Assessment of Aqua AMSR-E Ross Sea Ice Assessment of Aqua AMSR-E Ross Sea Ice Concentrations using Aqua MODISConcentrations using Aqua MODIS

Donald J. Cavalieri, Alvaro Ivanoff, and Thorsten Markus, Code 614.1, NASA GSFC

An assessment of Aqua AMSR-E Ross Sea ice concentrations is made using ice concentrations derived from Aqua MODIS surface reflectivities. The approach is similar to that used previously in a comparative study of Aqua AMSR-E and Landsat 7 ETM+ Arctic sea ice concentrations (Cavalieri et al., 2006). Although MODIS has a coarser spatial resolution than ETM+, it has the advantage of being on the same spacecraft as AMSR-E thereby eliminating temporal difference issues.

Areas of largest error correspond to sea ice with a lower surface reflectivity. From a separate analysis we show that the lower surface reflectivity and the large negative AMSR-E ice concentration bias result from flooding of the ice surface.

Figure 2: MODIS image for Day 275 (0205)

Figure 1: Sea Ice Concentration Difference (AMSR-E-MODIS) Map for Day 275 (0205)

Hydrospheric and Biospheric Sciences Laboratory

MODIS Image2005 Day No.

(Time)

AMSR-E Pixels

Covered By MODIS

MODIS Ice

Conc. Range

Mean AMSR-E Ice Conc.

Mean MODIS Ice Conc.

AMSR BiasRelative toMODIS

AMSR-MODIS

RMS Diff.

Day 274 (0120) 3930 1-100% 97.3% 98.5% -1.2% 4.1%

Day 274 (0300) 7729 0-100% 97.3% 98.8% -1.5% 4.2%

Day 275 (0025) 4244 95-100% 98.1% 93.3% +4.8% 9.8%

Day 275 (0205) 6962 0-100% 83.1% 89.8% -6.7% 12.1%

Table 1. Aqua AMSR-E/MODIS Sea Ice Concentration Comparison Statistics

Names: Donald J. Cavalieri, Alvaro Ivanoff, and Thorsten Markus, NASA GSFCContact Email: Donald.J.Cavalieri@nasa.govContact Phone: 301-614-5901

References:Cavalieri, D. J., T. Markus, D. K. Hall, A. Gasiewski, M. Klein, and A. Ivanoff, Assessment of EOS Aqua AMSR-E Arctic Sea Ice Concentrations Using Airborne Microwave and Landsat 7 Imagery, IEEE Trans. Geosci. Remote Sensing, vol. 44, pp. 3057-3069, Nov. 2006.

Data Sources:1) Aqua Advanced Microwave Scanning Radiometer for EOS (AMSR-E) standard sea ice concentration level 3 product on a

12.5 km polar stereographic grid. Sea ice products are produced by the AMSR-E Science Investigator-led Processing System (SIPS) at Marshall Space Flight Center in Huntsville, AL and archived at the National Snow and Ice Data Center in Boulder, CO.

2) Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) band 1 (620-670 nm at a 250 m spatial resolution) radiances were used to obtain sea ice concentrations following a method described by Cavalieri et al. (2006). The MODIS Level 1b data are available through the Level 1 and Atmosphere Archive and Distribution System (LAADS) website: http://ladsweb.nascom.nasa.gov. 

Technical Description of Table and Figures:Table 1: Summary statistics comparing AMSR-E and MODIS ROSS Sea ice concentrations for four MODIS images.Figure 1: Color-coded sea Ice concentration difference (AMSR-E-MODIS) map for Day 275 (0205), 2005 showing areas of AMSR-E sea ice concentration negative bias relative to MODIS. The largest errors are found within the marginal ice zone (MIZ). Figure 2: Aqua MODIS image showing the lower surface reflectivity of the MIZ compared with the brighter sea ice regions within the ice pack. A separate analysis shows that the lower MODIS surface reflectivity and the large negative AMSR-E ice concentration bias are explained by flooding of ice surface (insets show flooded and non-flooded ice).Comparison of Figures 1 and 2: Areas of largest error correspond to sea ice with a lower surface reflectivity. From a separateanalysis we show that the lower surface reflectivity and the large negative AMSR-E ice concentration bias result from flooding of the ice surface.

Scientific Significance and Relevance to Future Science: The AMSR-E is a state-of-the-art microwave radiometer launched in May 2002 on the EOS Aqua spacecraft. The significance of this work is that it provides a baseline for AMSR-E sea ice concentration retrieval accuracy for the Antarctic sea ice cover. Knowledge of the retrieval accuracy is essential if these data are to be scientifically useful in current and future climate studies. Our knowledge of the global sea ice cover stems largely from records derived from satellite passive microwave observations which began in the early 1970’s and continues today with AMSR-E.

Hydrospheric and Biospheric Sciences Laboratory

Satellite-Derived High-Resolution Distributions of Particulate and Satellite-Derived High-Resolution Distributions of Particulate and Dissolved Organic Carbon and CDOM in the Coastal Ocean Dissolved Organic Carbon and CDOM in the Coastal Ocean

This study shows the application of novel satellite algorithms with high spatial resolution MODIS-Aqua data processing code from Goddard’s Ocean Biology Processing Group to generate high resolution satellite products to study the carbon cycle in the coastal ocean.

The presence of high levels of colored dissolved organic matter (CDOM) in coastal waters significantly reduces the accuracy of satellite chlorophyll, which is applied to estimate primary productivity in the ocean.

Dissolved organic carbon (DOC) is an integral component of ocean productivity, comprising >70-90% of the total organic carbon (TOC) found in coastal and deep ocean regions. Satellite-derived CDOM (aCDOM), DOC, and particulate organic carbon and nitrogen products allow us to estimate processes such as ecosystem production of DOC and particles and sunlight decomposition of CDOM. With these algorithms, MODIS-Aqua can be applied to monitor impacts of climate change and anthropogenic activities in the coastal ocean.

Figure 2

Figure 1: High-resolution MODIS-Aqua coastal ocean productsHydrospheric and Biospheric Sciences Laboratory

Antonio Mannino, Code 614.2, NASA GSFC

Name: Antonio Mannino, NASA/GSFC E-mail: antonio.mannino@nasa.govPhone: 301-286-0182

References: Mannino, A., M.E. Russ and S.B. Hooker. 2008. Algorithm Development and Validation for Satellite-Derived Distributions of DOC and CDOM in the U.S. Middle Atlantic Bight. J. Geophys. Res. Oceans, Vol. 113, C07051, doi:10.1029/2007JC004493.Franz, B.A., P.J. Werdell, G. Meister, E.J. Kwiatkowska, S.W. Bailey, Z. Ahmad, and C.R. McClain (2006). MODIS Land Bands for Ocean Remote Sensing Applications, Proc. Ocean Optics XVIII, Montreal, Canada, 9-13 October 2006.

Data Sources and Analyses: We conducted multiple expeditions within the coastal ocean region along the U.S. Mid-Atlantic in 2005-2006 to collect measurements of Particulate (POC) and Dissolved Organic Carbon (DOC), Colored Dissolved Organic Matter absorbance, and in-water radiometry to develop algorithms to compute CDOM, POC and DOC from NASA’s MODIS-Aqua satellite sensor. The CDOM algorithms relate in situ radiometry (reflectance band ratios) to measurements of CDOM, and then DOC is correlated to reflectance band ratios through the CDOM to DOC relationships. We evaluated the accuracy of the satellite measurements through comparisons with independent measurements collected at sea.

Technical Description of Image:Figure 1: High spatial resolution (pseudo-250m) MODIS-Aqua images of the validated CDOM absorption coefficient at 355 nm (aCDOM355, m-1), DOC (µM C), percent of DOC of total organic carbon (TOC), POC (mg C m-3), and particulate nitrogen (PN, mg N m-3) for Spring (May 12, 2006) and Summer (June 30, 2006) within the U.S. Mid-Atlantic coast. High-resolution MODIS processing code for land and ocean bands from NASA GSFC’s Ocean Biology Processing Group is applied to produce the MODIS-derived ocean reflectances at 250m resolution (Franz et al. 2006) used to generate the ocean products shown here. The satellite images illustrate the significant seasonal and spatial variability of aCDOM, DOC, POC and PN within the coastal ocean. The elevated levels of each product on June 30th along the coast and particularly within the Chesapeake and Delaware Bay plumes are due to the flux of terrigenous materials from the record-level precipitation and subsequent freshwater discharge from coastal watersheds during June 2006. Generally, much higher DOC levels are found in Summer compared to early Spring due to net ecosystem production that promotes accumulation of DOC. Our results demonstrate that the accuracies of satellite-derived measurements are within 9% for DOC, 20% for aCDOM and 25% for POC for our coastal ocean study region. These results compare very well with accuracy for satellite estimates of ocean chlorophyll globally.

Scientific significance: This study contributes the first validated algorithms for satellite retrieval of coastal ocean DOC and aCDOM. The aCDOM and POC algorithms can be applied beyond this study region, potentially to the global ocean, if the appropriate field datasets are available to extend the range of the algorithm. By adjusting the algorithm coefficients with values derived from other region-specific aCDOM to DOC relationships, regionally tuned DOC algorithms may be applied “globally” within continental margins.

Relevance for future science and relationship to Decadal Survey: High spatial resolution ocean satellite products can be generated from MODIS-Aqua to simulate datasets that can be obtained from a GEO-CAPE decadal survey mission concept. Furthermore, this high-resolution capability permits satellite retrieval of larger portions of the coast due to the reduction in straylight from adjacent bright targets such as land mass or clouds. Satellite analysis of aCDOM can be used to quantify photooxidation rates (CDOM and DOC loss), track the inputs of terrigenous organic matter from rivers or estuaries into the coastal ocean and beyond, and trace water masses with different CDOM signatures. Satellite-derived DOC and POC measurements can be applied to carbon cycle studies to quantify the fluxes of DOC entering the coastal ocean and exported from the continental margin to the open ocean, estimate DOC produced through ecosystem productivity, and assess the standing stock of DOC and POC. Satellite observations can be applied to investigate interannual and decadal-scale variability in CDOM, POC and DOC within continental margins and evaluate how climate change and anthropogenic activities impact coastal ecosystems.

Hydrospheric and Biospheric Sciences Laboratory

Hydrospheric and Biospheric Sciences Laboratory

NASA research to NOAA operationsEdward Walsh, Code 614.6, NASA GSFC

Its initial flight, into Hurricane Bonnie, provided the first comprehensive measurement of a hurricane wave field. The second flight provided the first comprehensive measurement of the temporal/spatial variation of the mound of water that is the storm surge of a landfalling hurricane

In a cooperative research program, the NASA Scanning Radar Altimeter (SRA) was mounted onboard a NOAA hurricane research aircraft.

Figure 1: Black radials extend in wave propagation direction a distanceproportional to ocean wavelength, width proportional to wave height.

Figure 2: SRA storm surge measurements (dots) fall between the predictions of two storm surge models (curves) which differed because they assumed different hurricane tracks.

Since the public needs to evacuate before landfall, numerical storm surge models have been developed to enable emergency managers to issue timely evacuation orders.

The SRA flight into Hurricane Bonnie demonstrated (Wright et al. 2008) that an airborne microwave altimeter could measure the temporal/spatial variation of the surge to evaluate and improve model performance

Hydrospheric and Biospheric Sciences Laboratory

References:

Black, P. G., E. A. d’Asaro, W. M. Drennan, J. R. French, P. P. Niiler, T. B. Sanford, E. J. Terrill, E. J. Walsh, J. A. Zhang, 2007: Air–sea exchange in hurricanes, synthesis of observations from the Coupled Boundary Layer Air–Sea Transfer Experiment, Bull. AMS, 88, 357-374.

Fan, Y., I. Ginis, T. Hara, C. W. Wright and E. J. Walsh, 2008: Numerical simulations and observations of surface wave fields under an extreme tropical cyclone, in preparation.

Moon, Il-Ju, Isaac Ginis, Tetsu Hara, H. L. Tolman, C. W. Wright and E. J. Walsh, 2003: Numerical simulation of sea surface directional wave spectra under hurricane wind forcing, J. Phys. Oceanogr., 33, 1680-1706.

Walsh, E. J., C. W. Wright, D. Vandemark, W. B. Krabill, A. Garcia, S. H. Houston, S. T. Murillo, M. D. Powell, P. G. Black, and F. D. Marks, 2002: Hurricane directional wave spectrum spatial variation at landfall, J. Phys. Oceanogr., 32, 1667-1684.

Wright, C. W., E. J. Walsh, D. Vandemark, W. B. Krabill, A. Garcia, S. H. Houston, M. D. Powell, P. G. Black, and F. D. Marks, 2001: Hurricane directional wave spectrum spatial variation in the open ocean, J. Phys. Oceanogr., 31, 2472-2488.

Wright, C. W., E. J. Walsh, W. B. Krabill, W. A. Shaffer, S. R. Baig, M. Peng, L. J. Pietrafesa, A. W. Garcia, F. D. Marks, Jr., P. G. Black, J. Sonntag, B. D. Beckley, 2008: Storm Surge Measurement with an Airborne Scanning Radar Altimeter. J. Atmos. Oceanic Tech., submitted.

Data Sources:

NASA Scanning Radar Altimeter (SRA). NOAA Hurricane Tracking Aircraft

Technical Description of Image:

Figure 1: The SRA quantified the general mariner knowledge that the largest waves in a hurricane are in the right-front quadrant. It also showed that the right rear quadrant contained the most complex wave field, frequently being trimodal. The waves are young, steep and short in the right-rear quadrant and older, flatter and long in the right front and left front quadrants. To the left-rear and left-front of the eye, the wind and dominant waves are about at right angles to each other. The SRA data have provided a basis for verifying (Wright et al. 2001; Walsh et al. 2002; Moon et al. 2003) and improving (Fan et al. 2008) the performance of numerical wave models and better understanding the processes occurring within hurricanes (Black et al. 2007).

Figure 2: The first-order differences in the predictions of the two models shown in Figure 2 occurred because they used different hurricane tracks, both of which were issued by NHC, which temporally and spatially bracketed the actual track of Hurricane Bonnie.

Scientific significance: Because the public needs to evacuate before landfall, numerical storm surge models have been developed to enable emergency managers to issue timely evacuation orders. Over the years, hurricane track and intensity forecasts and the surge models and the digital terrain and bathymetry data they depend on have improved. The area of least improvement has been in obtaining detailed data on the temporal/spatial variation of the storm surge dome of water as it evolves and inundates the land to evaluate and improve the performance of the models. Tide gages in the vicinity of the landfall are frequently destroyed by the surge and survey crews dispatched after the event provide no temporal information and only indirect indications of the maximum surge envelope over land. The SRA flight into Hurricane Bonnie demonstrated (Wright et al. 2008) that an airborne microwave altimeter could measure the temporal/spatial variation of the surge to evaluate and improve model performance.

Name: Edward Walsh, NASA/GSFC, NOAAE-mail: Edward.Walsh@nasa.govPhone: 303-497-6357