<ul><ul><li>By </li></ul></ul><ul><ul><li>HOJJAT SEYYEDI </li></ul></ul><ul><ul><li>GEM 2008 </li></ul></ul><ul><ul><li>Ja...
Contents <ul><li>Introduction </li></ul><ul><li>Background </li></ul><ul><li>Significance of research </li></ul><ul><li>Re...
Introduction  <ul><li>Satellite precipitation estimates are widely used to measure global rainfall on near realtime and mo...
Background <ul><li>Heinemann and Kerényi, 2003; Grose et. al., 2002 and Ebert et. al. 1998  using geostationary satellite ...
Background <ul><li>Elbert et al. (1998), Smith et al. (1998) and Turk et al. (2000) implemented microwave (MW) radiometers...
Background <ul><li>The combination of both kinds of data, microwave (MW) data from polar orbiting satellites and IR (10.7 ...
Background <ul><li>Multi-sensor Precipitation Estimate (MPE) newly developed algorithm  by EUMETSAT. Blending passive micr...
Background <ul><li>Radar data as reference data because of  high temporal (5-10 min) and spatial (1 km) resolution, repres...
Significance of the research - The primary reason of implementing meteorological satellites is avoid the coverage and time...
Research Objective <ul><li>General Objective </li></ul><ul><li>Evaluating EUMETSAT MPE products in comparison with ground ...
Source data and study area (1) <ul><li>The study area is Northwestern Europe with focus on  the Netherlands and some parts...
Source data and study area (2) EUMETSAT MPE products and blending algorithm (mm/h) (mm/h) Geostationary satellite  (MET8 a...
Rainfall events (20090514, 19:00 UTC to 20090517, 23:45 UTC)  (20090525, 0000 UTC to 20090527, 23:45 UTC) CLAI -  Heavy ra...
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 gri...
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...
Research question and method (1) What are the differences in spatial distribution of EUMETSAT MPE products from METEOSAT 8...
Research question and method (2) What are the differences in spatial distribution of EUMETSAT MPE products from METEOSAT 8...
Categorical statistical results obtained   Instantaneous One hour accumulated Three hours accumulated Statistical Score Ra...
Research question and method (1) What are the differences in estimated values by EUMETSAT MPE products from METEOSAT 8 (5 ...
Research question and method (2) What are the differences in estimated values by EUMETSAT MPE products from METEOSAT 8 (5 ...
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 <ul><li>The null hypothesis (H0):  “no difference in spatial accuracy of EUMETSAT MPE products from METEOSAT8 ...
Recommendations <ul><li>The spatial accuracy assessment shows the METEOSAT 9 MPE product has higher statistical scores for...
Thank you
2*2 contingency table Ground based RADAR Yes No RSS_METEOSAT8 YES Hits False alarms Estimated Yes NO Misses Correct negati...
Scatter graph for MPE products, instantaneous comparison, second event The diagonal dotted line in the graph shows the ide...
Regressions for mean rainfall values in MPE products, α=0.05 Instantaneous One hour accumulated Three hours accumulated Ra...
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...
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MPE data validation

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  • 8.37 sq.km
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  • MPE data validation

    1. 1. <ul><ul><li>By </li></ul></ul><ul><ul><li>HOJJAT SEYYEDI </li></ul></ul><ul><ul><li>GEM 2008 </li></ul></ul><ul><ul><li>January 14, 2010 </li></ul></ul>Comparing satellite derived rainfall with ground based radar for Northwestern Europe Under the supervision of Dr. Ben Maathuis Dr. Chris Mannaerts
    2. 2. Contents <ul><li>Introduction </li></ul><ul><li>Background </li></ul><ul><li>Significance of research </li></ul><ul><li>Research objective </li></ul><ul><li>Study area </li></ul><ul><li>Research questions and results </li></ul><ul><li>Conclusions </li></ul><ul><li>Recommendations </li></ul>
    3. 3. Introduction <ul><li>Satellite precipitation estimates are widely used to measure global rainfall on near realtime and monthly timescales for climate studies, numerical weather prediction (NWP) data assimilation, now-casting and flash flood warning, tropical rainfall potential, and water resources monitoring </li></ul><ul><li>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 </li></ul><ul><li>Use of global and local data have significantly limited because of lack of precipitation measurements over the oceans as well as uneven distribution of rain gauges and weather radars </li></ul><ul><li>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, V., et al. 2007). </li></ul>
    4. 4. Background <ul><li>Heinemann and Kerényi, 2003; Grose et. al., 2002 and Ebert et. al. 1998 using geostationary satellite IR data based on cloud top temperature(Infra Red Methods). </li></ul><ul><li>Advantages: high temporal and spatial resolution for near real estimation of rainfall in areas with a sparse radar network (e.g. Central Africa) as well as for the long-term monitoring. </li></ul><ul><li>Disadvantage: There is no specific separation point between rain and no rain based on cloud top radiometric temperature. </li></ul>(Source: Heinemann and Kerényi, 2003)
    5. 5. Background <ul><li>Elbert et al. (1998), Smith et al. (1998) and Turk et al. (2000) implemented microwave (MW) radiometers for estimating precipitation. (Passive Microwave Methods) </li></ul><ul><li>Advantages: The MW can penetrate the cloud so it can contact directly to the hydrometeor. more accurate result than IR data </li></ul><ul><li>Disadvantages: poor spatial and temporal resolution of microwave sensors leads to significant sampling error in the estimation of accumulated precipitation. MW radiometers are not available on geostationary satellites. </li></ul>(Source: Turk et al. 2000)
    6. 6. Background <ul><li>The combination of both kinds of data, microwave (MW) data from polar orbiting satellites and IR (10.7 μm) data from geostationary systems (Vicente and Anderson 1993, Turk et al. 1998, Huffman et al., 2006). (Blended Global Products) The blended method mainly consist of three general steps i.e.: collocation, integration and transformation (Maathuis 2006). </li></ul><ul><li>Advantages: Higher spatial and temporal resolution, Higher accuracy, Global coverage </li></ul>(Source: Turk et al. 2000)
    7. 7. Background <ul><li>Multi-sensor Precipitation Estimate (MPE) newly developed algorithm by EUMETSAT. Blending passive microwave data from the SSM/I instrument on the US-DMSP satellites and images in the Meteosat IR channel for the estimation of instantaneous rain rates and daily rainfall averages on the resolution of METEOSAT (Heinemann and Kerényi, 2003). </li></ul><ul><li>MPE algorithm uses the passive MW rain rate measurements as calibration values therefore adjusts it geographically and temporarily. (Heinemann and Kerényi, 2003). </li></ul>Geostationary stellite1(meteosat 8) High temporal-spatial resolution Cloud infrared images Polar MW- hourly rainfall measurement Combination Algorithm Multi-sensor Precipitation Estimate (MPE)
    8. 8. Background <ul><li>Radar data as reference data because of high temporal (5-10 min) and spatial (1 km) resolution, representative of timescale and areal value estimated by satellites </li></ul><ul><li>The C-band Doppler weather radar employs scattering of radio-frequency waves (5.6 GHz for C-band) to measure precipitation and other particles in the atmosphere ( Rinehart, 2004) </li></ul><ul><li>Radar images are themselves indirect measurements of rainfall and are prone to errors of various kinds, the sources of error affecting quantitative weather radar observations and quantitative precipitation estimation (QPE) at mid-latitudes, are non-uniform vertical profile of reflectivity (VPR), variability of the drop size distribution (DSD), and attenuation due to strong precipitation intensity (Michelson et al. 2005, Holleman and Beekhuis, 2004; Huuskonen and Holleman, 2006) </li></ul>(source: Holleman, 2006)
    9. 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. 10. Research Objective <ul><li>General Objective </li></ul><ul><li>Evaluating EUMETSAT MPE products in comparison with ground based rain radar data </li></ul><ul><li>Sub Objectives </li></ul><ul><li>Implementing categorical verification statistics for assessing how the spatial distribution of the MPE products from METEOSAT8 and METEOSAT9 differ from the reference data. </li></ul><ul><li>Implementing continuous verification statistics for assessing how the values of the MPE products from METEOSAT8 and METEOSAT9 differ from the reference data. </li></ul>
    11. 11. Source data and study area (1) <ul><li>The study area is Northwestern Europe with focus on the Netherlands and some parts of Belgium . </li></ul><ul><li>Due to the Earth’s curvature, the distance over which weather radars observe the entire cloud is limited a maximum range of 200 km . </li></ul><ul><li>- Water bodies are masked out the weather radar observations to exclude unrealistically high rain rate values caused by sea clutter. </li></ul>Gauge adjusted ground based radar data (mm/h) (dBZ) Z - R Z = 200R 1.6
    12. 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. 13. 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 <ul><li>Heavy showers complexes over a large part of the Netherland </li></ul><ul><li>Showers were accompanied by exceptionally active thunderstorms </li></ul>1st event 2 nd event
    14. 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. 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. 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. 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. 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. 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. 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. 21. Results of continuous comparison Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event
    22. 22. Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event Results of continuous comparison
    23. 23. Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event Results of continuous comparison
    24. 24. Instantaneous 1 Hour accumulated 3 Hours accumulated First event Second event Results of continuous comparison
    25. 25. Conclusions <ul><li>The null hypothesis (H0): “no difference in spatial accuracy of EUMETSAT MPE products from METEOSAT8 and METEOSAT9” is rejected. </li></ul><ul><li>For the normal rainfall events analyzed using MPE product from METEOSAT9 seems to be more reliable. </li></ul><ul><li>The null hypothesis (H0): “no difference in estimated values by EUMETSAT MPE products from METEOSAT8 and METEOSAT9” is rejected. </li></ul><ul><li>MPE products is over estimating the severe event analyzed. </li></ul>
    26. 26. Recommendations <ul><li>The spatial accuracy assessment shows the METEOSAT 9 MPE product has higher statistical scores for north western Europe. However, it would be better to do a similar high temporal and spatial comparison for the other parts of the world. </li></ul><ul><li>The study was conducted on a short time scale only, so further work on longer series can help to improve the understanding of the accuracy of MPE products, e.g. on a seasonal basis. </li></ul><ul><li>Applying diagnostic verification methods such as fuzzy verification, which yield more in depth information about the nature of the errors. </li></ul><ul><li>Using Cloud Analysis Image (CLAI) in conjunction with MPE products to study the relationship between cloud type and rainfall estimation accuracy by METEOSAT 8 and METEOSAT 9. </li></ul><ul><li>Applying multi categorical analysis for a variety of rain thresholds to see how the performance depends on the rain intensity. </li></ul>
    27. 27. Thank you
    28. 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. 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. 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. 31. Cloud types Source :Dr. Maathuis presentation on METEOSAT-MSG
    32. 32. Illustration of the IR signal from different cloud types Source :Dr. Maathuis presentation on METEOSAT-MSG
    33. 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

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