Radiometer Considerations Lectures in Maratea 22 – 31 May 2003 Paul Menzel NOAA/NESDIS/ORA.

Slides:



Advertisements
Similar presentations
Copyright © 2009 Pearson Education, Inc. Chapter 35 Diffraction and Polarization.
Advertisements

The waves spread out from the opening!
The Wave Nature of Light
MODIS/AIRS Workshop MODIS Level 2 Cloud Product 6 April 2006 Kathleen Strabala Cooperative Institute for Meteorological Satellite Studies University of.
Spectrometer Sam Valerio. Shows spectral distribution of a light source in the form of a graph. This specific one in the Imaging Science Center is called.
Diffraction of Light Waves
Radiation and the Planck Function Lectures in Benevento June 2007 Paul Menzel UW/CIMSS/AOS.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Remote sensing in meteorology
BASIC RADIATIVE TRANSFER. RADIATION & BLACKBODIES Objects that absorb 100% of incoming radiation are called blackbodies For blackbodies, emission ( 
Telescopes (Chapter 6). Based on Chapter 6 This material will be useful for understanding Chapters 7 and 10 on “Our planetary system” and “Jovian planet.
Fiber-Optic Communications James N. Downing. Chapter 2 Principles of Optics.
Chapter 25: Interference and Diffraction
This Set of Slides This set of slides deals with telescopes. Units covered: 26, 27, 28, 29, and 30.
Quick Review of Remote Sensing Basic Theory Paolo Antonelli CIMSS University of Wisconsin-Madison Benevento, June 2007.
Chapter 5 Remote Sensing Crop Science 6 Fall 2004 October 22, 2004.
High Spectral Resolution Infrared Land Surface Modeling & Retrieval for MURI 28 April 2004 MURI Workshop Madison, WI Bob Knuteson UW-Madison CIMSS.
14 October Observational Astronomy SPECTROSCOPY and spectrometers Kitchin, pp
Infrared Interferometers and Microwave Radiometers Dr. David D. Turner Space Science and Engineering Center University of Wisconsin - Madison
Remote Sensing and Image Processing: 7 Dr. Hassan J. Eghbali.
New Products from combined MODIS/AIRS Jun Li, Chian-Yi Liu, Allen Huang, Xuebao Wu, and Liam Gumley Cooperative Institute for Meteorological Satellite.
Measurement of Thermal Infrared Radiation Emitted by the Atmosphere Using FTIR Spectroscopy By Narayan Adhikari Charles Woodman 5/11/2010 PHY 360.
Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.
The waves spread out from the opening!
Lecture 27-1 Thin-Film Interference-Cont’d Path length difference: (Assume near-normal incidence.) destructive constructive where ray-one got a phase change.
University of Wisconsin - Madison Space Science and Engineering Center (SSEC) High Spectral Resolution IR Observing & Instruments Hank Revercomb (Part.
Atmospheric Soundings, Surface Properties, Clouds The Bologna Lectures Paul Menzel NOAA/NESDIS/ORA.
As components of the GOES-R ABI Air Quality products, a multi-channel algorithm similar to MODIS/VIIRS for NOAA’s next generation geostationary satellite.
Chapter 15 Preview Objectives Combining Light Waves
Radiometer Considerations And Cal/Val Lectures in Bertinoro 23 Aug – 2 Sep 2004 Paul Menzel NOAA/NESDIS/ORA.
Atmospheric Temperature, Moisture, Ozone, and Motion – Infrared
MODIS Preprocessing (before L1B) Changes over the Last Year (and looking forward) Chris Moeller and others CIMSS; Univ. Wisconsin July 13, 2004 Thanks.
GOES-12 (Channel Radiometer) Channels are typically independent of each other Need to know each channel’s – Spectral response function – Noise characteristics.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
Polarization analysis in MODIS Gerhard Meister, Ewa Kwiatkowska, Bryan Franz, Chuck McClain Ocean Biology Processing Group 18 June 2008 Polarization Technology.
CoRP Cal/Val Symposium July 13, 2005
Quick Review of Remote Sensing Basic Theory Paolo Antonelli CIMSS University of Wisconsin-Madison South Africa, April 2006.
1 Atmospheric Radiation – Lecture 13 PHY Lecture 13 Remote sensing using emitted IR radiation.
Collect 5 Calibration Issues Chris Moeller and others Univ. Wisconsin March 22, 2005 Presented at MCST Calibration breakout meeting, March 22, 2005.
Interannual Variability and Decadal Change of Solar Reflectance Spectra Zhonghai Jin Costy Loukachine Bruce Wielicki (NASA Langley research Center / SSAI,
Summary Remote Sensing Seminar Summary Remote Sensing Seminar Lectures in Maratea Paul Menzel NOAA/NESDIS/ORA May 2003.
Quick Review of Remote Sensing Basic Theory Paolo Antonelli SSEC University of Wisconsin-Madison Monteponi, September 2008.
Fourier Transform Infrared (FTIR) Spectrometer Subhashree Mishra ATMS Grad Student, UNR W. P. Arnott Physics, UNR Introduction to Atmospheric Instrumentation.
Electro-optical systems Sensor Resolution
Collect 5 Calibration Issues Chris Moeller and others Univ. Wisconsin March 22, 2005.
Copyright © 2009 Pearson Education, Inc. Chapter 35-Diffraction.
Cloud Detection: Optical Depth Thresholds and FOV Considerations Steven A. Ackerman, Richard A. Frey, Edwin Eloranta, and Robert Holz Cloud Detection Issues.
Passive Microwave Remote Sensing
Phys102 Lecture 26, 27, 28 Diffraction of Light Key Points Diffraction by a Single Slit Diffraction in the Double-Slit Experiment Limits of Resolution.
Chapter 35-Diffraction Chapter 35 opener. Parallel coherent light from a laser, which acts as nearly a point source, illuminates these shears. Instead.
Paper under review for JGR-Atmospheres …
Pre-launch Characteristics and Calibration
V2.0 minus V2.5 RSAS Tangent Height Difference Orbit 3761
Basic Concepts of Remote Sensing
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
An Overview of MODIS Reflective Solar Bands Calibration and Performance Jack Xiong NASA / GSFC GRWG Web Meeting on Reference Instruments and Their Traceability.
AIRS (Atmospheric Infrared Sounder) Instrument Characteristics
How to Use This Presentation
Chapter 35-Diffraction Chapter 35 opener. Parallel coherent light from a laser, which acts as nearly a point source, illuminates these shears. Instead.
Meteosat Second Generation
Instrument Considerations
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
Diffraction vs. Interference
By Narayan Adhikari Charles Woodman
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
Early calibration results of FY-4A/GIIRS during in-orbit testing
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
Remote sensing in meteorology
The waves spread out from the opening!
Presentation transcript:

Radiometer Considerations Lectures in Maratea 22 – 31 May 2003 Paul Menzel NOAA/NESDIS/ORA

Relevant Material in Applications of Meteorological Satellites CHAPTER 12 - RADIOMETER DESIGN CONSIDERATIONS 12.3Design Considerations Diffraction The Impulse Response Function Detector Signal to Noise Infrared Calibration Bit Depth12-5

Remote Sensing Instrument Considerations Radiometer Components Opticscollect incoming radiation separate or disperse the spectral components (dichroics, grating spectrometer, interferometer, prism,...) focus the radiation to field stop Detectorsrespond to the photons with a voltage signal Electronics voltage signal is amplified by the electronics A/D converts into digital counts. Performance Characteristics Responsivitymeasure of the output per input Detectivityratio of the responsivity per noise voltage Calibrationattempts to reference the output to known inputs. Design Considerations Diffractionfunction of the mirror size Impulse Responsedetermines how sharp edges appear Signal to Noisehow clean is the image Infrared Calibrationenables quantitative use of measurements Bit Depthtruncation error can limit precision of data Satellite Orbits Geostationary vs Polar orbiting vs Other

Approaches To Separate Radiation into Spectral Bands radiometer - uses filters to separate spectrum by reflection and transmission (wavelengths are selectively reflected and transmitted) prism - separates spectrum by refraction (different wavelengths bend into different paths) grating spectrometer - spatially separates spectrum by diffraction (wavelets from different slits will be in phase in different locations depending on wavelength) interferometer - separates spectrum by interference patterns spread out temporally (wavelets from different paths will be in phase at different times depending on wavelength)

Radiation is characterized by wavelength and amplitude a

Interference: positive (a) for two waves almost in phase and negative (b) for two waves almost out of phase

Combining two waves of slightly different wavelength

Separation of Spectra

Spectral Separation with a Prism: longer wavelengths deflected less

Spectral Separation with a Grating: path difference from slits produces positive and negative wavelet interference on screen

Spectral Separation with an Interferometer - path difference (or delay) from two mirrors produces positive and negative wavelet interference

Interferometer measurements compared with atmospheric physics calculations CO 2 Lines

Design Considerations (1) Diffraction Mirror diameter defines ability of radiometer to resolve two point sources on the earth surface. Rayleigh criterion indicates that angle of separation, θ, between two points just resolved (maxima of diffraction pattern of one point lies on minima of diffraction pattern of other point) sin θ = λ / d where d is diameter of mirror and λ is wavelength. Geo satellite mirror diameter of 30 cm at infrared window wavelengths (10 microns) has resolution of about 1 km. This follows from 10-5 m / 3 x 10-1 m = 3.3 x 10-5 = r / 36,000 km or r = 1 km = resolution.

Energy distribution from diffraction through a circular aperture Max numberenergylocation of ring Central maxE0  1.22 λ / d Second max 0.084E1.22  2.23 λ / d Third max0.033E2.23  3.24 λ / d Fourth max0.018E3.24  4.24 λ / d Fifth max0.011E4.24  5.24 λ / d Thus for a given aperture size more energy is collected within a given FOV size for shorter vs. longer wavelengths Central

Energy distribution of 4 micron radiation going through a geo 30 cm diameter circular aperture to the focal point Max number% Energyradius of source Central max82%0.58 km Second max 91%1.06 km Third max94%1.54 km Fourth max95%2.02 km Tenth max98%4.88 km Twentieth max99%30.3 km Fortieth max99.5%50.6 km Central

Energy distribution of 10 micron radiation going through a geo 30 cm diameter circular aperture to the focal point Max number% Energyradius of source Central max82%1.45 km Second max 91%2.65 km Third max94%3.84 km Fourth max95%5.04 km Tenth max98%12.2 km Twentieth max99%75.7 km Fortieth max99.5%126.4 km Central

Energy distribution of 10 micron radiation going through a geo 50 cm diameter circular aperture to the focal point Max number% Energyradius of source Central max82%0.84 km Second max 91%1.59 km Third max94%2.30 km Fourth max95%3.02 km Tenth max98%7.32 km Twentieth max99%45.4 km Fortieth max99.5%75.8 km Central

Distribution of 10 um energy sources focused by 30 cm mirror onto 112 urad square detector (total detected signal emanating from circle of given size) % of signalemanating from circle with diameter of (FOV = 4km) 60%one FOV 73%1.25 FOV 79%1.5 FOV Effect of nearby 220 K clouds on 300K clear scene for clear sky brightness temperature (CSBT) to be within 1 K clear area must have at least 30 km diameter Rule of thumb is 1% 220 K cloud and 99% 300 K clear sky results in CSBT off by 0.5 K at 10 microns

Calculated diffraction effects for Geo 30 cm mirror for infrared window radiation with a 2 km radius FOV in a clear scene of brightness temperature 300 K surrounded by clouds of 220, 260, or 280 K. Brightness temperature of a 10 radius clear hole is too cold by about 1.5 K.

Design Considerations (2) Impulse or Step Response Function Detector collects incident photons over a sampling time and accumulates voltage response, which is filtered electronically. This is characterized by impulse (or step) response function, detailing what response of sensor is to delta (or step) function input signal. Response function is determined from characteristics of prealiasing filter which collects voltage signal from detector at sampling times. Perfect response of detector continuously sampling scene with 100% contrast bar extending one FOV. Scene radiance Detector response 

Percentage of total signal appearing in samples preceding and following correlated sample peak; for GOES-8 infrared window samples sample N-2 has 4.3% of total signal, N-1 has 26.5%, N peaks with 44.8%, N+1 has 23.4%, and N+2 has 1.0%. This causes smearing of cloud edges and other radiance gradients.

Design Considerations (3) Detector Signal to Noise Noise equivalent radiance for infrared detector can be expressed as NEDR( ) =  [Ad Δf] 1/2 / [Ao  (Δ ) Ω D* Δ ] where  is preamplifier degradation factor Ad is detector area in cm2 Δf is effective electronic bandwidth of radiometer Ao is mirror aperture area in cm2  (Δ ) is transmission factor of radiometer optics in spectral interval Δ Ω is solid angle of FOV in steradians D* is specific spectral detectivity of detector in spectral band in cm Hz 1/2 / watt, and Δ is spectral bandwidth of radiometer at wavenumber in cm-1. NEDR for GOES-8 imager

Design Considerations (4) Infrared Calibration Radiometer detectors are assumed to have linear response to infrared radiation, where target output voltage is given by Vt = α Rt + Vo and Rt is target input radiance, α is radiometer responsivity, and Vo is system offset voltage. Calibration consists of determining α and Vo. This is accomplished by exposing radiometer to two different external radiation targets of known radiance. A blackbody of known temperature and space (assumed to emit no measurable radiance) are often used as the two references. If z refers to space, bb blackbody, calibration can be written as Vz = α Rz + Vo Vbb = α Rbb + Vo where α = [Vbb - Vz]/[Rbb - Rz] Vo = [Rbb Vz - Rz Vbb]/[Rbb - Rz] Using Rz=0 this yields Rt = Rbb [Vt - Vz] / [Vbb - Vz].

Design Considerations (5) Bit Depth Range of radiances expected for earth and atmosphere in a given spectral band must be converted to digital counts of fixed bit depth. This introduces truncation error. For n bit data, the radiance range, must be covered in 2 n even increments. GOES-8 imager truncation errors are indicated below.

Examples from MODIS Instrument configuration Qualitative radiance considerations IR Cal Val NEDR Image artifacts TPW product validation

Atmospheric Profile Retrieval from MODIS Radiances p s I =  sfc B (T(p s ))  (p s ) -  B (T(p)) [ d  (p) / dp ] dp. o I1, I2, I3,...., In are measured with MODIS P(sfc) and T(sfc) come from ground based conventional observations  (p) are calculated with physics models Regression relationship is inferred from (1) global set of in situ radiosonde reports, (2) calculation of expected radiances, and (3) statistical regression of observed raob profiles and calculated MODIS radiances Need RT model, estimate of  sfc, and MODIS radiances

MODIS bands MODIS bands 30-36

MODIS Emissive Band Cal/Val from ER-2 Platform Transfer S-HIS cal to MAS Co-locate MODIS FOV on MAS Remove spectral, geometric dependence WISC-T2000, SAFARI-2000, TX-2001 MODIS FOV MAS 11um CO 2 H2OH2O Windows MAS, SHIS on ER-2  20 km 705 km MODIS on Terra

SHIS calibration transfer to MAS on March 22, 2001 MAS BT SHIS BT 3.90um 8.6um 11.0um 13.90um

MODIS IR Spectral Bands, MAS FWHM

Accounting for Broadband Spectral Response c 2 / T B(,T) = c 1 /{ 5 [e -1] } Summing the Planck function over a spectral response function SR ( ) can be approximated c 2 / eff (a+bT)  B(, T) SR ( ) = B( eff,T) = c 1 /{ eff 5 [e -1] } Adjusted brightness temperature accounts for spectral smearing of the Planck function.

ER-2 Level MODIS B30, 9.6um (Ozone) MODIS B33, 13.3um (CO2) MODIS B35, 13.9um (CO2) MODIS B36, 14.2um (CO2) MAS B43, 9.6um (Ozone) MAS B48, 13.2um (CO2) MAS B49, 13.8um (CO2) MAS B50, 14.3um (CO2)

Influence of Altitude Difference between MODIS and MAS Atmospheric absorption above the ER-2 altitude (20 km) is important for O 3 and CO 2 sensitive bands. O3O3 CO 2

Switch to Side B S/MWIR bias adjustment to 79/110 Side A MODIS first light Side A JanFebMarAprMayJunJulAugSepOctNovDec 2000 JanFebMarAprMayJunJulAugSepOctNovDec 2001 WISC-T2000SAFARI-2000 TX-2001 S/MWIR bias at 79/190

MODIS - SHIS > 0 MODIS too warm < 0 MODIS too cold Calibration Noise Residual

MODIS Radiance (BT) Bias wrt CART Site

Band 36; 14.2um Band 35; 13.9um Band 34; 13.7um Band 33; 13.4um Detector to detector calibration

MODIS NEdR Estimate Based on Earth Scene Data Day 01153, 20:10 UTC Clear scenes of the Pacific Ocean Note: Some SG present in MWIR Used 150 x 28 box (420 data points per detector) Band um.007 mW/m2/ster/cm-1 Band Band Band Band Band Band Band Band Band Band Band Band Band Band Band

MODIS Terra

cont.

MODIS Aqua

Striping in MODIS L1B Band 26 Day UTC V2.5.4

Original L1B (V003)Destriped MODIS Band 27 (6.7  m), :45 UTC On-orbit correction largely effective, but temporal dependence of the correction is evident in testing.

Band 27; 6.77um

11um 6.7um Day Day Detector striping prevalent in Band 27 (6.7um)

Noisy detectors in MODIS L1B Band 34 Day UTC Striping fades and amplifies across the scan swath. No procedure has been identified to correct this.

Band 34 Noisy Detectors

Considerable effort required to tune the correction of the optical leak at 11um for MODIS. Estimated accuracy limited to 1- 2% by residual optical crosstalk influence in atmospheric bands. Baja 11um14.3um Pre launch correction Post launch correction

New MODIS TPW Algorithm: Comparison with NOAA-15 Advanced Microwave Sounding Unit (AMSU) for June 2, 2001 TPW (cm) MODIS new TPW (cm) June 2, 2001 AMSU TPW (cm) June 2, 2001

MODIS TPW 22 May 2002 SSMI

TPW Comparisons of MODIS, GOES, and Raob versus MWR at CART Site

CART Site TPW Comparison: Sample of One Case December 18, 2001 MODIS Science Team Meeting