MSG/SEVIRI CHANNEL 4 Short-Wave IR 3.9 m IR3.9 Tutorial

Transcription

1 MSG/SEVIRI CHANNEL 4 Short-Wave IR 3.9 m IR3.9 Tutorial HansPeter Roesli EUMETSAT satmet.hp@ticino.com Contributions: D Rosenfeld (HUJ) J Kerkmann (EUM), M Koenig (EUM), J Prieto (EUM), HJ Lutz (EUM), COMET

2 I compiled the lecture Distance Learning,

3 Content IR3.9 (channel 4) most complex of MSG/SEVIRI channels IR3.9 excels for its versatility Technicalities Physical aspects Special effects Image rendering Applications Distance Learning,

4 Channel technicalities IR3.9 is a window channel (similar to IR10.8) + offering: Minimal absorption by atmospheric constituents ( IR10.8 (central part inherently cleaner than Good signal strength Low detector efficiency around 4 m for adequate - signal-to-noise ration requires trade-off between spatial resolution, detector temperature and channel width: Spatial resolution 3km SSP* Detector cooling ~95K Channel width limited by neighbouring H 2 O and CO 2 absorption bands *SSP Sub-Satellite Point Distance Learning,

5 Spectral position and width of IR3.9 channel H 2 O absorption CO 2 absorption IR3.9 Distance Learning,

6 Specification of IR3.9 escapes from H 2 O buys into CO 2 Other radiometers like AVHRR, (A)ATSR, MODIS, or MTSAT have channel centred at 3.7/3.8 m Slight shift of SEVIRI channel centre to 3.9 m with two consequences: + Negligible H 2 0 contamination on short-wave side - CO 2 contamination on long-wave side increased Correction for H 2 O attenuation no more necessary (difficult for low levels due to varying ( ratio water vapour mixing Correction of CO 2 attenuation inherently easier ( constant (mixing ratio ~ IR3.9 Distance Learning,

7 CO 2 attenuation Due to CO 2 attenuation IR3.9 generally 1K to 4K cooler than IR10.8 Not much of a problem for typical night time applications like fog detection just a bias To be taken into account for solar-thermal repartition - solar part indicative of cloud microphysical properties Distance Learning,

8 Limb cooling due to CO 2 attenuation IR3.9 weighting function shows absorption due to CO2 in middle-high troposphere Atmospheric path to satellite increases towards ( limb ) the outer parts of the Earth disc CO 2 column over emitting objects increases More absorption/attenuation towards limb Less signal received apparent cooling Limb cooling (brightening/darkening), i.e. full-disc images brighter (darker) along the limb Distance Learning,

9 Limb cooling temperature difference IR10.8-IR3.9 L L L Limb cooling in the IR3.9 channel L L Larger differences in cloud-free limb areas (cooler IR3.9 brightness ( temperature Distance Learning,

10 Sunshine Earthshine Signal in IR3.9 comes from reflected solar AND emitted thermal radiation! Consequence for Planck's relation between radiance and temperature: In daytime temperature derived from Planck's law is NOT representative for insitu temperature (too high)! Figure by COMET Distance Learning,

11 Extraction of solar part with CO 2 correction concept + Use IR10.8 IR13.4 radiance difference* as proxy to CO2 attenuation above image scene and add part of it to IR3.9 radiance Use IR10.8 radiance as estimate of thermal - contribution to IR3.9 radiance and subtract it from already CO 2 -corrected IR3.9 radiance Problems: Works satisfactorily only with IR3.9-wise thick clouds Error growth towards large sun zenith angles ( set (separation limit typically For details see MSG Channel Interpretation Guide * IR13.4 is situated in the CO 2 absorption band at 13 m-15 m Distance Learning,

12 Solar-part extraction extraction terminator early morning sun zenith angle 80 no separation separation IR3.9 total IR3.9 solar Distance Learning,

13 Image rendering 2 alternatives when using grey scales IR3.9's response to thermal emission and solar reflection offers two ways of image rendering when using grey scales Physical rendering where image brightness follows radiance levels measured by IR3.9 detector weak radiance / cold EBBT* dark grey shades strong radiance / warm EBBT bright grey shades Meteorological (inverse) rendering with appearance similar to visible imagery weak radiance / cold EBBT bright grey shades strong radiance / warm EBBT dark grey shades *EBBT Equivalent Black Body Temperature Distance Learning,

14 Physical rendering preferred by science/us IR39 tot '295K' '265K' IR39 solar ' 248K' '225K' 276K 276K IR K 218K IR39 therm Distance Learning,

15 Meteorological rendering preferred in EUMETSAT IR39 tot inv IR39 solar IR108 inv IR39 therm inv Distance Learning,

16 Meteorological vs physical rendering IR39 tot inv IR39 tot meteorological ( tutorial (used in this physical Distance Learning,

17 Meteorological rendering NIGHT Cold ice clouds cold snow surface mid-level clouds water clouds land surface ocean, lake fire Warm Only thermal contribution: clouds brighter (colder) than ocean surface Distance Learning,

18 Meteorological rendering DAY Low reflectance / Cold ice clouds snow surface ocean, lake cold land surface warm land surface water clouds hot land surface fire, sun glint High reflectance / Warm Thermal and solar contribution: low clouds darker than ocean surface Distance Learning,

19 Meteorological applications Low clouds and fog [day and night] Thin cirrus [day and night] and multi-layer clouds [day] Cloud phase & particle size [day and night] Sea and land surface temperature [night] (Forest) fire [day and night] Urban heat island [night] Super-cooled clouds [day and night] Cloud top structure (overshooting tops) [day] Distance Learning,

20 Fog and low stratus / stratocumulus emissivity IR3.9/IR10.8 Distance Learning,

21 Main application - fog and low stratus / stratocumulus Identification of fog and stratus at night remains main application of IR3.9, supported by high temporal resolution Compared to IR10.8 emissivity of water clouds at IR3.9 is smaller and water clouds reflect more of cold atmosphere above NOT the case for cloudfree surfaces! Absolute difference for SEVIRI larger by 2K-4K than for other radiometers due to CO2 attenuation! Breakdown of method at dawn-dusk and over sandy desert surfaces due to cancellation effect IR8.7 a valid alternative to IR3.9 for these cases and, in daylight, VIS0.6/0.8/1.6 anyway Method also valid for higher water clouds identification of super-cooled clouds Distance Learning,

22 Fog and low stratus / stratocumulus IR3.9-IR10.8 example night dawn day Fog or low stratus Clear ground High cloud Way out for dusk-dawn: replace IR3.9 by IR8.7 ( capability (IR8.7-IR10.8 even has excellent 24-hour Distance Learning,

23 Thin cirrus Cirrus is more transparent in IR3.9 than in IR10.8 (see red arrows) leading to stronger response of IR3.9 to warm radiation from below In addition, thin cirrus is often patchy and only partially fills the radiometer field-of-view, further ( later enhancing response of IR3.9 (see Thin cirrus is easily detected on IR3.9-IR10.8 ( difference images (large positive Distance Learning,

24 Thin cirrus over land and sea DAY VIS0.8 IR10.8 IR3.9-IR10.8 Distance Learning,

25 Snow cover NIGHT ( cirrus EBBT -3K snow cover (thin EBBT > -3K cirrus EBBT >> -3K thick ice cloud EBBT < -3K bare ground EBBT << -3K water (low) cloud Discrimination not optimal conflict with thin cirrus Improved performance in combination with other channels and channel differences, e.g. Night Cloud Microphysic RGB IR3.9-IR10.8 Distance Learning,

26 Reflected solar radiation phase and size of cloud particles Reflection in IR3.9 sensitive to cloud phase and very sensitive to particle size Higher reflection from Water droplets than from ice particles Small particles than from large particles Figure by COMET Typical particle size: water 5 m - 20 m ice often 50 m In meteorological view Clouds with small water droplets (St/Sc) darker than ice clouds Marine Sc (large water droplets) brighter than continental Sc Distance Learning,

27 Cloud phase Due to high reflection of sunlight by water droplets (low) water clouds appear much darker than (high) ice clouds marine stratocumulus off Portugal and NW Africa Distance Learning,

28 More structured iced cloud tops proxy for convective intensity IR3.9 1 Very small particles strong convection 2 Small particles weaker convection 3 Large ice particles weak/old convection IR10.8 Distance Learning,

29 Multi-layer clouds Low water clouds 2 High ice clouds 1 Distance Learning,

30 Supercooled clouds DAY Cloud tops consisting of supercooled water droplets may be located by using: IR3.9 imagery to identify phase supercooled water clouds reflect much, appear dark IR10.8 imagery to determine cloud top temperature supercooled water clouds have top temperature down to -40 C Distance Learning,

31 Supercooled clouds DAY IR3.9 IR10.8 VIS0.8 ( 5 C - Low water clouds (top ( 20 C - Mid-level SUPERCOOLED water clouds (top Snow 1 Distance Learning,

32 Supercooled clouds NIGHT At night water clouds can also be distinguished from ice clouds by using the fog product IR10.8- IR3.9 Similar to daylight the "fog product" and IR10.8 imagery can be applied together to locate supercooled cloud tops at night Supercooled clouds look like high fog Distance Learning,

33 Sun glint Like other solar window channels also IR3.9 shows specular reflection at sun/satellite are positioned at equal opposite zenith angles Effect enhanced due to detector saturation and ( later hot-spot sensibility (see Sun glint appearance: Over large water surfaces (nearly) circular area Over land reflecting rivers and lakes Over arid areas blurred spot (better seen when having ( sequence it moving across scene in animated image Possible application over water: detection of oil spills and estimate of wind speed Distance Learning,

34 Afternoon sun glint over the Atlantic Distance Learning,

35 Noon sun glint over the Kongo river Distance Learning,

36 ( 1 ) Morning sun glint over Arabia detector saturation IR3.9 total sun glint IR3.9 total no sun glint IR3.9 solar sun glint IR3.9 thermal sun glint observe eroded coast outline from solar contribution Distance Learning,

37 ( 2 ) Morning sun glint over Arabia detector saturation detectors (3 per scan line) only slowly recovering Distance Learning,

38 Sub-pixel response If there is variability within a radiometer field-of-view (~pixel) then the radiance for that FOV is a linear combination of the (! power separate radiances (NOT of their temperature values - IR3.9: B = T 13.6 B=(B1+B2+B3+B4)/4 T=B 1/13.6 T = 451 K 300 K 500 K IR10.8: B = T 4.8 B=(B1+B2+B3+B4)/4 T = B 1/4.8 T = 392 K 300 K 300 K example: hot spot within one pixel observation Distance Learning,

39 Sub-pixel response sensitivity to scene temperature Radiance not linear in temperature: B = T / Response to changes in scene temperature is much larger at shorter wavelengths Figure by COMET Distance Learning,

40 Hot spots fire ( EBBT(IR10.8 EBBT(IR3.9) - Strong sensitivity to subpixel "hot areas" makes IR3.9 channel very useful in fire detection With only 5% of pixel area at 500K: IR3.9 sees 360K (measures only ~335K due to ( saturation detector IR10.8 measures <320K With large pixel fractions covered by fire, both channels easily detect it Distance Learning,

41 Hot spots small and large fires very large fires also detected in IR10.8 IR3.9 MSG/SEVIRI adds temporal component! IR10.8 Distance Learning,

42 Hot spots fractional cloud cover Strong sensitivity to subpixel variations makes IR3.9 also useful for fractional cloud cover (neglecting effects of emissivity and atmospheric ( moisture Distance Learning,

43 Hot spots fractional cloud cover IR desert surface 2 closed cloud deck 3 broken clouds 4 cirrus difference image much more structured 4 4 IR3.9 - IR10.8 Distance Learning,

44 Better radiance contrast at warm scene temperature As compared to IR10.8 at IR3.9 for EBBT>~280K radiance increases faster with increasing EBBT, leading to improved contrast Small changes in temperature produce large radiance variations Figure by COMET Distance Learning,

45 Warm-temperature contrast flooding and marshes flooded one month later Flooded areas and marshes remain warmer at night (Upper Nile and Sudd marshes in Sudan Photo by WFP Sudan VAM Unit

46 Hot spots / warm-temperature contrast urban heat islands Under clear-sky conditions SEVIRI IR imagery detects urban heat islands IR3.9 (at night!) better suited than IR10.8 due to high sensitivity to sub-pixel temperature ( fires variations (warm areas in cities similar to Stronger contrast (temperature difference city - surroundings) in IR3.9 than in IR10.8 Paris: 291K Surroundings: 286K Distance Learning,

47 Strong signal noise at cold scene temperature As compared to IR10.8 at IR3.9 for EBBT<230K radiance is small and EBBT response very flat noise at cold temperature Small changes in radiance produce large temperature variations Figure by COMET Distance Learning,

48 Strong signal noise at cold scene temperature speckles IR3.9: noisy picture IR3.9 imagery well suited for warm scenes at night but not useful for temperature determination of cold scenes like thunderstorm tops However, noisy appearance indicates very cold (overshooting) Cb tops, e.g. accountable for speckled colour shade in Night Cloud Microphysics RGB composite Distance Learning,

49 Conclusions Not a pure window channel (CO 2 ) Limb cooling due to increasing CO 2 attenuation Detector saturation strong sun glint, hot spots Noise limits EBBT > 230K cold cloud Enhanced contrast at warm temperature heat island and marsh land Solar contribution cloud microphysics Emissivities differ from IR10.8 St, Sc, thin Ci Very sensitive to sub-pixel temperature variations hot spots, broken cloud Distance Learning,

50 Outlook MTG Back to 3.7/3.8 m-centred band Band width narrow enough (improved detector efficiency) to minimise both H2 O and CO 2 attenuation Dynamical range extended beyond fire temperature (non-linear gain) better estimate of biomass burning and no saturation artefacts Distance Learning,