Analysis of Chesapeake Bay Sea-Level Variability Using the Regional Ocean Modeling System (ROMS) John Moisan, Code 614.2, NASA GSFC Sea-level variance within the Chesapeake Bay from tidal (Figure 1- left) and non-tidal (Figure 1- right) components. The spatial scales of variability in both elements encompass the full length of the Bay. The results are important for supporting the development of NASA’s Surface Water Ocean Topography Mission. (SWOT) The 1 st (Figure 2-left) and 2 nd (Figure 2-center) EOF maps of the non-tidal sea-level variability account for 97.8% on the non-tidal variability and are highly correlated to the NW/SE and N/W wind components. The sum of tides and these EOFs (Figure 2-right) account for most of the sea-level variability. The Bay's sea-level is most sensitive in the NW area of the Bay to variations in sea-level. And, this sensitivity is highest for winds coming from the SE (NW winds). Hurricanes going NW up along the western side of the bay (recall 2003 Hurricane Isabel) would drive strong such NW-oriented winds and the Bay resulting in highest flooding in the NW sections of the Bay. A similar impact would not occur for any other wind coming from any other direction. Non-Tidal Sea-level Variance [m 2 ] Tidal Sea-level Variance [m 2 ] Sea-level Variance of EOF 1 [m 2 ] Sea-level Variance of EOF 2 [m 2 ] Percent Total Sea-level Variance From Tides + 1 st and 2 nd EOFs [n.d.] Figure 1: Tidal variability is highest at both the southern and northern extents of the Bay, while non-tidal sea-level variability is isolated to the heads of the Bay’s tributaries and to the northern part of the Bay. Figure 2: The residual’s (sea-level minus tides) are then further subjected to Empirical Orthogonal Function (EOF) analysis in order to discern what physical processes are involved.

Name: John R. Moisan, NASA/GSFC Phone: References: Bruder, B. L., J. R. Moisan, and J. Xu, Analysis and modeling of sea-level variability in the Chesapeake Bay using the Regional Ocean Modeling System (ROMS), In prep. Xu, J., W. Long, L. W.J. Lanerolle, J. D. Wiggert, R. R. Hood, C. W. Brown, R. Murtugudde and T. F. Gross, 2010, Climate forcing and salinity variability in the Chesapeake Bay, USA. Estuarine and Coastal Shelf Science. (In prep.) Data Sources: The data from this effort are derived from U.S. National Oceanic and Atmospheric Administration (NOAA) ocean weather stations and sea-level stations located within the Chesapeake Bay and along the adjacent coastal ocean. The analysis is a joint effort with Jiangtao Xu, a scientist at NOAA/NOS and a summer graduate intern from Georgia Inst. of Tech., Brittany Bruder. The task is to analyze the ocean’s free surface field from a regional 3D circulation model that had been configured to simulate the circulation features of the Bay in The model’s state variables are obtained at 2 hour intervals over the full 2006 model run. Technical Description of Image: Figure 1: Each individual data point in the model’s free surface is used in a harmonic analysis to obtain the 37 tidal components necessary for characterizing tidal variability. The resulting tidal predictions, after validation with the local sea-level observations around the Bay, were then used to remove the tidal signatures from the time series. The level of variance in the Bay’s sea-level due to tidal and non-tidal processes varies with location in the Bay. Tidal variability is highest at both the southern and northern extents of the Bay, while non-tidal sea-level variability is isolated to the heads of the Bay’s tributaries and to the northern part of the Bay. While this seems to indicate that the variability may be linked to fluvial inputs, further analysis yields that the wind field is the primary controlling factor. Figure 2: The residual’s (sea-level minus tides) are then further subjected to Empirical Orthogonal Function (EOF) analysis in order to discern what physical processes are involved. The first EOF shows a large scale quarter wave feature that is lowest at the mouth of the Bay and increases in a direction oriented to the NW. This EOF accounts for 91.2% of the observed variability in the subtidal sea-level. The EOF’s time series correlates (>0.7) with the NW/SE component of the large scale wind field. The second EOF shows a N/S-oriented, large scale feature, and accounts for 6.6% of the subtidal variance. It correlates (>0.7) with the N/S component of the large scale wind field. Together, the tides along with the 1 st and 2 nd EOFs account for the majority of the sea-level variability. Scientific significance: Sea-level variability in the Chesapeake Bay is a critical issue for many coastal cities. Understanding the physical processes involved in the control of this is important for management of potential flooding events, such as can occur during winter storms and hurricanes. The EOF analysis can be used with tide models to quickly assess the impact of storm wind forcing on the various coastal areas in the Bay. Relevance for future science and relationship to Decadal Survey: NASA is developing Surface Water Ocean Topography (SWOT) mission which is designed to observe coastal sea levels. The present study is being carried out to support the mission by providing answers on the spatial and temporal scales of sea-level variability due to various physical forcing processes such as storms, tides, precipitation-evaporation, climate change, and fluvial fluxes. Using models to address these complex questions in a highly complex coastal region is an effective way to obtain answers to various questions that arise during the development of both the science and engineering issues related to the SWOT mission.

C HARACTERIZATION OF F OREST O PACITY U SING M ULTI -A NGULAR E MISSION AND B ACKSCATTER D ATA Peggy O’Neill (NASA GSFC), Code and Mehmet Kurum (ORAU), Code Figure 2: Effective” vegetation optical thicknesses from the multi-angular emissivity data Figure 3: Vegetation optical thicknesses comparison. Figure 1: The “measured” vegetation opacity values obtained using the radar returns at several azimuth locations at incidence angle of 45º on September 15, Figure 4: Pictures of the trihedral corner reflector (a) and ComRAD (b) deployed over a natural stands of Virginia pine forest. Three independent approaches are applied to the microwave data to determine vegetation opacity of coniferous trees.  First, a ratio between radar backscatter measurements with a corner reflector under trees and in an open area is calculated to obtain “measured” tree propagation characteristics (see Fig. 1).  Second, a zero-order radiative transfer model is fitted to multi-angular microwave emissivity data in a least-square sense to provide “effective” vegetation optical depth (see Fig. 2).  Finally, the “theoretical” propagation constant is determined by forward scattering theorem using detailed measurements of size/angle distributions and dielectric constants of the tree constituents (see Fig. 3). Results indicate that the “effective” attenuation values are smaller than but of similar magnitude to both the “theoretical” and “measured” values. (a) (b)

Name: Peggy O’Neill and Mehmet Kurum Phone: (301) References: M.Kurum, P. O’Neill, R. H. Lang, A Joseph, M. Cosh, T. Jackson, “Characterization of Forest Opacity Using Multi-Angular Emission and Backscatter Data ”, in Proceedings, International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, July 24 – 30, M.Kurum, P. O’Neill, R. H. Lang, C Utku, A Joseph, M. Cosh, T. Jackson, ”Passive Measurements over Conifer Forest at L-Band: Modeling of the Forest Floor”, presented, 11th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, Washington, DC (USA), March 1 – 4, Data Source: NASA’s Terrestrial Hydrological Program has funded a three-year field experiment to measure the L band microwave response to soil moisture under different types of small to medium tree canopies (Fig. 4b). The project was a collaboration between GSFC, the George Washington University, and USDA. The truck-mounted ComRAD radar/radiometer instrument system was used to obtain microwave data over deciduous and coniferous trees coincident with measurements of soil and vegetation properties. Technical Description of Image: Current baseline soil moisture retrieval algorithms for ESA's Soil Moisture Ocean Salinity (SMOS) mission and candidate retrieval algorithms for NASA's Soil Moisture Active Passive (SMAP) mission are based on the tau-omega model, a zero-order radiative transfer approach where scattering is largely ignored and vegetation canopies are generally treated as a bulk attenuating layer. In this approach, vegetation effects are parameterized by tau and omega, the microwave vegetation opacity and single scattering albedo, respectively. This model has been validated over grasslands, agricultural crops, and generally light to moderate vegetation. Its applicability to areas with a significant tree fraction is unknown, especially with respect to specific tree types, anisotropic canopy structure, and presence of leaves and/or understory. Although not really suitable to forests, Ferrazzoli et al proposed that a zero-order tau-omega model might be applied to such vegetation canopies with large scatterers, but that equivalent or effective parameters would have to be used. They determined these effective parameters by minimizing a cost function computed from the difference between measured multi-angular dual-polarized emissivity and modeled data (tau-omega model). Fig. 3 compares the effective vegetation parameters (given in Fig. 2) computed from multi-angular pine tree microwave emissivity data with the results of two independent approaches which provide “theoretical” and “measured” vegetation characteristics. These two techniques are based on forward scattering theory and radar corner reflector measurements (given in Fig. 1), respectively. The results demonstrate that “effective” vegetation optical depths are lower than “theoretical” and “measured” ones. Resolving the discrepancies between these three estimates of tau could lead to simple effective parameters being used when retrieving soil moisture over vegetated terrain. Scientific Significance: Soil moisture is recognized as an important component of the water, energy, and carbon cycles at the interface between the Earth’s surface and atmosphere. Several planned microwave space missions, most notably ESA’s SMOS mission (launched November 2009) and NASA's SMAP mission (to be launched 2014/15), are focusing on obtaining accurate soil moisture information over as much of the Earth's land surface as possible. Their large radiometer footprints will be partially or completely covered with forests. Thus, knowledge of L-band vegetation features appears to be of great importance when the tau-omega approach is applied to dense vegetation (i.e. forest, mature corn, etc.) where scattering from branches and trunks (or stalks in the case of corn) is likely to be very important. Our work involves a theoretical/experimental examination of the vegetation characterization by utilization of microwave modeling, together with ground truth and L-band brightness temperatures. These results could be used with future global soil moisture mission data to more accurately interpret soil moisture products over densely vegetated landscapes. Relevance for future science and relationship to Decadal Survey: Effective characterization of a forest canopy should help scientists to extend accurate soil moisture retrievals from global space mission to more areas to the Earth's surface than are currently feasible. Once completed, this project should provide quantitative assessments of vegetation scattering and attenuation, leading to improved SM retrievals and vegetation characterization for moderately to densely vegetated areas from microwave missions in space.