Remote Sensing Technology Institute, DLR Germany. Synergetic use of Radar and optical Satellite Images to Support Severe Storm Prediction

Transkript

1 Remote Sensing Technology Institute, DLR Germany Synergetic use of Radar and optical Satellite Images to Support Severe Storm Prediction S. Brusch, S. Lehner, J.-S. Stellenfleth SEASAR 2008, Frascati-Rome-Italy

2 1. Introduction / Motivation 2. Data and Methodology 3. Case studies and comparisons to models Folie 2

3 Introduction / Motivation MERIS L2 FR Nov, :26 UTC ENVISAT ASAR Nov, :26 UTC Folie 3

4 Introduction / Motivation MERIS L2 FR Nov, :26 UTC ENVISAT ASAR Nov, :26 UTC Folie 4

5 Introduction / Motivation MERIS L2 FR Nov, :26 UTC ENVISAT ASAR Nov, :26 UTC Folie 5

6 Introduction / Motivation Synergy ESA web interface: EOLI Web - Multi-Provider Interoperable Catalogue Service Optical data from different sensors MERIS, MODIS, MSG, AVHRR, Folie 6

7 Data and Methodology Wind field retrieval Cloud statistics and properties: Raining or non-raining? Convective or stratiform clouds? Folie 7

8 Data and Methodology Wind field retrieval Wind fields are derived from SAR data using a geophysical model function CMOD 4 or 5 (relating the NRCS of the ocean surface to the local near surface wind speed) : Wind direction SAR image Visible Streaks (or 2D-FFT, ) and Interpolation SAR image calibration Normalized Radar Cross Section Inversion of CMOD-5 Wind speed Incidence angle Folie 8

9 Data and Methodology Cloud statistics and properties Folie 9

10 Data and Methodology Cloud Classification 1. Type (convective, stratiform, ) ISCCP cloud statistics according to Stephens and Rossow MERIS / MODIS cloudy pixels (COT, eff radius, CTP, ) Folie 10

11 Data and Methodology Cloud Classification 2. Precipitation COT > 5 (optical thick clouds) and additionaly: according to Nauss: and model data (COSMO / EU) Nauss, 2006: Discriminating raining from non-raining clouds at mid- total convective precipitation (snow+rain) latitudes using multispectral satellite data Probability of precipitation > 45 % 0.10 mm/h Folie 11

12 Case studies Storm Britta on Nov, Meteorological situation 2. Wind field 3. Estimation of precpitation 4. Results Folie 12

13 Storm Britta Meteosat Second Generation (MSG) true color composite acqiured on Nov, at 10:00 UTC Surface weather analyse Nov 1, 2006 at 06:00 UTC (source: German Weather Service, DWD) Folie 13

14 Storm Britta North Sea MERIS L2 FR Nov, at 10:26 UTC ENVISAT ASAR Nov, at 10:26 UTC Folie 14

15 Storm Brítta Folie 15

16 Storm Brítta subscene (70x70km) Folie 16

17 Storm Brítta N 2 processes: - downdrafts due to precipitation (cooling) in clouds only valid for strong convective cells with high precipitation rates - transport of horizontal momentum in unstable MBL S Folie 17

18 Storm Britta Precipitation estimation: yellow lines: propability > 45 % METEOX.COM subscene (70x70km) Folie 18

19 postfrontal open cell convection sketch theoretical surface elevation with this model it is not entirely possible to explain high sea surface roughness in front of observed cloud lines Folie 19

20 u strong vertical wind shear Folie 20

21 u Rolls shape is different -> cloud structure is changing -> surface elevation (roughness) is shifted in the direction of the ambient wind field Folie 21

22 postfrontal open cell convection sketch theoretical surface elevation u downward transport of horizontal momentum in cold air parcels (red circles) Folie 22

23 postfrontal open cell convection sketch theoretical surface elevation u increasing wind speed on the surface theoretical shifted surface elevation Folie 23

24 Storm Brítta Comparisons to models HIRLAM, LSM (LM) Folie 24

25 Storm Brítta 25 m/s ENVISAT ASAR WSM + u 10 wind speed Nov, at 10:24 UTC 5 m/s ocean wave model (LSM) (3rd generation WAM) wind field input (COSMO/EU) 7x7 km, at 9:00 UTC Folie 25

26 Storm Brítta wind speed ENVISAT ASAR: arrows LSM: background scale: 5 m/s - 25 m/s Scotland (Highlands) Folie 26

27 Storm Brítta wind speed scale: 5 m/s - 25 m/s high-resolution limited area model (HIRLAM, SMHI) 22x22 km, analysis, 09:00 UTC ocean wave model (LSM) (3rd generation WAM) wind field input (COSMO/EU) 7x7 km, 09:00 UTC Quikscat wind field (morning pass) Folie 27

28 Polar Low on Mar, Folie 28

29 Polar Low HIRLAM (SMHI) 18 UTC analyse Polar Low on Mar 15, 2005 during LOFZY compaign 1 The HIRLAM analysis produces one trough-like disturbance (1) instead of the two PL's. The PL's did not show up in the forecasts ENVISAT ASAR WSM 20:45 UTC + u 10 Windgeschwindigkeit Folie 29

30 Polar Low ENVISAT ASAR WSM and overlaid MODIS observed clouds that are higher than 800 hpa Isolines of cloud top pressure (Red: 600 hpa, Yellow: 450 hpa) Folie 30

31 SeaSAR software tool TerraSAR-X, ENVISAT (ASAR, MERIS), MODIS, ERS-1/2 -> SAR: quicklooks, vessel detection, wind fields, currents -> optical data: cloud products, ocean color products -> future: automatic wind field retrievel (use of model data) Folie 31

32 Conclusion - It was demonstrated how the synergy of multiple instruments improve the application of earth observation satellites for oceanography: 1. Wind speed of a PL that was not detected by HIRLAM analyse could be measured using ENVISAT ASAR images 2. Differences in wind speed in cloudy areas of up to 15 m/s are observed (impact of gusty winds on storm surge prediction) 3. Small scale features, e.g., gusty winds or downburst are detected in SAR images - Differences between numerical models and remote sensing data are obseved, e.g,: LSM/LM shows to long wind shadows behind Norway HIRLAM shows a too smooth wind field compared to well tested and validated scatterometer wind field -Remote Sensing data can be used to validate models or probably to improve numerical model prediction by assimilating Folie 32

33 ACKNOWLEDGMENT We thank ESA for providing ENVISAT ASAR data in the framework of the AO project Wind farm, the Meteorological Institute hosted at the FU-Berlin for providing COSMO / EU data, Thomas Bruns (German Weather Service, DWD) for providing LSM data, the Swedish Meteorological and Hydrological Institute (SMHI) for providing HIRLAM data, and a part of the work was sponsored by the Helmholtz Association of German Research Centers (HGF) in the framework of the virtual institute EXTROP and the research network EOS. Folie 33