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The MEaSUREs PAR Project. Robert Frouin Scripps Institution of Oceanography La Jolla, CA _______________________________________ OCRT Meeting, 4-6 may 2009, New York, NY. Objective

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The measures par project

The MEaSUREs PAR Project

Robert Frouin

Scripps Institution of Oceanography

La Jolla, CA

_______________________________________

OCRT Meeting, 4-6 may 2009, New York, NY


The measures par project

Objective

-To produce a 12-year (and continuing) time series of daily PAR at the ocean surface from SeaWiFS, MODIS-Terra, and MODIS-Aqua.

-The product will cover the global oceans, with a spatial resolution of about 9.3x9.3 km (equal area grid), the resolution of Level 3 ocean-color products from those sensors.

-The time series will start in September 1997, i.e., at the beginning of the SeaWiFS operational phase.


The measures par project

Definition

-PAR is defined as the quantum energy flux from the Sun in the spectral range 400-700 nm. It is expressed in Einstein/m2/day. Daily PAR is the average flux during a day (24 hours).

-The factor required to convert units of mW cm-2μm-1 to units of Einstein m-2 day-1 is equal to 1.193 to an inaccuracy of a few percent regardless of meteorological conditions (Kirk, 1994).


The measures par project

Importance

-PAR controls the growth of phytoplankton and, therefore, the development of crustaceans, fish, and other consumers.

-It ultimately regulates the composition and evolution of marine ecosystems.

-Knowing the distribution of PAR over the oceans, spatially and temporally, is critical to:

•Understanding bio-geo-chemical cycles of carbon, nutrients, and oxygen, and

•Addressing climate and global change issues, such as the fate of anthropogenic atmospheric carbon dioxide.


The measures par project

Satellite Estimation

-PAR is computed as the difference between the 400-700 nm solar flux incident on the top of the atmosphere (known) and reflected back to space by the atmosphere and surface (measured), taking into account atmospheric absorption (modeled).

-Knowledge of pixel composition is not required, eliminating the need for cloud screening and arbitrary assumptions about sub-pixel cloudiness.


The measures par project

Model

-The PAR model uses plane-parallel theory and assumes that the effects of clouds and clear atmosphere can be de-coupled. The planetary atmosphere is therefore modeled as a clear sky atmosphere positioned above a cloud layer.

-The solar flux reaching the ocean surface is given by

E = Eclear(1 – A)(1 – As)-1

where A is the albedo of the cloud-surface system and As the albedo of the surface.

-Eclear = E0cos(qs)Td Tg is the solar flux that would reach the surface if the cloud/surface system were non reflecting and non-absorbing.

-In clear sky conditions, A reduces to As.


The measures par project

Algorithm

-In order to compute E, A is expressed as a function of the radiance measured by the ocean-color sensor in the PAR spectral range (e.g., in bands 1 through 6 for SeaWiFS). This includes:

(1) converting the TOA radiance into reflectance,

(2) correcting the TOA reflectance for gaseous absorption and scattering by the atmosphere/surface system, and

(3) transforming the corrected reflectance into albedo.


The measures par project

Procedure No. 1:

-A daily PAR estimate is obtained for each instantaneous pixel, assuming the cloud/surface system is stable during the day and corresponds to the satellite observation.

-Daily PAR estimates obtained separately from different orbits and individual sensors are binned using a simple, linear averaging scheme (arithmetical mean).

Procedure No. 2:

-The Level 1b data from each sensor is processed individually, as in the first procedure, but up to the albedo of the cloud-surface system A.

-The daily fields of A and associated sun zenith angle, obtained for each orbit of each satellite, are used to perform daily integration and generate the Level 3 binned PAR product.


The measures par project

-Since MODIS-Terra, SeaWiFS, and MODIS-Aqua cross the Equator at different local times (i.e., 10:30, 12:00, and 13:30) the diurnal variability of clouds is taken into account by averaging individual estimates (Procedure No. 1).

-Procedure No. 2 is preferred, since the multiple sensor data, obtained at different times during the day, are used in the daily integration of instantaneous PAR.

-To ensure consistency of the PAR data over time, estimates using data from one, two, or three sensors need to be compared, and statistical adjustment factors determined.

-Procedure No. 1 has been used to generate the operational SeaWiFS PAR product. It has been adapted to POLDER, GLI, MODIS, and MERIS. It has been applied by the GlobColour project to create a merged MERIS/SeaWiFS PAR product.


The measures par project

Daily PAR Equator at different local times (i.e., 10:30, 12:00, and 13:30) the diurnal variability of clouds is taken into account by averaging individual estimates (Procedure No. 1).

05 July 2003

MERIS

SeaWiFS

Merged

MERIS/

SeaWiFS


The measures par project

8-Day PAR Equator at different local times (i.e., 10:30, 12:00, and 13:30) the diurnal variability of clouds is taken into account by averaging individual estimates (Procedure No. 1).

04-11 July 2003

MERIS

SeaWiFS

Merged

MERIS/

SeaWiFS


The measures par project

Monthly PAR Equator at different local times (i.e., 10:30, 12:00, and 13:30) the diurnal variability of clouds is taken into account by averaging individual estimates (Procedure No. 1).

July 2003

MERIS

SeaWiFS

Merged

MERIS/

SeaWiFS


The measures par project

Evaluation Equator at different local times (i.e., 10:30, 12:00, and 13:30) the diurnal variability of clouds is taken into account by averaging individual estimates (Procedure No. 1).

-The SeaWiFS PAR algorithm has been evaluated against in situ measurements from

•moored buoys off the west coast of Canada (Halibut Bank data set, 49.34N-123.73W) and in the central equatorial Pacific (ep1 data set, 0.00N-155.00W).

•the COVE platform off the west coast of the US (2 PAR sensors, 36.90N-75.71W)


The measures par project

Comparison statistics of SeaWiFS PAR versus in situ PAR Equator at different local times (i.e., 10:30, 12:00, and 13:30) the diurnal variability of clouds is taken into account by averaging individual estimates (Procedure No. 1).

at Halibut Bank and ep1 (Frouin, Franz, Werdell, 2003).


The measures par project

COVE site (36.90N-75.71W), 2006 Equator at different local times (i.e., 10:30, 12:00, and 13:30) the diurnal variability of clouds is taken into account by averaging individual estimates (Procedure No. 1).

r2 = 0.867

Bias = 2.57 mW/cm2/µm (7.3%)

RMS Diff. = 6.79 mW/cm2/µm (19.3%)

Nb. Points = 239

SeaWiFS PAR versus in situ PAR, in mW/cm2/µm, daily average.


The measures par project

COVE site (36.90N-75.71W), 2006 Equator at different local times (i.e., 10:30, 12:00, and 13:30) the diurnal variability of clouds is taken into account by averaging individual estimates (Procedure No. 1).

r2 = 0.947

Bias = 1.81 mW/cm2/µm (5.1%)

RMS Diff. = 3.77 mW/cm2/µm (10.7%)

Nb. Points = 46

SeaWiFS PAR versus in situ PAR, in mW/cm2/µm, 8-day average.


The measures par project

COVE site (36.90N-75.71W), 2006 Equator at different local times (i.e., 10:30, 12:00, and 13:30) the diurnal variability of clouds is taken into account by averaging individual estimates (Procedure No. 1).

r2 = 0.986

Bias = 2.00 mW/cm2/µm (5.7%)

RMS Diff. = 2.58 mW/cm2/µm (7.3%)

Nb. Points = 12

SeaWiFS PAR versus in situ PAR, in mW/cm2/µm, monthly average.


The measures par project

-The good statistical performance, i.e., 15-20%, about 10%, and 6-8% RMS errors on daily, 8-day, and monthly time scales, makes the algorithm suitable for large-scale studies of aquatic photosynthesis, especially in view of the difficulty to estimate efficiency factors.

-But satellite estimates are biased high by about 5%.


The measures par project

The bias between satellite PAR estimates and and 6-8% RMS errors on daily, 8-day, and monthly time scales, makes the algorithm suitable for large-scale studies of aquatic photosynthesis, especially in view of the difficulty to estimate efficiency factors. in situ measurements may be due to uncertainties in the modeling and/or effects of diurnal cloud variability.

-Clear sky PAR values agree well with those from accurate and validated RT code, i.e., the bias is not explained by the modeling of clear sky PAR.

-In maritime convective regions, clouds tend to occur mostly in the afternoon, and in maritime non-convective regions in the morning and late afternoon.

-Smaller PAR values from MODIS-Terra (10:30am) or MODIS-Aqua (1:30pm) suggest that diurnal cloud variability may explain, at least partly, the higher SeaWiFS values.


The measures par project

Clear sky PAR from SeaWiFS PAR code V1.4 and 6S RT code. Aerosols are of maritime type with optical thickness of 0.2 at 550 nm, and ozone amount is 0.34 atm.cm.


The measures par project

Tau = 5 Aerosols are of maritime type with optical thickness of 0.2 at 550 nm, and ozone amount is 0.34 atm.cm.

Tau = 20

Factor, F, used to convert reflectance of the cloud/surface system into albedo, for cloud optical thickness of 5 (left) and 20 (right).


The measures par project

Comparison of MODIS-Terra PAR estimates with SeaWiFS PAR estimates for April 2003 (monthly average).


The measures par project

Comparison of MODIS-Aqua PAR estimates with SeaWiFS PAR estimates for April 2003 (monthly average).


The measures par project

Comparison of MODIS-Aqua PAR estimates with MODIS-Terra PAR estimates for April 2003 (monthly average).


The measures par project

Modifications to Version 1.4 estimates for April 2003 (monthly average).

-Computation of the ozone transmittance and the spherical albedo of the atmosphere, to fix minor bugs.

-Tests for missing aerosol optical thickness and Angström coefficient.

-Surface albedo parameterization as a function of aerosol optical thickness and sun zenith angle, based on extensive measurements at the COVE site (Jin et al., GRL, 2004).


The measures par project

Paramerization of surface albedo as a function of sun zenith angle and aerosol optical thickness at 500 nm. Vertical bars represent variability due to wind speed and chlorophyll concentration. (Based on Jin et al., GRL, 2004.)


The measures par project

Modeled daily clear sky PAR (in mW/cm angle and aerosol optical thickness at 500 nm. Vertical bars represent variability due to wind speed and chlorophyll concentration. (Based on Jin et al., GRL, 2004.)2/µm) as a function of day during the year for various latitudes (Version 1.7 of the SeawiFS PAR code).


The measures par project

Difference between daily clear sky PAR (in mW/cm angle and aerosol optical thickness at 500 nm. Vertical bars represent variability due to wind speed and chlorophyll concentration. (Based on Jin et al., GRL, 2004.)2/µm) computed with versions 1.7 and 1.4 of the SeaWiFS PAR code.


The measures par project

Modeled daily clear sky PAR (in mW/cm angle and aerosol optical thickness at 500 nm. Vertical bars represent variability due to wind speed and chlorophyll concentration. (Based on Jin et al., GRL, 2004.)2/µm)as a function of day during the year for various latitudes (Version 1.7 of the SeawiFS PAR code). Input radiance is 20 mW/cm2/µm/sr. VZ = 45 deg., RelAz =90 deg..


The measures par project

Difference between daily PAR (in mW/cm angle and aerosol optical thickness at 500 nm. Vertical bars represent variability due to wind speed and chlorophyll concentration. (Based on Jin et al., GRL, 2004.)2/µm) computed with versions 1.7 and 1.4 of the SeaWiFS PAR code. Input radiance is 20 mW/cm2/µm/sr.


The measures par project

Future Work angle and aerosol optical thickness at 500 nm. Vertical bars represent variability due to wind speed and chlorophyll concentration. (Based on Jin et al., GRL, 2004.)

-Continue to test Version 1.4 and test Version 1.7 against in situ measurements (COVE and BOUSSOLE sites). Understand biases, and make adjustments.

-This using SeaWiFS and MODIS data. Application to MODIS requires selecting spectral bands that do not saturate over clouds (land bands).

-Generate a first 12-year time series of daily PAR using one, two, or three sensors.

-Determine adjustment factors, by comparing estimates with one, two, or three sensors. Generate a second time series.