Final Report

The New York Bight Shelf – Harbor Dynamic Study: Ocean Forecast Sensitivity to Forecasts of Atmospheric Forcing

Nickitas Georgas and Alan F. Blumberg

Center for Maritime Systems Stevens Institute of Technology

Castle Point on Hudson Hoboken, New Jersey, 07030

phone: 201-216-5218 fax: 201-216-8214 email: ngeorgas@stevens.edu

Award (ONR Grant) Number: N00014-06-1-1027 http://www.stevens.edu/maritimeforecast/

LONG-TERM GOALS The long-term goals of the study are (a) to understand the forces that control the dynamics and dispersion of the Hudson River plume in the New York Bight, (b) to enhance the predictive capabilities of hydrodynamic forecasting systems for the area through sufficient incorporation of these driving mechanisms, and (c) to demonstrate the benefits of continuous scientific cooperation among Mid-Atlantic Coastal Ocean Observing Regional Association (MACOORA) members. Stevens Institute of Technology (Stevens) and Rutgers University (Rutgers) expanded their existing partnership through this joint study that leveraged the complimentary strengths and ongoing investments of the partner institutions. In the collegiate spirit of the Integrated Oceanographic Observing Systems (IOOS) and our regional association (MACOORA), members of a third academic institution, the School of Marine and Atmospheric Sciences of the State University of New York at Stony Brook (SoMAS at SUNY Stony Brook), exchanged data with Stevens for this study. NAVAL RELEVANCE The Navy requires nowcasts and forecasts of atmospheric and ocean conditions in the approaches to and within estuaries and harbors in friendly and in denied-access regions. Coupled meteorological and ocean models are required, each with high enough spatial resolution to resolve complex bathymetric, topographic, oceanic, and atmospheric features. The forecasts must be initialized and updated based on hydrologic and meteorological models, remote sensing data from satellites, aircraft, bi-static radars, and/or in situ data collected by newly emerging autonomous systems. Conducting experiments with these systems to develop new capabilities and ensure their robustness in denied areas is problematic. A more cost effective approach is through the development of a local test-bed that can leverage prior knowledge, existing infrastructure, and the support of other ongoing programs. The New York Bight Harbor and Shelf provides a unique naval test-bed for the development of a robust forecasting capability within and around an urbanized harbor environment. Stevens, Rutgers, and SUNY Stony Brook have decades of experience working in this region. Significant real-time observation networks are already in place and operated by these institutions. Both meteorological and

ocean models are being run continuously, but the inclusion of high-resolution atmospheric forcing was not fully implemented or tested in the ocean models. The support of several ongoing ONR, NSF, NOAA, NOPP, DHS, DoD, and State of NJ projects was leveraged in this effort. The project added to this ongoing research investment a methodology for testing complete, high-resolution atmospheric forcing, and its incorporation in estuarine and coastal ocean forecast models. The model development and validation benefited from these leveraged resources, and the projects benefited from a readily available, enhanced forecast product. QUESTIONS OF NAVAL INTEREST The effect of synoptic-scale wind forcing on the flows in the Estuary has been studied in the past. However, additional work is required to assess whether surface forcing by other atmospheric processes (barometric pressure load, heat fluxes) plays a role. Very little is known about the impact of meso-scale meteorological events such as the enhanced sea breezes in the New York Bight Apex. It was our hypothesis that these effects need to be included in forecasting and observation programs in denied areas, a critical Navy need. Yet, different meteorological models may predict local effects like sea breezes with variable skill, if at all. In addition, meteorological models of different physics and scale create different atmospheric forecasts. We also hypothesized that in areas for which air-sea interactions drive or disperse plumes, uncertainty in meteorology will lead to uncertainty in oceanic response. One of the questions asked was how well do ocean models predict the features that carry sediments downstream and out to sea, given the uncertainty in atmospheric forcing forecast by the meteorological models. SPECIFIC OBJECTIVES Existing capabilities included Stevens’ New York Harbor Observing and Prediction System (NYHOPS, www.stevens.edu/maritimeforecast), the Rutgers’ glider fleet (http://marine.rutgers.edu/cool/auvs), and the SoMAS short-range ensemble forecast (SREF) system (http://atmos.msrc.sunysb.edu/html/alt_mm5.cgi). NYHOPS has been operational since 2004, creating, disseminating, and quality-controlling daily 48-hr forecasts of 3D hydrodynamic fields in and around the NY/NJ Harbor estuary. In January 2007, NYHOPS transitioned to a high-resolution variable grid, down to 50m horizontal in New York Harbor. Limited 2D atmospheric forcing (surface level adjustment and heat fluxes from 2D atmospheric fields) was included in the original NYHOPS system. The meteorological SREF system creates and ensembles atmospheric forecasts from a multitude of individual meteorological model runs (end-members, each employing different modeling engines, physics, initial conditions, and spatial resolutions), to bound uncertainty in the resulting atmospheric ensemble mean prediction product. In this work, we merged the capabilities of the three aforementioned institutions to:

(a) Develop a fully 2D Atmosphere-Ocean forcing Module (AOM) in the Stevens high-resolution NYHOPS forecasting model covering the New York Bight Shelf and Harbor,

(b) Quantify the benefits of using the AOM module in the new high-resolution NYHOPS system

through comparison of AOM-forced NYHOPS forecasts to fixed-station, satellite, and glider observations, and,

(c) Assess the sensitivity of the NYHOPS forecasts for the Hudson River and its New York Bight plume to atmospheric forcing provided by different meteorological models at different resolutions. APPROACH NYHOPS model overview The ocean observing component of NYHOPS is overseen by M. Bruno. The high-resolution NYHOPS ocean forecasting system (Figure 1) is being developed by N. Georgas and overseen by A. Blumberg. Graduate students Wei Li, Ganesh Gopalakrishnan, Jei Ko, and Shashi Bhushan of Stevens also played critical parts in this project. The three-dimensional hydrodynamic model employed to forecast the ocean circulation across the domain shown in Figure 1 is a derivative of the Princeton Ocean Model (POM, Blumberg and Mellor, 1987). The hydrodynamic code includes significant features not included in the original POM, such as wetting-and-drying and thin-dam formulations, data assimilation, as well as coupled sediment transport, wave, and atmospheric modules (Georgas et al 2007). A water-quality module within NYHOPS is also funded by ONR. The NYHOPS domain is centered on the focus region of the NY/NJ Harbor Estuary and the tidal Hudson River. The open ocean boundary (OOB) of the NYHOPS grid (Figure 1) is situated along the 200m isobath of the continental shelf slope and extends from the coast of Maryland to the coast of Nantucket, MA. Other areas included in NYHOPS are Long Island Sound, NJ coast - and back-bays, Delaware - and Narragansett Bays (Figure 2). The resolution varies from 5km at the OOB to 50m inside some areas in the NY/NJ Harbor Estuary. The hydrodynamic model is initiated at 0 hrs local every day, and completes a 24hr hindcast cycle based on observed forcing, and a 48hr forecast cycle based on forecast forcing. NYHOPS provides forecasts for water level, 3D circulation fields (currents, temperature (T), salinity (S), density, speed of sound), significant wave height and average period, and CDOM concentrations. OOB conditions in NYHOPS are specified from:

• Tides; 8 tidal constituents from the East Coast database based on the ADCIRC model, Mukai et al 2002,

• Storm surge; hindcast from observations, forecast cycle from the NOAA/NWS/MDL extra-tropical storm surge model (http://www.nws.noaa.gov/mdl/etsurge/, Chen et al 1993; Kim et al 1996),

• Historical mean southwestward along-shore flow at the NY Bight through specification of constant cross-shelf elevation tilt at the northeast and west OOB (Blumberg and Galperin 1990).

• Monthly T/S climatology; from the NOAA Levitus 1998 compiled historic database of the last 100 years, http://www.cdc.noaa.gov/cdc/data.nodc.woa98.html, optimally interpolated on the OOB, and

• Significant wave height and direction; from an optimally interpolated new 4’ resolution NOAA/NWS/NCEP Coastal Atlantic WaveWatch III model; MAT4, ftp://polar.ncep.noaa.gov/pub/waves/develop.

NYHOPS internal forcing is specified for: • 93 river systems; hindcast from 74 USGS gages (http://waterdata.usgs.gov/nwis/rt), forecast

from 27 NOAA/AHPS 6-hourly predictions (http://www.nws.noaa.gov/oh/ahps/) or persistence, watershed-area adjusted,

• 241 major freshwater dischargers; monthly-averaged flows and effluent temperatures compiled from EPA EnviroFacts Warehouse Permit Compliance System Discharge Monitoring Reports (PCS DMR, http://www.epa.gov/enviro/html/pcs/pcs_query_java.html), quality controlled,

• 39 major thermal dischargers; monthy-averaged flows and thermal intake/effluent input/outputs compiled from EPA EnviroFacts Warehouse PCS DMR, quality controlled.

Operational NYHOPS surface boundary conditions (SBC) for wind and heating and cooling are based on the North American Mesoscale model (NAM, ftp://ftpprd.ncep.noaa.gov/pub/data/nccf/com/nam) run at NOAA’s National Centers for Environmental Prediction. NAM is a Weather-Research-and-Forecasting (WRF)-type model. Each forecast cycle is initiated from a GSI (formerly 3DVAR) -assimilated initial condition (AIC) every 6hrs. Predicted atmospheric fields from each forecast cycle are provided on a sigma-pressure grid with mean horizontal resolution of 12km every 3hrs. For the NYHOPS 24hr hindcast period, a concatenation of timestamps from the most recent NAM runs is used: NYHOPS uses the 0z (AIC) and 3z (forecast) from the 0z NAM run, then the 6z (AIC) and 9z (forecast) from the 6z NAM run, etc. Thus, NYHOPS takes the times closest to the NAM AIC, ensuring the truest representation of the observed and near-term forecasted surface atmospheric fields. As described, complete 2D NAM winds at 10m elevation are extracted from NAM and used for wind stress forcing and wind wave growth in NYHOPS (Figure 1). Although available from NAM, the operational NYHOPS model has not been using 2D atmospheric fields related to ocean surface heat fluxes. Rather, 3-hourly surface heat flux input variables are extracted from NAM at the location of the JFK airport (Figure 2) and used to form a one-dimensional-in-time (1Dt) 3hrly time series used everywhere at the NYHOPS surface boundary. The JFK airport is centrally located within the NYHOPS focus region. The surface heat flux input variables at JFK used in the operational NYHOPS are total cloud cover, air temperature at 2m above surface, 10m-above-ground wind, relative humidity, and barometric pressure reduced to mean sea level (Box 1). Note that long-wave radiation and convective heat fluxes vary in 2D, as they are a function of the NYHOPS-predicted local surface water temperature (Box 1). Also note that barometric pressure has only been used to calculate the latent heat flux component of the air-sea heat fluxes through the calculation of vapor pressure (Box 1). Barometric pressure load forcing as a free surface adjustment mechanism has not been incorporated into the operational NYHOPS. Rather, external storm surge is included through the OOB condition. Several questions are addressed:

1) What is the effect of including the barometric pressure gradient (as provided by NAM) into NYHOPS as a forcing term? Does this inclusion of barometric pressure load forcing create better NYHOPS forecasts for water level through surface adjustment and inverse barometer processes?

2) What is the effect of including complete 2D atmospheric heat flux variability (2D heat flux

atmospheric variables from NAM instead of 1Dt from JFK) into NYHOPS? Does this inclusion create better NYHOPS forecasts in the Hudson River proper and plume area?

3) Would there be an improvement in NYHOPS predictive skill if meteorological models of higher spatiotemporal resolution than NAM were to be used, as available, to drive the NYHOPS surface boundary forcing?

4) How sensitive are the NYHOPS oceanographic forecasts to wind stress, barometric pressure,

and heat flux forcing provided by different, available, meteorological forecast models? In particular:

a. What is the sensitivity of general circulation in the Hudson River and plume region due

to variability in forecasted surface adjustment atmospheric processes (surface wind stress and barometric pressure)?

b. What is the sensitivity of general circulation in the Hudson River and plume region due to variability in forecasted surface adjustment and heat flux atmospheric processes?

Inclusion of full 2D barometric pressure and heat flux forcing in the NYHOPS AOM Given these questions, the NYHOPS code was first altered to include barometric pressure gradient and full 2D-variable atmospheric forcing. A beta code was received from Mr. Nicholas Kim of HydroQual, Inc., debugged, and embedded into NYHOPS. The incoming solar radiation, for example, was expanded to allow for longitudinal variability due to local solar zenith changes with regard to local time. Cloud cover was allowed to vary in 2D, so that the penetrating solar radiation at the water surface was adjusted by local overcast albedo (see example in Figure 3). 2m above ground air temperature was allowed to vary spatially, so that longwave and convective heat fluxes are calculated from local water and local air temperature. Relative humidity and barometric pressure were allowed to vary spatially, so that the local vapor pressure of air could be included in the latent heat flux formulation of NYHOPS (Box 1). As it pertains to barometric pressure load forcing (ocean current acceleration),

Ptu

yx,0

1∇∝

∂∂

ρ (1)

the barometric pressure gradient was included in the circulation forcing terms, as barometric pressure was assigned from the spatially variable forecast fields. In the new NYHOPS AOM, the deterministic part of the S2(p) atmospheric tide included in mean-sea-level- (MSL)-reduced barometric pressure fields predicted by atmospheric models can be estimated and removed, as it has been recognized as the part of the radiational Y2

2 component of the S2 ocean tide (see Box 2 and references therein). Inquiry for and use of SoMAS SREF atmospheric forecasts with various resolution and physics To specifically tackle questions (3) and (4) above, the NOAA/NCEP NAM meteorological model used to force the daily NYHOPS forecasts was complemented by two out of a possible twelve different end-member models used in the SoMAS SREF atmospheric ensemble (Figure 4) for a 120-day period beginning March 1 2007. SoMAS SREF resolutions varied from 36km down to 4km, and models included MM5 and home-brewed WRF’s to supplement the NAM model. The fourteen end-members provide different near-surface winds, atmospheric pressure, air temperature, and relative humidity forecast fields compared to NAM or each other (Table 1). We concentrated on the use of two of these

end-member atmospheric models (Figure 4) to force “parallel” NYHOPS ocean model simulations, because they were the only ones provided at both 12km (similar to NAM), and 4km meteorological grids (from here on, “high-resolution”). It should be noted, however, that the high-resolution 4km grids did not cover the entire NYHOPS oceanic domain (Figure 4).

MM5 Members: Folder Name Days between Mar 1 2007 and Jun 10 2007 with >5hr gaps

**WRF-NMM (Grell, MRF, Sice) data9a 03/31, 04/01, 05/16, 05/25

WRF-NMM (Grell, M-Y, Reis2) data.GRMY-Reis.NEUS.eta 04/01, 04/06, 04/16, 05/25, 06/10

GFS (Betts-Miller, M-Y, Sice) data.BMMY-CCM.NEUS.avn 04/01, 04/21, 05/07

GFS (KF2, MRF, Reis2) data.K2MRF-Reis.NEUS.avn 03/06, 04/01, 05/09.

NOGAPS (Grell, Blackadar, Sice) data.GRBLK-CCM2.NEUS.nogaps 03/21, 03/22, 04/01, 04/06, 04/20

CMC (KF2, M-T, Sice) data.K2MY-CCM2.NEUS.cmc 03/14, 04/01, 04/07, 04/09

18 Z GFS + FDDA (Grell, Blackadar, Sice) data.GRBLK-CCM2-fdda.NEUS.avn 03/31, 04/18, 04/25

WRF-ARW Members:

**WRF-NMM (KF2, YSU, Ferrier) 221.YSU.KFE.FERR.RRTM.WRF 03/19, 04/08, 04/13

WRF-NMM (Betts-Miller, M-Y, WSM3) 221.MYJ.BMJ.WSM3.RRTM.WRF 04/08

GFS (Grell, YSU, Ferrier) GFS.YSU.GRE.FERR.RRTM.WRF 04/08

GFS (KF2, M-Y, Ferrier) GFS.MYJ.KFE.WSM3.RRTM.WRF 04/07, 04/08, 05/30

NOGAPS (Betts-Miller, YSU, WSM3) NOG.YSU.BMJ.WSM3.RRTM.WRF 03/21, 03/22, 03/24, 04/06, 04/08, 04/20

CMC (KF2, M-Y, WSM3) CMC.MYJ.KFE.WSM3.RRTM.WRF 03/09, 03/10, 03/14, 03/18, 04/08, 04/09, 05/02

Table 1. Available and selected MM5/WRF meteorological members from SoMAS SREF. Selected models (**) are run at both 12-km (d2 domain) and 4-km (d3 domain) horizontal resolution.

The data9a MM5 meteorological end-member model (Table 1) is integrated from an outer 36-km domain that nests a smaller 12-km domain, and an even smaller 4-km domain (Figure 4). Thirty-three stretched sigma pressure levels are used in the vertical, with a maximum resolution in the boundary layer. It employs the Grell convective parameterization (Grell CP), the Medium Range Forecast Model planetary boundary layer scheme (MRF PBL), and the “simple ice” (Sice) bulk microphysical parameterization of cloud and orographic precipitation (Colle et al 2005, Jones et al 2007). The WRF-ARW end-member model (221.YSU.KFE.FERR.RRTM.WRF, Table 1) uses similar nesting techniques, but employs the Kain-Fritch-2 convective parameterization (KF2 CP), the Yonsei University planetary boundary layer (YSU PBL), the eta grid-scale cloud and precipitation scheme (Ferrier), and the Rapid Radiative Transfer Model RRTM (Shamarock et al 2005). The selected MM5

and WRF end-members use initial and boundary conditions from the NCEP NAM (WRF-NMM). The 12km nested grids (d2 domains) are integrated from 00z (a NAM-derived AIC) to 48z once daily. However, due to computational limitations, the 4km nested grids (d3 domains) are integrated from 12z to 36z once daily. This creates a 12hr separation from assimilated NAM initial conditions (12z from 00z) for the 4km runs. The analysis that follows will show that this 12hr separation carries important adverse consequences on forecasting skill for both the meteorological and ocean models. All four total selected runs (MM5 12km and 4km, and WRF 12km and 4km; Table 1) provide hourly output of atmospheric variables, compared to the 3hrly NAM. Both MM5- and WRF-type models had near-surface wind speed and direction, barometric pressure, air temperature, and relative humidity forecasts available that may be used to force the NYHOPS oceanographic forecasts. All of MM5/WRF models listed in Table 1 were received in daily GRIB format. They were decoded at Stevens by using Linux shell scripts and the wgrib utility. Individual forecasts were concatenated; if more than one forecast was available for the same valid time, the latest available forecast was used. Missing data were interpolated. However, for the few days when winds were not available for more than 5hrs, winds were set to zero to avoid occurrence of potentially unrealistic constant high winds. Forecasts with missing data greater than 5hrs are shown in Table 1. For the 12km SREF 48-hr forecasts, which were run daily for 00z to 48z, a gap in the concatenated time series only occurs when two successive forecasts are missing; otherwise, the latest forecast is used. For the 4km SREF 24-hr forecasts, which cover only 12z to 36z (24hrs), a gap occurs each of the days listed on table 1. For NAM, forecasts did not have any gaps, and the concatenated time series were created from the 6hrly-initialized 3hrly outputs. In our investigative NYHOPS simulations based on each of the selected meteorological models, we used the same short-wave radiation and cloud cover from NAM, but different winds, pressure, convective and long-wave heat flux-related variables as provided by each model and calculated by the new NYHOPS AOM. Since MM5/WRF models have grids different from that of the NYHOPS model (Figure 4), atmospheric forecasts were interpolated to the NYHOPS’ water-only grid cells. All 13 separate members of MM5 and WRF models were formatted in NYHOPS “synop_wind” and “synop_met” input data files for each day of model prediction and concatenated for the entire model simulation period from March 1 2007 to June 17 2007. For the two selected members of MM5 and WRF models, two sets of “synop_wind” and “synop_met” NYHOPS simulations were prepared.

• “synop_wind” NYHOPS model runs used 2D atmospheric surface adjustment (SA) forcing data (wind and barometric pressure). In these runs, meteorological data needed for heat flux calculation came from the “standard” location at JFK airport. This is a standard method used for the current NYHOPS’ daily forecasting system with the exception of the inclusion of barometric pressure gradient load; the latter is an additional forcing not used in standard NYHOPS.

• “synop_met” model runs used 2D data for meteorological forcing as well as heat flux calculation. In these runs, 2D input variables were expanded to include short-wave radiation, cloud cover, relative humidity, and atmospheric temperature. In addition, 2D winds, rather than winds at JFK, were also used for the calculation of local latent and sensible heat fluxes (Box 1).

By running models with the expanded meteorological forcing physics and different forecast data for heat flux calculation (2D vs. JFK only), effects of using a more elaborate 2D field of heat flux data can be assessed. Inter-run comparisons can also be made. Although the model-run time increases by about

a factor of two with 2D data for heat flux, it might be well worthwhile if the model accuracy improves significantly. The rest of the oceanographic data (OOB conditions, river inflows, etc.) required by NYHOPS were taken from the standard NYHOPS forecast input files (“run_data”) and used in all NYHOPS simulations performed for this study. In parallel, tidal and statistical analysis, atmospheric and ocean model performance skill, and visualization algorithms for the full 120-day simulation, as well as 15-day-averaged meteorological input forcing and NYHOPS predictive forecasts were created in FORTRAN and MATLAB. These algorithms were used to analyze and present differences in the various NYHOPS simulations as a whole, and compared to observations from multiple sources (“point” stations shown in Figure 2, SST from a NOAA satellite, and Rutgers glider deployment data). They were also used to “look deeper” into a “critical” 15-day period between April 15 2007 and April 30 2007. This 15-day period included the significant “tax day” Nor’easter storm of April 15-16 2007 that flooded big parts of northern NJ and southern NY with rain and dumped big amounts of snow on the Upper Hudson and Catskills watersheds, and the long recession from it that followed [see an NWS summary of the event at http://www.erh.noaa.gov/er/aly/Past/2007/Apr_15-16_2007/Apr_15-16_2007.htm]. Another significant storm for the area, the St. Patrick’s Snowstorm, occurred between March 16 and 17 2007. With the developed algorithms at hand, the results from these simulations were compared to meteorological and oceanographic observations, each other, and a standard “synop_wind” NYHOPS run (without barometric pressure gradient load forcing and heat fluxes based on JFK atmospherics alone). Each 120-day synop_wind run took 3.5 days – 7 days for a synop_met run – on a newly acquired 8-way parallel computer. Each run also required about 120GB of storage space, before post-processing. Hence, another reason the study focused on the selected end-members from the ones available in Table 1 was that it was not feasible to complete and analyze all 26 [(12+1) x 2-synop] runs within the current project scope. An expansion will be sought to accommodate all runs, and include the possibility of an ensemble oceanographic forecasting product similar to meteorological ensemble forecasts currently been developed.

Figure 1. The high-resolution New York Harbor Observing and Prediction System (NYHOPS).

NYHOPS HIGH-RES. GRID

Figure 2. Locations of places and stations referenced in the study.

Box 1. Wind-stress and surface heat flux formulations used in the operational NYHOPS model. WINDS NOAA/NCEP WRF-NMM (NAM) 3hrly forecasts of 2Dt winds (w) at 12km resolution.

ztu

∂∂

∝∂∂ τ

ρ

0

1

wwC WDa

,ρτ =

+=−

−

−

3

3

3

,

10*115.2

10*)065.049.0(10*2.1

wC WD

∞∈

smsm

smw

/),25(/)25,11(

/)11,0( Large and Pond, 1982.

SURFACE HEAT FLUXES: Calculated at each NYHOPS surface cell, forced by: a) Short-wave radiation = f (incoming solar radiation minus cloud cover losses; Rosati & Miyakoda, 1988) b) Net atmospheric long-wave radiation = f (incoming gain from air temperature, cloud cover, and emitted loss from local water temperature; Wunderlich 1972 – modified Stefan-Boltzmann laws):

−+

−

42

66

)17.01(

)1037.9(

s

a

TC

Txεσ

c) Sensible heat flux = f (air temperature, local water temperature, wind speed function; Edinger et al. 1974):

))(34.09.6( 2asT TTWC −+

d) Latent heat flux of evaporation = f (wind speed function and air temperature, local water temperature, barometric pressure and relative humidity for vapor pressures at air-sea interface; Edinger et al. 1974; Buck 1981; Cole and Buchak 1995):

))(34.09.6( 2as eeW −+

Underlined heat-flux variables extracted from NAM at one central location (JFK airport), then used everywhere in the NYHOPS domain (1Dt inputs to the heat flux module, even though winds are 2D for wind stress). ε Emissivity of water es Saturated vapor pressure of surface water σ Stefan-Boltzmann constant, W/(m2K4) ea Vapor pressure of air above ground Ts Surface water temperature, K CD,W Surface wind drag coefficient Ta Air temperature at 2m above ground, K u Current C Cloud cover fraction (0-1) ρ0 Reference density of water CT Bulk transfer coefficient for conductive flux ρα Air density W Wind speed, m/s τ Surface wind stress

Figure 3. 15-day (Apr 15 – Apr 30 2007) mean water surface solar radiation computed by the full 2D NYHOPS AOM based on local time adjustments and 2Dt cloud cover from NAM. For these 15 days, solar zenith, the 2Dt cloud cover distributions, and their occurrence within the day/night cycle created an estimated 20% spatial variability in the mean atmosphere-penetrating shortwave radiation over the NYHOPS domain.

Box 2. Atmospheric Tides Correction in NYHOPS AOM. Over the past two decades, it has been recognized that the diurnal, S1(p), and semidiurnal, S2(p), atmospheric tides should not be included as part of barometric pressure forcing in global ocean models, as they are traditionally considered part of the oceanic tidal signal (e.g. Ray 1993, Wunch and Stammer 1997, Ponte and Ray 2002, Ponte and Vinogradov 2007). Chapman and Lindzen (1970) summarize work from Haurwitz (1956, 1965) to provide approximate expressions for the surface pressure fluctuations due the S1(p), and semidiurnal, S2(p) air tides, depending on local standard time non-dimensionalized by the 24hr solar day, tLST, in hours, and colatitude, θ, in degrees.

)209440.0sin(sin593.0),(

)05949.24sin()1cos3(0425.0)75762.24sin(sin16.1),(3

1

232

+=

+−++=LST

UTCLST

tmbarpSttmbarpS

θ

πθπθ (E.Box2)

It should be mentioned that S2(p) is almost invariant of seasons, while S1(p) may vary with seasons (not shown, Chapman and Lindzen 1970). As an example, consider Atlantic City (AC), NJ, where θ=(90-39.35)º=50.65º and tUTC=tLST+5hrs/24hrs,

)209440.0sin(274.0,@)(

)67748.44sin(009.0)75762.24sin(536.0,@)(

1

2

+=

+++=LST

LSTLST

tmbarACpSttmbarACpS ππ

So, for AC, S1(p) is roughly half of S2(p). Note that the semidiurnal air tide reaches maximum at 9:45 (am and pm), while the diurnal tide reaches maximum around 8:40am Eastern Standard Time. Also note that the standing oscillation part of S2(p) has a much smaller amplitude than the travelling part. Through the “local inverse barometer” approximation (e.g. Ponte et al 1991, Ray 1993, Ponte and Vinogradov 2007), a semidiurnal barometric pressure oscillation with a 0.536mbar maximum at 9:45 EST would roughly translate to a semidiurnal oscillation in water level with a 5.36mm minimum at 9:45 EST. This semidiurnal water level oscillation is considered part of the S2 ocean tide. It so happens that the composite S2 tide maximum at AC happens around 11:30 EST (am and pm), or approximately half way to quadrature. Thus, the small radiational part of the S2 ocean tide caused by the atmospheric S2(p) signal is diminished by about a factor of 2 (~2.5mm) by the beating of the out-of-phase sum of the rest of the semidiurnal components that comprise the S2 ocean tide. What this all comes out to is that, at AC, if we include the barometric pressure from a model (or observations) without first removing the atmospheric tidal components, we would have a case of double counting. In the power spectrum of Figure b1, one can see that the NAM model under-predicts the observed S2 component at AC. We calculated that, when the uncorrected NAM barometric pressure is used to force the NYHOPS model, the model-resolved S2 tidal component decreases from 118 to 117mm, a double-counting effect on the order of 1mm, consistent with the under-prediction of the ~2.5mm S2(p) effective component of S2 by NAM. Satisfaction of the Rayleigh criterion for separation of the K2/S2 and K1/P1 pairs requires an analysis period at least 182 days long (Arbic et al 2004). An 120-day period was available for this tidal analysis, so the double-counting of the S1(p) and S2(p) components appeared on these oceanic tidal constituents as well, again on the order of 1mm or less.

Given the very small relative contribution of these tides on the oceanic components, and the error bars around their resolution within atmospheric models as seen in Figure Box 2, we decided not to remove the S1(p) and S2(p) barometric components through the use of the, also approximate, equation E.Box2 in this application of the NYHOPS AOM. Figure Box2. Power spectrum of observed, NAM-modeled, and cross-spectral barometric pressure at Atlantic City, NJ, (mbar2*hr), identifying the S1(p) and S2(p) atmospheric tidal components.

Figure 4. Meteorological models used in this study to force the NYHOPS ocean model through the new NYHOPS AOM. Dr. Brian Colle from Stony Brook’s SoMAS generously provided several end-members of their ensemble atmospheric forecasting modeling product. We concentrated on the two (one MM5-based and one WRF-based) run at both NAM-equivalent 12km resolution grid (d2 domain), as well as at 4km “high-resolution” grid (d3 domain).

WORK COMPLETED A new NYHOPS Atmosphere-Ocean forcing module (AOM) was coded, that includes full 2D atmospheric forcing on the NYHOPS ocean model. Eleven 120-day NYHOPS ocean simulations (Table 2) were set up, completed, and stored at Stevens servers:

• A standard-NYHOPS-like run with heat flux variables from NAM at JFK, and 2D winds from NAM (no barometric pressure load gradient forcing). This run is referred to as the “standard NYHOPS run.”

• One “synop_wind” (2D winds and pressure; heat flux variables at JFK) and one “synop_met” (full 2D atmospheric forcing) NYHOPS model simulations with forcing from the standard NCEP/NAM meteorological model at 12km resolution.

• Four MM5 (MM5/data9a)-forced NYHOPS simulations: “synop_wind” and “synop_met” models with atmospheric forcing based on the selected SoMAS SREF MM5 end-member meteorological model at 12km (d2 domain) and 4km (d3) resolution (2 run types x 2 resolutions of MM5).

• Four WRF (WRF/221.YSU.KFE.FERR.RRTM.WRF)-forced NYHOPS simulations: “synop_wind” and “synop_met” models with atmospheric forcing based on the selected SoMAS SREF WRF end-member at 12km (d2) and 4km (d3) resolution (2 runs types x 2 resolutions of WRF).

Atmospheric and ocean in situ observations were collected, as available from the stations along the tidal Hudson River, NY Harbor, along the NJ coast where the River plume is usually found (e.g. Chant et al 2008), and on the locations of four NDBC buoys in the NY Bight (Figure 2). A standard, FORTRAN-based, NOAA/NOS statistics package for ocean model evaluation (NOAA 2003) was successfully adopted and applied to compare the eleven NYHOPS simulations to a number of collected in situ observations. The NOS statistics package was expanded to include model evaluation for atmospheric variables (wind, barometric pressure, relative humidity, air temperature), and also MATLAB plotting routines. Model performance results were calculated, tabulated, and plotted. For the April 15-30 2007 time-period, 2D maps of event-mean and standard deviation atmospheric and ocean variables were created for each of the eleven simulations. Also, 2D maps of mean difference (bias), RMS difference, relative bias (bias over standard mean), and relative RMS difference (RMS difference over standard deviation) for all atmospheric and ocean variables and between each of the non-standard NYHOPS runs and the standard NYHOPS run were created. These plots were used to investigate variability in the NYHOPS forecasts, especially along the Hudson River region, depending on the meteorological model used. Along-track observations from four Slocum glider expeditions within the NYHOPS domain between March and May 2007 were made available by Rutgers. They were post-processed at Stevens, and compared to results from the NYHOPS simulations. Model skill improvement (MSI) due to the new AOM was calculated based on root mean square errors (Oke et al 2001):

STANDARD

AOM

rmsermseMSI −= 1 (1)

where rmseAOM is the root mean square error between results from a 2D AOM-including NYHOPS run and glider observations, while rmseSTANDARD is the mean square error between the standard NYHOPS run and glider observations. MSI values in the [0-1] range denote skill improvement. NOAA AVHRR-17 and -18 and GOES-12 satellite SST data for the 120-days of the NYHOPS simulations have been downloaded and tabulated. AVHRR post-processed data were received from Rutgers. The post-processed data appear to be in need of quality control for the coastal region. Preliminary RMS errors between the AVHRR SST data and coastal station in situ data calculated at Stevens is higher (>3ºC) than expected. The GOES-12 satellite data were downloaded along with QAQC flags, and appear more promising for future satellite SST-to-NYHOPS comparisons, even though limited by a 4km resolution.

“Synop_wind” runs: NYHOPS runs forced with “1D” surface heat flux (1D-HF) variables at JFK used globally.

NYHOPS run Standard

Operational “1D” NAM-12km

“1D” MM5-12km

“1D” MM5-4km

“1D” WRF-12km

“1D” WRF-4km

Meteorological model (→) used to provide NYHOPS with… (↓)

NAM Resol.: 12km Cycle: 48hrs IC: 0,6,12z, ... Output: 3hrly

NAM Resol.: 12km Cycle: 48hrs IC: 0,6,12z,… Output: 3hrly

MM5-d2 Resol. 12km Cycle: 48hrs IC: 0,24z Output: hourly

MM5-d3 Resol. 4km Cycle: 24hrs IC: 12z Output: hourly

WRF-d2 Resol. 12km Cycle: 48hrs IC: 0,24z Output: hourly

WRF-d3 Resol. 4km Cycle: 24hrs IC: 12z Output: hourly

wind stress

NAM, 2D

NAM, 2D

MM5-d2, 2D

MM5-d3, 2D

WRF-d2, 2D

WRF-d3, 2D

barometric pressure

None.

NAM, 2D

MM5-d2, 2D

MM5-d3, 2D

WRF-d2, 2D

WRF-d3, 2D

air temperature, relative humidity, barometric pressure, and winds used for surface heat fluxes

NAM at JFK

NAM at JFK

MM5-d2 at JFK

MM5-d3 at JFK

WRF-d2 at JFK

WRF-d3 at JFK

cloud cover

NAM at JFK

NAM at JFK

NAM at JFK

NAM at JFK

NAM at JFK

NAM at JFK

“Synop_met” runs: NYHOPS runs forced using the new fully 2D NYHOPS AOM.

NYHOPS run 2D

NAM-12km 2D MM5-12km

2D MM5-4km

2D WRF-12km

2D WRF-4km

Meteorological model (→) used to provide NYHOPS with… (↓)

NAM Resol.: 12km Cycle: 48hrs IC: 0,6,12z,… Output: 3hrly

MM5-d2 Resol. 12km Cycle: 48hrs IC: 0,24z Output: hourly

MM5-d3 Resol. 4km Cycle: 24hrs IC: 12z Output: hourly

WRF-d2 Resol. 12km Cycle: 48hrs IC: 0,24z Output: hourly

WRF-d3 Resol. 4km Cycle: 24hrs IC: 12z Output: hourly

wind stress

NAM, 2D

MM5-d2, 2D

MM5-d3, 2D

WRF-d2, 2D

WRF-d3, 2D

barometric pressure

NAM, 2D

MM5-d2, 2D

MM5-d3, 2D

WRF-d2, 2D

WRF-d3, 2D

air temperature, relative humidity, barometric pressure, and winds used for surface heat fluxes

NAM, 2D

MM5-d2, 2D

MM5-d3, 2D

WRF-d2, 2D

WRF-d3, 2D

cloud cover

NAM, 2D

NAM, 2D

NAM, 2D

NAM, 2D

NAM, 2D

Table 2. Synopsis of atmospheric forcing for the eleven NYHOPS ocean model simulations carried out in this study.

RESULTS The effect of meteorological model resolution on NYHOPS predictions Figure 5 identifies an issue with the resolution of the tidal Hudson River proper shared by all available operational meteorological models used in this study. The River’s width varies from about 1.5km west of Manhattan, to about 5km at Haverstraw Bay, to less than 500m in each northern span to the federal dam at Troy, NY 240km upstream of the Battery, NY. Thus, the 12km grids do not resolve the tidal part of the Hudson River at all, while the 4km grids only resolve Haverstraw Bay as water (Figure 5; panels D and F). Within Figure 5, panel B shows the geopotential height (elevation above sea level) in NAM. Although the Hudson River valley is partially resolved by the 12km meteorological model, the River itself is still modeled as land, and the NAM elevation along the “Hudson River” varies from near sea level at the lower Harbor entrance, to 200m above water level in the Bear Mountain area between Haverstraw and Newburgh, NY, to about 100m above water level from Newburgh to Troy. The direct applicability of these meteorological model outputs to force high-resolution ocean models like NYHOPS within embayments, estuaries, and tidal rivers comes into question. An example of this predicament is seen in Figure 6 for two experimental NYHOPS simulations forced with the 12km NAM atmospheric solutions, but using two different barometric pressure output variables from the NAM model: One simulation used barometric pressure at surface while the other used the “corrected” barometric pressure reduced to mean water level. Were the Hudson River resolved as water, the barometric pressure at surface would have been the same as the barometric pressure reduced to water level. The elevated Hudson River area shows significantly (~20mbar) lower average barometric pressure due to hypsometric effects (Figure 6, panel A). The lower barometric pressures in turn raise the average water level of the Hudson River due to inverse barometric effects (Figure 6, panel B). When the barometric pressure is reduced to mean water level (Figure 6, panel C) the mean atmospheric pressure gradient is an order of magnitude smaller (~1mbar/240km), and the resulting average water level has a much smoother gradual upstream slope (Figure 6, panel D) primarily associated with the Hudson River inflow head elevation. The 15-day period used to average the results of Figure 6 includes the significant “Tax-Day” Storm, which brought significant rainfall in the area, “swelling” the Hudson River more than normal as seen in Figure 6, panel D. The water-level-reduced mean barometric pressure from NAM (Figure 6, panel C) shows a positive 1.2 mbar spatial gradient from the Battery to Troy. An inverse barometric response to that gradient would cause about 1.2 cm (12mm) mean water level set down at Troy compared to the Battery for the 15-30 April 2007 period. But is this mean gradient in barometric pressure true, or an artifact of the poor NAM resolution in the Hudson River region? Moreover, does inclusion of barometric pressure gradient load from NAM (given the resolution issues) improve the skill of NYHOPS sea level prediction, and where along the Hudson River and plume? Comparisons of observed residual water level processed from optical NOS and USGS gages, versus NYHOPS residual water level model predictions for the Mar 1 to June 17 2007 time period were carried out (Table 3). Figure 7 shows that the root-mean-square error (RMSE) between the NYHOPS model and observations increased after including NAM 12km barometric pressure load forcing in all stations along the Hudson River. After application of atmospheric pressure forcing, NYHOPS sea level prediction performance weakens with increase in latitude and distance from the open ocean. It is thus concluded that barometric pressure forcing as provided by NAM along the Hudson River proper

should not be included in the NYHOPS model, due to the poor resolution of NAM in the area. The mean increase in sea level error after inclusion of the NAM 12km atmospheric pressure forcing does not translate to an increase in error at all times of the simulation. Figure 8 shows that including barometric pressure could lead to up to 20cm worse, or up to 15cm better residual water level predictions at Albany, NY, depending on the time considered.

Root-mean-square error, cm,

in residual water level

Station (NOS or USGS) Latitude Standard NYHOPS

without P load With NAM 12km

P load Albany, NY 42.64611 17.37 19.52 Poughkeepsie, NY 41.65083 10.29 11.07 West Point, NY 41.38611 9.63 10.32 Hastings-on-Hudson, NY 40.98778 8.60 8.90 The Battery, NY 40.70000 10.00 10.14 Sandy Hook, NJ 40.46667 9.46 9.54 Table 3. RMSE statistics for residual water level (total – tides) between NYHOPS predictions and in situ observations along the tidal Hudson River, before and after atmospheric pressure forcing is included from NAM 12km, for the period Mar 1 2007 to June 17 2007.

Barometric Pressure RMSE, mbar Number (Figure 10) Station Name

NAM-12km

WRF-12km

WRF-4km

MM5-12km

MM5-4km

1 Raritan Bay at Keansburg, NJ 0.78 1.22 1.53 1.22 1.61 2 Sandy Hook, NJ 0.61 1.09 1.46 1.13 1.65 3 Point Lookout, NY 0.69 1.24 1.53 1.20 1.64 4 Ambrose Light, NY 0.68 1.29 1.60 1.20 1.69 5 Point Pleasant, NJ 0.71 1.26 1.55 1.20 1.66 6 Barnegat Light, NJ 0.70 1.24 1.56 1.15 1.64 7 Absecon Channel, NJ 0.72 1.20 1.45 1.15 1.58 8 Atlantic City, NJ 0.68 1.16 1.39 1.11 1.51 9 26 NM out of Cape Henlopen, DE 0.69 1.11 1.49 1.14 1.61

10 33 NM South of Islip, NY 0.64 1.20 1.48 1.15 1.60 11 23 NM SE of Montauk, NY 0.69 1.21 1.48 1.22 1.67 12 Buzzards Bay, MA 0.69 1.22 1.49 1.25 1.68

Average “resolved” 0.69 1.20 1.50 1.18 1.63 Pier40, NY 1.37 1.45 1.65 1.60 1.99 Table 4. Comparison statistics for meteorological model error in barometric pressure prediction for the stations and correlograms in Figure 10. Note that the various models used different initiation strategies and were concatenated differently for use in NYHOPS, so inter-comparison across models is not possible, as explained in the text. Rather, differences across models may hint to degradation of forecasting skill with time since the beginning of forecasts (assimilated initial conditions).

Unfortunately, very limited barometric pressure observations in the waters of the Hudson north of the Battery existed for the period in question. A Stevens NYHOPS meteorological station at Pier 40, NY, west of Manhattan, included barometric pressure readings between May 1 and June 17 2007 (Figure 9). Based on this 40-day period, the demeaned RMSE in barometric pressure between the NAM 12km meteorological model and the in situ observations was 1.37 mbar (Table 4), roughly double the demeaned RMSE in all stations outside the NY/NJ Estuary (Figure 10 and Table 4). This absence of in situ barometric pressure data over the waters of the Hudson River has lately being addressed with the onset of the Hudson River Environmental Conditions Observing System (HRECOS, http://hudson.dl.stevens-tech.edu/hrecos/), which currently includes three stations reporting Patm north of the Battery. Further investigation in meteorological model discrepancies versus observed barometric pressure will now be possible based on these new data. Hopefully, this system will be further expanded to include more than three meteorological stations in the 240km span north of the Battery. Outside the Hudson River proper, NAM predicts barometric pressure with an excellent short-term forecast skill (Table 4 and Figure 10 based on the 00z and 03z concatenated time series). The R-square (R2) statistic for all 12 stations on Table 4 is 0.99, and the regression slope between model and observations is between 0.97 and 1.00. The effect of meteorological model initiation time (forecast degradation) on NYHOPS predictions Table 4 also lists statistics on barometric pressure prediction for the various meteorological models used in this study. It may appear than NAM performs best among the 5 models used, and that the 12km runs actually provide better predictions than the more high-resolution 4km runs for barometric pressure. This is misleading. The NAM 12km output used, was a concatenation of largely assimilated (00z) or 3hr-after-assimilation (03z) timestamps (Table 2). In other words, the NAM predictions used in the study were 0-3hrs separated from nowcasts. The 12km MM5 and WRF SoMAS SREF records used mostly 0-24hr forecasts as aforementioned, so they could be separated up to 24hrs from the initial 00z nowcast (up to 48hrs for missing forecast days in Table 1). The 4km MM5 and WRF SREF records used 12-36hr forecasts, so they could be separated up to 36hrs from the assimilated condition. What is then seen in Table 4 is the well-known degradation of meteorological forecast skill with time from initial conditions (smallest for the 0-3z NAM, greater for the 0-24hr 12km MM5 and WRF, and even greater for the 12-36z 4km MM5 and WRF models). The files were concatenated in such manner to use the best-available information at the time of NYHOPS initiation from each model. Similar degradation in meteorological model performance with time appears in other atmospheric fields as well. Figure 11 and Table 5 show results similar to Figure 10 and Table 4 for air temperature. Air temperature is better predicted at the “open ocean” stations than at “coastal stations” (Table 4) for which the day/night heating cycle is less pronounced and which are not affected by sea breezes or land/ocean resolution issues. Relative humidity RMSE was between 9% and 16% in two stations with data, Pier 40, NY and Point Pleasant, NJ and varied similarly among the five meteorological models. Any resolution benefits then from the high-resolution (4km) meteorological models are, at least partially, negated by their larger initialization lag compared to the 12km models when used to force the NYHOPS ocean model. Table 6 list RMSE in sea surface temperature (SST) as predicted by NYHOPS when forced by the five meteorological models. Apart from the station at Sandy Hook, NJ which appears to be degraded the most both in forcing (Tables 4 and 5) and response (Table 6), the NYHOPS performance skill does not depend significantly on the meteorological model chosen to force it, given

the forecast availability limitations (initialization lag issue). Yet, it will be later shown that the uncertainty in resulting ocean circulation parameters may be large, especially outside NY Harbor, where winds dominate. In other words, similarly skilled meteorological models may create different oceanic response in time even though similar on the time-average.

Air Temperature RMSE, degrees C

Station Name NAM-12km

WRF-12km

WRF-4km

MM5-12km

MM5-4km

Pier 40, NY (short record May 1 – June 17 2007) 2.44 2.87 2.84 2.68 2.73 Raritan Bay at Keansburg, NJ 2.50 2.63 3.05 3.15 3.02 Sandy Hook, NJ 1.32 1.73 2.31 1.66 2.00 Point Lookout, NY 1.27 1.78 2.19 1.94 2.02 Ambrose Light, NY 1.52 1.83 2.24 1.80 2.00 Point Pleasant, NJ 1.53 2.17 2.32 1.97 2.21 Barnegat Light, NJ 1.33 2.00 2.31 1.90 2.14 Atlantic City, NJ 1.65 2.24 2.42 2.14 2.40 Average "Coastal" 1.59 2.05 2.41 2.08 2.26 26 NM out of Cape Henlopen, DE 0.79 1.07 1.18 1.14 1.27 33 NM South of Islip, NY 0.71 0.97 1.18 1.02 1.11 23 NM SE of Montauk, NY 0.76 0.90 1.10 0.99 1.10 Buzzards Bay, MA 1.13 1.29 1.62 1.52 1.66 Average "New York Bight" 0.85 1.06 1.27 1.17 1.29 Table 5. Comparison statistics for meteorological model error in air temperature prediction. Differences across models may hint to degradation of forecasting skill with time since the beginning of forecasts (assimilated initial conditions).

RMSE in SST, degrees C

Station NAM-12km

WRF-12km

WRF-4km

MM5-12km

MM5-4km

Albany, NY 0.4 0.4 0.4 0.4 0.4 Poughkeepsie, NY 0.4 0.6 0.7 0.6 0.6 West Point, NY 0.5 0.5 0.5 0.6 0.7 GWB, NJ 0.8 0.8 0.8 0.9 0.8 Sandy Hook, NJ 1.0 1.6 1.5 1.3 1.5 Atlantic City, NJ 1.9 2.1 1.9 1.7 1.7 26 NM out of Cape Henlopen, DE 0.9 1.6 1.6 1.1 1.1 33 NM South of Islip, NY 1.4 2.0 2.0 1.7 1.6 23 NM SE of Montauk, NY 1.0 1.3 1.4 1.1 1.2 Buzzards Bay, MA 0.9 1.2 1.2 0.9 0.8 Table 6. Comparison statistics for NYHOPS model error in sea surface temperature (SST) prediction. The top group of stations is greatly influenced by the Hudson River inflow and water temperature, which is gaged at Troy, NY.

Improvement in model prediction due to full 2D heat fluxes implemented in the new NYHOPS AOM Figure 12 shows the improvement in NYHOPS SST prediction along the Hudson River and plume region due to the new NYHOPS AOM with full 2D atmospheric heat flux forcing. The increase in model skill is substantial: An overall 46% decrease in RMSE from 1.27 to 0.87 ºC. This is true for most fixed stations with the exception of Sandy Hook, NJ, where no improvement in skill was found. Interestingly, even stations largely influenced by the Hudson River inflow temperature at Troy, NY improved significantly (Figure 12). Figure 13 shows an example for West Point, NY before and after inclusion of 2D heat flux forcing. For the fixed stations in Figure 12, water temperature RMS errors for simulations with similar heat flux physics (1D- versus 2D-grouped) but based on different meteorological models were within 5% of each other. Within NY Harbor, no significant improvement in NYHOPS prediction skill for salinity (Table 7) and currents (under the Verrazano Narrows Bridge) was found: The Model Skill Improvement (MSI), though having a positive sign for all these stations, was less than 1%. The RMSE for near-surface currents along the principal current direction at the Narrows was 19cm/s, or 13% of the observed current range. Simulation Group GWB, NY 79th Street, NY Pier40, NY Sandy Hook, NJ Grand Mean Average 1D Heat Flux 1.88 1.96 2.09 2.30 1.44 Average 2D Heat Flux 1.88 1.94 2.09 2.29 1.43 Table 7. RMSE Statistics in near-surface salinity (psu) among simulations with 1D versus 2D heat flux forcing provided by meteorological models

T RMS, ºC Temperature S RMS, psu Salinity Glider Period (1D) (2D) MSI (%) (1D) (2D) MSI (%) G1 03/07 – 04/03 ‘07 2.33 2.20 +5.6% 0.62 0.58 +6.5% G2 04/03 – 04/06 ‘07 1.01 0.41 +59.4% 0.20 0.21 -5.0% G3 03/07 – 04/06 ‘07 1.10 0.97 +11.8% 0.26 0.26 0.0% G4 04/26 – 05/21 ‘07 1.78 1.67 +6.2% 0.44 0.45 -2.3% Table 8. Depth-averaged RMS differences between glider observations and NYHOPS results for temperature (T) and salinity (S) before (1D) and after (2D) implementation of the new NYHOPS 2D AOM forced with NAM 12km atmospheric inputs. MSI is the model skill improvement as described in the text: positive values show improvement, negative values show decrease in NYHOPS skill.

Figures 14-16 and Table 8 summarize comparisons of NYHOPS model results (water temperature and salinity) to along-track Slocum glider observations provided by Rutgers University. Overall, after implementation of the new 2D AOM in NYHOPS the performance of the ocean model in predicting water temperature along the glider paths improved significantly (Table 8), even for depths below the surface, with the exception of mid-depths during the G1 and G4 glider deployments (Figure 16). The biggest improvement was during the very short deployment of glider G2 (Table 8), due to a significant decrease in mean bias (Figures 14 and 15). In contrast, the performance of the model in predicting salinity was less affected by the atmospheric forcing scheme, as expected. NYHOPS salinity skill improved somewhat at the nearshore Hudson River plume region (G1 25-day deployment which included the St. Patrick’s Day storm of March 16-17 2007), while remaining largely unaffected at the

offshore (G4 25-day deployment), perhaps due to the imposition of the historical-monthly-mean OOB conditions. These historic OOBs appear to carry bias from the true dynamic ocean boundary conditions that is modulated by the imposition of the 2D AOM but not removed. The latter finding is consistent with recently published results obtained from an HF-Radar-currents assimilating version of NYHOPS (Gopalakrishnan 2008). That study found improvement in NYHOPS temperature predictions against the same glider observations after observed surface currents were assimilated, while salinity was largely unaffected by the assimilation. Uncertainty in ocean model predictions due to uncertainty in atmospheric forcing As discussed above, similarly skilled meteorological models with differences in physics (PBL, PC, microphysics) and resolution (12km versus 4km) provided atmospheric forcing to a multitude of NYHOPS ocean simulations. It was also shown that the NYHOPS circulation results from these different simulations were similar when viewed in terms of bulk statistical comparisons (RMSE) to available observations, with differences mostly due to how the atmospheric forcing was applied (1D versus 2D AOM, and initiation scheme), than to the meteorological forecasts themselves. Yet, the NYHOPS circulation results did vary substantially among different simulations forced with the improved 2D AOM and best available meteorological forecast from the different meteorological models. Figures 17-18 show the range (and ensemble average) of atmospheric predictions at Atlantic City, NJ and Sandy Hook, NJ, respectively, among the five meteorological models discussed. These figures also include the range (and ensemble average) of the low-pass filtered sea level response to the atmospheric forcing as computed by the 2D AOM NYHOPS runs. Figure 19 concentrates on the NYHOPS response for the major circulation variables at the same locations and for two periods: the complete 120-day simulation period, and the last 15 days in April 2007 that included and followed the Tax Day storm. In situ observations are also included in the figures, where available. Figures 17-18 indicate that the atmospheric fields used to force water and surface heat fluxes in NYHOPS carry different levels of uncertainty (blue range of solutions in the figures), with the most uncertain being relative humidity, followed by wind speed and direction, air temperature, and lastly barometric pressure. Figure 19 similarly shows that the NYHOPS ocean circulation fields most affected by this atmospheric forcing uncertainty are currents, then salinity, then water temperature, then elevation. This order for the uncertainty of ocean circulation fields is largely consistent with a recent study that used First Order Variance Analysis (FOVA) and Dimensionless Sensitivity Coefficients (DSC) to quantify circulation driver/response uncertainty in ocean/estuarine models and NYHOPS in particular (Blumberg and Georgas 2008). For New York Harbor, currents and salinity were the most sensitive circulation fields to atmospheric forcing in both the present and the published study; Total water level (tide + residual) and water temperature were the least sensitive. Compared to that study, the present work shows that water temperature uncertainty due to uncertainty in atmospheric forcing is larger than total water level uncertainty (Figure 19), especially over or near shallow water that can heat and cool more rapidly. Uncertainty in 2D heat fluxes was not present in the published work which only explored sensitivity to wind forcing (as well as, independently, to river inflow and bathymetry). Comparison between Figure 19 and a similar figure based on 1D heat flux runs (not shown) shows an approximately twofold increase in surface and bottom water temperature uncertainty range due to atmospheric forcing uncertainty for Atlantic City, NJ.

Uncertainty is linked to the dynamics of a model. Figure 20 shows grand-mean R2 regression coefficient values between all models and observed data for all meteorological and oceanic parameters evaluated. Variables appear to be better predicted in order of least uncertainty.

E-W wind component NAM WRF-12km WRF-4km MM5-12km MM5-4km MEAN Pier 40, NY 0.45 0.50 0.50 0.42 0.38 0.45 Keansburg, NJ 0.78 0.63 0.54 0.62 0.56 0.63 Pt. Pleasant, NJ 0.65 0.53 0.45 0.52 0.48 0.52 Absecon Channel, NJ 0.78 0.77 0.65 0.74 0.69 0.73 Atlantic City, NJ 0.86 0.78 0.68 0.74 0.68 0.75 Ambrose Light, NY 0.89 0.78 0.68 0.78 0.71 0.77 Buzzards Bay, MA 0.85 0.80 0.67 0.80 0.70 0.76 26 NM out of Cape Henlopen, DE 0.88 0.83 0.73 0.80 0.72 0.79 23 NM SE of Montauk, NY 0.91 0.88 0.75 0.84 0.77 0.83 33 NM S of Islip, NY 0.88 0.81 0.78 0.78 0.76 0.80 MEAN 0.79 0.73 0.64 0.71 0.64 0.70 N-S wind component NAM WRF-12km WRF-4km MM5-12km MM5-4km MEAN Pier 40, NY 0.74 0.68 0.67 0.65 0.61 0.67 Keansburg, NJ 0.60 0.51 0.49 0.47 0.47 0.51 Pt. Pleasant, NJ 0.60 0.50 0.42 0.47 0.44 0.49 Absecon Channel, NJ 0.77 0.71 0.60 0.70 0.65 0.69 Atlantic City, NJ 0.80 0.70 0.62 0.67 0.58 0.67 Ambrose Light, NY 0.84 0.76 0.66 0.75 0.67 0.74 Buzzards Bay, MA 0.87 0.79 0.71 0.79 0.70 0.77 26 NM out of Cape Henlopen, DE 0.91 0.82 0.70 0.81 0.72 0.79 23 NM SE of Montauk, NY 0.89 0.84 0.75 0.82 0.74 0.81 33 NM S of Islip, NY 0.81 0.77 0.76 0.79 0.71 0.77 MEAN 0.81 0.74 0.65 0.73 0.65 0.72 Table 9. Regression coefficients for wind components for the meteorological models as used in this study. Italics denote coastal stations; the rest of the stations are in open waters.

Table 9 lists regression coefficients (R2 values) for the two wind components for various coastal and open ocean stations. The meteorological models exhibited a better skill in predicting winds over open ocean stations than near the coast, even though winds are generally more variable in the open ocean (Figure 21; panels A, B, C). Meteorological model forecasting skill was worse at the NY Harbor stations, possibly due to sheltering and resolution issues. Variation across models is also shown on Figure 21, due to the resolution and initiation cycle issues discussed earlier as well as to differences in the physics resolved (e.g. sea breezes; Wilczak et al 2002, Glenn et al 2007). The effect of uncertainty in atmospheric forcing to the Hudson River Plume prediction Blowing over a relatively wide and shallow shelf like the New York Bight, winds play a dominant role in the sea level variability and surges at the coastal shorelines (e.g. Resio and Westerink 2008). Even for a significant Nor’easter storm like the Tax Day event (April 15-16 2007), for which the barometer dropped by more than 50 mbar over a few hours, the sea level response in coastal NJ and NY (Figures

17 and 18) was roughly twice what could be attributed to the local inverse barometer (which has also been estimated in the present study to be an over-prediction of the effect of barometric pressure on sea level in the region), presumably due to wind-driven surge. Sea level is a critical part of the Hudson River plume dynamics. Upon exiting the New York Harbor into the New York Bight Apex, the river plume forms a buoyant bulge and a recirculation zone that are susceptible to wind forcing. When winds blow from the north the plume curves to its right and flows along the NJ Atlantic Ocean seashore (Chant et al 2008). In April 2005, bulge formation was found to be reinforced by persistent sea breezes (Glenn et al 2007). Figure 22 shows biases between the WRF-4km-forced and the NAM-12km-forced NYHOPS simulated fields in the NY Bight Apex for the period between April 15 and April 30 2007 that included the Tax Day storm. NAM-12km mean winds for the 15-day period (Figure 21, panel D) were on the order of 1m/s over the NY Harbor entrance, directed to the southeast. WRF-4km mean winds for the same period (not shown) were on the order of 0.5m/s over the same region, directed south. The difference of these two average vectors divided by the mean wind speed of the baseline NAM-12km run is their relative bias. Thus, averaged over the whole period, the WRF-4km surface winds were about 100% weaker than those predicted by NAM-12km for the Apex with a relative change in direction pointing towards the harbor’s interior (northwest), similar to the effect of a constant sea breeze. The response of the ocean should then be (and is) similar to the response observed for sea breeze periods (Glenn et al 2007): a) A reinforced 15-day average Hudson River bulge predicted by NYHOPS forced by WRF-4km, increasing the average sea level at the center of the bulge by about 1cm (about 5% of the mean) compared to the NAM-12km run (Figure 22, panel A), b) an increase in the northward return flow along the Sandy Hook spit (Figure 22, panel B), and c) a spread of the coastal plume toward Long Island seen in the mean surface salinity bias (Figure 22, panel C). Figure 22, (panel D) also shows the RMS difference in surface salinity between the two runs outlining the variation in the Hudson River plume position in the Apex. The dynamics of the Hudson River plume exiting NY Harbor are sensitive to winds which carry uncertainty among different meteorological models (Figure 21). Then, prediction of the advection and dispersion of the plume in the NY Bight proper may be inexact due to possible errors (uncertainty) in both its initial location in the Bight Apex and the local over-ocean wind forcing. The uncertainty in local wind forcing will also affect the location and timing of Ekman upwelling and downwelling events, while the uncertainty in all atmospheric fields combined will affect the local surface heat fluxes. Figure 23 (and 24) visually summarize the resulting uncertainty as an RMS difference in SSS (and SST), among 2D AOM NYHOPS runs forced by the SoMAS 4km meteorological models versus the 2D AOM NYHOPS run forced with NAM-12km. The relative RMS difference (RMS difference divided by the standard deviation is also shown). Local root-mean square differences may be as high as 5psu for SSS and 1.5ºC for SST, or up to 300% and 100% of the local standard deviation, respectively (Figures 23-24). In other words, uncertainty in SSS primarily due to uncertainty in wind forcing, may be three times as high locally as the typical local SSS range, while uncertainty in SST due to uncertainty in atmospheric forcing, may be as high locally as the typical SST range. Both WRF-4km and MM5-4km had the same initiation scheme (12z) and cycle (12z-36z). Figure 25 is similar to Figures 23-24 but compare high-resolution 4km-forced 2D AOM NYHOPS models to each other, taking the issue of meteorological forecast initiation and resolution out of the equation. This is

by and large a comparison between oceanic model response to meteorological models with differences in atmospheric physics alone (MM5/data9a versus WRF/221.YSU.KFE.FERR.RRTM.WRF). Even so, the RMS differences between these runs are singificant, especially in areas of coastal plumes, like the Hudson River, or, also, the Connecticut and Delaware Rivers (Figure 25). Maximum and minimum RMS differences between meteorological and oceanic variables are summarized in Table 10. Max RMS difference Min RMS difference 4km meteorological models Wind speed, m/s 3.8 1.6 Relative humidity, % 11.6 6.7 Air Temperature, ºC 4.0 0.6 Barometric Pressure, mbar 1.0 2.0 Forced 2D AOM ocean models Surface current, cm/s 30.9 0.2 Surface salinity, psu 5.7 0.0 SST, ºC 1.9 0.0 Water level, cm 9.3 0.2 Table 10. Maximum and minimum RMS difference for high-resolution (4km) runs with the same initiation cycle but different meteorological models (MM5 vs. WRF). Given the differences among meteorological runs and the apparent sensitivity of the ocean models to surface atmospheric forcing highlighted in Table 10, forecasts of oceanic circulation based on a single, hand-picked, meteorological model may carry significant uncertainty. It is possible that some of this uncertainty may be reduced by ensemble ocean forecasts based on multiple NYHOPS end-member simulations forced by different meteorological models. The black lines and blue ranges in Figure 19 show an example based on just five NYHOPS end-member simulations. This approach could be used to create a mean oceanic ensemble forecast that could be compared to observations to test its predictive value. This approach has the benefit of also quantifying the areas of uncertainty around the ensemble forecast. For atmospheric fields, SoMAS has used this ensemble strategy in the SREF meteorological product using the end-members listed in Table 1. Atmospheric predictions from each of these meteorological end-members are pooled into an ensemble average meteorological forecast that has been shown to improve the meteorological forecast skill compared to each end-member alone (Jones et al 2007). An alternative, computational-resource-conserving approach that requires further investigation could be to force NYHOPS with the SoMAS SREF meteorological ensemble fields, rather than the individual meteorological end-members, and assess the performance of that NYHOPS simulation against the significant amount of data compiled for this study.

NAM Land/Water Mask

NAM Geopotential Height, meters

WRF 12km Land/Water Mask

WRF 4km Land/Water Mask

MM5 12km Landuse Types

MM5 4km Landuse Types

Figure 5. Land/water masks for the meteorological models used and approximate location of the tidal Hudson River. The River is modeled as land. Note changes in geopotential height in NAM.

Haverstraw

A B

C D

E F

Bear Mountain

Long Island

Hudson River

Long Island

Long Island

Hudson River

Long Island

Hudson River

Figure 6. 15-day average barometric pressure from NAM (A and C) versus 15-day average water level <WL> from NYHOPS (B and D) based on two NAM barometric pressure forcing fields: A,B) Using surface barometric pressure, and C,D) Using barometric pressure reduced to water level. Note the very different color bar scales for the average barometric pressure fields (mbar).

C

A

<WL> cm

<WL> cm

B

D

0.0

0.5

1.0

1.5

2.0

2.5

40.040.541.041.542.042.543.0Latitude along the Hudson

from Albany, NY to Sandy Hook, NJ

Incr

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Figure 7. Increase in NYHOPS tidal-residual water level prediction error along the Hudson River due to barometric pressure load forcing inclusion from NAM 12km. The effect of including the unresolved barometric load from NAM into NYHOPS is detrimental, and the water level error due to this inclusion increases with distance upstream from the lower NY Harbor (Sandy Hook, NJ) to the end of the tidal Hudson at Albany, NY.

Figure 8. Time series of error increase in NYHOPS-predicted tidal-residual water level at Albany, NY after barometric pressure inclusion from NAM 12km. The increase in error can be as high as 20cm, but, at times, inclusion of atmospheric pressure forcing can reduce error by 15cm. Overall an increase of 2.2cm was observed, as seen in Figure 7.

Pier 40, NY: Barometric Pressure. mbar

Pier 40, NY: Local inverse barometer approximation for barometric water level adjustment,

Pier 40, NY: Non-tidal residual water level, m

Figure 9. Time series of barometric pressure (A) local inverse barometer water level approximation (B), and residual water level (C) between the demeaned NAM 12km prediction and observed data at the Pier 40, NY hydro-meteorological station maintained by Stevens. Note the very loose correlation between the local inverse barometer approximation (B) and the local water level response (C). See, for example, Ponte et al 2001 for an explanation of the breakdown of the local inverse barometer approximation on short timescales and semi-enclosed coastal areas: in short, incompressibility forces water to move horizontally due to the barometric pressure gradient, not vertically due the local hydrostatic weight (e.g., Gill 1982).

Tax

Day

Sto

rm

Tax

Day

Sto

rm

A NAM 12km

B Based on NAM 12km

C NAM-forced NYHOPS

Figure 10. Correlograms of NAM 12km atmospheric pressure against demeaned in situ observations for the 12 stations in Raritan Bay and the Mid-Atlantic Bight listed on Table 4.

1 2 3

4 5 6

7 8 9

10 11 12

Figure 11. Correlograms (left) and time series (right) of NAM 12km 2m air temperature against demeaned in situ observations for representative stations listed on Table 5.

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Figure 12. Comparison of RMSE in SST prediction for the ensemble-averaged NYHOPS simulations with 1Dt heat fluxes from JFK (averaged over all five cases forced by the various meteorological models; AVERAGE 1D HF) versus the ensemble-averaged NYHOPS simulations with full 2D heat fluxes for stations along the Hudson River and plume. Overall, the inclusion of full 2D heat flux forcing in the new NYHOPS AOM decreased the average RMSE in SST prediction by approximately 50% from 1.27 to 0.87 ºC.

Figure 13. Model/observations SST time series before (left) and after (right) inclusion of full 2D heat fluxes in the new NYHOPS AOM. Example shown is for West Point, NY.

Figure 14. Water temperature differences (ºC) between Rutgers glider observations versus NYHOPS 1D AOM model results for four glider deployments between March and May 2007.

G1 G2

G3 G4

N N

N N

Figure 15. Water temperature differences (ºC) between Rutgers glider observations versus NYHOPS 2D AOM model results for four glider deployments between March and May 2007.

G1 G2

G3 G4

N N

N N

Figure 16. Model Skill Improvement, MSI [-1,1], for water temperature (red lines) and salinity (blue lines) versus depth within the water column along the glider paths shown in Figures 15 and 16. Positive MSI shows improvement in model performance after the 2D AOM was implemented in NYHOPS with NAM 2D atmospheric forcing. Depth-averaged MSI values are listed in Table 8. G2 was a very short 3-day deployment.

G1 G2

G3 G4

Figure 17. Range of forecast atmospheric fields from meteorological models and low-passed water level from NYHOPS 2D AOM simulations at Atlantic City, NJ for the simulated period Mar 1 2007 to Jun 17 2007. Red dots and lines show observations. Black lines are for the ensemble means. Blue color denotes range of simulated values from all models pulled into the ensemble.

NYHOPS Results

Figure 18. Range of forecast atmospheric fields from meteorological models and low-passed water level from NYHOPS 2D AOM simulations at Sandy Hook, NJ for the simulated period Mar 1 2007 to Jun 17 2007. Red dots and lines show observations. Black lines are for the ensemble means. Blue color denotes range of simulated values from all models pulled into the ensemble. Wind fields are from Ambrose Light, NY, a nearby but less sheltered station (Figure 2).

NYHOPS Results

Atlantic City, NJ

Sandy Hook, NJ

Figure 19. Range of forecast estuarine circulation fields from NYHOPS 2D AOM simulations at Atlantic City, NJ (top) and Sandy Hook, NJ (bottom) for the complete simulated period Mar 1 2007 to Jun 17 2007 (left) and the last 15-days of April 2008 (right zoom). Red dots and lines show observations. Black lines are for the ensemble means. Blue color denotes range of simulated values from all models pulled into the ensemble.

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.)

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Figure 20. Grand-mean regression coefficients between models and observed fixed-station data for all meteorological and oceanic fields evaluated. The values shown are the averages of regression coefficients across all stations and models evaluated. Meteorological model variables are capitalized and are: BARO (Barometric pressure), AIRT (Air Temperature), WINDU (East-West wind component), WINDV (North-South wind component), RHUM (Relative humidity). Ocean circulation model variables are written with small letters and are: elev (Water level broken down in a tidal and a residual part), wtmp (water temperature), and salt (salinity).

15-day Standard Deviation in Wind Speed from the 4km models WRF-4km MM5-4km

NAM-12km winds 15-day standard deviation 15-day mean

Figure 21. Standard deviation in wind speed (panels A, B, C) for three meteorological models (A: WRF-4km, B: MM5-4km, C: NAM-12km) through the April 15 to April 30 2007 period that included the Tax Day Storm. The effect of the storm’s passing over New York City is visible on the 15-day means of wind (panel D) and barometric pressure (not shown). Differences among meteorological models in panels A through C reflect differences in physics, resolution, and initiation cycles.

A B

C D

Sea level bias Surface current vector bias

Surface salinity bias Surface salinity RMS difference

Figure 22. Bias (mean difference) between the WRF-4km and the NAM-12km meteorological models in the NY Bight Apex for the 15-day period of April 15 to April 30 2007 that included the Tax Day storm. The relative response of the ocean is similar to the response observed for sea breeze periods (Glenn et al 2007): a) A reinforced 15-day average Hudson River bulge predicted by NYHOPS forced by WRF-4km (panel A), b) an increase in the northward return flow along the Sandy Hook spit (panel B), and c) a spread of the coastal plume toward Long Island seen in the mean surface salinity bias (panel C). Panel D shows the RMS difference in surface salinity between the two runs.

A B

C D

RMS Difference in surface salinity

WRF-4km vs. NAM-12km MM5-4km vs. NAM-12km

Relative RMS Difference in surface salinity

WRF-4km vs. NAM-12km MM5-4km vs. NAM-12km

Figure 23. RMS difference (top) and relative RMS difference to local standard deviation (bottom) in surface salinity between NYHOPS runs forced with the SoMAS SREF 4km models and the NYHOPS run forced with NAM-12km. Left panels show differences between NYHOPS runs forced with WRF-4km versus NAM-12km; Panels on the right show differences between NYHOPS runs forced with MM5-4km versus NAM-12km. The period for which the statistics were calculated was April 15 to April 30 2007.

A B

C D

RMS Difference in SST

WRF-4km vs. NAM-12km MM5-4km vs. NAM-12km

Relative RMS Difference in SST

WRF-4km vs. NAM-12km MM5-4km vs. NAM-12km

Figure 24. RMS difference (top) and relative RMS difference to local standard deviation (bottom) in SST between NYHOPS runs forced with the SoMAS SREF 4km models and the NYHOPS run forced with NAM-12km. Left panels show differences between NYHOPS runs forced with WRF-4km versus NAM-12km; Panels on the right show differences between NYHOPS runs forced with MM5-4km versus NAM-12km. The period for which the statistics were calculated was April 15 to April 30 2007.

A B

C D

Surface wind Surface current

Surface Salinity Surface Temperature

Figure 25. RMS differences for surface winds and three ocean circulation variables between two NYHOPS runs forced with the SoMAS SREF 4km models (a WRF-4km and an MM5-4km run). The period for which the statistics were calculated was April 15 to April 30 2007. Differences in the ocean fields are by and large due to different physics in the meteorological models. Both runs used the same resolution (4km) and the same initiation scheme and forecast cycle (12z-36z).

A B

C D

IMPACT/APPLICATIONS Benefits of and hurdles for using the full 2D meteorological formulations were quantified for NYHOPS, while several issues with regard to applicability/optimization of atmospheric inputs to NYHOPS were investigated. Results from eleven 120-day NYHOPS runs based on different meteorological models highlight issues with resolution and forecast initiation time. The comparability of NYHOPS hydrodynamic results points to a possible need for oceanic ensemble forecasts based on end-members forced with similarly-skilled meteorological models, if uncertainty around these results is to be quantified. Atmospheric resolution and masking issues may lead to errors that make the inclusion of barometric pressure load into NYHOPS problematic. This item may need further investigation to examine whether there are events/times where atmospheric load inclusion could be of benefit. On the contrary, the inclusion of fully 2D atmospheric heat fluxes improved NYHOPS SST forecasts by about 40% at the surface in the NY/NJ Harbor area, and about 15% on a depth-averaged basis in the New York Bight area. Surface salinity predictions in the estuarine and ocean waters show only a slight improvement, a finding consistent with concurrent data assimilation attempts. It is recommended that the newly developed 2D AOM become part of the operational NYHOPS, even though the forecast run time will be doubled. Ocean circulation forecasts are shown to be highly sensitive to atmospheric forcing. Thus, significant uncertainty in atmospheric forcing leads to significant uncertainty to ocean circulation predictions. Meteorological forecast uncertainty is greater for winds and relative humidity, and smaller for air temperature and barometric pressure. The resulting ocean model uncertainty is greater for currents, then salinity, then water temperature, and smallest for water level. This finding is consistent with a prior published study based on First Order Analysis of Variance, but the water temperature prediction is found to carry greater uncertainty than found earlier due to uncertainty in atmospheric heat flux forcing. Variability among wind forecasts (from different meteorological models, even with the same resolution and initiation cycle but different atmospheric parameterization schemes), produced significantly different NYHOPS ocean circulation predictions. This uncertainty in hydrodynamic predictions (measured by RMS differences among the various NYHOPS runs) can, in some areas, be the same order of magnitude as the local range of ocean variables (measured by the local standard deviation). Thus, a forecast produced by an ocean model forced by one meteorological model alone may be of doubtful value in some locations, due to the uncertainty carried within the atmospheric forecast fields. The various NYHOPS ocean simulations appeared to have overall similar forecast performance in the areas and for the periods investigated, as if the variability due to predicted atmospheric forcing is chaotic, at least in a time-averaged sense. These results point to a possible need for oceanic ensemble forecasts based on end-members forced with similarly-skilled atmospheric models, if uncertainty around these results is to be quantified and/or reduced. This appears to be critical in the Hudson River plume area, where winds have been shown to play a critical role in initial dispersal and lateral advection of the plume. Predictions of coastal transport of sediment or other waterborne substances carried with the plume will be bound by similar uncertainty. Such an oceanic ensemble product and its benefits can be tested by extending the NYHOPS simulations to include all twelve (x2) end-members of the SoMAS SREF meteorological ensemble. A new scope of work is requested. Higher resolution atmospheric heat flux inputs did not improve NYHOPS results in the harbor or the NJ coast area due to the 0z to 12z forecast degradation issue. The National Weather Service is geared toward improving the resolution of meteorological forecasts in the near future. See, for example,

http://cstar.cestm.albany.edu/wrf/wrf_info.htm for a 5km local WRF (AWR/MM6) model that includes the Hudson River region and is ran at SUNY Albany in cooperation with the local NWS WFO. The initiation lag issue would still pertain to this model, as the model is run twice a day at 00z and 12z, compared to the NAM 6hourly scheme. It appears that more computational power is needed, as well as improved microphysical schemes to be able to resolve the Hudson River proper in meteorological models while at the same time provide a frequent initiation cycle. Though not primarily driven by wind forcing, resolving other meteorological variables such as barometric pressure and air temperature along the 240km river span may lead to a significant decrease in circulation errors downstream in the New York Harbor and Bight extension. The new observations from the HRECOS system of stations can be used to test the quality of current and future forecasts along the Hudson River proper. The work presented here provides a major step forward to the operational (two-way) coupling of real-time ocean and weather observation systems with a modeling system. The coupled system has also served as a valuable vehicle to enable the development of reliable, operational fixed and vessel-based observation systems. NYHOPS provides a wealth of real-time data and accurate forecasts in the waters of New York and New Jersey. This information is now available to serve the maritime user community in the same way that weather forecasting has served the on-land population. All of the data is available over the Internet 24 hours a day by means of weather forecast-like maps that can be used effectively by Harbor pilots, sailors, power boaters, swimmers, and fishermen as well as port security officials and emergency management personnel. The observational network and modeling system are parts of the integrated, sustained ocean observing system envisioned by the National Oceanographic Partnership Program (NOPP), under the OCEAN.US office, and the Integrated Ocean Observing System (IOOS). The PI’s have been active participants in the leadership of the Mid-Atlantic Coastal Ocean Observing Regional Association (MACOORA), now designated as a formal Regional Association under the IOOS program. RELATED PROJECTS ONR N00014-06-1-1027: Investigation of Coastal CDOM Distributions Using In-Situ and Remote Sensing Observations and a Predictive CDOM Fate and Transport Model REFERENCES Arbic, B.K., S.T. Garner, R.W. Hallberg, H.L. Simmons, 2004: The accuracy of surface elevations in forward global barotropic and baroclinic tide models. Deep-Sea Research II 51, 3069-3101. Blumberg, A. F., and G. L. Mellor, 1987: A description of a three-dimensional coastal ocean circulation model. Three Dimensional Coastal Models, Coastal and Estuarine Sciences, 4, N. S. Heaps (ed.), Amer. Geophys. Union Geophysical Monograph Board, 1-16. Blumberg, A.F., and B. Galperin, 1990: On the summer circulation in New York Bight and contiguous estuarine waters. Coastal and estuarine studies, Volume 38, R.T. Cheng (ed.), Residual Currents and Long-Term Transport, Springer-Verlag, NY, 451-467.

Blumberg A. F. and N. Georgas, 2008: Quantifying Uncertainty in Estuarine and Coastal Ocean Circulation Modeling. Dennis A. Lyn, Ph.D., (ed). Journal of Hydraulic Engineering. American Society of Civil Engineers. 134 (4), 403-415. Chant, R.J., S.M. Glenn, E. Hunter, J. Kohut, R.F. Chen, R.W. Houghton, J. Bosch, and O. Schofield, 2008: Bulge Formation of a Buoyant River Outflow. Journal of Geophysical Research 113, C01017, doi:10.1029/2007JC004100. Chapman, S. and R.S. Lindsen, 1970: Atmospheric Tides: Thermal and Gravitational. Springer. 201 pages. Chen, J., W. Shaffer, and S. Kim, 1993: A forecast model for extra-tropical storm surge. Advances in HydroScience and Engineering. Vol. 1, pp. 1437-1444. Colle, B.A., M.F. Garvert, J.B. Wolfe, C.F. Mass, and C.P. Woods, 2005: The 13-14 December 2001 IMPROVE-2 Event. Part III: Simulated Microphysical Budgets and Sensitivity Studies. Journal of the Atmospheric Sciences, 62 (10), 3535-3558. Georgas, N., A. F. Blumberg, T. O. Herrington, 2007: An Operational Coastal Wave Forecasting Model for New Jersey and Long Island Waters, Reinhard E. Flick, Ph.D. (ed.), Shore & Beach, American Shore and Beach Preservation Association. 75 (2), 30-35. Glenn, S., O. Schofield, R. Chant, J. Kohut, H. Roarty, J. Bosch, L. Bowers, D. Gong, and J. Kerfoot, 2007: Wind-driven response of the Hudson River plume and its effect on dissolved oxygen concentrations. ISSN 1392-1649. Environmental Research, Engineering, and Management, 1(39), 14-18. Gill, A.E. 1982: Atmosphere-ocean dynamics. International Geophysics Series 30. London: Academic Press. 663 pp. Gopalakrishnan, G. 2008: Surface current observations using high-frequency radar and its assimilation into the New York Harbor Observing and Prediction System. PhD dissertation. Stevens Institute of Technology. 225pp. Haurwitz, B., 1956: The geographical distribution of the solar semidiurnal pressure oscillation. Meteorological Papers, 2 (5). New York University. Haurwitz B., 1965: The diurnal surface-pressure oscillation. Archives of Meteorology Geophysics and Bioclimatology, A12, 426–434. Jones, M.S., B.A. Colle, J.S. Tongue, 2007: Evaluation of a Mesoscale short-range ensemble forecast system over the Northeast United States. Weather and Forecasting, 22 (1), 36-55. Kim, S.-C., J. Chen, and W.A. Shaffer, 1996: An operational forecast model for extratropical storm surges along the U.S. east coast. Preprints Conference on Coastal Oceanic and Atmospheric Prediction, Atlanta, Amer. Meteor. Soc., 281-286.

Mukai, A.Y., J.J. Westerink, R.A. Luettich Jr., D. Mark, 2002: Eastcoast 2001. A Tidal Constituent Database for Western North Atlantic, Gulf of Mexico, and Caribbean Sea. Final Report. US Army Corp of Engineers. Engineer Research and Development Center. Coastal and Hydraulics Lab. Vicksburg, MS. Report No. A337804. 196pp. NOAA, 2003: NOS Standards for Evaluating Operational Nowcast and Forecast Hydrodynamic Model Systems. NOAA Technical Report NOS CS 17. NOAA/NOS/CSDL. Silver Spring, Maryland. October 2003.

Oke, P.R., J.S. Allen, R.N. Miller, G.D. Egbert, and P.M. Kosro, 2002: Assimilation of surface velocity data into a primitive equation coastal ocean model. Journal of Geophysical Reseasrch, 107, 3122, doi: 10.1029/2000JC000511. Ponte, R.M., D.A. Salstein, R.D. Rosen, 1991: Sea level response to pressure forcing in a barotropic numerical model. Journal of Physical Oceanography, 21, 1043-1057. Ponte, R.M. and R.D. Ray, 2002: Atmospheric pressure corrections in geodesy and oceanography: A strategy for handling air tides. Geophysical Research Letters, 29 (24), 2153, 6-1 – 6-4. Ponte, R.M. and S.V. Vinogradov, 2007: Effects of stratification on the large-scale ocean response to barometric pressure. Journal of Physical Oceanography, 37, 245-258. Ray, R.D., 1993: Global Ocean Tide Models on the Eve of TOPEX/POSEIDON. IEEE Transactions on Geoscience and Remote Sensing, 31 (2), 355-364. Resio, D.T. and J.J. Westerink, 2008: Modeling the physics of storm surges. Physics Today. American Institute of Physics. S-0031-9228-0809-010-8. September 2008, 33-38. Shamarock, W.C., J.B. Klemp, J. Dudhia, D.O. Gill, D.M. Barker, W. Wang, J.G. Powers, 2005: A description of the Advanced Research WRF Version 2. NCAR Technical Note NCAR/TN-468+STR. National Center for Atmospheric Research. Mesoscale and Microscale Meteorology Division. Boulder, Colorado. June 2005. 100pp. Wilczak, J., L. Nance, I. Djalalova, D. Gottas, S. Benjamin, G. Grell, S. Peckham, 2003: Evaluation of the Eta, RUC, WRF, and MM5 meteorological models during NEAQS 2002. American Geophysical Union, Fall Meeting 2003. December 2003, San Fransisco, CA. 2003AGUFM.A51E0733W8-12. Eos Transactions, AGU, 84(46). Wunch, C. and D. Stammer, 1997: Atmospheric loading and the oceanic “inverted barometer” effect. Review of Geophysics, 35 (1), 79-107. PUBLICATIONS Ahsan, Q., A. F. Blumberg, A. J. Thuman, and T. W. Gallagher. 2005. Geomorphological and Meteorological Control of Estuarine Processes – Modeling Analyses using a High Resolution 3D Hydrodynamic Model. J. Hydraulic Engineering, 131, 259-272.

Blumberg A. F. and N. Georgas. 2008. “Quantifying Uncertainty in Estuarine and Coastal Ocean Circulation Modeling”, Dennis A. Lyn, Ph.D., Journal of Hydraulic Engineering, American Society of Civil Engineers. 134 (4), 403-415. Blumberg, A.F. and F. L. Hellweger. 2005. “Hydrodynamics of the Hudson River Estuary”, In: Hudson River Fishes and their Environment, Waldman, J., K. Limburg, and D. Strayer, Eds. American Fisheries Society, Alpharetta, Georgia. Bruno, M.S., A.F. Blumberg and T.O. Herrington. 2006. The Urban Ocean Observatory - Coastal Ocean Observations and Forecasting in the New York Bight. Journal of Marine Science and Environment, No C4, IMarEST, 1-9. Fan, S., A F. Blumberg, M. S. Bruno, D. Kruger, and B. Fullerton. 2006. The Skill of an Urban Ocean Forecast System, Estuarine and Coastal Modeling, ASCE, 720- 731. Georgas, N., A. F. Blumberg, T. O. Herrington. 2007. “An Operational Coastal Wave Forecasting Model for New Jersey and Long Island Waters”, Reinhard E. Flick, Ph.D., Shore & Beach, American Shore and Beach Preservation Association. 75 (2), 30-35. HONORS/AWARDS/PRIZES Alan F. Blumberg, Stevens Institute of Technology, Elected Fellow, American Society of Civil Engineering. Alan F. Blumberg, Stevens Institute of Technology, Appointed to the Science Advisory Board (SAB), U.S. Environmental Protection Agency. Michael S. Bruno, Stevens Institute of Technology, Elected Fellow, American Society of Civil Engineering. Michael S. Bruno, Stevens Institute of Technology, appointed member Marine Environmental Committee, Transportation Research Board. Nickitas Georgas, Stevens Institute of Technology, 2006 American Shore and Beach Preservation Association Educational Award.