Near Surface

• Wind/wave and surface roughness

• Air temperature

• Specific Humidity

• Pressure

Wind from passive remote sensing

• Cloud motion winds and similars

• Wind at the sea surface from passive MW radiometry

• Sunglint

Data coverage (received)

Another type of inversion: Polar WV winds from MODIS

Source: P. Menzel, 2003

Sunglint

In principle wind intensity can be estimated over ocean by measuring the angular extension of the sunglint area.

POLDER partial (6 out of about 14) sequence of images observing the same pixel

SU

NG

LIN

T

Specular (flat sea) reflection component

Surface emissivity - Oceans

Plane surface:Sea-water permittivityFresnel equations (I, Q)

Wind roughened surface:Sea-water permittivityFresnel equations (I, Q)Large-scale wavesGravity-capillary, capillary waves (> 2m/s)Whitecaps (> 7 m/s)Foam (> 10-12 m/s)

Directional wind roughened surface: Sea-water permittivity Fresnel equations (I, Q, U, V)Large-scale wavesGravity-capillary, capillary waves (> 2m/s) Whitecaps (> 7 m/s)Foam (> 10-12 m/s)

Modelled emissivity - Oceans10.7 GHz 19.35 GHz = 53.1o

= 0, 180o

v-pol.

h-pol.

37.0 GHz 85.5 GHz

Wind speed [m/s]

Comparison of different ocean surface emissivity models

2007/02/13, Local Morning Passes

2007/02/13, Local Evening Passes

Available observations for latent heat flux over oceans based on SSM/I:

Motivation

Temporal resolution of observationSpatial resolution

WindSat Payload DescriptionThe 6.8 channel is dual-polarization (vertical and horizontal), and is more

sensitive to sea surface temperature (SST) than to winds. Thus it is used to remove environmental noise due to variations in SST. Similarly, the 23.8 channel has dual-polarization and is highly sensitive to atmospheric water vapor. Thus, measurements at 23.8 GHz enable algorithms to correct for the effects of atmospheric attenuation on signals from the ocean surface.

WindSat uses a 1.8-m offset reflector antenna fed by 11 dual-polarized feed horns. The antenna beams view the Earth at incidence angles ranging from 50 to 55°. Table 1 shows the nominal beamwidth and resulting surface spatial resolution of each band. The Coriolis satellite orbits Earth at an altitude of 840 km in a Sun-synchronous orbit. The satellite completes just over 14 orbits per day. The orbit and antenna geometry result in a forward-looking swath of approximately 1000 km and an aft-looking swath of about 350 km. The fully integrated WindSat payload stands 10 ft tall and weighs approximately 675 lbs.

http://www.nrl.navy.mil/WindSat/Description.php

Sensitivity of 10.7 GHz third (a) and fourth (b) Stokes parameters to wind direction. The colors represent different wind speed ranges. Wind vector truth data supplied by the NOAA GDAS system. Figure courtesy of NOAA/NESDIS/ORA.

Wind ‘problem’

Scatterometers

ASCATAdvanced SCATterometer

Scatterometro che misura il vento sulla superficie del mare secondo la rugosità della

superficie osservata

Prodotti di ASCAT• Velocità e direzione del vento sulla superficie degli oceani

• Analisi di vegetazione tropicale

• Analisi di ghiaccio polare

• Osservazione di comportamenti climatici

• Strato di permafrost

• Desertificazione

• Profili verticali di temperatura e pressione

Funzionamento

• Le antenne trasmettono un lungo segnale e poi rilevano il segnale di back-scattering

• Il segnale varia con l’intensità del vento

• Le antenne sono orientate a 45° 135°e 225°, cioè a 90° tra loro in modo da avere osservazioni consecutive

Copertura e scansione

Il tipo di scansione è along-track ed hanno una copertura a destra e a sinistra della direzione del satellite per 384 km.

Ogni direzione offre osservazioni per un’area di 500 km

Near Surface

• Wind/wave and surface roughness

• Air temperature

• Specific Humidity

• Pressure

• Aerosols

• Precipitations

Air Temperature: Ta

• The air temperature needed for computation of sensible heat in principle refers to an atmospheric level too close to the surface to be really sensed by passive remote sensing sounders.

• Most of the algorithms computes Ta from the SST, or from mixing ratio assuming a fixed value of relative humidity or neural network (input SST, TPW etc..)

• Weaknesses of current techniques: because of the strong dependence from the SST the use for sensible heat computation may induce biases especially for estimation of instantaneous values.

• Accuracy: >1.5 K• Data availability and access: in general the Ta is not a

distributed product but is used in the Satellite based sensible heat database.

EXAMPLES:HAOPS: “Air temperature is derived using the mean of two simple bulk approaches: From the near surface specific humidity assuming a constant relative humidity of 80% at any time and from the SST assuming a constant temperature difference of 1K. Therefore the quality of this parameter may be of limited accuracy under certain conditions.”

Hong et al. 2003:

Ta=0.98*SST + 1.45

Jordan & Gautier 1995: Ta=a* SQRT[1-b/(c+W**2)] W: total precipitable water vapour

Meng,L., He,Y., Chen, J., Wu, Y., 2007: Neural network retrieval of ocean surface parameters from SSM/I data, Mon. Weather Rev, 135, 586-597

Near Surface

• Wind/wave and surface roughness

• Air temperature

• Specific Humidity

• Pressure

• Aerosols

• Precipitations

Near surface humidity: Qs

• The near surface humidity needed for computation of latent heat in principle refers to an atmospheric level too close to the surface to be really sensed by passive remote sensing sounders.

• Most of the algorithms computes Qs with regression from the SST, TPW and integrated water vapour in the lowest 500 m.

• Weaknesses of current techniques: because of the dependence from the SST the use for latent heat computation may induce biases especially for estimation of instantaneous values.

• All algorithms retrieving Qs use SSM/I observations and therefore have relatively coarse spatial resolution 30 km and problems close to coast

Ref Method Time scale Accuracy EqLHF(in the same paper)

Sate llite /instrument

1. Liu, 1986 (L86)

Simple polinomial regression (Q=f(W)

Monthly mean 8-10(-4) g/kg NO Soundings

LHF --- 20W/m2

2.Singh at al,2006

Genetic Algorithm (globally iterative, numerical optimisation method) (GA) ; Input data: W,Wb,SST ; Q=f(SST,W,Wb)

Daily averages RMS=1.6 g/kgQ=a*SST*Wb+b*W*WB+c*SST*(Wb**2)+ct

NO SSM/I, ship obs.

3.Schulz et al, 1993 (SC93)

Multivariate regression Q=f(Wb)Instantaneous values

Estimated standard error. 1.2 g/kg

Q=a +b*Wb NO SSM/I

[estimated standard error for Wb: 0.07 g/cm2]

Wb=a0+a1*Tv19+a2*Th19+a2Tv22

4. Chou et al, 1995

Principal component Analysis (6 global EOFs)

Monthly mean from daily values

0.75 g/kg --- 1.6 g/kg Q=f(W,Wb) YESSSM/I, radiosondes

6 sample population of humidity profiles, defines as a function of W ranges

1.72 g/kg Wb from Schulz et al, 1993

5. Wagner et al, 1990

Principal Component Analysis (7 regionalized EOFs, applied/applicable only for North Atlantic)

Daily0.9 g/kg in the surface layer 1.4 g/kg at 800 hPa

Qa=f(W,Wb,SST) based on EOFs

SSM/I

for W(between SSM/I and soundings) RMS=3.7 kg/m2

Wb from SSM/I, using Gloersen et al, 1994

Neural Network Td: RMS=1.51 C YES SSM/I(input: SSM/I TB Ta: RMS=1.47 C

Output: Ua, SST,Ta,Tdewpoint)Instantaneous values

SST: bias=0,07 C; RMS= 1.54 C

Bulk algorithm: Fairall et al, 1996; Chu et al, 2006

Ua: RMS=1.48 m/s

LHF RMS: 4.9 --- 37.85 W/m2

6. Meng et al, 2007

Methods to retrieve near-surface specific humidity

Meng,L., He,Y., Chen, J., Wu, Y., 2007: Neural network retrieval of ocean surface parameters from SSM/I data, Mon. Weather Rev, 135, 586-597

Singh,R., Joshi, P.C., Kishtwal, C.M., Pal, P.k, 2006: A new method for estimation of near surface specific humidity over global oceans, Met. And Atm. Phys., 94, 1-10

GA: Qa=0.729*SST*Wb+ 0.259*W*Wb-0.259*SST*Wb2+3.26

SC93: Qa=a+b*Wb

CH97: Qa=f(Wb,W) found using PCA

L86: Q=f(W) based on simple polinomial regression (for monthly mean)

Accuracy

Daily mean of mixing ratio at the surface for the period 1990-2003 at Trapani station.

2007/02/13, Local Morning Passes

2007/02/13, Local Evening Passes

Temporal resolution of observationSpatial resolution

Available datasets for latent heat flux over oceans:

•Hamburg Ocean-Atmosphere Parameters and Fluxes from Satellite Data (HOAPS(HOAPS) : daily and monthly, 1987-1998,

www.hoaps.zmaw.de 0.5o x 0.5o resolution

•Goddard Satellite-Based Surface Turbulent Fluxes

GSSTF 1GSSTF 1: daily and monthly, 1987-1994,

http://disc.gsfc.nasa.gov/precipitation/gsstf1.0.shtml 2o lat x 2.5o lon

GSSTF2GSSTF2: daily and monthly, 1987-2000,

http://daac.gsfc.nasa.gov/CAMPAIGN DOCS/hydrology/ hd gsstf2.0.htm 1o x 1o

•Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations

(J-OFUROJ-OFURO):

http://dtsv.scc.u-tokai.ac.jp/j-ofuro/ monthly, 1991-1995, 1o x 1o

3-days , 1992-2000, 1o x 1o

Based on Special Sensor Microwave/Imager (SSM/I)

Near Surface

• Wind/wave and surface roughness

• Air temperature

• Specific Humidity

• Pressure

• Aerosols

• Precipitations

PAOBSBy the early ‘70s new skills were developed to derive from satellite imagery estimates surface MSL and 1000/500 geopotential thickness and a paper by Kelly et al. (1978) describes a semi-objective procedure to modify mean sea level pressure and 1000-500 hPa thickness using cloud vortex patterns obtained from satellite imagery. The method combines the previous work of Nagle & Hayden (1971) and Troup & Streten (1973), and is designed for operational use, particularly in the Southern Hemisphere. The method is capable of reproducing synoptic-scale structure that can be deduced from cloud data and incorporated into a numerical analysis system using "bogus“ observations. Figure 1 illustrates the method, the green circles are the automatically generated bogus observations for use by the numerical analysis.

Kelly, G.A.M., G.A. Mills and W.L. Smith 1978. Impact of Nimbus-6 temperature soundings on Australian region forecasts. Bull. Amer. Mereor. Soc., 59: 393-405Nagle,R.E. & C.M.Hayden, 1971: The use of satellite observed patterns in the northern hemisphere 500 mb numerical analysis. NOAA Tech. Rep. NESS 55, 25 pp.Streten, N.A. & A.J.Troup, 1993: A synoptic climatology of satellite observed cloud vortices over the Southern Hemisphere. Quart.J.Roy.Meteor.Soc., 99 56-72

lidar

Comment: only 1 wavelength

Near Surface

• Wind/wave and surface roughness

• Air temperature

• Specific Humidity

• Pressure

• Aerosols

• Precipitations

Precipitations: physical basis• VIS/IR: is based on indirect relationship between cloud top

properties (e.g. temperature, reflectance, particle effective radius) and precipitation. Better performances for convective clouds (fixed bottom and varying top) than for frontal clouds (fixed top varying bottom). Good spatial resolution and temporal sampling (15’)

Precipitations: physical basis• Lighting: is based on the detection of emission from O2

stimulated by the energy discharge in a ligthning. Good spatial (<10 km) and temporal resolution. Limited to precipitating systems that have lighting. Need a quantitative relationship between lightning characteristics and precipitation

Precipitations: physical basis• Passive MW: based on interaction between precipitating particles and radiation.

Sensitivity to the vertical structure. Low sensitivity to solid precipitation or weak liquid precipitations (drizzle). Relatively low spatial resoltion (>10 km). Problems with coasts and islands. Temporal sampling: Low orbit satellites 2 sampling/day

• Radar up to now only over tropics and single frequency (14 GHz). Good spatial resolution. Real vertical profile