Ralf Bennartz

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Microwave remote sensing of atmospheric properties using Aqua's AMSR and AMSU Ralf Bennartz Atmospheric and Oceanic Sciences University of Wisconsin

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Outline 1/2 Introduction Physical basis Observation geometry Microwave instruments Advanced Microwave Sounding Unit (AMSU) Advanced Scanning Microwave Radiometer (AMSR) Environmental parameters Water vapor Cloud liquid water

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Outline 2/2 Precipitation Precipitation identification Quantifying precipitation amount Comparison with ground radar Phase of precipitation at surface Sampling issues Soil wetness in Amazon river region Conclusions

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Microwave spectral range H2O H2O O2 O2

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Microwave instruments on AQUA Advanced Microwave Sounding Unit (AMSU) Cross track scanning instrument Main dedication is temperature and humidity soundings Several channels in oxygen absorption band 50-60 GHz Three channels in water vapor absorption line at 183 GHz Four window channels 23, 31, 89, and 150 GHz All channels with mixed polarization Spatial resolution varies with scan position and frequency between 15 x 15 km and about 100 km Advanced Microwave Scanning Radiometer (AMSR) Conical scan geometry Main dedication is integrated atmospheric properties, soil moisture, sea surface temperature and wind 6, 10, 18, 23, 37, 89 GHz All channels dual polarization Spatial resolution varies with frequency between 6 x 9 km and 70 x 50 km

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AMSR channels H2O H2O O2 O2

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Passive microwave observation geometry

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Comparison of direct broadcast versus global data (different calibration pathes) Differences smaller than 0.3 K for all channels

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Retrieval of water vapor column

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Dry bias in water vapor column for cloud-free observations

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Dry bias for cloud-free observations

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Cloud liquid water

MODIS versus microwave : 

MODIS versus microwave True color images

Liquid water from microwave: 

Liquid water from microwave LWP MODIS and microwave

Comparison with satellite microwave data: 

Comparison with satellite microwave data LWP MODIS and SSM/I

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Precipitation: Demands on precipitation estimates Nowcasting/short range forecasting Timely product generation Best possible spatial resolution Absolute accuracy in many cases not of major importance (three to four intensity classes are sufficient) Climate research High absolute accuracy needed to detect climate signals Global observations Temporal and spatial averages Long term observation Monitoring of extreme events

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Visible/NIR/IR (AVHRR/MODIS) + high spatial resolution + convective cells, even small ones, can be well identified - no strong coupling between spectral signature and rain - area of potential rain overestimated generally low likelihood - intensity and likelihood not really decoupled Microwave (AMSR, AMSU) - lower spatial resolution - small convective cells sometimes missed + stronger coupling between rain and scattering signature + rain areas better delineated + more independent intensity and likelihood information - sometimes spurious light rain - not applicable over snow and ice Precipitation from Satellites

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Passive microwave precipitation signal Most directly linked to surface precipitation Over cold (water) surfaces only All types of surfaces More indirect

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The scattering index Has been found to be a linear measure for precipitation intensity Predict brightness temperature T* in absence of scattering from low frequencies (functional relation is found by inverse radiative transfer modeling or global brightness temperature statistics) Take observed high frequency brightness temperature and subtract T*

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Four classes of precipitation intensity from co-located radar data Rain rate Class 1: Precipitation-free 0.0 - 0.1 mm/h Class 2: Risk for precipitation 0.1- 0.5 mm/h Class 3: Light/moderate precipitation 0.5 - 5.0 mm/h Class 4: Intensive precipitation 5.0 - ... mm/h Precipitation identification

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Precipitation identification using AMSU NOAA15 overpass 13 September 2000, 06:43 UTC

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NOAA15 overpass 13 September 2000, 06:43 UTC

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Passive microwave precipitation signal Most directly linked to surface precipitation Over cold (water) surfaces only All types of surfaces More indirect

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Z-R conversion radar Marshall-Palmer (1948): Z=200 R1.6 (Figure from Battan, 1981) Sekhon-Srivastava (1970): Z=2000 R2.0

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Moments of drop size distribution

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Average the high resolution data to the passive mw resolution and account for: passive mw beam shape up to 2.5 times 3-db radius parallax errors for both datasets (Bennartz, JAOT, 1999; Bennartz and Michelson IJRS, 2003) Convolution of high resolution data to passive mw observations

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Observation geometry Altitude of radar beam (elevation 0.5°): @100km distance: 2.2 km @200km distance: 5.2 km 273 K isothermal typically at 2-3 km

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Thunderstorm Graupel (Cold air outbreak) Frontal precipitation Radar reflectivity [dBz] Three different cases High LWP everywhere east of Gotland WVP 25 kg/m2 Dry air WVP 12 kg/m2 Isolated showers Moist air WVP 35 kg/m2

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Radar versus passive microwave precipitation estimate Thunderstorm Graupel (Cold air outbreak) Frontal precipitation

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Radar versus passive microwave precipitation estimate Thunderstorm Graupel (Cold air outbreak) Frontal precipitation Need information about type of precipitation event

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Likelihood of snowfall versus rain at the surface derived from AMSR-E estimates of the freezing level Upper panel: Thick lines: Surface observer reports liquid rain at the surface(741 cases) Thin line: Surface observer reports snowfall at the surface (449 cases) Lower panel: likelihood of frozen precipitation as function of freezing level

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AMSR-E derived frequency of frozen precipitation over the North Atlantic October 2002 January 2003

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Sampling of precipitation

Sampling issues at 60ON: 

Sampling issues at 60ON Sampling of precipitation

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Linear dependence of Tb on fraction of land (Bennartz, JAOT, 1999)

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Show movies of Amazon river

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Conclusions Microwave imaging instruments provide a number of key parameters for atmospheric and hydrological research and forecasting: These are column integrated water vapor, cloud liquid water, rainfall, and soil wetness information The clear advantage of microwaves over vis/nir/ir instruments is their ability to penetrate clouds Disadvantage is the lower spatial resolution compared to e. g. MODIS

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MSG SEVIRI Provides complementary information to mw sensors (cloud detection, cloud micprohysical parameters) Provides high temporal coverage (developing thunderstorms) GPM/EGPM will NOT have VIS/IR sensors

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MSG Daytime SEVIRI 19.3.2003 10:45 UT (RGB: 3.9 / 0.8 / 0.6 micron)

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MSG Nighttime SEVIRI 20.3.2003 23:45 UT (RGB: 12 / 11 / 3.9 micron)

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Radar frequencies

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AMSU-A and AMSU-B effective field of view (EFOV) as function of scan position Resulting from the continuous motion of the AMSU-B’s main reflector the EFOV of the AMSU-B is broadened in the cross-track direction This is not the case for the stepwise scanning AMSU-A

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NOAA15 overpass 18 June 2000, 07:39UTC

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NOAA15 overpass 13 September 2000, 06:43 UTC

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NOAA12 overpass 13 September 2000, 05:48 UTC