2007

1979

Melt Trend 1%/yr

Year

Mea

n M

elt A

rea

(sq.

km

)Greenland Melt Continues Its Rise

• A distinct melt signature in the Passive Microwave (PM) satellite data enable the tracking of melt area on the Greenland ice sheet (Abdalati and Steffen, 2001)

• May-Sept. mean ice sheet melt area in 2007 was highest of the 29-year PM record

• As of 2007, melt has been declining at ~10% per decade– Matches 29-year rate of perennial arctic sea ice decline

Areas that experienced melt at start and end of

PM record

Waleed Abdalati, Code 614.1 NASA GSFC

Hydrospheric and Biospheric Sciences Laboratory

Name: Waleed Abdalati, NASA/GSFC E-mail: Waleed.Abdalati@nasa.govPhone: 301-614-5696

References:

The figure is an update of:Abdalati, W., and K. Steffen, Update on the Greenland ice sheet melt extent, J. Geophys. Res. Vol. 106, No. D24, pp. 33,983-33,988, 2001.

Data Sources:Scanning MultiChannel Microwave Radiometer (SMMR), and Special Sensor Microwave Imager (SSM/I)

Technical Description of Image:The line plot is a year to year assessment of the average extent of melt on the Greenland ice sheet for the time period May 1 through Sept. 30 of each year. It is intended to show the year to year variability of the melt quantity based on a daily (or every other day in the case of SMMR) classification of pixel wetness. The figures at the right show the areas on the Greenland ice sheet that experienced melt for at least 3 days during the May-Sept. melt season for the start of the record (1979) and the end of the record (2007).

Scientific significance:The trend shows a significant increase in the melt of the ice sheet over the last 29 years, and 2007 is the highest melt of the record. The 10% per decade increase is similar to the 10%/decade rate of reduction in perennial sea ice cover over the same period, and observed by the same instrumentation. The increase in melt is important because it contributes to the shrinkage of the Greenland ice sheet (and subsequently sea level rise) directly through the runoff of melt water. It also contributes indirectly by providing more melt water to lubricate the interface between the ice and the bedrock on which it rests, as this meltwater penetrates to the bottom of the ice, causing the ice to flow faster toward the sea during high melt periods. This penetration to the bottom, and the associated acceleration have been demonstrated by Zwally et al., 2002, Surface melt acceleration of Greenland ice sheet flow, Science, Vol. 297, No. 5579, 218-222.

Relevance for future science:The state of melt of the Greenland ice sheet is important because of its direct and indirect contributions to sea level rise. As a result, there is a continued interest in understanding how it is evolving over time. In addition, these melt observations facilitate the interpretation of ice sheet change data from ICESat, GRACE, and any of their respective follow-on missions. It also facilitates the interpretation of InSAR data from our international partners. These results can also be derived at higher spatial resolution for the AMSR data for a more detailed look at the processes in a spatial context.

Hydrospheric and Biospheric Sciences Laboratory

Measuring Hurricane Storm Surge with a Scanning Radar AltimeterMeasuring Hurricane Storm Surge with a Scanning Radar Altimeter

Much of the damage to coastal areas from land falling Hurricanes is caused by the accompanying wall of water known as the storm surge. Measurements of the height of this surge are often either unavailable or must be deduced by indirect indications from the damage caused. However, accurate measurements are essential to assess the performance of the surge models that public safety decision makers depend on.

The NASA Scanning Radar Altimeter (SRA) made direct measurements of the storm surge during the landfall of Hurricane Bonnie at Cape Fear, North Carolina. The SRA data indicated that existing storm surge models are inconsistent. One model better predicted the peak of the surge north of Cape Fear while a different model better predicted the depression of the water surface caused by the offshore winds west of Cape Fear.

Figure 1 SRA track (o) off the North Carolina coast and elevation contours from two storm surge models

Figure 2 SRA storm surge(.) and two model predictions

Edward Walsh, Code 614.6 NASA GSFC

Hydrospheric and Biospheric Sciences Laboratory

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

References:

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. Phys. Oceanogr., In Preparation.

Walsh, E. J., C. W. Wright, D. Vandemark, W. B. Krabill, A. W. Garcia, S. H. Houston, S. T. Murillo, M. D. Powell, P. G. Black, 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.

Data Sources: The SRA measurements were a joint effort between NASA and the NOAA/Atlantic Oceanographic and Meteological Laboratory/Hurricane Research Division (HRD) in which the NASA Scanning Radar Altimeter (SRA) flew into Hurricane Bonnie as it was making landfall on 26 August 1998 aboard one of the NOAA/Aircraft Operations Center (AOC) WP-3D hurricane research aircraft.

Technical Description of Image:Figure 1: The circles identifying the track of the NOAA aircraft carrying the NASA SRA are spaced at 25 km intervals along the track relative to latitude 34N. The thick dashed contours are predicted water elevations (m) from the NOAA SLOSH storm surge model. The thin contours are the predicted water elevations from the North Carolina State University storm surge model.

Figure 2: The dots indicate the NASA SRA determinations of the storm surge surface elevations along the flight track shown in Figure 1. The circles identify the storm surge elevations along the track predicted by the SLOSH model and the other curve indicates the NC State model predictions. Neither model agrees with the observations, but the SLOSH model better predicts the peak of the surge while the NC State model better predicts the depression of the water surface caused by the offshore winds on the left side of the hurricane track as it makes landfall. The SRA data gap near -140 km and the erratic nature of the NC State model were due to the SRA track grazing the shoreline.

Scientific significance: The storm surge from Hurricane Katrina exceeded 8 m. With much of the densely populated Atlantic and Gulf Coast shorelines less than 3 m above mean sea level, storm surge has the potential to destroy lives and property and cut off escape routes. Public safety depends upon the accuracy of storm surge model predictions. This work is the first to demonstrate that an airborne radar altimeter could provide targeted measurements of storm surge that could thoroughly assess the accuracy of storm surge models and provide insights for their improvement.

Relevance for future science: After flying on one of the NOAA hurricane research aircraft for eight seasons, the NASA SRA was decommissioned. But the success of the joint research prompted NOAA to build their own SRA to carry on the measurements. NOAA has funded Ed Walsh to develop analysis software for their new system and the NOAA SRA should be operational for the 2008 hurricane season.

Hydrospheric and Biospheric Sciences Laboratory

Figure 1Figure 1: 1km LIS SWE in the Yakima Basin (top), and in Yakima’s Naches sub-basin (right) for April 1, 2004.

NASA’s Satellite Data Products and the Land Information SystemNASA’s Satellite Data Products and the Land Information System for Improved Snowpack for Water Managementfor Improved Snowpack for Water Management

Figure 2Figure 2: Improvement of CLM with MODIS SCA and other model calibration adjustments to reduce the model’s inherent biases.

NASA's satellite data products and the Land Information System (LIS) may provide improved hydrologic estimates and predictions (e.g., snowpack) critical for water management. The LIS modeling helps integrate and assimilate a variety of Earth science satellite, meteorological, and surface data.

Current work is towards improving the US Bureau of Reclamation reservoir operations system (i.e., “RiverWare) that relies heavily on estimating current and future snow pack. MODIS snow cover area (SCA) is used to help “adjust” the LIS Community Land Model (CLM) to provide improved snow water equivalent (SWE) estimates and predictions critical for western US reservoir management.

Kristi Arsenault, Code 614.3, NASA-GSFC and UMBC-GEST

Hydrospheric and Biospheric Sciences Laboratory

0

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0 30 60 90 120 150 180 210 240 270 300

Day of Water Year 2004

SW

E (

inch

es)

SNOTEL

CLM2 Default

CLM2 - DI of MODIS SCA; Temp Corr

Average of 17 Snotel Stations

(Oct 1)

Assimilated MODIS SCA into CLM2 of LIS::

WY2004 for the Yakima Basin, WA

1 km LIS – Yakima River Basin, 1 km LIS – Yakima River Basin, WAWA

Name: Kristi Arsenault (NASA-GSFC/UMBC), Ted Engman (NASA-GSFC/SAIC), David Toll (NASA-GSFC), Paul Houser (GMU/CREW), Sujay Kumar (NASA-GSFC/UMBC), Joe Nigro (NASA/GSFC/SSAI), Steve Hunter and Ra Aman (USBR/TSC) E-mail: Kristi.Arsenault@nasa.gov Phone: 301-286-9176

References:Arsenault, K., S. Kumar, S. Hunter, R. Aman, P. Houser, D. Toll, T. Engman, J. Nigro, 2007. Land Surface Model Biases and their Impacts on the Assimilation of Snow-related Observations, To be presented as a poster at the 2007 Fall AGU Meeting, in San Francisco, CA.

Data Sources: The main satellite data used in this project include the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) products, which include 1) the 500-m MODIS snow cover area product (archived at the National Snow and Ice Data Center (NSIDC) in Boulder, CO), 2) the 1km MODIS land cover classification dataset derived at Boston University, and 3) the 1km MODIS leaf area index (LAI) product which is also developed at Boston University.

Atmospheric forcing data for model simulations were drawn from the NASA-GSFC NLDAS forcing database (http://ldas.gsfc.nasa.gov).

Technical Description of Image:

Figure 1:Figure 1: The LIS modeling environment has been “customized” for the Yakima River Basin in WA state for a water management applications project with the US Bureau of Reclamation. LIS was set up on a 0.01 deg (~1km) spatial scale to capture better spatial variability, especially for a mountainous area like the Yakima Basin. For this project, CLM2 LSM is used and “guided” with the binary 500m MODIS snow cover area (SCA) product in LIS to obtain more realistically distributed snow fields. Adjustments were made to CLM2 to correct for its model biases and to improve its overall snow modeling capabilities.  SWE map (in mm) generated using the integrated MODIS SCA and LIS CLM2 modeling system for April 1, 2004.

Figure 2:Figure 2: For the same Water Year of 2004, a comparison is shown of averaged SWE from in-situ SNOTEL data (black line), the default version of CLM2 in LIS (red line), and the updated version of CLM2, using MODIS SCA and a temperature adjustment to the snowpack layers (blue line).

Scientific significance: Water resources managers in the Western U.S. rely on in-situ based snow water equivalent observations in helping to make their water management decisions, since up to 90% of the water supply out West comes from snow in the mountains. However, snow depth and SWE are highly variable within a short distance, especially in mountainous areas, making point-based SWE observations not representative for such areas. Having accurate and high spatial resolution SWE maps are quite relevant to capturing such spatial variability and also to improving water resources managers’ knowledge of the amount of water stored in the snowpack, providing them better estimates of how much water may fill the reservoirs and run-off to the river systems in spring and summer months.

Relevance for future science and relationship to Decadal Survey: The estimation and prediction of snowpack, especially to the timing and amount of spring runoff, is critical for western US water management. The most optimal way for future enhancements of snowpack are to better use and integrate the various satellite, modeling and surface information such as through improved modeling as in the NASA Land Information System and improved satellite products. The Decadal Survey recommended (2013-2016 timeframe) SCLP (Snow and Cold Land Processes) satellite with two radar (passive and active) sensors will provide much improved snow accumulation estimation for water resources.

Hydrospheric and Biospheric Sciences Laboratory