W
WATER AND ENERGY CYCLES
Taikan Oki1 and Pat J.-F. Yeh2
1
Institute of Industrial Science, University of Tokyo,
Tokyo, Japan
2
Department of Civil and Environmental Engineering,
National University of Singapore, Singapore, Singapore
Synonyms
Global water balance; Global water budget; Global water
cycle
Definition
The global hydrological cycle can be described by the
following physical processes which form a continuum of
water movement. Complex pathways include the passage
of water from the gaseous envelope around the planet
called the atmosphere through the bodies of water on the
surface of Earth such as the oceans, glaciers, and lakes
and at the same time (or more slowly) passing through
the soil and rock layers underground. Later, the water is
returned to the atmosphere. A fundamental characteristic
of the hydrological cycle is that it has no beginning and
it has no end.
Introduction
The role of hydrological cycles in the Earth system, the
amount of water on the Earth’s surface, and its distribution
in various reserves are first introduced together with the
water cycles on the Earth. The concept of mean residence
time, water stored in various parts over the Earth’s surface
as various phases, such as glacier, soil moisture, water
vapor, and water flux among these reserves, such as
precipitation, evaporation, transpiration, and runoff, is
briefly explained with their roles in global climate system,
and their quantitative estimates are presented. The zonally
averaged net transport of freshwater and the role of rivers
in the global hydrological cycle are quantitatively shown.
Finally, the measurements of certain fluxes and storages in
the global hydrological cycle by using satellite remote
sensing are reviewed.
Earth system and water
The Earth system is unique in that water exists in all three
phases, that is, water vapor, liquid water, and solid ice,
compared to the situations in other planets. The transport
of water vapor is regarded as energy transport because of
its large amount of latent heat exchange during
phase change to liquid water (approximately 2.5 106 J
kg1); therefore, water cycle is closely linked to energy
cycle. Even though the energy cycle on the Earth is an
“open system” driven by solar radiation, the amount of
water on the Earth does not change on shorter than geological timescales (Oki, 1999; Oki et al., 2004), and the water
cycle itself is a “closed system.”
On the global scale, the hydrological cycles are associated with atmospheric circulation, which is driven by the
unequal heating of the Earth’s surface and atmosphere in
latitude (Peixoto and Oort, 1992).
Annual mean absorbed solar energy at the top of the
atmosphere is maximum near equator with approximately
300 W m2, decreases suddenly at higher latitudes, and is
approximately 60 W m2 at Arctic and Antarctic regions.
Emitted terrestrial radiative energy from the Earth at the
top of the atmosphere is approximately 250 W m2 for
20 north and south, gradually decreases at higher
latitudes, and is approximately 175 W m2 at Arctic and
150 W m2 at Antarctic region. As a consequence, the
net annual energy balance is positive (absorbing) for
tropical and subtropical regions in 30 north and south
and negative in higher latitudes (Dingman, 2002).
If there are no atmospheric and oceanic circulation on
the Earth, temperature difference on the Earth should have
E.G. Njoku (ed.), Encyclopedia of Remote Sensing, DOI 10.1007/978-0-387-36699-9,
© Springer Science+Business Media New York 2014
896
WATER AND ENERGY CYCLES
Water and Energy Cycles, Table 1 World water reserves (simplified from Table 9 of “World water balance and water resources
of the Earth” by UNESCO Korzun (1978). The last column, mean residence time, is from Table 34 of the report)
Form of water
Covering area (km2) Total volume (km3) Mean depth (m) Share (%) Mean residence time
World ocean
Glaciers and permanent snow cover
Groundwater
Ground ice in zones of permafrost strata
Water in lakes
Soil moisture
Atmospheric water
Marsh water
Water in rivers
Biological water
Artificial reservoirs
Total water reserves
361,300,000
16,227,500
134,800,000
21,000,000
2,058,700
82,000,000
510,000,000
2,682,600
148,800,000
510,000,000
510,000,000
1,338,000,000
24,064,100
23,400,000
300,000
176,400
16,500
12,900
11,470
2,120
1,120
8,000
1,385,984,610
been more drastic; temperature in the equatorial zone
should have been high enough that the outgoing terrestrial
radiation balances the absorbed solar energy, and temperature in the polar regions, which should have been low
enough, as well. In reality, there are atmospheric and
oceanic circulations that lessen this expected temperature
gradation in the absence of circulations.
Both atmosphere and ocean carry energy from the
equatorial region toward both polar regions. In the case
of atmosphere, the energy transport consists of sensible
heat and latent heat fluxes (Masuda, 1988). The global
water circulation is this latent heat transport itself, and
water plays an active role in the atmospheric circulation;
it is not a passive compound of the atmosphere, but it
affects atmospheric circulation by both radiative transfer
and latent heat release of phase change.
Water reserves, fluxes, and residence time
The total volume of water on the Earth is estimated as
approximately 1.4 1018 m3, and it corresponds to
a mass of 1.4 1021 kg. Compared with the total mass
of the Earth (5.974 1024 kg), the mass of water constitutes only 0.02 % of the planet, but it is critical for the survival of life on the Earth, and the Earth is called Blue
Planet and Living Planet.
There are various forms of water on the Earth’s surface.
Approximately 70 % of its surface is covered with salt
water, the oceans. Some of the remaining areas (continents) are covered by freshwater (lakes and rivers), solid
water (ice and snow), and vegetation (which implies the
existence of water). Even though the water content of the
atmosphere is comparatively small (approximately 0.3 %
by mass and 0.5 % by volume), approximately 60 % of
the Earth is always covered by clouds (Rossow et al.,
1993). The Earth is the planet whose surface is dominated
by various phases of water.
Water on the Earth is stored in various reserves, and
various water flows transport water from one to another.
Water flow (mass or volume) per unit time is also called
water flux.
3,700
1,463
174
14
85.7
0.2
0.025
4.28
0.014
0.002
96.539
1.736
1.688
0.0216
0.0127
0.0012
0.0009
0.0008
0.0002
0.0001
2,718
100.00
2,500 years
1,600 years
1,400 years
10,000 years
17 years
1 years
8 days
5 years
16 days
A few hours
72 days
The mean residence time in each reserve can be simply
estimated from the total storage volume in the reserve and
the mean flux rate to and from the reserve; there is even
a distribution of flux rate coming in and going out
from the storage (Chapman, 1972). The last column of
Table 1 presents the values of the global mean residence
time of water. Evidently, the water cycle on the Earth
is a “stiff” differential system with the variability
on many timescales, from a few weeks to thousands
of years.
Tmean ¼
Total storage volume
Mean flux rate
(1)
The mean residence time is also important to consider
when water quality deterioration and restoration are
discussed, since the mean residence time can be an index
of how much water is turned over. Apparently, river water
or surface water is more vulnerable than groundwater to be
polluted; however, any measure to recover better water
quality works faster for river water than groundwater.
Since the major interests of hydrologists have been the
assessment of volume, inflow, outflow, and chemical and
isotopic composition of water, the estimation of the mean
residence time of certain domains has been one of the
major targets of hydrology.
Existence of water on the earth
Table 1 (simplified from a table in Korzun, 1978)
introduces how much water is stored in which reserves
on the Earth:
The proportion in the ocean is large (96.5 %). Even
though the classical hydrology has traditionally
excluded ocean processes, the global hydrological
cycle is never closed without including them. The
ocean circulation carries huge amounts of energy and
water. The surface ocean currents are driven by surface
wind stress, and the atmosphere itself is sensitive to the
sea surface temperature. Temperature and salinity
WATER AND ENERGY CYCLES
determine the density of ocean water, and both factors
contribute to the overturning and deep ocean general
circulation.
Other major reserves are solid water on the continent
(glaciers and permanent snow cover) and groundwater.
Glacier is the accumulation of ice of atmospheric origin
generally moving slowly on land over a long period.
Glacier forms discriminative U-shaped valley over land
and remains moraine when it retreats. If a glacier
“flows” into an ocean, the terminated end of the glacier
often forms an iceberg. Glaciers react in comparatively
longer timescale against climatic change, and they also
induce isostatic responses of continental scale
upheavals or subsidence in even longer timescale. Even
though it is predicted that the thermal expansion of oceanic water dominates the anticipated sea level rise due
to global warming, glaciers over land are also a major
concern as the cause of sea level rise associated with
global warming.
Groundwater is the subsurface water occupying the saturated zone. It contributes to runoff in its low-flow
regime, between floods. Deep groundwater may also
reflect the long-term climatological situation. Groundwater in Table 1 includes both gravitational and
capillary water. Gravitational water is the water in the
unsaturated zone (vadose zone), which moves under
the influence of gravity. Capillary water is the water
found in the soil above the water table by capillary
action, a phenomenon associated with the surface
tension of water in soils acting as porous media.
Groundwater in Antarctica (roughly estimated as
2 106 km3) is excluded from Table 1.
Soil moisture is the water being held above the water
table. It influences the energy balance at the land surface as a lack of available water suppresses evapotranspiration and as it changes surface albedo. Soil moisture
also alters the fraction of precipitation partitioned into
direct runoff and percolation. The water accounted for
in the runoff cannot be evaporated from the same place,
but the water infiltrated into soil may be uptaken by the
capillary suction and evaporated again.
The atmosphere carries water vapor, which influences
the heat budget as latent heat. Condensation of water
releases latent heat, heats up the atmosphere, and
affects the atmospheric general circulation. Liquid
water in the atmosphere is another result of condensation. Clouds significantly change the radiation in the
atmosphere and at the Earth’s surface. However, as
a volume, liquid (and solid) water contained in the
atmosphere is quite little, and most of the water in the
atmosphere exists as water vapor. Precipitable water
is the total water vapor in the atmospheric column from
land surface to the top of the atmosphere. Water vapor is
also the major absorber in the atmosphere of both shortwave and longwave radiation.
Water in rivers is very tiny as stored water all the time
however, the recycling speed, which can be estimated
as the inverse of the mean residence time, of river water
897
(river discharge) is relatively high, and it is important
because most social applications ultimately depend on
water as a renewable and sustainable resource.
Overall, the amount of water stored transiently in a soil
layer, in the atmosphere, and in river channels is relatively
minute, and the time spent through these subsystems is
short, but, of course, they play dominant roles in the global
hydrological cycle.
Water cycle on the earth
The water cycle plays many important roles in the climate
system, and Figure 1, revised from Oki and Kanae (2006),
schematically illustrates various flow paths of water
(Oki, 1999). Values are taken from Table 1 and also calculated from the precipitation estimates by Xie and Arkin
(1996). Precipitable water, water vapor transport, and its
convergence are estimated using ECMWF objective
analyses, obtained as 4 year mean from 1989 to 1992.
The roles of these water fluxes in the global hydrological
system are now briefly introduced:
Precipitation is the water flux from atmosphere to land
or ocean surface. It drives the hydrological cycle over
land surface and changes surface salinity (and temperature) over the ocean and affects its thermohaline circulation. Rainfall refers to the liquid phase of
precipitation. Part of it is intercepted by canopy over
vegetated areas, and the remaining part reaches the
Earth’s surface as throughfall. Highly variable, intermittent, and concentrated behavior of precipitation in
time and space domain compared to other major hydrological fluxes mentioned below makes the observation
of this quantity and the aggregation of the process complex and difficult.
Snow has special characteristics compared with rainfall.
Snow may be accumulated, the albedo of snow is quite
high (as high as clouds), and the surface temperature
will not rise above 0 C until the completion of snowmelt. Consequently, the existence of snow changes the
surface energy and water budget enormously. A snow
surface typically reduces the aerodynamic roughness,
so that it may also have a dynamical effect on the
atmospheric circulation and hydrological cycle.
Evaporation is the return flow of water from the surface to
the atmosphere and takes the latent heat flux from the surface. The amount of evaporation is determined by both
atmospheric and hydrological conditions. From the atmospheric point of view, the fraction of incoming solar
energy to the surface leading to latent and sensible heat
flux is important. Wetness at the surface influences this
fraction because the ratio of actual evapotranspiration to
the potential evaporation is reduced due to drying stress.
The stress is sometimes formulated as a resistance, and
such a condition of evaporation is classified as hydrology
driven. If the land surface is wet enough compared to the
available energy for evaporation, the condition is classified as atmosphere driven.
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WATER AND ENERGY CYCLES
Water and Energy Cycles, Figure 1 Global hydrological fluxes (103 km3/year) and storages (103 km3/year) with natural
and anthropogenic cycles are synthesized from various sources. Big vertical arrows show total annual precipitation and
evapotranspiration over land and ocean (103 km3/year), which include annual precipitation and evapotranspiration in major
landscapes (103 km3/year) presented by small vertical arrows; parentheses indicate area (106 km3/year). The direct groundwater
discharge, which is estimated to be about 10 % of total river discharge globally, is included in river discharge (Revised from
Oki and Kanae, 2006).
Water and Energy Cycles, Table 2 Annual freshwater transport from continents to each ocean (1015 kg/year) mean for 1985–1988.
“Inner” indicates the runoff to the inner basin within Asia and Africa. HH Q· indicate the direct freshwater supply from the
atmosphere to the ocean. N.P., S.P., N. At., and S. At. represent North Pacific, South Pacific, North Atlantic, and South Atlantic Ocean,
respectively
N.P.
From rivers
From atmosphere
Asia
Europe
Africa
N. America
S. America
Australia
Antarctica
Total
HH Q
Grand total
4.7
2.9
0.5
8.1
9.9
18.0
S.P.
0.4
0.4
0.1
1.0
1.9
11.1
9.2
Transpiration is the evaporation of water through
stomata of leaves. It has two special characteristics different from evaporation from soil surfaces. One is that
the resistance of stomata is related not only to the dryness
N. At.
0.2
1.7
0.2
4.8
5.7
12.2
12.7
0.5
S. At.
Indian
0.9
3.3
0.0
0.2
Arctic
2.7
0.7
Inner
0.1
0.4
1.1
8.3
0.1
9.3
14.0
4.7
0.1
0.8
4.0
14.0
10.0
4.5
2.2
6.7
0.3
0.3
Total
11.4
2.4
0.1
8.8
14.9
0.2
1.9
39.7
39.7
0.0
of soil moisture but also to the physiological conditions
of the vegetation through the opening and closing of stomata. Another is that roots can transfer water from
deeper soil than in the case of evaporation from bare soil.
WATER AND ENERGY CYCLES
Vegetation also modifies surface energy and water balance by altering surface albedo and by intercepting precipitation and evaporating this rainwater.
Runoff at the hillslope scale is nonlinear and a complex
process. Surface runoff could be generated when rainfall
or snowmelt intensity exceeds the infiltration rate of the
soil or precipitation falls over saturated land surface. Saturation at land surface can be formed mostly by topographic concentration mechanism along hillslopes.
Infiltrated water in the upper part of the hillslope flows
down the slope and discharges at the bottom of the hillslope. Because of the highly variable heterogeneity of
topography, soil properties such as conductivity and
porosity, and precipitation, basic equations such as
Richards’ equation, which can express the runoff process fairly well at a point scale or hillslope scale, cannot
be directly applied to the macroscale because of its
nonlinearity.
Runoff returns water to the ocean which may have
been transported inland in vapor phase by atmospheric
advection. The runoff into oceans is also important for
the freshwater balance and the salinity of the oceans.
Rivers carry not only water mass but also sediments,
chemicals, and various nutritional matters from continents to seas. Without rivers, the global hydrological
cycles on the Earth will never close.
The global water cycle unifies these components
consisting of the state variables (precipitable water, soil moisture, etc.) and the fluxes (precipitation, evaporation, etc.).
Zonally averaged net transport of freshwater
The meridional (north–south direction) distribution of
the zonally averaged annual energy transports by the
atmosphere and the oceans has been evaluated, even
though there are quantitative problems in estimating such
values (Trenberth and Solomon, 1994). However, the
corresponding distribution of water transport has not often
been studied although the cycles of energy and water are
closely related. Wijffels et al. (1992) used values of the
convergence of water vapor flux in the atmosphere (Q)
from Bryan and Oort (1984) and discharge data from
Baumgartner and Reichel (1975) to estimate the freshwater transport by oceans and atmosphere, but their results
seem to have large uncertainties, and they did not present
the freshwater transport by rivers.
The annual freshwater transport in the meridional
(north–south) direction can be estimated from Q and river
discharge with the geographical information such as the
location of river mouths and basin boundaries (Oki et al.,
1995). Results are introduced in the next section.
Rivers in global hydrological cycle
The freshwater supply to the ocean has an important effect
on the thermohaline circulation because it changes the
salinity and thus the density. The impacts of freshwater
supply to ocean are enhanced in the case of large river
basins because they concentrate freshwater from large
899
Water and Energy Cycles, Figure 2 The annual freshwater
transport in the meridional (north–south) direction by
atmosphere, ocean, and rivers (land) (Oki et al., 1995). Water
vapor flux transport of 20 1012 m3/year corresponds to
approximately 1.6 1015 W of latent heat transport. Shaded bars
behind the lines indicate the fraction of land at each latitudinal
belt.
areas to their river mouths. It also controls the formation
of sea ice and its temporal and spatial variations. Annual
freshwater transport by rivers and the atmosphere to each
ocean is summarized in Table 2 based on the atmospheric
water balance (Oki, 1999). Some part of the water vapor
flux convergence remains in the inland basins. There are
a few negative values in Table 2, suggesting that net freshwater transport occurs from the ocean to the continents.
This is physically impossible and is caused by errors in
the source data. Although detailed discussion of the values
in Table 2 may not be meaningful, it is nevertheless
interesting that such an analysis does make at least qualitative sense using the atmospheric water balance method
with the geographical information on basin boundaries
and the location of river mouths. In this analysis, it should
be noted that the total amount of freshwater transport into
the oceans from the surrounding continents has the same
order of magnitude as the freshwater supply that comes
directly from the atmosphere, expressed by Q.
The annual freshwater transport in the meridional direction has also been estimated based on the atmospheric
water balance with the results shown in Figure 2. The estimates in Figure 2 are the net transport, that is, in the case of
oceans, it is the residual of northward and southward
freshwater fluxes by all ocean currents globally, and it cannot be compared directly with individual ocean currents
such as the Kuroshio and the Gulf Stream. It should be
noted that the directions of river flows are mostly steady
unlike the ocean or atmospheric circulations and concentrate the freshwater in one direction throughout the year.
900
WATER AND ENERGY CYCLES
Transports by the atmosphere and by the ocean have
almost the same absolute values at each latitude but with
different signs. The transport by rivers is about 10 % of
these other fluxes globally (this may be an underestimation because Q tends to be smaller than the river discharge
observed at a land surface). The negative (southward)
peak by rivers at 30 S is mainly due to the Paraná River
in South America, and the peaks at the equator and
10 N are due to rivers in South America, such as the Magdalena and Orinoco. Large Russian rivers, such as the Ob,
Yenisey, and Lena, carry freshwater toward the north
between 50 N and 70 N.
These results suggest that the hydrological processes
over land play nonnegligible roles in the climate system,
not only by the exchange of energy and water at the land
surface but also through the transport of freshwater by
rivers, which affects the water balance of the oceans and
forms a part of the hydrological circulation on the Earth
among the atmosphere, continents, and oceans.
Remote sensing in global hydrological cycle
Over the past 20 years, significant progress has been made
toward routine monitoring of certain fluxes and storages in
the global hydrological cycle by satellite remote sensing,
while continued progress is anticipated from upcoming
missions. Table 3 gives an overview on the past, current,
and planned future hydrological remote sensing
capabilities. Some of these important achievements in
measuring global water cycle components are discussed
below:
Precipitation – The remote sensing precipitation obser-
vation systems were originated about three decades ago
(Griffith et al., 1978). Most rainfall products measured
from spaceborne platforms, for example, NEXRAD
(Next-Generation Radar) and TRMM (Tropical
Rainfall Measuring Mission) Precipitation Radar
rainfall measurements, are typically provided at large
space–time scales suitable for coarse-scale meteorological applications, such as climatologic analysis and
water balance studies. Satellite rainfall retrieval is
subject to errors caused by various factors ranging from
infrequent sampling to the high complexity and
variability in the relationship of the measurement to
precipitation parameters. Therefore, advanced retrieval
algorithm and spatial downscaling techniques (e.g.,
PERSSIAN, Precipitation Estimation from Remotely
Sensed Information Using Artificial Neural Networks;
Sorooshian et al., 2000) are necessary to be applied to
the satellite rainfall data for the purpose of the study
of global water cycle. The planned Global Precipitation
Measurement (GPM; http://gpm.gsfc.nasa.gov/), which
is an international satellite mission scheduled to launch
in 2014, envisions a large constellation of passive
microwave sensors to provide global rainfall products
at the temporal resolution of 3 h and spatial resolution
Water and Energy Cycles, Table 3 Summary of past, current, and planned future hydrological satellite remote sensing capabilities
Satellite sensors/missions
Hydrological variables measured
Time period of observation
Geostationary Operational Environmental Satellite
(GOES)
Special Sensor Microwave/Imager (SSM/I)
Scanning Multichannel Microwave Radiometer
(SMMR)
European Remote Sensing-1 (ERS-1) Radar Altimeter
and SAR
TOPEX/Poseidon
European Remote Sensing-2 (ERS-2) Radar Altimeter
and SAR
Tropical Rainfall Measuring Mission (TRMM)
Advanced Microwave Sounding Unit (AMSU)
Moderate Resolution Imaging Spectroradiometer
(MODIS)/Terra
Geosat Follow-On Mission
Atmospheric Infrared Sounder (AIRS)/Aqua
Advanced Microwave Scanning Radiometer – EOS
(AMSR-E)/Aqua
Moderate Resolution Imaging Spectroradiometer
(MODIS)/Aqua
ENVISAT Radar Altimeter-2 and ASAR
Jason-1
Gravity Recovery and Climate Experiment (GRACE)
Soil Moisture and Ocean Salinity (SMOS)
Soil Moisture active Passive (SMAP)
Global Precipitation Measurement (GPM)
Precipitation
1978–present
Precipitation, snow water equivalent, snow extent
Snow water equivalent, snow extent
1987–present
1978–1987
Surface water height, soil moisture
1991–2000
Surface water height
Surface water height, soil moisture
1993–2005
1996–present
Precipitation
Precipitation
Snow extent, evapotranspirationa
1998–present
1998–present
2000–present
Surface water height
Water vapor
Soil moisture, snow water equivalent
2000–present
2002–present
2002–present
Snow extent, evapotranspirationa
2002–present
Surface water height, soil moisture
Surface water height
Total water storage, soil moisture,a groundwatera
Soil moisture
Soil moisture
Precipitation
2002–present
2002–present
2002–present
2009 - present
Scheduled in 2014
Scheduled in 2014
a
Not directly measured; ancillary data required in the estimation
WATER AND ENERGY CYCLES
of 100 km2 (Hossain and Lettenmaier, 2006; Smith
et al., 2007). The GPM mission will provide almost
real-time rainfall information at three hourly sampling
interval on a global basis, thus allowing hydrologists an
opportunity to improve flood prediction capability for
medium to large river basins, especially in the underdeveloped world where in situ precipitation gauge
networks are sparse. Another current community-wise
agenda on the satellite precipitation missions is the
“Program for Evaluation of High Resolution Precipitation Products (PEHRPP),” which is an effort led by the
International Precipitation Working Group (IPWG) to
evaluate the quality of currently available high-resolution
satellite rainfall products (Ebert et al., 2007).
Terrestrial water storage – Another contribution of
remote sensing technologies to understand the global
hydrological cycle has been the measurement of terrestrial water storage (TWS) and its components
(Famiglietti, 2004). The major achievements in this
aspect include (1) soil moisture retrieval (Jackson
et al., 2002; Njoku et al., 2003) from the Soil Moisture
and Ocean Salinity (SMOS; Pellarin et al., 2003) and
Hydrosphere Satellite Mission (Hydros; Entekhabi
et al., 2004), (2) surface water height measurement
using the altimetry (Alsdorf and Lettenmaier, 2003;
Alsdorf et al., 2007), and (3) integrated measurement
of TWS from the Gravity Recovery and Climate
Experiment (GRACE) mission (Tapley et al., 2004),
among others. TWS is a fundamental component of
the global water cycle and an integrated measure of surface and subsurface water availability (i.e., the sum of
soil moisture, groundwater, snow and ice, waters in
vegetation and biomass, and surface water in lakes,
reservoirs, wetlands, and river channels snow). It has
great importance for the management of water
resources, agriculture, and ecosystem health. TWS controls the partitioning of precipitation into evaporation
and runoff and the partitioning of net radiation into
the sensible and latent heat fluxes, with significant
implications for precipitation recycling, hydrological
extremes (i.e., flood and drought), and land memory
processes (Shukla and Mintz, 1982; Eltahir and Bras,
1996; Eltahir and Yeh, 1999; Koster et al., 2004), while
TWS change is a basic quantity in closing the terrestrial
water balance from local to regional and global scales
(Ngo-Duc et al., 2005; Güntner et al., 2007; Yeh and
Famiglietti, 2008). Despite its importance, its role in
the global hydrological cycle has received little
attention relative to other hydrological processes
(Lettenmaier and Famiglietti, 2006), and there are no
extensive networks currently in existence for monitoring TWS changes. Satellite observations of the Earth’s
time-variable gravity field from the GRACE mission
(Tapley et al., 2004) present a new opportunity to
explore the feasibility of monitoring TWS variations
from space. Short-term (monthly, seasonal, and
interannual) temporal variations in gravity on land are
largely due to corresponding changes in vertically
901
integrated terrestrial water storage (Wahr et al., 2004).
This has allowed for the first time observations of variations in total TWS at large river basins (Swenson et al.,
2003; Chen et al., 2005; Seo et al., 2006; Winsemius
et al., 2006) to continental scales (Wahr et al., 2004;
Ramillien et al., 2005; Schmidt et al., 2006; Klees
et al., 2007; Syed et al., 2008); for new approaches to
remote estimation of discharge (Syed et al., 2005) and
evapotranspiration (Rodell et al., 2004); of groundwater variations (Rodell and Famiglietti, 2002; Yeh
et al., 2006) and snow water storage (Frappart et al.,
2005); and for validation and improvement of the
terrestrial water balance in the global land surface
hydrological models (Niu and Yang, 2006; Swenson
and Milly, 2006).
Water vapor – Another example of remote sensing measurements is the global water vapor distribution. Water
vapor is one of the most important greenhouse gases;
small amounts of water vapor in the form of clouds
can strongly affect both shortwave and longwave radiations. Buoyancy created by changes in the phase of
water largely drives the vertical motion of the atmosphere. The Atmospheric Infrared Sounder (AIRS) on
board NASA’s Earth Observing System (EOS) Aqua
spacecraft measures water vapor at 2 km vertical resolution with 10–15 % accuracy in clear sky conditions
(Moustafa et al., 2006). Such information is available
only in a small percentage of the globe because most
of the AIRS pixels are cloud contaminated. Success in
applying AIRS data has been achieved via selectively
choosing cloud-free pixels. Additionally, surface lidar
can also provide useful information on water vapor,
temperature, and winds in clear air below clouds
although they are rather expensive and delicate instruments of limited deployment and coverage. Global Positioning System (GPS) also holds promise to be used as
a water vapor sensor when combined with independent
temperature analyses.
Summary
The global hydrological cycle consisting of oceans, water
vapor in the atmosphere, and terrestrial water, is essential
to the Earth system. The cycle is closed by the exchange
of water and energy fluxes between these reservoirs.
Although the amounts of water in the atmosphere and river
channels are relatively small, their fluxes are large, and
hence a critical role in society, which is dependent on water
as a renewable resource. The ultimate goal of the hydrological science is to enhance the understanding of global water
and energy cycles on various spatial and temporal scales
through monitoring and modeling, and the outcomes
should also be beneficial and accessible to other scientific
disciplines, the general public, and the decision makers.
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Cross-references
Cloud Properties
Ice Sheets and Ice Volume
Rainfall
Terrestrial Snow
Water Vapor
903
WATER RESOURCES
Taikan Oki1 and Pat J.-F. Yeh2
1
Institute of Industrial Science, University of Tokyo,
Tokyo, Japan
2
Department of Civil and Environmental Engineering,
National University of Singapore, Singapore, Singapore
Synonyms
Freshwater resources; Runoff
Definition
Water resources mainly correspond to freshwater which
can be utilized by human beings for irrigation, industrial
purposes, and domestic uses. Even though the stocks of
water in natural and artificial reservoirs are helpful to
increase the available water resources for human society,
the flow of water should be mainly regarded as water
resources since water is a naturally circulating resource
that is constantly recharged. Maximum (potential) availability of renewable freshwater resources under given climatic condition has been traditionally regarded as
precipitation minus evapotranspiration, which corresponds to runoff. However, transpiration from soil moisture through crops is contributing to human society and
the flux is regarded as water resources as well. Therefore,
evapotranspiration from soil moisture in croplands is
called as green water nowadays, and conventional water
resources withdrawn from rivers, surface water, and
groundwater are called as blue water.
Introduction
All organisms, including humans, require water for their
survival. Therefore, ensuring adequate and sufficient
water supplies is essential for human well-being. Stored
waters, such as deep groundwater and water in reservoirs,
are often considered as water resources. However, they are
a part of hydrological cycles, and groundwater is also circulating even though its speed could be rather slow. Some
groundwater is called “fossil water,” which implies that
the aquifer was recharged long term ago and that it will
be depleted if being overly exploited just as fossil fuels.
From this point of view, flows of water should be considered as water resources in the assessments, designing, and
planning of sustainable water usage.
Global water balance and water resources
Conventionally, available freshwater resources are
defined as annual runoff estimated by compiling observed
river discharge data or by using water balance equation
(i.e., the residual of annual precipitation minus evapotranspiration; see Baumgartner and Reichel, 1975;
Korzun, 1978). Such an approach has and is continuing
to provide information on the annual freshwater resources
for many countries. Theoretically, available annual
freshwater resources can be derived using annual
904
WATER RESOURCES
precipitation (P) and evapotranspiration (E) measurements; however, the residual term annual runoff (R),
namely, annual available freshwater resources, is relatively small compared to P and E. P and E are approximately 1,100 and 1,200 mm/year, respectively, over the
ocean and 800 and 500 mm/year over the land (Oki,
1999). Therefore, the estimation of R could contain certain
amount of uncertainties due to even comparatively small
errors in the estimation of P and E.
Atmospheric water balance computation using the
information of water vapor flux convergence could be
alternatively used to estimate global runoff distribution
owing to the advent of four-dimensional data assimilation
(4DDA) technique in atmospheric science (Oki et al.,
1995). Even though the microwave remote sensing of precipitable water (vertically integrated water vapor) and
temperature profiles is contributing to improve the quality
of 4DDA data, this approach is suitable for global overview of the distribution of available freshwater resources
(Trenberth et al., 2007).
Model-based estimation of available
freshwater resources
Relatively simple water balance models have been used
to estimate grid-based available freshwater resources in
the world (Alcamo et al., 2000; Vörösmarty et al.,
2000). Later, land surface models (LSMs) were applied
for the estimation of global water cycles (Oki et al.,
2001; Dirmeyer et al., 2006) and for the global water
resources assessment by comparing the demand side for
both the twentieth century and the future (Shen et al.,
2008). Some of these estimates on global water balance
were calibrated by multiplying an empirical factor
inferred from available observed river discharge data.
However, recent model developments are capable to estimate runoff with adequate accuracy without the need of
calibration (Hanasaki et al., 2008a). Such estimates by
using numerical models require external atmospheric
forcing data such as precipitation and temperature, and
the accuracy of estimated freshwater resources is highly
dependent on the quality of the forcing data (Oki et al.,
1999). Satellite remote sensing can provide reasonable
estimates of the forcing data for the regions with low density of in situ observations, particularly with regard to the
quantities such as precipitation and downward shortwave
and longwave radiation. Additionally, 4DDA reanalysis
products have been commonly used as the forcing data
for air temperature, humidity, and wind speed. In order
to assure the quality of the estimations, the results of land
surface simulations have been validated, typically, by
comparing with in situ discharge observations. However,
recently, various satellite remote sensing observations
have been used to validate hydrological variables other
than discharge such as inundated area (Prigent et al.,
2007) and total terrestrial water storage (Kim et al.,
2009). Also, some remote sensing variables, such as topsoil moisture (Reichle and Koster, 2005) and snow cover
(Zaitchik and Rodell, 2009), have been assimilated into
modeling system to reduce the simulation uncertainty.
Global distribution of available
freshwater resources
Figure 1 illustrates the global distribution of annual runoff
estimated by land surface models from the multi-model
ensemble simulations under the Global Soil Wetness Project (Dirmeyer et al., 1999). Annual runoff (Figure 1) can
be considered as the maximum available renewable freshwater resources (RFWR) if waters from upstream cannot
be reused at downstream due to consumptive use or water
pollution (Oki et al., 2001). Runoff is accumulated
through river channels and realized as river discharge
(Figure 1). River discharge can be considered as the potentially maximum available RFWR if all the water from
upstream can be used. Both runoff and river discharge
are concentrated in limited areas, and their amounts range
from nearly zero in desert areas to more than 2,000 mm/
year in the tropics and greater than 200,000 m3/s of discharge on average near the river mouth of the Amazon
(Oki and Kanae, 2006).
Some macroscale hydrological models recently developed for water resources assessments have been equipped
with a reservoir operation scheme (e.g., Haddeland et al.,
2006; Hanasaki et al., 2006) in order to simulate the “real”
hydrological cycles and provide information on actually
available freshwater resources. These water resources
have been significantly influenced by anthropogenic
activities and modified from the “natural” hydrological
cycles even on the global scale in “Anthropocene”
(Crutzen, 2002).
Water resources for crop growth
An integrated water resources model can further be linked
to a crop growth sub-model designed for inferring the
timing and quantity of irrigation requirement and estimating environmental flow (Hanasaki et al., 2008a). Such an
approach enables the assessment of the balances between
demand and supply of water resources on a daily time
scale. Using this approach (Hanasaki et al., 2008a),
a gap in the sub-annual distribution of water availability
and water use can be detected in the Sahel, the Asian monsoon region, and southern Africa, where the conventional
water scarcity indices such as the ratio of annual water
withdrawal to water availability or the available annual
water resources per capita (Falkenmark and Rockström,
2004) cannot properly detect the stringent balance
between water demand and supply (Hanasaki et al.,
2008b).
Moreover, macroscale numerical models can be associated with a scheme tracing the origin of flow path as if
tracing the isotopic ratio of water (Yoshimura et al.,
2004). Such a water flow-tracing function, if incorporated
into an integrated water resources model (e.g., Hanasaki
et al., 2008a) with the consideration of multiple sources
of water withdrawals including streamflow, medium-size
WATER RESOURCES
905
Water Resources, Figure 1 Global distribution of (a) mean annual runoff (mm/year), (b) mean annual discharge (million m3/year),
and (c) water scarcity index Rws. Water stress is higher for regions with larger Rws (Oki and Kanae, 2006).
906
WATER RESOURCES
Water Resources, Figure 2 (a) The ratio of blue water to the total evapotranspiration during a cropping period from irrigated
cropland (the total of green and blue water). The ratios of (b) streamflow, (c) medium-size reservoirs, and (d) nonrenewable
groundwater withdrawals to blue water (Hanasaki et al., 2010).
reservoirs, and nonrenewable groundwater in addition to
precipitation on the croplands, is able to trace the origin
of water used to produce the major crops (Hanasaki
et al., 2010).
Figure 2a illustrates the ratio of blue water to the total
evapotranspiration during the cropping period in irrigated
croplands. Here the blue (green) water is defined as the
amount of water evapotranspiration originated from irrigation (precipitation) (see Falkenmark and Rockström,
2004). Figure 2a shows distinctive geographical distribution of the pattern of the dependence on blue water. Total
annual blue water consumption is estimated approximately as 1,500 km3/year, which is about 20 % of the total
consumptive use of approximately 7,000 km3/year of
water resources in croplands during the cropping period
(Hanasaki et al., 2009). Further, the ratios of the source
of blue water are shown for streamflow including the
influence of large reservoirs, medium-size reservoirs,
and nonrenewable groundwater in Figure 2b–d, respectively. Areas highly dependent on nonrenewable groundwater are detected in the Pakistan, Bangladesh, and in
western part of India, north and western parts of China,
some regions in the Arabian Peninsula, and the western
part of the United States and Mexico. Cumulative
nonrenewable groundwater withdrawals estimated by the
model correspond fairly well with the country statistics
of total groundwater withdrawals, and such an integrated
model has the ability to quantify global virtual water flow
(Allan, 1998; Oki and Kanae, 2004) and “water footprint”
(Hoekstra and Chapagain, 2007) through the major crop
water consumption (Hanasaki et al., 2009).
Remote sensing applications in water resources
The last two decades have witnessed significant achievements toward routine monitoring of global hydrologic
cycle components by using remote sensing techniques,
while continued progress is anticipated from upcoming
missions. Among these space efforts, the following missions are particularly relevant to water resources
applications:
1. Integrated measurement of terrestrial water storage
(TWS) from the Gravity Recovery and Climate
Experiment (GRACE) mission (Tapley et al., 2004),
launched in 2002
2. Soil moisture retrieval from the Soil Moisture and
Ocean Salinity (SMOS; Kerr et al., 2000), launched
WATER RESOURCES
by the European Space Agency (ESA) in 2009, and the
Soil Moisture Active and Passive (SMAP) mission
(Entekhabi et al., 2004), planned to launch by NASA
in 2014
3. Surface water height measurement using the altimetry
from the Surface Water Ocean Topography (SWOT)
mission (Alsdorf and Lettenmaier, 2003), planned to
launch by NASA in 2014
TWS is a fundamental component of in closing the terrestrial water balance from local to regional and global
scales. As an integrated measure of surface and subsurface
water availability, TWS bears significant implications for
water resources planning and management. Despite its
importance, there are no extensive networks currently in
existence for monitoring TWS changes. Satellite observations of Earth’s time-variable gravity field from the
GRACE mission present a new opportunity to explore
the feasibility of monitoring TWS variations from space.
Short-term (monthly, seasonal, and interannual) temporal
variations in gravity on land are largely due to
corresponding changes in vertically integrated terrestrial
water storage (Wahr et al., 2004). This has allowed for
the first time observations of variations in total TWS at
large river basins (Swenson et al., 2003) to continental
scales (Wahr et al., 2004). Also, application using GRACE
data has been made in the estimation of discharge (Syed
et al., 2005), evapotranspiration (Rodell et al., 2004),
groundwater variations (Yeh et al., 2006), snow water
storage (Frappart et al., 2005), surface water dynamics
(Han et al., 2009), lateral redistribution of water storage
through river networks (Kim et al., 2009), and validation
and improvement of global land surface hydrological
models (Niu and Yang, 2006).
The SMOS and upcoming SMAP missions are the first
dedicated satellite missions to measure surface soil moisture levels globally. Soil moisture is an important factor
which interfaces water and energy exchanges between
the land surface/atmosphere and is the most important
hydrological quantity for agriculture. Water management
for irrigation is a critical issue for global crop production
and food safety. Root zone soil moisture, which is strongly
related with transpiration (green water), will be more reliably estimated by merging SMOS and SMAP observations with a land surface model in a data assimilation
system, even though the enhanced technologies in those
satellites are still limited to directly observe only topsoil
moisture. It will enable meteorology and food agencies
to forecast crop yield and enhance the capabilities of crop
water stress decision support systems, monitor global climate change, detect droughts, and conduct flood forecasting and weather prediction.
The SWOT satellite mission and its wide-swath
(20–120 km) altimetry technology for repeated elevation
measurements can measure the water height variations of
the global oceans and terrestrial surface waters accurately.
For water resources applications, hydrological observations of the temporal and spatial variations in water
907
volumes stored in all wetlands, lakes, and reservoirs are
extremely important. However, because of coarse spatial
(>100 km) and temporal (>1 month) resolutions, previous researches using altimetry (e.g., Birkett et al., 2002)
have been used in only limited conditions and objectives.
By measuring water height and area variations in higher
spatial (2 m 10 m to 2 m 60 m) and temporal (2
weeks) resolutions which allow accurate estimation of
river discharges and lake/wetland storages in remote
regions, SWOT will contribute to a fundamental understanding of the terrestrial branch of the global water cycle
and hence benefit global water resource planning and
management.
Conclusions
Current advancement of remote sensing technology has
not proved to be well capable of observing global water
fluxes; thus, it is not an easy task to accurately estimate
available freshwater resources based on remotely sensed
data. However, remote sensing technique can help in providing necessary information such as meteorological forcing data, land use and land cover, extent of surface water
bodies, and topography for the modeling estimation of
water resources. Remote sensing of cropland coverage,
planting date, and harvesting date, and detection of irrigated areas are necessary for assessing the balances
between demand and supply of water resources in addition
to the social information, such as population distribution,
urban area, industrial water use, and domestic water use.
Remote sensing of hydrological quantities, such as snow
cover, soil moisture, and temporal change of total terrestrial water storage, can provide great values to constrain
and validate hydrological model simulations and thereby
assure accurate estimates of freshwater resources from
model simulations of water storages and fluxes.
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WATER VAPOR
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Cross-references
Agriculture and Remote Sensing
Crop Stress
Earth Radiation Budget, Top-of-Atmosphere Radiation
Irrigation Management
Rainfall
Surface Water
Snowfall
Soil Moisture
Water and Energy Cycles
WATER VAPOR
Eric Fetzer
Jet Propulsion Laboratory, California Institute of
Technology, Pasadena, CA, USA
Synonyms
Atmospheric humidity; Atmospheric moisture
Definition
Water vapor. Water in gaseous form, usually mixed with
dry air in the Earth’s atmosphere.
Water vapor mixing ratio. The ratio of density of water
vapor to the density of air. Typical values range from
a few grams/kilogram in the tropical lower troposphere
to a few micrograms/kilogram around the tropopause.
Relative humidity. The ratio of water vapor partial pressure
to saturation vapor pressure. A common measure of water
vapor amount, but not conserved as an air parcel changes
temperature. Mixing ratio is conserved.
Saturation vapor pressure. Maximum partial pressure of
water vapor adjacent to a plain surface of water or ice.
Water vapor saturation vapor pressure follows the
Clausius-Clapeyron relation for water and varies approximately exponentially with temperature.
Clausius-Clapeyron relation. Equation relating equilibrium pressure of a gaseous substance adjacent to a solid
or liquid surface. The Clausius-Clapeyron relation varies
exponentially with temperature at about 7 %/K near
909
freezing. In terrestrial atmospheric sciences, this term
generally refers to water vapor with respect to liquid water
or ice.
Latent heat of vaporization. Heat required to convert a unit
mass of liquid water to water vapor. The same heat is
released when water vapor condenses to liquid, as happens
in clouds. Analogous latent heat of fusion refers to conversion between ice and liquid. Water vapor latent heats are
very large: about 2.3 106 J/kg for conversion of liquid
water to vapor compared to dry air heat capacity of
717 J/kg-K.
See American Meteorological Society (2010) and Wallace
and Hobbs (2006) for additional terms, including dew
point, frost point, and specific humidity.
Introduction
Water vapor varies significantly throughout the atmosphere. While a trace species in the middle atmosphere,
it is the third most abundant gas in the Earth’s lower troposphere. It has three important roles in weather and climate.
First, water vapor is the dominant greenhouse gas so it has
a significant effect on the planetary energy balance. Second, because the atmospheric capacity for water vapor
varies exponentially with temperature, its radiative effects
may act to amplify any surface warming; water vapor
feedbacks are believed to roughly double anthropogenic
warming (Intergovernmental Panel on Climate Change,
2007). Third, clouds and precipitation both begin as water
vapor. Thus, water vapor acts indirectly on radiatively
important clouds, while its condensation releases latent
heat and affects precipitation. A major challenge in
weather forecasting is improved precipitation forecasts,
and water vapor is the atmospheric source for precipitation. Latent heat release represents about half the warming
of the tropical atmosphere and makes a small but important contribution at higher latitudes, especially in storm
systems (Hartmann, 1994). The importance of water vapor
has made its observation a cornerstone of remote sounding
techniques for decades.
Influence on weather
Thunderstorms and severe weather are significantly
enhanced by the presence of water vapor, especially at
low levels. Latent heat released by water vapor condensing onto cloud liquid droplets causes warming and
reduced density, enhancing convective instability
(Emanuel, 1994). This instability is most pronounced
where cooler, dry air overlies warm, moist air, as is common in spring, summer, and fall in the American Midwest;
high convective instability is a major factor in the formation of tornadoes. Latent heat release is also a major factor
in organized tropical convective systems, including
hurricanes.
Radiative effects and climate feedbacks
Water vapor is the dominant greenhouse gas in the atmosphere, with strongest absorption in the middle and upper
910
WATER VAPOR
troposphere (400–100 hPa pressure) where many of its
infrared spectral lines become saturated (Liou, 1992).
Water vapor is not well mixed (unlike other greenhouse
gases such as carbon dioxide or methane), so estimating
its radiative effects has required direct observations of its
distribution. This has presented a challenge, because only
recently have high information content water vapor data
sets become available from satellites (see below). The
global upper tropospheric water vapor record prior to
2002 was based on regression models of local relative
humidity versus satellite-observed brightness temperatures in the 6.3 mm infrared band from broadband radiometers (Soden et al., 2005). Other early studies addressed
upper tropospheric water vapor variability using balloonborne sensors, but only in the early twenty-first century
did those sensors become sensitive enough to detect the
very small water vapor amounts typical of the upper troposphere (Voemel et al., 2007).
Water vapor is also an important factor in climate feedbacks. Atmospheric water vapor has an enormous source
at the ocean surface, depends strongly on temperature
through the Clausius-Clapeyron relation, and acts as
a greenhouse gas. Combined, these factors give water
vapor a positive feedback (amplifying effect) on surface
warming or cooling. Dessler et al. (2008) used satellite
observations and El Nino-Southern Oscillation as
a proxy for carbon dioxide-induced surface warming and
examined radiative forcing kernel functions from climate
models. They showed that climate models’ water vapor
feedback in response to warming roughly doubled surface
warming, consistent with the observed atmospheric
response (see also Dessler and Sherwood, 2009). In further confirmation of water vapor response to surface
warming, Santer et al. (2007) attributed an increase in
a 20 year record of total water vapor as a response to
anthropogenic warming.
Direct confirmation of upper tropospheric water vapor
response to surface warming – and verification of
a positive water vapor feedback – remains a challenge
(Boers and van Meijgaard, 2009; National Research
Council, 2003). Also, the mechanisms whereby water
vapor is mixed throughout the troposphere are not fully
understood (Gambacorta et al., 2008). The vertical distribution of water vapor is important in feedback processes
because lower tropospheric water vapor strongly couples
surface changes to the atmosphere. Climate models are
based on deep convective parameterizations, which can
lead to biased water vapor distributions in models (Pierce
et al., 2006) along with height-dependent temperature
biases (John and Soden, 2007). Because water vapor and
temperature are coupled by deep convection, these biases
are related and their radiative contributions partly cancel
(National Research Council, 2003; Bony et al., 2006).
John and Soden (2007), following Held and Soden
(2006), show evidence that climate model feedbacks are
robust despite biases in mean fields. While the water vapor
feedback is well understood, parameterization of cloud
physics in climate models (including the coupling
between clouds and water vapor) is a major source of
uncertainty in climate projection (Stephens, 2005;
National Research Council, 2003).
Remote sensing of water vapor
The basis of all remote sensing or remote sounding systems is an instrument, or set of instruments, to observe
electromagnetic radiation. (Remote sounding is the process of inferring vertical structure of temperature, clouds,
and constituent gases like water vapor from observed
spectral information.) In the case of water vapor, the
observations are typically in the 6.3 mm infrared band or
the 183 GHz microwave band. In addition to an instrument making observations, remote sensing systems usually include a numerical post-processing system to
retrieve vertical distributions of geophysical parameters
from the calibrated observed radiances. Therefore, remote
sensing “observations” of water vapor (and many other
atmospheric quantities) are in fact inferences about the
state of the atmosphere. This makes the attribution of
uncertainties an especially important and challenging
component of remote sounding.
History of satellite remote sensing of water vapor
Satellite remote sensing of atmospheric water vapor
extends back to the 1960s, when a variety of remote sensing instruments were launched into polar and geosynchronous orbits (Kidder and Vonder Haar, 1995). Many of
these instruments included observations at wavelengths
sensitive to water vapor. By the 1970s, basic methodologies for observing water vapor from space were
established, and the United States began launching the
TIROS Operational Vertical Sounder (TOVS) series of
satellite instruments dedicated to tropospheric sounding.
TOVS was first launched in 1978 and included the HighResolution Infrared Sounder (HIRS) and the Microwave
Sounding Unit. The highest-quality long-term satellite
water vapor record is from Special Sensor Microwave
Imager (SSM/I) instrument, though only as total water
vapor. Other improvements in water vapor include the
incorporation of four microwave water vapor channels
with the Advanced Microwave Sounding Unit-B
(AMSU-B) in 2000. AMSU-A, part of the Advanced
TOVS sounders, included only a single water vapor channel. Technological advances have produced infrared
instruments with higher spectral resolution than their
microwave counterparts. This improved spectral resolution leads to greater information about water vapor vertical
structure. The strength of microwave instruments is their
greater coverage in the presence of clouds, though at the
cost of vertical resolution. The Atmospheric Infrared
Sounder (AIRS) instrument, launched in 2002 by the
United States National Aeronautics and Space Administration (NASA) on the Aqua spacecraft into a 1:30 equator
WATER VAPOR
crossing orbit, provides detailed information about water
vapor (and a number of other quantities) from the surface
to the upper troposphere with its 2000+ infrared channels
and 20 microwave channels in a co-boresited AMSU-A
instrument (Chahine et al., 2006). A similar instrument,
the Infrared Atmospheric Sounding Interferometer (IASI),
launched in 2006 by the European Space Agency provides
similar information in a 9:30 local orbit on the first of three
Meteorological Operational satellites (Centre National
D’Etude Spatiales, 2010). IASI observations are collocated with microwave observations from the Microwave
Humidity Sounder (Polar Orbiting Environmental Satellite, 2010) for additional constraints on the water vapor
distribution. Other modern satellite instruments observing
water vapor include the Microwave Limb Sounder (MLS),
a limb-viewing instrument with sensitivity from the upper
troposphere to the stratosphere and carried on the NASA
Aura platform. This is a follow-on to an MLS instrument
on the Upper Atmosphere Research Satellite (UARS) but
with sensitivity to both temperature and water vapor
(UARS MLS could detect variations in relative humidity
only; see Fueglistaler for a review of instruments sensing
the upper troposphere and stratosphere). Aura carries the
Tropospheric Emission Spectrometer, with the unique
ability to sense the water vapor isotopologue HDO in the
troposphere (Worden et al., 2007).
Acknowledgment
This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract
with the NASA.
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912
WEATHER PREDICTION
WEATHER PREDICTION
Peter Bauer
European Centre for Medium-Range Weather Forecasts
(ECMWF), Reading, UK
Synonyms
Weather forecasting
Definition
Weather. State of the atmosphere and its day-to-day variation, mostly described by temperature, wind, cloudiness,
and precipitation.
Weather Prediction. Prediction of weather at a given time
and location using numerical models and observations.
Numerical atmospheric modeling
Numerical models are used to simulate the evolution of all
those processes in the atmosphere and at surfaces that
affect the atmospheric state. Increasingly, these models
add complexity to the modeled components of atmosphere
and surfaces that go beyond the basic formulation of mass,
heat, and momentum transport. With increasing complexity of the models, a wider range of spatial and temporal
process scales has to be accounted for and the diversity
and nonlinearity of the modeled processes increases as
well (Kalnay, 2003).
In many applications, numerical models or model
output are combined, for example, by nesting smallerscale models into larger-scale models; by coupling of
models for the atmosphere and land surfaces (see
“Ocean-Atmosphere Water Flux and Evaporation”;
“Land-Atmosphere Interactions, Evapotranspiration”),
hydrology, waves, and oceans; and by coupling of
atmospheric dynamics with chemistry models.
Global numerical models usually employ a set of primitive equations to describe atmospheric dynamics under
the assumption of momentum conservation, heat
exchange through thermodynamics, and mass conservation (see “Atmospheric General Circulation Models”).
Processes with scales well below the scales that the
numerical model can resolve are described by physical
parameterizations that approximate the effect of sub-grid
processes on the resolved scales and vice versa. Among
these are radiation processes (see “Radiation, Electromagnetic”), cloud condensation and convection, orographic
drag, turbulent diffusion, and most surface processes
(Kalnay, 2003).
Since most processes exhibit nonlinear behavior and
are discretized in space and time, the corresponding equations are solved numerically. The choice of solution
method depends on the model type. Some global models
are spectral models, where the horizontal dimension is
described by a set of waves and where the horizontal resolution is proportional to the highest defined wave number. Others solve the above equations on structured or
unstructured horizontal grids. The vertical dimension is
usually defined by finite layers with varying choice of
coordinate systems (e.g., sigma, eta, theta, hybrid
coordinates; e.g., Warner, 2011).
At operational numerical weather prediction (NWP)
centers, numerical models are run as part of the analysis
system that estimates the state of the (global) atmosphere
at a given time as well as for producing the forecast over
the desired time range. The analyses are used to initialize
the forecast model runs. These deterministic forecasts
are often complemented by ensemble forecasts for which
a set of forecasts are produced from perturbed initial conditions, possibly using further stochastic input. The perturbations are supposed to represent the uncertainty of the
initial state estimate as well as the model, so that the
ensemble of forecasts provides an estimate of forecast
uncertainty. To reduce computational cost, the ensemble
forecasts are run at lower spatial resolutions than the deterministic model. With increasing forecast range (monthly/
seasonal), ensemble-type modeling becomes more important because the nonlinear response to small differences in
the initial conditions and model errors become large and
the knowledge of ensemble forecast spread is crucial for
interpreting forecast skill.
Atmospheric data assimilation
Data assimilation systems (see “Data Assimilation”) in
NWP provide the mathematical framework for performing
atmospheric analyses that represent the best estimate of
the state of the atmosphere at a certain time. The quality
of the forecast depends on both the accuracy of the numerical model and the accuracy of the initial state estimate.
The initial state estimate is called the analysis and represents an inversion problem. In general, this inversion
problem is underdetermined so that the analysis must
employ information from a priori data (in NWP usually
short-range forecasts initialized with previous analyses)
and observations using a mathematical framework to
optimally combine the two.
The complexity of the data assimilation system to be
used depends on the application and the affordable computational cost and can range from simple interpolation techniques to four-dimensional variational and ensemble
Kalman filter schemes or even nonlinear methods (Daley,
1991; Ide et al., 1997; Rabier, 2005). Global analysis
systems are solving the above inversion problem with state
vector dimensions of the order of 108 (the product of the
number of grid points, number of levels, and dimension of
state vector) and observation vector dimensions of the order
of 107 (product of number of observation points, number of
levels, or satellite instrument channels/range gates).
Today, most global operational NWP centers are operating 4D-Var (Lewis and Derber, 1985) data assimilation
systems that are capable of combining sparse and
heterogeneously distributed data with a dynamical model.
A computationally efficient derivative is the incremental
4D-Var method that is based on the assumption that model
behavior is nearly linear in the vicinity of a good
WEATHER PREDICTION
short-range forecast of the model state (first guess) so that
the analysis is produced from incrementally updating the
first-guess estimate (Bouttier and Courtier, 2002; Rawlins
et al., 2007). The advantage of 4D-Var methods lies in the
fact that (1) they produce meteorological fields that are
dynamically consistent because the optimization is
performed over a time window through which the model
is integrated and (2) that computationally efficient adjoint
models can be used with incremental methods (Courtier
et al., 1993). Systems like this are currently in use at
ECMWF, the UK Met Office, Météo-France, the Meteorological Service of Canada (MSC), the Japan Meteorological Agency (JMA) (for the regional model), and in the
USA at the Naval Research Laboratory (NRL).
At regional scales, ensemble-based methods (Andersen, 2001; Houtekamer and Mitchell, 2001) become
increasingly implemented because they do not require
the development of adjoint models and are capable of
explicitly calculating analysis error statistics than can be
used for estimating model errors and for initializing
ensembles of forecasts (Lorenc, 2003). Due to the necessity of running ensembles, the computational effort is considerable and currently not affordable at global scales with
the same horizontal resolutions as obtained with variational assimilation methods.
However, increasingly hybrid systems composed of
high-resolution variational and low-resolution ensemble
data assimilation are implemented in which the ensemble
system provides background error statistics for the highresolution analysis.
Satellite data in weather prediction
Satellite data products
Satellite data can be assimilated as level 1 (e.g., calibrated
and geo-located electromagnetic radiances) or level 2
(e.g., derived geophysical products such as temperature
and humidity; see “Geophysical Retrieval, Overview”)
data. The choice depends on various factors, most prominently on the amount of maintenance required in an operational system. Level 2 product retrieval often employs
a similar inversion framework as NWP data assimilation
system to derive the desired parameters, namely, a priori
information from NWP models or climatologies, radiative
transfer, error, and bias models. Most level 2 products,
however, employ different models and a priori constraints
than used in NWP modeling, and their error characteristics
are often not well defined or difficult to account for in data
assimilations systems (Joiner and Dee, 2000). Further,
when retrieval algorithms or instrument characteristics
(e.g., loss of channels, increase of noise, and instrument
recalibration) change, the NWP data assimilation system
must be tuned to the performance of the new product,
which is often cumbersome in an operational framework.
On the other hand, level 1 satellite data assimilation
requires running radiative transfer models (see “Radiative
Transfer, Solution Techniques”; “Radiative Transfer,
Theory”) that simulate observation-equivalent radiances
913
from the model state. For the most important application
of satellite data in NWP, that is, the observation of temperature and moisture structures in the atmosphere, radiative
transfer models are very fast and accurate (Saunders
et al., 1999; Han et al., 2006), and they allow the flexible
use of radiometer channels as a function of situationdependent sensitivity and potential channel corruption.
They also greatly simplify observation error and bias
estimation.
The same approach of level 1 data assimilation is
increasingly used for observations sensitive to clouds, precipitation, aerosols, surface characteristics and trace gases
and observables from active sensors.
History of satellite data usage
Derived wind vectors obtained from geostationary cloud
(later also water vapor) feature tracking were among the
first products used in the early days of satellite data assimilation. The associated cloud feature heights are retrieved
from infrared window channels (Nieman et al., 1993),
and the data produced good impact on analysis and forecast quality, mainly in the Southern Hemisphere where little conventional observations were available. With the
launch of infrared and microwave sounders onboard polar
orbiting satellites, more information on vertical temperature
structures became available. At that time, the assimilation
of retrieved geophysical products was preferred over radiances due to less efficient radiative transfer models, more
simple observation operators, and the uncertain impact of
satellite data in general. Initial experiments with sounder
data produced even negative impact due to unknown bias
characteristics of the retrieved profiles and model error statistics that were not tuned to deal with profile data obtained
from observations other than radiosondes.
An intermediate step between the assimilation of
retrieved products and radiance assimilation is based on
1D-Var techniques. Here, a single-column (onedimensional variational or 1D-Var) retrieval is performed
with radiance observations, and the retrieval product is
assimilated globally by the corresponding global analysis
scheme (optimum interpolation, 3D/4D-Var). The advantage of such an approach is that the constraints and
assumptions used in the 1D-Var retrieval are very similar
to those used in the global scheme because they are
performed within the same analysis system. One of the
earliest protagonists of this approach were Eyre et al.
(1993) in Europe, producing retrieved temperature profiles from TOVS data that comprises observations from
the HIRS, MSU, and SSU instruments. This approach
has greatly facilitated the promotion of satellite data usage
in NWP and was later extended to the use of moisturesensitive channels and instruments such as the AMSU-B
and the SSM/I (Phalippou, 1996). Most of these systems
were later replaced by direct radiance assimilation,
that is, excluding the intermediate retrieval step, for the
abovementioned reasons (Andersson et al., 1994;
Derber and Wu, 1998).
914
WEATHER PREDICTION
The initial concerns over general satellite data impact
were mostly overcome by the time 4D-Var data assimilation systems were established, mainly because of the
improved treatment of spatial and temporal collocation
between data and model simulations, better forecast model
error formulations, and the improved interaction of temperature and moisture with model dynamics (Andersson
and Thépaut, 2008). In the 1990s and early years of the
twenty-first century, the dominant impact of satellite data
on numerical weather forecast skill was contributed by
ATOVS data that combines HIRS AMSU-A and
AMSU-B observations. The first ATOVS sensor package
was launched with NOAA-15 in 1998 and has been continued until NOAA-19 (launched in 2009). For both
NOAA-18 and NOAA-19, the AMSU-B has been
replaced with MHS. Since 2006, the same package is also
available on the EPS series METOP.
The microwave sounder system is complemented by,
so-called, microwave imagers (e.g., SSM/I, and the follow-on instrument SSMIS, AMSR-E onboard Aqua,
TMI onboard TRMM, AMSR-2 onboard GCOM-W) that
contribute information on sea-surface temperature, nearsurface wind speed, integrated atmospheric moisture,
clouds, and precipitation (see “Microwave Radiometers”).
A major step forward has been the development of
infrared spectrometers (grating spectrometer AIRS
onboard EOS Aqua in 2002, interferometer IASI onboard
METOP-A/B in 2006/2012 and CrIS onboard Suomi NPP
in 2011) that provide unprecedented vertical resolution
and measurement accuracy by making available thousands of channels with very high spectral resolution covering the 4–15 mm range. These instruments have produced
substantial impact on NWP (Collard and McNally, 2009)
and will complement microwave sounding radiometers for
the next 20 years. Due to their superior spectral resolution,
these instruments are also crucial for atmospheric chemistry
and air quality applications (Clerbaux et al., 2009).
Further, the utilization of information on atmospheric
temperature and moisture structures obtained from the
bending of rays of actively transmitted radio waves by
the GNSS has vastly increased over the past 5 years (Eyre,
1994; Healy and Thépaut, 2006; see “Limb Sounding,
Atmospheric”; “GPS, Occultation Systems”). Currently,
the assimilation of GPS signals in occultation mode by
dedicated receivers (onboard CHAMP, GRAS onboard
METOP, COSMIC Formosat-3 constellation) provides
radiosonde-equivalent measurement accuracy, mainly in
the upper atmosphere where NWP models tend to exhibit
significant errors.
Apart from temperature structures in the upper atmosphere, trace gas observations (mainly ozone) can provide
valuable contributions to weather prediction since ozone
has a strong impact on radiative heating (see
“Stratospheric Ozone”). Solar backscattered ultraviolet
instruments (e.g., SBUV, OMI, and GOME-1/2) provide
the bulk of the observations when sunlight is available,
while infrared spectrometers complement the observations at nighttime.
Apart from clear-sky satellite data, efforts toward
cloud-affected data assimilation have been successful in
recent years (Bauer et al. 2010; Geer et al. 2010; Bauer
et al. 2011. Bauer et al., 2006a, b). This was mainly
achieved by the greatly improved global model moist
physical parameterizations and the enhanced computational capabilities that allow the operational employment
of multiple scattering radiative transfer models
(Greenwald et al., 2002; Bauer et al., 2006c). The explicit
treatment of clouds and precipitation in operational analysis systems is accompanied by a large set of uncertainties,
for example, greater model nonlinearity, potential
dynamic instabilities, large and unknown error structures,
as well as unknown model biases (Errico et al., 2007).
Atmospheric modeling requires accurate constraints of
energy and water fluxes at the interface with land and
ocean surfaces. Over oceans, the interaction between wind
and waves is treated by wave models. Here, near-surface
wind observations from passive (microwave radiometers)
or active (scatterometers; see “Radar, Scatterometers”)
satellite data and direct observations of wave height from
altimeters (see “Radar, Altimeters”) and directional wave
spectra from synthetic aperture radars (see “Radar,
Synthetic Aperture”) are part of the operational set of
assimilated data.
Over land surfaces, a recent development in NWP is the
use of microwave radiometer observations to constrain
soil moisture analysis (see “Soil Moisture”). This can be
accomplished by fixed aperture radiometer observations
at 6–10 GHz from TMI and AMSR-E (Reichle et al.,
2007) and moderate resolution observations at 1.4 GHz
from synthetic aperture imagery by SMOS (Merlin et al.,
2006). Note that over land surfaces, RFI can impose serious limitations on data quality and usefulness for NWP
(see “Radio-Frequency Interference (RFI) in Passive
Microwave Sensing”). Also scatterometer is used to
extract information on soil properties.
Snow and sea-ice products, mostly obtained from passive and active microwave instruments, provide important
information for NWP systems on how to define surface
albedo and heat fluxes.
Figure 1 illustrates the dramatic increase of satellite
data diversity and volume since 1996 as well as a prediction until 2017 at ECMWF. The historic evolution of used
data does not necessarily reflect the sequential launch of
individual satellites but rather the ability of NWP data
assimilation systems and computers to digest the available
information. Today, data from about 50 different instruments is used constraining geophysical parameters in the
atmosphere and at both land and ocean surfaces. The gap
between data volume contributed by conventional (i.e.,
nonsatellite) and satellite data has largely widened. More
WEATHER PREDICTION
915
80
Number of satellite data products actively assimilated at ECMWF
70
60
50
40
30
20
10
0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
POES
COSMIC
Megha Tropiques
Oceansat
TERRA/AQUA AMV
Sentinel 1
Suomi-NPP
COSMIC-2
AQUA
HY-2A
Cryosat
Sentinel 3
DMSP
CNOFS
AURA
Meteosat
SMOS
Metop
GRACE
FY-3A/B
GOES
EarthCARE
ERS-1/2
GCOM-W1
QuikSCAT
MTSAT
ADM Aeolus
ENVISAT
TRMM
JASON-1/2/3
FY-2C/D
GOSAT
60
Total number of observations monitored at ECMWF
50
CONV+AMV
TOTAL
40
30
20
10
0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Weather Prediction, Figure 1 History and prediction of satellite data usage in data assimilation at ECMWF in terms of instruments
(top) and data volume per day for conventional and satellite observations (bottom).
916
WEATHER PREDICTION
Anomaly correlation of 500hPa height forecasts
Northern hemisphere
Southern hemisphere
98
D+3
95
90
D+5
80
70
D+7
60
50
40
OPERATIONS
30
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
98
D+3
95
90
ERA-INT
ERA-40
80
D+5
70
D+7
60
50
40
REANALYSIS
30
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Weather Prediction, Figure 2 Evolution of ECMWF 500 hPa height forecast skill score, expressed as anomaly correlation. Top
panel shows day 3 (blue), day 5 (red), day 7 (green) scores between Northern (thick lines) and Southern (thin lines) Hemispheres. Bottom
panel shows corresponding scores from ECMWF reanalysis, namely, ERA-40 (gray) and ERA-Interim (colors).
than 40 million satellite observations are monitored per
day, 95 % of which are assimilated directly as radiances
while the remainder is composed of retrieved products.
Impact of satellite data on prediction skill
Data impact in NWP systems can be quantified in various
ways and by assessing the impact on both analyses and
forecasts. It is assumed that better analyses will provide
better initial conditions for forecasts. Analysis quality is
usually quantified by the comparison of NWP model
fields to all available observations before and after they
have been assimilated. A better system is expected to produce a consistently better fit of the analysis to the
observations and should maintain this advantage up to
the next short-range forecast that has been initialized with
this analysis. Forecast skill can be quantified by comparing forecasts to both observations and analyses.
Figure 2 shows the evolution of the ECMWF forecast
model skill over the period 1980-2010 for the 3, 5, and
7 day forecasts over the Northern and Southern Hemispheres from the operational system (Figure 2a) and from
the ECMWF reanalyses ERA-40 (gray, Uppala et al.,
2005) and ERA-Interim (in colors, Uppala et al., 2008).
Figure 2a illustrates the substantial increase of skill over
three decades and the strong reduction of the difference
between skill over the Northern and Southern
WEATHER PREDICTION
Hemispheres. While the former is the result of the combined evolution of model physics, data assimilation, and
observing systems, the latter is only explained by the
contribution of satellite data due to the sparseness of
conventional data over Southern Hemispheric oceans.
Figure 2b provides another angle at disentangling the individual contributions to the time series of NWP forecast
skill: The reanalyses have been produced with a model
version that has been frozen in 2001 (ERA-40) and 2006
(ERA-Interim), respectively. The small increase of skill
over the ERA period is mostly due to the advancement
of satellite observations, while the difference between
ERA-40 and ERA-Interim is mainly due to the improvement of model and data assimilation system between
2001 and 2006. The larger gap of Northern and Southern
Hemisphere forecast skill of ERA-40 compared to
ERA-Interim in the overlap period between 1989 and
2001 illustrates the fact that the data assimilation system
of ERA-Interim exploits the same satellite observations
more effectively than ERA-40.
If the impact of individual observing systems (satellites
or instruments) is evaluated, the most prominent assessment tool is the so-called Observing System Experiment
(OSE), in which new data is added to an existing system
and the relative difference to a control system is evaluated.
Similarly, individual data sources can be withdrawn from
a full system to assess the impact of the withdrawn data.
More sophisticated methods involve the model operators that are used in the data assimilation system. Based
on forecast error estimates from the difference between
forecasts and verifying analysis, the model and observation operator adjoints employed in 4D-Var can be used
to deduce the dependence of this forecast error on individual observation types that were used in the initializing
analysis (Zhu and Gelaro, 2008). An alternative is the
use of ensemble-based analysis and forecasting techniques that evaluate forecast impact from ensemble spread
(that represents model error) with or without specific
observation types. Finally, Observing System Simulation
Experiments (OSSE) provide a framework for evaluating
the potential impact of observations that do not yet exist
and therefore require an observation simulation from independent NWP models (Arnold and Dey, 1986; Tan et al.,
2007).
A major OSE impact study was conducted in 2006–
2007 to evaluate the impact of the satellite observing system in global NWP at ECMWF (Kelly and Thépaut,
2007). The experiments demonstrated that (a) infrared
spectrometers (AIRS, IASI) produce the largest impact
per single instrument on geopotential height and temperature forecast skill and (b) that the currently available constellation of 4–5 microwave sounders (AMSU-A/B/MHS)
produces a very similar relative impact compared to one
advanced infrared sounder. The results are similar for the
Northern Hemisphere but with a smaller dynamic range
due to the stronger constraint from denser conventional
observations obtained over the continents. Further studies
917
suggest that, apart from infrared and microwave sounders,
GPS radio-occultation and scatterometer data produce significant contributions to forecast accuracy since they are
most directly related to temperature and surface wind
(pressure) with good global coverage.
In terms of atmospheric moisture, Kelly and Thépaut
(2007) confirmed previous investigations showing that
SSM/I data has the strongest impact in the lower troposphere over oceans complemented by AMSU-B data in
the mid and upper troposphere (Andersson et al., 2007).
The impact of clear-sky microwave imager data is about
as strong as that of cloud-affected data (Kelly et al., 2008).
Figure 3 shows a different measure of global forecast
impact from selected observation types that is obtained
from the 24 h forecast error sensitivity to the accumulated
sensitivity to all observation types (Cardinali, 2009). The
methodology is able to estimate observational impact
without having to add/withdraw them but only applies to
short-range forecast impact estimation. In Figure 3a,
the total impact of the most prominent satellite and
conventional observations for a 4 month period
(September–December 2008) is shown, while Figure 3b
shows the impact per individual observation.
It is evident that infrared spectrometers and microwave
sounders produce the strongest impact followed by radiooccultation observations. Figure 3b shows that surface
observations of pressure over the oceans from drifting
buoys but also all types of direct wind observations have
a strong impact, which suggests the importance of accurate wind observations from satellites as expected from
ADM/Aeolus in the future (see “Lidar Systems”).
It is important to note that the impact of individual
observations depends on the NWP system and the weight
assigned to the observations in the analysis. It is therefore
crucial to evaluate the NWP model, the data assimilation
system, and the entire set of used observations together
to characterize the importance of existing satellite data
for NWP and to estimate the potential impact of future
systems.
Summary
Current weather forecast skill is strongly driven by the
sophistication of the physical processes represented in
numerical models and advanced data assimilation
schemes allowing vast amounts of data from conventional
sources and satellites to be used. Globally, more than 40
million observations per day are used from about 50 different satellite instruments to produce atmospheric analyses with which the forecast models are initialized. The
most important instruments are passive radiometers that
measure infrared and microwave radiation emitted by the
surface-atmosphere system and that are mostly exploited
to derive information on temperature and moisture structures. Increasingly, observations of clouds and rain, surface waves, land surface characteristics, and atmospheric
trace gases are added. In parallel, numerical models
become increasingly capable of representing more
918
WEATHER PREDICTION
GOES-Rad
MTSAT-Rad
MET 9-Rad
MET 7-Rad
AMSU-B
MHS
AMSR-E
SSMI
GPS-RO
IASI
AIRS
AMSU-A
HIRS
TEMP-mass
DRIBU-mass
AIREP-mass
SYNOP-mass
SCAT-wind
MODIS-AMV
MET-AMV
MTSAT-AMV
GOES-AMV
PILOT-wind
TEMP-wind
DRIBU-wind
AIREP-wind
SYNOP-wind
0
2
4
6
8
10
12
14
16
18
20
FEC %
GOES-Rad
MTSAT-Rad
MET 9-Rad
MET 7-Rad
AMSU-B
MHS
AMSR-E
SSMI
GPS-RO
IASI
AIRS
AMSU-A
HIRS
TEMP-mass
DRIBU-mass
AIREP-mass
SYNOP-mass
SCAT-wind
MODIS-AMV
MET-AMV
MTSAT-AMV
GOES-AMV
PILOT-wind
TEMP-wind
DRIBU-wind
AIREP-wind
SYNOP-wind
0
5
10
15
20
25
30
FEC per OBS %
Weather Prediction, Figure 3 Relative contribution of different observing systems to 24 h forecast error reduction for September–
December 2008 (Cardinali, 2009). Top panel shows contribution per observing system; bottom panel shows contribution per
single observation.
WEATHER PREDICTION
complex physical and chemical processes at smaller
scales. Ensemble analysis and forecasting systems allow
the estimation of analysis and forecasting uncertainties –
a crucial information in forecasting highly nonlinear atmospheric phenomena. Future satellite observing systems
will develop toward more hyper-spectral instruments covering wider spectral ranges with fine spectral resolution as
well as active instruments that sample vertical structures
and wind very accurately.
Abbreviations
4D-Var–Four-Dimensional Variational Assimilation
AIRS–Atmospheric Infrared Sounder
IASI–Infrared Atmospheric Sounding Interferometer
ATOVS–Advanced TIROS Operational Vertical Sounder
TOVS–TIROS Operational Vertical Sounder
HIRS–High-Resolution Infrared Sounder
AMSU-A–Advanced Microwave Sounding Unit A
AMSU-B–Advanced Microwave Sounding Unit B
MHS–Microwave Humidity Sounder
SSM/I–Special Sensor Microwave/Imager
SSMIS–Special Sensor Microwave Imager Sounder
TMI–TRMM Microwave Imager
TRMM–Tropical Rainfall Measuring Mission
AMSR-E–Advanced Microwave Scanning Radiometer E
METOP–Meteorological Operational Polar satellite
EPS–EUMETSAT Polar System
NOAA–National Oceanic and Atmospheric
Administration
GNSS–Global Navigation Satellite System
GRAS–GNSS Receiver for Atmospheric Sounding
CHAMP–Challenging Minisatellite Payload
GPS–Global Positioning System
NWP–Numerical Weather Prediction
ERA–ECMWF Reanalysis
ECMWF–European Center for Medium-Range
Weather Forecasts
SMOS–Soil Moisture Ocean Salinity
EOS–Earth Observing System
COSMIC–Constellation Observing System for
Meteorology, Ionosphere, and Climate
OSE–Observing System Experiment
OSSE–Observing System Simulation Experiment
Suomi NPP–Suomi National Polar-orbiting Partnership
CrIS–Cross-track Infrared Sounder
GCOM-W–Global Change Observation Mission - Water
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WETLANDS
Cross-references
Atmospheric General Circulation Models
Data Assimilation
Geophysical Retrieval, Overview
GPS, Occultation Systems
Land-Atmosphere Interactions, Evapotranspiration
Lidar Systems
Limb Sounding, Atmospheric
Microwave Radiometers
Ocean-Atmosphere Water Flux and Evaporation
Radar, Altimeters
Radar, Scatterometers
Radar, Synthetic Aperture
Radiative Transfer, Solution Techniques
Radiation, Electromagnetic
Radiative Transfer, Theory
Radio-Frequency Interference (RFI) in Passive Microwave
Sensing
Soil Moisture
Stratospheric Ozone
WETLANDS
John Melack
Department of Ecology, Evolution and Marine Biology,
University of California, Santa Barbara, CA, USA
Definitions
Passive microwave radiation is emitted from the Earth’s
land, seas, and atmosphere at wavelengths generally
between 0.15 and 30 cm or, if expressed as frequencies,
between 1 and 200 GHz.
Emissivity is the ratio of energy radiated by a material to
energy radiated by a blackbody at the same temperature.
Introduction
Wetlands cover extensive areas worldwide (Lehner and
Döll, 2004), have important ecological and biogeochemical functions, and play critical roles in improving water
quality, mitigating floods, and providing habitat for fish
and wildlife. For many wetlands, remote sensing is the
preferred approach to obtain a synoptic view of inundation
and vegetative cover, and a suite of optical and microwave
sensing systems and analysis algorithms are being applied
to wetlands (Sahagian and Melack, 1998; Melack, 2004).
In the case of the large, temporally varying wetlands found
throughout the world, a remote sensing system with frequent, near-global coverage and sensitivity to wetness is
necessary. These requirements are met by passive microwave sensors.
Passive microwave systems and analyses
A global record of passive microwave radiation measured
from satellites is available from 1979 to the present. The
Scanning Multichannel Microwave Radiometer (SMMR)
was operated on board the Nimbus-7 satellite from 1979 to
921
1987, with global coverage every 6 days. The Special Sensor
Microwave/Imager (SSM/I) replaced SMMR in 1987 and
operates today with 3 day global coverage from a satellite
in the US Defense Meteorological Satellite Program. The
Tropical Rainfall Measuring Mission (TRMM), launched
in 1997, included a microwave radiometer similar to that
on SSM/I but providing higher spatial resolution because it
flies at lower altitude than the SSM/I. The Advanced Microwave Scanning Radiometer (AMSR), launched in 2002 on
the Aqua satellite, offers additional capabilities.
Measurements of passive microwave radiation are
expressed as brightness temperatures ( K) and are
recorded as vertical and horizontal polarizations at several
frequencies (Choudhury, 1989). To reduce effects of
atmospheric water vapor and temperature on the measurements, the difference between the two polarizations,
referred to as DT, is often used. However, surface roughness, exposed soil and rock, seasonal vegetation changes,
and other atmospheric conditions can affect the DT.
Prigent et al. (1998) have developed an approach to calculate microwave emissivities of land surfaces after removing
the contributions from the atmosphere, clouds, and rain and
modulation by surface temperatures by using ancillary
remotely sensed information and meteorological reanalyses.
In general, flooded regions have low microwave emissivities and high polarization differences relative to non-flooded
areas. Spatial resolutions of approximately 10–50 km limit
the application of the technique to large wetlands or to
regions where the cumulative area of smaller wetlands comprises a significant proportion of the landscape.
Calm water surfaces result in a strongly polarized emission at 37 GHz (e.g., SMMR DT ca. 60 K), although this is
attenuated to varying degrees by overlying vegetation. In
the absence of flooding, the dense vegetation and relatively level terrain typical of large wetlands present
a stable background of depolarized microwave emission
(e.g., SMMR DT averaging ca. 4 K). Fluctuations in the
extent of inundation can be quantified if the DT is raised
sufficiently above background. Inundation area can be
estimated from the DT by mixing models that incorporate
the microwave emission characteristics of the major landscape units (Sippel et al., 1994). The results have been validated against independent measures of flooding, such as
river-stage records in areas of floodplain where inundation
is known to be controlled by a large river.
Passive microwave applications
Inundation
A global monthly time series of inundation during the
1990s was produced by Prigent et al. (2007) based on
a combination of passive microwave surface emissivities,
scatterometer responses, and visible and near-infrared
reflectances for ca. 0.25 grid cells. The detection of inundation relied primarily on SSM/I data. In forested regions
it appears that the results do not indicate inundation if
standing water occupies less than 10 % of the pixel.
922
WETLANDS
In comparison to inundation determined under low and
high water levels at 100 m resolution with synthetic aperture radar for the central Amazon (Hess et al., 2003),
Prigent et al.’s results do fairly well, underestimating
low water area by 11 % and high water area by 30 %. Variations in wetland area match well with variations in water
level derived from TOPEX-Poseidon altimetry in the
Niger, Ganges, Pantanal, and Amazon basins on a 4 grid.
In tropical areas, passive microwave remote sensing
studies have focused on analysis of the inundation patterns
in seasonally flooded forests and savannas and on comparative analyses of hydrological patterns in the major wetlands in South America (Hamilton et al., 2002).
Observations with SMMR at the 37 GHz have been analyzed to determine spatial and temporal patterns of inundation on floodplains of the Amazon, Tocantins and Orinoco
basins, and the Pantanal wetlands of South America.
Ecological and biogeochemical studies
Information about inundation and wetland vegetation are
essential for the understanding of carbon dynamics in the
Amazon basin. By combining field measurements of carbon dioxide concentrations in surface waters with passive
and active remote sensing of inundation, Richey et al.
(2002) calculated the evasion (outgassing) of CO2 from
water to the atmosphere in the central Amazon basin. Similarly, by combining measurements of methane emission
from a variety of habitats and sites with inundation and
vegetation variations derived from microwave and optical
remote sensing analyses, Melack et al. (2004) estimated
methane emissions from the Amazon basin.
Variations in the distribution and inundation of floodplain
habitats play a key role in the ecology and production
of many commercially important freshwater fish. In
a comparison of flooded areas estimated from a monthly
series of passive microwave data (Sippel et al., 1998) with
the annual fish yield aggregated from the Brazilian Amazon,
Melack et al. (2009) found significant relationships for small
species at lower trophic levels generally at short lag times
(0–1 years), while those for large species at higher trophic
levels had considerably longer lag times (3–5 years).
Summary
The principal advantages of the passive microwave observations are their frequent global coverage and their ability
to reveal characteristics, such as inundation, of the land
surface beneath cloud cover and vegetation.
This encyclopedia includes no entries beginning with X, Y and Z.
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