New Estimation and Study of Freshwater Forcing at the air-sea interface
Balla Maggero
Marine & Oceanography Services. Ngong road 30259 Nairobi. e-mail: nambuye@yahoo.com
Abstract: The retrieval and application of sea-surface primary diagnostic variables with rainfall from space-borne passive
microwave and infrared sensors has grown upstream. These products are necessary for the calculation of turbulent heat
fluxes (evaporation) and the water-budget at the air-sea interface. The newly launched AMSR-E and MODIS instruments
on AQUA platiform gives good complementary observations for the derivation of these basic state variables and rainfall.
This article reviews concisely, the latest algorithm development for retrieval of these variables from AMSR-E/MODIS
combination for the purpose of studying freshwater forcing at the sea surface.
Keywords: OCGM, thermohaline circulation, data assimilation, sea-surface forcing
1 Introduction
The sources and sinks of freshwater at the sea surface
are crucial component of the global water balance. The
ocean losses about 10% more freshwater through
evaporation than it gains through precipitation
(Baumgartner and Reichel, 1975). The remaining 10% is
contributed by river run-off, with the change in storage of
freshwater in the oceans being a much smaller residual.
The surface freshwater flux determines upper ocean
mixing and the thermohaline component of ocean
circulation. The stabilizing effect of a local net freshwater
surface input limits the efficiency of ocean-atmosphere
communication: it impedes heat exchange with
subsurface layers, nutrient transport to the upper ocean,
and trace gas exchange with the atmosphere. In the
atmosphere these fluxes determine atmospheric
moisture content and latent heat that drive tropospheric
convection, horizontal advection and precipitation
patterns. Surface freshwater forcing is believed to play a
primary role in setting the background state and timing of
the large interranual phenomena of ENSO. Theory
suggests that equatorial upper-ocean heat content and
mixed-layer depth are crucial in setting ENSO time scales
(Zebiak and Cane, 1987). Over the western Pacific
warm pool region, the upper ocean mixed layer is
observed to very shallow, on the order of 30m, apparently
due to a combination of stabilization by precipitation and
highly intermittent wind forcing (Lukas and Lindstrom,
1991). Long-term observations that permit the accurate
estimation of precipitation, evaporation and wind stress
are therefore likely to be important for an understanding of
ENSO, and for the development of a quantitative ENSO
prediction capability. Satellite observations, as it will be
shown later, offer the unique opportunity to sample large
oceanic areas with high temporal resolution. The
accuracy of satellite estimates, however, have to be
compared against in situ observations so that reliable
estimates are assessed. The theme of this review is the
retrieval algorithm for evaporation basic state variables
together with precipitation from the Advanced
Microwave Scanning Radiometer (AMSR) and the
Moderate Resolution Spectroradiometer (MODIS),
(currently on-board AQUA mission). The problem of
modeling thermohaline circulation and ocean data
assimilation are briefly introduced.
2. The AMSR and the MODIS
The AMSR is a twelve-channel, six frequency, total power
passive-microwave radiometer system. It measures TB at
6.9, 10.7, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and
horizontal polarized measurements are taken at all
channels. The earth-emitted microwave radiation is
collected by an off-set parabolic reflector 1.6m in diameter
that scans across the Earth along an imaginary conical
surface, maintaining a constant Earth incidence angle of 550
and proving a swath width of 1445km. The reflector focuses
radiation into an array of six feedhorns which then carry
radiation to radiometers for measurement. Calibration is
accomplished with observations of the cosmic background
radiation and an on-board warm target. Spatial resolution of
the individual measurements varies from 5.4km at 89GHz
to 56km at 6.9GHz.
The MODIS instrument employs a conventional imagingradiometer concept, consisting of a cross-track scan mirror
and collecting optics, and a set of linear detector arrays with
spectral interference filters located in four focal planes. The
optical arrangement provides imagery in 36 discrete bands
from 0.4 to 15.5m, selected for diagnostic significance in
Earth science. The spectral bands have spatial resolutions
of 250m, 500m, or 1km at nadir; signal-to-noise rations of
greater than 500 at 1km resolution (at a solar zenith angle of
700; and absolute irradiance accuracies of  5 percent from
0.4 to 3m (2 percent relative to the sun) and 1 percent or
better in the thermal infrared (3 to 14.5 m). MODIS
provides daylight reflection and day/night emission spectral
imaging of any point on the Earth at least every two days,
operating continuously.
3.0 Data Set Evolution and Retrieval Schemes
The retrieval algorithms for the freshwater elements are
based on experience gained from previous sensors;
SMMI, SSM/I, TMI and AVHRR.
3.1 Precipitation
The AMSR rainfall algorithm is a slight modification of the
TMI and SSM/I algorithm (Wilheit at al., 1991)., The
information is obtained by matching the observed
brightness temperatures to database of a priori cloud
profiles derived from a variety of sources including cloud
dynamical models as well as ground-based radars. The a
priori is then used in Bayesian inversion scheme to derive
a linear combination of profiles that most resembles the
observed brightness temperatures. Despite differences,
the land and ocean algorithms have been combined into
a single framework to insure communication between the
algorithm components. The level 3 algorithm sacrifices
some accuracy of the instantaneous retrieval in order to
minimise assumptions that might contaminate a climate
record of rainfall. The frequency of occurrence of rain
intensities in different rate categories can be plotted as a
histogram or a smoothed curve that fits the histogram.
3.2 Evaporation
Sea Surface Temperature determination is based on
MODIS-calibrated mid- and far-IR radiances, using an
algorithm that exploits the differences in atmospheric
transimisivity in the different IR bands to enable highly
accurate estimation of the atmospheric effects, thereby
enabling ancillary input to the algorithm along with a land
mask, which is used to mark non water pixels while an ice
extent mask limits polar sea coverage. A sequence of
spatial and temporal homogeneity tests is applied to
validate the quality of cloud-free observations.
Sea-surface wind speeds: wind speed is retrieved using
non-linear approach from AMSR brightness
temperatures. The neural network provides the most
accurate estimates because of the unique relationship
with TB. This relationship is given by the radiative transfer
equation for atmosphere bounded at the bottom by a
rough sea surface. The radiative transfer equation can be
approximated by a relatively simple closed-form
expression, which is called the TB model function ( Wentz,
1983). It is accomplished by varying the wind speed until
the TB model function matches the observations. In order
to obtain the highest possible accuracy, the model must
include the effects of all other relevant parameters in the
radiative transfer equation, and the TB model must be
precisely calibrated.
Air Specific Humidity: Over the ocean, near surface
specific humidity is a direct function of the water vapor
column of the planetary boundary layer (Schulz et al.,
1993). Therefore, air specific humidity is retrievable. A
linear scheme employing 10.65, 18.7, 23.8, and 36.5
GHz TB at both polarisation is used.
4.0 Thermohaline Modelling
The development of a generalised vertical-coordinate
ocean model include the same physics, such as the
turbulance-closure model (which predicts the vertical
diffusion of momentum, temperature and salinity), as in the
Princeton Ocean Model (Blumberg and Mellor, 1987). The
latter model uses sigma-coordinates in the vertical where
the model layers follow the terrain and require considerable
smoothing of the topography with decreasing resolution.
Thus, modifications are necessary to accommodate some
detail in topography and a course-resolution grid
but
retaining some boundary-layer physics, which is important
for deep flows; thermohaline circulation. Inclusion of flexible
vertical-layer structure could also permit the use of density
surfaces to define layers because the layers have to be
updated every time step. The freshwater cycling in the
ocean is studied first in a diagnostic framework and later on
in quasi-equilibrium runs for a specified freshwater forcing.
Given the equilibration time scale of thousands of years for
the oceanic thermohaline circulation, even the 2X2 degree
model can be too expensive to run. However, tendencies in
the model behaviour can be equally illuminating of the
physics and adequate for the study of the sensitivity of the
interbasin export from the Pacific to the Atlantic via the Arctic
Ocean.
5.0 Data Assimilation
The OCGM are among the most demanding tasks
performed by computers today. Even when the fastest
computers are used, these models and the solutions to the
equations are only approximations. One way to correct for
errors in the model solution is to apply a procedure known
as “data assimilation”, which blends the approximations with
observations of the real ocean in a least-square error sense.
The blending takes account of errors in the model dynamics
and the thermodynamics, as well as the measurement
errors in the observations. The outcome is better than the
analysis using either a model or observation only. Weather
forecasting utilise mathematical models that encapsulates
our understanding of the evolution of the various
components of the system and the interactions between
each other. The essential pre-requisite for prediction is best
estimate of the initial state of the system, encoded as
numbers on a spatial grid which may have little
resemblance to the spatial pattern of available observations
for a system whose evolution exhibits sensitive dependence
on initial conditions (or chaos), such as the ocean. Several
methods exist for assimilating observations into a numerical
models. Two main approaches are usually seen in the
techniques following either the optimal control theory or the
statistical estimation theory (Ghil and Manalotte-Rizzoli,
1991). Seasonal climate forecasting is a revolutionary
technology in meteorology and oceanography aimed at
predicting long-range fluctuation in climate (as opposed to
day-to-day weather events) several months in advance.
This endeavour draws heavily on a wide range of satellite
observations which are synthesised via coupled
atmosphere-ocean models. The scientific basis for longterm climate prediction lies in the ocean, which acts as a ‘
pacemaker’ by slowly nudging the atmosphere into a
pontentially predictable mean state. In 1997, the severe ElNino was detected six months earlier by various satellites.
The anomalous warming of the ocean surface in the
equatorial Eastern Pacific was predicted. A data assimilation
system fed this results downwards into the ocean interior
component of a coupled model. The model in turn
successively predicted the onset of the event. Since
satellites observe only the surface, this capability is vital to
probing the interior processes such as the thermohaline
circulation.
Above images were captured by SSM/I and AVHRR flying on
DMSP and the NOAA satellites (courtesy of NASA's Earth
Science Enterprise and its Earth Observing System)
The methods discussed are the brightness temperature
model for wind speed, the split-window approach for the
SST, the Bayessian inversion scheme for precipitation, and
the retrieval of air specific humidity using a function that
correlates with the water vapor column of the planetary
boundary layer. Although the spatial resolution along the
tracks is good there are large gaps between them, and only
tantalizing glimpses of the split are provided by the basic
observations on each day. Coupled atmosphere-ocean
models have been used for forecasting warm events. When
numerical prediction models can enhance the value of
observations, data assimilation fuses disparate data
streams into a coherent picture of the evolving ocean. By
routinely confronting these models with observations, data
assimilation facilitates rapid advance to simulate the
profoundly complex, highly non-linear system, a capability
yet to be exploited in climate modelling. It provides the
essential scientific basis for using models to predict the
system evolution.
Future improvements are expected
from a new generation of instrument-suite to be in space in
the near future. The utilization of the planned Dualfrequency Precipitation Radar (DPR) together with the GPM
Microwave Imager (GMI) both on Global Precipitation
Mission (GPM) and expected dense sampling from
constellation satellites will be a great stride in the
measurements and study of freshwater fluxes.
Acknowledgment: The author is grateful to two reviewers for their
fruitful and constructive comments. The Information and data used
in this work include those produced through the funding of NASA’s
Earth Science Enterprise and the Earth Observing Systems Data
and Information System Archive at GSFC, Greenbelt Maryland.
References:
6.0 Summery
The distribution of freshwater from sea-ice, river runoff
and precipitation minus evaporation sources is of
importance to the thermohaline circulation by modifying
the surface-water masses and mixing processes. This
variability affects deep waters as well as mode water,
which can have a climate impact on decadal scale
because they will come into contact with the atmosphere
much faster than the deep waters. It is clear that
observations of freshwater state variables from most
recent advanced remote sensors pose special
advantages. Though still under research missions, the
AMSR and MODIS are superior in their respective class.
This article has presented some retrieval techniques for
the basic inputs for the ocean freshwater forcing.
Baumgartner, A and E. Reichel, The world Water Balance: Mean Annual
Global, Continental and Maritime Precipitation, Evaporation and Runoff.
Elsevier, New York, 179, 1975.
Blumberg, A.L., and G.L. Mellor: A description of a three-dimensional coastal
circulation model, Three Dimensional Coastal Models (ed. N.S. Heaps), 1 –
16, Am. Geophys. Union. Wash. DC. 1987.
Ghil , M. and P. Manalotte-Rizzoli: Data Assimilation in Meteorology and
Oceanography. Ad. Gephys. 23, 141 –265, 1991.
Lukas and Lindstrom, The mixed layer of the Western Equatorial Pacific
Ocean, J. Geophys. Res. 96, 3343-3357,1991
Schulz, J., P. Schluessel and H. Grassii : Water vapor in the Atmospheric
Boundary Layer over the Oceans from SSM/I Measurements. Int. J. Remote
Sens. 14, 2773-2789. 1993.
Wentz F.J. A Well Calibrated Ocean Algorithm for SSM/I. J. Geophys. Res.
102,8703-8718, 1997.
Wilheit, T.T., A.T.C. Chang and L.S. Chui : Retrieval of Monthly Rainfall indices
from Microwave Radiometric Measurements using Probability Distribution
Function, J. Atmos. Oceanic Technology, 8, 118-136, 1991.
Zebiak, S. A. and M. A. Cane, A model El Nino-Southern Oscillation. Mon.
Wea. Rev., 115, 2262-2278. 1987.