 20090527, 23:45 UTC) CLAI -  Heavy rain with flooding in this period ,[object Object],[object Object],1st event 2 nd  event
Visual comparison: Met8-Radar-Met9 1 st  event ( 20090514, 19:00 UTC to 20090517, 23:45 UTC)_UTM zone 32_WGS 84,3×3 km grid size Instantaneous 1 Hour accumulated 3 Hours accumulated mm/h mm/h mm/h mm/h mm/h mm/h mm/3h mm/3h mm/3h
Visual comparison : Met8-Radar-Met9 mm/h mm/h 2 nd  event  (20090525, 0000 UTC to 20090527, 23:45 UTC)_ UTM zone 32_WGS 84,3×3 km Instantaneous 1 Hour accumulated 3 Hours accumulated mm/h mm/h mm/3h mm/3h mm/3h mm/h mm/h
Research question and method (1) What are the differences in spatial distribution of EUMETSAT MPE products from METEOSAT 8 (5 min temporal resolution) and METEOSAT 9 (15 min temporal resolution) in comparison with reference data?   Categorical  comparison
Research question and method (2) What are the differences in spatial distribution of EUMETSAT MPE products from METEOSAT 8 (5 min temporal resolution) and METEOSAT 9 (15 min temporal resolution) in comparison with reference data?
Categorical statistical results obtained   Instantaneous One hour accumulated Three hours accumulated Statistical Score Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 POD 0.46 0.28 0.41 0.33 FAR 0.17 0.30 0.20 0.34 CSI 0.42 0.25 0.37 0.28 Accuracy 0.74 0.72 0.71 0.72 Bias 0.55 0.40 0.52 0.51 ETS 0.27 0.15 0.22 0.17 Statistical Score Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 POD 0.53 0.31 0.51 0.34 FAR 0.13 0.27 0.16 0.31 CSI 0.49 0.28 0.46 0.30 Accuracy 0.73 0.69 0.71 0.68 Bias 0.61 0.43 0.60 0.50 ETS 0.30 0.15 0.26 0.15 Statistical Score Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 POD 0.57 0.35 0.59 0.36 FAR 0.07 0.15 0.10 0.17 CSI 0.54 0.33 0.55 0.34 Accuracy 0.70 0.58 0.69 0.58 Bias 0.61 0.41 0.65 0.44 ETS 0.28 0.13 0.26 0.13
Research question and method (1) What are the differences in estimated values by EUMETSAT MPE products from METEOSAT 8 (5 min temporal resolution) and METEOSAT 9 (15 min temporal resolution) in comparison with reference data? Continuous  comparison
Research question and method (2) What are the differences in estimated values by EUMETSAT MPE products from METEOSAT 8 (5 min temporal resolution) and METEOSAT 9 (15 min temporal resolution) in comparison with reference data?
Results of continuous comparison Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event
Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event Results of continuous comparison
Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event Results of continuous comparison
Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event Results of continuous comparison
Conclusions ,[object Object],[object Object],[object Object],[object Object]
Recommendations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Thank you
2*2 contingency table Ground based RADAR Yes No RSS_METEOSAT8 YES Hits False alarms Estimated Yes NO Misses Correct negatives Estimated No Observed Yes Observed No N=Total
Scatter graph for MPE products, instantaneous comparison, second event The diagonal dotted line in the graph shows the ideal 1:1 relationship between reference values and estimated values by METEOSAT 8 and METEOSAT 9
Regressions for mean rainfall values in MPE products, α=0.05 Instantaneous One hour accumulated Three hours accumulated Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 Observations (n) 288 308 288 308 Slope coefficient, p-value 7.35, 3.56E-122 1.84, 3.2E-64 7.96, 1.51E-127 1.21, 1.42E-58 Intercept coefficient, p-value -0.11, 0.047 -0.03, 0.27 -0.16, .004 0.01, 0.50 r² 0.85 0.61 0.87 0.57 Significance F 3.57E-122 3.2E-64 1.51E-127 1.42E-58 Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 Observations (n) 72 77 72 77 Slope coefficient, p-value 7.60, 6.13E-35 1.84, 9.72E-17 8.23, 2.59E-36 1.20, 3.89E-15 Intercept coefficient, p-value -0.13, 0.18 -0.03, 0.58 -0.18, .06 0.01, 0.71 r² 0.89 0.60 0.90 0.56 Significance F 6.13E-35 9.72E-17 2.59E-36 3.89E-15 Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 Observations (n) 24 25 24 25 Slope coefficient, p-value 7.82, 1.79E-14 1.98, 6.61E-07 8.43, 2.61E-15 1.26, 7.46E-06 Intercept coefficient, p-value -0.50, 0.19 -0.13, 0.59 -0.65, 0.09 0.02, 0.92 r² 0.93 0.67 0.94 0.59 Significance F 1.79E-14 6.61E-07 2.61E-15 7.46E-06
Cloud types Source :Dr. Maathuis presentation on METEOSAT-MSG
Illustration of the IR signal from different cloud types Source :Dr. Maathuis presentation on METEOSAT-MSG
METEOSAT8 METEOSAT9 0º 70 º N 12 min 15º N 5min 70 º S 52º N 140/12=11.65º/min 11min to the Netherlands from start point 3.5min to the Netherlands from start point Time stamp on images are based on the end of scan time In case of RSS, 1.5min difference between real scan time and time stamp on image In case of MET9, 4min difference between real scan time and time stamp on image

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MPE data validation

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Significance of the research - The primary reason of implementing meteorological satellites is avoid the coverage and time gap of conventional ground-based rainfall data for a number of applications, above all hydrology and weather forecasting. - Similar to any observational data, investigating their accuracy and limitations is crucial. This is done by verifying the satellite estimates against independent data from rain gauges and radars (Levizzani, et al. 2007). - No study has compared the potential of METEOSAT 8 and METEOSAT 9 MPE products with ground based radar data and/or gauge in estimating rainfall in high spatial and temporal resolution.
  • 10.
  • 11.
  • 12. Source data and study area (2) EUMETSAT MPE products and blending algorithm (mm/h) (mm/h) Geostationary satellite (MET8 and MET9) High temporal-spatial resolution Cloud infrared images Polar-SSM/I MW- hourly rainfall measurement Blending Algorithm Multi-sensor Precipitation Estimate (MPE)
  • 13.
  • 14. Visual comparison: Met8-Radar-Met9 1 st event ( 20090514, 19:00 UTC to 20090517, 23:45 UTC)_UTM zone 32_WGS 84,3×3 km grid size Instantaneous 1 Hour accumulated 3 Hours accumulated mm/h mm/h mm/h mm/h mm/h mm/h mm/3h mm/3h mm/3h
  • 15. Visual comparison : Met8-Radar-Met9 mm/h mm/h 2 nd event (20090525, 0000 UTC to 20090527, 23:45 UTC)_ UTM zone 32_WGS 84,3×3 km Instantaneous 1 Hour accumulated 3 Hours accumulated mm/h mm/h mm/3h mm/3h mm/3h mm/h mm/h
  • 16. Research question and method (1) What are the differences in spatial distribution of EUMETSAT MPE products from METEOSAT 8 (5 min temporal resolution) and METEOSAT 9 (15 min temporal resolution) in comparison with reference data? Categorical comparison
  • 17. Research question and method (2) What are the differences in spatial distribution of EUMETSAT MPE products from METEOSAT 8 (5 min temporal resolution) and METEOSAT 9 (15 min temporal resolution) in comparison with reference data?
  • 18. Categorical statistical results obtained Instantaneous One hour accumulated Three hours accumulated Statistical Score Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 POD 0.46 0.28 0.41 0.33 FAR 0.17 0.30 0.20 0.34 CSI 0.42 0.25 0.37 0.28 Accuracy 0.74 0.72 0.71 0.72 Bias 0.55 0.40 0.52 0.51 ETS 0.27 0.15 0.22 0.17 Statistical Score Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 POD 0.53 0.31 0.51 0.34 FAR 0.13 0.27 0.16 0.31 CSI 0.49 0.28 0.46 0.30 Accuracy 0.73 0.69 0.71 0.68 Bias 0.61 0.43 0.60 0.50 ETS 0.30 0.15 0.26 0.15 Statistical Score Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 POD 0.57 0.35 0.59 0.36 FAR 0.07 0.15 0.10 0.17 CSI 0.54 0.33 0.55 0.34 Accuracy 0.70 0.58 0.69 0.58 Bias 0.61 0.41 0.65 0.44 ETS 0.28 0.13 0.26 0.13
  • 19. Research question and method (1) What are the differences in estimated values by EUMETSAT MPE products from METEOSAT 8 (5 min temporal resolution) and METEOSAT 9 (15 min temporal resolution) in comparison with reference data? Continuous comparison
  • 20. Research question and method (2) What are the differences in estimated values by EUMETSAT MPE products from METEOSAT 8 (5 min temporal resolution) and METEOSAT 9 (15 min temporal resolution) in comparison with reference data?
  • 21. Results of continuous comparison Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event
  • 22. Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event Results of continuous comparison
  • 23. Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event Results of continuous comparison
  • 24. Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event Results of continuous comparison
  • 25.
  • 26.
  • 28. 2*2 contingency table Ground based RADAR Yes No RSS_METEOSAT8 YES Hits False alarms Estimated Yes NO Misses Correct negatives Estimated No Observed Yes Observed No N=Total
  • 29. Scatter graph for MPE products, instantaneous comparison, second event The diagonal dotted line in the graph shows the ideal 1:1 relationship between reference values and estimated values by METEOSAT 8 and METEOSAT 9
  • 30. Regressions for mean rainfall values in MPE products, α=0.05 Instantaneous One hour accumulated Three hours accumulated Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 Observations (n) 288 308 288 308 Slope coefficient, p-value 7.35, 3.56E-122 1.84, 3.2E-64 7.96, 1.51E-127 1.21, 1.42E-58 Intercept coefficient, p-value -0.11, 0.047 -0.03, 0.27 -0.16, .004 0.01, 0.50 r² 0.85 0.61 0.87 0.57 Significance F 3.57E-122 3.2E-64 1.51E-127 1.42E-58 Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 Observations (n) 72 77 72 77 Slope coefficient, p-value 7.60, 6.13E-35 1.84, 9.72E-17 8.23, 2.59E-36 1.20, 3.89E-15 Intercept coefficient, p-value -0.13, 0.18 -0.03, 0.58 -0.18, .06 0.01, 0.71 r² 0.89 0.60 0.90 0.56 Significance F 6.13E-35 9.72E-17 2.59E-36 3.89E-15 Radar _METEOSAT 9 Radar _METEOSAT 8(RSS) Event1 Event2 Event1 Event2 Observations (n) 24 25 24 25 Slope coefficient, p-value 7.82, 1.79E-14 1.98, 6.61E-07 8.43, 2.61E-15 1.26, 7.46E-06 Intercept coefficient, p-value -0.50, 0.19 -0.13, 0.59 -0.65, 0.09 0.02, 0.92 r² 0.93 0.67 0.94 0.59 Significance F 1.79E-14 6.61E-07 2.61E-15 7.46E-06
  • 31. Cloud types Source :Dr. Maathuis presentation on METEOSAT-MSG
  • 32. Illustration of the IR signal from different cloud types Source :Dr. Maathuis presentation on METEOSAT-MSG
  • 33. METEOSAT8 METEOSAT9 0º 70 º N 12 min 15º N 5min 70 º S 52º N 140/12=11.65º/min 11min to the Netherlands from start point 3.5min to the Netherlands from start point Time stamp on images are based on the end of scan time In case of RSS, 1.5min difference between real scan time and time stamp on image In case of MET9, 4min difference between real scan time and time stamp on image

Editor's Notes

  1. 8.37 sq.km
  2. 8.37 sq.km