|Computational weather modelling
U. N. Sinha*,,
Ravi S. Nanjundiah** and T. N. Venkatesh*
*Flosolver Unit, National Aerospace Laboratories, Bangalore 560 017,
**Centre for Atmospheric and Oceanic Sciences, Indian Institute of
Science, Bangalore 560 012, India
have been major advances in the field of weather prediction by numerical methods, since
the 1920s. The details of modelling the atmosphere
along with the computational techniques are discussed. Some outstanding problems in the
Indian context are highlighted.
ONE of the earliest applications envisaged
for computers and computing techniques was weather forecasting. In the early twentieth
century, Richardson1 conceived of a factory where weather
forecasting would be conducted using a large number of personnel (in a sense he imagined
the use of a massively parallel system). His efforts, though pioneering, failed due to a
lack of understanding of numerical instabilities. Later Charney et al.2
used the earliest computer available to conduct computations with a simple atmospheric
model. From such early attempts at numerical weather prediction to the present day, this
field has always been considered one of the grand challenge problems in
computational sciences. Hence it is not surprising that some of the largest and most
powerful computers have been used for weather forecasting.
The atmosphere is a complex system. Phenomena as
diverse as turbulence and radiation need to be considered. Scales range from the extremely
large (such as the planetary waves with spatial scales of thousands of kilometres) to the
extremely small (e.g. turbulence, with spatial scales of a few centimetres). The presence
of moisture (with related changes of phase from vapour to liquid and ice and consequent
feedback of heating to the ambient atmosphere) makes the modelling of this system quite
difficult. In this paper we present a broad overview of computational weather prediction.
We begin with the governing equations and discuss the underlying assumptions in using
them. We also present a brief overview of the numerical methods, the computational methods
used for the integration of these models and some results. We will discuss the details of
numerical methods with specific reference to the model3 used by the National
Centre for Medium Range Weather Forecasts (NCMRWF), Delhi for Indian meteorological
The atmosphere is a fluid on a rotating sphere (the
earth). Evaporation of water from the surface, and its precipitation from the atmosphere,
makes air a mixed phase system. The reduction of density in the vertical makes the system
stratified. The wind velocity is calculated using the laws of motion (conservation of
mass, momentum and the equation of state). The major forcing for atmospheric motion is
radiative heating from the sun. Other factors that change the temperature are heating due
to phase change (from vapour to liquid) inside the clouds (commonly known as convective
heating), and heating of air at the surface (sensible heating). The changes in temperature
cause changes in pressure which in turn affects the flow. Furthermore, evaporation
increases the concentration of moisture while precipitation reduces its concentration. The
concentration can also change due to advection of moisture. These changes are accounted
for by a species conservation equation. Some of the more sophisticated models also have
species conservation equations for other tracers such as NOx, SOx,
aerosols, etc. (while aerosols are only recently being incorporated into atmospheric
models, it is interesting to note that Richardson1 thought that dust in the
atmosphere could be an important variable for weather prediction).
The laws of conservation of mass, momentum, energy,
and water vapour are coupled to each other through various feedback processes and
modelling these leads to a set of coupled nonlinear partial differential equations. For
describing atmospheric motion, the spherical system of coordinates (latitude f , longitude
l ) is the most convenient (and natural) system to use. The equations can be written as:
Law of conservation of mass: (1)
where r is the density and
Law of conservation of momentum:
where p is the pressure, F the geopotential, the earths angular velocity vector (W is
and represents the frictional forces.
Law of conservation of energy: (3)
where Q = T(p0/p)k
is the potential temperature, k = R/Cp (Cp
is the specific heat at constant pressure and R is the gas constant for air), T
the temperature and HT the diabatic heating term (due to radiation,
surface heating and phase change). p0 is a reference pressure usually
taken to be 1000 mb.
The equation of state is
p = r RT. (4)
Moisture conservation equation: (5)
where q is the specific humidity and S
represents sources and sinks of moisture.
In the above set of equations the unknowns are r ,
p, , T and q. The terms ,
S and H occur at scales which are much smaller than those resolved by models
and are represented by empirical relations commonly known as parameterizations (also
For forecasting the state of the atmosphere we need
to solve these equations numerically. However, before discretizing numerically, certain
simplifying assumptions are used for making the problem more tractable, and for reducing
the computational resources required to solve these equations. These assumptions are made
by using arguments of scale analysis4. Upon conducting scale analysis
considering large-scale (O(103) km) motion, we find that the wind field
is practically two-dimensional, i.e. the vertical velocity is much smaller than the
horizontal velocity. We also find that in the vertical direction the predominant terms in
the momentum equation are the pressure gradient force (¶ p/¶ z) and the
gravity (or weight) term (r g). The other terms in this equation are much smaller
(about 23 orders of magnitude) and to a first approximation, when considering
large-scale motion such as the scales resolved by the General Circulation Model (GCM) at
NCMRWF, the motion is in hydrostatic balance. The law of conservation of momentum in the
vertical direction therefore simplifies to:
This has many far-reaching implications:
The hydrostatic balance allows us to use pressure as a vertical
The hydrostatic balance filters out acoustic waves and thus allows
the use of larger time steps for temporal integration.
There is no vertical velocity term in the vertical
momentum equation. This quantity now needs to be computed indirectly.
In most models the non-dimensional variable s
= p/p* (where p* is the surface pressure) is used as the
vertical coordinate5. The use of the s coordinates allows the incorporation of
variations in the surface terrain. The drawback of using the hydrostatic assumption is
that vertical velocity is no longer prognostic, (i.e. there is no evolution equation for
the vertical velocity). The continuity equation in the s coordinates is
is the horizontal velocity and The components of are u and v.
This equation, in the vertically integrated form, is
used to calculate the variation of surface pressure:
Another form of the same equation is used to obtain
the vertical velocities:
Thus the effect of the hydrostatic assumption is
that the continuity equation is used to compute both the mass of the air column
(proportional to p*) and the vertical velocity . Also, it
should be noted that by scale analysis we may neglect the impact of other terms in the
vertical momentum equation (as they are small in comparison to the pressure gradient and
gravity terms of the vertical momentum equation), but their variations (in time) may be as
large as that of the vertical velocity itself. Specifically near the equator (Figure 1),
estimates of the vertical component of the Coriolis term (2W ucosf ) are as large
as the variations of the vertical velocity. That the neglect of this term has not been
considered entirely satisfactory by modellers can be noted from the following comment of
Richardson1: If the largest of the small terms [in the vertical momentum
equation] be included the equation runs
Here w is the speed of
earths rotation, f is the latitude and me the eastward momentum.
The term 2w mecosf may produce a
pressure change of 0.5 millibar at sea level under extreme conditions.W. H. Dines points
out that the term 2w mecosf may be taken to mean that air moving
eastward is lighter than the air moving westward and suggests that this may explain the
fact that westerly winds commonly increase aft much more than do easterly winds. If the
term 2w mecosf really has such important influence it ought not to be
neglected when one is dealing with parts of eddies. The impact of neglecting these
terms on the simulations in the tropical regions needs careful and systematic study.
Lions et al.6 recognize the limitations of the approximation and suggest
the use of matched asymptotic expansions for improving their model near the equator. This
is of particular significance as small differences in vertical motions can have
significant impact in the development of clouds and cumulus heat sources and their
consequent feedback to the large-scale flow.
Scale analysis also shows that in the horizontal
direction, the major balance is between the Coriolis force and the pressure gradient force
(commonly known as the geostrophic balance). The other terms in the horizontal momentum
equations are not neglected as these are only about one order of magnitude smaller than
the dominant terms. However, it is noteworthy that the magnitude of terms dropped in the
vertical momentum equation is comparable to that of terms included in the horizontal
momentum equations. It is also interesting to note that in the near equatorial region the
geostrophic balance is not valid, and there is no balance in the horizontal momentum
equations to a first order in this region (thus making accurate predictions more difficult
at low latitudes).
For global modelling the above assumptions are
generally used. With these assumptions, the equations as used in the model are as follows.
The momentum equation is thus
where T0 is a mean temperature, T¢
= T T0, h the vertical component
of the relative vorticity, a the radius of earth, U = ucosf
, V = vcosf and Ff , Fl the components of the
In spectral models we prefer to use scalar
quantities due to singularities at the poles which make vector representation difficult.
For this reason the momentum equation is written in terms of the horizontal divergence D
and vertical component of vorticity z .
as h = z + f and f = 2W
sinf is the Coriolis parameter. is
calculated from D and z .
The energy equation is
where g = RT/(Cps ) ¶
T/¶ s is the static stability parameter.
However, the hydrostatic assumption is no longer
valid when one is resolving the cloud scale (110 km), and the non-hydrostatic
system of equations (eqs (1)(4)) with the height (z) as the vertical
coordinate is used. Due to computational constraints such models can only be used for very
limited regions and short time periods. MM5 (ref. 7) and RAMS8 are widely used
Spatially the Partial Differential Equations (PDEs)
can be discretized either by using finite differences or by using the spectral technique
in the horizontal (finite differencing is always used in the vertical). For long, finite
difference techniques were preferred as computation of nonlinear quadratic terms (uv,
u2, etc.) was expensive in the spectral domain. However, the
path-breaking work of Orszag9 in using transform methods to compute these
terms, and of Bourke et al.10 in their application to atmospheric
modelling, have popularized the spectral method. Spectral techniques are generally
preferred now as they offer higher order accuracy for the same physical resolution. Using
spherical harmonics, a variable on a sphere can be represented as
and Pnm(cosf ) is the
associated Legendre function of order m and degree n (see Bandyopadhyay11
for derivations). In the spherical system of coordinates, poles are points of singularity
and computation of vectors in such regions is difficult. As atmospheric flow is
quasi-two-dimensional, divergent and rotational, we use the stream and potential functions
to describe the flow. The prognostic variables used in spectral models are divergence (D),
relative vorticity (z ), temperature (T), surface pressure (usually the natural
logarithm of this quantity is used, ln(p*)) and specific humidity q. In some
models (Krishnamurti et al.12), the dew-point temperature instead of
specific humidity is used as a prognostic variable.
Theoretically, an infinite number of spherical
harmonics is necessary to describe a variable. However, for computational purposes one
needs to truncate at a finite wave number. The highest wave number determines the
resolution of the model (for most weather forecasting purposes, about 126213 waves
are currently used). We therefore take
The relationship between the number of waves in the
zonal direction and the number of waves in the meridional direction defines the type of
truncation used in a model. If the two are equal (J = N) then the
truncation is said to be triangular (T). If the number of meridional waves is
greater than the zonal wave number by a constant quantity (J = N + |m|),
then the truncation is rhomboidal (R).
For alias-free computations (i.e. to prevent energy
of unresolved modes spuriously appearing in the resolved modes) of the quadradic terms (uv,
u2, etc.) the number of gridpoints required in the east-west direction
is 3N + 1 for both (T) and (R) truncations. In the
northsouth direction, the number of latitudes for exact Gaussian quadrature is (3N + 1)/2
for triangular truncation and 5N/2 for rhomboidal truncation. The resolution and
the method of truncation of spectral models is represented as T N
(Triangular) or R N (Rhomboidal).
Almost all weather forecasting centres in the world
currently use triangular truncation. The European Centre for Medium Range Weather
Forecasts (ECMWF) generates its forecasts at T-213 (corresponding to a resolution
of about 70 km at the equator), while the National Centres for Environmental
Prediction, USA uses T-126, corresponding to an equatorial resolution of about
90 km. The Indian centre, NCMRWF, generates its forecasts using a resolution of T-80
corresponding to an equatorial resolution of 140 km. Plans are afoot to increase this
resolution to T-126 in the near future. In the Indian context, the most outstanding
problem is the prediction of the evolution and propagation of convective systems such as
the cyclones, depressions and cloud bands. These require resolutions of at least
50 km (Krishnamurti and Jha13 suggest a resolution of about 30 km to
resolve cyclones) over the area of interest. With global models such resolutions can be
obtained by spectral wave numbers of 170 or more.
It should be noted that use of spherical harmonics
is not without problems. Spherical harmonics are specifically ill-suited for variables
that do not have smooth and continuous variations such as specific humidity and surface
terrain heights. The Gibbs phenomenon (spurious oscillations that occur when a
discontinuity or a sharp change in a variables value is represented by a linear
combination of waves) is quite noticeable in these quantities; and in the case of specific
humidity, undershooting leads to negative values (a physical impossibility) while
overshooting leads to spurious precipitation (commonly called spectral rain).
The Gibbs oscillation can also have large impact especially near mountains in the
specification of terrain heights leading to errors in the calculation of the pressure
Temporally, finite difference techniques are used
for time-marching. The most common method is the semi-implicit leap-frog technique. The
linear parts of the model, i.e. terms involving pressure gradient, divergence and
temperature, are computed implicitly, else an explicit method would require time steps
that would be governed by the speed of gravity waves, which for the atmosphere could be as
large as that of acoustic waves. The other terms involving Coriolis force and advection
are computed explicitly. The stability aspects have been discussed by Asselin14.
As the species continuity equation for specific
humidity and the prognostic equation for vorticity do not have the linear terms discussed
above, they are computed in a purely explicit manner.
The temporal integration is conducted in the
spectral space. However, it is impossible to compute some of the physical forcing terms
such as radiative heating, cloud parameterization, modelling of the boundary layer and
related transfer of fluxes at the surface in the spectral space. These and the quadratic
terms (uv, u2) are computed on the physical grid. The sub-grid
models which deal with computation of boundary layer processes, cloud parameterization and
radiation are commonly termed as model physics. The parts of the model that
are related to the inviscid parts of the laws of momentum are termed as model
dynamics. Generally, model physics deals with those parts that are essentially
empirical, while model dynamics deals with the more theoretically exact parts of the
It is also interesting to note that while
considerable progress has been made in improving model physics from the time of Bourke et
al.10, little change has occurred in the realm of model dynamics. Almost
all models use the semi-implicit scheme suggested by them. The division of computation on
spectral and physical domains requires transformation from one space to another. This
transformation is achieved in the following way. From physical to spectral domain, the
procedure is to:
- Compute the Fourier coefficients of the variables (usually done using
FFTs) on latitude circles, and
- Multiply the Fourier coefficients with associated Legendre
polynomials and compute spherical harmonics using Gaussian quadrature across latitude
circles for summation.
To go from spectral to physical domain,
- Compute the Fourier coefficients on a latitude-circle from spherical
harmonics by summing the product of spherical harmonics and associated Legendre
polynomials for that latitude, and
- Conduct an inverse Fourier transform to compute the physical grid
Expressed differently, multi-dimensional transforms
Initial and boundary conditions
The accuracy of a weather forecast is highly
dependent on the quality of the initial conditions. These are garnered from a variety of
sources such as ground observations, radio-sonde launches, ships, buoys and satellites.
These data are irregularly spaced both in space and
time and as such the data obtained cannot be directly fed into the model. Also it is
necessary to access the quality of the incoming data and reject erroneous data. The
process of including data into the model is called assimilation. A four-dimensional
variational assimilation in space and time is commonly used for this purpose.
Lately it has been shown that initialization of the
input data using inverse algorithms for model physics (such as cumulus parameterization),
i.e. observing the current rainfall rates, inferring the heating rates therefrom, and
producing a wind field in balance with this heating field, dramatically improves forecasts
especially for tropical convective systems (Krishnamurti et al.12). Due
to recent advances in satellite observation systems, satellites such as the INSAT 2E now
routinely transmit data at resolutions as high as 8 km ´ 8 km (ref.
15). Since most global models have a typical resolution of 80 km it is necessary
to explore data assimilation techniques that can effectively incorporate this
information for improving forecasts for the Indian region.
The discretized PDEs need to be integrated in time
to generate the forecasts. These models are computationally intensive. At a resolution of T-213
with 31 levels in the vertical, a typical machine with a sustained speed of 1 GFLOPS (109
FLoating point OPerations per Second) would take about 2025 min for a one-day
simulation (based on data provided by Dent and Mozdzynski16 for a 46-processor
Fujitsu VPP700, and by Drake et al.17 for a 512-processor Paragon).
Typically we generate 510-day forecasts. Due to uncertainty in initial conditions,
ensemble forecasts, i.e. a group of forecasts with slightly varying initial
conditions, are generated, and the most likely scenario is obtained from this ensemble.
Therefore, even with a 1 GFLOPS machine a ten-day forecast would take about 200 min
and an ensemble of 12 simulations would take about 40 h. Since the mid-eighties,
weather forecasting has been largely done on vector machines such as the Cray, either in a
pure sequential mode or using shared memory parallel processing. Even today computers with
sustained processor speeds of 1 GFLOPS for weather forecasting are quite expensive and
hence it is imperative that massive paral-
lel processing be used for the task. However, while
it is necessary to use high performance computing for numerical weather prediction, this
does not preclude the use of lower-end computers for research and development in this
field; e.g. it has been shown that with careful re-engineering of the NCMRWF model code
and exploiting various features of modern programming languages such as Fortran-90, such
Figure 3 cd. Simulation
of a convective system during the 1993 monsoon season with the NCMRWF model at T-80
models can be executed even on PCs (Nanjundiah and
Sinha18). A one-day simulation on a Pentium II PC takes about 45 min.
Specifically in the Indian context, massive parallel
processing based on low-end processors such as Pentiums seems to be an attractive
Parallel computing of meteorological models
Parallel computing involves the decomposition of a
single large task into smaller multiple tasks which can be carried out concurrently on a
large number of processors. The most common method used for parallel processing is domain
decomposition. For atmospheric models the global domain can be split into smaller
sub-regions and the computations in each sub-region could be allocated to a processor. At
required intervals during the temporal integration, data need to be communicated between
processors. It should be borne in mind that interprocessor communication is an additional
overhead for parallel computing and the time required for this should be a minimum for
optimum throughput and maximum scalability, (i.e. increase in processing speeds with
increasing number of processors). For most finite difference techniques data need to be
communicated between neighbouring processors, and so a high degree of scalability is
assured. However, spectral models need the communication to be global to transform
variables from grid space to spectral space and vice-versa. This increases communication
overheads and generally results in poorer scalability19. In most parallel
implementations of spectral models, only the computations in grid space are parallelized.
As the number of processors increases, the sequential component becomes more prominent
leading to reduced scalability. However, with careful parallelization of the spectral
component the scalability can be increased substantially. With proper stencils of
communication, efficiencies greater than 90% can be achieved20. Drake et al.17
have also shown that the spectral model CCM2 is also highly scalable on a variety of
platforms. However, it should be noted that the computational flow in CCM2 is far more
amenable to parallel computing than in the NCMRWF model used by Venkatesh et al.20.
The NCMRWF model has been implemented in most of the
leading parallel computing centres in India. It has been implemented on a variety of
platforms at the National Aerospace Laboratories including a PC. In Figure 2 we show a
typical forecast with the model at T-80 (standard resolution used at NCMRWF) and at
T-170. We find that the higher resolution generates finer scale structures. In
Figure 3, we show the evolution of a tropical depression over the Bay of Bengal as
predicted by the model at a resolution of T-80. The movement of the depression is
Computational weather modelling has made rapid
strides from the days of Charney et al.2. Today, resolutions as fine as
50 km are possible with global models. In consonance with this, medium-range
(510 days) predictions have improved dramatically in quality. However, the
quality of the predictions in the tropics is still inferior to those in the mid-latitudes,
being only about one-third of that in the northern hemisphere. Shukla21
attributes this to the differing nature of the convective disturbances while Krishnamurti22
suggests that the difference in data density is the primary cause. It must be noted that
most major weather forecasting centres (such as ECMWF and NCEP) are in the mid-latitudes
and their regions of interest are essentially the temperate regions. Thus improvement in
the forecasts in these regions may not be surprising. It is necessary for Indian
scientists to address problems relevant to tropical regions; furthermore, it is now being
widely realized that further improvements at higher latitudes may become feasible only
when tropical forecasts improve, because of global connections between different regions.
In the Indian context, the most important problems
that need to be addressed are the following.
- The prediction of the evolution and movement of cloud bands and the
changes in associated rainfall. Predicting these will be of immense benefit to farmers and
help them determine sowing and cropping patterns.
- An accurate prediction of the movement of cyclones could result in
improved disaster management.
It should be noted that realistic simulation of the
seasonal mean patterns of the Indian monsoon with GCMs remains elusive23. It
has been shown that no GCM captures all the major features of the Indian monsoon.
Addressing these issues requires improvements to the
model in terms of increased resolution and development of model physics and dynamics
appropriate to the higher resolutions and the availability of good assimilated data. The
computational resources have to be augmented
to generate predictions with such high resolution models in real time. Given the
availability of expertise in
the fields of modelling and high performance computing in the country, achieving these
objectives seems feasible.
- Richardson, L. F., Weather Prediction by Numerical Process,
Cambridge University Press (reprinted Dover, 1965), 1922,
- Charney, J., Fjortoft, R. and von Neumann, J., Tellus, 1950, 2,
- Kalnay, E., Sela, J., Campana, K., Basu, B. K., Schwarzkopf, M.,
Long, P., Kaplan, M. and Alpert J., Documentation of the research version of the NMC
medium range forecast model,
National Centres for Environmental Prediction, Washington DC, USA, 1988.
- Holton, J., Introduction to Dynamic Meteorology, Academic
Press, New York, 1972, p. 319.
- Phillips, N. A., Mon. Weather Rev., 1959, 87,
- Lions, J., Temam, R. and Wang, S., Commun. Pure Appl. Math.,
1997, L, 07070752.
- Grell, G. A., Dudhia, J. and Stauffer, D. R., NCAR Tech. Note,
NCAR/TN-398+IA, October, 1993.
- Walko, R. L., Tremback, C. J. and Hertenstein, R. F. A., RAMS, The
Regional Atmospheric Modeling System, Users Guide, Mission Research Corporation, Fort
Collins, CO 80522, USA, 1995.
- Orszag, S. A., J. Atmos. Sci., 1970, 27, 890895.
- Bourke, W., McAveney, B., Puri, K. and Thurling, R., Methods in
Computational Physics (ed. Chang, J.), Academic Press, 1977, vol. 17, pp.
- Bandyopadhyay, S., Project Document, NAL PD FS9817,
- Krishnamurti, T. N., Bedi, H. S. and Hardiker, V. M., An
Introduction to Global Spectral Modelling, Oxford University Press, 1997, p. 237.
- Krishnamurti, T. N. and Jha, B., Sadhana, 1998, 23,
- Asselin, R., Mon. Weather Rev., 1972, 100,
- Bhatia, R. C., Bushan, B. and Rao, V. R., Curr. Sci., 1999, 76,
- Dent, D. and Mozdzynski, G., Proceedings of the Seventh ECMWF
Workshop on the Use of Parallel Processors in Meteorology (eds Hoffman, G. R. and Kreitz,
N.), World Scientific, Singapore, 1996, pp. 3651.
- Drake, J., Foster, I., Michalakes, J., Toonen, B. and Worley, P., J.
Parallel Comput, 1995, 21, 15711591.
- Nanjundiah, R. S. and Sinha, U. N., Curr. Sci., 1999, 76,
- Basu, B. K., Curr. Sci., 1998, 74, 508516.
- Venkatesh, T. N., Rajalakshmy, S., Sarasamma, V. R. and Sinha, U. N.,
Curr. Sci., 1998, 75, 508516.
- Shukla, J., Adv. Geophys. B, 1985, 28, 87122.
- Krishnamurti, T. N., Annu. Rev. Fluid Mech., 1995, 27,
- Gadgil, S. and Sajani, S., Clim. Dyn., 1998, 14,
ACKNOWLEDGEMENTS. We thank NCMRWF for
providing the initial datasets for the simulations.