*Drafting Panel:*

Chris Bretherton

Judith Curry

Chris Fairall

James Hudson

George Isaac

Donald Lenschow

Michael King

Steven Krueger

David Randall (Chair)

William Rossow

Bruce Wielicki

*Final Version, April 1996*

Executive Summary

FIRE, the First ISCCP (International Satellite Cloud Climatology
Project) Regional Experiment, has conducted four successful field
missions in support of a program of research that includes remote
sensing and modeling studies of clouds and radiation as they relate to
climate.

FIRE is now preparing to go to the Arctic in order to study a variety of
Arctic cloud systems under spring, summer, and possibly autumn
conditions. At the same time, analysis of previously collected FIRE data
will continue. The primary motivations for the Arctic phase of FIRE are:

• Arctic boundary-layer clouds are poorly simulated by current climate
models;

• the physical processes at work in the Arctic cloudy boundary layer are
poorly understood;

• Satellite remote sensing algorithms currently cannot accurately
retrieve Arctic surface and cloud characteristics.

FIRE's Arctic field program will strongly interact with both Surface
Heat Budget of the Arctic (SHEBA) (primarily sponsored by National
Science Foundation (NSF) and Office of Naval Research (ONR)) and
Atmospheric Radiation Measurements (ARM) (sponsored by Department of
Energy (DOE)). SHEBA will provide an ice station which will (it appears)
be built around an ice breaker. ARM will provide some of the instruments
to be deployed at the SHEBA ice station. Both SHEBA and ARM also include
modeling components. FIRE's role is to provide aircraft and
satellite-based measurements, along with further modeling studies.

The aim of the FIRE III field experiment is to produce an integrated
data set that:

\(1\) supports the analysis and interpretation of physical processes
that couple clouds, radiation, chemistry, and the atmospheric boundary
layer;

\(2\) provides in situ data for testing of satellite and ground-based
remote sensing analyses; and

\(3\) provides initial data, boundary conditions, forcing functions, and
test data to support Arctic FIRE modeling efforts.

FIRE's strategy is to use aircraft to take in situ and remote
measurements of the Arctic cloud and surface characteristics. The
aircraft observations will be supplemented by surface measurements
provided by SHEBA and ARM. In addition, FIRE will provide studies based
on satellite remote sensing, and will also use in situ data to evaluate
these remote sensing techniques.

The interrelationships among FIRE, SHEBA, and ARM are summarized below:

**What ARM provides for FIRE:** high quality surface radiation
measurements.

**What SHEBA provides for FIRE:** time series of surface-based
observations of atmospheric vertical structure (including cloud
structure) and description of the sea ice surface over a region of 100
km surrounding the SHEBA ice station.

**What FIRE provides for ARM and SHEBA:** satellite analysis of cloud
characteristics; technology, observations and analysis of aircraft data;
improved parameterizations of Arctic cloudy boundary layer process.

The winter clouds are believed to consist of lower tropospheric ice
crystals, which lead to radiative fluxes to be 10 - 40 W m*-2* greater
than expected for clear sky conditions. Many of these ice clouds may be
produced by moist convective plumes associated with leads in the ice.
Arctic summertime stratus are often observed to form in multiple layers
in the lowest kilometer of the atmosphere. Key scientific issues
relating to Arctic clouds are as follows:

• What is the influence of leads and other open water on cloud
properties when there is a large surface temperature contrast with the
ice?

• How does the extreme static stability and low atmospheric water vapor
content of the Arctic lower troposphere, particularly during winter,
affect the flow energy across the air-sea interface?

• What is the mechanism that leads to the spectacular multiple-layering
of Arctic Ocean summer cloud systems?

• How does the transition of low clouds from liquid to crystalline
depend on temperature and aerosol characteristics, and how does the
springtime transition differ from the autumnal transition?

• Does the formation of "diamond dust" differ in polluted vs. unpolluted
atmospheres?

The highly-reflecting snow/ice surface of the Arctic ocean strongly
affects the transfer of shortwave radiation. Clouds alter the
distribution of the solar flux between direct and diffuse components,
which is important because the penetration of shortwave radiation into
the sea ice depends on the ratio of diffuse to direct radiation. A
peculiarity of the polar regions is the dominance of large solar zenith
angles for solar illumination, especially in the spring and fall
seasons. Because the central Arctic spends a greater portion of the year
with little or no solar illumination, longwave radiation plays a
particularly important role in the surface energy balance. The longwave
opacity of the Arctic atmosphere is so low that the appearance of clouds
significantly increases the emission by the atmosphere of longwave
radiation both downwards to the surface and upwards to space, even
though Arctic clouds have longwave emissivities significantly less than
unity. Arctic stratus often form in the warmest, most humid layer, so
that the downward longwave flux in winter can be larger than the upward
flux from the sea ice. Key scientific issues related to Arctic radiation
are as follows:

• What is the spectral distribution of longwave radiation and in
particular what is the role of the 20 mm "rotation-band window" region
in regulating the surface and atmospheric temperature in the Arctic?

• What are the effects of springtime "Arctic haze" on the absorption of
solar radiation in polar clouds?

• How do the reflectance and transmittance of the clouds and the surface
depend on the low solar zenith angles typical of the Arctic?

• What is the role of "clear-sky" ice crystal precipitation in
determining the longwave radiation fluxes?

• What are the shortwave radiative effects of the horizontally
inhomogeneous stratocumulus clouds over the horizontally inhomogeneous,
highly-reflecting snow/ice surface?

• How do the optical properties of the Arctic surface vary in response
to changes in snow characteristics (thickness, age, temperature,
contamination), thinning of the ice, and melt pond formation?

Aerosols can strongly regulate the optical properties of the Arctic
clouds and even have the potential to influence the macrostructure of
the Arctic boundary layer, including the depth and vigor of the
turbulence that vertically transports energy and moisture. Key questions
regarding the chemistry of the Arctic atmosphere are as follows:

• What are the actual supersaturations at which various cloud
condensation nuclei (CCN) are activated?

• What is the shape of the CCN spectrum?

• Which particles are actually incorporated into droplets?

• What are the sizes of the CCN?

• What is the chemical composition of the CCN? Are organics important?

• How do the CCN distributions interact with the cloud droplet
distributions?

• What is the nature of the ice forming nuclei (IFN)?

1.0 Introduction

1.1 A FIRE on the ice

FIRE, the First ISCCP (International Satellite Cloud Climatology
Project) Regional Experiment, has conducted four successful field
missions in support of a program of research that includes remote
sensing and modeling studies of clouds and radiation as they relate to
climate. The field programs included cirrus studies in Wisconsin and
Kansas, a stratocumulus study off the coast of California, and a
stratocumulus transition experiment in the subtropical north Atlantic. A
review of the first ten years of FIRE has been written by Randall et al.
(1995).

As described in the FIRE Phase III Science Plan, FIRE is now preparing
to go back to the field. The next mission will be in the Arctic, where
we will study a variety of Arctic cloud systems under spring and summer
conditions. The scientific goals and motivations of our work are
discussed only briefly here, because they have already been spelled out
in detail in the Science Plan. The primary motivations are:

• Arctic boundary-layer clouds are poorly simulated by current climate
models;

• the physical processes at work in the Arctic cloudy boundary layer are
poorly understood;

• Satellite remote sensing algorithms currently cannot accurately
retrieve Arctic surface and cloud characteristics.

The present document is the "Implementation Plan" for the upcoming
Arctic phase of FIRE. The purpose of the Implementation Plan is to
explain many of the particulars of how we will conduct our collective
research, with a moderate degree of detail.

The following subsection gives a discussion of how Phase III of FIRE
will maintain continuity with earlier FIRE research, with emphasis on
the boundary-layer cloud components of FIRE. Next, we briefly summarize
the scientific objectives of Arctic FIRE, as spelled out in the Science
Plan.

1.2 Connections to previous FIRE research

FIRE in the Arctic will build upon the knowledge, instrumentation and
many interdisciplinary collaborations fostered by the earlier phases of
FIRE, but in several ways represents an exciting new philosophical
thrust.

Previous FIRE boundary layer field campaigns documented the
cloud-radiation-sea surface interactions that are responsible for the
areal extent and optical properties of subtropical stratocumulus clouds,
and led to major advances in remote sensing algorithms for cloud
properties. They led to an appreciation of the relation of boundary
layer cloud amount, cloud radiative properties and boundary layer
thermodynamic structure to dynamic forcing, the properties of the
surface and overlying air, the diurnal cycle of solar radiation, and the
concentration and size distribution of aerosol. These insights are being
incorporated in many GCM parameterizations.

The previous FIRE research efforts also produced highly integrated data
sets, in which local and remote sensing observations from surface sites,
aircraft and satellite data, and large scale and regional scale analysis
from forecast models are blended. This type of study requires close
collaboration between multiple investigators and requires several years
to bring fully to fruition. Continuing studies based on the FIRE I and
Atlantic Stratocumulus Transition Experiment (ASTEX) data are currently
yielding important insights on the fundamental mechanisms of subtropical
stratocumulus to trade cumulus transition,
cloud/aerosol/drizzle/radiation feedbacks, and mesoscale variability of
stratocumulus clouds. FIRE investigators are continuing to work with
climate and forecast modelling groups to develop new or more refined
parameterizations of marine cloud topped boundary layers.

The Arctic activities of FIRE will continue these traditions. Further
analysis and modelling based on earlier datasets will remain a crucial
component of the project, as these activities provide some of the most
important scientific payoffs to the larger climate community. They will
continue in parallel with the Arctic cloud experiment. Continuity of
personnel, instrumentation, and goals from FIRE I and II will allow
efficient comparison of Arctic cloud systems with their subtropical
counterparts. The summertime Arctic cloud-topped boundary layer shares
many important characteristics with subtropical stratocumulus. To name
just a few, cloud-radiative interactions (as opposed to synoptic-scale
lifting) are crucial in maintaining the observed structure, the boundary
layer is often observed to be decoupled into dynamically separate
layers, cloud radiative/microphysical/aerosol interactions are thought
to be very important, and there are strong feedbacks between the
boundary layer structure and the nature of the underlying surface.

A continued emphasis on platform integration, synthesis and
interdisciplinary collaboration in Arctic FIRE will also increase its
effectiveness and distinguish it from previous studies of Arctic clouds.
FIRE investigators have achieved increasing skill in this kind of
synthesis during FIRE I and ASTEX. However, the Arctic FIRE Arctic cloud
experiment will coordinate with related investigations much more closely
than in previous FIRE campaigns. The Arctic FIRE Arctic cloud experiment
will rely heavily on another related experiment, SHEBA. SHEBA will
measure surface fluxes and advanced ground-based remote sensing
instrumentation for clouds, radiation, and thermodynamic structure at a
site on the sea ice for a year. This provides an invaluable context for
the periods during which FIRE will obtain in-situ cloud measurements.
FIRE investigators will coordinate data collection and analysis
extremely closely with SHEBA, maximizing the ability of the two groups
of scientists to gain from each others insights and measurements. Past
experiments have documented the coupling of clouds, thermodynamics and
turbulence from aircraft (Arctic Ice Dynamics Joint Experiment (AIDJEX),
the Arctic Stratus Experiment, Beaufort Arctic Storms Experiment
(BASE)), documented the interactions of microphysics, aerosols and
radiation Arctic Radiation Measurement in Column Atmosphere Surface
System/Cloud, Aerosol, and Radiation Arctic Field Experiment
(ARMCAS/CARAFE), or taken extended time surface observations. However,
no experiment has integrated all three of these with measurements of the
evolution of the underlying snow/ice surface and analyses of the
synoptic-scale atmospheric structure. Arctic FIRE and SHEBA plan to do
this and thereby simultaneously provide all the information necessary to
test the suite of physical parameterizations that determine the
climatological predictions of general circulation models (GCM) for the
Arctic. Close collaborations with the ARM North Slope site in Barrow and
a Canadian hydrological experiment in the MacKenzie river basin, CAGES,
are also anticipated, providing a yet broader spectrum of measurements
to FIRE investigators. The Arctic cloud experiment and SHEBA should both
benefit from FIRE experience with arranging real time assimilation of
data into operational numerical weather prediction centers, such as was
successfully carried out in ASTEX.

The Arctic cloud experiment broadens the scope of earlier FIRE boundary
layer cloud campaigns by introducing several new challenges in
measurement, modelling and interpretation. These include strong
inhomogeneity of the underlying surface, mixed phase clouds, sampling
clouds above the boundary layer, and difficulty in the satellite
detection of clouds over a bright and nonuniform surface. A successful
attack on these complications must be grounded in an understanding of
fundamental feedbacks in and measurement strategies for cloud-topped
boundary layers. The strategies of the Arctic cloud experiment are
therefore strongly rooted in successful experiences and collaborations
from earlier FIRE campaigns.

1.3 Summary of scientific objectives

1.3.1 Clouds

Satellite estimates of wintertime cloud cover are somewhat greater than
the surface values (e.g. Curry et al. 1996). These winter clouds are
believed to consist of lower tropospheric ice crystals, which lead to
radiative fluxes to be 10 - 40 W m*-2* greater than expected for clear
sky conditions. Many of these ice clouds may be produced by moist
convective plumes associated with leads in the ice. Ice crystal
precipitation may contribute to snow accumulation in the Arctic. The ice
clouds persist into the spring, so that they do interact with solar
radiation. Measurements of particle size, habit, and the associated
phase functions for solar radiation are needed so that we can understand
the effects of these ice clouds. We also need to understand the effects
of springtime Arctic haze on the absorption of solar radiation in polar
clouds. For this purpose, we will need multispectral and multi-angle
observations in the diffusion regime within the cloud, allowing
determination of the single scatter albedo at both multiple wavelengths.

Arctic summertime stratus are often observed to form in multiple layers
in the lowest kilometer of the atmosphere (Herman and Goody, 1976). The
temperature and moisture soundings are similarly layered, and the
temperature of the cloud bases can affect the surface energy balance. In
summer, a shallow stably stratified layer with cloud is often observed
beneath an upper cloud-capped mixed layer, with little or no turbulent
mixing between the layers. The upper cloud layers appear to persist
without any turbulent mixing of moisture up from the surface. The models
that have been proposed to explain the observed layering have been
heavily simplified, especially in their treatment of turbulence. There
have been no large-eddy simulations of Arctic stratus to date.

Key scientific issues relating to Arctic clouds are as follows:

• What is the influence of leads and other open water on cloud
properties when there is a large surface temperature contrast with the
ice?

• How does the extreme static stability and low atmospheric water vapor
content of the Arctic lower troposphere, particularly during winter,
affect the flow energy across the air-sea interface?

• What is the mechanism that leads to the spectacular multiple-layering
of Arctic Ocean summer cloud systems?

• How does the transition of low clouds from liquid to crystalline
depend on temperature and aerosol characteristics, and how does the
springtime transition differ from the autumnal transition?

• Does the formation of "diamond dust" differ in polluted vs. unpolluted
atmospheres?

1.3.2 Radiation

Clouds are the dominant modulators of the Arctic radiation climate.
Cloud radiative properties depend on the amount of condensed water, the
size and shape of the cloud particles, and the phase of the particles
(liquid or ice). Recent studies (e.g. Curry and Ebert 1992) suggest that
cloud emissivity is less than unity for all cloud types and during all
seasons, that over the course of the year clouds have a net warming
effect on the surface, and that the top-of-the-atmosphere cloud
radiative forcing is dominated by the shortwave flux.

The highly-reflecting snow/ice surface strongly affects the transfer of
shortwave radiation. The surface albedo is modified by meltpond area and
depth, lead area, ice thickness and age, snow thickness and age, and the
effects of cloud on surface albedo. Clouds alter the distribution of the
solar flux between direct and diffuse components, which is important
because the penetration of shortwave radiation into the sea ice depends
on the ratio of diffuse to direct radiation.

A peculiarity of the polar regions is the dominance of large solar
zenith angles for solar illumination, especially in the spring and fall
seasons. Studies are needed to examine the accuracy of current radiative
models for solar zenith angles beyond about 80 degrees.

Because the central Arctic spends a greater portion of the year with
little or no solar illumination, longwave radiation plays a particularly
important role in the surface energy balance. The surface emissivity
varies between 0.8 and 1.0, depending on snow grain size. The longwave
opacity of the Arctic atmosphere is so low that the appearance of clouds
significantly increases the emission by the atmosphere of longwave
radiation both downwards to the surface and upwards to space. Arctic
clouds may generally be optically thin enough that their longwave
emissivities are significantly less than unity which will alter their
effect on the radiation balance. Moreover, Arctic stratus often form in
the warmest, most humid layer, which can lie well above the surface, so
that the downward longwave flux in winter could, at times, be larger
than the upward flux from the sea ice. Downward surface longwave flux
calculations require accurate modeling of water vapor absorption at the
low mixing ratios typical of polar conditions.

Key scientific issues related to Arctic radiation are as follows:

• What is the spectral distribution of longwave radiation and in
particular what is the role of the 20 mm "rotation-band window" region
in regulating the surface and atmospheric temperature in the Arctic?

• What are the effects of springtime "Arctic haze" on the absorption of
solar radiation in polar clouds?

• How do the reflectance and transmittance of the clouds and the surface
depend on the low solar zenith angles typical of the Arctic?

• What is the role of "clear-sky" ice crystal precipitation in
determining the longwave radiation fluxes?

• What are the shortwave radiative effects of the horizontally
inhomogeneous stratocumulus clouds over the horizontally inhomogeneous,
highly-reflecting snow/ice surface?

• How do the optical properties of the Arctic surface vary in response
to changes in snow characteristics (thickness, age, temperature,
contamination), thinning of the ice, and melt pond formation?

1.3.3 Chemistry

The concentration and size distribution of cloud droplets (microphysics)
depends in part on the preexisting aerosol. To isolate the aerosol
effects, we need measurements of cloud condensation nuclei, or CCN. CCN
are quite variable in space and time, which causes considerable
variability in cloud microphysics (e.g. Hudson and Li 1995), cloud
albedo (e.g. Twomey 1977) and precipitation efficiency (Albrecht 1989;
Twomey 1991).

Anthropogenic sources make up a large but as yet unknown component of
global CCN. This constitutes a significant but as yet unquantified
anthropogenic effect on climate (e.g. Kaufman et al. 1991). It has been
suggested by Karl et al. (1993) that this effect could be responsible
for the asymmetric diurnal warming that has been observed in
industrialized regions of the world.

The parameter that characterizes CCN, the critical supersaturation,
*Sc*, is related to the number of soluble ions within a particle. CCN
are composed of soluble electrolytes, usually salt and often sulfate.
CCN provide a conceptual link between atmospheric chemistry and cloud
properties. Although the *Sc* is the only aerosol property needed to
predict the cloud interactions, the actual chemistry of the CCN must be
known in order to understand the sources of CCN, which seem to be highly
variable (e.g. Hudson 1993). It is important to distinguish between
natural and man-made sources.

CCN measurements are commonly done at only one supersaturation (e.g.
0.3%), which is chosen to represent the clouds under consideration. Much
more work needs to be done to correctly determine cloud
supersaturations, which appear to be quite variable. Full spectral CCN
measurements (e.g. 0.01% to 3%) are needed because of the different
supersaturations even within the same clouds due to different updraft
velocities. This is especially important in stratus clouds where the
updraft velocity is low and comparable to the variability of the updraft
velocity. Comparisons should be made between various regimes, e.g. the
Arctic with lower latitudes.

Variations among cloud parcels indicate that the shape of the CCN
spectrum can also be important in determining cloud microphysics (Martin
et al. 1994; Hudson and Svensson 1995). This may be especially true of
cloud droplet spectral width, which is important for precipitation. CCN
measurements should be made aboard all research aircraft and CCN spectra
should be measured when possible. The vertical distribution of CCN is
also important to determine whether sources are below or above the
clouds. Scavenging of CCN by the cloud droplets through coalescence and
Brownian capture is also important for the CCN budget. These processes
probably lead to the depletion of CCN in the cloudy boundary layer
(Hudson and Frisbie 1991). A more positive identification of which
particles actually get incorporated into cloud droplets (i.e. with the
counterflow virtual impactor (CVI) Twohy and Hudson (1995) is necessary
for a better understanding of CCN-cloud interactions.

The physical size of CCN is important for a number of considerations
including atmospheric removal rates both in clouds and in the cloud-free
atmosphere. In general particle size distributions are more easily and
commonly measured than CCN spectra. Thus the determination of the
physical sizes of CCN would be important for relating to other
measurements and to understanding the global cycle of CCN. This can be
done by passing the particles through an electrostatic classifier, also
known as a differential mobility analyzer (DMA), before entering the CCN
spectrometer (Hudson and Da 1996). This also offers important clues
about particle composition because if the particles are totally soluble
then their sizes can be predicted from knowledge of composition and they
do not display much variability for most soluble materials such as
sulfates. However insoluble material internally mixed with the CCN
increases the size of the CCN. This technique can be used as an indirect
measurement of CCN composition. Another indirect technique is thermal
fractionation or particle volatility. Most volatility measurements
indicate that most CCN are sulfate particles (i.e. Twomey 1971). CCN
size measurements and volatility measurements by Hudson and Da (1996)
indicate systematic differences depending on particle origin where the
anthropogenic particles are often internally mixing with insoluble
material whereas natural particles may be pure soluble substances.

Direct measurements of particle chemistry are desirable to confirm these
indications and to better pin down CCN sources. Important gaseous
precursors such as SO2, DMS, O3, OH should be measured. Direct
measurements of aerosol composition should also be made but these should
be concentrated on the submicrometer size range where CCN exist.
Measurements of particulate sulfate and organic material are needed in
order to determine whether these are the substances that make CCN.
Sulfates certainly act as CCN. Organics are also associated with CCN
(Novakov and Penner 1993), but they may just ride along with the CCN. If
organics act as CCN themselves, then removal of sulfate from fuels will
not necessarily reduce CCN outputs.

Ice particle concentrations also have an analogous dependence on the
aerosol with analogous ice forming nuclei (IFN). However this connection
is more complex and less well-understood. If valid IFN measurements can
be demonstrated they should also form a component of Arctic FIRE as many
of the clouds are composed of ice particles. However, this technology
has not yet developed to the stage of routine measurements as is the
case for CCN.

The CCN measurements should be compared with cloud microphysical
measurements in a comprehensive fashion. It is not enough to merely
compare spectra. It is most desirable to compare CCN spectra with cloud
droplet spectra within adiabatic cloud parcels, but this requires
careful analysis. Comparisons with nonadiabatic parcels may reveal the
effects of entrainment and drizzle on cloud microphysics. Trajectory
analyses are needed in order to trace source regions of particles.
Connections with gaseous precursors are needed to understand production
processes. The Arctic spring offers interesting chemistry that can occur
between the Arctic haze season and the polar sunrise, which invokes
photochemistry. The Arctic haze arises from the long range transport of
material into the Arctic. The persistent stratus clouds of the summer
present significant cloud scavenging and perhaps large vertical
gradients in particle concentrations (Hudson and Frisbie 1991). Analysis
of these data should reveal the possible sensitivity of Arctic stratus
to anthropogenic CCN and the present and estimated future contributions
to Arctic CCN.

In summary, key questions regarding the chemistry of the Arctic
atmosphere are as follows:

• What are the actual supersaturations at which various CCN are
activated?

• What is the shape of the CCN spectrum?

• Which particles are actually incorporated into droplets?

• What are the sizes of the CCN?

• What is the chemical composition of the CCN? Are organics important?

• How do the CCN distributions interact with the cloud droplet
distributions?

• What is the nature of the IFN?

1.4 Discussion

The next section of this document summarizes prior field studies in the
Arctic, and explains the main objectives of Arctic FIRE. Section 3
discusses our plans for measurements using surface-based systems,
aircraft, and satellite sensors. Section 4 deals with data integration
and modeling studies. Section 5 lists concluding remarks. Appendix A and
B summarizes the SHEBA and ARM science plans, respectively. Appendix C
describes the sampling strategies for satellite intercomparisons.
Appendix D lists details of FIRE's organization and data management
protocols. Appendix E defines the acronyms. Appendix F lists the
abstracts of the FIRE Science Team researchers. Appendix G lists the ARM
surface instruments at the ice station and Pt. Barrow. Appendix H
outlines the research plans for continuing FIRE-II research. Appendix I
lists the references.

2.0 General Approach

A multi-faceted strategy is required to achieve the objectives outlined
in section 1. The strategy includes three main elements: a field
experiment, satellite remote sensing studies, and modeling studies.

2.1 Summary of previous Arctic cloud and radiation research

During the last two decades, several aircraft datasets of relevance to
FIRE III have been obtained over the Arctic Ocean: ARMCAS/CARAFE, BASE,
Arctic Stratus Experiment, Lead Experiment (LEADEX), and Arctic Gas and
Aerosol Sampling Program (AGASP). AGASP consisted of many springtime
flights (using many different aircraft) over the Arctic Ocean during the
1980\'s to measure the Arctic haze pollution aerosol. Unfortunately,
most of the aircraft did not make concurrent cloud microphysics and
radiation measurements, although some relevant data was obtained from
the University of Washington C-131A aircraft (Curry et al. 1990; Meyer
et al. 1991).

The Beaufort Arctic Storms Experiment (BASE) is an atmospheric component
of the Canadian Global Energy and Water Cycle Experiment (GEWEX)
experiment whose focus is the hydrologic balance of the Mackenzie River.
The objectives of BASE are to improve understanding of weather systems
in the Canadian Arctic, particularly as related to precipitation. The
BASE field experiment was conducted during September and October 1994 in
the Beaufort Sea, north of the mouth of the Mackenzie River. The
principal dataset relevant to FIRE III was obtained using the NCAR
C-130Q aircraft; it includes measurements of radiation fluxes, cloud
microphysical properties, and high frequency measurements of winds,
temperature and humidity. Radiative and microphysical properties of the
full range of cloud types occurring during the Beaufort Sea during
autumn were sampled: cirrus and altostratus (over ice and over open
water); convective clouds over open water; stratus decks over open
water; low-level clouds over ice (both liquid and mixed phase); and "sea
smoke associated with leads. In addition, oceanographic measurements
were obtained to determine temperature and salinity budgets for the
Mackenzie Shelf.

The Arctic Stratus Experiment was conducted during June 1980 over the
Beaufort Sea. The objectives of this experiment were to concurrently
measure cloud microphysical properties with atmospheric radiative fluxes
in the boundary layer stratus clouds and to interpret the radiative
fluxes in the context of the cloud microphysical properties. In addition
to radiation fluxes and cloud microphysical properties, high frequency
measurements of winds, temperature and humidity were obtained from the
National Center for Atmospheric Research (NCAR) Electra aircraft (see
Herman and Curry 1984 for a description of the aircraft
instrumentation). Although no in situ surface measurements were made,
European Centre for Medium-range Weather Forecasting (ECMWF) analyses
and Defense Meteorological Satellite Program (DMSP) visible and infrared
satellite images provided the large scale context for the measurements.
Using these data, the radiative properties of the clouds were analyzed
and modeled (Herman and Curry, 1984; Curry and Herman, 1985; Tsay et al.
1989), the cloud microphysical properties were described (Tsay and
Jayaweera, 1984) and interpreted in the context of turbulence and
radiative processes (Curry 1986), and the mean and turbulence structure
of the cloudy atmospheric boundary layer were analyzed (Curry et al.
1988). This dataset includes several examples of the multiple-layered
boundary layer stratus. No surface-based observations were obtained
during this experiment.

The Arctic Radiation Measurement in Column Atmosphere-surface System
(ARMCAS) and Cloud, Aerosol and Radiation Arctic Field Experiment
(CARAFE) was conducted in June 1995, including over the Beaufort Sea.
Both the University of Washington C-131A and the NASA ER-2 aircraft
participated in the experiment; there were no surface-based observations
over the ocean. The objectives of the experiment were to measure the
cloud microphysical characteristics and their variabilities, solar
absorption of Arctic stratus, aerosol physical properties and optical
depth, and to compare in situ with aircraft remote sensing observations.

While a large amount of useful data has been obtained from previous
field experiments, and useful technology has been developed and tested,
a comprehensive dataset with surface observations has not been obtained
using recent technological advances in both ground-based remote sensing
and aircraft measurements.

2.2 FIRE's objectives, and cooperation with SHEBA and ARM

The aim of the FIRE III field experiment is to produce an integrated
data set that:

\(1\) supports the analysis and interpretation of physical processes
that couple clouds, radiation, chemistry, and the atmospheric boundary
layer;

\(2\) provides in situ data for testing of satellite and ground-based
remote sensing analyses; and

\(3\) provides initial data, boundary conditions, forcing functions, and
test data to support Arctic FIRE modeling efforts.

FIRE's overall strategy is to use aircraft to take in situ and remote
measurements of the Arctic cloud and surface characteristics. In
addition, FIRE provides detailed analysis of satellite observations. The
aircraft and satellite data are supplemented by surface measurements
provided by SHEBA and ARM.

SHEBA is a research program designed to document and understand the
physical processes that couple the atmosphere, ice, and ocean in the
Arctic (funded by NSF and ONR). The central theme of SHEBA emerges from
issues related to the surface energy balance, especially the ice-albedo
and cloud-radiation climate feedback mechanisms. The essence of SHEBA as
a project is to conduct a field experiment at a drifting station on the
pack ice of the Arctic Ocean for a year (starting Autumn 1997), in
combination with remote sensing and modeling analyses of the entire
Arctic Basin. The observational program emphasizes the physical
processes associated with interactions among the radiation balance, mass
changes of the sea ice, storage and retrieval of energy and salt in the
mixed layer of the ocean, and the formation of clouds and their
influence on the surface energy balance. The modeling effort aims to
improve the parameterizations of crucial air-sea-ice interactive
processes in climate models. The primary focus is on processes that
determine the ice-albedo feedback and the cloud-radiation feedback. The
SHEBA field experiment will begin in Autumn, 1997 and continue for the
duration of an annual cycle. The measurements will be staged from and
distributed around a research ice-breaker ship located on the surface of
a multi-year ice floe in the Beaufort Sea. Scientific and operational
considerations suggest that the optimal site for deploying the SHEBA
camp is the eastern Beaufort Sea, perhaps in the vicinity of 76°N, 135°W
(see Fig. 1). Appendix A gives a summary of the SHEBA Science Plan
(available in draft form as this FIRE Arctic Implementation Plan was
written).

The ARM program of the U. S. Department of Energy is designed to improve
our understanding of radiative transfer and the role of clouds in
climate. Its centerpiece is a long-term series of ground-based
measurements at several sites, including one in Barrow, Alaska. ARM will
contribute radiation hardware to the SHEBA ice station, and will perform
analysis of this data. A summary of the relevant parts of ARM Science
Plan is given in Appendix B. In addition, ARM funds several PIs to
conduct research that is directly relevant to FIRE III, to conduct
relevant modeling and data analysis studies dealing with:

• one-dimensional radiative transfer modeling;

• cloudy boundary layer modeling;

• ARCSyM regional climate modeling;

• atmospheric general circulation modeling.

Each of the collaborating programs (FIRE, SHEBA, and ARM) has distinct
programmatic elements, but at the same time their goals and efforts
support larger goals and objectives related to Arctic climate as
described in the SHEBA Science Plan. Close coordination of FIRE III with
SHEBA and ARM is accomplished by members of the FIRE Executive Committee
and the FIRE Science team being members of key SHEBA and ARM committees.
The interrelationships among the three programs are summarized below:

**What ARM provides for FIRE:** high quality surface radiation
measurements.

**What SHEBA provides for FIRE:** time series of surface-based
observations of atmospheric vertical structure (including cloud
structure) and description of the sea ice surface over a region of 100
km surrounding the SHEBA ice station.

**What FIRE provides for ARM and SHEBA:** satellite analysis of cloud
characteristics; technology, observations and analysis of aircraft data;
improved parameterizations of Arctic cloudy boundary layer process.

3.0 Observing Platforms and Sampling Strategies

3.1 Surface Measurements

Surface-based measurements over the ice pack will be concentrated in the
SHEBA domain. Exact details are not know, but the SHEBA science plan
(Curry et al., 1995) outlines a scenario for a central SHEBA ice station
with most of the instruments and at least 3 remote sites with much more
limited instruments. SHEBA plans a set of Continuous Core Measurements
(CCM) in the ocean, ice and atmosphere. For FIRE Phase III the
atmospheric and surface characterization measurements are the most
relevant: measurements on the atmospheric side of the interface will be
made by combing systems from the ARM program and systems specially
deployed for SHEBA. ARM is developing a portable, semi-autonomous
measurement system called an "ARCS" (Atmospheric Radiation and Clouds
Station; see Clements et al., 1995) which will become available starting
in 1995; several units will be manufactured, and plans call for various
component from one to be deployed in the Arctic, at the SHEBA ice
station. The ARCS will form the foundation for atmospheric measurements
made from surface platforms. However, a complete ARCS will not be
available for SHEBA, so additional measurements will be required. A
summary of the surface-based observations planned or expected for the
SHEBA program is described in this section. These observations will
include measurements of surface radiation, cloud properties, water
vapor, aerosols, boundary layer structure, and surface meteorology. They
will be obtained with a combination of ground-based in situ and remote
sensing systems.

Because of the extreme environmental conditions which will be
encountered in the Arctic, a critical operational component will be a
full test of surface instrumentation before it is deployed on the ice.
Present planning calls for integration of the ETL radar (ETL is NOAA's
Environmental Technology Laboratory) and wind profiler into the DOE/ARCS
shelter and data systems in the autumn of 1996, with a full cold test
scheduled in Barrow, Alaska starting in January of 1997. This activity
will provide for a comprehensive instrument shake down period when
modifications and repairs can be made relatively inexpensively. It will
also provide an important prototype Arctic data set over a period of a
month or more which can be used by the modeling and satellite community
to initialize development of the procedures for integrating the surface
data sets into models and satellite verification schemes. The surface
observationalists will concentrate on producing operational products of
cloud microphysical and radiative properties using retrieval techniques
which have previously only been applied on a case study basis. Tight
coordination between ETL and the DOE ARCS will take advantage of mutual
shelter, data management and logistical needs to cost effectively
eliminate duplication of effort between the two agencies. Other surface
based groups such as the Tethered Balloon consortium will be encouraged
to participate in the Barrow cold test.

3.1.1 Central meteorological tower

Turbulent and radiative fluxes, mean temperature, humidity, wind speed
and direction, and selected chemical/aerosol concentration measurements
will be made from standard meteorological towers. For some variables,
multiple levels are required. Sample instruments are given in Table 1.
Because of practical considerations (i.e. not having to climb a 100 m
tall tower to work on and repair failing sensors), modest towers (e.g.
10 to 30 m tall) designed for easy maintenance are indicated.

  ----------------------- ----------------------- -----------------------
  Table 1 - Tower                                 
  meteorological                                  
  measurements.                                   

  Instrument              Variable                Height (m)

  Sonic anemometer        Stress, heat flux       3, 7, 16

  Anemometer              Wind speed, direction   1,2,4,10,25

  Thermometer             Air temperature         1,2,4,10,25

  Hygrometer              Air humidity            1,2,4,10,25

  Radiometer              Surface temperature     Surface

  Barometer               Surface pressure        Surface

  Thermometer             Ice temperature         Subsurface
  ----------------------- ----------------------- -----------------------

Several scientific aspects affect the tower siting strategy for SHEBA
and FIRE Phase III. The focus of these projects is on the surface energy
budget so the tower designs must accomplish a credible representation of
the mean fluxes over some relevant area. The variety of ice/snow types
and the small scale of the local surface variability make this a very
difficult task. Clearly, a single tower measuring fluxes at a single
level will not meet this requirement (Blanc, 1983). Multiple levels of
flux measurement will allow us to resolve vertically the surface layer,
will generally lend credibility/consistency and will allow us to use the
flux-footprint concept to sample increasingly larger areas farther from
the tower as the height of the measurement increases (Schuepp et al.,
1990; Horst and Weil, 1994). The combination of flux and mean variable
profiles are required to sort out the confusing effects of surface
variability versus non-constant flux profiles caused by non-stationarity
and boundary layer scale vertical gradients. Five levels of mean
measurements (1, 2, 4, 10, 25 m) with 3 interlaced levels of turbulent
flux sensors would be reasonable for the main meteorological tower.
Other measurements (e.g. radiation, aerosol) would only be made at one
level.

The specific instruments used on the tower will be, whenever possible,
standard instruments with a history of successful use in polar climates.
Mean sensors for air temperature, humidity, and wind speed/direction are
readily available, although operation in extreme cold may call for
special considerations (e.g., Anderson, 1994).

Several sonic anemometer units, despite well-known deficiencies (e.g.,
Grelle and Lindroth, 1995), have proven performance to -50 °C and are
commercially available; and at least one specially modified unit has
been used at South Pole Station to -65 °C (Neff, private communication).
Note the sonic anemometer will yield both the surface stress (momentum
flux) and the sensible heat flux (Schotanus et al., 1983), but a
separate fast humidity sensor is required for direct measurements of
latent heat flux (Kaimal and Finnigan, 1994). An accuracy of about °C is
required for surface temperature (see the discussion of bulk flux
parameterization requirements in Fairall et al., 1995), which is beyond
the capabilities of the pyrgeometer (Persson et al., 1995). Thus, a high
quality infrared surface temperature sensor will be necessary, with both
upward and downward looking channels to allow for surface reflection.

Two additional smaller towers are needed to address the following
issues: elimination of contamination by the SHEBA ice station;
examination of the effects of surface inhomogeneities on the surface
fluxes; and the importance of intermittency and subgrid variability of
the surface fluxes. These two additional towers will be portable 3-m
towers that measure turbulent fluxes at only one level.

Fluxes at several selected sites can be sampled periodically with one or
two small portable flux systems deployed by helicopter or snow mobile.
In LEADEX (Ruffieux et al., 1995) such systems were gasoline generator
powered, but a low power system might be practical (although this
depends to some extent on the radiometer dome frost problem). The
portable flux units could also form the core of any lead studies
associated with SHEBA. Ideally, such a system could be dropped off at
the desired site and then serviced or moved every few days. It is also
not necessary to use the portable systems continuously, one or two days
a week might provide a sufficient sample. The remainder of the week,
they could be sited quite close to camp where power is available. This
would provide a combination of direct intercomparison with the camp
tower system and independent samples from a few hundred meters away.

3.1.2 Ground-based in situ profiling systems

3.1.2.1 Rawinsondes

Rawinsondes are needed in addition to the ground-based remote sensing
systems to determine vertical profiles of temperature, wind, and
humidity. The remote sensors have the advantage of continuous high
resolution coverage while the rawinsonde has the advantage of greater
altitude coverage. To use the rawinsondes as a reference standard for
the remote sensors, care must be taken to correct for response time and
radiation-induced errors (Cole, 1993; Luers and Eskridge, 1995) and
Global Positioning System (GPS) navigated sondes will probably be
required. Performance of Vaisala rawinsondes in regions of low
temperature and humidity has been shown to be adequate for our purposes,
although humidity corrections are required at the lowest temperatures
(Oltmanns, private communication). The cost of importing helium, sondes,
balloons, etc. and maintaining extra personnel on the ice to launch and
analyze rawinsondes precludes the use of frequent sonde launches for
SHEBA. We recommend 2 sondes per day, with higher time resolution
profile information provided by remote sensing systems. This is made
more palatable by the weak diurnal cycle.

To represent a realistic alternative to frequent sonde launches, the
wind profiler/RASS system must have sufficient height coverage to
capture the primary transition from the ice-interaction dominated
boundary layer to the advective region above. This transition is
typically identified as a maximum in temperature and water vapor at
roughly 1 km altitude of the atmosphere; most of the short-term
variability in temperature and moisture gradient occurs below 2.5 km
(Persson et al., 1992). Wind profiles in the lowest 100 m can be sampled
with 5 to 10-m resolution with a sodar (see section 3.1.3).

3.1.2.2 Tethered balloons

A tethered balloon is potentially valuable for probing the boundary
layer and low clouds, particularly for measuring cloud microphysics,
subsaturation, and radiation fluxes (Hignett, 1991). A tethered balloon
has many advantages over aircraft, including lower cost, daily
measurements, better vertical resolution, better low altitude operation,
long time series at a fixed location, and better sampling for some
microphysical instruments. The low altitude performance is important.
SHEBA must depend heavily on remote sensors because of its infrequent
radiosonde launches, yet the active sensors like lidar, mm radar, and
RASS all can only "see" above their first range gate, which is typically
several times higher than the top of the meteorological tower.

The tether system envisioned is of the small, lightweight type
manufactured by Atmospheric Instrumentation Research, Inc. (AIR) in
Boulder. A number of groups, including one from NASA Wallops that
participated in FIRE/Spectral Radiation Experiment (SPECTRE) at
Coffeyville, have had favorable experience with these systems. The
balloons are only 3-7 cubic meters in volume, manageable by a single
person, and have a lifting capacity of several kg. AIR has developed a
data system capable of managing up to 6 meteorological packages hanging
at different places along the tether.

If a larger balloon can be operated at the SHEBA ice station, then a
small cloud radiation package could be put together from existing
instruments. Besides a meteorological package, the most important
instruments would be, in rough priority order:

\(1\) a liquid water content meter (King probe or Gerber PVM);

\(2\) a droplet effective-radius meter (Gerber PVM);

\(3\) net shortwave and longwave flux radiometers of the Valero caliber;

\(4\) a chilled mirror hygrometer to examine subsaturation in Arctic
clouds, if possible; and

\(5\) pairs of net flux radiometers to measure flux divergence in Arctic
clouds.

Microphysical properties in low level clouds and fogs can also be
measured by replicator sondes, and video-based ice particle or droplet
samplers. Work is in progress to develop systems which can run power up
the tethers which will substantially increase the operating capabilities
of the balloon borne sensors. Some of these tether systems can be
winched up and down to sample atmospheric and cloud profiles up to 1.5
km. In addition to the important information that these systems can
provide on near-surface profiles (for instance snow drift intensities),
they will be critical in providing validation for the surface remote
sensing instrumentation. Because surface based, remote sensing
retrievals of cloud microphysics will be used extensively for model and
satellite verification studies, it is important that these techniques be
validated in turn with the pristine in situ measurements that can best
be made from balloons.

3.1.3 Ground-based remote sensing

Table 2 lists suggested surface-based profiling remote sensors. These
instruments will provide standard atmospheric wind, temperature, and
moisture profiles (from conventional sondes and remote sensors), plus a
variety of remotely sensed cloud, aerosol, and (perhaps) chemical
properties. The SHEBA remote sensors must be heavily slanted toward the
"unattended, continuously operating" style as opposed to highly capable
research and development sensors requiring constant attention,
adjustment, and repair. The wind profiler/RASS, microwave radiometer,
and minisodar already fit this description. A ruggedized, fully
automated scanning 0.52 mm wavelength lidar will be built for SHEBA. A
vertically pointing 35 GHz cloud radar is presently under development at
ETL for the ARM program; an identical system will be provided for SHEBA.
The fourier transform interferometers will incorporate a special
refrigeration unit and will not require a continuous supply of liquid
nitrogen.

  ----------------------- ----------------------- -----------------------
  Table 2 - Surface-based                         
  remote sensors                                  
  (\*denote ARCS).                                

  Instrument              Variable                Resolution/Max Altitude

  449 MHZ Profiler        wind components,        0.1/2.5 km, 0.4/3.5 km
                          turbulence              

  1 KHz (acoustic) RASS   virtual temperature     0.1/2.0 km

  Minisodar               wind components,        0.005/0.2 km
                          turbulence              

  0.52 Micron lidar       Backscatter, polar.     0.03/20 km

  35 GHz Cloud radar      Cloud scatter, vertical 0.02/10 km
                          velocity                

  Microwave radiometer\*  Integrated water        \-\--
  ----------------------- ----------------------- -----------------------

3.1.3.1 Clouds

The diagnosis of cloud microphysical properties from the remotely sensed
information is, with a few exceptions, indirect (Gossard, 1994; White et
al., 1996). That is, the fundamental parameters measured directly by a
cloud radar are the backscatter coefficient and the radial velocity.
This information is obtained in an array of height bins with a time
resolution on the order of seconds. Just from this one sensor, a variety
of cloud properties can be determined by simple processing: cloud top
height, cloud base height, and in-cloud velocity variance profiles.
Using models and/or assumptions about cloud properties (e.g., adiabatic
liquid water profiles, constant droplet number density as a function of
height), such data can be processed to give information about cloud
microphysical properties such as droplet size (Frisch et al., 1995).
When data from various systems are combined, we can obtain more
estimates of cloud variables. Thus, SHEBA must include a carefully
chosen set of surface-based remote sensors and must include a research
component of corresponding in situ cloud measurements (either aircraft
or tethered balloons) to develop the various synergistic algorithms
necessary for the experimental objectives.

Cloud microphysical properties have been studied in FIRE II and ASTEX
with combinations of ground-based remote sensors (Miller and Albrecht,
1995; White et al., 1995; Frisch et al., 1994, 1995). Such studies will
also be useful in these Arctic stratus studies. Doppler radar and IR
radiometer measurements have been used in FIRE II to determine profiles
of ice crystal sizes, concentrations, and ice mass content (Matrosov, et
al. 1994). An alternative method utilizes the radar/lidar backscatter
ratio to estimate similar cloud properties (Intrieri, et al., 1993).
Such techniques will be effectively used in Arctic stratus ice clouds
(Pinto et al., 1995). Such measurements should be possible out to ranges
of 10 to 15 km. The frequent occurrence of satellite overpasses in the
Arctic region will provide frequent opportunities to use the
ground-based remote sensors to evaluate some of the satellite-based
atmospheric retrieval techniques and to develop others. Satellite
retrievals are especially uncertain in upper latitudes and FIRE III will
provide an excellent opportunity to improve retrieved products from
satellites, especially those relating to cloud properties.

3.1.3.2 Profiles of winds and atmospheric thermodynamic properties

To represent a realistic alternative to frequent sonde launches, the
wind profiler/Radio Acoustic Sounder System (RASS) system must have
sufficient height coverage to capture the primary transition from the
ice-interaction dominated boundary layer to the advective region above.
This transition is typically identified as a maximum in temperature and
water vapor at roughly 1 km altitude. Thus we need a system capable of
providing nearly continuous winds and temperature to about 2.5 km. The
system must also have adequate vertical range spacing to accurately
resolve this structure, on the order of 100 m. \[Note that the lowest
levels of the atmosphere, where the surface layer transition and jet
structures exist, can be sampled with 10-m resolution with a sodar
(Ruffieux et al., 1994).\] The wind profiler used in LEADEX (Wolfe et
al., 1992) gave 90% height coverage to 0.6 km at 100-m resolution and
1.0 km at 200-m resolution. The 90% height coverage for RASS temperature
was about 0.5 km. This means that a much more powerful profiler will be
required for SHEBA. Based on a comprehensive study of wind profiler
performance (Martner et al., 1993), it is presently planned to use a 449
MHz profiler (a derivative of the NOAA operational network systems)
instead of the 915 system used previously. Such a system will have no
problem with the altitude coverage for winds and will have a clear
advantage in RASS temperature coverage because of the reduced sound
absorption at the lower frequency, but vertical resolution will be no
better than 100 m.

3.1.4 Radiation

The central Arctic is exposed to little or no solar illumination a large
portion of the year so that, in contrast to other parts of Earth, the
longwave fluxes play a much more important role in the total energy
balance, particularly at the surface. Also, the stable stratification of
the polar atmosphere, especially in winter, can cause large differences
in surface skin temperature and air temperature. This effect must be
understood in order to properly determine longwave fluxes. Because of
the predominance of ice clouds during colder seasons, angularly and
spectrally resolved solar radiation is also particularly important for
SHEBA.

The Arctic atmospheric longwave opacity is so low that the appearance of
clouds significantly increases the emission of longwave radiation both
downwards to the surface, and upwards to space in the presence of
temperature inversions which are frequent phenomena. Arctic clouds may
generally be optically thin enough that their longwave emissivities are
significantly less than unity which will alter their effect on the
radiation balance. Moreover, Arctic stratus often form in the warmest,
most humid layer, which can lie well above the surface, so that the
downward longwave flux in winter could conceivably, at times, be larger
than the upward flux from the sea-ice if strong inversions are present.

**For these reasons, downward surface longwave flux calculations require
accurate modeling of water vapor absorption at the low mixing ratios
typical of polar conditions. Verification of downward longwave flux
calculations should follow the process used in SPECTRE, i.e. combining
interferometer and broadband measurement of longwave radiation with
radiosonde profiles and Raman lidar water vapor profiles. Several
independent measurements of longwave broadband fluxes are required given
the current uncertainties (10 W m-2) in the absolute calibration of
these devices. The instruments planned are given in Table 3. Commercial
pyranometer and pyrgeometer units are usable, although a special
calibration program will be required to meet the accuracies needed for
SHEBA (better than W m-2). Some method must be employed to maintain
radiometer domes free of frost without compromising their accuracy.
There are now several commercially available ventilation systems
available.**

  ----------------------------------- -----------------------------------
  Table 3 - Surface-based radiative   
  flux sensors, all of which are part 
  of the ARCS.                        

  Instrument                          Variable

  Pyranometer                         Solar irradiance

  Pyrgeometer                         IR irradiance

  Rotating shadowband rad.            Diffuse solar irradiance

  Sun photometer                      Direct solar irradiance

  IR-radiometer                       Sky temperature

  Whole-sky imager                    Cloud fraction/structure

  Fourier Transform Interferometer    Humidity, temperature
  (IR)                                

  Fourier Transform Interferometer    Solar spectrum
  (solar)                             
  ----------------------------------- -----------------------------------

3.2 Aircraft observing strategy

Aircraft are invaluable tools for investigating the complex cloud
structure and characteristics in the complex Arctic atmosphere. Although
continuous time series observations from the ice breaker contribution of
SHEBA can be used to study the transient nature of clouds at a single
point over sea ice over an extended period of time, aircraft provide an
essential element of our strategy to evaluate both the surface
observations obtained from the ice breaker and the satellite
observations of reflected and emitted radiation. Aircraft can provide
descriptions of the vertical structure and horizontal distribution of
the boundary layer and associated clouds, as well as sample the complex
multi-layer cloud system prevalent over the Beaufort Sea. Aircraft will
enable extensive observations of cloud microphysics, both clear-sky and
cloud radiative properties, chemical composition, aerosol properties,
vertical structure, thermodynamic characteristics, and surface
characteristics including the distribution of leads and melt ponds.

We expect clouds in the Arctic basin to be more complex than those
observed during FIRE 1987 and ASTEX, in part due to the influence of the
cold sea ice surface with many fractured leads. This promotes both
low-level Arctic stratus clouds in summer, which are composed largely of
liquid water drops of fairly uniform size, as well as frequent
incursions of mid-level altocumulus and "high-level" cirrus clouds. Due
to the low altitude of the stratosphere at 70°N, the cirrus clouds occur
at much lower altitudes than in the tropical and subtropical
environments studied extensively in the past.

A long transition period occurs between April, when low-level clouds are
primarily crystalline, and mid-June, when the dominant cloud type is
low-level water clouds, with less frequent, but regular, mid-level
altocumulus. We have adopted a strategy to characterize this transition
period over a seven-week period or longer with a serial deployment of
low-mid-level aircraft (outlined below), together with a four-week
period of high-altitude ER-2 overflights. During this component of
Arctic FIRE (IFO I; here "IFO" means a period of "Intensive Field
Observations"), we anticipate utilizing the Canadian National Research
Council (NRC) CV-580, the NCAR C-130Q, and the University of Washington
(UW) CV-580. All three of these aircraft are turboprops that fly at 120
m s-1, but can be slowed down to 80 - 100 m s-1 for sampling cloud
microphysics. These aircraft are all equipped with an extensive set of
PMS cloud microphysics probes, a Gerber PVM-100A liquid water content
and effective radius probe, Johnson-Williams and King hot wire probes, a
Nd:YAG lidar, thermodynamic state variable measurements, and selected
chemistry instrumentation. As expected, however, these aircraft also
have unique and distinguishing characteristics.

In addition to IFO I, we envision a second IFO for Arctic FIRE in the
summer of 1998, consisting of the NCAR C-130Q (SHEBA-supported) to
overfly the ice breaker. If the Earth Observing System (EOS) AM-1
satellite launches in June 1998, as expected, then the EOS program will
likely sponsor the NASA ER-2\'s participation at this time as well. In
addition, we envision participation by the UW CV-580.

This preferred deployment strategy for IFO I and IFO II campaigns is
summarized in Fig. 2. The overlap periods envisioned for IFO I, will be
used for (i) intercomparisons of aircraft instrumentation, and (ii)
two-aircraft missions (in addition to coordination with the higher
flying ER-2). Intercomparisons can be done by flying the boundary layer
aircraft in formation and comparing both mean and turbulence
measurements of thermodynamic, dynamic, microphysics and radiation.
Examples of two-aircraft missions include: (i) flying at two levels with
one aircraft above the other to obtain simultaneous in situ and remote
sensing measurements of cloud layers, and (ii) carrying out a Lagrangian
study of cloud evolution by having the second aircraft replace the first
aircraft along an air trajectory.

3.2.1 Aircraft platforms

The Canadian Convair CV-580 carries, in addition to a comprehensive
suite of microphysical instrumentation highlighted above, a gust probe,
a nadir-viewing monostatic Nd:YAG lidar (which will have both upward-
and downward-looking capability by 1998), pyranometers, CN and CCN
counters, and two downward-looking radiometers (a three channel visible
radiometer and a Landsat Thematic Mapper (TM) simulator). Chemistry
instrumentation can be put on board to measure many trace gases (SO2,
O3, NO/NO2, H2O2) as well as organic and inorganic composition of the
aerosol, cloud liquid, and cloud ice. DMA and DMS measurements are also
possible. Instrumentation unique to the NRC CV-580 include the Russian
Nevzorov total water content probe that measures cloud water and ice
contents at concentrations as low as 0.005 g m-3. It should also be
possible to install a Russian cloud extinction probe to directly measure
volume extinction coefficient. These probes will be especially useful in
mixed phase clouds or all ice clouds, a situation where Particle
Measurement System (PMS) probes provide only coarse indicators of
particle concentration. Environment Canada is also installing passive
downward and possibly upward, dual polarization, microwave radiometers
operating at 19, 37, and 85 GHz. Additional instrumentation will be
sought to enable the size and phase of particles to be distinguished in
the size range of 50 to 150µmm.

The UW CV-580 is equipped with cloud physics, cloud radiation, aerosol,
chemistry, and monostatic lidar instrumentation. For spectral and
angular radiation measurement between the UV-b (0.30 mm) and
near-infrared (2.3 mm), there is a state-of-the-art Cloud Absorption
Radiometer described by King et al. (1986). This instrument can be used
either for internal scattered radiation measurements (for determining
the spectral single scattering albedo of clouds) or for bidirectional
reflectance function measurements of cloud, snow, tundra and sea ice
surfaces. It can also be used as a cross-track scanner when oriented to
scan from horizon to horizon. In addition, the UW CV-580 has a CCN
spectrometer and space available for the addition of other principal
investigator instrumentation (such as a spectrometer and/or spectral
flux radiometer). Although this aircraft is capable of measuring phase
and imaging some ice crystals, its strength lies in liquid water clouds
(hence its preference towards the end of IFO I).

Both the NCAR C-130Q and NRC CV-580 have comparable turbulence and cloud
microphysics instrumentation, and include the standard suite of
thermodynamic state variables as well as a monostatic lidar. Unique
instrumentation available on the NCAR C-130Q includes a passive
microwave radiometer capable of imaging through clouds and observing sea
ice and lead distributions. When flying at low altitude the microwave
radiometer gives about 30 m resolution, which is sufficient to pick up
all but the smallest leads (which have little effect on the atmosphere
anyway). The NCAR C-130Q will also be equipped with a dropwindsonde
capability to: (i) add to the array of synoptic observations, and (ii)
provide estimates of horizontal advection of moisture and possibly of
divergence. These will be deployed on the ferry legs to and from the
primary research area, and possibly along the closed flight paths to aid
in estimating line integrals around closed flight tracks. Finally, the
C-130Q contains the Multispectral Cloud Radiometer described by Curran
et al. (1981) and King (1987), which requires careful attention to
calibration (true of all visible and near-infrared radiometers).

The NASA ER-2 is the platform of choice for high-altitude surveys of
cloud radiative properties. It will link the low-level aircraft and
surface-based measurements to satellite observations. The primary
instrumentation of interest to Arctic FIRE includes a downward-looking
lidar (Spinhirne et al. 1983), a multispectral along-track scanning
radiometer (under development), a microwave imaging radiometer (Wang et
al. 1995; Raccette et al. 1996), and a multichannel scanning
spectrometer (King et al. 1996). These sensors all have analogs to
future satellite sensors under development for launch between 1998 and
2000. The long range and high altitude (20 km) of this aircraft make it
an excellent platform from which to select cloud features to study, to
map the cloud fields that are being sampled by the low-level aircraft,
to test algorithms for cloud retrievals, and to intercompare with
coordinated satellite overpasses (especially following launch of the EOS
AM-1 spacecraft, which is currently planned for June 1998).

3.2.2 Aircraft-based aerosol and chemistry measurements

The NCAR C-130 will also carry aerosol and chemistry instrumentation
from university and NCAR investigators as was done in the ACE-1 project
in 1995.

The Desert Research Institute (DRI) instantaneous cloud condensation
nuclei (CCN) spectrometer (Hudson 1989) will determine the spatial
variability of CCN because it continuously obtains the entire CCN
spectrum in situ within time periods as short as 1 s. Its spectral range
extends to 0.01%, which is well below the 0.2% limit of other CCN
instruments; this is significant because of the low supersaturations of
stratus clouds and because of increasing evidence that the shape of the
CCN spectrum below the actual parcel supersaturation affects droplet
spectral width, which is important for precipitation development
(section 1.3.3). Since this same instrument was employed in both of the
earlier FIRE stratus cloud projects in 1987 and 1992 it will allow
comparisons of the supersaturations in these different stratus regimes
in addition to valid aerosol comparisons. This instrument can also be
used to determine additional aerosol characteristics such as the
physical sizes of the CCN (Hudson and Da 1996)(section 1.3.3). This is
especially advantageous since there are now two matched DRI CCN
spectrometers; both were successfully operated on the NCAR C-130 in two
earlier projects. One spectrometer continuously monitors the ambient
spectrum while the other measures spectra of processed samples such as
dry particle sizes using a DMA. Both the ambient spectrum and the size
resolved spectrum are needed to determine the size of the CCN because
the spectra can change while flying. Measurements of the total particle
size distribution can be made in conjunction with this by using a CN
counter. A separate DMA can also be employed to obtain semi-continuous
particle size distributions. Total particle (CN) measurements with a TSI
model 3010 will reveal the CCN/CN ratio. The NCAR CVI should also be
employed with the CCN spectrometer (Twohy and Hudson 1995) to
specifically determine which CCN are within the cloud droplets and to
separately collect particles on filters to determine chemistry that is
segregated according to cloud droplet sizes. Separate size-resolved
filters should also be collected in, below, and above cloud air.

If a new airborne IFN instrument (Rogers 1988) planned for operation on
the NASA DC-8 in 1996 is successful then it should be employed on the
C-130 in Arctic FIRE.

The NCAR chemiluminescent ozone detector would be used to estimate
entrainment and mixing into the cloudy boundary layer.

As in the ACE-1 project with the NCAR C-130 gaseous precursors of
particles need to be measured. These begin with DMS, which comes from
natural oceanic sources, and SO2, which may serve as an intermediate
species or may come from pollution sources. The oxidants OH and NOx need
to be measured as these are important in the pathways of conversion of
the DMS or SO2 to particulates. Ammonia is important because it is quite
variable and ammonia is a component of most atmospheric CCN either as
ammonium sulfate or ammonium bisulfate.

Concentrations of greenhouse gases will be determined using the Fourier
Transform Interferometer Radiometer (FTIR). Vertical profiles of aerosol
extinction coefficient will be made with lidar.

Some aerosol sampling could be done on the meteorological tower.
Instrument Neutron Activation Analysis (INAA) will be used to
differentiate anthropogenic sulfate from ocean-biogenic sulfate. Since
INAA filters for multi-element analysis will require a number of cubic
meters of sample volume in Arctic conditions, this measurement will only
be achievable on the ground. The utility of the INAA type of analysis is
that it can give a fairly unambiguous differentiation of "natural" and
anthropogenic aerosol whereas the airborne techniques will be generally
somewhat ambiguous in this regard.

Ground-based filter sampling seems quite practical. Filter packs and
pumps are fairly robust and have been employed in environments even more
hostile than the Arctic (e.g., volcanic caldera). They can be stored for
long periods of time at low temperatures and shipped out periodically
for analysis. Some of the desired ground based measurements that require
more elaborate equipment (e.g., DMAs, CCN counters) may not be
practical.

3.2.3 Sampling strategy

In order to address the competing requirements of measuring horizontal
variability and vertical structure, as well as obtaining a sufficient
sample length, the exact details of the flight tracks will need to be
fine-tuned in later planning exercises. Generally, however, there will
be flight segments at constant level of at least 60 km in length to
obtain a sufficient sample length for turbulence and radiation
measurements; shorter legs would probably suffice for microphysics and
trace constituent measurements. There will also be slow accents and
descents to obtain continuous vertical profiles of these variables, as
well as a series of horizontal flight legs at 4-5 different levels. Many
of the flight tracks will be in the environs of the SHEBA ice breaker to
complement the fixed-base measurements and provide in situ data for
comparisons with ground-based remote sensing observations. The aircraft
can also provide a valuable data set to help extend the long time series
fixed-point measurements obtained from the surface to large spatial
scales.

One approach for deploying the aircraft for meeting the objectives of
measuring surface energy, mass and momentum budgets is to fly closed
flight paths around the surface station at several levels, starting at
the lowest safe altitude, and extending up through the top of the region
being considered. This would allow measurement of the turbulent flux
profiles, as well as cloud microphysics, radiation, and mean
thermodynamic structure of the atmosphere. The surface station
observations complement these measurements by providing long time series
and detailed structure of the atmosphere in the vicinity of the fixed
point. If the flight path is long enough and if a turbulent boundary
layer exists up through the airplane flight levels, it may be possible
to estimate the entrainment velocity at the top of the boundary layer by
measuring the flux profile of a trace chemical species such as ozone,
total water, or dimethyl sulfide. The velocity can then be estimated
from the ratio of the flux at the top of the boundary layer to the jump
in concentration across the top of the boundary layer. An independent
estimate of entrainment velocity may be possible by measuring the mean
divergence within the closed flight track and the time rate of change of
the boundary layer depth. To do this requires flight tracks of 60-100 km
in diameter, flown both clockwise and counterclockwise to eliminate the
mean offset in the measurement of the lateral wind component.

These results will complement ground-based measurements, both by
extension to large spatial scales and by providing in situ confirmation
to ground-based remote sensing observations (e.g., radar, lidar and
microwave radiometer).

3.2.4 Calibration and intercomparisons

Since several aircraft will be used during the field campaign, it is
essential that the instruments be calibrated and intercompared in a
consistent manner. Whenever possible, the aircraft should be flown in
formation or at least along the same path with a minimal time
separation. However, because it will be difficult to get all the
aircraft together at one time, and it would be difficult to intercompare
in-cloud measurement type probes, standardized techniques should be
developed for probe intercomparisons. This might include circulating
equipment that would fire glass beads through the sampling volume of
many PMS probes. Alternatively, wind tunnel intercomparisons could be
done for cloud droplet and liquid water content probes. Chemistry probes
might be calibrated with known standards. Radiation sensors, CN or CCN
counters could be compared in a laboratory setting. One of the major
challenges will be to intercompare the gust probe measurements from each
aircraft. This might only be possible with in-flight measurements. Data
analysis software must also be checked using the same data set.

It is recommended that one individual be assigned the responsibility of
organizing such calibrations and intercomparisons for each aircraft, and
a lead scientist should be designed for coordinating the efforts between
aircraft.

3.2.5 Logistics

The choice of locations for deploying the various aircraft are as
follows: (i) the NASA ER-2 and NCAR C130Q aircraft will be based in
Eielson Air Force Base (25 miles south of Fairbanks, Alaska), (ii) the
UW CV-580 will be based in Prudhoe Bay, Alaska, close to the sea ice,
start of the Alaska pipeline, and adjacent to a highway with access from
Fairbanks (via the Brooks Range), and (iii) the NRC CV-580 will be based
in Inuvik, Northwest Territories, Canada. This site is on the McKenzie
River inland from the coast of the Beaufort Sea (on the edge of the
tundra and boreal forest), and is the northernmost deployment site in
Canada. Hence, the ice breaker, which may be located in the vicinity of
77°N, 135°W, will entail long transits of all aircraft involved. Hence
the maximum coordination will occur, in almost all cases, near the SHEBA
ice breaker. To the maximum extent possible, the Alaska Synthetic
Aperture Radar (SAR) Facility at the University of Alaska-Fairbanks,
will provide assistance in viewing the sea ice and lead distribution
acquired, and processed, from Canada\'s Radarsat and the European ERS-2
SAR satellite sensors. In addition, the NOAA National Weather Service
station in Fairbanks is a valuable, and supportive, facility for mission
planning purposes.

3.2.6 Issues

Issues that require further study include basing in light of the ranges
of the various aircraft and the location of the SHEBA ice station; and
development of a list of strawman flight plans and objectives, with
attention to satellite coordination, inter-aircraft coordination,
calibration intercomparisons, and vertical sampling strategy.

In case the NCAR C-130Q is not available for use by FIRE in the Arctic,
current thinking suggests that during both IFOs we would simply conduct
the measurements without the NCAR C-130Q, but otherwise as indicated in
Fig. 2.

3.3 Satellite remote sensing

3.3.1 Evaluation of polar ISCCP/EOS cloud retrievals

Satellites can measure many but not all of the desired cloud properties.
ISCCP currently provides global estimates of cloud fraction, cloud
height (radiating temperature), and cloud optical depth. Experimental
studies have begun during FIRE Phase II to add estimates of cloud
particle phase (ice, water) and size, liquid water path (LWP), ice water
path, cloud physical thickness, and cloud overlap. These new estimates
have met with varying degrees of success: the most reliable being water
droplet size and LWP, while the least reliable are cloud overlap and
cloud IWP. This section will briefly describe some of the specific
remote sensing evaluation and development work which will be carried out
during Arctic FIRE polar cloud observations.

3.3.2 Cloud detection/cloud amount

Detection of clouds is typically accomplished using visible and thermal
infrared imaging channels on meteorological satellites such as
Geostationary Orbiting Environmental Satellite (GOES) and Advanced Very
High Resolution Radiometer (AVHRR) (e.g. Rossow 1989). Cloud detection
typically relies on the fact that clouds reflect more solar radiation
and emit less thermal radiation than clear-sky conditions. Given the
snow/ice backgrounds and large temperature inversions common in polar
regions, these typical approaches enjoy only partial success in polar
regions. More recent methods add either spatial structure (i.e. texture)
detection (Welch et al. 1992), or particle size/phase detection
(Yamanouchi et al. 1987). In general, snow and ice surfaces behave like
low level clouds with very large ice particles (especially old snow and
ice) and infinite optical thickness. In this case, the same remote
sensing techniques that remotely sense cloud water/ice phase and
particle size, can in principle be used for cloud detection.
Difficulties are expected to arise with either fresh snow (particle
sizes similar to some cirrus) and optically thin cloud, where reflection
signals are a mixture of surface and cloud properties. The optimal case
is for daytime detection of water cloud over old snow or ice background.
The most difficult case is expected to be night-time detection of
optically thin ice cloud over fresh snow, or low level optically thick
cloud with large ice particles.

A related issue is the determination of fractional cloud coverage. Here
the issue is two fold: how much optically thin cloud area is missed,
versus how much overestimate was caused by detection of partially
covered satellite fields of view as filled with cloud? FIRE II used
Landsat data to show that both of these effects are important over ocean
backgrounds for boundary layer clouds but are of minor importance for
cirrus, when analyzing satellite data with the 4-8 km field of view data
used by ISCCP (Wielicki and Parker 1992). For the 250 m or 500 m
Moderate resolution Imaging Spectrometer (MODIS) fields of view on
EOS-AM, detection of optically thin cloud is the dominant error source.
Arctic FIRE will extend these results to studies of clouds over snow and
ice backgrounds.

Arctic FIRE will examine the accuracy of cloud detection/amount using
several evaluation data sources:

• lidar on the ER-2 collocated with the MAS radiometer used to simulate
MODIS and AVHRR spectral channels. Limitation: only tests nadir view
angle.

• surface lidar and radar on the SHEBA and ARM surface sites.

• surface observers, when sufficient solar or lunar illumination is
present.

• Landsat satellite data (May not be available during the IOP but useful
work can be done now.)

3.3.3 Cloud top height/radiating temperature/thickness/overlap

The primary difficulty in polar regions for estimation of cloud height
is the difficulty in obtaining accurate temperature profiles at the time
of observation. If the cloud is optically thick, determination of the
cloud radiating temperature is straightforward. If the cloud is
optically thin, two methods have been used extensively:

• infrared sounder retrieval (e.g. Menzel et al. 1992);

• solar channel determination of visible optical depth which is
converted to an infrared emittance and used to correct non-black cloud
emission temperatures (Rossow et al. 1991; Minnis et al. 1993).

An additional problem for the second method (ISCCP) is that a highly
reflective surface precludes accurate estimation of the optical
thickness of thin cloud. Additional methods are being developed which
use changes in thermal emission with wavelength at 3.7/11/12 mm to
estimate the non-black cloud emission temperature, and may provide an
improvement over current methods. Combinations of infrared sounder data
and passive microwave are also being examined. All of these methods,
however, experience difficulty in conversion of radiating temperature to
cloud height, if given inaccurate temperature profiles, so more effort
to retrieve better profiles of temperature and humidity from weather
satellites is also needed (e.g., Francis 1994). Arctic FIRE can examine
the relative trade-offs of these methods in polar regions: especially
sounder versus imager approaches.

Recently, attempts have begun to examine the estimation of cloud
physical thickness as a function of optical depth (Minnis et al. 1992),
as well as methods to handle some cases of multi-layered cloud:
optically thin upper cloud over an optically thick lower cloud using
AVHRR/High Infrared Radiation Sounder (HIRS) (Baum et al. 1994), and
water cloud under thick cirrus over an ocean background by combining
imager and passive microwave (Liu et al. 1995). None of these methods
has been tried in polar regions.

Arctic FIRE will examine the accuracy of remotely sensed cloud
height/temperature using two primary evaluation data sources:

• lidar on the ER-2 collocated with the MODIS Airborne Simulator (MAS)
radiometer used to simulate MODIS and AVHRR spectral channels.
Limitation: only tests the nadir viewing angle, and cannot penetrate
upper clouds with optical depths greater than 3 to verify overlapped
lower cloud layers or cloud base altitude. This may not be a strong
limitation for polar clouds.

• surface lidar and radar at the SHEBA and ARM surface sites. These
allow penetration of all cloud layers, but require long time series to
gather a significant number of independent cases.

3.3.4 Water cloud optical depth, effective radius, liquid water path

ISCCP began the systematic global estimation of daytime cloud visible
wavelength optical depth from satellite data. Estimates of LWP have
traditionally been made from passive microwave (Greenwald et al. 1993).
Recently, significant progress has been made in developing new
approaches to remotely sense water cloud optical depth/particle size/and
LWP for daytime observations using 0.65, 3.7, and 11 mm on AVHRR (land
or ocean background; Han et al. 1994), and 0.74, 1.6, and 2.2 mm (ocean
background) which will be available on MODIS (King et al. 1992).

For polar regions, night-time estimation of cloud optical depth
(emissivity) using thermal infrared multi-spectral techniques (imager or
sounder), will be similar to estimation over land or ocean, with the
complication of loss of some of the signal caused by relatively small
temperature differences between surface and cloud for polar atmospheres
(smaller lapse rates and lower clouds). Daytime estimation of cloud
optical depth in the visible imager channels over snow and ice also has
a smaller sensitivity and larger uncertainty because of the large
surface reflectance. Thermal infrared methods will again be more useful
in this case.

Evaluation of satellite channel estimation of the effective radius show
systematic discrepancies of 30-50% depending on which aircraft probe
(Forward Scattering Spectrometer Probe (FSSP) versus Gerber) and
spectral channels are compared. Horizontal variations in effective
radius appear reasonable, but mean values do not agree. Arctic FIRE
should continue the examination of simultaneous performance of FSSP and
Gerber probe data, as well as the potential differences in effective
radius derived using 1.6,/2.2, 3.7, 8.5, 11/ 12 mm data. Documentation
of the systematic vertical variation of effective radius within the
cloud layer is also required. For optically thick cloud, daytime
observations can still derive cloud particle size, but daytime
observations of cloud with visible optical depth less than about ten
will sense a mixture of cloud particle size and surface snow/ice
particle size. Theoretical studies are needed to examine this dependence
as a function of cloud optical depth.

The (FSSP and Gerber probes are fast response instruments. They are best
utilized through the execution of many rapid ascents and descents
through the clouds. The major differences in cloud microphysics occur in
the vertical, but there is considerable variability in the way that
droplet sizes and the variance of droplet size increases with height
above cloud base. This depends on entrainment and precipitation
(including coalescence growth that may or may not lead to precipitation)
and on the distance from cloud base and cloud top, which also varies.
Horizontal legs are thus not very useful for measurement of these
important vertical variations and of variations in cloud base and top,
which to a great extent determine the amount of condensed water and thus
droplet size. The vertical variations of the microphysics are of utmost
importance for remote determinations of cloud properties. Thus for
understanding cloud physics and for comparisons with satellite and
ground based remote sensing, long horizontal legs are not the best
strategy. For these purposes it is best to have as many vertical
soundings as possible. This can best be accomplished by deep porpoising
(cloud base to cloud top) maneuvers. Many soundings are needed in order
to better characterize the vertical structure of the cloud microphysics.

Studies of passive microwave LWP have demonstrated that it is primarily
useful for optically thicker (optical depth greater than five) water
clouds over ocean (Lin and Rossow 1996). Land and snow/ice backgrounds
have not been examined because of the expected decrease in sensitivity
to cloud water over these high emissivity surfaces. A general
characteristic is that microwave frequencies (active or passive) are
optimal for clouds with moderate to large visible optical depth, while
optical imagers are optimal for low to moderate visible optical depths.
A disadvantage of satellite passive microwave is the inability to
resolve cloud LWP at scales less than about 50 km with current Special
Sensor Microwave/Imager (SSM/I) data.

Arctic FIRE will utilize two primary approaches for evaluation of water
cloud optical thickness, effective radius, and LWP.

• Use ER-2 and satellite overflights of SHEBA and ARM surface sites. The
surface sites will use combined lidar/radar/radiometers to vertically
profile cloud phase/particle density/particle size/LWC. Uplooking
passive microwave will measure LWP. The accuracy of the surface remotely
sensed cloud data is verified against in-situ aircraft flights within
clouds over the surface sites.

• Fly stacked ER-2 (MAS, lidar) and cloud in-situ aircraft missions for
verification of cloud particle size. Intercompare FSSP and Gerber probe
data for effective radius.

Finally, consideration must be given to theoretical studies of the
significance of non-plane parallel effects: both horizontal
inhomogeneity and spherical shell solutions to radiative transfer for
solar zenith angles greater than about 80 degrees. While recent results
for boundary layer oceanic cloud and midlatitude cirrus show reasonably
small biases using the independent pixel approximation (IPA; Cahalan et
al. 1994), this must be examined for the polar clouds. The best data for
this test will be the surface site remotely sensed vertical profiles of
cloud microphysics as input to 2D radiative calculations.

3.3.5 Ice cloud optical depth, effective diameter, ice water path

Progress in the determination of ice cloud visible optical depth,
effective particle diameter, and IWP is less well developed than for
water clouds (Wielicki et al. 1990; Minnis et al. 1993; Lin and Rossow
1996). The difficulties are caused by the large and complex variability
in particle shape, resulting in difficulties specifying:

• accurate single scatter phase functions

• accurate single scatter albedo

• accurate conversions of particle size and number to ice water content.

As a result, Arctic FIRE has several efforts devoted to improving the
characterization of ice particle microphysics:

• development and analysis of ice replicators for balloon and aircraft
which extend the small size range of detected ice crystals from the
20-50 mm lower limit of 2D-C probes, to 5-10 mm.

• development and analysis of ice particle imaging systems (cloud
scope).

• aircraft probe to measure the scattering phase function asymmetry
parameter ("g-meter").

• aircraft nephelometer to measure the full scattering phase function of
ice crystals.

The above microphysical and single-scattering studies are expected to
substantially improve our understanding of the interaction of radiation
with ice crystals. In addition, theoretical studies are being used to
apply ray tracing (Takano and Liou, 1989; Macke, 1993), T-Matrix
(Mishchenko and Travis 1994), discrete dipole, and other techniques to
predict single scattering properties of a wide range of ice crystal
shapes and sizes (hexagonal, bullet, rosette, plates, capped-plates,
fractal shapes). These theoretical results will be compared to the
g-meter and nephelometer measurements, combined with imaging probes to
provide crystal shape estimates.

These studies of single-scatter properties are fundamental, but do not
directly prove the ability to remotely sense a given cloud property.
This final step is accomplished using data similar to that for the
evaluation of retrieved water droplet cloud properties:

• Use ER-2 and satellite overflights of SHEBA and ARM surface sites. The
surface sites will use combined lidar/radar/radiometers to vertically
profile cloud phase/particle density/particle size/IWC. The accuracy of
the surface remotely sensed cloud data is verified against in-situ
aircraft flights within clouds over the surface sites.

• Fly stacked ER-2 (MAS, lidar) and cloud in-situ aircraft missions for
verification of cloud particle size. Intercompare 2D-C, cloud scope, and
replicator results for cloud particle size/shape distributions.

3.3.6 Surface and in-atmosphere radiative fluxes

There have been several detailed attempts to infer radiative fluxes in
the polar regions at the top of the atmosphere and at the surface, as
well as the cloud effects on them, but the uncertainties are large (see
review in Curry et al. 1996). Rossow and Zhang (1995) combined several
satellite datasets, including ISCCP cloud properties, to determine the
radiation budget. The EOS CERES (Clouds and the Earth\'s Radiative
Energy System; Wielicki et al. 1996) investigation will produce
estimates of shortwave (SW) and longwave (LW) broadband radiative
fluxes:

• at the top of the atmosphere,

• at the surface, and

• at several levels within the atmosphere

using the CERES broadband and MODIS narrowband radiation/cloud
measurements on EOS-AM and EOS-PM. In addition, the GEWEX SRB (Surface
Radiation Budget) data set will produce estimates of surface fluxes
using ISCCP cloud data. FIRE investigators will refine these analyses in
polar regions using SHEBA and ARM surface flux observations. The ability
to evaluate retrievals of in-atmosphere fluxes will be limited, but will
utilize stepped aircraft profile data collected over the SHEBA and ARM
sites as near as possible to the satellite overpass time. If the SHEBA
surface site is deployed in August 97 - September 98 as planned, there
may only be 2 months of overlap with CERES EOS-AM estimates and SHEBA
surface and aircraft measurements. Later, ARM surface site data will be
used to extend this study: both at the surface and using aircraft
overflights. CERES and SRB are expected to perform this analyses as part
of their EOS and Pathfinder investigations.

3.3.7 Time/space extension of cloud properties for model testing

Surface sites typically provide one-dimensional (1D) instantaneous
profiles at a point, or two-dimensional (2D) vertical slices of the
cloud/atmosphere properties as clouds advect over the site (frozen
turbulence approximation). For some models, this is not sufficient
information. In particular, it is often necessary to extend the
observations to larger time and space scales, as well as to two
horizontal dimensions to verify:

• what is the areal extent of the cloud?

• is the cloud fundamentally 2D (streets) or 3D (cumulus cells)?

• how rapidly is the cloud evolving in time? (A frozen turbulence
approximation cannot distinguish spatial change from time change).

For this reason, FIRE will analyze an extended region and set of times
for cloud properties in the region of the two surface sites (SHEBA,
ARM). This region will be roughly 2000 km square and will rely on the
NOAA polar orbiters as the data source of the cloud analyses. The GOES
satellite views are too oblique to be useful at these latitudes (viewing
zenith angle \> 70 degrees). Cloud analysis will be provided both by
ISCCP (D1 gridded data and DX pixel data) and by individual FIRE
investigators.

3.3.8 Sampling considerations

There are two outstanding sampling issues in testing both cloud models
and cloud remote sensing data:

• How many independent samples are required?

• What constitutes an independent sample?

The answer depends greatly on the particular scientific question being
asked. It is not possible to determine accuracy estimates for every
possible cloud question which might be asked. As discussed in Appendix
C, the types of clouds, seasons, climate regions, time and space scales
define too large a range of options; there are about 4000 different
cloud/observing combinations.

The optical depth, cloud overlap, solar zenith, viewing zenith, and
viewing azimuth affect the nature of remotely sensing cloud properties
from surface, aircraft, or space-based passive radiance measurements.
For purely thermal infrared techniques, the viewing azimuth and solar
zenith restrictions can be removed. We argue in Appendix C that for
Arctic FIRE/SHEBA, there are about 72 cases for evaluation of passive
cloud remote sensing methods, while for evaluation of active remote
sensing methods and cloud modeling there are only 4 cases each. In other
words, testing of passive remote sensing algorithms is the key issue.

The analysis given in Appendix C leads to the following conclusions:

• We cannot get sufficient aircraft flight hours to evaluate satellite
polar cloud retrievals within any reasonable expenditure of manpower and
cost.

• We can obtain sufficient surface data taken continuously under
satellite overpasses within a 1-5 year time record of ARM data. The
longer the time record, the more accurate the evaluation of the
satellite ensemble mean for testing climate models.

This suggests a bootstrap approach to evaluation of satellite remote
sensing algorithms:

i\. Use surface based lidar/radar/radiometers to determine required
cloud properties at the SHEBA ice breaking ship from August 97 -
September 98.

ii\. Evaluate the retrieved surface based cloud properties against
aircraft in-situ data. This will require flying aircraft vertical
stepped profiles of microphysics over the SHEBA surface site.

iii\. Evaluate the retrieved satellite derived cloud properties against
the surface based cloud property profiles

iv\. Use year of SHEBA surface cloud data to evaluate ISCCP-retrieved
cloud properties using the two NOAA polar orbiter sets of SHEBA
overpasses. With two satellites (one AM, one PM) we expect roughly a
doubling of the number of independent samples per day over the EOS-AM
analysis noted earlier (40 independent samples per case in one year).
Only the last two months of SHEBA will likely overlap with EOS-AM.

v\. In August/September 1998, a 3-week mission involving the ER-2 and
in-situ aircraft will underfly EOS-AM cloud observations to obtain
initial samples over the ice sheet. If this mission focuses on
evaluation of stratus/stratocumulus retrievals only over ocean and ice
backgrounds (single and multi-layer) then it can obtain 10 flights times
4 independent samples per flight / 2 overlap conditions = 20 independent
samples of single and multi-layer Arctic stratus. Other cloud types will
not be sampled in this approach, but in the summer, St/Sc is observed
63% of the time. If vertical profiles of microphysics are tested, (4
levels) then only 5 independent samples for St/Sc cases will be
obtained.

vi\. After SHEBA, the ARM DOE Point Barrow site will come on line with
similar lidar/radar/radiometer profiling of Arctic cloud properties.
EOS-AM and EOS-PM retrievals will be evaluated using this ARM data over
a one to five year period, with increasing accuracies as a longer
evaluation period is obtained. The one weakness of this approach is if
the summer Arctic clouds over the ice sheet are so different from the
Point. Barrow clouds, that they are not representative of clouds over
Arctic ice. It is expected that winter clouds will be the same for
either Barrow or the ice sheet. The dominant boundary layer cloud
affected by the ice sheet is sampled in August/September by the ER-2.

vii\. After SHEBA is over, and the ARM Point Barrow site is operational,
fall and winter aircraft missions are required to evaluate the retrieved
vertical profiles of cloud microphysics derived from the surface site.

The sampling strategy outlined above is efficient, and maximizes the use
of aircraft and surface data to evaluate satellite retrievals.

There are some issues that will have to be monitored as experience is
gained, however. In particular, the sampling strategy assumes that the
uncertainties in converting active lidar/radar/radiometer data to
microphysical vertical profiles are fundamentally more tractable than
those incurred from passive satellite remote sensing. In this event, it
is further assumed that the evaluation of the active cloud properties
requires many fewer samples to understand and evaluate the underlying
radiative transfer algorithms used to invert the active remote sensing
data, and thereby allow its extension to other cases that were not
directly sampled. For example: if we evaluate the water droplet
microphysics profile derived from active lidar/radar surface
observations: it does not matter whether the water cloud was single
layer versus overlapped, or even whether the water cloud was Cb, Cu,
St/Sc, or As/Ac. The active remote sensor observation probes a specific
point in the cloud, and its performance should only be a function of the
particle size/shape (i.e. single scattering properties) and the
attenuation of signal to and from the point in the cloud. This suggests
that evaluation of the surface active sensing microphysics profiles will
require more aircraft evaluation for ice cloud (poorly known single
scattering properties) than for water cloud. In fact, the evaluation
strategy should be based on "cloud type" only in the sense that certain
cloud types tend to have certain particle size/phase/shape. In fact, the
largest changes in microphysics often occur in the vertical profile of
particle size/shape within the same cloud at one time. Therefore, a
single aircraft stepped profile can sample many "independent" cloud
microphysics cases. This suggests the following aircraft evaluation
strategy:

  ------------ ------------ ---------------- ------------ ----------------
  Table 4 -                                               
  Aircraft                                                
  evaluation                                              
  strategy.                                               

  Observing    Cloud        Observations per Conditions   Observations per
  strategy     Particle     flight           needed       flight
               Cases Needed                               

  Surface      4 water      2                one thick,   2
  lidar,                                     one thin     
  radar/Ac                                                

               8 ice        4                one thick,   2
                                             one thin     
  ------------ ------------ ---------------- ------------ ----------------

The four water cloud cases are effective droplet radii of approximately
4, 8, 16, and 32 mm. On any given flight, the aircraft will typically
sample 2 of these sizes in the vertical profile. The eight ice cloud
cases are different ice particle size/habit from 5 mm to more than 200
mm. A typical cirrus cloud case is expected to have a vertical profile
with perhaps half of these particle cases. The observation conditions
(thick/thin) refer to regions of the cloud with little attenuation (near
cloud base) and high attenuation (large optical depth into the cloud).
Mixed phase microphysics (water and ice in the same volume) should be a
superposition of the water and ice solutions, and will be sampled
adequately as a subset of the water and ice cloud cases above. The ideal
cases will be overlapped cloud so that retrievals for both water and ice
cloud could be evaluated in the same aircraft flight. An hour of
aircraft time should be sufficient to measure eight stepped levels (20
km, 4 minute crosswind legs centered over the surface site). However,
the SHEBA ship surface site will probably be a 2 hour transit flight for
the aircraft, so 1 hour of on-site time is essentially a 5 hour flight
mission. Note that the legs are stepped to allow collection of accurate
broadband radiative fluxes from the aircraft, for testing of the
vertical profile of radiative heating. Flight levels will be radioed to
the aircraft based on real-time observation of cloud levels by the
surface active remote sensors. A target number of evaluation samples
from SHEBA/Arctic FIRE is 3 per microphysics class:

3 samples per case = 3 \* (4/2 + 8/4) \* (2/2) = 12 flights = 60 hrs.

This is a reasonable number of flight hours for the spring and summer
1998 in-situ aircraft deployments (4 aircraft, roughly 240 hours total:
i.e. 25% for vertical profiles over the SHEBA site). This data set will
have to be combined with additional aircraft microphysical flights over
the ARM site on Point Barrow and in Oklahoma to extend the verification
samples of the surface remotely sensed microphysics as a function of
particle phase/size/habit. Classification of the results will be done by
effective radius and shape. The uncertainty in the surface based
microphysical profiles will be reduced with time as additional aircraft
evaluation missions are flown.

For ice clouds, the use of ice replicator sondes should be explored.
Each sonde launch could replace an aircraft flight. A difficulty with
the sonde launches, however, is the horizontal advection of the sonde
causing spatial matching noise when comparing to the surface site. SHEBA
may have a tethered balloon with the ability to profile microphysics for
low clouds. This data source can in principle replace many of the
aircraft flights if successful. The aircraft could then focus on the
upper level ice clouds.

3.4 Numerical weather prediction models

Documentation of the large-scale atmospheric structure is critically
important for modeling studies as well as for interpretation of the
large-scale context and effects of the smaller-scale process with which
a FIRE field program is most directly concerned. Numerical Center for
Environmental Prediction (NCEP) (and possibly Canadian Meteorological
Centre (CMC) and ECMWF) operational medium range forecasts will be used
to provide gridded regional analyses with high vertical resolution
(25-50 hPa) and horizontal resolution (50-100 km) over the Arctic. These
analysis will include standard meteorological parameters (geopotential,
temperature, mixing ratio, winds) and also special parameters such as
cloud amount and properties, radiation fields, surface fluxes, vertical
velocity, and contributions of individual physical parameterizations to
the tendencies of model prognostic variables. This will allow a detailed
intercomparison of in situ measurements with the model's physical
parameterizations, which indirectly affect its analyses. To improve the
quality of the analyses in the FIRE/SHEBA operations region, soundings
taken during SHEBA and other collaborative experiments will be encoded
in World Meteorological Organization (WMO) format and broadcast on the
GTS (Global Telecommunications System) so that they can be assimilated
by the forecasting centers in real time. We will rely on SHEBA personnel
to work with NCEP, CMC and ECMWF to ensure that these experimental
soundings are recognized and accepted by the quality control systems of
their numerical models. Whenever possible, aircraft soundings and
dropwindsondes will be taken during FIRE to improve observations of the
advective contributions to moisture and heat and at the SHEBA ice
station. C. Bretherton will work with the meteorological centers to
incorporate these soundings as part of reanalyses.

While no routine radiosonde observations are taken in the Arctic Ocean,
satellite-derived atmospheric temperature retrievals and surface
pressure and temperature data from drifting sea ice surface buoys do
provide data. Because of possible model deficiencies in high latitudes,
these analyses must nevertheless be used with caution, particularly for
the moisture field.

4.0 Analysis of Data After the Field Phase

4.1 Data integration and diagnostic studies

The scientific payoff from FIRE does not come merely from collecting a
set of measurements, even if the set is comprehensive and some of it has
been analyzed to retrieve key quantities. Rather the real payoff comes
from using the retrieved results to answer questions about what is going
on in the Arctic. The key overarching question for FIRE is:

What is the cloud-radiative feedback on the Arctic energy balance?

The subsidiary questions are:

What are the properties of Arctic clouds, including their synoptic and
annual variations?

How do Arctic clouds alter radiative exchanges between the surface,
atmosphere and space?

Why are these particular clouds produced in the Arctic?

To answer these questions from observations requires, first, the
reduction of the measurements of the most important quantities that are
associated with the controlling cloud and radiative processes, with
particular emphasis on characterizing the variations of these quantities
over the relevant space and time scales that are indicative of the
processes at work. Most of the proposed investigations concentrate on
this aspect of the observations. However, each of the measurement
systems has limitations of coverage and resolution and many quantities
must be inferred from remote sensing measurements from all the
platforms. Thus, the FIRE experiment is not complete until combined
analyses of observations from many platforms are integrated to address
the main scientific questions.

Although there are a few proposals to conduct such "integrative"
studies, the scope of the needed studies really requires a greater team
effort to be effective. Therefore, to insure that adequate attention is
paid to coordinating research efforts to accomplish these combined
diagnostic studies, three Integration Task Teams will be formed from the
FIRE team membership. These teams will be responsible for informing the
larger team of observation and data reduction needs and for encouraging
and coordinating research that leads to integrated datasets and analysis
results.

4.1.1 Satellite remote sensing evaluation

Given the combined expertise of the aircraft and satellite investigators
on the FIRE team, the FIRE team has a lead role within SHEBA for
developing needed satellite remote sensing tools to complete the SHEBA
analysis, particularly for observations of clouds, the atmosphere and
surface (SHEBA expertise will lead remote sensing of sea ice
properties). As described in the Satellite Observation section, we
already have routine satellite measurements of temperature and humidity
profiles (by NOAA operations - TIROS Operational Vertical Sounder (TOVS)
products, Smith et al. 1979, McMillin 1991) and cloud properties (cover,
top temperature and height, optical thickness by ISCCP, Rossow and
Schiffer 1991) in polar regions, but they have not been sufficiently
evaluated. Moreover, we have well-developed methods for retrieval of
liquid water cloud particle sizes (Han et al. 1994, Minnis et al. 1992)
and liquid water path over open water (Greenwald et al. 1993, Liu and
Curry 1993, Lin and Rossow 1994), but these have not yet been tried in
polar regions. There are also some ideas for retrieving the some
properties of ice phase clouds (Wielicki et al. 1990, Minnis et al.
1993, Young et al. 1994, Lin and Rossow 1994) that have not been tested
thoroughly. We can already anticipate significant limitations of some of
these analysis methods for polar studies because of instrument design
limits or because the surface-cloud-atmosphere variations are too
complex. A key analysis concept that may mitigate some of these problems
is the analysis of coincident observations from many different
instruments. In particular combinations of multi-channel optical and
microwave imagers and infrared and microwave profilers has been
suggested by some work (Baum et al. 1994, Liu et al. 1995) but is not
fully developed. The primary obstacle to progress is lack of
observations for evaluation.

The heterogeneous characteristics of available and future
instrumentation preclude a straightforward comparison of different
measurements. Rather a statistical comparison is required to account for
different sampling of smaller space and time scales. The strategy is to
use the in situ and higher resolution aircraft measurements to check the
larger datasets that can be acquired from surface remote sensing
instruments. These larger datasets, complemented by in situ measurements
of surface properties from SHEBA and ARM, can then be used to evaluate
the satellite retrievals. Thus, a subset of the FIRE team must conduct
investigations that integrate a comprehensive collection of coincident
observations from aircraft, the surface and satellites, where similar or
"identical" quantities are retrieved, to determine how well cloud,
atmosphere and surface properties can be determined from satellites
alone. This study includes analyses of multiple satellite observations.

Some examples of this type of study are Wielicki et al. (1990), Nakajima
et al. (1991), Minnis et al. (1993), Rossow et al. (1993) and Han et al.
(1995), where statistical comparisons are made of satellite and surface
and/or aircraft observations. The only proposed studies of this type are
by Minnis et al., Rossow et al, and Han and Welch, though this approach
is implicit in the proposal of Tsay and King. The first part of these
studies will be to compare the cloud properties already being retrieved
and to extend the development of methods for retrieval of ice phase
cloud properties. Specifically, the ISCCP analysis has already been
revised to incorporate a treatment of ice phase clouds based on earlier
FIRE results, but this needs evaluation for polar ice clouds. The
particular complexity of the Arctic atmosphere is that the atmosphere
and surface also undergo very large annual variations, so that attention
must also be paid to improving satellite measurements of surface and
atmospheric properties as well clouds. The study by Rossow et al
includes some study of improved surface retrievals. These three studies
will combine observations from a large number of satellite instruments
with available surface lidar/radar and aircraft microphysics
information.

Note that none of the aircraft or surface investigations proposed to
conduct this type of analysis (however, the approach is implicit in the
proposal of Tsay and King at that from ETL). The critical weakness that
this situation indicates, based on past experience with FIRE I and II,
is that the amount of aircraft data and surface data that is actually
reduced will be insufficient for this type of study. Every effort must
be made to complete the analysis of much larger amounts of aircraft and
surface observations and to coordinate/compare analysis results or the
remote sensing evaluation objective of FIRE will not be met. The first
Integration Task Team (proposed co-leaders: B. Wielicki and M. King)
will focus on this combined task.

Specific study tasks include:

\(1\) evaluate the treatment of radiative transfer in ice clouds and
quantify uncertainties in retrieval of ice cloud properties from
satellites,

\(2\) quantify the effects of small-scale horizontal variability and
vertical structure in clouds on remote sensing analyses of their
properties,

\(3\) check the accuracy of temperature retrievals in inversion
conditions,

\(4\) check the accuracy of humidity profiles obtained from infrared and
microwave satellite instruments, and

\(5\) evaluate current surface temperature and albedo retrieval methods.

4.1.2 Cloud effects on radiative fluxes

Measurements of the properties of clouds, surface and atmosphere allow
one to infer radiative fluxes, in principle. Such analyses have been
done for the Arctic based on ISCCP (Darnell et al. 1992, Schweiger and
Key 1994, Rossow and Zhang 1995), but these calculations are much more
detailed than available validating datasets with the exception of
top-of-atmosphere fluxes that can be compared with NIMBUS-7 Earth
Radiation Budget (ERB) and Earth Radiation Budget Experiment (ERBE)
values. The extensive nature of the satellite datasets even allows for
such an analysis over the whole Arctic basin and over the annual cycle
(Curry et al. 1996), but we cannot verify some important conclusions
that are implied in these results. Two crucial examples are that, given
indications that Arctic cloud cover is very nearly complete year-round
(Warren et al. 1988, Curry and Ebert 1992, Rossow and Garder 1993), the
ice- albedo feedback may be significantly weakened by the presence of
clouds and ability of the Arctic ocean to cool in winter may be
significantly inhibited. Does this mean that clouds are a strong
negative feedback on Arctic climate? We do not know.

Although there continue to be some important uncertainties in how clouds
are to be represented in radiative transfer calculations (Stackhouse
1995), current uncertainties in these radiative flux analyses are
dominated by uncertainties in the measured physical variables. The most
important quantities include cloud water content and particle size and
surface albedo \-- most particularly their annual variation. The only
proposed diagnostic study (Rossow et al) will implement the improved
cloud and surface retrievals discussed above and compare the calculated
fluxes with any available surface and in situ measurements of spectral
and broadband fluxes. Although one objective of this investigation is a
general improvement of this type of analysis for global studies, the
opportunity exists for producing a special diagnostic radiative flux
product by using the combination of surface, aircraft and satellite
observations within the FIRE/SHEBA experiment area to provide the needed
input quantities. This task requires systematic analysis of the
observations from as many instruments over the whole time period as
possible, rather than limited case-study analyses. The key uncertainties
in the representation of clouds in radiative transfer models are three:
treatment of ice phase clouds, treatment of small-scale horizontal
inhomogeneity, and treatment of cloud vertical structure, particular
phase changes. Two investigations (Mishchenko et al, Liou et al)
specifically focus on the first question and are coordinated with
satellite analyses (Rossow et al, Minnis et al, respectively). These
studies will be complemented by some laboratory measurements planned by
Lawson. The study by Han and Welch also addresses some similar issues.
However, only the Mishchenko et al. proposal articulates a specific
observation and analysis strategy that uses combined observations from
surface, aircraft and satellite platforms. Many other proposals concern
making measurements related to the microphysical properties of ice
clouds, but do not mention any combined-instrument analyses. Only one
investigation (Wielicki) addresses the horizontal inhomogeneity question
directly, but this study relies solely on the very high resolution
Landsat data and does not indicate a combined analysis of long-term
lidar/radar datasets. No investigations plan combined diagnostic studies
of cloud vertical structure, though there will relevant measurements
from aircraft and the lidar/radar instruments. The radiative flux
calculations planned by Rossow et al include calculated flux profiles,
so that diagnosis of radiative heating/cooling rate profiles for the
FIRE/SHEBA experiment region may be possible.

The second Integration Task Team (proposed co-leaders: W. Rossow and S.
Pilewskie) will focus on this combined task.

Specific tasks:

\(1\) compare calculated surface radiative fluxes and flux profiles to
surface and aircraft observations, respectively, as function of cloud
and atmospheric properties,

\(2\) quantify uncertainties and their causes in the treatment of ice
phase clouds,

\(3\) evaluate effects on radiative fluxes of large cloud particle size
changes or phase changes with altitude,

\(4\) evaluate treatment of SW fluxes at extremely low solar zenith
angles,

\(5\) confirm spectral dependence of fluxes under various meteorological
conditions, and

\(6\) incorporate combined datasets for more comprehensive
heating/cooling rate profile diagnosis.

4.1.3 Aerosols and chemistry

When simultaneous CCN spectra and cloud droplet concentrations are
matched the effective cloud supersaturations can be determined. These
can be compared with those obtained in other stratus cloud projects
(e.g. Hudson and Svensson 1995). Appropriate cloud droplet
concentrations and CCN from FIRE on the Ice should be compared with FIRE
and ASTEX to determine the relative role of CCN in affecting cloud
microphysics. This analysis should be extended to drizzle drop
concentrations to determine if the aerosol effect extends to
precipitation as well as cloud albedo. If variations are found in
droplet spectral width then these will be compared with CCN spectral
shape to determine if this affects the susceptibility to precipitation.
IFN measurements should also be compared with ice particle
concentrations. Comparisons of CCN spectra with chemical measurements of
the size segregated aerosol can better determine aerosol sources
especially when coupled with trajectory analyses. The size of the CCN
can also be compared with particle chemistry to determine if soluble and
insoluble material can be identified. The chemistry of the particles
that are found in CVI filter measurements can be compared with the CCN
spectra that are found in the CVI. These can be compared with ambient
measurements of CCN volatility and particle sizes to better characterize
those particles that really act as CCN. The effect of the clouds on the
aerosol can also be ascertained by comparing CCN spectra at various
altitudes to determine whether physical (i.e. coalescence scavenging) or
chemical (i.e. conversion of SO2 to sulfate) processes within the clouds
have altered the aerosol.

4.2 Modeling overview

Direct measurement of cloud formation/decay processes will require
resolving the relevant space-time scales of atmospheric motions and
variations in water, including phase changes and transports from lower
latitudes (Curry 1983) and to and from the surface, particularly over
leads (Pinto and Curry 1995). Some analogous studies have been or are
being conducted for tropical cases (e.g. Xu and Randall 1996).

Moisture transport involves processes occurring in the shallow Arctic
atmospheric boundary layer (McInnes and Curry 1995, Pinto and Curry
1995, Walsh et al. 1993). Such studies should also be complemented by an
indirect approach that examines larger scale variations of clouds and
atmospheric dynamics. There are several modeling proposals that are
focused on aspects of this topic, primarily at the smaller scales of the
direct approach, but their data analysis plans are not described
(Bretherton, Kogan, Krueger, Lenschow and Wang, Randall, Wang). Only
three modeling proposals include explicit plans to attempt such a
diagnosis from observations (a possible exception is a "cirrus" proposal
by Ackerman et al.): Curry and Cotton for Large Eddy Simulation (LES)
models, Del Genio for a GCM, and Isaac for a GCM. The latter includes
special emphasis on the role of chemistry and aerosol processes in cloud
formation.

The third Integration Task Team (proposed co-leaders: C. Bretherton and
J. Curry) will provide a small planning group to communicate the
evolving data analysis needs of the model investigations to the FIRE
team and to coordinate the proposed model experiments. Specific tasks
include the following:

\(1\) compile (with the help of the whole FIRE team) comprehensive case
study datasets, including atmospheric large scale circulation, boundary
layer turbulence, cloud and surface properties;

\(2\) compile simulations of these cases from column models, LES models
and GCMs;

\(3\) diagnose moisture fluxes and radiative fluxes and their seasonal
variations.

As mentioned earlier, cloud microphysical properties have been studied
with combinations of ground-based remote sensors in FIRE II and ASTEX.
Processing and integration of these measurements are required to relate
them to cloud-scale model simulations. The high frequency of
observations possible with ground-based remote sensors will also allow
an evaluation of the representativeness of the relatively infrequent
rawinsonde observations of atmospheric structure.

The fourth integration Task Team (proposed leaders: S. Krueger and T.
Uttal) will seek to ensure that measurements from ground-based remote
sensors are processed in ways that will allow them to be integrated with
measurements from other platforms (surface, rawinsonde, and satellite),
and to be used to evaluate cloud-scale model simulations.

Arctic FIRE will employ a variety of models, ranging from cloud-scale
models to global climate models. Models with dynamical components will
require relevant large-scale boundary conditions such as advective
tendencies and vertical motion, which will be derived from gridded
analyses from NCEP, and possibly CMC and ECMWF.

4.2.1 Approaches

We will couple three basic modeling approaches during Arctic FIRE: 3D
cloud-scale models, 2D cloud-scale models, and 1D models. These
approaches will be used to help develop and evaluate parameterizations
of cloud-scale processes, boundary layer structure, and cloud-aerosol
interaction in global climate models.

4.2.2 Cloud-scale models

Cloud-scale models simulate the larger scales of turbulence. They
include 3D (also called large-eddy simulation or LES) models and 2D
(also called cloud ensemble or eddy-resolving models). The premise of
such models is that given observed boundary conditions and large-scale
forcing, the model is able to simulate the dynamical response, which in
many cases of interest includes convection and clouds. Simulations can
be modified to study the role of particular physical processes.

A cloud-scale model (or eddy-resolving model) has a grid size on the
order of 50 m or less in the vertical and horizontal directions, and can
explicitly represent all of the important flux-producing eddies that are
at work inside clouds. Cloud-scale models also include microphysics and
interactive solar and infrared radiation parameterizations. 3D
cloud-scale (or large-eddy) simulations are very expensive
computationally, typically do not cover more than a few simulated hours,
and are restricted to domains that are much smaller than GCM grid boxes.

2D cloud-scale models are particularly useful if detailed microphysical
or aerosol schemes are included, or if broader horizontal domains
including surface inhomogeneities such as leads are simulated. 2D
cloud-scale models can also be used for multi-day simulations. One of
the 2D models to be employed during Arctic FIRE, the University of Utah
model, includes a sophisticated turbulence closure model for small-scale
turbulence.

2D cloud-scale models have proven to be very useful for studying the
stratus to cumulus transition in subtropical marine boundary layer
(Krueger et al. 1995 a, b; Wyant et al. 1996). This cloudiness
transition is primarily due to the response of the stratocumulus-topped
boundary layer to increasing SST. A case study of such a transition was
observed in detail during the first Lagrangian experiment of ASTEX.
Simulations of this case performed with 2D cloud scale models for the
second GEWEX Cloud System Studies (GCSS) Boundary Layer Clouds Modeling
Workshop show generally good agreement with the observations.

4.2.3 1D models

1D models to be used during Arctic FIRE include turbulence closure
models and single column models. A single-column model (SCM) includes
the entire set of physical parameterizations of a GCM active in a single
grid column. A fundamental use of a SCM is to develop and evaluate
parameterizations for GCMs. As part of Arctic FIRE, we will also conduct
GCM simulations using improved versions of these parameterizations.

4.3 Arctic low-level cloud regimes: What are we trying to model?

The principal thrusts of our modelling efforts will include:

\(1\) The vertical and temporal distribution of cloud, microphysical
parameters and radiation in the area of the SHEBA site over the annual
cycle.

\(2\) Multilayer summertime Arctic stratus

\(3\) Springtime mixed-phase clouds and the effects of leads.

\(4\) Aerosol/Arctic cloud interactions, especially during spring and
summer.

We will now discuss each of these in more detail.

4.3.1 Vertical and temporal distribution of cloud properties at the
SHEBA site

In conjunction with SHEBA, D. Randall will develop a single-column model
of the coupled Arctic atmosphere-ice-ocean system based on the CSU GCM
and compare it to measurements taken at the SHEBA site over the entire
annual cycle. The model will be forced by the large scale advective
tendencies analyzed by operational models. FIRE aircraft measurements
will help evaluate the assumptions behind the cloud parameterizations.
Intercomparison with Arctic FIRE cloud scale models will also be used to
improve the parameterization framework. Important specific issues that
will be addressed with this SCM include:

• Studies of the sensitivity of the coupled system to external
perturbations, e.g. CCN perturbations, moisture and temperature
advection perturbations.

• Simulation of Arctic summer stratus, including formation, dissipation,
convective transports, and optical properties, and also including a
faithful representation of the dependence of cloud amount, cloud depth,
and cloud optical properties on the properties of the lower boundary,
including the fraction of open water, melt-pond coverage, and snow
cover.

• Simulation of the formation and dissipation of Arctic winter "diamond
dust" clouds, including their effects on the downward longwave radiation
at the surface.

• Simulation of the seasonal transitions, including the coupled
evolution of cloudiness and the ocean / ice surface.

• Investigation of the extent to which the cloud amount and optical
properties of the Arctic summer stratus depend on the state of the lower
boundary, e.g. ice cover and thickness, snow-cover and thickness,
melt-pond cover and thickness.

• Study of the ways in which Arctic clouds affect the surface energy
budget, both instantaneously in all seasons, and integrated over an
annual cycle.

In addition, C. Bretherton will work with at least one of NCEP, CMC or
ECMWF to compare the SHEBA data with diagnosed surface and radiative
fluxes, cloud properties, and turbulent transports from an operational
forecast model. The goal is to test and improve forecast model
parameterizations in the Arctic, but this intercomparison should also
help us assess the quality of the operational analyses which will be
used to drive the SCMs.

4.3.2 Multilayer summertime Arctic stratus

2D and 3D cloud scale models and 1D turbulence closure models will be
used to better understand the multilayer structure of Arctic summertime
stratus cloud and distinguish between the various hypotheses described
in the FIRE Phase III Science Plan. Two groups (W. Cotton and J. Curry,
and Y. Kogan and D. Lilly) will use 3D large-eddy simulation (LES)
models with detailed microphysics, while two other investigators (C.
Bretherton and S. Krueger) will use 2D and 3D cloud-scale models with
bulk microphysics. S. Krueger will also use a 1D version of his model.

4.3.3 Springtime mixed-phase clouds and the effects of leads.

To understand the cloud and boundary layer processes during the
springtime, models must include representations of mixed phase
microphysics. Y. Kogan and D. Lilly will expand their bin-resolved
microphysical model to include the size spectrum of ice particles, and
compare their results against springtime measurements. In addition, the
surface fluxes of heat, moisture and perhaps even aerosols in plumes
from leads are very important in the winter and spring. S. Krueger will
use a 2D model to examine the plume structure and the aggregate effects
of plumes on the boundary layer. A related SHEBA investigation by Q.
Shao will study the same problem with a 3D model. W. Cotton and J. Curry
will study the microphysical structure and ice crystal formation in
plumes.

4.3.4 Aerosol/Arctic cloud interactions, especially during spring and
summer.

The LES model of Y. Kogan and D. Lilly will also be used to investigate
the sensitivity of cloud structure and radiative fluxes to aerosol
abundance and size spectrum within the boundary layer.

4.4 Observations needed to make modeling studies possible

4.4.1 Initialization and boundary conditions

Modeling studies require data for initialization, to provide
observationally specified boundary conditions in some cases, and for
evaluation of the model results.

As mentioned earlier, all models require some specification of the
large-scale advective tendencies, including those due to vertical
motion, and the geostrophic wind. We will rely on analyses from the
operational weather services to best integrate the available data.
However, the Arctic basin is a data void, although it is surrounded by
fairly dense observational networks. Additional observations made from
the ice should considerably improve these analyses. Particularly
valuable observations will include those from rawinsondes and wind
profilers operated at the SHEBA ice station, and from dropwindsondes
deployed from FIRE aircraft. The SHEBA rawinsonde data will be
assimilated into the NCEP, CMC and ECMWF operational forecast models
(see section 3b).

For models that prescribe surface characteristics rather than predict
them, the following additional observations are necessary: statistical
descriptions of spatial distributions of surface temperature (to
incorporate the effects of leads and melt ponds), surface spectral
albedos and emissivity, and surface roughness length. Area-averaged
surface heat and moisture fluxes are also needed. SHEBA is expected to
provide all of the above measurements. For models that do not predict
cloud particle size distributions, observed profiles of droplet spectra
are needed. For models that incorporate aerosol-microphysical
interactions and/or the radiative effects of aerosols, aerosol size
spectra and chemical composition are required. If upper-level clouds are
present, the downwelling radiative fluxes must be specified at the
domain top for models with limited vertical domain size.

4.4.2 Model evaluation

Measurements are required not only to perform simulations, but to
evaluate them as well. These include profiles of temperature and water
vapor, obtainable from radiosondes, dropwindsondes and aircraft
soundings, as well as less readily available observations of surface
fluxes of sensible and latent heat and spectrally-resolved up and
downwelling radiative fluxes (to be provided by SHEBA), and various in
situ and remotely sensed radiation and cloud measurements. Most useful
for model evaluation are liquid and ice water content and effective
radius profiles, cloud fraction profiles, and turbulent and radiative
flux profiles. These will be provided/determined/estimated from
measurements made by a combination of SHEBA surface-based remote sensing
instruments, FIRE aircraft in situ and remote sensing instruments, and
operational satellite sensors.

5.0 Concluding Remarks

FIRE's foray into the Arctic, in collaboration with SHEBA and ARM,
represents an important broadening of the scientific scope of the FIRE
program as a whole. This strategic step is motivated by the acknowledged
importance of the Arctic for the global climate system, and an
appreciation of how poorly we understand Arctic clouds and the energy
budget of the Arctic surface.

At the same time, this Implementation Plan for the Arctic phase of FIRE
is quite consistent in approach with the earlier phases of FIRE's
"boundary-layer cloud" projects, first off the coast of California in
1987, and then in ASTEX during 1992. The emphasis is on a combination of
aircraft measurements and satellite retrievals. Surface data will be
provided primarily by SHEBA and ARM.

Further information about the FIRE program and FIRE's planned Arctic
field mission can be obtained by contacting the FIRE Project Manager,
David S. McDougal, Mail Stop 483, NASA Langley Research Center, Hampton,
VA 23681, 804-864-5832, d.s.mcdougal@larc.nasa.gov. A summary of FIRE's
organization and data management structure is given in Appendix D.

**Appendix A - Summary of SHEBA Science Plan**[^1]

SHEBA is a research program designed to document and understand the
physical processes that couple the atmosphere, ice, and ocean in the
Arctic. The central motivation behind SHEBA is the large discrepancies
among global circulation model (GCM) predictions of present and future
climate in the Arctic, and the uncertainty about the impact of the
Arctic on climate change. These difficulties are due, in large part, to
incomplete understanding of the physics of vertical energy exchange
within the ocean-ice-atmosphere system. In particular, the central theme
of SHEBA emerges from issues related to the surface energy balance,
especially the ice-albedo and cloud-radiation climate feedback
mechanisms. The SHEBA program is based on the premise that improved
understanding of the physical processes involved in air-sea-ice
interactions is needed to address the issues of climate feedback in the
Arctic and to improve our ability to model the Arctic climate. Hence,
the goal of SHEBA is to acquire the knowledge needed to correctly
parameterize the physical processes in numerical models of the
atmosphere-ice-ocean system, and to integrate these processes into GCMs.

The essence of SHEBA as a project is to conduct a year-long field
experiment (starting Autumn 1997) at a drifting station on the pack ice
of the Arctic Ocean, in combination with remote sensing and modeling
analyses of the entire Arctic Basin. The primary focus is on processes
that determine the ice-albedo feedback and the cloud-radiation feedback.
The observational program emphasizes a coordinated and comprehensive
measurement effort examining the physical processes associated with
interactions among the radiation balance, mass changes of the sea ice,
storage and retrieval of energy and salt in the mixed layer of the
ocean, and the formation of clouds and their influence on the surface
energy balance. The modeling effort aims to improve the
parameterizations of crucial air-sea-ice interactive processes in
climate models. The large-scale context for the SHEBA field site will be
provided by geophysical data products derived from satellite-borne
sensors.

The timeliness of SHEBA derives from the recognition of a need to
clarify the role of high latitudes in global climate on various time
scales, a desire to improve the physics included in air-ice-ocean
models, the availability of new observational technology, including
satellite derived geophysical products, and a shared motivation with
several other programs. Primary sponsorship of SHEBA is provided by the
National Science Foundation Arctic System Science (ARCSS) Program and
the Office of Naval Research (ONR) High Latitude Program. In addition,
SHEBA is being designed in collaboration with the Submarine Science Ice
Experiment (SCICEX), sponsored by a consortium of federal agencies led
by ONR; the First ISCCP Regional Experiment III (FIRE III), sponsored
principally by the National Aeronautics and Space Administration; the
Department of Energy Atmospheric Radiation Measurement (ARM) program;
and the NASA sponsored EOS- POLES project. International collaboration
is coordinated under the auspices of the Arctic Climate System Study
(ACSYS) of the World Climate Research Programme.

**Appendix B - Summary of Relevant Parts of the ARM Science Plan**

ARM chose the North Slope of Alaska as a locale because the Arctic is
hypothesized to have large climatic feedbacks linking surface and
tropospheric temperatures, surface albedo, evaporation, cloud cover,
deep ocean water production (the global thermohaline ocean circulation
pump), and the polar atmospheric heat sink. The North Slope of
Alaska/Adjacent Arctic Ocean (NSA/AAO) ARM site will be centered at
Barrow, Alaska. A supplementary site will be established at Atqusuk,
which is about 100 km inland from the coast

The specific scientific objectives to be addressed at the NSA/AAO site
focus on improving the performance of climate models at high latitudes
by improving our understanding of specific physical processes. The
specific objectives are enumerated below, in the form of questions.

Document the radiative environment and how it is determined by
atmospheric constituents and thermodynamics.

Determine the physical, chemical and dynamical processes responsible for
determining the Arctic cloud characteristics.

Understand the radiation-climate feedback processes operating in the
Arctic.

Aircraft measurements on a campaign basis are needed for in situ
evaluation of the surface-based remote sensing systems. ARM will
coordinate with aircraft campaigns from other programs in the Arctic. A
major campaign will take place in conjunction with SHEBA and FIRE III in
the Beaufort Sea. These aircraft campaigns are critical for providing in
situ measurements to evaluate the ground-based remote sensing
retrievals. Additionally, there are several suitable aircraft based on
the North Slope for which appropriate instrumentation packages could
fairly easily be prepared. Use of such non-dedicated aircraft would make
periodic in situ measurements of several different types much more
affordable, particularly dropsonde deployment. Because of logistical
difficulties and cost limitations on the North Slope, dropsondes are an
attractive option relative to surface-based radiosonde launch facilities
at boundary sites.

Satellite remote sensing must play a major role in observing the Arctic
environment, because of the paucity of conventional observations. The
NSA/AAO observations will be used to evaluate and interpret satellite
observations in this region, and thus extend the observational
time/space domain.

ARM will test improved parameterizations using single-column models. To
study the formation, maintenance, and dissipation of cloud systems, and
to improve parameterization of these processes in climate models,
atmospheric mesoscale models are also needed.

Virtually all of ARM's scientific objectives will be pursued in
collaboration with the Surface HEat Budget of the Arctic Ocean (SHEBA)
and Arctic FIRE.

A**ppendix C - Analysis of Sampling Strategies**

This appendix describes in detail the sampling strategy discussed in
section 3.3.8.

There are two outstanding sampling issues in testing both cloud models
and cloud remote sensing data:

• How many independent samples are required?

• What constitutes an independent sample?

The answer depends greatly on the particular scientific question being
asked, and would differ greatly for the following examples:

• What is the decadal average global cloud cover?

• What is the yearly averaged cloud height over Brazil?

• What is the seasonal cycle in cloud optical depth off the coast of
California?

• What is the ENSO variation of high cloud amount over the western
tropical Pacific?

• What is the seasonal cycle of cloud particle size and phase over the
Arctic?

• What is the cloud height at 1800 GMT on January 15, 1999 over the
Oklahoma ARM site?

It is not possible to determine accuracy estimates for every possible
cloud question which might be asked. The types of clouds, seasons,
climate regions, time and space scales define too large a range of
options.

There are two obvious ways to focus the possibilities:

• Define a reasonably small set of representative cloud types.

• Define a reasonably small set of representative climatic regimes.

Both of these approaches have been tried in analyses of surface-observed
cloud statistics (Hahn et al., 1982; Sherr et al., 1968).

FIRE has followed the "cloud type" approach, and has examined 3 of the 6
types to date:

  ------------ ----------------------- ------------------ ------------------
  St/Sc        Stratus/Stratocumulus   July, 1987         Off the California
                                                          Coast

                                       June, 1992         Azores

  Cu           Cumulus                 June, 1992         Azores

  Ci/Cs/Cc     Cirrus                  October, 1986      Wisconsin

                                       November, 1991     Kansas
  ------------ ----------------------- ------------------ ------------------

Remaining cloud types are Cumulonimbus (Cb), Nimbostratus (Ns), and
Altostratus/Altocumulus (As/Ac). Is midlatitude oceanic stratus
fundamentally the same cloud as polar stratus, however? Given the large
changes in surface temperature, moisture fluxes, and atmospheric
stability, a model successfully tested on oceanic boundary layer cloud
in the subtropics may well fail when applied to boundary layer clouds
over the Arctic ice pack. Further, the remote sensing of clouds over
ocean, land, and especially snow/ice surfaces can differ greatly. This
suggests the inadequacy of a simple cloud type classification.

Evaluation of remote sensing algorithms suggests a need to test a
minimal set of:

• 6 cloud types as listed above,

• 7 climate regions:

ocean: tropics and midlatitudes

land: tropics, midlatitudes, and poles

desert: midlatitudes

snow/ice: poles

• 2 optical thickness (thin, thick)

• 2 cloud overlap conditions (single layer, multi-layer)

• 3 solar zenith angles (0, 60, night; i.e. cosine of the zenith angle =
1, 0.5, 0)

• 3 viewing zenith angles (0, 45, 65. i.e. cosine of the viewing zenith
angle = 1, 0.7, 0.4)

• 3 viewing azimuth angles (0, 90, 180)

This gives a total of (6)(7)(2)(2)(3)(3)(3) = 4536 different
cloud/observing combinations. Of course, some of the cloud types,
climate regions, and optical depths are mutually exclusive (Cb at the
poles over snow/ice, or optically thin Ns) so that this number might be
reduced to closer to 4000.

The optical depth, cloud overlap, solar zenith, viewing zenith, and
viewing azimuth affect the nature of remotely sensing cloud properties
from surface, aircraft, or space-based passive radiance measurements.
For purely thermal infrared techniques, the viewing azimuth and solar
zenith restrictions can be removed. For active lidar and radar, the
solar zenith and azimuth can be eliminated, for a factor of 10 fewer
cases (order 400). For any given cloud type and climate region, (say
midlatitude land cirrus) the number of cases to test reduces to 108,
with some solar zenith angles being inappropriate (e.g. overhead sun for
polar ice/snow). In particular, for Arctic FIRE/SHEBA, we might be left
with:

(2 depth)(2 overlap)(2 solar zenith)(3 viewing zenith)(3 viewing
azimuth) = 72 cases

for evaluation of passive cloud remote sensing methods.

For evaluation of active remote sensing methods (e.g. combined surface
nadir uplooking lidar/radar/radiometer profiles of cloud microphysics)
the number of cases per cloud type simplifies to:

(2 depth)(2 overlap) = 4 cases

For cloud modeling tests, the number of cases would be:

(2 overlap)(2 solar zenith) = 4 cases

for each cloud type considered. The above analysis suggests that
evaluation of remote sensing algorithms requires at least an order of
magnitude more cases to test than a dynamical cloud model.

The preceding analysis suggests that the number of cases that must be
studied is an order of magnitude larger for passive remote sensing than
for active remote sensing or for cloud model evaluation. It also
suggests that the total number of samples required, *Nt*, can be thought
of as:

*Nt* = (*Nc*) (*Ns*)

where *Nc* is the number of cloud type/observing condition cases and
*Ns* is the number of independent samples of each case. *Ns* will
determine how well we can estimate the uncertainties in modeling or
measuring any given case. For a gaussian error in measuring a given
cloud case, we can derive the following simple table for the 95%
confidence limits as a function of the number of independent samples:

  -------------- ---------------------------- ------------------------------
  Ns             *Standard Deviation*         *Mean*

  5              σ~true\ \<\ 1.5\ σest~       m~true\ =\ mest\ ±\ 1.2\ σ ~

  10             σ~true\ \<\ 1.35\ σest~      m~true\ =\ mest\ ±\ 0.7\ σ ~

  30             σ~true\ \<\ 1.2\ σest~       m~true\ =\ mest\ ±\ 0.2\ σ ~

  100            σ~true\ \<\ 1.1\ σest~       m~true\ =\ mest\ ±\ 0.1\ σ ~
  -------------- ---------------------------- ------------------------------

For example. if we have 30 independent samples of cirrus cloud height
determination which estimate a mean bias error of 500 m, and a standard
deviation of 1.5 km, we can say with 95% confidence that the true
standard deviation of the cloud height error is less than (1.5)(1.2) =
1.8 km. At the same time we can say that the bias error in cloud height
is with 95% confidence between 200 m and 800 m. If our concern is
climate research, so that the most critical confidence bound is that of
the ensemble mean, then clearly the number of independent samples
required to estimate the ensemble mean depends directly on the noise of
the measurement technique. For most current remote sensing techniques,
the standard deviation of the error is roughly 10-20% of the mean value,
so that knowledge approaching 1-2% in the ensemble mean would require
evaluation against 100 independent samples.

What constitutes an "independent sample?" Leith (1973) considered this
issue for climate statistics and concluded that for typical "red"
atmospheric spectra, that a workable definition of independence was
twice the time or distance required for the autocorrelation function
value to fall to 1/*e*. For midlatitude geopotential height, Leith found
the independence scale at midlatitudes to be 6.5 days. At 10 m
s^-1\ this\ is\ roughly\ 5000\ km\ (i.e.\ \~\ wavenumber\ 6).\ Cahalan\ et\ al.\ (1982)\ examined\ 2.5\ degree\ gridded\ NOAA\ heat\ budget\ longwave\ flux\ data\ and\ concluded\ that\ this\ scale\ was\ roughly\ 1000\ km.\ This\ number\ is\ probably\ a\ rough\ indication\ of\ cloud\ height\ independence\ scale.\ It\ is\ probably\ an\ upper\ estimate,\ however,\ given\ that\ the\ large\ grid\ size\ (250\ km)\ causes\ an\ overestimate,\ and\ given\ that\ this\ data\ included\ both\ large\ clear\ areas\ (very\ uniform\ and\ not\ pertinent\ to\ cloud\ tests)\ as\ well\ as\ the\ less\ uniform\ cloudy\ regions.\ In\ this\ sense,\ the\ estimate\ of\ Cahalan\ et\ al.\ (1982)\ represents\ the\ distance\ between\ different\ cloud\ types\ (i.e.\ high\ cloud\ to\ low\ cloud),\ as\ opposed\ to\ the\ independence\ scale\ of\ any\ given\ cloud\ type.\ A\ similar\ estimate\ of\ the\ scale\ of\ independence\ between\ different\ cloud\ types\ was\ obtained\ by\ Jones\ (1992),\ who\ examined\ the\ autocorrelation\ of\ surface\ observer\ cloud\ fraction\ and\ suggested\ an\ independence\ scale\ of\ about\ 700\ km.^

An examination of the FIRE I San Nicolas Island LWP time series (1
minute samples for 19 days) gives an independence scale of about 300 km
for stratocumulus. It is likely that the independence scale (hereafter
denoted by *L*) for different cloud variables differs, with *L*(cloud
height) \> *L*(cloud particle size) \> *L*(LWP or optical depth). Any
short time series, however, will tend to systematically underestimate
the true independence scale. Further work is needed to define these
scales as a function of cloud type and cloud physical property. Critical
data sets would include:

• cloud top/base from LITE space-borne lidar data (700 m or 7 km
resolution)

• cloud optical depth from AVHRR LAC (best) or GAC (acceptable)

• cloud particle size from AVHRR LAC(best) or GAC (acceptable)

• cloud fraction from AVHRR LAC or GOES 1-km: need to define a grid size
for cloud fraction: 5, 20, 100, 500 km.

For conditional sampling, one difficulty will be objective determination
of each cloud type from a large set of satellite or surface
observations. From earlier work, the appropriate independence scales for
cloud properties probably lie between 100 and 500 km.

Suppose that the correct scale is 250 km. In this case, we can estimate
how rapidly a surface site, an aircraft, and a satellite can collect
independent samples:

  ---------------- ---------------- --------------------- ----------------
  *Data Source*    *Speed*          *Time to move 250 km* *Samples/Time*

  ARM/SHEBA:       10m/s            7 hours               3.5 / day

  ER-2:            200m/s           0.3 hours             17/flight

  Satellite:       7000m/s          0.5 minutes           3000 / day
  ---------------- ---------------- --------------------- ----------------

How many independent samples can FIRE/SHEBA obtain? It depends on what
we are after.

First, consider "Cloud Type Frequency of Occurrence." The first step is
to determine the expected cloud types for the Spring (April, May)
SHEBA/Arctic FIRE deployment. Using Hahn et al., 1982, March/April/May,
for 75N, 135W cloud frequency of occurrence is taken from surface
observations. Assuming that the time scale for changing cloud type is
greater than 1 day (i.e. changing synoptic systems), we have the
following expected conditions for 1 month of field observations:

  ----------------------- ----------------------- -----------------------
                          *Days/Month*            *Days/Month*

  Cloud Type              Single-Layer            Multi-Layer

  St/Sc                   8                       13

  Ci/Cs/Cc                3                       11

  As/Ac                   1                       11

  Ns                      0                       8

  Cu                      1                       3
  ----------------------- ----------------------- -----------------------

Two days are predicted to be clear. We then find a total of 15 days with
either clear or single-layer cloud, and 15 days with multi-layer cloud.

Next, consider "Satellite observation frequency of
occurrence/conditions." The following satellite observations are
expected from EOS-AM 10:30 am sun-synchronous orbit: (correct later to
NOAA am and pm sun-synchronous)[^2] for April 1, 75N, 140W:

  ----------------------------------- -----------------------------------
  *Orbit overpasses per day*          *8.5*

  Range of local times                1130pm - 1230pm, every 1.7 hours

  Daily time gap                      11 hours

  Solar zenith angle                  70 - 90 degrees, daily

  Viewing zenith angle                0 - 65 degrees, daily

  Viewing azimuth angle               85 - 92 degrees, daily

  Phase function scattering angle     90 - 105 degrees, daily
  ----------------------------------- -----------------------------------

Using the above statistics: we expect over the SHEBA or ARM sites to
observe:

  ----------------------- ----------------------- -----------------------
  *Cloud/Climate Class*   *Comments*              *Observing Strategy*

  5 Cloud types           (no Cb)                 1 day = 1 type

  2 Climate regions       (ice, land)             SHEBA = ice, ARM = land

  2 Overlap conditions    (single, multi)         1 day = 1 type
  ----------------------- ----------------------- -----------------------

  ----------------------- ----------------------- -----------------------
  *Observing Condition*   *Comments*              *Observing Strategy*

  2 Optical thicknesses   (thin, thick)           within each day: cloud
                                                  scale

  2 Solar Zenith          (70, \> 90 degrees)     within each day

  3 Viewing Zenith        (0, 45, 65 degrees)     within each day

  1 Viewing Azimuth       \(90\)                  within each day
  ----------------------- ----------------------- -----------------------

We can now take the above Classes, and predict the number of independent
samples expected for different sampling strategies. For the estimates
below we predict the average time to get 30 independent samples of each
Cloud/Observing Class.

+------------------+-----------+-----------+-------------+-----------+
| *Observing       | *Cloud    | *Cases*   | *Obs        | *Co       |
| Strategy*        | Cases     |           | Conditions  | nditions* |
|                  | Needed*   | *Obs/day* | Needed*     |           |
+------------------+-----------+-----------+-------------+-----------+
| Obs/day          | 2 \* 5    | 1         | 12          | 8         |
+------------------+-----------+-----------+-------------+-----------+
| Surface Site vs. | o         |           | 2τ\*2θ~     |           |
| Satellite        | vlp\*type |           | 0\*lq\*lφ ~ |           |
+------------------+-----------+-----------+-------------+-----------+

30 samples per case = 30 \* 10 \* 1 \* (12/8) = 450 days = 1.5 yrs

100 samples per case = 100 \* 10 \* 1 \* (12/8) = 1500 days = 4 yrs

Missing Observation Conditions: None.

Disadvantages: no oversampling to beat down instrument noise.

  ------------------- ------------ ------------- -------------------- -------------
  *Observing          *Cloud Cases *Cases        *Obs Conditions      *Cond.
  Strategy*           Needed*      Obs/Flight*   Needed*              Obs/Flight*

  ER-2 / ER-2         2 \* 5       8             4                    4

  (MAS?Lidar)         ovlp\*type                 2τ\*2θ~0\*lq\*lφ ~   
  ------------------- ------------ ------------- -------------------- -------------

30 samples per case = 30 \* 10 \* (1/8) \* (4/4) = 38 flts = 225 hrs

100 samples per case = 100 \* 10 \* (1/8) \* (4/4) = 125 flts = 750 hrs

Missing Observation Conditions: Only validates nadir satellite viewing
angle, and only validates cloud

height and cloud amount.

Advantage: oversampling to reduce instrument noise effects.

  ------------------- ------------ ------------- -------------------- -------------
  *Observing          *Cloud Cases *Cases        *Obs Conditions      *Cond.
  Strategy*           Needed*      Obs/Flight*   Needed*              Obs/Flight*

  ER-2/In-Situ        2 \* 5       4             4                    4

  (MAS/In-Situ)       ovlp\*type                 2τ\*2θ~0\*lq\*lφ ~   
  ------------------- ------------ ------------- -------------------- -------------

30 samples per case = 30 \* 10 \* (1/4) \* (4/4) = 75 flts = 450 hrs

100 samples per case = 100 \* 10 \* (1/4) \* (4/4) = 250 flts = 1500 hrs

Missing Observation Conditions: no vertical profiles of effective
particle size.

Evaluation of models of viewing zenith dependence requires a sawtooth
flight pattern by in-situ aircraft under the ER-2: further exacerbating
the large difference in flight speed of the two aircraft. Stacked
in-situ aircraft vertical profiling of microphysics greatly reduces the
number of independent samples obtained per flight. If 4 levels are
measured, we get 1/4 the independent samples, so we need 4 times as many
flights as determined above.

Advantage: oversampling to reduce instrument noise effects.

**Appendix D - FIRE Scientific Organization and Data Management**

1.0 LEAD MANAGEMENT

FIRE has been a multi-agency effort since its inception. The goals of
FIRE are not bounded by a single agency\'s mission; neither is the
talent base required to address the complex problems of cloud-climate
research. Close interagency cooperation in the past has made possible
FIRE\'s advancements in our understanding of these climatically
important cloud systems. It is intention of FIRE\'s participants to
maintain and further refine this spirit of interagency cooperation.

**While NASA will continue to serve as the lead agency for FIRE-III,
other federal agencies including National Science Foundation (NSF),
Office of Naval Research (ONR), Department of Energy (DOE), Department
of Defense (DOD), and National Oceanic and Atmospheric Administration
(NOAA) will remain strong participants in FIRE-III. The Radiation
Sciences Program, Atmospheric Science Branch, NASA Headquarters, will
provide overall cognizance of the FIRE project and will represent FIRE
to the Working Group on Data Management for WCRP Radiation Projects.
Contact points are located at the following offices:**

+-----------------------+-----------------------+-----------------------+
| Dr. Robert Curran     | Dr. Robert Abbey, Jr. | Dr. Michael Ledbetter |
|                       |                       |                       |
| Atmospheric Science   | Office of Naval       | Arctic Systems        |
| Branch                | Research              | Science Program       |
|                       |                       |                       |
| NASA Headquarters/YS  | Code 1122 MM          | Office of Polar       |
|                       |                       | Programs              |
| Washington, DC 20546  | 800 N. Quincy Street  |                       |
|                       |                       | National Science      |
|                       | Arlington, VA 22217   | Foundation            |
|                       |                       |                       |
|                       |                       | 2401 Wilson Boulevard |
|                       |                       |                       |
|                       |                       | Arlington, VA 22230   |
+-----------------------+-----------------------+-----------------------+
| Dr. Jay Fein          | Dr. Pam Stephens      | Dr. Richard Lawford   |
|                       |                       |                       |
| Atmospheric Sciences  | Atmospheric Sciences  | NOAA/OGP              |
|                       |                       |                       |
| National Science      | National Science      | Mail Stop GP          |
| Foundation            | Foundation            |                       |
|                       |                       | 1100 Wayne Avenue     |
| 1800 G Street, NW     | 1800 G Street, NW     |                       |
|                       |                       | Silver Spring, MD     |
| Washington, DC 20550  | Washington, DC 20550  | 20910                 |
+-----------------------+-----------------------+-----------------------+
| Dr. Pat Crowley       |                       | Dr. Rex Fleming       |
|                       |                       |                       |
| Department of Energy  |                       | Director, Climate     |
|                       |                       | Observations          |
| Environmental         |                       |                       |
| Sciences Division     |                       | NOAA/ERL              |
|                       |                       |                       |
| Research ER-74        |                       | 3300 Mitchell Lane,   |
|                       |                       | Suite 175             |
| Washington, DC 20585  |                       |                       |
|                       |                       | Boulder, CO 80301     |
+-----------------------+-----------------------+-----------------------+

2.0 PROJECT MANAGEMENT

The FIRE project activities will be managed by the FIRE Project Office
at NASA\'s Langley Research Center. The FIRE project manager will be
responsible for the overall management, coordination, and reporting of
the project activities. These responsibilities will include interacting
with the Radiation, Dynamics, and Hydrology Branch and Physical Climate
and Hydrology Branch management at NASA Headquarters, the Meteorology
Research Program management at ONR, and the other agency
representatives; overall cognizance of project planning, schedules, and
field operations; and allocation of approved funding to support the
scientific objectives. The project manager will be assisted by a project
scientist and a project staff, which will include: an Operations
Manager, a Data Manager, and a Project Coordinator.

Project Scientist - The FIRE Project Scientist will be responsible for
all scientific aspects of FIRE. The project scientist will (a) act as a
scientific advisor to the project manager; (b) represent the members of
the science team in their relationship with the project manager; (c)
maintain an oversight of the scientific integrity of the project; (d)
review and make recommendations of proposed modifications to selected
proposals as warranted; and (e) participate in negotiations regarding
allocations of specialized project resources among approved
investigations.

Operations Manager - The Operations Manager will have overall
responsibility for organizing, scheduling, and conducting field
operations, preparation of mission plans, establishing mission
objectives with the MST Chairman, determining special support
requirements, conducting planning and debriefing sessions, and
operational procedures. The Operations Manager is also responsible for
designing the specific and facilitating operationally feasible flight
profiles to meet the scientific objectives and for coordinating the
experimental requirements of measurement platforms for resources,
testing, and integration for all field measurements. Lastly, the
Operations Manager is responsible for the setup, testing, and operation
of the mission operations site.

Data Manager - The Data Manager will be responsible for coordinating the
types, scope, and quantity of data collected during the intensive field
and extended-time activities and its archiving. The Data Manager will
serve as the Chairperson for the Data Management Working Group. The Data
Manager will ensure that the data is submitted as required to the FIRE
Central Archive and eventually released to the scientific community.

Project Coordinator - The Project Coordinator will provide
administrative support to the FIRE Project Office, including logistical
and reimbursement support in the planning and conducting of FST meetings
and field missions as required.

3.0 FIRE-III SCIENCE TEAM

The FIRE-III Science Team (FST) will be comprised of the principal
investigators of those agency-approved proposals directly related to
FIRE-III research. Appointment to the FST will be based on the
recommendation of the supporting agency. The term of participation on
the team will continue as long as the approved research continues. The
team shall determine its own structure and method for interaction. Ex
officio members may be appointed to the team, as needed, by the project
manager.

The FST will be responsible for implementing the broad scientific
objectives of the project in accordance with the selected investigators.
Additional responsibilities will be to:

a\. Develop implementation plans for FIRE research including:

1\. field experiments.

2\. data management.

3\. research programs.

b\. Recommend and perform coordinated research activities, including the
interface of modeling and experiments.

c\. Conduct individual investigations in accordance with approved
proposals.

d\. Hold meetings and workshops, as needed, to plan and assess research
programs.

e\. Designate the makeup and responsibilities of special working groups,
as needed, to address special task areas.

f\. Establish a data protocol that will promote the timely publication
and dissemination of scientific results.

**Table 1 lists the FST members, their institutions, and working groups
in which they will be participating. S refers to both stratus and Arctic
cloud systems. Figure 1 shows a schematic of the relationships between
the agencies, the agency program managers, the FIRE Project Office, and
the FST.**

  --------------------------- ---------------- ------------- -------------------
  Table 1 -  FIRE-III Science                                
  Team: C = Cirrus only;                                     
  [C]{.underline},A = Cirrus                                 
  principal, Arctic/Stratus                                  
  secondary; C,A = Cirrus and                                
  Arctic/Stratus equal;                                      
  C,[A]{.underline} = Cirrus                                 
  secondary, Arctic/Stratus                                  
  principal; A =                                             
  Arctic/Stratus only                                        

  Name                        Organization     Sponsoring    Research Thrust
                                               Agency        

  Tom Ackerman                PSU              NASA          C

  Bruce Albrecht              PSU              ONR           A

  Chris Bretherton            U Washington     NASA          A

  Kenneth Campana             NMC              NOAA          [C]{.underline},A

  Bill Cotton                 CSU              NASA          C,A

  Steve Cox                   CSU              NASA          [C]{.underline},A

  Judy Curry                  U Colorado       NASA          A

  Tony Del Genio              GISS             NASA          C,A

  Leo Donner                  GFDL             NOAA          C

  Wynn Eberhard               NOAA-ETL         NOAA          C,[A]{.underline}

  Chris Fairall               NOAA-ETL         NOAA          A

  Hermann Gerber              Gerber           NASA          C,A
                              Scientific                     

  Jim Hack                    NCAR             NSF           [C]{.underline},A

  John Hallett                DRI              NASA          [C]{.underline}, A

  Mike Hardesty               NOAA-ETL         NOAA          C,[A]{.underline}

  Andy Heymsfield             NCAR             NASA          [C]{.underline},A

  Peter Hobbs                 U Washington     NASA          A

  Jim Hudson                  DRI              NASA          C,[A]{.underline}

  George Isaac                AES              NASA          A

  Mike King                   GSFC             NASA          C,[A]{.underline}

  Yefim Kogan                 U Oklahoma       ONR           A

  Sonya Kreidenweis           CSU              NSF           [C]{.underline},A

  Bob Kropfli                 NOAA-ETL         NOAA          C,[A]{.underline}

  Steven Krueger              U Utah           NASA          A

  Paul Lawson                 SPECinc          NASA          [C]{.underline},A

  Don Lenschow                NCAR             NASA          A

  Kuo-Nan Liou                U Utah           NASA          C

  Martin Miller               ECMWF            NASA          C,[A]{.underline}

  Pat Minnis                  LaRC             NASA          C,A

  M. Mishchenko               GISS             NASA          [C]{.underline},A

  Peter Pilewskie             ARC              NASA          C,A

  Michael Poellot             U North Dakota   NASA          C

  David Randall               CSU              NASA          C,[A]{.underline}

  Bill Rossow                 GISS             NASA          C,[A]{.underline}

  Ken Sassen                  U Utah           NASA          C

  Jim Spinhirne               GSFC             NASA          C,A

  David Starr                 GSFC             NASA          C

  Graeme Stephens             CSU              NASA          [C]{.underline},A

  Brian Toon                  ARC              NASA          [C]{.underline},A

  Si-Chee Tsay                GSFC             NASA          C,[A]{.underline}

  Taneil Uttal                NOAA-ERL         NASA,NOAA     C,[A]{.underline}

  Francisco Valero            ARC              NASA          C,A

  Shouping Wang               MSFC             ONR, NASA     A

  Ron Welch                   SDSMT            NASA          C,A

  Bruce Wielicki              LaRC             NASA          C,A

  Don Wylie                   U Wisconsin      NASA          C,A

  Qing-Yu Zhao                NMC              NOAA          [C]{.underline},A
  --------------------------- ---------------- ------------- -------------------

3.1 FIRE Working Groups

FIRE has been designed to address a number of complicated scientific
problems. The FIRE strategy is to assemble those members of the
scientific community with interests in these problems and to create an
environment which encourages them to collectively pursue the solutions
to these problems. FIRE working groups representing the themes of cirrus
cloud systems, marine stratocumulus and arctic cloud systems, and data
management will be constituted from the FST membership as follows:

i\. Cirrus Working Group

ii\. Arctic Working Group

iii\. Data Management Working Group

The Cirrus and Arctic Working Groups will accept the following
responsibilities:

i\. Define specific goals to be collectively pursued; these include, but
are not limited to the following:

a\. satellite intercomparisons;

b\. satellite-in situ intercomparisons;

c\. case studies;

d\. GCM parameterizations.

ii\. Identify responsible individuals in each of the collective study
areas referred to in item i and monitor the progress of the collective
research.

iii\. Define data sources, requirements and schedules essential to
working group studies.

iv\. Exchange data among FST and working group members.

v\. Oversee publication of joint research results from tasks identified
in item i.

The Data Management Working Group (DMWG) will be comprised of key
members from the Cirrus and Arctic working groups, as well as from the
FIRE Central Archive. The FIRE Data Manager will chair the DMWG. The
responsibilities of the DMWG are:

i\. provide interaction and coordinate data management decisions among
the FIRE data gathering working groups, the FIRE Central Archive, and
the principal investigators.

ii\. ensure uniform FIRE data characteristics (throughout all working
groups and FIRE researchers)

iii\. define listings of uniform parameter units and conventions for
reduced data products.

iv\. oversee the plans, progress, and performance of the FIRE Central
Archive (see section 4.1.3).

4.0 DATA MANAGEMENT

Data management is an ongoing activity, beginning immediately in the
pre-experiment phase and continuing through the end of FIRE-III.
Collaboration and data exchange of data among FIRE investigators will be
required to produce integrated analyses of multi-platform, multi-scale,
and multi-spectral data sets; these integrated analyses are central to
the accomplishment of the FIRE-III science objectives. In addition, the
data management activities will seek to ensure the availability of FIRE
data and analysis products to the entire science community. To maximize
these data management goals, the FST will develop a data management and
protocol plan consistent with the supporting agencies\' goals for the
sharing of the data, the rapid dissemination of results, and
documentation of measurements obtained.

At a minimum, the data management activities will disperse most of the
data reduction and processing functions to the FIRE investigators
engaged in collecting and analyzing the data, but to hold the resultant
data sets and analysis products centrally for ready access by all FIRE
investigators. The FIRE Central Archive, located at the Langley Research
Center EOSDIS Distributed Data Active Archive (DAAC), and the FIRE
Project Office will provide a centralized source of information and
copies of data which are centrally archived. The FST, composed of the
investigators, will coordinate the decentralized data reduction and
analysis activities. In addition, the FIRE Central Archive will
transfer, from time to time, certified FIRE data to the scientific
community.

Data management activities in FIRE will insure the exchange of data
among FIRE investigators that is required to produce the integrated
analyses of multi-platform, multi-scale, and multi-spectral data sets;
these integrated analyses are central to the accomplishment of the FIRE
science objectives. In addition, the data management activities will
insure the availability of FIRE data and analysis products to the entire
science community. These activities will be carried out by four
organizational components within FIRE:

\(1\) the Cirrus and Arctic Working Group,

\(2\) the individual FIRE principal investigators,

\(3\) the FIRE Central Archive, and

\(4\) the FIRE Project Office.

The strategy embodied in this organization is to disperse most of the
[data reduction]{.underline} and [processing]{.underline} functions to
the FIRE investigators engaged in collecting and analyzing the data, but
to hold the resultant data sets and analysis products centrally for
ready access by all FIRE investigators. The FIRE Central Archive and the
FIRE Project Office will provide a centralized source of information and
copies of data which are [centrally]{.underline} archived, whereas the
FIRE Working Groups, composed of the investigators, will coordinate the
[decentralized]{.underline} data reduction and analysis activities. In
addition, the FIRE Central Archive will transfer, from time to time,
certified FIRE data to EODIS and/or other organizations for permanent
archive and for open access by the scientific community.

4.1 Data Management Responsibilities

4.1.1 Working Groups

There are two data-gathering FIRE working groups - a Cirrus Working
Group, and an Arctic Working Group. These working groups will be
composed of FIRE principal investigators pursuing research relevant to
that working group. The data management responsibilities of the
individual principal investigators, as dispersed elements of the FIRE
data processing system, could become onerous if not coordinated
properly. Therefore, the FIRE Working Groups must govern these
individual activities to insure progress toward the FIRE science
objectives. The data management functions of the FIRE Working Groups
are:

1\. To determine the content and format of all principal investigator
data sets to be submitted to the Langley Research Center Distributed
Active Archive System (LaRC DAAC). If requested, only FIRE-III
researchers will have access to data in the LaRC DAAC during the time
period for data certification.

2\. To set standards for data quality, documentation of all data sets,
and certification criteria for data products that will be released to
the at-large scientific community.

3\. To assign a data "category" (section 8.5) to each data parameter
that reflects it\'s usefulness to subsequent data analysis and whether
and when it should be archived.

4\. To select case study data sets for special intensive processing
(including reformatting) by all relevant principal investigators and to
identify other additional processing of data to accomplish FIRE-III
objectives.

5\. To coordinate data management decisions and ensure uniform FIRE-III
data characteristics.

6\. To certify, within 6-24 months after acquisition, those data
products from the LaRC DAAC that will be released to the scientific
community.

4.1.2 Principal Investigators

All data reduction and analysis functions in FIRE-III reside with the
scientists carrying out their research as part of the program. To
encourage the interactions of these researchers needed to integrate the
various observations and models into a more comprehensive understanding
of clouds and their effects, FIRE-III investigators will have free and
timely access to all data collected during FIRE-III, either by
individual principal investigators or collected from satellites.
Coordination of data analysis and modeling activities requires all
principal investigators to perform certain other tasks as part of the
FIRE-III data management scheme. These functions are:

1\. To provide to the LaRC DAAC written information concerning data
holdings, including all data collected as part of FIRE-III and other
data deemed relevant to FIRE-III research.

2\. To save all data collected in unreduced form for five years so that
reduction of data can be repeated if necessary.

3\. To provide to the LaRC DAAC copies of all category 1, 2, 3 (when
practical), and 4 (if desired) observations in a mutually-agreed upon
format (see section 7.2 for definition of data category) accompanied by
complete instrument, reduction algorithm and data format documentation.

4\. To provide, within 9 months after acquisition, to the LaRC DAAC
copies of any data analysis products deemed relevant to the
accomplishment of FIRE-III objectives, accompanied by appropriate
documentation.

5\. To provide for archival of all submitted data sets by producing
back-up copies of all data and taking other necessary precautions to
ensure the preservation of the FIRE-III data.

6\. To provide to other FIRE-III investigators or the LaRC DAAC, upon
request, copies of other data sets acquired for FIRE-III research, that
are relevant to other FIRE-III studies.

7\. To provide to other FIRE-III investigators reasonable access to
unreduced observations to facilitate particularly crucial multi-data
analyses.

4.1.3 Langley DAAC

The LaRC DAAC will serve as the FIRE-III Central Archive. The LaRC DAAC
is responsible for archiving and distributing NASA science data in the
areas of radiation budget, clouds, aerosols, and tropospheric chemistry.
It will also archive some of the data sets which result from the EOS
program and other elements of Mission to Planet Earth.

The DAAC has developed an on-line computer system which allows the user
to logon, search through the DAAC\'s data inventory, choose desired data
sets, and place an order. Data may be received either electronically
(via FTP) or on media such as 4mm tape, 8mm tape, or CD-ROM (prepackaged
datasets only).

Users with an X-Windows terminal (e.g., Motif) or a Sun Open Windows
display system with access to Internet, may log onto the system by
entering:

xhost + eosdis.larc.nasa.gov

(or: xhost + 192.107.191.17)

telnet eosdis.larc.nasa.gov

login name: ims password: larcims

At the prompts, enter x for the X-Windows interface and then your
display name (name of your workstation followed by ":0" or internet
address followed by ":0").

Users with access to NCSA Mosaic can use the following URL address:

http://eosdis.larc.nasa.gov/

Users without access to a terminal with an X-Windows display system but
who have access to Internet may log onto the system by entering:

telnet eosdis.larc.nasa.gov

login name: ims password: larcims

At the prompt, enter c for the character interface and then press
return.

Users who cannot access the system or who have questions concerning the
Langley DAAC may contact:

Langley DAAC User and Data Services

Mail Stop 157B

NASA Langley Research Center

Hampton, VA 23681-0001

Phone: (804) 864-8656 FAX: (804) 864-8807

email: larc@eos.nasa.gov

For further information on LaRC DAAC contact Ms. Sue Sorlie, Langley
Research Center, 804-864-8660, s.e.sorlie@larc.nasa.gov or John Olson at
804-864-8609, olson@magician.larc.nasa.gov.

The LaRC DAAC will provide a centralized data holding and on-line system
in order to facilitate easy access to all FIRE-III data by all FIRE-III
investigators. Since most of the satellite data are not collected
directly by FIRE-III principal investigators, the LaRC DAAC will also be
responsible for holding of the satellite data sets required for FIRE-III
research from the relevant satellite operating agencies. The specific
data management functions of the FIRE-III Data Archive are:

1\. To hold all reduced observations and data analysis products
submitted by individual principal investigators or groups of principal
investigators upon the request of the FIRE-III Science Team.

2\. To hold all satellite data sets required for FIRE-III as specified
by the FIRE-III Science Team.

3\. To provide, upon request, copies of any data sets to FIRE-III
investigators (at minimal cost to investigators).

4\. To produce a data ingest request of the complete FIRE-III data
holdings of the archive and the individual principal investigators
indicating the current analysis status of these data. The data ingest
request entries should provide information about the location of the
data holding, the instrument(s) performing the observations, the
temporal and spatial resolution of the data, and the format of the data.

5\. To update the data ingest request every year and to make it
available to FIRE-III investigators in both hard copy and electronically
(on-line dial-up) form.

6\. To restrict access to the data products in the LaRC DAAC to only
researchers associated with the FIRE-III project, if so requested by the
FST. The at-large scientific community will not then have access to
these data sets until they are released.

7\. To "release", on an annual basis, certified FIRE-III data from the
permanent archive for unrestricted access by the at-large scientific
community. The "transfer" process removes the access restraints to the
at-large scientific community.

8\. To provide for archival of the permanent FIRE-III data in the data
archive by producing back-up copies of all data and taking other
necessary precautions to ensure the preservation of the data.

9\. To publish documentation, such as a FIRE-III Data Archive Users
Manual that describes the contents of the FIRE-III Data Archive, data
formats, data request information, and other pertinent descriptive
material.

4.1.4 Project Office

FIRE data management structure vests the primary data processing
function with the individual scientific investigators, the information
and archival functions with the Central Archive, and the decision-making
with the FIRE Working Groups. The Project Office must provide for
liaison among these different groups. The specific data management
functions of the project office are:

1\. To provide liaison between the FIRE Working Groups (and individual
principal investigators) and the data collecting agencies and agencies
operating observing platforms required by FIRE.

2\. To provide liaison between the FIRE Central Archive and the
satellite and other data collecting groups to facilitate the acquisition
of the data sets needed for FIRE.

3\. To provide for a close working relationship among the FIRE Working
Groups and the Central Archive by including a representative of the
Central Archive on the Project Office staff who can attend FIRE Science
Team meetings.

4.2 Data Products

There are several types of data products that will be acquired,
submitted, and archived at the LaRC DAAC. These products, which will be
obtained from a variety of instruments onboard satellite, airborne, or
surface-based platforms during the field experiment, are as follows:

1\. Guide/Summary\--written information about the data holdings of the
individual PI\'s, including location of the data holdings instrument(s)
performing the observations; resolution and area coverage of the data;
date, time, and location of the observations; format of the data; and
analysis status of these data.

2\. Raw data\--original observations acquired by the instrument, in
instrument units (voltages, etc.).

3\. Reduced data\--observations converted to the physical quantity
directly sensed by the instrument with quality control inspection and
removal of bad data.

4\. Value-added products\--physical quantities derived from the
observations, including documentation on the analysis algorithm and any
auxiliary data sets used in the analysis.

5\. Calibration, quality, and navigation information\--describes the
conversion to physical units, the conditions of observation and the
location of the observation.

6\. Instrument documentation and data tape format description.

7\. Data for special case studies which have been arranged for
intercomparison of multi-platform observations.

8\. Data selected for special processing to facilitate model studies.

9\. Status and bibliography of FIRE-III publications.

The data acquired by the individual experimenters will be reduced to
final values and forwarded to the LaRC DAAC. The format for the
submittal of data to the archive must be in ASCII or HDF/netCDF (see
Appendix F). In addition, a written description of the readme file or
pertinent remarks should be included in the data file or in a separate
file to be submitted with the data. The Langley DAAC will assist data
producers putting data into HDF.

Transfer of the final data between the investigators and the LaRC DAAC
may be accomplished by electronic data transfer using ftp.

Transfer of the data from the data archive to the investigators will be
done either physically (using DATs, 9-track tapes or floppy disks) or
interactively (via electronic transfer) where such systems are available
(email or ftp).

4.3 Data Categories

**Each of the data products will be assigned a data "category" by the
science team which will reflect its usefulness to subsequent data
analysis and whether and when it should be archived. Table 2 outlines
the definition of the four data categories. The FIRE-III Science Team
will be responsible for these assignments, coordinated with the
responsible PIs.**

  -------------- ------------------- ------------------- -------------------
  Table 2 -                                              
   Definition of                                         
  Data                                                   
  "Categories"                                           

  **Category**   **Definition**      **Usefulness**      **Archive?**

  1              Fundamental         Critical            ASAP or 3-6 months
                 measured parameter                      

  2              Fundamental         "A lot of people    9-18 months
                 measured parameter  will use it"        

  3              Reduced or          Useful              Welcomed; not
                 value-added                             required
                 parameter                               
  -------------- ------------------- ------------------- -------------------

4.4 Data Protocol and Publications Plan

The FIRE-III data protocol and publication plan has been prepared to
encourage an orderly and timely analysis, interpretation, and
publication of the data obtained during FIRE-III. It is hoped that the
development and distribution of this plan will enhance the science
output by promoting cooperation among the investigators and encouraging
the early publication of results, thereby enriching the scientific
interpretation of the data obtained from single and ensemble of
instruments.

The FIRE-III Science Team (FST) is responsible for the certification of
data submitted to the permanent FIRE-III data archive located at the
Langley Research Center (LaRC) Distributed Active Archive Center (DAAC).
The certification process will normally take 6-24 months after
acquisition. During the certification process period, the following set
of data protocol and publication ground rules will be agreed upon and
abided to by all FST members as a condition of their participation in
the FIRE-III project.

4.4.1 Data Protocol

1\. FST members will have free and timely access to all FIRE-III data
acquired during the project. The normal vehicle for data dissemination
will be a transfer of data via the LaRC DAAC; however, direct transfer
of data between investigators is also encouraged.

2\. Each investigator\'s data is proprietary until the data appear in
publication or, if the data are included in the LaRC DAAC, until this
archive is published/released to the scientific community. FST members
who collect FIRE-III data are responsible for the reduction, analysis,
interpretation and publication of their data and research results.

3\. An investigator whose unpublished data are to be used in an
investigation has the right to be included among the authors of any
resulting publication. The investigator may refuse co-authorship but not
the use of his data. The investigator must provide information
concerning the quality of the data and may require that suitable caveats
regarding the data be included in the publication. It is the
responsibility of the sponsoring investigator to solicit the
participation of the investigator whose data are to be used as early as
possible during the formative stages of the investigation.

4\. FST members may release their own data to whomever they wish. They
may not release the data of other investigators without consent.

5\. Selected sets of reduced data obtained by investigators
participating in collaborative research will be made available to FST
participants within 9 months following acquisition.

6\. The FST will normally provide the forum in which collaborative
investigations are planned and executed; however, this is not meant to
discourage collaborative investigations outside the scope of FIRE-III.

7\. Any data sets resulting from collaborative investigations among FST
members will be made available to the LaRC DAAC. This includes all
collaborative efforts both within and outside the FST.

8\. Scientists who are not FST members, co-investigators, or associates
may participate in investigations using unpublished FIRE-III data
provided they are sponsored by a FST member and they make available
whatever data they plan to use to the LaRC DAAC at the beginning of the
participation. Co-investigators and associates may participate in the
investigation of a FST member.

4.4.2 Data Publication

Early publication of results from FIRE-III research is strongly
encouraged. Towards this goal, the following minimum publication plan
has been developed:

1\. An overview synopsis of the FIRE-III program will be prepared by
program personnel and key FST researchers for publication in an
appropriate journal. The paper will describe the scientific objectives,
operational plans, and potential results of the major FIRE-III
activities to the scientific community during the early stages of
FIRE-III.

2\. A synopsis of the key operational activities and possible results
from the FIRE-III field experiment will be prepared by project personnel
and key FST researchers for publication in an appropriate journal. This
paper will be designed to be a "quick look" publication to inform the
scientific community at an early stage of the implementation of the
mission and possible highlighted observations.

3\. Publication of results from the FIRE-III field experiment may be in
a special issue of an appropriate journal. The special issue decision
will be made by the FST. The issue will contain (a) an overview paper
and (b) science papers.

The overview paper will be co-authored by program personnel and key
researchers and will include a statement of the goals of the particular
field mission. It will describe the field site, the instrumentation
involved in the deployment, flight plans, and other operational
activities.

The science papers will be contributions from the FIRE-III
investigators. They will be "stand-alone" papers that the investigators
will prepare summarizing measurements, data interpretation, and data
correlations. Collaborative papers between different groups are strongly
encouraged.

4\. A firm timetable for the publication of the special issue papers
will be established whereby all of the papers will be submitted for
publication prior to a mutually agreed date, usually within a year after
the field experiment completion.

5\. Oral presentations of selected results by the investigators and the
project may be presented together at an appropriate conference.

6\. Additional publications or presentations by FIRE-III investigators
beyond those identified above are expected and encouraged. Other
publications should, however, be in harmony with the data protocol and
publication plan contained in this document.

5.0 INTENSIVE FIELD PROGRAM MANAGEMENT

5.1 Operations Plans

The FIRE Project Office and FST will prepare detailed operations plans
for the two observational components of FIRE: the Arctic Cloud
Experiment and a possible Tropical Cirrus Experiment. The Arctic plan
must be developed as soon as possible in light of the short lead time on
the field experiment.

5.2 Lead Mission Scientists

Dr. Judy Curry, University of Colorado, has been appointed as the Lead
Mission Scientist for the Arctic Cloud Experiment; Dr. David Starr,
GSFC, has been appointed as the Lead Mission Scientist for the potential
Tropical Cirrus IFO. The Lead Mission Scientists will be the scientific
spokespersons for their respective field experiments. They will
coordinate the planning and implementation of the science objectives,
conduct planning and debriefing sessions, and be the chief
representatives of and arbitrators for the participants in the field
missions.

5.3 Mission Selection Team

During the intensive field phases of the cirrus and Arctic activities, a
Mission Selection Team (MST) will be formed. The MST will be comprised
of no more than five FST scientists representing the multiple goals of
FIRE; it will be chaired by the FST Lead Mission Scientist who has the
following responsibilities immediately prior to and during the intensive
field observation periods. The MST will accept the following
responsibilities:

i\. solicit and represent ideas of other FIRE scientists in operations,
decisions, and scheduling.

ii\. review on a daily basis the candidate missions proposed by the MPT
(see section 5.4) and select the planned operations and scheduling of
all FIRE platforms for the following day.

iii\. select on a daily basis a Mission Scientist (DMS) and an Alternate
Mission Scientist (see section 5.5) to plan and carry out operations
selected by the MST.

iv\. assemble forecast information for use in daily operations planning.
\*

v\. maintain up-to-date experiment accomplishment records for use in
daily operations planning. \*

vi\. maintain current status reports on all data gathering components
throughout the experiment. \*

vii\. review daily post mission reports prepared by the Mission Planning
Team (see section 5.4).

\*assisted by the FIRE Project Office.

All deliberations of the Mission Selection Team will be open and in the
absence of a clear consensus among its members; the chairman will assume
responsibility for making operations decisions.

On issues concerning specific platforms such as aircraft or special
rawinsonde ascents, an appropriate spokesperson from the contributing
organization will be given the opportunity to advise the MST and to
participate on the MPT.

5.4 Mission Planning Team

A Mission Planning Team (MPT) will be active during the cirrus and
Arctic IFO phases. The Mission Planning Team will contain at least three
FST members, individuals representing participating aircraft facilities,
and FIRE Project Office support personnel. The MPT members for the
following day will be identified on a daily basis by the MST. Ideally,
on any given day, the MPT will contain candidate mission scientists for
the following day\'s operations. It will not be uncommon for members of
the MST to also serve as members of the MPT. The MPT\'s responsibilities
are listed below.

i\. Prepare candidate missions for the following day\'s operations and
present these to the MST for consideration.

ii\. Following a decision by the MST, support the Mission Scientist in
preparing a detailed mission plan for the following day\'s operations.

iii\. Prepare a post mission summary of each day\'s operations including
an evaluation of the success of the operations. This daily summary will
be made available to the FIRE Project Office and the MST and become a
part of the FIRE data archive.

5.5 Daily Mission Scientist

A Daily Mission Scientist (DMS) will be identified on a daily basis by
the MST at the same time the following day\'s mission is selected. In
the event that the DMS plans to be aboard an aircraft during the
mission, an Alternate Mission Scientist will be selected by the MST and
will perform ground-based functions during the execution of the mission.
The DMS will be in charge of the detailed planning of the following
day\'s operations and the execution of that plan. He will be responsible
for making any real time decision required during the execution of the
plan. He will oversee the preparation of a post mission summary of each
day\'s operations including an evaluation of the success of the
operations.

A Mission Scientist must have an overall grasp of the scientific
objectives of FIRE as well as an appreciation of operational constraints
of the various platforms and personnel.

The Mission Scientist will be selected from a pool of lay FST
researchers according to the scientific objectives to be addressed in
the upcoming mission and the scientific background of the individual.
Ideally, the Mission Scientist would be the previous mission\'s
Alternate Mission Scientist so as to provide continuity between missions
and familiarity with on-going meteorology conditions and forecasts.

5.6 IFO Meeting Schedule

The schedule of IFO meetings (and data management milestones) is given
in Table 7.

5.6.1 Simulated IFO

Approximately 2-3 months before the intensive field mission, a simulated
IFO will be conducted at an appropriate site. The purpose of the meeting
will be to simulate the mission planning, based on realistic
meteorological conditions and using those major platform instruments
that will participate in the actual IFO. Representatives from the
satellite, aircraft, and surface-based instruments will participate, as
well as the IFO meteorologist/forecaster and other key researchers.
Meteorology forecasts will be based on actual meteorology conditions
experienced by the IFO area one year previously.

The mission planning will include: research objectives to be addressed;
platforms/instruments that will operate; and deployment strategy
including flight plans, operating schedules, data taking modes, etc.
Constraints to be considered include finite resources (aircraft flight
hours allocated, cost of rawinsonde/satellite data, etc.), realistic
operational schedules (aircraft/crew constraints, personnel/instrument
fatigue, etc.), uncertainty of meteorology forecast, etc. The mission
plans will be evaluated for expected results, based on actual
meteorological conditions experienced on the mission day, and
improvements/modifications will be discussed. The meteorological
forecast for the next day will be presented and the cycle repeated.

5.6.2 Pre-Mission Meeting

A pre-mission meeting will be held on the day before the start of the
intensive field mission. The purpose of the meeting will be to welcome
all participants, describe the mission operations strategy, provide
information on local logistics, determine the operational status of the
participating platforms/instruments, and allow the local media an
opportunity to interact with the mission, principal investigators, and
other participants. A mission planning meeting for the first day of
operations will follow immediately.

5.6.3 Post-Flight Debrief

After each experiment flight there will be a post-flight debriefing of
all the experimenters. This debriefing is intended to communicate and
document pertinent subjective observations made during the completed
mission and allow the experimenters an opportunity to modify subsequent
plans or procedures for the following experiment. Each experimenter
should have a "quick-look" capability for inspection of his sensor
performance. A copy of the "quick-look" data (raw strip charts, tables,
etc.) may be submitted to the Data Manager for possible
comparison/correlation with other experiment data.

5.6.4 Post-Mission Debrief

A post-mission debriefing will be held on the day following the
conclusion of the mission. Each experimenter will describe his sensor
performance, a summary of sensor operating times, a sample of data
obtained, a description of the data format that is planned to be
submitted to the data archive, and a listing of experiment days to be
analyzed in a priority order. The Working Group will review the missions
and measurements obtained during the mission. If appropriate, it will
prioritize experiment days to be analyzed, key areas of data reduction
and analysis, identify possible data collaboration and exchange, and
modify the data reporting schedule.

5.6.5 Science Data Workshop-I

A preliminary data reduction workshop will be held approximately 4
months after the conclusion of the mission. The experimenters will
describe the performance of their sensors, a sample of the preliminary
reduced data, an estimate of the sensor accuracy and precision, and
report on key measurement results. The FIRE-III Science Team will review
the measurements and analyses obtained to date, determine a priority
list of case study days, identify possible collaborative research
activities, and discuss future plans. The DMWG will ascertain the data
parameters that have been acquired during the experiment, decide on
category elements such as reduced, value-added, and investigator-held
data, and assign category priorities and data submittal schedules.

5.6.6 Science Data Workshop-II

Approximately one year after the field experiment, a Science Data
Workshop-II will be held to review the key research results. The
FIRE-III Science Team will review each of the major scientific
objectives in light of the measurements and analyses performed to date,
review the status and availability of the data, explore synergistic
relationships among the data, actively plan collaborative research
activities, and discuss future plans. The DMWG will review the reduced
data parameters that have been submitted, review the data category
elements and priorities, and recommend possible verification and quality
assurance activities.

5.6.7 Science Conference

About 24 months after the field experiment, the FIRE-III Science
Conference will be held for the presentation of individual and group
science results. This conference may be either a stand alone meeting or
possibly held as part of an international science conference. The DMWG
will review all data products that have been submitted, review the
status of the quality assurance and verification activities, and make
recommendations concerning the certification process of the data sets
that will eventually be released to the scientific community.

5.7 Data Management Schedule

The schedule of events for the submittal of data to the archive is given
below and in table 3.

5.7.1 Summary Information and Preliminary Category-1 Data Submittal

Approximately 3 months after FIRE-III, the experimenters will submit the
summary information and preliminary category-1 data to LaRC DAAC.

5.7.1 Final Category-1 Data Submittal

Approximately 6 months after FIRE-III, the experimenters will submit the
final category-1 data information to LaRC DAAC.

5.7.2 Preliminary Category-2 and-3 Data Submittal

Approximately 9 months after FIRE-III, the experimenters will submit the
preliminary reduced category-2 and category-3 (if desired) data products
to LaRC DAAC.

5.7.3 Final Category-2 and -3 Data Submittal

Approximately 18 months after FIRE-III, the experimenters will submit
the final reduced category-2 and-3 (if desired) data products to LaRC
DAAC.

5.7.4 Release of Data to Science Community

**After the certification of the data has been completed (probably
within 6-24 months), the LaRC DAAC will release the data to the
scientific community. Any proprietary rights (see section 4.4.1) to the
data and data interpretation will be voided at this time.**

  ------------ ----------------------------- -----------------------------
  **Table 3 -                                
  IFO Meeting                                
  and Data                                   
  Management                                 
  Schedule**                                 

  **Time,      **IFO Meetings**              **Data Management Activity**
  Months**                                   

  -3           Simulated IFO                 

  -1 (day)     Pre IFO Review; First Mission 
               Planning                      

  0            IFO                           

  IFO+1 (day)  Post IFO Review; prioritize   
               case study days               

  3            Science Data Workshop-I;      Submit preliminary summary
               definition of data product    information and category-1
               categories, identification of data
               case study investigations     

  6                                          Submit final category-1 data

  9                                          Submit preliminary category-2
                                             and -3 data

  12           Science Data Workshop-II;     
               review data status and        
               availability, recommend       
               verification and quality      
               assurance activities          

  18                                         Submit final category-2 and
                                             -3 data

  24           Science Conference; authorize Release data to scientific
               release of data to scientific community
               community                     
  ------------ ----------------------------- -----------------------------

6.0 FIRE MISSION/MEETING MILESTONES

**The listing of the key FIRE missions and meetings is given in Table 4.
An attempt has been made to coordinate the various meetings between the
Cirrus and Arctic working groups so that those researchers who are
participating in both missions can attend. Whenever the two groups hold
coincident meetings, a full-up FST meeting will also be held so as to
integrate the extended-time and modeling activities as much as possible.
The milestone list reflects a minimum meeting schedule; other meetings
may be scheduled (i.e., meetings to plan for/review Tropical Cirrus
Mission) as required. Also included are some key cirrus collaborative
experiments, such as the Subsonic Aircraft Contrail and Cloud Special
Study (SUCCESS) and the Stratospheric Tropospheric Experiment:
Radiation, Aerosols, and Ozone (STERAO). As a minimum, the entire FST
will meet at least once per year.**

+----------+---------------------+---------------------+---------------+
| **Table  |                     |                     |               |
| 4 -      |                     |                     |               |
| T        |                     |                     |               |
| entative |                     |                     |               |
| Schedule |                     |                     |               |
| of FIRE  |                     |                     |               |
| Acti     |                     |                     |               |
| vities** |                     |                     |               |
+----------+---------------------+---------------------+---------------+
| *        | **1996**            | **1997**            | **1998**      |
| *Month** |                     |                     |               |
+----------+---------------------+---------------------+---------------+
| January  |                     |                     |               |
+----------+---------------------+---------------------+---------------+
| February |                     |                     | Arctic Cloud  |
|          |                     |                     | Site          |
|          |                     |                     | Inspection    |
|          |                     |                     |               |
|          |                     |                     | Arctic Cloud  |
|          |                     |                     | Simulated     |
|          |                     |                     | Experiment    |
+----------+---------------------+---------------------+---------------+
| March    | [FIRE-III ST        | STERAO-B-I          |               |
|          | M                   | Experiment          |               |
|          | eeting]{.underline} |                     |               |
|          |                     |                     |               |
|          | SUCCESS Simulated   |                     |               |
|          | Exp                 |                     |               |
|          |                     |                     |               |
|          | .                   |                     |               |
+----------+---------------------+---------------------+---------------+
| April    | SUCCESS Experiment  | STERAO-B-I          | Arctic Cloud  |
|          |                     | Experiment          | Experiment    |
+----------+---------------------+---------------------+---------------+
| May      | SUCCESS Experiment  | Arctic Cloud Site   | Arctic Cloud  |
|          |                     | Survey-B            | Experiment    |
|          |                     |                     |               |
|          |                     | [FIRE-III ST        |               |
|          |                     | M                   |               |
|          |                     | eeting]{.underline} |               |
+----------+---------------------+---------------------+---------------+
| June     |                     | STERAO-B-II Site    |               |
|          |                     | Inspection          |               |
+----------+---------------------+---------------------+---------------+
| July     |                     |                     |               |
+----------+---------------------+---------------------+---------------+
| August   | Arctic Cloud Site   | STERAO-B-II         | [FIRE-III ST  |
|          | Survey-A            | Experiment          | Meeting       |
|          |                     |                     | ]{.underline} |
+----------+---------------------+---------------------+---------------+
| S        | STERAO-B-II Site    | STERAO-B-II         |               |
| eptember | Survey              | Experiment          |               |
+----------+---------------------+---------------------+---------------+
| October  | [Arctic Cloud WG    | Tropical Cirrus     |               |
|          | M                   | Site Survey-B       |               |
|          | eeting]{.underline} |                     |               |
|          |                     |                     |               |
|          | [Cirrus WG          |                     |               |
|          | M                   |                     |               |
|          | eeting]{.underline} |                     |               |
|          |                     |                     |               |
|          | SUCCESS Science     |                     |               |
|          | Workshop            |                     |               |
+----------+---------------------+---------------------+---------------+
| November | Tropical Cirrus     | [FIRE-III ST        |               |
|          | Site Survey-A       | M                   |               |
|          |                     | eeting]{.underline} |               |
|          |                     |                     |               |
|          |                     | STERAO-B Science    |               |
|          |                     | Workshop            |               |
+----------+---------------------+---------------------+---------------+
| December |                     |                     |               |
+----------+---------------------+---------------------+---------------+

7.0 NATIONAL AND INTERNATIONAL COLLOBORATIONS

FIRE Phase III is a research project to be conducted by U.S. scientists
to provide a U.S. national contribution to the ISCCP of the World
Climate Research Program. In the sense that the scientific problems
addressed by FIRE-III are truly international, it is in the best
interests of FIRE- III to encourage the free exchange of information
with our national and international colleagues. Although resources do
not permit direct support of foreign participation in FIRE-III, ways
will be sought to allow collaboration, such as full participation and
data sharing as members of the FST. Other forms of collaboration will
also be encouraged. Cooperation and coordination with the research
projects of other countries will be accomplished through the ISCCP
Working Group on Data Management (DMWG), which is responsible for this
function under the Joint Scientific Committee of WMO and International
Committee of Scientific Unions. Reports of FIRE-III activities will be
made at the annual ISCCP WGDM meetings.

**Appendix E - Acronyms**

  ---------------------- ------------------------------------------------
  Table 1 - List of      
  Acronyms.              

  2D-C                   An instrument used to detect small ice crystals

  2D-P                   Heymsfield abstract

  ACE-1                  Atmospheric Chemistry Experiment; first mission

  ACSYS                  Arctic Climate System Study

  AEAP                   Atmospheric Effects of Aviation Program

  AES                    Atmospheric Environmental Service (Canada)

  AGASP                  Arctic Gas and Aerosol Sampling Program

  AIDJEX                 Arctic Ice Dynamics Joint Experiment

  AIRS                   Atmospheric Infrared Sounder

  AMS                    Alternate Mission Scientist, American
                         Meteorological Society

  ARC                    Ames Research Center

  ARCS                   Atmospheric Radiation and Clouds Station

  ARCSS                  Arctic System Science

  ARCSyM                 Arctic Regional Climate System Model

  ARM                    Atmospheric Radiation Measurements Program

  ARMCAS                 Arctic Radiation Measurement in Column
                         Atmosphere-surface System

  ASCII                  American Standard Code for Information Exchange

  ASTEX                  Atlantic Stratocumulus Transition Experiment

  AVHRR                  Advanced Very High Resolution Radiometer

  BASE                   Beaufort Arctic Storms Experiment

  C-130Q                 NCAR aircraft

  C-131A                 University of Washington aircraft

  CAGES                  A Canadian hydrological experiment planned for
                         the MacKenzie river basin

  CAR                    Cloud Absorption Radiometer

  CARAFE                 Cloud, Aerosol and Radiation Arctic Field
                         Experiment

  CARSS                  Liou abstract

  CCD                    Charged-Coupled Diode

  CCM                    Continuous Core Measurements

  CCN                    cloud condensation nuclei

  CEPEX                  Central Equatorial Pacific Experiment

  CERES                  Clouds and the Earth\'s Radiant Energy System

  CFD                    Continuous Flow Diffusion

  CIMMS                  Cooperative Institute for Mesoscale
                         Meteorological Studies

  CIRES                  Cooperative Institute for Research in
                         Environmental Science

  CMC                    Canadian Meteorological Centre

  CN                     Cloud Nuclei

  COARE                  Coupled Ocean-Atmosphere Regional Experiment

  CSU                    Colorado State University

  CV-580                 Canadian National Research Council aircraft

  CVI                    Counterflow Virtual Impactor

  D1                     An ISCCP data type

  DAAC                   Distributed Active Archive Center(EOS)

  DMA                    Differential Mobility Analyzer

  DMS                    Daily Mission Scientist, dimethyl sulfide

  DMSP                   Defense Meteorological Satellite Program

  DMWG                   Data Management Working Group

  DOD                    Department of Defense

  DOE                    Department of Energy

  DRI                    Desert Research Institute

  DX                     An ISCCP data type

  ECMWF                  European Center for Medium-range Weather
                         Forecasts

  EDX                    Energy-dispersive x-ray

  ENSO                   El Nino Southern Oscillation

  EOS                    Earth Observing System

  EOS-AM                 series of EOS satellites with morning crossings
                         of equator

  EOS AM-1               first launch in EOS series

  EOSDIS                 Earth Observing System Data and Information
                         System

  EOS PM                 series of EOS satellites with afternoon
                         crossings of equator

  ER-2                   NASA ARC aircraft

  ERB                    Earth Radiation Budget instrument (NIMBUS 6 &7)

  ERBE                   Earth Radiation Budget Experiment (on ERBS)

  ERBS                   Earth Radiation Budget Satellite

  ERL                    Environmental Research Laboratory(NOAA)

  ERS                    European Remote-sensing Satellite

  ETL                    Environmental Technology Laboratory(NOAA)

  FARS                   Facility for Atmospheric Research (U. Utah)

  FIRE                   First ISCCP Regional Experiment

  FIRE I                 FIRE Phase I(1984-1989)

  FIRE II                FIRE Phase I(1990-1994)

  FIRE III               FIRE Phase III(1995-)

  FSSP                   Forward Scattering Spectrometer Probe

  FST                    FIRE Science Team

  FTIR                   Fourier Transform Interferometer Radiometer

  GAC                    Global Area Coverage

  GCM                    General Circulation Model

  GCSS                   GEWEX Cloud System Studies

  GEWEX                  Global Energy and Water Cycle Experiment

  GFDL                   Geophysical Fluid Dynamics Laboratory

  GISS                   Goddard Institute for Space Studies

  GOES                   Geostationary Orbiting Environmental Satellite

  GPS                    Global Positioning System

  GSFC                   Goddard Space Flight Center

  GTS                    Global Telecommunications System

  HDF/netCDF             hierarchical data format/net common data format

  HIRS                   High Infrared Radiation Sounder

  HIS                    High-resolution Interferometric Sounder

  IFN                    ice forming nuclei

  IFO                    intensive field observations

  IN                     ice nuclei

  INAA                   Instrument Neutron Activation Analysis

  INDOEX                 Indian Ocean Experiment

  ISCCP                  International Satellite Cloud Climatology
                         Experiment

  IWC                    ice water content

  IWP                    ice water path

  LAC                    local area coverage

  Landsat                NASA Earth observing satellite series

  LaRC                   Langley Research Center

  LEADEX                 Lead Experiment

  LES                    large-eddy simulation

  LITE                   Lidar In-space Technology Experiment

  LWC                    liquid water content

  LWP                    liquid water path

  MAS                    MODIS airborne simulator

  MAST                   Monterey Area Ship Track Experiment

  MODIS                  Moderate-resolution Imaging Spectrometer

  MPT                    Mission Planning Team

  MSFC                   Marshal Space Flight Center

  MST                    Mission Selection Team

  NASA                   National Aeronautics and Space Administration

  NCEP                   National Center for Environmental Prediction

  NIMBUS                 NASA series of research meteorological
                         satellites(1-7)

  NOAA                   National Oceanic and Atmospheric Administration

  NRC                    National Research Council

  NSA/AAO                North Slope of Alaska/Adjacent Arctic Ocean

  NSF                    National Science Foundation

  N.W.T.                 Northwest Territories

  OGP                    Office of Global Programs

  ONR                    Office of Naval Research

  PDL                    Polarization Diversity Lidar

  P.I.                   Principal Investigator

  PMS                    Hallett abstract

  POLES                  Polar Exchange at the Sea Surface

  PSU                    Penn State University

  PVM                    Particulate Volume Monitor

  RAMS                   Radiation Airborne Measurement System

  RASS                   Radio Acoustic Sounding System

  SAR                    Synthetic Aperture Radar

  SASS                   Subsonic Assessment Program (part of AEAP)

  SBIR                   Small Business Initiative Research

  SCAR-B                 Smoke, Clouds and Radiation-Brazil

  SCICEX                 Submarine Science Ice Experiment

  SCM                    Single-Column Model

  SDSMT                  South Dakota School of Mines and Technology

  SEM                    scanning electron microscopy

  SHEBA                  Surface Heat Budget of the Arctic Ocean

  SOCEX-I & II           Southern Ocean Cloud Experiments

  SPEC                   Stratton Park Engineering Co. Inc.

  SPECTRE                Spectral radiation Experiment

  SPFR                   Spectral Flux Radiometer

  SRB                    Surface Radiation Budget

  SSM/I                  Special Sensor Microwave/Imager

  SUCCESS                Subsonic Aircraft: Control and Cloud Effects
                         Special Study

  T-39                   NASA LaRC aircraft

  TEM                    Transmission Electron Microscopy

  TIROS                  Television Infrared Observation Satellite

  TIROS N                Third generation of NOAA operational
                         meteorological satellites

  TM                     Thematic Mapper

  TOGA                   Tropical Ocean and Global Atmosphere

  TOVS                   TIROS Operational Vertical Sounder

  TSC                    Tilt-Scan Camera

  TWP                    Tropical Western Pacific

  UHF                    Ultra-High Frequency

  UND                    University of North Dakota

  URL                    Universal Resource Locator (a web address)

  UW                     University of Washington

  WB-57F                 NCAR aircraft

  WCRP                   World Climate Research Program

  WGDM                   Working Group on Data Management

  WPDN                   Ackerman abstract

  WMO                    World Meteorological Organization
  ---------------------- ------------------------------------------------

**Appendix F - FIRE Abstracts**

Coordinated Parameterization Development and Large-Eddy Simulation for
Marine and Arctic Cloud-Topped Boundary Layers 1

C. S. Bretherton, University of Washington

Application of LES to Understanding and Parameterizing the Arctic Cloudy
Boundary Layer 1

Judith Curry, University of Colorado, William Cotton, Colorado State
University

Boundary-Layer Dynamics and Cloud-radiative Studies in SHEBA:
Development of an Atmospheric Lidar and a Wind Profiler 2

C. W. Fairall, C. Grund, and R. G. Strauch, NOAA Environmental
Technology Laboratory

FIRE III Research 2

Peter Hobbs, University of Washington

FIRE III Research 3

James Hudson, Desert Research Institute

Canadian Participation in FIRE III 4

George Isaac, Atmospheric Environment Service

Remote Sensing and Retrieval of Arctic Stratus Clouds and Surface
Characteristics 5

Michael King and Si-Chee Tsay, NASA/Goddard Space Flight Center

Study of midlatitude and Arctic aerosol-cloud-radiation feedbacks based
on LES model with explicit ice and liquid phase microphysics 5

Yefim Kogan, Cooperative Institute for Mesoscale Meteorological Studies,
Douglas Lilly, School of Meteorology, University of Oklahoma

Cloud-Scale Numerical Modeling of the Arctic Boundary Layer 6

Steven Krueger, Dept. of Meteorology, University of Utah

FIRE-III Research 7

D. H. Lenschow and Q. Wang, National Center for Atmospheric Research

FIRE III Research 8

David Randall, Colorado State University

Radiative Energy Exchanges In The Polar Regions 8

William Rossow and Barbara Carlson, Institute for Space Studies, Goddard
Space Flight Center

Studies of the Radiative and Microphysical Properties of 9

Cloud, Aerosol and Surface Bi-Directional Reflectance 9

Si-Chee Tsay and Michael King, NASA Goddard Space Flight Center

FIRE III Research 9

Tanneil Uttal, NOAA Environmental Technology Laboratory

A Parameterization Study of Marine Boundary-Layer Clouds 10

Shouping Wang, NASA Marshall Space Flight Center

**Appendix F - Abstracts of FIRE Phase III Arctic Projects**

Coordinated Parameterization Development and Large-Eddy Simulation for
Marine and Arctic Cloud-Topped Boundary Layers

C. S. Bretherton, University of Washington

Large-eddy simulations of subtropical and summertime Arctic cloud-topped
boundary layers will be carried out. The subtropical boundary layer
simulations will focus on two issues: (1) improving entrainment closures
based on in-cloud turbulent kinetic energy and cloud-top jumps, which
are currently quite uncertain, and ascertaining what resolution in the
entrainment zone is required for an LES to reliably predict entrainment,
and (2) assessing the role of penetrative cumulus entrainment in
stratocumulus breakup. We hope to incorporate key findings in both of
these areas to improve current GCM parameterizations of the cloud-topped
boundary layer. A third area of study will be testing the feasibility of
several proposed mechanisms for multiple cloud layering in the
summertime Arctic in an LES and comparing against field data.

Application of LES to Understanding and Parameterizing the Arctic Cloudy
Boundary Layer

Judith Curry, University of Colorado, William Cotton, Colorado State
University

The goal of this research is to improve our understanding of physical
processes occurring in the arctic cloudy boundary layer and our ability
to parameterize them in climate models. The approach taken here is to
use Large-Eddy Simulation (LES) to interpret the interactions among
turbulence, cloud microphysics and radiative transfer in the following
different arctic cloudy boundary layer types: warm-season layered clouds
with decoupled mixed layers; and wintertime ice crystal plumes emanating
from leads. The LES results will be compared with observations, and
parameterizations suitable for large-scale models will be developed. The
results will be evaluated in the context of the forthcoming Arctic FIRE
experiment, particularly the optimal measurements to be obtained from
aircraft (including flight patterns) and the large-scale measurements
required to interpret cloud life-cycles. Specifically, we propose to
accomplish the following:

\* develop and implement a new subgrid-scale parameterization and other
modifications for the LES to accommodate the stable boundary layers that
are present in the Arctic;

\* introduce new parameterizations into the LES related to the impact of
turbulence on droplet spectra and the influence of droplet spectra on
the radiation fluxes;

\* simulate and interpret the interactions among turbulence, cloud
microphysics and radiative transfer in the different arctic cloudy
boundary layer types;

\* compare the LES output with case studies obtained from the Arctic
Stratus Experiment and BASE;

\* evaluate the importance of diabatic terms in second-moment equations,
and develop a parameterization for these terms for second-order closure
models if necessary;

\* evaluate the applicability of existing cloud fractional coverage
schemes to Arctic stratus and develop a new scheme if necessary; and

\* examine scale interactions using the LES nested in a mesoscale model
(RAMS).

Boundary-Layer Dynamics and Cloud-radiative Studies in SHEBA:
Development of an Atmospheric Lidar and a Wind Profiler

C. W. Fairall, C. Grund, and R. G. Strauch, NOAA Environmental
Technology Laboratory

This is an NSF SHEBA Phase-1 project to develop, construct, and test a
lidar and wind profiler for future deployment in the ice station of the
Surface HEat Budget of the Arctic Ocean (SHEBA) program, planned for an
12-month field program beginning in March 1997. These systems will be
used in conjunction with other surface-based remote sensors (e.g.,
Doppler cloud radar, microwave radiometer, Doppler sodar, FTIR
radiometers) and in situ sensors for studies of boundary-layer dynamical
and cloud-aerosol/radiative effects on the ice surface energy budget.
The work will be coordinated with the FIRE-III Arctic Cloud research
program. The wind profiler will be a portable, scaled down version of
the NOAA network UHF profilers and be designed to provide wind and
temperature profiles continuously to 2.5 km altitude. Through additional
data processing, it will also provide information on velocity and scalar
turbulence profiles and precipitation. Based on an analysis of LEADEX
data, most of the temporal variability in the vertical gradients of
temperature, dew point, and (to a lesser extent) winds occurs below this
altitude. The lidar will be a derivative of a system being developed for
cloud and aerosol monitoring and will measure optical depth and provide
cloud ice/water discrimination. It will feature both vertically pointing
and zenith angle scan capabilities. The scanning capability is shown to
be critical for obtaining information about complex ice crystal habit
and structure. Both systems will feature high reliability in Arctic
conditions and will operate "unattended" except for occasional changing
of storage media or transfer of data. In the first year of the project,
the lidar will be designed and constructed. An existing prototype radar
(being developed for another program) and the lidar then will be tested
in Alaska for one month. In the second year of the project, the radar
will be constructed following evaluation of the test results. Both
systems will be field tested in Alaska a second time then deployed at
the SHEBA ice station.

FIRE III Research

Peter Hobbs, University of Washington

Subject to the availability of funding, the Cloud and Aerosol Research
Group at the University of Washington intends to bring its new
twin-engine, turbo-prop, Convair-580 research aircraft to the Arctic to
participate in a portion of the 6-week FIRE-III intensive field project.
The aircraft will be instrumented for state-of-the-art measurements of
aerosols, cloud microstructures, and cloud radiative properties,
including the NASA-built CAR 13-wavelength radiometer and lidar. Our
proposed research, which will be carried out in collaboration with Drs.
Michael King and Si-Chee Tsay, includes the following:

\(1\) Measurements of cloud-active aerosols and their precursors.

\(2\) Measurements of the statistical properties of the microphysical
structures of arctic stratus and other clouds.

\(3\) Direct measurements of the spectral radiative properties of arctic
stratus, and comparisons with values derived from simultaneous
measurements of cloud structure.

\(4\) Comparisons of in situ measurements of cloud properties with
remote sensing measurements from the ER-2 and satellites.

We expect also to carry out a number of measurements concerned with the
radiative properties of sea-ice, snow, tundra etc., and the energy
balance of the arctic. However, since these are more related to SHEBA,
they are not discussed here.

FIRE III Research

James Hudson, Desert Research Institute

I plan to mount both of the DRI instantaneous CCN spectrometers (Hudson
1989) aboard a research aircraft and operate them throughout the
project. The most likely aircraft is the NCAR C-130, where they have
been in two previous projects-Telford California stratus project
(December 1994) and SCMS (July-August 1995). Two spectrometers are
desirable in order to efficiently do sample processing upstream of one
of the CCN spectrometers. This allows the other instrument to
continuously monitor the ambient CCN spectrum. Continuous ambient
spectra are vital in aircraft operations where the concentrations change
rapidly with position (e.g., there are often vertical differences in
concentration and/or spectral slope that are significant for cloud
microphysics; also horizontal differences due to scavenging and
production). The sample processing mechanisms include:

\(1\) Volatility-subjecting the sample to various temperatures to
indirectly estimate particle composition (Hudson and Da 1996) especially
with respect to NaCl, sulfates, and carbon.

\(2\) Size vs. supersaturation-classifying the sample according to dry
particle size with an electrostatic classifier (DMA). This allows an
estimate of the relative solubility of the particles. Hudson and Da
(1995) showed that CCN from polluted air masses tend to act as though
they have more insoluble material internally-mixed with the soluble
material that makes them CCN. CCN from cleaner air masses, however, act
like pure soluble substances. These two techniques are complementary;
when taken together they derive significant information about CCN
composition.

\(3\) Different inlets-The CCN spectra of droplet residue material from
the counterflow virtual impactor (CVI) can be used to determine the
effects of entrainment on cloud droplet spectra (Twohy and Hudson 1995).
If the CVI is not available then a cloud interstitial inlet can also
produce similar information about the effects of cloud dynamics on cloud
droplet spectra. When both spectrometers monitor the ambient there is
increased confidence in the measurements not to mention the enhanced
reliability of two instruments. In addition to comparing the cloud
droplet and CCN concentrations I am

keenly interested in comparing the shapes of the two spectra. Martin et
al. (1994) suggested that the shape of the cloud droplet spectrum is
related to the shape of the CCN spectrum. Indeed Hudson and Li (1995)
found significant differences in the shape of the CCN spectra in
polluted and clean air masses; this is an additional difference to the
well-known difference in number concentration. However this may or may
not go the same way in the Arctic. The shape (breadth) of the droplet
spectrum is very important for the initiation of drizzle
(autoconversion), which has been suggested to be related to CCN
concentrations (e.g., Albrecht et al. 1995). Drizzle initiation may also
depend on CCN spectral shape, testing this requires measurements at
supersaturations lower than the actual cloud supersaturations, which are
generally quite low for stratus clouds anyway (e.g., Hudson and Svensson
1995). Such low supersaturations (\<0.1%) are only possible with the DRI
spectrometer. We expect significant variations in CCN concentrations,
spectral shape, and composition during FIRE-III. CCN spectral
measurements are necessary in order to keep separate the effects on
cloud microphysics of differences in cloud dynamics from the differences
in the aerosol. Comparisons with identical CCN measurements in several
other stratus projects-FIRE, ASTEX, SOCEX-1 and 2, MAST, ACE-1-will be
indispensable.

Canadian Participation in FIRE III

George Isaac, Atmospheric Environment Service

Observations collected by the Canadian PIs, together with the other FIRE
III data, will be used to understand Arctic cloud formation,
development, and their effect on climate change. Special emphasis will
be put on the interaction of clouds with Arctic aerosols, the influence
of the aerosol chemistry upon cloud microphysical characteristics, and
the general role clouds play in redistributing, transforming, and
transporting Arctic chemical constituents. Using the observations,
microphysical, chemical, dynamical and radiative properties, as well as
the vertical and horizontal spatial distribution characteristics, of the
clouds studied will be determined. Parameterizations will be developed,
describing these properties, that might be used in GCMs. Where possible,
these parameterizations will be tested and evaluated in column models,
cloud resolving models and full scale GCMs. It is anticipated that the
National Research Council Convair 580 will be involved and would fly out
of Inuvik, N.W.T. Some specialized ground instrumentation, such as
lidars and radars, might also the used. The possibility of placing
instrumentation for measuring chemical constituents on the ice flow and
other surface sites will be explored. This research will involve a large
number of Canadian scientists and is dependent upon Canadian funding
approval. Formal plans for this work, including PIs designated for each
task, have yet to be developed.

Remote Sensing and Retrieval of Arctic Stratus Clouds and Surface
Characteristics

Michael King and Si-Chee Tsay, NASA/Goddard Space Flight Center

NASA Goddard Space Flight Center This investigation continues studies of
the radiative and microphysical properties of Arctic stratus clouds and
snow/sea ice surface. The Moderate Resolution Imaging Spectroradiometer
(MODIS), being developed as part of the Earth Observing System (EOS) and
scheduled to be launched in 1998 on the EOS AM platform, is a NASA
facility instrument designed to meet the scientific needs for satellite
remote sensing of clouds, aerosols, water vapor, and land and ocean
surface properties. To support the development of MODIS remote sensing
and retrieval algorithms, the MODIS Airborne Simulator (MAS) was
developed for NASA\'s high-altitude ER-2 aircraft. This airborne sensor
is capable of providing narrowband spectral coverage similar to MODIS of
reflected solar and emitted thermal radiation. Over the past few years,
MAS has flown in many field campaigns throughout the world, including
recently in Alaska. These diverse data sets provide critical
measurements for assessing the scientific capability and usefulness of
MODIS channels, as well as enabling unique information to be acquired
from studies of the Earth\'s atmosphere and surface. Analyses of various
cases obtained during previous FIRE-I and II experiments are an
important element of this investigation. The intent of this
investigation is to support our participation in the FIRE-III field
campaigns and to analyze data obtained by MAS during previous campaigns.
Specifically, we intend to:

\(1\) participate in flight planning and direction of the NASA ER-2
aircraft over Arctic stratus clouds off the north slope of Alaska during
the 1997 field campaign,

\(2\) build a well-calibrated and feature-rich data set to improve MODIS
cloud masking algorithms,

\(3\) distinguish clouds from snow covered tundra and sea ice surfaces,

\(4\) retrieve stratus cloud properties (i.e., optical thickness and
effective particle radius) over highly reflecting surfaces, and

\(5\) perform statistical analyses of stratus cloud properties by using
high spatial resolution MAS data.

Study of midlatitude and Arctic aerosol-cloud-radiation feedbacks based
on LES model with explicit ice and liquid phase microphysics

Yefim Kogan, Cooperative Institute for Mesoscale Meteorological Studies,
Douglas Lilly, School of Meteorology, University of Oklahoma

The complexity of cloud-radiation problem results in large uncertainties
in estimating the cloud forcing in global climate models. Of particular
importance are cloud-radiation feedbacks in midlatitude, subtropical,
and high-latitude low level clouds. Continuing investigation of these
cloud systems is a major goal of the FIRE III program. The proposed work
will have two major thrusts:

\(1\) Continuing the study of marine stratocumulus clouds using LES
simulations based on FIRE I and FIRE II/ASTEX observational data. The
data will be used to validate the CIMMS LES model and to improve our
understanding of the interaction between the microphysical, radiative,
and thermodynamical processes. Validation of the model against
observations will result in further refinement and improvement in the
model physical and numerical formulation.

\(2\) Large-eddy simulations of arctic cloud systems, including low
level stratus with liquid and mixed phase microphysics. The modeling
part of the research will be based on the CIMMS 3-D LES model of a
stratocumulus cloud layer that includes an explicit formulation of
aerosol and cloud drop size-resolving microphysics and radiation. The
study of mixed phase clouds will use the new version of the CIMMS model
which includes also explicit formulation of the ice-phase microphysics.
Depending on the physics and the scale of the studied phenomena, the
model may be also formulated in a 2-D framework with bulk treatment of
microphysics. In accordance with the FIRE III objectives, model
simulations and data analysis will aim at the improvement of existing
parameterizations of cloud and radiative processes in LES and GCM
models. The analysis of ASTEX and FIRE III observational data sets will
be made in collaboration with the researchers from British
Meteorological Office, University of British Columbia and University of
Colorado.

Cloud-Scale Numerical Modeling of the Arctic Boundary Layer

Steven Krueger, Dept. of Meteorology, University of Utah

The interactions between sea ice, open ocean, atmospheric radiation, and
clouds over the Arctic Ocean exert a strong influence on global climate.
Uncertainties in the formulation of interactive air-sea-ice processes in
global climate models (GCMs) result in large differences between the
Arctic and global climates simulated by different models. Arctic stratus
clouds are not well-simulated by GCMs, yet exert a strong influence on
the surface energy budget of the Arctic. Leads (channels of open water
in sea ice) have significant impacts on the large-scale budgets during
the Arctic winter, when they contribute about 50 percent of the surface
fluxes over the Arctic Ocean, but cover only 1 to 2 percent of its area.
Convective plumes generated by wide leads may penetrate the surface
inversion and produce condensate that spreads up to 250 km downwind of
the lead, and may significantly affect the longwave radiative fluxes at
the surface and thereby the sea ice thickness. The effects of leads and
boundary layer clouds must be accurately represented in climate models
to allow possible feedbacks between them and the sea ice thickness. The
FIRE III Arctic boundary layer clouds field program, in conjunction with
the SHEBA ice station and the ARM North Slope of Alaska and Adjacent
Arctic Ocean site, will offer an unprecedented opportunity to greatly
improve our ability to parameterize the important effects of leads and
boundary layer clouds in GCMs. We will make extensive use of FIRE III
measurements and a high-resolution numerical model, the University of
Utah Cloud Resolving Model, to increase our understanding of the
physical processes that determine (1) the formation and structure of
Arctic stratus clouds and (2) how leads over the Arctic Ocean affect the
large-scale budgets of sensible heat, water vapor, and condensate. This
is necessary before developing lead and boundary layer cloud
parameterizations based on general physical principles. We will:

\(1\) Determine in detail how large-scale processes, in combination with
cloud-scale radiative, microphysical, and dynamical processes, govern
the formation and multi-layered structure of Arctic stratus clouds.

\(2\) Quantitatively determine the effects of leads on the large-scale
budgets of sensible heat, water vapor, and condensate in a variety of
Arctic winter conditions.

FIRE-III Research

D. H. Lenschow and Q. Wang, National Center for Atmospheric Research

Our intended FIRE-III research will utilize previous field observations
of Arctic clouds, boundary layer, and ice surface to perform integrated
analyses of both the Arctic clouds and boundary layer, and evaluate
instrument capabilities for making measurements in the Arctic
environment. We plan to use the data set obtained from the Beaufort and
Arctic Seas Experiment (BASE) as the primary data source for two
important reasons. First, BASE obtained measurements in the Arctic
boundary layer during the fall/winter transition season which is not
well documented. Several types of structure were measured during this
experiment, including stably-stratified boundary layers, mixed-phase
boundary-layer clouds, boundary layers modified by leads and polynyas,
and multi-layered cloud structures. Second, a primary platform for BASE
was the newly-instrumented NCAR C-130 which will likely be deployed in
FIRE-III/SHEBA. Evaluation of the capability of the instruments on the
C-130 to measure in the Arctic is an essential step in preparing for
FIRE-III/SHEBA. In addition to the BASE data set, we plan to analyze
measurements from the Arctic Stratus Experiment and the Arctic Ice
Dynamics Joint Experiment (AIDJEX) which measured the summertime Arctic
boundary layer; and from the Lead Experiment (LEADEX) which measured the
effects of open leads during winter/spring. Although these detests
(excluding BASE) have been studied by other researchers, our analyses
will focus on addressing problems that were uncovered during and after
these experiments with a view towards improving measurements in
FIRE-III/SHEBA. The primary objectives of the proposed research are to:

\(1\) understand the Arctic cloud and boundary layer structure,
especially the interactions among cloud microphysics, turbulence, and
radiation using in situ aircraft measurements;

\(2\) understand the moisture, heat, and momentum exchange between the
atmosphere and the Arctic ocean water/ice surface;

\(3\) assess the instrumentation capability on the newly-deployed NCAR
C-130 for making measurements in the Arctic environment and provide a
basis for improved measurement capabilities for the FIRE-III/SHEBA field
campaign;

\(4\) optimize the aircraft measurement strategy in the Arctic for
FIRE-III/SHEBA;

\(5\) provide analyses from in situ measurements useful for interpreting
and validating satellite remote sensing and retrieval algorithms, and
for validating results from numerical modeling of the Arctic clouds and
boundary layer (in collaboration with Judith Curry, University of
Colorado).

FIRE III Research

David Randall, Colorado State University

We propose to investigate the Arctic climate simulated by the current
version of the CSU GCM, with particular attention to cloudiness and the
surface energy budget. We propose to develop a one-dimensional model of
the Arctic atmosphere-sea ice-ocean system, which will exist in two
versions, which differ only in their treatment of the atmosphere. The
first version will be based on the second-order bulk model developed by
Randall et al. (1992), and the second will use a one-dimensional version
of the third-order closure developed by Krueger (1988). These
one-dimensional models will be tested using the FIRE Arctic data in
"single-column model" mode. Following these tests, at least one of the
two versions will be transferred to the CSU GCM, and used in simulations
of the present climate and of climate change due to increasing CO2.

Radiative Energy Exchanges In The Polar Regions

William Rossow and Barbara Carlson, Institute for Space Studies, Goddard
Space Flight Center

Some evaluations of the surface energy balance in the polar regions,
that currently maintains a persistent sea and land ice cover, suggest
that it may be very sensitive to climate perturbations; however, little
change in polar ice/snow cover over the past century has been
documented, even though global temperatures have changed somewhat. This
situation reflects a generally poor understanding of the polar energy
balance and the feedback processes controlling it. There has been recent
significant progress in quantitative methods for analysis of satellite
measurements of the atmosphere, clouds and surface; however, the paucity
of quality in situ observations has prevented verification of these
methods under more difficult polar conditions. Very few of these methods
have even been applied to study of the polar regions. As one step
towards determining the polar energy balance and the key controlling
processes, we propose to combine all of the available satellite
measurements into a single consistent analysis that measures the key
properties of the atmosphere, clouds and surface required to infer the
surface and top-of-atmosphere radiation balance and its seasonal
variations. Building and improving upon the ISCCP analysis of imaging
radiometer data, we will add measurements by infrared and microwave
sounders to deal with the complex variations of surface and cloud
properties. Using energy balance models, together with these
observations, we can infer the required energy transports (mostly by the
atmosphere) required to balance radiative fluxes and examine the
sensitivity of ice and snow cover to variations in these fluxes. If this
work is funded as part of a U.S. contribution to the Canadian GEWEX
experiment called BASE and a new project called SHEBA, then the value of
our combined satellite analysis will be greatly enhanced by comparison
with coordinated surface and aircraft observations and more complete
atmospheric and sea ice modeling efforts of collaborators.

Studies of the Radiative and Microphysical Properties of

Cloud, Aerosol and Surface Bi-Directional Reflectance

Si-Chee Tsay and Michael King, NASA Goddard Space Flight Center

This investigation continues studies of the microphysical, optical and
radiative properties of Arctic clouds and aerosols, and characterizes
the anisotropy of various types of Arctic surfaces. Tools of this
investigation include a multispectral scanning radiometer (CAR; Cloud
Absorption Radiometer), which is mounted on the University of
Washington\'s C-131A aircraft for the purpose of measuring the angular
distribution of scattered radiation deep within a cloud layer, from
which the single-scattering albedo of clouds can be determined as a
function of wavelength. Recent improvements in the CAR further advance
its capability in characterizing the radiative properties of aerosols
and surfaces. Over the past few years, the CAR has flown in many
national and international field campaigns, providing unique information
in studies of cloud, aerosol and surface properties. Analyses of various
cases obtained during previous experiments are an important element of
this investigation. The intent of this investigation is to participate
in the 1997 FIRE-III and upcoming SCAR-B (Smoke, Cloud And Radiation -
Brazil) field campaigns, and to analyze data obtained by the CAR as well
as other ancillary and coordinated microphysics/radiation data sets
during previous campaigns. Specifically, we intend to:

\(1\) validate experimentally the relationship between cloud
single-scattering albedo and effective particle radius from CAR
radiation measurements in the diffusion domain,

\(2\) determine the radiative properties (i.e., optical thickness,
single-scattering albedo and phase function) of aerosol from CAR
radiation measurements for use in developing atmospheric corrections,
and

\(3\) characterize the bidirectional reflectance distribution function
(BRDF) over Arctic snow and sea ice surfaces, as well as other types of
surfaces, to develop physically based surface BRDF models.

FIRE III Research

Tanneil Uttal, NOAA Environmental Technology Laboratory

The National Oceanic and Atmospheric Administration (NOAA) Environmental
Technology Laboratory (ETL) is presently involved in studies of
radiatively important cloud properties, utilizing data from the First
ISCCP Radiation Experiment (FIRE II) and the Atlantic Stratocumulus
Transition Experiment (ASTEX). Important remote sensing techniques based
on radar, lidar and IR radiometer measurements have been developed at
ETL that remote sensing of profiles of cloud properties such as particle
concentrations, sizes, LWC,and IWC from the ground that were not
possible even a few years ago. The proposed work includes analysis of
FIRE II and ASTEX data sets with an emphasis on comparison of cloud
properties between different climatic regimes. In addition, NOAA/ETL has
received separate funding for FY96 to conduct a similar measurement
campaign in the tropical Western Pacific as part of the DOE/ARM program.
In this proposal we also plan to augment the mid-latitude, sub-tropical
and tropical data sets with an arctic data set collected in conjunction
with the FIRE III and SHEBA programs in 1997. Deployment of a 35 GHz
radar, lidar, and IR radiometer will allow characterization of arctic
clouds, haze, and aerosol which will be directly comparable to FIRE III,
ASTEX, and TWP results. This will provide the radiation/climate
community with an unprecedented data set for four major climatic zones.
The emphasis in this study will be on the intercomparison of cloud
properties to answer important questions of global variations of upper
tropospheric and boundary layer clouds.

A Parameterization Study of Marine Boundary-Layer Clouds

Shouping Wang, NASA Marshall Space Flight Center

The goal of the proposed research is to formulate a new parameterization
of marine boundary-layer clouds for a large-scale model. We propose to
develop a model based on the buoyancy sorting concept developed by
Raymond and Blyth (1986), mass flux-layered model of Wang (1993), and
the prognostic cloud scheme developed by Tiedtke (1993). For the
application in the marine boundary layer, we need to improve the
representation of the cloud-top entrainment, large-scale condensation
and evaporation in the cloud scheme. We also emphasize the interaction
between the cumulus and stratocumulus clouds in the boundary layer. In
the processes, some large-eddy simulation runs will be conducted to
study the interaction that is crucial to our new parameterization. We
will also implement the new prognostic cloud scheme into a traditional
second-order turbulence closure model. Thus, the stratocumulus,
trade-wind cumulus regimes and the transition between them can be
predicted with a unified cloud scheme. All the one-dimensional models
will be tested with ASTEX and FIRE data. Following the tests, we plan to
implement the models into a general circulation model.

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> Table 5: BaIloon-Based Measurements and Surface Characterization

[Instrument Data Products Loc Op Mode USER\* Provider]{.underline} C
Test?\*\*

Balloon

BBSS WS, WD T, RH Profiles S,B Periodic All \*\*\* Y

Radio- WS, WD, T, RH Profiles S Periodic All FIRE Y

sondes

TBS BLWS,WD,T.RH Pro- S Periodic All ? Y

files

> BL Cloud Microphys Pro­files
>
> BL Cloud Rad Profiles

Surface Characterization

Ground Rad plus Sky Rad above (measured at same places) give:

Albedo

Spectral Albedo

Emissivity

WSfl Area Surf Images (tower) S,B Periodic All ? Y

Snow, Ice Coverage

SDG Array Average Snow Depth S,B All 7

Peri-­

odic

Eddy Corr Sensi & latent Heat Fluxes S Continuous ab ? Y

Over Major Surf Types

> Notes: Cold test of instrumentation which has riot previously
> satisfactorily performed on a routine basis under the conditions
> expected during Arctic FIRE t is very important The assumption that in
> instrumentation typically used at lower latitudes will perform
> satisfactorily in the Arctic without modification is usually wrong
> Hence, a test, modification, retest cycle should be incorporated in P1
> planning. On request, ARM will provide access to its Cold Test Site in
> Barrow during the winter of 96\~97.

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> (Albrecht, Considine)
>
> 12\. Diurnal variation of cloud and boundary layer structure from
> surface‑based remote sensing (Miller, Albrecht)
>
> 13\. Entrainment zone studies using 94 GHz radar data (Albrecht)
>
> 14\. Turbulence structure and boundary layer evolution during ASTEX
> Lagrangian IOPS (Wang, Lenschow)
>
> 15\. Intercomparisons of convective updrafts and downdrafts observed
> from aircraft and surface‑based cloud Doppler radar (Wang, Frisch,
> Lenschow)
>
> 16\. Spectral analysis of microphysics (Gerber)
>
> 17\. Correlate CCN with drizzle and standard deviation (Hudson)
>
> 18\. Examine SOCEX and ACE‑1 data and look at whole spectrum,
> including tail of drop size distribution (Hudson)
>
> **[Modeling Studies/Integrated Data Sets: Selected Recent
> Projects]{.underline}**
>
> 1\. GCSS (GEWEX Cloud System Study) Boundary Layer Cloud Modeling
> Intercomparison Workshops:
>
> a\. Stratocumulus‑topped boundary layer (FIRE‑I) (Moeng et al., 1996)
>
> b\. ASTEX Lagrangian cases (FIRE‑II) (Krueger, Bretherton)
>
> 1D models:
>
> ‑ AERO (Peter Bechtold ‑ France)
>
> ‑ ECMWF (Joao Texeira ‑ UK)
>
> ‑ KNMI (Eric van Meijgaard)
>
> 2D models:
>
> ‑ CSU (Bjorn Stevens‑USA)
>
> ‑ U Utah (Steve Krueger, Ching‑Hsuan Chen ‑ USA)
>
> ‑ U Washington (Matt Wyant/Chris Bretherton ‑ USA)
>
> 2\. Cloud parameterization (ASTEX) (Xu, Randall, 1996)
>
> 3\. Drizzle parameterization (ASTEX) (Randall, Chen)
>
> 4\. Microcell simulation (ASTEX) (S. Wang)
>
> 5\. Mesoscale structure (ASTEX) (Bretherton, Rand)
>
> 6\. ECMWF boundary layer model tests (ASTEX)

**[Modeling Studies/Integrated Data Sets: Planned
Future/Ongoing]{.underline}**

**[Projects]{.underline}**

> 1\. ASTEX cloud climatology (Uttal, et al.)
>
> 2\. ASTEX Lagrangian turbulent fluxes (Wang)
>
> 3\. Idealized modeling studies
>
> 4\. ASTEX Lagrangian studies (Krueger, Bretherton) ‑ have compiled
> results from 6 models
>
> 1D: AERO, KNMI, ECMWF
>
> 2D: CSU, Utah, Washington
>
> 5\. Further idealized modeling studies of stratocumulus to cumulus
> transition (Krueger) ‑ dynamics of decoupling (2D CEM) ‑ roles of
> various forcings (2D CEM) ‑1D turbulence closure modeling
>
> 6\. 3D direct numerical simulations of smoke‑topped boundary layers
> (Krueger) ‑ model developed to explicitly simulate entraining eddies ‑
> gain insight into laboratory flows.
>
> **[Satellite Remote Sensing]{.underline}**
>
> On‑going FIRE Phase II satellite remote sensing research focuses on
> four types of studies:
>
> \(1\) Improvement of radiative transfer models used to retrieve cloud
> properties from satellite radiance measurements;
>
> \(2\) Testing of satellite analysis schemes against more detailed
> field datasets;
>
> \(3\) Comparison of results obtained from different satellite
> instruments;
>
> \(4\) Comparison of behavior of target cloud types in different
> locations and climate regimes.
>
> [Cirrus clouds]{.underline}
>
> \(1\) Studies are underway to examine, in detail, different models of
> the radiative effects of irregular and regular ice crystal shapes and
> shape distributions, as well as sizes and size distributions.
> Radiative transfer models are being compared with a variety of field
> datasets. Existing models are being extended to other infrared
> wavelengths to retrieve more information about nighttime cirrus,
> including temperature, optical thickness and particle size.
>
> \(2\) Several team efforts are exploring different ways to derive
> cloud layer information from different combinations of wavelengths and
> satellite instruments. These results are being tested against lidar
> and radar observations.
>
> \(3\) Two global analyses of cirrus clouds based on HIRS measurements
> are being compared with ISCCP and SAGE information about upper
> tropospheric cloudiness. By combining these analyses, a much clearer
> characterization of cirrus is being developed.
>
> \(4\) Cirrus particle size retrievals have been developed and tested
> against FIRE data. Global surveys of cirrus behavior (optical
> thickness and particle size variations) are underway to compare and
> contras\" cirrus already studied by FIRE and cirrus in other climate
> regimes, particularly the polar and tropical regions.
>
> [Boundary-layer Clouds]{.underline}
>
> \(1\) Improved microwave models are being developed to retrieve
> boundary layer cloud top temperature and liquid water path information
> in the presence of upper‑level clouds.
>
> \(2\) Most retrieval methods developed for satellite data have already
> been tested against FIRE data. Studies of more detailed behavior are
> underway using higher resolution satellite datasets. Studies of
> possible aerosol effects are also underway**.**
>
> \(3\) Global intercomparisons of visible, infrared and microwave
> retrievals of the properties of marine boundary layer clouds have been
> completed. Studies are beginning for continental cases.
>
> \(4\) Higher resolution climatologies of marine stratus regimes have
> been completed. Comparisons of behavior in different regions are
> underway, including extension to continental clouds. Detailed
> examination of the statistics of small‑scale optical thickness
> variations and their radiative consequences in marine boundary layer
> clouds show potential to identify the dynamical processes that
> determine these finer scale properties.

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[^1]: . Available in draft form as this FIRE Arctic Implementation Plan
    was written.

[^2]: . (Note: We need to extend these same statistics for 7 am, 2 pm
    NOAA orbits for the SHEBA field phase in 1998 for evaluation of
    ISCCP. But the above statistics will apply to EOS-AM overpasses of
    the ARM north slope site after the SHEBA/FIRE field deployments have
    been completed.)