Introduction For more than four decades, satellites have been observing the ocean from several hundred kilometers above the surface of the Earth. From the vantage of space, they have given us a global view of the ocean surface and the atmosphere and their variability. Satellite instruments that are in orbit today detect a broad range of ocean variables that touch on all aspects of oceanography These include sea surface temperature, surface wind velocity, chlorophyll, sea surface elevation, and ice cover, among others. The unifying element of satellite remote sensing is not just that measurements are made from space, but more specifically that satellite instruments detect electromagnetic radiation that is either emitted from or reflected off the surface of the ocean. The main attention will be given to passive and active microwave remote sensing of the ocean-atmosphere system.

Lecture consists of two parts:

Fundamentals of microwave remote sensing and Applications of satellite microwave data to study the ocean-atmosphere system

1 Outlines I. Fundamentals

Introduction to microwave radiometry and radar sensing. The electromagnetic spectrum. Brief history. Application of microwave remote sensing data. Microwave radiometry. Dielectric properties of natural media. Depth of penetration. Emissivity. Brightness temperature. Radiative transfer in the atmosphere-underlying surface system. Radar sensing. Backscattering. Altimeters. Scatterometers. Real Aperture and Synthetic Aperture Radars.

Outlines

• Microwave antennas and receivers. Directivity, sidelobes and loss. Sensitivity and calibration. Antenna temperature. • Multichannel scanning microwave radiometers: SSM/I, TMI, AMSU, AMSR-E. Surface Moisture and Ocean Salinity (SMOS) and Aquarius missions. Meteor-M N1 MTVZA, GCOM-W1 AMSR2. • Radars: QuikSCAT and ASCAT. ERS-1/2 SAR, Envisat ASAR. RADARSAT -1/2, ALOS PALSAR. • Passive microwave remote sensing. Absorption by atmospheric gases, clouds and precipitation. Sea surface temperature, salinity and wind speed. Sea ice concentration and age. Total atmospheric water vapor content and total cloud liquid water content. Retrieval algorithms for geophysical parameters. • Active microwave remote sensing. Sea-surface wind speed. Oceanic dynamic phenomena. Sea ice. Oil pollution. Imprints of atmospheric phenomena on radar images.

2 Outlines

II. Applications

Microwave remote sensing of oceanic and atmospheric phenomena. Case studies. Multi-sensor approach. Currents, eddies, bottom topography, etc. Tropical and extratropical cyclones, intense mesoscale convective vortices, cold air outbreaks, etc.

Remote sensing The ocean-atmosphere system is characterized by high temporal and spatial variability. The detailed study and monitoring of the Earth is the pressing problem. The use of remote sensing in different spectral bands to estimate geophysical fields is extremely successful. Electromagnetic radiation (EM) occurs as a continuum of wavelengths  and frequencies  from short wavelength, high frequency cosmic waves, to long wavelength, low frequency radio waves. The wavelengths that are of the greatest interest in remote sensing are visible and near infrared (IR) radiation in the range of 0.4-3 µm, (IR) radiation in the range of 3-14 µm and microwave radiation in the range of 1 mm – 1 m (frequency 0.3 – 300 GHz).

3 Electromagnetic spectrum, atmospheric transmission

Two natural sources of radiation, the sun and Earth, are of particular importance in remote sensing.

Spectral features of remote measurements

Wavelength Microwave Infrared Visible range Regime Passive Active Passive Active Passive Active Day / Night + + + + - + Cloudiness + + - - - - Spatial low low and high medium high high-medium high resolution Penetration < mm – m < mm - m < mm < mm < mm (land) < mm depth > 100 m m (ice) (land) (land ice) < m – 20 m m (ice) (water) < m – 20 m (water) 30 [cm]  Three measurements are used to describe EM waves: [GHz] wavelength () in µm, cm or m, frequency (ν) in hertz (Hz) and velocity (c) in m/s. 1 GHz = 109 Hz.    70 (degrees) 0.5 D

4 Sea Surface Height (SSH)

Active measurements using microwave radar Pulse sent from satellite to earth, measure returtn time With appropriate processing and averaging, it is possible to calculate: Ocean currents, eddies (scales > 60-100 km) Deviations in ocean surface due to bathymetry Gradual sea level rise due to global waring Deviation in ocean surface due to internal physical variability (heat, salinity)

5 Improving Models

•¼° spatial resolution •Hourly •Seasonal Simulations •Project Columbia (16 CPUs) •1 Week Wallclock

Thanks to Project Columbia Visualization 13

6 Brightness temperature of the ocean-atmosphere system C TB MICROWAVE RADIOMETER

↓ T B atm

h θ ↑ T B atm

TB ocean(ν,θ)=κ(ν,θ) T0

Brightness temperature of the ocean-atmosphere system H H  ( ,h)secdh T ( , )  ( , )T e ( )sec  T (h)e h secdh  B s  0 h   ( ,h)secdh [1( , )]T (h) ( ,h)e 0 secdhe ( )sec  0

[1( , )]T C ( )e2 ( )sec

TB is the brightness temperature at frequency ,  is the incidence angle,

Ts is the thermodynamic temperature and  is the emissivity of the sea surface,

T(h) is the air temperature at height h, H is the satellite height,

  o   (h)dh is the opacity (total absorption) of the atmosphere, 0

(h) is the absorption coefficient, C T = 2.69 + 0.003625 is the cosmic background radiation on the atmosphere top.

7 Spectra of the brightness temperature of the ocean-atmosphere system (curves 1) and the ocean at the lower (curves 2) and upper (curves 3) boundaries of the atmosphere. Solid lines – vertical polarization, dotted lines – horizontal polarization. Total water vapor content V = 59 kg/m2, total cloud liquid water content Q = 0.0 kg/m2 (black lines), V = 28 kg/m2, Q = 0 kg/m2 (blue lines); Q = 0.6 kg/m2, V = 61 kg/m2(red lines).

H20 O2 280 O2 H20 1 1 240 2 200 2 160 120 80

Brightness temperature, К Brightnesstemperature, 40 3 3 0 0 40 80 120 160 200 Frequency, GHz

V ,H V ,H  ( , )   V ,H  ( , ) TB ( ,)  TBocean(,)e  TBatm( ,)  TBatm( ,)[1 ( ,)]e  V ,H 2 ( , ) TC [1 (,)]e

V ,H V ,H TBocean(,)   (,)TS is the brightness temperature of the ocean

 TBatm (, ) is the upwelling brightness temperature of the atmosphere

 TBatm (, ) is the downwelling brightness temperature of the atmosphere

MICROWAVE RADIOMETER C TB

↓ T B atm

h θ ↑ T B atm

TB ocean(ν,θ)=κ(ν,θ)T0

8 Spectra of brightness temperature of the ocean-atmosphere system and the ocean at the lower TBocean = Ts and upper TBocean = Ts [exp(-sec) boundaries of the atmosphere for vertical and horizontal polarization calculated at various values of the atmospheric (V and Q) and oceanic (SST) parameters at incidence angle  = 55.

300 H2O 1 O2 2 250 3 4 200 5 6 7 150 8 9 100 10 11 12 BRIGHTNESS TEMPERATURE (K) TEMPERATURE BRIGHTNESS 50 13 14 0 15 0 10 20 30 40 50 60 70 80 90 100 16 FREQUENCY (GHZ)

Absorption by atmospheric gases and clouds

(,h) = ox(,h) + wv(,h) + cl(,h)

(,h) = F[, T(h), P(h), a (h),  (h)]

T(h), P(h), a (h) and  (h) are vertical profiles of air temperature, atmospheric pressure, absolute humidity and cloud liquid water content

ox(h) = oxreson(h) + oxnonreson(h) is molecular oxygen absorption,

wv(h) = wvreson(h) + wvnonreson(h) is water vapor absorption,

cl(h) is cloud absorption

Resonance absorption: shape of resonance lines, line strength,

dependence on P(h), collisions O2 - O2, O2 - N2, interaction between lines, etc.

9 Recent publications on WV absorption 1. Payne V.H., J.S. Delamere, K.E. Cady-Pereira, et al. Air-broadened half-widths of the 22- and 183-GHz water-vapor lines. IEEE TGRS, 2008, vol. 46, no 11, pp. 3601–3617. 2. Kneifel S., S. Crewell, U. Löhnert and J. Schween Investigating water vapor variability by ground-based microwave radiometry: Evaluation using airborne observations. IEEE Geoscience Rem. Sens. Lett. 2009, vol. 6, no. 1, pp. 157–161.

3. Turner D.D., M.P. Cadeddu, U. Löhnert et al. Modifications to the water vapor continuum in the microwave suggested by ground-based 150-GHz observations. IEEE TGRS, 2009, vol. 47, no. 10, pp. 3326-3337. 4. Cimini D., F. Nasir, E.R. Westwater et al. Comparison of groundbased millimeter- wave observations in the Arctic winter, IEEE TGRS, 2009, vol. 47, no. 9, pp. 3098–3106. 5. Payne V., K. Cady-Pereira and J.-L. Moncet Water vapor continuum absorption in the microwave. Abstracts of 11th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment. 1-4 March 2010. Washington, DC. USA. P. 58. GPS. Dependence between phase delay of electromagnetic waves and total water vapor content V. Del L = 0.6 cm/(kg/m2)

Absorption by atmospheric gases and clouds

(,h) = ox(,h) + wv(,h) + cl(,h)

ox(h) is molecular oxygen absorption,

wv (h) is water vapor absorption, cl(h) is cloud absorption

() = ox() + wv() + cl() is total atmospheric absorption

100 Spectra of total absorption by

3 10 2 1 2 water vapor at V = 59 kg/m 4 4 1 2 0 20 40 60 80 100 120 140 160 180 200 and 28 kg/m (curves 1 and 2),

0,1 molecular oxygen (curve 3)

0,01 and clouds at Q = 0.6 kg/m2 Frequency, GHz o 0,001 and tcl = 0 C (curve 4).

10 Absorption by atmospheric gases and clouds     (h)dh 0

 () = ox() + wv() + cl() is total atmospheric absorption  WV ( )  WV (,h)dh  k[v,a(h)]V 0  V  a(h)dh is total water vapor content 0

  cl ( )   cl [,(h),T(h)]dh  k[v,tcl ( )]Q 0

 Q  (h)dh is total cloud liquid water content 0

Cloud absorption  1    cl    0.06283 Im    0.1885  2 2   2   2    =   + j  is the complex dielectric permittivity of water,

Im is an imagery part,  is the cloud liquid water content

0.20 1.2 Total cloud liquid water (a) (b) content Q  (h)dh 1.0 89.0  0.15 19.35 0.8 Dependence of total 85.5 cloud absorption with 0.10 0.6 Q = 1 kg/m2 on cloud 10.65 0.4 droplet temperature at

0.05 37.0 SSM/I, TMI and AMSR 0.2 22.24 frequencies of 6.9, 10.65 6.9 0.00 0.0 and 19.35 GHz (a), and -30 -20 -10 0 10 20 -30 -20 -10 0 10 20 22.24, 37.0, 85.5 and Cloud temperature, t C Cloud temperature, t C 89.0 GHz (b).

11 Dielectric permittivity of water The complex dielectric permittivity of water is a function of frequency, temperature and salinity S

s   (v,T,S)     j  ' j'' 1 j2 20

where s and  are, respectively, the static and high frequency dielectric -12 coefficients of the sea water, o = 8.85x10 F/m is the permittivity of free space,  is relaxation time in seconds,  is the ionic conductivity of the dissolved salts in mho/m, and  is the frequency in Hz.

 S 0   o  is a real part 1 (2 )2

S 0    2 2  is an imagery part 1 (2 ) 200

Real part

Water temperature

Imagery part

Frequency, Hz Spectral dependence of the complex permittivity of water at various temperatures and various degrees of salinity (Schanda, 1976).

12 Dielectric constant of saline water at 20oC and salinity 33ppt

Real

Imagery

Dielectric constant of pure water at 20oC

Real

Imagery

13 Dielectric permittivity of water determines…. 4   emissivity of smooth water surface  p 12  q2

where

0.5 0.5 k    k    2 2 0.5 p  1 q  1 k      2 2 1 2  L  penetration depth 4q of electromagnetic wave into water

Dielectric permittivity of water determines….

1.8  2  cl  2 2 - cloud absorption coefficient  1  2   2

  2 1  2 - phase delay of cl  1 3 2 2    1  2   2  electromagnetic wave in clouds with Q = 1 kg/m2

For solving remote sensing problems precise values of permittivity of water should be known T. Meissner and F.J. Wentz. The complex dielectric constant of pure and sea water from microwave satellite observations. IEEE TGRS. 2004, vol. 42, pp. 1836-1849. E. Sharkov, Passive Microwave Remote Sensing of the Earth: Physical Foundations, ser. Springer Praxis Books in Geophysical Sciences. Berlin, Germany: Springer-Verlag, 2003.

14 Emissivity of smooth water surface The reflectance  of a surface is defined to be the ratio of the irradiance M reflected from the surface to the irradiance E incident on the surface  = M/E Reflectivity R is used to describe the reflectance from a plane dielectric. The spectral reflectance of a plane dielectric is given by the

Fresnel reflection coefficients RV,H = 1 –  V,H

2 2 2 2 V p  cos   q H cos  p  cos  q R  R  p  cos 2  q2 cos  p2  cos  q2

1 1 2 2 0.5 0.5 p  {[A2  ('')2 ]0.5  A}0.5 q  {[A  ('') ]  A} 2 2

2 A   cos  1

Dependence of emissivity of the smooth sea surface on incidence angle, polarization, and temperature of water

The emmisivity of sea water at vertical The emmisivity of water as a function and horizontal polarization as a function of water temperature. Solid curves - of an incidence angle at frequencies of fresh water, dotted curves - saline water 10.0 (1), 37.5 (2) and 100.0 GHz (3). (S = 35%o).

15

Δκocean(wind speed) = FF κfoam+ (1-FF) κrough

Ocean emission Foam emission Ocean emission

Dependence of emissivity on sea surface wind speed

Brightness temperature TBocean as a function of wind speed: slope versus frequency (Webster et al., 1976). Aziz M.A. et al. Effects of air–sea interaction parameters on ocean surface microwave emission at 10 and 37 GHz. IEEE TGRS. 2005, vol. 43, pp. 1763-1774. Boukabara S.-A. and F. Weng. Microwave emissivity over ocean in all-weather conditions: Validation using WINDSAT and airborne GPS dropsondes. IEEE TGRS. 2008, vol. 46, pp. 376-384. Uhlhorn E.W., P.G. Black, J.L. Franklin, et al. Hurricane surface wind measurements from an Operational Stepped Frequency Microwave Radiometer. Monthly Weather Rev., 2007, vol.135, no. 9, pp. 3070–3085.

16 Dependence of emissivity on sea surface wind speed

Wind-speed sensitivity of surface emissivity versus frequency, for horizontal and vertical polarization (Rosenkranz, 1992).

SST radiometric sensitivity

Plot of SST radiometric sensitivity (defined as change in surface brightness temperature caused by a 1C change in SST) as a function of frequency for vertical polarization at SST = 5, 15 and 25C, incidence angle 53 and ocean salinity 36%o (Galloway et al., 1997).

17 Microwave radiometers

Satellite microwave radiometers (1968 – 1987) Special Sensor Microwave Imager*) (SSM/I) TRMM Microwave Imager (TMI) Advanced Microwave Scanning Radiometer (Aqua AMSR-E and ADEOS-II AMSR)

*) Imager is a satellite instrument that measures and maps the Earth and its atmosphere. Imager data are converted by computer into pictures

Satellite microwave radiometers (1968 – 1987)

Satellite Instrument Resolution Frequency NEΔΤ (km) (GHz) (K) Kosmos-243 40 – 15 3.5, 8.8, 22.23, 37.5 0.5 / 2

Skylab S-193/S-194 10 / 280 13.90 / 1.41 1.0 / 1.0 Nimbus-5, 6 ESMR 25 /20 19.35 / 37.0 1.5 (47ms) Nimbus-5 NEMS 185 22.24, 31.4, 53.65, 54.9, 58.8 0.24-0.29 (2 s) Nimbus-6 SCAMS 145 22.24, 31.65, 52.85, 53.85, 55.45 1.0 / 1.5 (1 s) Nimbus-7, SMMR 121 6.63, 0.9 (126 ms) Seasat 74, 44, 38, 21 10.69, 18.0, 21.0, 37.0 0.9-1.5 (62 ms) Bhaskara-1,2 SAMIR 125 /200 19.35, 22.24 1.0 (0.21 s) MOS-1, 2 MSR 40 / 30 23.8, 31.4 0.75/0.9 (47ms)

18 Kosmos-243 Microwave Radiometer Specifications Wavelength (cm) 8.5 3.4 1.35 0.8 Center frequency (GHz) 3.5 8.8 22.2 37.5 Antenna pattern width (degree) 8.6 4.0 3.6 4.0 Efficiency (%) 80 85 76 95 Sensitivity (K) 0.7 0.5 0.9 1.3 IFOV*) (apogee) (km x km) 50 x 50 22 x 22 20 x 20 22 x 22 IFOV (perigee) (km x km) 35 x 35 15 x 15 13 x 13 15 x 15 Integration time (sec) 2.0 2.0 2.0 2.0 Receiver Total power Incidence angle (degree) 0 *) IFOV is Instantaneous Field Of View

Kosmos-243, the first satellite with microwave radiometers, was launched on 23 September 1968. Orbit inclination 71.3, apogee - 319 km, perigee - 210 km.

Kosmos-243 measurements

TB (K) Rains, ITCZ Brightness temperature variations across the Clouds and rains Pacific ocean measured at: 1 - 8.5 cm, 2 - 3.4 cm and 3 - climatic distribution

SST (oC) SST section across the Pacific ocean retrieved from TB(8.5) and TB(3.4): 1 – climatic, 2 – retrieved

19 Kosmos-243 measurements

Ice concentration map constructed from satellite microwave data: C > 50% (1) and C < 50 % (2).

Kosmos-243 measurements

Precipitable water over the Northern Pacific Ocean constructed from TB data acquired on 23 September 1968 with the superimposed atmospheric fronts

20 SSM/I (Special Sensor Microwave/Imager) SSM/I consists of seven separate total-power radiometers, each simultaneously measuring the microwave emission coming from the Earth and the intervening atmosphere. Dual-polarization measurements are taken at 19.35, 37.0 and 85.5 GHz, and only vertical polarization is observed at 22.235 GHz. Spatial resolutions vary with frequency. http://podaac.jpl.nasa.gov:2031/SENSOR_DOCS/ssmi.html http://www.ngdc.noaa.gov/dmsp/descriptions/dmsp_sensors.html

Frequencies 19.35 22.23 37.0 85.5 (GHz) Bandwidth 240 240 900 1400 (MHz) Polarization V/H V V/H V/H Sensitivity (K) 0.3 0.6 0.6 0.6 IFOV (km x 69 x 43 60 x 40 37 x 28 15 x 13 km) Sampling rate 25 x 25 25 x 25 25 x 25 12.5 x (km x km) 12.5 Integration 2.6 2.6 2.6 1.3 time (msec)

TRMM Microwave Imager - TMI

Center frequencies (GHz) 10.65 19.4 21.3 37.0 85.5 Bandwidth (MHz) 100 200 400 1000 3000 Polarization V/H V/H V V/H V/H Sensitivity (K) 0.6 0.6 0.6 0.6 1.1 IFOV (km x km) 46 x 26 25 x 15 23 x 14 14 x 8 6 x 4 Sampling rate (km x km) 10 x 10 10 x 10 10 x 10 10 x 10 5 x 5 Integration time (msec) 2.6 2.6 2.6 2.6 1.3 TMI is similar to the SSM/I instrument. There are some key differences: the addition of vertically and horizontally polarized channels at 10.6 GHz, the scan geometry is the same for every scan rather than alternating between an A scan and a B scan and the water vapor channel was moved from 22.2 to 21.3 GHz to avoid saturation in the tropics. TMI has a conical scanning geometry. It receives upwelling radiation from 49 degree off nadir. The swath width is 758.5km. This swath is covered by 104 low resolution pixels or 208 high resolution pixels. http://www-trmmrt.gsfc.nasa.gov/trmmrt/nsstart.htm

21 Aqua Advanced Microwave Scanning AMSR Radiometer (AMSR and AMSR-E)

AMSR measured the brightness temperatures of the atmosphere-ocean system with the vertical (V) and horizontal (H) polarizations at frequencies of 6.9, 10.7, 18.7, 23.8, 36.5, 50.3, 52.8 and 89.0 GHz.

AMSR only 89.0 89.0 Center frequency (GHz) 6.925 10.65 18.7 23.8 36.5 50.3 52.8 A B Band width (MHz) 350 100 200 400 1000 200 400 300 Polarization Vertical and Horizontal Vertical V H 3dB width (degree) 1.8 1.2 0.65 0.75 0.35 0.25 0.25 0.15 0.15 IFOV (km x km) 40x70 27x46 14x25 17x29 8x14 6x10 6 x10 3 x 6 Sampl. interval (kmxkm) 10 x 10 5 x 5 Temp. sensitivity (K) 0.34 0.7 0.7 0.6 0.7 1.8 1.6 1.2 Incidence angle, deg. 55.0 54.5 Dynamic range (K) 2.7 - 340 Swath width ( km) Approximately 1600 Scanning cycle (sec) 1.5

Спутник GCOM-W1 (Япония). Запуск в 2011 г.

Усовершенствованный микроволновый сканирующий радиометр AMSR2 для наблюдения собственного излучения земных покровов, поверхности океана и атмосферы на 7 частотах в диапазоне от 7 до 89 ГГц. Самая большая в мире спутниковая вращающаяся антенна.

22 Spectra of the brightness temperature of the ocean-atmosphere system (curves 1) and the ocean at the lower (curves 2) and upper (curves 3) boundaries of the atmosphere. Solid lines – vertical polarization, dotted lines – horizontal polarization. Total water vapor content V = 59 kg/m2, total cloud liquid water content Q = 0.0 kg/m2 (black lines), V = 28 kg/m2, Q = 0 kg/m2 (blue lines); Q = 0.6 kg/m2, V = 61 kg/m2(red lines).

280 1 1 240 2 200 2 160 120 80

Brightness temperature, К Brightnesstemperature, 40 3 3 0 0 40 80 120 160 200 Frequency, GHz

Sensitivity of TB to salinity vs microwave frequency

23 SMOS. Earth Explorers. ESA's Water Mission SMOS, http://www.esa.int/esaLP/ESAMBA2VMOC_LPsmos_0.html

Artist's impression of SMOS Data on ocean salinity are vital for improving our understanding of ocean circulation patterns. A novel 2-D interferometric microwave radiometer has been developed that is capable of observing ocean salinity by capturing images of emitted microwave radiation around the frequency of 1.4 GHz (L-band).

Aquarius – USA - Argentina Scheduled for launch in 2011, the international Aquarius/ SAC-D satellite will begin a mission to map the global sea surface salinity (SSS) field and its variability from space.

Salinity psu Aquarius will record more SSS observations in two months than have been measured since such observations began about 125 years ago.

24 SMOS: OCEAN SALINITY

Comparing SSS map generated with 3 days of SMOS data (ascending orbits 29-31 Jan 2010) and World Ocean Atlas climatology for January No data veraging Degraded results in field-of-view borders, near coast, and in areas of strong wind

FIRST SMOS SALINITY MAP by J. Tenerelli, CLS, Brest, 10 Feb 2010

25 The Amazon plume

processed by MTS processed by MTS

I. Corbella

Higher brightness temperature due to fresh water is seen away of the coast

26 NASA-Coordinated Satellite Systems (GEOSS Precursor?)

54

S. Pinori, R. Crapolicchio, S. Mecklenburg. Preparing the ESA- SMOS (Soil Moisture and Ocean Salinity) mission -Overview of the User Data Products and Data Distribution Strategy. Microrad’08. Florence. Italy. March 2008. SMOS L1 Processor - L1a to L1b Data Processing Model (DPM). Availble: http://www.smos.com.pt/downloads/release/documents/ SODS-DME-L1PP-0008-DPM-L1b.pdf SMOS L1 Processor – L1c Data Processing Model (DPM). Availble: http://www.smos.com.pt/downloads/release/documents/SO-DS- DMEL1PP-0009-DPM-L1c.pdf. Berger M., Camps A., Font J., et al Measuring Ocean Salinity with ESA's SMOS Mission, ESA Bulletin ,111, 113f. Drinkwater,M., Y.Kerr, J.Font, M.Berger, () The Soil Moisture and Ocean Salinity Mission. Exploring the Water Cycle of the Blue Planet. ESA Bull-Eur Space, 2009, 137 Piles, M., A. Camps, M. Vall-llossera, M. Talone (2009). Spatial resolution enhancement of SMOS data: a deconvolution-based approach, IEEE TGRS,

27 Outlines • II. Applications • Aqua AMSR-E sensing of synoptic-, subsynoptic- and mesoscale marine weather systems over different parts of the World’s ocean. • Comparison of the satellite microwave measurements and fields of the retrieved geophysical parameters with relevant satellite and in situ data (Terra and Aqua MODIS, NOAA AVHRR, QuikSCAT- derived wind fields, Envisat ASAR and ALOS PALSAR images, surface analysis maps and radiosonde reports). • Case studies of weather systems Tropical cyclones. Warm core Extratropical cyclones Cold air outbreaks, etc.

Weighting functions

H

TBatm(, ) = T(h) K(,h,) dh, 0 where

T(h) = vertical profile of the atmosphere temperature, h = height above the sea level,

   ( ,h)secdh K(,h,) = (,h,) e h sec = temperature weighting function, and  = atmospheric absorption.

28 Weighting functions

(a) (b)

Weighting functions for the tropical atmosphere at clear sky (a) and at cloudiness with Q = 0.14 kg/m2 (b) at frequencies of 50.3, 52.8, 53.8, 54.8, and 55.4 GHz

Warm core

10 km AMSR

48 km AMSU

110 km MSU

Cross section of temperature anomalies through Hurricane Hilda (1964) [after Hawkins and Rubsam (1968)]

29 Cross section of temperature anomaly through Hurricane Bonnie at 12:00 UTC 25 Aug 1998 retrieved from AMSU data

Maemi

Trajectory and central pressure 4-16 September 2003

Maemi is the Korean name for a cicada that legend says chirps madly to warn of a coming typhoon.

30 (a) Typhoon Maemi, TB(89H)

(c)

ΔTB  7 K

10 Sep 2003 02:33 UTC

Pmin  910 mb

Typhoon Maemi

89V 36.5V 23.8V 18.7V 10.6V 6.9V

89H 36.5H 23.8H 18.7H 10.6H 6.9H

AMSR brightness temperatures of typhoon Maemi taken on 10 Sep 2003 at 13:43UTC

31 Typhoon Maemi 10 Sep 2003 at 13:43 UTC

AMSR brightness temperatures over typhoon Maemi ΔT  8 K Pmin  910 mb B at 50.3 GHz (a) and 52.8 GHz (b) and sections through its center (c) and (d).

ADEOS-II Maemi

11 Sep 02:07 UTC

32 Central pressure vs thermal anomaly

PC = 1001 – 8.39ΔTB

Typhoon Sinlaku 10 September 2008

(а) (c)

Envisat ASAR image at 01:31 UTC (а), QuikSCAT-derived wind speed at 22:08 UTC superimposed on Тb(85H) measured by SSM/I at 22:27 UTC (b) and Тb(36H) measured by Aqua AMSR-E at 17:50 UTC (c)

33 Fanapi. 18 September 2010. 17:30 UTC

2 kg/m

Polar low. 5 February 2008

а b c Vladivostok Vladivostok Vladivostokк

Korea Korea Honshu Honshu

Aqua MODIS (a) and AMSR-E brightness temperatures at 36.5 GHz with horizontal polarization at 04:15 UTC (b) and at 15:18 UTC (c)

34 QuikSCAT-derived wind fields

а Vladivostok б Vladivostok

Корея Хонсю Корея Хонсю

20:54 UTC, 4 December (а) and 09:14 UTC 5 December 2005 (b)

11 November 2003 (a) (b) Cold air outbreak on Ice 10 Jan 2007

Mesoscale convective rolls and cells on satellite images acquired by

7 December 2003 (a) NOAA-17 AVHRR at Sakhalin 11:39 UTC and (b) Envisat ASAR at 11:46 UTC

Hokkaido

35 11 November 2003 Cold air outbreak on 10 Jan 2007

Surface analysis map of the Japan

Kamchatka Meteorological Agency for 12:00 UTC. Sakhalin Red rectangle marks the boundaries of Envisat ASAR image taken at 11:46 UTC.

Hokkaido

Cold air outbreaks on 10 January 2007

QuikSCAT-derived wind field acquired on 09:13 UTC. Dark lines mark the boundaries of Envisat ASAR image taken at 11:46 UTC. Sakhalin

Hokkaido

36 Cold air outbreak on 10 January 2007

Ice (a) Ice (b)

Ice Ice Sakhalin

Brightness temperatures with H-pol measured by Aqua AMSR-E at 36.5 GHz (a) and at 89.0 GHz (b) at 16:35 UTC Hokkaido

Cold air outbreak on 10 January 2007

(a) Ice (b) Ice

Ice 7 December 2003 Ice Sakhalin

2 kg/m kg/m2

Aqua AMSR-E-derived total water vapor content (a) and total cloud liquid water content (b) over the Okhotsk Sea at 16:35 UTC Hokkaido

37 Cold air outbreak on 20 December 2002

(a) (b)

Sakhalin Sakhalin

GMS-5 visible (a) and infrared (b) images of the Okhotsk Sea taken on 20 Dec 2002 at 02 UTC (a) and at 12 UTC (b) showing the organization of convection into 2D roll clouds over and downstream of the MIZ of the Okhotsk Sea.

Brightness temperatures at 89 GHz, H-pol during cold air outbreak on 20 December 2002

2.74 2.95

Kamchatka

Kamchatka

Sakhalin Sakhalin

1.89

194A; 02:30 105D; 16:30 UTC UTC K K

38

Total water vapor content

Kamchatka

Kamchatka

Sakhalin Sakhalin

15

02:30 UTC 16:30 UTC

Brightness Kamchatka temperature at 89 GHz, H-pol during cold air outbreak on 21 Dec 2002 at 01:30 UTC

K

39 Mesoscale convective open cells

Kamchatka

K

21 Dec 2002, 15:30 UTC 89 GHz, H-pol

K

Open cells. 21 Dec 2002, 15:30 UTC, 89 GHz, H-pol

ΔTb = Tbcl – Tbo = aoQ + a1ΔV + a2 ΔW

48.7 N

47.8 N Tbmin = 180 K Tbmax = 210 K

46.3 N

40