Mesoscale Modeling of Boundary Layer Refractivity and

Mesoscale Modeling of Boundary Layer Refractivity
and Atmospheric Ducting
Tracy Haack1, Changgui Wang2, Sally Garrett3, Anna Glazer4,
Jocelyn Mailhot4, Robert Marshall5
1
Naval Research Laboratory
Monterey, California USA
2
Met Office, Joint Centre for Mesoscale Meteorology (JCMM)
Reading RG6 6BB United Kingdom
3
Defence Technology Agency
Auckland, New Zealand
4
Environment Canada, Meteorological Service of Canada
Dorval, Québec Canada
5
Naval Surface Warfare Center
Dahlgren, Virginia USA
Submitted to Journal of Applied Meteorology and Climatology
9 Oct 2009
Revision: 17 Mar 2010
Corresponding author address: Tracy Haack, Naval Research Laboratory,
Marine Meteorology Division, Monterey, California 93943-5502, USA
Email: [email protected] Phone: 831-656-4727
ABSTRACT
In this study four different mesoscale forecasting systems were used to investigate the
four-dimensional structure of atmospheric refractivity and ducting layers that occur
within rapidly evolving synoptic conditions over the eastern seaboard of the U.S. The
aim of this study was to identify the most important components of forecasting systems
that contributed to refractive structures in a littoral environment. Over a seven day period
in April/May of 2000 near Wallops Island, Virginia meteorological parameters at the
ocean surface and within the marine atmospheric boundary layer (MABL) were measured
to characterize the spatio-temporal variability contributing to ducting. Using traditional
statistical metrics to gauge performance the models were found to generally over-predict
MABL moisture resulting in fewer and weaker ducts than were diagnosed from vertical
profile observations. Mesoscale features in ducting were linked to highly resolved SST
forcing and associated changes in surface stability, and within 100 km of the coast, to
interactions of the MABL with local sea breeze flows. Sensitivity tests that permit
greater mesoscale detail to develop on the model grids revealed that the initialization of
the simulations followed by inclusion of a high resolution, evolving sea surface
temperature, are the most critical factors for accurate predictions of coastal refractivity.
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1. Introduction
Numerical weather prediction (NWP) and mesoscale modeling products are routinely
used by national defense agencies around the world for applications far beyond weather
forecasting. In addition to providing spatial and temporal variability of the atmosphere at
high-resolution over regions of military interest, the modeled fields are often ingested by
a variety of tactical decision aids yielding weapon and radar performance and
communication and surveillance guidance.
Many naval applications utilizing mesoscale models involve at-sea and coastal
scenarios where discontinuities between land and ocean can create sharp gradients in air
temperature and water vapor content. Vertical gradients, particularly of water vapor,
have a significant impact on the propagation of electromagnetic (EM) waves, bending
them toward or away from the earth’s surface. The modified refractivity, based upon
Snell’s Law, denotes how EM energy will travel from its point of origin through a given
vertical layer (Bean and Dutton 1968). The four refractivity regimes: subrefraction,
normal refraction, superrefraction and trapping are each characterized by a range of
modified refractivity slope values as shown schematically in Fig. 1 along with definitions
of ducting layer characteristics. Surface-based ducts are an important subset of ducting
events, defined as those having a duct base height equal to 0 m, because of their potential
to alter the propagation environment at low elevations thus affecting surface radars and
communications.
This study presents the coastal refractivity and ducting characteristics represented by
four mesoscale forecasting systems during a seven day intensive observation period
(IOP) near Wallops Island, Virginia offshore of the Delmarva Peninsula in April and May
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of 2000. Hereafter we refer to the experiment as Wallops-2000, or Wallops-2000
Microwave Propagation Measurement Experiment (MPME). The field study was
designed by the Naval Surface Warfare Center Dahlgren Division (NSWCDD) to
measure both environmental and propagation conditions contributing to the emergence of
refractive features in a challenging coastal environment (Stapleton et al. 2001).
The intercomparison team was formed from the four countries: United States of
America (U.S.), United Kingdom (U.K.), Canada and New Zealand (N.Z.). These so
called “ABCANZ” countries have an international exchange agreement on scientific
research in several specialized areas of technology. Each country employed the
numerical weather prediction tool used to support their national defense and navy
missions to simulate the entire seven-day Wallops-2000 IOP. The hind cast nature of this
study, eight years after the field campaign, created some limitations in model
initialization and data assimilation that were unavoidable. We gained considerable
insight from those limitations however, and identified several sensitivity tests as a result.
The team’s primary objective was to identify critical components of each country’s
independent forecasting system that contribute greatly to refractive structures in a littoral
environment. We utilized the intercomparison to establish a validation benchmark of
mesoscale modeling capabilities of coastal refractivity and atmospheric ducting. This
process is of vital importance with the ever increasing use of high resolution NWP fields
to represent the four-dimensional environment in EM propagation codes and clutter
models. Further, such information provides valuable guidance on where to focus system
development for substantive improvements to high-resolution refractivity forecasts.
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Large-scale influences on ducting have been studied extensively and were first
documented by Rosenthal and Helvey (1979) and then Helvey et al. (1995) who
developed a schematic synoptic-refractive relationship model. They found increased duct
frequency over the eastern and equatorward sectors of mid-latitude high pressure areas
coinciding with the strongest subsidence inversions and near standard refraction behind
low pressure troughs. Those findings were reinforced by von Engeln and Teixeira (2004)
from a six year global ducting climatology from European Centre for Medium-Range
Weather Forecasts (ECMWF). Rosenthal and Helvey’s synoptic-refractive relationship
model was noted as being more appropriate for the open ocean however, due to
mesoscale features commonly observed near coastlines.
In the littoral environment, vertical gradients in the atmosphere are frequently
generated by interactions between mesoscale and large-scale processes. Mesoscale
structure may be imparted by complex topography and coastline geometry creating
topographic flows and diurnally driven sea breezes. During weak background flow for
example, land breeze circulations were reported to influence radar observations in the
Wallops Island area (Meyer 1971). In another coastal region, severe radio signal fades
were coincident with the formation and inland advection of a sea-breeze, as documented
by Reddy and Reddy (2007). Abrupt changes in surface stability, from a spatially
complex and highly resolved sea surface temperature, can also have dramatic effects on
the overlying MABL. High-resolution SST analyses, when compared to climatological
SST forcing, were found to alter coastal processes and MABL structure in the vicinity of
the Gulf Stream (Doyle and Warner 1995). Such complexities make the characteristics of
these layers a challenge to predict and validate.
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The first highly idealized case studies of refractive effects were presented by Silveira
and Massambani (1995) over the Sãn Paulo river basin and water reservoir in Brazil
showing the effects of lake-land breeze circulations on ducting and line-of-sight
microwave propagation links. Burk and Thompson (1997) obtained more realistic coastal
refractivity by initializing model runs with real-data analyses for a five-day period over
the Southern California Bight. They found that the trapping layer depth and strength
evolved with the diurnal cycle and the entrance of synoptic low pressure trough
eliminated the ducting layer entirely. Additional high-resolution real-data model runs
along California coast during summertime were performed by Haack and Burk (2001)
and showed modulation of refractive layers by the marine atmospheric boundary layer’s
(MABL) interaction with topography.
Refractivity studies were also conducted in the Persian Gulf using the Ship Antisubmarine Warfare Readiness/Effectiveness Measuring exercise (SHAREM-115) data.
Atkinson et al. (2001) revealed that simulating correct ducting structures depended upon
inhomogeneous model initialization using a profile appropriate for over land or water
surfaces. A more comprehensive set of modeling experiments by Atkinson and Zhu
(2006) identified four factors influencing propagation in that region: the sea breeze,
coastal configuration, orography and ambient wind, while an observational study by
Brooks et al. (1999) showed sensitivity of duct depths to SST.
Forecasting refractivity in the coastal zone relies upon representation of evolving
synoptic scale systems along with a detailed, high-resolution description of the lower
boundary (topography, coastline, land types and sea surface temperature) and
sophisticated surface and boundary layer parameterization schemes to capture the
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inhomogeneity and nonlinearity in the lower atmosphere. Previous modeling studies
were limited somewhat by low spatial resolutions or fairly idealized initialization
methods. The present work not only takes advantage of the advancements in model
parameterizations schemes and real-data initialization procedures over the last decade,
but also utilizes high resolution model grids to improve the mesoscale representation of
coastal ducting layers. The benefits of an intercomparison study help discern the impact
of differences in initialization, data assimilation, analysis and forecast modeling system
components, while elucidating the limitations of each model in specifying the
environment for EM propagation purposes. The paper is organized as follows: Section 2
describes the Wallops-2000 MPME field campaign. In section 3, each NWP modeling
setup is described. An overview of the large-scale environment is provided in section 4
and the model validation in section 5. Section 6 presents the mesoscale structure in the
simulated refractivity fields and sensitivity tests used to discern the relative importance of
initialization and boundary conditions. The conclusions are given in section 7.
2. Wallops-2000 MPME field campaign
The Wallops-2000 field program was conducted in April and May 2000 to collect
meteorological measurements and radar frequency one–way propagation data along
onshore-offshore radials extending up to approximately 60 km from the Virginia
shoreline near Wallops Island (Fig. 2). During the seven-day IOP, between 28 April and
4 May 2000, a wide range of refractive conditions were observed.
The Naval Postgraduate School (NPS) deployed a fixed buoy approximately 13 km
offshore from a shore-based, meteorological observing tower. Both platforms reported
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temperature, relative humidity, pressure and winds at heights of approximately 4 m and
10 m respectively. Sea surface temperature was also collected at the NPS buoy. An
instrumented helicopter (HELO), outfitted by John Hopkins University/Applied Physics
Laboratory (JHU/APL), collected measurements of temperature, relative humidity and
pressure along the radials, primarily within the lowest 150 m of the atmosphere. The
HELO measurements have been used in refractivity related research by Babin (1995) and
Babin (1996) who investigated surface ducting and subrefractive environments in around
the Wallops Island area. Details about the instrumentation including measurement
response times, accuracy and resolutions are given in Babin and Rowland (1992).
The helicopter radial positions were matched by an instrumented boat, the Sealion
which recorded temperature, relative humidity, pressure, winds and SST. The
Microwave Propagation Measurement System (II) developed by NSWCDD was used to
collect one-way radio frequency propagation loss between a transmitter mounted on the
Sealion and a shore-based receiver. Analysis of the propagation data and EM ducting
depends upon the details of the transmitter and receiver and will be undertaken in a
subsequent paper. The focus of the present study is on the meteorological measurements,
MABL refractivity and atmospheric ducting conditions.
3. Model setup
Each of the models used in this study were setup to be as similar as possible (Table
1). This included using similar latitude and longitude boundaries for the inner 4-km nest
on which most of the subsequent analysis and data comparisons were conducted (Fig. 2).
Roughly half of this nest resides over water and includes a portion of the Gulf Stream in
8
its southeastern corner. The intricate coastline in this region contains the Potomac River
basin and the Delmarva Peninsula on which Wallops Island is located.
For the U.S., mesoscale model prognostic fields were produced by the Naval
Research Laboratory Coupled Ocean/Atmosphere Prediction System (COAMPS1). Of
the four models investigated, only COAMPS could be run in a manner consistent with its
typical operational setup. The four COAMPS domains (36, 12, 4, and 1.33-km grid
spacing) were initialized three days prior to the start of Wallops-2000 from Navy
Operational Global Analysis and Prediction System (NOGAPS) 1.0°x1.0° analyses (27
pressure levels), allowing higher resolution detail and vertical structure to develop.
Lateral boundary tendencies for the outer 36-km grid were computed from 6-hourly
NOGAPS fields. The 12-hour forecast fields, and an analyzed SST, were corrected every
12 hours by a multivariate optimum interpolation (MVOI) analysis of available satellite,
station, aircraft, ship, and buoy data residing within each grid. This procedure produced
twice daily (00 and 12 UTC) 1-12 hour forecasts forced at the lower boundary by a 4-km
resolution, 12-hourly updated SST analysis from the Navy Coupled Ocean Data
Assimilation (NCODA) system.
The U.K. utilized the Met Office’s Unified Model (MetUM). For their initial set of
model runs, MetUM produced seven, 24 hour-long simulations using three grids (12, 4,
and 1-km grid spacing), reinitializing daily at 12 UTC by dynamical downscaling from
ECMWF global analyses T319L60 (~0.5°, 60 level). The boundary conditions for their
outer grid (12-km) were generated from ECMWF data at 6 hour intervals. Observational
data had to be incorporated indirectly into the MetUM through the 4D-variational
1
COAMPS is a registered trademark of the Naval Research Laboratory
9
(4DVAR) data assimilation on the global fields because the Met Office mesoscale data
assimilation scheme could not easily be run in hind cast mode. The SST values on all
grids were obtained from the coarser resolution ECMWF SST field and updated every 24
hours at 12 UTC.
The N.Z. Defence Technology Agency (DTA) used the Penn State-National Center
for Atmospheric Research 5th generation mesoscale model (MM5). The MM5 grids (36,
12, and 4-km grid spacing) were initialized at 00 UTC 25 April from the National Center
for Environmental Prediction (NCEP) 1.0°x1.0° global reanalysis (27 pressure levels).
No subsequent re-initialization took place for MM5 so that observational data were only
incorporated by nudging the boundaries of the MM5 outer grid (36-km) to the NCEP
reanalysis every 6 hours. A coarse 1.0° resolution NCEP SST analysis field was
interpolated to all three MM5 grids and updated daily at 00 UTC.
Environment Canada ran their Global Environmental Multiscale model (GEM).
Seven analyses were produced daily at 00 UTC for the GEM simulation on a global
variable resolution grid (~15-km grid spacing) initialized from the Canadian
Meteorological Center’s (CMC) global analysis fields (~24-km grid spacing, 28 pressure
levels). This 00 UTC initialization allowed for a 6-hour spin up of the global simulation
to provide fields for a 12-km nested grid, which after another six hours provided the
fields for a 4-km nested grid. For the 4-km grid, the model was run for 12 hours starting
at 12 UTC daily. Thus, GEM fields were not continuous over a full 24 hour cycle.
Boundary conditions were updated every hour on their 4-km and 12-km nests. As in the
MetUM and MM5 models, the Wallops hind cast prevented their 3DVAR data
assimilation from being run on the GEM mesoscale grids. Hence, all observational
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information was provided by the 00 UTC CMC analyses. The SST fields for GEM were
produced daily at 00 UTC from a coarse 100 km CMC SST analysis.
In the remainder of this paper, we use the term ‘model’ to signify the complete
forecast system used by each country for this study. The terms ‘forecast’ and ‘forecast
length’ are used to indicate the length of time from model initialization which as
indicated above, was not the same for all models. Given those differences, we do not
attempt to evaluate more subtle albeit important aspects of the mesoscale models, such as
their numerics, dynamics and physical parameterization schemes. Rather, we used the set
of model predictions to draw common conclusions and point to areas for further study, a
strategy that was effective in motivating the sensitivity tests detailed in section 6b.
4. Synoptic forcing
The SST field for the region in springtime depicts cold coastal waters over the
continental shelf that form a sharp gradient with the Gulf Stream as it separates from the
coast near Cape Hatteras, North Carolina. Monthly mean fields of AVHRR SST indicate
that temperatures increase throughout the spring, more rapidly over the continental shelf,
maintaining about a 10 K SST front with the warm waters of the Gulf Stream in April
and May (Mesias et al. 2007). Satellite data also reveal a propensity for warm or cold
core eddies between the continental shelf and the Gulf Stream (Lee and Cornillon 1996).
The Gulf Stream is located about 200-300 km offshore of Wallops Island, which can be
seen in the 3-day composite SST ending 2 May 2000 (Fig. 2), within the southeastern
corner of the 4-km grids.
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The sharp SST front and two cyclonic eddies imply a spatially complex SST that can
impart abrupt changes in surface fluxes and stability. One of the seminal papers on airsea interaction (Sweet et al. 1981), described the modifications to the lower atmosphere,
and to the atmosphere’s refractive index, by the stability change across the Gulf Stream.
Figure 3 presents the SST field used by each model near the middle of the Wallops IOP
on 1 May. The NCODA SST analysis used by COAMPS (Fig. 3a) closely depicted the
observed distribution with a meandering SST front, cold coastal SST and a warm Gulf
Stream. The SST gradients were not well resolved in MetUM or MM5, having about
30% of the SST gradient that was present in COAMPS, and the MM5 SST contained an
east-west orientation. The SST gradient in GEM was about 70% of COAMPS, but
lacked the sharp front and eddies comprising the Gulf Stream. These SST differences
permitted an examination of atmospheric ducting sensitivities to the ocean boundary
condition from which subsequent SST sensitivity tests were devised.
Rapid transitions in synoptic weather regimes are well documented over the Wallops
Island region during the spring season (Babin 1996, Goldhirsh et al. 1994, Goldhirsh and
Musiani 1999). The present study begins on 28 April with a deep trough over the eastern
U.S. that slowly moved out of the area early on 30 April. During these first two days,
shortwave passage, upper-level disturbances and convective activity complicated weather
patterns with rapid adjustments in near-surface winds and in the free atmosphere above
the MABL. A frontal passage on 30 April marked the transition to a more tropical air
mass as weak ridging combined with a surface anticyclone off the coast of North
Carolina advecting warm, dry air over the study area. This began a period of sustained,
increasingly strong ducting from 12 UTC 30 April until 2 May when an approaching low
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pressure cell elevated the MABL inversion and a cold front mixed away vertical
gradients. The last two days of the IOP (3-4 May) were controlled by weak synoptic
forcing associated with a surface high pressure cell offshore of the Canadian coast that
brought in cold, subsiding air in a long, over-water fetch.
Time-series of hourly surface meteorological parameters at the NPS buoy (diamond
in Fig. 2) reveals many of the synoptic features discussed above (Fig. 4). The observed
SST (shown by the black line) displays the warming trend over the seven-day IOP as well
as a maximum diurnal perturbation of about 2 K. All models except COAMPS had
nearly constant SST, and contained large 2-3 K biases in temperature. The NCODA
analysis produced the observed warming trend but its weak 0.5 K diurnal signature was
out of phase with the observations. The remaining variables generally followed the
observed trends, revealing two episodes of offshore flow with warmer temperatures and
lower relative humidity (30 April and 2 May). Both days were followed by high pressure
(1st and 3-4th of May) although each corresponded to very different air masses over
Wallops Island, as suggested by the wind shift to southerlies on 1 May and to northeasterlies on 3 May. The MetUM correctly predicted the magnitude of low relative
humidity during the two offshore flow periods and overall had more accurate large-scale
evolution, while synoptic transitions appear to be delayed in GEM and COAMPS by 3-6
hours.
The time-series of model computed duct strength at the buoy location (Fig. 5) is used
to assess temporal variations in ducting and associate them with the changing synoptic
environment. These data suggest that ducting is a common feature for this time and
location occurring about half the time in all the models except MM5. Periods of
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observed high pressure were conducive to strong ducting as was present in the models on
1 May, although the high pressure event on 3-4 May was problematic for some of the
models. Weaker ducts developed during periods of offshore flow, occurring after about
18 UTC on 29 and 30 April and 2 May, by a heated planetary boundary layer advecting
over much colder coastal waters. These stable internal boundary layer (SIBL) ducts
began at the coastline as shallow, thin surface-based ducts that expanded downwind,
gradually subsiding after ~03 UTC. Periods with no ducting coincided with a negative
pressure tendency and an approaching low pressure center or front, which was the case
early on 28 April, 29 April and 2 May. During these times the MABL inversion was
lifted and gradients were weakened due to dynamic processes and vertical mixing. In
general, the evolution of the simulated ducts was in accord with the expected synopticrefractivity relationships displayed in Rosenthal and Helvey’s (1979) schematic model.
In Table 2, the mean ducting conditions at the near-shore buoy location are contrasted
with those 100 km offshore as simulated by each model. For profiles having multiple
ducting layers, the strongest duct was retained, which may not have been the lowest. In
comparing mean values between the buoy and offshore location, the models generally
had less than 10% difference with respect to duct frequency (DFQ) and strength (DST),
while duct thicknesses (DTK) increased with distance from the coast by 30-40%.
COAMPS was the exception here having 25% stronger ducting at the offshore location.
Mean duct base heights (DBH) conveyed the most variability between models and
locations with all but GEM having higher DBH offshore. Roughly half to two-thirds of
these ducts were surface-based ducts (SBD) in the models , with slightly fewer SBD
occurring at the offshore location.
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The difference between near shore and offshore DBH was considerably less in
COAMPS (20%) compared to the other models (65-50%), which was likely due to
COAMPS uniformly low SST over this 100 km distance. The cold expanse of water and
the sharp SST front in COAMPS was also responsible for its prediction of stronger
ducting at the offshore location, which resulted from the MABL’s rapidly adjustment to
the change in surface stability, an abrupt lowering of the boundary layer and
intensification of the inversion on the cold side of the front. This process is more gradual
in the other models which feature weaker SST gradients. It is important to note that
GEM statistics are weighted more toward the SIBL ducts that form in the afternoon and
evenings with offshore flow because their simulations were only from 12-00 UTC.
Hence, their statistics did not include the strongest ducting event between 00-12 UTC on
1 May, giving the impression that GEM had fewer and weaker ducts than the other
models.
5. Model validation
Validation of the simulations is based upon standard statistical methods including
means, biases (defined as observation minus model) and root mean square (rms) errors
along with duct event contingency table statistics used to establish a benchmark set of
values by which to judge the impact of sensitivity tests to follow. Means were computed
for the near surface variables at the buoy location using the hourly time series of buoy
measurements, and for the vertical profiles at the HELO locations using the subset of
times corresponding to each HELO descent leg.
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The mean statistics for buoy measured quantities provide an estimate of overall
predictive skill for near-surface variables (Table 3). Despite biases in air temperature,
which were mostly a reflection of those in SST, MetUM had more accurate synoptic
evolution yielding lower rms errors particularly for relative humidity. Both COAMPS
and GEM had difficulties with some of the synoptic transitions (Fig. 4), resulting in
slightly larger rms errors. Although MM5 displayed the general large-scale trends over
the seven-day IOP, significant errors in near-surface fields were the result of the long
simulation length, with large-scale adjustments made only at the outer grid boundary.
We evaluated profiles of potential temperature, specific humidity and modified
refractivity simulated by each model, against those computed from HELO measurements.
The model profiles were extracted from fields at the nearest hour and grid point location
corresponding to the average latitude and longitude position at the start and end of each
HELO descent. Mean statistics for the vertical profiles were obtained by sub-sampling
the HELO data to the model heights. If the HELO profile did not extend below the
lowest model level, data were duplicated below the lowest observational height so as not
to artificially introduce any ducting by extrapolation or interpolation to the surface.
Because GEM and MM5 were run at different vertical resolutions, their models’ profiles
were first interpolated to the vertical grid identified for analysis (70 level distribution).
Although the 190 HELO profiles had high vertical resolution of less than 10 m, only
about 30 profiles reached 500 m. Less than 10 profiles extended above 650 m, thus
limiting the ability to adequately validate the model fields above the lowest 100 meters.
Moreover, if a model produced a duct but it was above the height of the observed profile,
it was not considered in the ducting statistics to follow. The data were also limited
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temporally to daytime hours between 12 and 23 UTC. As enumerated in Table 4, some
days were heavily observed and others were not. Consequently, mean statistics for the
vertical profiles might suggest poor model performance if it did not simulate the timing
or distribution of ducting particularly well on either 29 April or 4 May, when nearly 50%
of all HELO data were collected. Profiles were not discarded if they failed to represent
an independent observation or in instances whereby consecutive HELO descent legs
corresponded to the same model profile. This strategy was adopted in order to retain
consistency in post-processing methods used by all countries.
The results of 190 HELO observations (Table 5) indicate that the lower atmosphere
was on average 1.5 g kg-1 drier and 2.5 K warmer at 112 m than at 5 m. The minimum in
mean modified refractivity occurred at a height of ~75 m suggesting a propensity for
surface-based ducting in the observed profiles. Generally all the models had increasingly
cold and wet biases with height leading to larger rms errors at 112 m than near the
surface. The vertical variation of the moisture field largely defined the slope of the
modified refractivity in each layer thus characterizing the layer’s refractivity regime. In
the statistics, the models had rather large differences in the vertical variation of mean
temperatures, but all models tended to over-estimate the moisture above the surface layer.
The largest decrease in mean moisture was given by COAMPS (1.0 g kg-1) followed by
the MetUM (0.7 g kg-1) while the remaining models had less than 0.5 g kg-1.
Since mean statistics only give a broad picture of the models’ performance, we
further investigated three specific times for individual profile comparisons, so chosen
because each contained an observed surface-based duct developing under very different
background flow characteristics. Figure 6a shows the SIBL structure that developed in
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the offshore flow on 30 Apr 17 UTC following a frontal passage. All four models
predicted the duct despite significant discrepancies in temperature (Fig. 6a). The ducting
layer was well represented in the models as it was predominately a function of the
relatively dry, warm mixed layer over land that was advected over a near-saturated layer
adjacent to the much colder ocean surface.
For 1 May 15 UTC, the observed profiles depict a strong, shallow inversion and sharp
moisture decrease generated by upper level subsidence (Fig. 6b). Both GEM and
COAMPS had larger changes in temperature and humidity with height than did the
models forced by a warmer ocean. As evidenced by the time series at the NPS buoy
location (Fig. 5), all the models captured strong ducting associated with high pressure
ridging during 1 May, but had large fluctuations in duct strength. Temporal fluctuations
were commonplace in the models’ time series and also in duct strength animation loops.
COAMPS developed pulses in the ducted layer after 12 UTC on 1 May as veering winds
became more southerly, down-sloping from the topography and within a deep, wellmixed MABL to the south. This finding suggests that model errors can arise due to a
lack of subsidence and imprecise timing of synoptic transitions and/or mesoscale
oscillations.
The 4 May 18 UTC case had shoreward directed flow, also crossing the warm Gulf
Stream to colder near shore ocean waters. While this period was dominated by high
pressure, a cold, dry air mass aloft originated over Wallops from the east. The observed
profiles display a complicated vertical structure with a mixed layer and embedded
internal boundary layer (Fig. 6c). At this time, the upwind SST distribution and the local
sea breeze return flow contributed to the layering in the lower atmosphere. Each model
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successfully simulated the cold, dry air aloft, however COAMPS also predicted a warmer
upwind ocean relative to the other models, leading to a well-defined mixed layer and
sharp drop in specific humidity, albeit above the level of the HELO profile. None of the
models captured the weak surface-based duct present in the observations at this time. We
return to the above three cases to discuss ubiquitous mesoscale features present in
simulated ducting in the next section.
The mean ducting characteristics from each model at the HELO locations (Table 6)
indicate that ducts occurred about half of the time they were observed for COAMPS and
a third of the time for the MetUM and GEM. The relatively little ducting depicted by
MM5 was due to insufficient observational information in that model run as discussed in
section 3. The DFQ percentages are roughly half those reported at the buoy location
(Table 2), and the majority of these are surface-based. The HELO statistics are more
representative of daytime ducting phenomena within the lower MABL being restricted to
times and heights of those measurements. In terms of mean DST, COAMPS overprediction stemmed from an inability to capture the timing of transitions on 1 May, while
the other models tended to under-predict mean DST because of weaker upper level
subsidence. The cold shelf waters in COAMPS also yielded more stable surface fluxes
within 100 km of the coast, thus augmenting the near-shore subsidence leading to greater
DFQ and SBD percentages, and stronger ducts with lower DBH in COAMPS.
Statistics derived from a contingency table comprising duct/no duct events from each
NWP model reveal that the models all have a greater percentage of missed ducts than
hits, and with the exception of COAMPS, have a greater percentage of errors than percent
correct (Table 7). The hit rate minus the false alarm rate yields discrimination scores of
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25 or less. The highest discrimination score of 100 is attained when the hit rate is 100%
and there are no false alarms. Discrimination scores were somewhat sensitive to the
method used for computing ducting statistics. Scores could be improved by removing
duplicate profiles, increasing discrimination scores by about 14 points in COAMPS, and
by extending model profiles by one level (to include modeled ducts that were slightly
deeper than observed). The latter had more of an impact on the coarse grid resolutions
(discussed in section 6a below) increasing COAMPS grid 1 discrimination score by 9
points. The above changes tended to increase DFQ percentages and hit rates, but
otherwise had little effect on the mean ducting statistics.
6.
Mesoscale structure
To show the grid-wide horizontal variability in simulated ducts, we revisit the three
profile dates and times presented in Figure 6. Much of the structure in the 30 April SIBL
ducts arose from complexity in the coastline and bays (Fig. 7). GEM’s ducts are less
extensive inhibited by a deeper surface layer, perhaps due to their coarser vertical
resolution. In the wake of the frontal passage, upper level subsidence moved
southeastward across the region forming ducts where the moist, mixed layer over the
warm ocean surface met with the advancing subsidence. Because the subsidence was
more pronounced in the simulations with colder shelf waters, and more moisture could be
fluxed into the MABL by a warm Gulf Stream, GEM and especially COAMPS had
stronger ducting in that location.
In the alongshore case, warm, dry air in the free atmosphere combined with surface
southerlies around the backside of a high pressure cell located to the southeast of the
20
study area. The duct strength maps in Figure 8 reveal substantial differences between
models and within a given model over very short distances that can be explained by
differences in SST and in synoptic transitions. Veering winds promoted ducting across
much of the ocean domain aided by the large, downward sensible heat flux on this day.
Ducting was inhibited by upward sensible heat flux over warm water regions. The
absence of any appreciable ducting in MM5 at this time clearly demonstrated the
importance of correcting mesoscale model fields with observations or reinitializing to
obtain a more accurate description of the large-scale environment. When combined with
their coarsely resolved SST, little vertical structure developed in that model as depicted in
their profiles (Figure 6b).
In the other models, an abrupt transition to ducting occurred downwind of the surface
stability change, driven by the collapse of the MABL and reduction in turbulent mixing
over colder ocean. This same mechanism was responsible for strong coastal ducting
south of Wallops Island in GEM and COAMPS. Away from the stable surface flux
transition, inversions lifted and weakened, diminishing or eliminating ducts along the
north coast of Wallops Island. Shallow inversions were re-established in COAMPS over
the cold waters offshore of Delaware Bay and to the northeast, increasing duct strengths
there. The MetUM synoptic transition was more rapid than the other models, having
already advected in a deep layer of warm, moist air into the southern portion of the
domain, virtually eliminating their ducting south of Wallops Island. Due to the rapid
evolution of ducting events that contain substantial mesoscale structure, even subtle
timing or position errors can negatively characterize model performance when compared
with observations at specific locations and times.
21
For the onshore case of 4 May, the upwind SST created a deep MABL capped by
large-scale subsidence. Differences in ducting distributions were substantial on this day
(Fig. 9), and largely related to the upwind SST east of the 4-km boundary. The Gulf
Stream SST front in that region created a deep, moist mixed layer that resulted in strong,
elevated ducting over most of the ocean domain in COAMPS. Ample mesoscale
structure resulted from changes in MABL depth and inversion strength as the layer
approached the coast and interacted with the local sea breeze return flow. While the
elevated duct was predicted by MetUM over the warm water regions, it lacked
subsidence closer to shore to sustain ducting there. In contrast, weak near surface ducting
developed in GEM and MM5 only over their cold water regions, but neither models’
simulation contained enough of a mixed layer to support the offshore, elevated ducting.
a. Grid resolutions
Using COAMPS four grids, we explore relationships between the large-scale
dynamics and mesoscale forcing in generating variability in ducted layers. The outer grid
had 36-km grid spacing and each embedded nest increased in horizontal resolution by a
factor of three from its parent nest (i.e. 12-km, 4-km and 1.33 km). The 4 May date was
selected to emphasize resolution differences associated with large-scale subsidence above
easterly flow. The resultant strong, elevated duct is contrasted with 2 May when a stable
internal boundary layer advected more than 100 km offshore creating weak, surfacebased ducting. The duct strength distributions for these two cases reveal substantial
mesoscale structure in the 4-km and 1.33-km grids (Fig. 10 and Fig. 11).
22
For the onshore case of 4 May, the trapping layer, represented by red in the crosssections, has a down-sloping ducted layer below 1 km on all four grids. The 36-km grid
however, had no evidence of a sea breeze, this being indicated by the drop in MABL
heights near the land/sea boundary in the cross-sections. The sea breeze is represented in
the refractive structures by the subrefraction aloft and in the duct distributions by
increased duct strengths immediately at and just offshore of the coast. The transition
between sea breeze-enhanced subsidence and large scale subsidence corresponds to a
region of much weaker ducting about 50 km from shore, notably absent on grid 1 (Fig.
10). The small-scale detail in the duct strength pattern is directly related to the resolvable
wave activity supported by the higher resolution grid as shown in the vertical crosssections of potential temperature.
Differences between grids were not limited to this example, being clearly visible on
many of the Wallops experiment days. The offshore flow case of 2 May 21 UTC
indicates the sensitivity of SIBL ducting to resolution (Fig. 11). This date was chosen
because of its weaker background flow, permitting retention of mesoscale responses. In
general, the higher resolution grids tended to have stronger inversions and tighter
gradients defining the ducted layer. Both features contributed to stronger and more
prevalent ducting on grids 3 and 4. Neither the 36-km nor 12-km grids had sufficiently
strong surface inversions to confine the moisture, and both grids lacked the weak,
shallow SIBL ducts at this time. GEM’s coarser vertical resolution also resulted in
minimal ducting for the 30 April case, suggesting that SIBL ducts may require at least 5km grid spacing and an average of 60 m vertical resolution to be adequately resolved.
23
The mean ducting characteristics at the HELO locations are given in Table 8 for each
of COAMPS four grids. The number of duct hits was increased by utilizing a 00 UTC
initialization on 4 May and including one additional model level in sampling the profiles
for a ducting event. This procedure retained the slightly deeper ducts modeled by grid
1’s more diffuse inversions in the statistics. Comparing the means from COAMPS grids,
DTK, DBH and SBD were all nearly the same. Decreased horizontal resolution had a
large impact on DST however, with mean values for grid 1 less than half that of grid 4.
Mean DFQ percentages were also reduced but only for the coarsest resolution grid,
generating 20% less ducting than the other grids at the HELO locations.
Table 9 shows the corresponding ducting contingency statistics. It is worth noting
that the higher resolution 1.33-km grid gave poorer statistics in terms of ducting forecasts
than the 4-km grid. This result is typical of the ‘double penalty’ problem of verifying
high-resolution simulations using traditional verification metrics (rms error, bias and
contingency tables) (Anthes 1983). Because the finer grids can resolve small-scale
features (Fig. 10d and 11d), the potential for greater and larger error occurs. Further,
greater accuracy and granularity of land surface databases and ocean surface forcing
through two-way coupled simulations may be necessary to fully realize the benefits of
increased grid resolutions.
b. Sensitivity tests
Several sensitivity tests and modifications to the computations were identified based
upon evaluation of each country’s initial simulations. An obvious difference deemed
critical for establishing accurate stratification and MABL vertical structure, was the lower
24
boundary condition over the ocean. Additional runs were performed by both GEM and
MetUM to study the effect of replacing their coarsely resolved SST fields with the
NCODA SST analysis utilized in COAMPS. Another potential source for improvement
was to lengthen the time between initialization from global fields and the forecast period
of interest, thus allowing greater spin-up of mesoscale detail on the 4-km grid. The 00
UTC initialization test was performed by MetUM. An earlier 00 UTC initialization was
also done by COAMPS for 4 May to avoid a large over-correction to the 12 UTC
moisture field done by their data assimilation scheme.
Improving the mesoscale forcing through a highly resolved SST and longer spin up
period produced a dramatic change to the ducting hit rate, increasing them by 10-27%.
All the models were able to achieve a greater percentage of correctly simulated ducts/no
ducts than they were error. In GEM, the modest 10% increase in hit rate, and a more than
doubling of their discrimination score, was accomplished despite an increase in their rms
errors for modified refractivity (Tables 10 and 11). This error was likely related to the
slight increase in rms error for specific humidity while producing a larger near surface
mean value and larger drop with height compared to the original run.
With an earlier initialization, the ducting simulated by MetUM substantially
improved. Downscaling from ECMWF occurred 12 hours earlier, allowing terrain
induced responses and gradients to be fully developed on the 4-km grid. This result was
consistent with the grid resolution comparisons with COAMPS, in which considerable
vertical structure was achieved simply by increasing the horizontal resolution (Figs. 10
and 11). The earlier initialization significantly increased duct hit rates, elevating
discrimination scores to 24 in MetUM and 47 in COAMPS (Table 11). However,
25
differences in COAMPS were not due to mesoscale initialization, since it retained the
previous 12-hour forecast as a first guess, but primarily to a poor data assimilation
correction on COAMPS inner grids (4 and 1.33 km). With COAMPS MVOI analysis,
the inner grids analyses were heavily influenced by the single vertical sounding at
Wallops Island. COAMPS has since advanced to a 3D-variational data assimilation
method that performs the analysis on the outer grid at the resolution of the inner grid,
thereby allowing for the influence of observations outside of the smaller domains.
When the MetUM also included the NCODA SST, the stability of the lower
atmosphere increased. The model developed a much shallower MABL allowing dry air
to descend to lower levels, thus strengthening the vertical moisture gradient. The overall
effect of earlier initialization and NCODA SST in terms of the profile statistics was a
reduction of their specific humidity rms errors by half, and interestingly, an increase in
their potential temperature rms errors, particularly near the surface (Table 10). However,
those errors had little influence on the modified refractivity which is more strongly
altered by the ‘wet’ term containing the contribution due to vapor pressure (Bean and
Dutton 1968). As a result, modified refractivity errors were also reduced by about half
from the original MetUM statistics representing a significant improvement in their
simulated ducts. Although the stronger inversion supported more ducts, it also increased
their false alarm rate, nonetheless raising the MetUM discrimination score to 34.
Additional initialization improvement could be achieved with mesoscale data
assimilation permitting mixed layers and sharp vertical gradients to be retained in model
first guess fields.
26
7. Conclusions
This modeling study examines the seven-day Wallops-2000 IOP data collected off the
eastern shores of the Delmarva Peninsula during a highly synoptically active period in
April/May of 2000. The primary observational dataset used to evaluate four mesoscale
model simulations (COAMPS, MetUM, MM5, GEM) included instrumented fixed buoy
time series and helicopter vertical profiles. This dataset provided a unique description of
the temporal and spatial changes in atmospheric ducting associated with rapidly evolving
synoptic and mesoscale forcing. Comparisons of simulated fields from each model’s 4km resolution domain were made with observed meteorological conditions and with the
mean diagnosed modified refractivity and ducting characteristics for the full seven-day
period. This study has established a validation benchmark of mesoscale modeling
capability for representing the atmospheric ducting phenomena in this region using
standard verification tools such as mean, bias and rms error statistics. Combined with
ducting event contingency table statistics, these metrics provide the basis by which we
assessed the models’ simulations, the effect of different grid resolutions, and sensitivity
test improvements.
From analysis of observed data in conjunction with the models’ predictions, the
broad-scale ducting patterns followed the general synoptic-refractivity model of
Rosenthal and Helvey (1979), in which favorable ducting conditions occurred during
periods of high pressure due to upper level subsidence and unfavorable conditions during
periods of negative sea level pressure tendency indicating approaching low pressure
systems. Differences between the models’ simulations were often linked to the SST and
to synoptic transitions which differed due to initialization methods and lateral boundary
27
conditions. The study was designed at the outset to consider each country’s complete
mesoscale modeling system which necessarily included differences in the models
themselves as well as in the tools and techniques used for obtaining global model fields,
for initialization and spin up of mesoscale simulations, for assimilation of observations,
and for specifying the SST. We used the models to explore commonality in the simulated
ducting patterns, to gain an understanding of important sensitivities of atmospheric
refractivity to the large-scale and mesoscale forcing present in each modeling system.
The four models were found to generally over-predict the mean moisture above the
surface layer resulting in a weaker vertical gradient in specific humidity, thus producing
fewer and weaker MABL ducts than were observed. However, some of the errors
resulted from either timing or position inaccuracies in the simulated ducting layers,
neither of which reflects well upon model performance. Improved ducting was achieved
by earlier initialization of mesoscale simulations in MetUM and by using the high
resolution, twice daily updated NCODA SST as a lower boundary condition in MetUM
and GEM. Improvements to COAMPS were made by eliminating a problematic moisture
correction on 4 May from the single Wallops Island sounding in the MVOI data
assimilation scheme.
Considerable mesoscale variability was present in simulated ducting events during
Wallops-2000 IOP. Using case study examples of offshore, alongshore and onshore
flow, we illustrated the complexities of the meteorology in this region and their effect on
atmospheric refractivity and ducting. Events characterized by weak background winds
had ample mesoscale structure in ducted layers. The horizontal variability was associated
with changes in the strength of gradients defining the layer and in their depth. More
28
abrupt transitions occurred across changes in surface stability. Mesoscale forcing
evolved from the spatially complex SST, by the development of surface layer and MABL
structure over and downwind of the meandering Gulf Stream and cold shelf waters. In
the coastal zone, interactions with sea breeze return flows induced additional mesoscale
variability in ducted layers with 50-100 km of shore. On other days, topographically
induced local pressure gradients developed over the diurnal cycle, and during a period of
rapid synoptic transition, oscillations in the free atmosphere were excited, both affecting
the presence and strength of coastal ducts. Analysis of COAMPS four grids showed that
the mesoscale features and intricate vertical structure were only possible for grid
resolutions of 4-km or higher, and also required a concurrently high resolution SST
analysis.
The findings from the initial simulations and subsequent sensitivity tests revealed
some of the most critical aspects of mesoscale model systems necessary for simulating
atmospheric refractivity and ducting. In order of importance, these include but are not
limited to:
1) Accurate large-scale forcing in initial fields and at lateral boundaries
2) Horizontal grid spacing of at least 5 km and average vertical spacing of at least 60
m in the lowest 1 km of the atmosphere
3) Mesoscale structure retained in analysis or allowed to spin up on finer grids
4) Accurate SST field of equivalent resolution to the model grid spacing
5) 3D and 4DVAR data assimilation techniques for proper moisture analysis
Some aspects of the modeling intercomparison remain for future work. A study of
model physics, numerics and dynamics could be made by initializing all models with the
29
same global fields, and an evaluation of the propagation measurements during Wallops2000 will help determine the degree to which predicted refractive layers yield accuracy in
microwave radar signals. More importantly, new observations are currently being
analyzed from a recent field campaign in the Bay of Plenty, New Zealand. Over a span
of 14-days, the four ABCANZ countries archived their 4-km model forecasts, each run in
near real-time utilizing its standard operational configuration, providing an opportunity
for more complete evaluation of national defense forecast capabilities of mesoscale
coastal refractivity.
Acknowledgments. Our gratitude extends to Stéphane Gaudreault, a recent addition to the
ABCANZ model intercomparison team and a contributor to our ongoing effort. We wish
to thank Ross Rottier and others at JHU/APL for the helicopter measurements and
Kenneth Davidson of NPS for supplying the buoy data. We are grateful to Duncan Cook,
Dan Dockery and two anonymous reviewers whose suggestions helped shape the
manuscript. The ABCANZ model intercomparison collaboration was supported by the
Office of Naval Research, Program Element 0602271N. Contributions from the U.S.
authors were also supported by Program Element 0602435N and from the U.K. author by
the DERTP programme funded by the Ministry of Defence, UK. This paper is British
Crown Copyright.
30
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33
36, 12, 4
70 sigma
(~45 m)
MM5
0Z 25 Apr
from 12-km
grid
0 & 12Z
from 4-km
grid 12-hour
forecast
Daily at 12Z
from 12-km
grid
0Z 25 Apr from
1° NOGAPS
global analysis
Daily at 12Z
from TL319L60
ECMWF global
analysis
0Z 25 Apr from
1° NCEP global
reanalysis
IC
4-km grid
IC
Grid 1
6-hourly
from NCEP
6-hourly
from
ECMWF
6-hourly
from
NOGAPS
BC Update
Grid 1
Every 30
minutes
from 12-km
grid
Every time
step from
12-km grid
BC
Update
4-km grid
Every time
step from
12-km grid
GEM
40 Pressure
(~80 m)
15, 12, 4
Daily at 0Z
Daily at 12Z None (15
1-hourly
from 24 km
from 12-km km grid is
from 12-km
CMC global
grid 6-hour
global)
grid
analysis
forecast
COAMPS:
U.S. Navy’s Coupled Ocean/Atmosphere Mesoscale prediction system
MetUM:
U.K. Met Office Unified Model
MM5:
Penn State-National Center for Atmospheric Research 5th generation mesoscale model
GEM:
Global Environmental Multiscale model of Canada
NOGAPS:
Navy Operational Global Analysis and Prediction System
ECMWF:
European Centre for Medium-Range Weather Forecasts
NCEP:
National Center for Environmental Prediction
CMC:
Canadian Meteorological Center
MVOI:
Multivariate optimum interpolation analysis
3D & 4DVAR: 3- or 4-dimensional variational data assimilation.
12, 4, 1
70 hybrid ht
(~60 m)
MetUM
36, 12,
4, 1.33
Resolution
Vertical Horizontal
(km)
COAMPS 70 sigma
(~60 m)
Model
Daily at 0Z
from CMC
analysis
(~100 km)
0 & 12Z
MVOI
analysis on
each grid
Daily at 12Z
from
ECMWF
analysis
From 1°
NCEP
reanalysis
SST
Update
Meso Data
Assimilation
0 & 12Z
MVOI
analysis on
each grid
None,
4DVAR in
ECMWF
analysis
None,
3DVAR in
NCEP
reanalysis
None,
3DVAR in
CMC global
analysis
Table 1. Wallops-2000 modeling experiment setup. The number in parenthesis is the average vertical resolution in the lowest 1 km.
The letters IC are for ‘initial conditions’ and BC are for ‘boundary condition’. Other abbreviations and acronyms are given below.
Table 2. Mean ducting characteristics at NPS buoy location and 100 km offshore
(Symbols in Fig. 2) computed from each model forecast. DFQ is duct frequency
percentage, DST is duct strength (M-units), DBH is duct base height (m), DTK is
duct thickness (m), SBD is the percentage of ducts that are surface-based (defined as
ducts with DBH=0 m).
COAMPS
DFQ
(%)
DST
(M-u)
DBH
(m)
DTK
(m)
SBD
(%)
Count
MetUM
NPS
100km
NPS
100km
73
73
52
57
7.3
9.8
5.8
133
166
125
66
168
MM5
GEM
100km
NPS
100km
42
39
44
25
5.3
5.2
5.1
5.0
5.1
73
208
161
314
41
20
175
95
134
134
206
93
137
60
56
44
41
17
85
157
NPS
168
83
91
Table 3. Near-surface statistics at NPS buoy (diamond in Fig. 2) computed from
hourly measurements and each model forecast. In each row, the upper number is the
mean and the lower two numbers are the bias (observation minus model) and root
mean square error (bias / rmse).
Buoy
COAMPS
MetUM
MM5
GEM
SST
(°C)
11.67
11.1
0.6 / 0.9
13.0
-1.3 / 1.7
13.5
-1.9 / 2.2
9.1
3.0 / 3.1
AirT
(°C)
12.4
12.2
0.2 / 1.1
14.1
-1.5 / 1.7
13.3
-1.0 / 1.7
11.1
1.8 / 2.2
RH
(%)
81.43
83.1
-1.7 / 9.8
79.8
0.9 / 7.2
82.9
-1.4 / 10.8
84.5
-5.5 / 12.7
WSP
(m s-1)
4.7
4.4
0.3 / 1.9
5.1
-0.3 / 1.6
5.1
-0.4 / 2.5
5.7
-1.1 / 2.7
WDR
(deg)
150.7
174.7
-24.0 / 96.5
149.6
4.3 / 74.0
160.7
-8.1 / 80.3
151.6
7.8 / 89.6
PRS
(hPa)
1014.3
1018.2
-3.9 / 4.0
1018.0
-3.4 / 3.5
1018.8
-4.6 / 4.8
1019.1
-4.2 / 4.4
Count
168
168
157
168
91
2
Table 4. Number of descending HELO legs used as observed profiles on each day of
Wallops-2000.
Date
28 Apr
29 Apr
30 Apr
1 May
2 May
3 May
4 May
Total
Profiles
18
39
29
21
13
16
54
190
3
Table 5. Vertical profile statistics at helicopter locations (Blue radial in Fig. 2) for
levels 5, 45 and 112 m of specific humidity (g kg-1), potential temperature (K) and
modified refractivity (M-units) from each model forecast and helicopter
measurements (HELO). In each row, the upper number is the mean and the lower
two numbers are the bias (observation minus model) and rmse (bias / rmse).
Specific Humidity (g kg-1)
Ht (m)
HELO
5.9
112
6.7
45
7.3
5
COAMPS
6.4
-0.5 / 1.2
7.0
-0.3 / 0.8
7.4
-0.1 / 0.5
MetUM
7.2
-1.4 / 1.8
7.6
-1.3 / 1.5
7.9
-0.7 / 1.1
MM5
7.3
-1.4 / 2.1
7.4
-0.8 / 1.8
7.5
-0.3 / 1.5
GEM
6.8
-0.9 / 1.4
7.0
-0.4 / 0.9
7.1
0.1 / 0.6
Potential Temperature (K)
Ht (m)
HELO
288.4
112
287.0
45
285.8
5
COAMPS
285.6
2.8 / 3.0
284.3
2.7 / 2.9
283.3
2.5 / 2.7
MetUM
286.8
]1.6 / 2.0
286.1
0.8 / 1.5
285.5
0.2 / 1.2
MM5
284.3
4.1 / 4.7
284.3
2.6 / 3.4
284.4
1.4 / 2.2
GEM
286.6
1.8 / 2.3
285.4
1.6 / 2.1
284.1
1.8 / 2.3
Mod Refractivity (M-units)
Ht (m)
HELO
330.4
112
328.7
45
329.3
5
Count
190
COAMPS
337.8
-7.5 / 11.1
334.9
-6.2 / 8.4
333.9
-4.7 / 6.1
MetUM
341.1
-11.0 / 14.4
336.2
-7.7 / 11.2
334.5
-5.3 / 7.9
MM5
346.1
-15.7 / 18.6
338.1
-9.0 / 13.8
333.4
-4.3 / 10.2
GEM
340.6
-10.4 / 13.0
335.2
-6.7 / 9.1
332.2
-2.9 / 5.6
190
190
190
190
4
Table 6. Mean ducting characteristics at helicopter locations (blue radial in Fig. 2)
computed from each models’ 4-km resolution grid and helicopter measurements
(HELO). DFQ is duct frequency percentage, DST is duct strength (M-units), DBH is
duct base height (m), DTK is duct thickness (m), SBD is the percentage of ducts that
are surface-based (defined as ducts with DBH=0 m).
HELO COAMPS
DFQ
(%)
DST
(M-u)
DBH
(m)
DTK
(m)
SBD
(%)
Count
MetUM
MM5
GEM
64
34
24
4
24
7.0
8.8
4.7
2.4
3.5
1.2
3.2
16.7
84.6
0.0
73
77
60
104
46
91
190
88
190
78
190
25
190
100
190
5
Table 7. Duct occurrence contingency table statistics. For MetUM column, statistics
for the original 12 UTC initialization is on the left and the sensitivity test with earlier
00 UTC initialization is on the right.
COAMPS
MetUM
12
00
MM5
GEM
Event Freq
(%)
64
64
64
64
64
Correct (%)
57
44
56
37
48
Error (%)
43
56
44
63
52
Hit (%)
43
25
41
6
28
Miss (%)
False Alarm
(%)
Correct Null
(%)
Discrimination
Score
57
75
59
94
72
19
22
17
2
16
81
78
83
98
84
25
3
24
4
12
6
Table 8. Mean ducting statistics (as in Table 6) except for COAMPS four grids (See
text for details). Grid resolutions are 36-km for Grid 1, 12-km for Grid 2, 4-km for
Grid 3, and 1.33 km for Grid 4. The mean ducting from HELO measurements are
shown for comparison.
HELO
DFQ
(%)
DST
(M-u)
DBH
(m)
DTK
(m)
SBD
(%)
Count
Grid 1
Grid 2
Grid 3
Grid 4
64
30
54
52
50
7.0
4.1
5.0
7.5
8.3
1.2
1.4
0.6
0.7
2.7
73
89
89
84
89
91
190
98
190
95
190
95
190
91
190
7
Table 9. Duct occurrence contingency table statistics (as in Table 7) except for
COAMPS four grids (See text for details). Grid resolutions are 36-km for Grid 1, 12km for Grid 2, 4-km for Grid 3, and 1.33 km for Grid 4.
Grid 1
Event Freq
(%)
Correct
(%)
Error
(%)
Hit
(%)
Miss
(%)
False Alarm
(%)
Correct Null
(%)
Discrimination
Score
Grid 2
Grid 3
Grid 4
64
64
64
64
61
70
74
55
39
30
26
45
43
69
70
34
57
31
30
66
6
28
19
6
94
72
81
94
37
41
51
28
8
Table 10. Vertical profile statistics (as in Table 5) except for sensitivity tests. The
sensitivity test labeled ’00 UTC’ represents earlier initialization and ‘NCODA’
represents use of 4-km NCODA SST (See text for details). In each row, the upper
number is the mean and the lower numbers are the bias and rmse (bias / rmse).
Specific Humidity (g kg-1)
HELO
COAMPS
00 UTC
112
5.9
45
6.7
5
7.3
6.1
-0.2 / 0.8
6.7
0.0 / 0.8
7.2
0.1 / 0.5
Ht (m)
MetUM
ECMWF SST
00 UTC
6.7
-0.9 / 1.7
7.2
-0.6 / 1.4
7.7
-0.5 / 1.0
MetUM
NCODA SST
00 UTC
6.0
-0.1 / 1.1
6.6
-0.0 / 1.0
7.1
0.1 / 0.5
GEM
NCODA SST
7.0
-1.1 / 1.5
7.2
-0.6 / 1.0
7.6
-0.4 / 0.8
Potential Temperature (K)
HELO
COAMPS
00 UTC
112
288.4
45
287.0
5
285.8
285.5
2.8 /3.0
284.2
2.8 / 3.0
283.4
2.4 / 2.7
Ht (m)
MetUM
ECMWF SST
00 UTC
286.8
1.6 / 2.1
286.1
1.0 / 1.3
285.4
0.4 / 1.2
MetUM
NCODA SST
00 UTC
286.8
1.6 /1.9
275.3
1.7 / 1.9
284.1
1.7 / 1.9
GEM
NCODA SST
286.9
1.5 / 2.0
286.1
0.9 / 1.5
285.4
0.4 / 1.2
Mod Refractivity (M-units)
HELO
COAMPS
00 UTC
112
330.4
45
328.7
5
329.3
Ht (m)
Count
190
MetUM
NCODA SST
00 UTC
333.6
-3.5 / 8.9
331.4
-3.0 / 8.0
331.1
-1.9 / 4.0
GEM
NCODA SST
335.8
-5.4 / 8.2
333.9
-4.2 / 7.3
332.5
-3.3 / 5.4
MetUM
ECMWF SST
00 UTC
339.2
-9.1 / 14.1
334.8
-6.4 / 11.2
333.6
-4.4 / 7.2
190
190
190
190
9
341.4
-11.2 / 13.5
335.9
-7.3 / 9.6
333.9
-4.6 / 7.1
Table 11. Duct occurrence contingency table statistics (as in Table 7) except for
sensitivity tests. The sensitivity test labeled ’00 UTC’ represents earlier initialization
and ‘NCODA’ represents use of 4-km NCODA SST (See text for details).
Event Freq
(%)
Correct
(%)
Error
(%)
Hit
(%)
Miss
(%)
False Alarm
(%)
Correct Null
(%)
Discrimination
Score
COAMPS
00 UTC
MetUM
00 UTC
MetUM
NCODA
00 UTC
GEM
NCODA
64
64
64
64
71
56
67
56
29
44
33
44
65
41
68
38
35
59
32
62
18
17
33
13
82
83
67
87
47
24
34
25
10
Figure Captions
Figure 1. Schematic representation of (a) modified refractivity profile labeled with
refractive layers and duct characteristics: duct strength, base height and thickness, and (b)
the three slope values (dM/dz) that delineate the four refractivity regimes: subrefraction,
normal refraction, superrefraction and trapping.
Figure 2. AVHRR 3-day composite SST (K) ending 2 May 2000 for the Wallops-2000
field experiment area. The domain covers the region of the 4-km model grids. The white
dot indicates Wallops Island, the blue line shows the primary radial flown by JHU/APL
helicopter, and the red symbols are the locations of time series at the NPS buoy
(diamond) and approximately 100 km offshore (asterisk). The composite SST is made
available by JHU/APL at http://fermi.jhuapl.edu/avhrr.
Figure 3. Sea surface temperature distribution for 1 May 12 UTC from (a) COAMPS,
(b) MetUM, (c) GEM, and (d) MM5, contoured every 1 K.
Figure 4. Time series of near surface (5-m) model forecasts at NPS buoy location
(Diamond in Fig. 2) and buoy observations (black) for 7-day Wallops-2000 IOP.
The ‘UM’ is for MetUM and ‘CMP’ is for COAMPS.
Figure 5. Time series of model forecast duct strength (M-units) at NPS buoy location
(Diamond in Fig. 2) for 7-day Wallops-2000 IOP. Periods of offshore flow and high
pressure ridging are labeled ‘O’ and ‘H’ respectively. The ‘UM’ is for MetUM and
‘CMP’ is for COAMPS.
Figure 6. Model forecast and helicopter profiles of potential temperature (K), specific
humidity (g kg-1), and modified refractivity (M-units) during (a) offshore flow: 30 April
17 UTC, (b) alongshore flow: 1 May 15 UTC, and (c) onshore flow: 4 May 18 UTC. The
‘UM’ is for MetUM, and ‘CMP’ is for COAMPS.
Figure 7. Duct strength distributions (M-units) for 30 April 17 UTC from (a) COAMPS,
(b) MetUM, (c) GEM, and (d) MM5. Wind arrows at 45 m height are shown on
COAMPS for reference to the background wind direction.
Figure 8. Duct strength distributions (M-units) for 1 May 15 UTC from (a) COAMPS,
(b) MetUM, (c) GEM, and (d) MM5. Wind arrows at 45 m height are shown on
COAMPS for reference to the background wind direction.
Figure 9. Duct strength distributions (M-units) for 4 May 18 UTC from (a) COAMPS,
(b) MetUM, (c) GEM, and (d) MM5. Wind arrows at 45 m height are shown on
COAMPS for reference to the background wind direction.
Figure 10. Subdomain of COAMPS four grids on 4 May 18 UTC showing duct strength
(M-units) in column 1 and vertical cross-section in column 2, of refractivity regime
(color), potential temperature (isopleth) and circulation (grey lines) to height of 1 km
along plane A-B.
Figure 11. Subdomain of COAMPS four grids on 2 May 21 UTC showing duct strength
(M-units) in column 1 and vertical cross-section in column 2, of refractivity regime
(color), potential temperature (isopleth) and circulation (grey lines) to height of 1km
along plane A-B.
Figure 1. Schematic representation of (a) modified refractivity profile labeled with refractive
layers and duct characteristics: duct strength, base height and thickness, and (b) the three slope
values (dM/dz) that delineate the four refractivity regimes: subrefraction, normal refraction,
superrefraction and trapping.
New
Jersey
40°N
Delaware
Bay
Maryland
Wallops
Is.
38°
●
Virginia
♦
Delmarva
Peninsula
*
Chesapeake
Bay
Gulf
Stream
North
Carolina
36°
78°
76°
278
283
74°W
288
293
298
303 K
Figure 2. AVHRR 3-day composite SST (K) ending 2 May 2000 for the Wallops-2000 field
experiment area. The domain covers the region of the 4-km model grids. The white dot
indicates Wallops Island, the blue line shows the primary radial flown by JHU/APL helicopter,
and the red symbols are the locations of time series at the NPS buoy (diamond) and
approximately 100 km offshore (asterisk). The composite SST is made available by JHU/APL at
http://fermi.jhuapl.edu/avhrr.
(a)
(b)
(c)
(d)
280
282
284
286
288
290
292
294
296 K
Figure 3. Sea surface temperature distribution for 1 May 12 UTC from (a) COAMPS, (b)
Unified Model, (c) GEM, and (d) MM5, contoured every 1 K.
Date
Figure 4. Time series of near surface (4-m) model forecasts at NPS buoy location (Diamond in
Fig. 2) and buoy observations (black) for 7-day Wallops-2000 IOP.
35
Duct Strength (M-units)
30
25
20
15
10
5
0
O
H
O
H
Date
Figure 5. Time series of model forecast duct strength (M-units) at NPS buoy location (Diamond
in Fig. 2) for 7-day Wallops-2000 IOP. Periods of offshore flow and high pressure ridging are
labeled ‘O’ and ‘H’ respectively.
1000
(a)
Helo
UM
MM5
GEM
CMP
900
CMP
700
UM
UM
600
H t (m )
Height (m)
800
MM5
GEM
CMP
MM5
UM
GEM
CMP
500
GEM
400
MM5
300
200
100
Helo
Helo
Helo
0
1000
(b)
900
GEM
700
UM
CMP
600
H t (m )
Height (m)
800
MM5
500
400
Helo
CMP
300
MM5
200
GEM
Helo
100
GEM
UM
CMP
MM5
UM
Helo
0
1000
(c)
800
UM
CMP
700
UM
CMP
MM5
MM5
500
GEM
GEM
CMP
600
H t (m )
Height (m)
900
UM
400
MM5
300
GEM
200
100
Helo
Helo
Helo
00
282
0
284
286
288
Potential Temperature (K)
290
292
Potential Temperature (K)
0
2
4
6
-1
Specific Humidity (g kg )
320
8
-1
360
400
440
Modified Refractivity (M-units)
Specific Humidity (g kg ) Modified Refractivity (M-units)
Figure 6. Model forecast and helicopter profiles of potential temperature (K), specific humidity
(g kg-1), and modified refractivity (M-units) during (a) offshore flow: 30 April 17 UTC, (b)
alongshore flow: 1 May 15 UTC, and (c) onshore flow: 4 May 18 UTC.
(a)
(b)
(d)
(c)
0
4
8
12
16
20
24
28
32
36
40
Figure 7. Duct strength distributions (M-units) for 30 April 17 UTC from (a) COAMPS, (b)
Unified Model, (c) GEM, and (d) MM5. Wind arrows at 45 m height are shown on COAMPS
for reference to the background wind direction.
(a)
(b)
(c)
(d)
0
4
8
12
16
20
24
28
32
36
40
Figure 8. Duct strength distributions (M-units) for 1 May 15 UTC from (a) COAMPS, (b)
Unified Model, (c) GEM, and (d) MM5. Wind arrows at 45 m height are shown on COAMPS
for reference to the background wind direction.
(a)
(b)
(c)
(d)
0
4
8
12
16
20
24
28
32
36
40
Figure 9. Duct strength distributions (M-units) for 4 May 18 UTC from (a) COAMPS, (b)
Unified Model, (c) GEM, and (d) MM5. Wind arrows at 45 m height are shown on COAMPS
for reference to the background wind direction.
(a) Grid 1
A
B
(b) Grid 2
(c) Grid 3
5
10
15
20 25
Sub Normal Super Trap
30
(d) Grid 4
A
B
185
Figure 10. Subdomain of COAMPS four grids on 4 May 18 UTC showing duct strength (Munits) in column 1 and vertical cross-section in column 2, of refractivity regime (color), potential
temperature (isopleth) and circulation (grey lines) to height of 1 km along plane A-B.
(a) Grid 1
A
B
(b) Grid 2
(c) Grid 3
5
10
15
20 25
Sub Normal Super Trap
30
(d) Grid 4
A
185
B
Figure 11. Subdomain of COAMPS four grids on 2 May 21 UTC showing duct strength (Munits) in column 1 and vertical cross-section in column 2, of refractivity regime (color), potential
temperature (isopleth) and circulation (grey lines) to height of 1km along plane A-B.