Atmósfera 21(2), 191-212 (2008)
Combined IR-microwave satellite retrieval of temperature and
moisture profiles using the ICI inversion system and
its application in the MM5 model
D. SINGH
Department of Science and Technology, Technology Bhawan,
New Mehrauli Road, New Delhi-110016, India
S. SANDEEP, A. CHANDRASEKAR
Department of Physics and Meteorology,
Indian Institute of Technology, Kharagpur, India
Corresponding author: A. Chandrasekar; e-mail: chand@phy.iitkgp.ernet.in
Received December 2, 2006; accepted October 11, 2007
RESUMEN
Las mediciones de radiación desde satélite ofrecen la oportunidad de recuperar variables atmosféricas a una
resolución horizontal mucho mayor que la que se alcanza con mediciones in situ (p. ej. con radiosondas).
Sin embargo la exactitud de esos datos es crucial para su utilización y la naturaleza problemática del caso
impide una solución directa. Se han investigado varias aproximaciones de recuperación, incluyendo técnicas
empíricas, acopladas a modelos numéricos de predicción climática, y técnicas de análisis de datos como la
regresión. En este trabajo se utiliza el esquema de la inversión acoplada con imagen (ICI, inversion coupled
imager, en inglés) para obtener información de los perfiles verticales de temperatura y humedad por medio
de la luminosidad de las temperaturas infrarroja y de micro ondas obtenidas desde un satélite de órbita polar.
Las desviaciones del sesgo y de la raíz cuadrada de la media (RMS, por sus siglas en inglés) se determinaron, por
separado, para las condiciones de invierno y verano, sobre la tierra y el mar, utilizando datos del reanálisis del
Nacional Center for Environmental Prediction (NCEP). Los resultados muestran que el error RMS de temperatura
en la troposfera baja es de unos 2 K. Por otro lado se encontró que los errores RMS de los perfiles de humedad
están alrededor de 1 g kg-1. Sin embargo, debajo de 850 hPa los errores para los perfiles de temperatura y humedad
fueron, sobre tierra, de 3.5 K y 3 g kg-1 y sobre el mar de 2.5 K y 2.0 g kg-1. Para la predicción de dos ciclones
tropicales que se formaron sobre la Bahía de Bengala del 24 al 27 de noviembre de 2002 y del 16 al 18 de mayo de
2004, se diseñaron dos experimentos numéricos, una simulación de control sin asimilación de las observaciones
y otro en el que los perfiles de temperatura y humedad fueron recuperados de la unidad avanzada de sondeo de
micro ondas (AMSU, por sus siglas en inglés) y de la sonda de alta resolución de radiación infrarroja (HIRS, por
sus siglas en inglés). La corrida del modelo con las asimilaciones de AMSU y HIRS pudo simular mejor el viento
y las estructuras termodinámicas asociadas a un ciclón tropical que la corrida de control. El patrón espacial de
precipitación simulado con el modelo con asimilación concuerda bien con las observaciones de la Tropical
Rainfall Measuring Mission (TRMM) para el caso del ciclón de noviembre de 2004. Las series de tiempo
de la presión del nivel mínimo del mar y de la velocidad máxima del viento, simuladas con el modelo con
asimilación, son más cercanas a las observaciones correspondientes que las de la simulación de control.
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ABSTRACT
Radiance measurements from satellites offer the opportunity to retrieve atmospheric variables at much higher
horizontal resolution than is presently afforded by in-situ measurements (e.g., radiosondes). However, the
accuracy of these retrievals is crucial to their usefulness, and the ill-posed nature of the problem precludes
a straightforward solution. A number of retrieval approaches have been investigated, including empirical
techniques, coupling with numerical weather prediction models, and data analysis techniques such as regression. In this paper, the inversion coupled with imager (ICI) scheme is used to retrieve vertical temperature
and moisture profiles from infrared and microwave brightness temperatures from a polar-orbiting satellite.
The bias and root mean square (RMS) deviations were assessed for the winter and summer conditions over
land and sea, separately, using the National Center for Environmental Prediction (NCEP) reanalysis data.
The results showed the RMS error of temperature in the lower troposphere to be about 2 K. On the other
hand, the RMS errors of the moisture profiles are found to be about 1 g kg-1. However, below 850 hPa the
errors were of the order of about 3.5 K and 3 g kg-1 for the temperature and moisture profiles over the land
and about 2.5 K and 2.0 g kg-1 over the sea. Two numerical experiments are designed, one control simulation
without assimilation of observations, and another in which the advanced microwave sounding unit (AMSU)
together with high-resolution infrared radiacion sounder (HIRS) retrieved temperature and moisture profiles
are assimilated for the prediction of two tropical cyclones, which formed over the Bay of Bengal during
24 to 27 November 2002 and during 16 to 18 May 2004. The model run with assimilation of AMSU and
HIRS could simulate the wind and thermodynamic structures associated with a tropical cyclone better than
the control run. The spatial pattern of the precipitation simulated by the model with assimilation is in good
agreement with the Tropical Rainfall Measuring Mission (TRMM) rainfall observations for the November
2002 cyclone case. The time series of minimum sea level pressure and maximum wind speed simulated
by the model run with assimilation are closer to the corresponding observations when compared with the
control simulation.
Keywords: Satellite data retrieval, assimilaton, MM5.
1. Introduction
1.1 Satellite data retrieval
A proper depiction of the state of the atmosphere is crucial for accurately predicting future
conditions, especially in the case of such highly variable parameters as precipitation amount
(Wu et al., 1995). For precipitation processes, two of the most important variables are moisture
availability (in both vapor and liquid/solid form) and temperature; both of them together determine
the maximum availability of water vapor in the atmosphere. However, the observation of these
values has historically relied largely on a radiosonde network with a spatial resolution that is too
coarse to capture adequately the spatial distribution of moisture. This problem is most notably
true in the tropics (e.g., Krishnamurti et al., 1991), but is the case in the temperate zones as well.
Additional difficulties are presented by radiosonde instrument errors and by the horizontal drift of
the balloon as it ascends, which can result in significant geo-location errors.
Sensors mounted on satellite platforms provide global coverage that is relatively homogeneous
in space and is of much higher resolution than the current radiosonde network. However, these
instruments only provide a vertically integrated measure of the amount of outgoing radiation
(radiance) at the top of the earth’s atmosphere. Because these radiances are a function of the
vertical distribution of water vapor and temperature in the atmosphere and not simply of their
Retrieval of ATOVS data using ICI system and its application in MM5
193
average values, the retrieval of these vertical profiles from the radiances is an ill-posed problem
that cannot be solved directly (Isaacs et al., 1986). As a result, numerous approaches have been
taken for retrieving geophysical parameters from satellite radiances. The simplest approach is to
develop semi empirical relationships between satellite radiances and the parameters of interest (Wu
et al., 1995). Variables that are more readily estimated from satellite data (such as precipitation
amount) can also be used in conjunction with a model initialization scheme to retrieve values that
are less readily estimated, such as diabatic heating and vertical motion fields. This approach is
referred to as physical initialization (Krishnamurti et al., 1991). Retrievals can also be performed
in conjunction with a numerical weather prediction (NWP) model by using the model forecasts
as a first-guess field for satellite retrievals and the satellite retrievals, in turn, will influence the
NWP model solution (Lipton and Vonder Haar, 1990). A fourth approach is to use a data analysis
technique such as linear regression (Smith et al., 1970) to determine the relationships between
satellite radiances and geophysical parameters from a sample set of data.
1.2 Data assimilation
The performance of the numerical weather prediction (NWP) models depends on the accuracy
of initial conditions. The mesoscale models use the forecast of a global model as the initial and
lateral boundary conditions. The lack of conventional meteorological observations over the oceanic
region often results in an inadequate initial analysis for the model forecast. This problem can be
resolved to a great extent by using satellite observations to improve the model initial conditions.
However, in the case of tropical cyclones the infrared (IR) satellite sensors are found to be less
useful as the cyclones are associated with thick clouds through which IR radiation is not able to
penetrate. In this context, the microwave sensors are very helpful as the microwaves can penetrate
through non-precipitating clouds. The advanced microwave sounding unit (AMSU) (Kidder
et al., 2000), being a microwave sensor, can retrieve the data through non-precipitating clouds
associated with tropical cyclones and can provide vital information. English et al. (2000) found
that the assimilation of the AMSU temperature radiance observations reduced forecast errors in
the southern hemisphere by 20% and in the northern hemisphere by 5%. Sandeep et al. (2006)
studied the impact of assimilation of AMSU temperature and moisture profiles for the prediction of
a tropical cyclone over the Arabian Sea using the fifth generation mesoscale model (MM5). They
found that the model simulation with the assimilation of AMSU data were in general agreement
with the observations when compared to the model simulation without the AMSU data. In the
present study, the AMSU retrieved temperature and moisture profiles are used to improve the initial
conditions for the forecast of two tropical cyclones formed over the Bay of Bengal using the MM5
model. Four dimensional data assimilation (FDDA) (Stauffer and Seaman, 1990) using analysis
nudging technique is employed to ingest and assimilate the AMSU data.
2. Data sets and model
In this study, National Oceanographic and Atmospheric Administration (NOAA)-16 satellite data
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over India and its surrounding regions were used for reconstructing the temperature profiles for
the winter (January) and the summer (July) conditions of the year 2004. The meteorological data
used in the initialization were taken from the limited area model (LAM) (Roy Bhowmik, 2003)
operationally run by the India Meteorological Department (IMD) at New Delhi, India. The LAM
model assimilates these profiles on an operational basis. Since the satellite raw data is received
in the high resolution picture transmission (HRPT) format, it is necessary to process it before the
retrieval process. The advanced television and infrared observation satellite (TIROS) operational
vertical sounder (ATOVS) and advanced very high resolution radiometer (AVHRR) processing
package (AAPP) model was used to perform the ingestion and pre-processing of the HRPT data.
AAPP stands for ATOVS and AVHRR processing package. This procedure supplies calibrated
data of brightness temperature for all ATOVS and high-resolution infra-red sounder (HIRS)
channels, located in the terrestrial coordinates (latitude and longitude) and mapped in a common
grid resolution.
The inversion coupled with imager (ICI) system was developed at the Centre de Météorologie
Spatiale (CMS), of France where it has been operational since 1996. Its structure is based on
independent modules, which work separately and could be easily replaced. The key components
are: initial profiles library, inversion module and the tuning module, which is responsible for the
periodic ICI calibration (Lavanant et al., 1999a). The ICI version used in the current study uses
the rapid transmittance TOVS (RTTOV)-6 model, a fast radiative transference code (Eyre 1991;
Sanders et al., 1998) to simulate the brightness temperature during the retrieval process.
A cloud cover classification is performed from the MAIA (Mask AVHRR for Inversion ATOVS)
algorithm (Lavanant et al., 1999b) and applied in the last step of the AAPP model. A mean clear
percentage of cloud cover on HIRS field of view is calculated from AVHRR channels with the aid
of ancillary data (surface temperature and total precipitable water content (TPWC)). In the AMSU
microwave spectral channels, especially for AMSU-B, the presence of rain and ice hydrometeors
particles becomes important due to scattering and absorption processes. Thus, a technique based
on the 21, 23 and 89 GHz AMSU-A channels brightness temperature differences (Grody et al.,
1998) was used for precipitation and scattering identification over sea and land locations.
2.1 The forward model
The radiative transfer equation (RTE) describes the interaction between the radiation that arrives
in the satellite sensor and the atmosphere. For a non-scattering atmosphere in local thermodynamic
equilibrium, the RTE is written as:
R (T , q , v ) = τ s ( v ) ε s ( v ) B s ( v , T ) +
2
∫ B ( v , T ) d τ ( v ) + (1 − ε s ( v ) τ s ( v ))∫ B ( v , T ) d τ ( v ) / τ
2
(v )
(1)
where R is the spectral radiance; v is the channel frequency; B is the Planck function which is a
function of the temperature T and frequency n; e the surface emissivity; t the layer to space atmospheric
transmittance function and the subscript s denotes surface; q is the water vapor mixing ratio profile.
Retrieval of ATOVS data using ICI system and its application in MM5
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During the retrieval process, the atmospheric radiative transfer model is used many times;
thus, the forward model needs to be fast enough to work in an operational inversion scheme, but
sufficiently accurate to maintain the retrieval quality.
If the satellite observed brightness temperature R of each channel is known, then R can be
considered a nonlinear function of the atmospheric temperature profile (T), water vapor mixing
ratio profile (q), surface skin temperature (Ts), surface emissivity (es), ozone profile, uniformly
mixed gases profiles, etc. That is, R = R (T, q, Ts , qs …). The uniformly mixed gases are supplied
by internal coefficients and a standard ozone profile is utilized (US standard atmosphere 1976).
Thus, in general we have:
Ri = Ri (x), i = 1, . . . n
(2)
where n is the number of brightness temperatures and the vector x contains the 57 parameters to be
estimated: 40 atmospheric temperatures (that correspond to 40 pressure levels in the atmosphere,
from 1000 to 0.1 hPa), 15 atmospheric water vapor mixing ratio levels (from 1000 to 300 hPa),
one surface skin temperature and one surface water vapor mixing ratio.
2.2 Inversion scheme of the ICI model
One can separate the ICI retrieval process in two different steps; the first step is related to the guess
selection using a least square optimization in the brightness temperature space, and in the second
step the inversion process uses a Bayesian approach to retrieve the temperature and moisture vector.
The selection of the guess profile is performed through a search in the library of temperature and
moisture profiles. For each profile i, a distance d i is computed as:
d i = (r m - r (xi))t B-1 (r m - r (xi)) (3)
where r m and r(xi) represent the vector of brightness temperature of the observed profile and the
vector of brightness computed from the candidate to guess profile, respectively; and B is the
brightness temperature covariance matrix that is calculated in agreement with the type of prevailing
cloud coverage (clear, partially clear and cloudy) and air mass class (polar, middle latitude, tropical).
The guess profile is defined as the average of the 10 profiles that minimize the computed distances
d i. The library of the guess profile is built sampling the radiosonde and analyses profiles from the
15 previous days provided in the local acquisition zone. The library contains approximately 2000
profiles (temperature and moisture).
The ICI inversion process consists of finding the most probable atmospheric profile x given
the measurements rm, i.e. of maximizing the conditional probability of x given rm: Max P(x|rm).
According to the Bayes theorem, in the case of Gaussian error distributions, the most probable
solution is that which minimizes the objective function:
J(x) = (x - xb)T C-1(x - xb) + (r m – r(x))T (E)-1 (r m - r(x))
(4)
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where x b is the background profile; C is the error covariance matrix of the background; r(x) is
the radiative transfer model or forward model; E is error covariance matrix of the combined
measurement and forward model errors. An approach to obtain a minimum for Equation (4) is to
use the Newtonian iteration method, which employs the following updating rule:
Xn+1 = Xn – J´´(xn)-1 J ´(xn)
(5)
where J’(x) the gradient of J(x) with respect to x and J”(x) is their second derivative of the Equation
(4). By matrix manipulation, one can find the following estimation scheme for this problem:
Xn+1 = Xn + (Xb – Xn) + Wn (r m – r(Xn) – Kn (Xb – Xn))
Wn = C KTn (Kn C KTn + E)-1
(6)
where K (x) contains the partial first-derivatives of r (x) with respect to the elements of x (Eyre,
1989). Iteration in Equation (6) ends when the increment (Xn + 1 – X n) is small enough and when
(r m – r (xn+1)) is of the order of the measurement error in all channels.
2.3 The MM5 model description and numerical experiment
The MM5 model (Grell et al., 1994) utilized a single domain with 30 km (130 × 118 grid cells in
east-west and in north-south directions) horizontal resolution and 23 sigma levels in the vertical
direction. Figure 1a depicts the model domain for the November 2002 cyclone while Figure 1b
shows the model domain for May 2004 cyclone. The model physics options used for this study are
mixed phase Reisner scheme for explicit moisture, Grell scheme for cumulus parameterization and
medium range forecast (MRF) scheme for planetary boundary layer. The model utilized a cloud
radiation scheme and a multi level soil model. The MM5 model utilized the NCEP-final (FNL)
analysis data available at a horizontal resolution of 1° × 1° latitude and longitude and a time interval
of 6 hours for the initial and lateral boundary conditions. Two numerical experiments were designed
to bring out the impact of ingesting and assimilating AMSU and HIRS temperature and moisture
profiles data on the forecasting of a tropical cyclone formed over the Bay of Bengal from 24 to
27 November 2002. In the control (CTRL) experiment the model is integrated from 24 November
06UTC to 26 November 00UTC in a free forecast mode. In the four-dimensional data assimilation
(FDDA) experiment the model is integrated from 24 November 00UTC to 24 November 18UTC
with improved initial conditions by the assimilation of AMSU temperature and moisture profiles.
The model has been nudged throughout this period. From 24 November 18UTC to 26 November
00UTC the model is integrated in free forecast mode. Similarly, another two experiments were
designed for the prediction of the tropical cyclone formed over the Bay of Bengal during 15-18
May 2004. In the CTRL run the model is integrated from 15 May 06UTC to 17 May 2004 00UTC.
The FDDA experiment was designed such that the AMSU and HIRS temperature and moisture
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Retrieval of ATOVS data using ICI system and its application in MM5
profiles are ingested and assimilated at 15 May 06UTC and 15 May18UTC. From 15 May 18UTC
to 17 May 00UTC the model is integrated in free forecast mode.
(b)
(a)
27N
21N
21
15
15
6N
9
EQ
3N
6S
66E
72
78
84
90
96E
63E
69
75
81
87
93E
Fig. 1. Model domains for November 2002 cyclone (a) and May 2004 cyclone (b).
3. Results and discussion
In this paper, a physical iterative has been demonstrated to retrieve vertical profiles of temperature
and humidity from satellite brightness temperatures. Infrared and microwave data were used
together to combine their relative strengths: finer horizontal resolution of the infrared instrument
and relative transparency to (water) clouds for the microwave. The combination of microwave
and IR channels (20 channels of ATOVS + 20 channels of HIRS) is used in the present study to
retrieve profiles at 40 different pressure levels. The advantage of this combination is to retrieve
the profiles even under the partially cloudy conditions. The validation is carried out using NCEP
reanalysis data at spatial resolution of 1° × 1° latitude and longitude over the domain area, which
covers EQ-35 °N and 65 °E-105 °E.
Figure 2 shows the error statistics concerning the mean (bias) and the standard deviation of the
difference between the simulated and the measured brightness temperature. In practice, these errors
are caused by various sources such as forward models approximations and measurement errors,
among others (Uddstrom and Mcmillin, 1993). As expected, the error levels vary considerably
from channel to channel. The accuracy also depends on the surface type (sea and land). In general,
surface channels yield the worst result due to their high sensitivity to surface parameters such as the
emissivity and the surface temperature. The channels associated with the water vapor absorption
band (moisture channels) also show a larger error (stdanderd deviation). We can also see that the
values of standard deviation are higher over land, especially in the AMSU channels. This occurs
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because the surface parameters have lower variability and are easier to be estimated over the
sea. However, one can notice that the forward model underestimates the brightness temperature,
although a bias correction scheme was applied in the measured brightness temperature prior to its
use in the inversion process.
(a)
4
3
Bias (K)
2
1
0
-1
1
2 3
4 5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
1
2 3
4 5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
-2
-3
-4
(b)
7
Standard deviation
6
5
4
3
2
1
0
Land
Sea
Fig. 2. Error statistics concerning (a) the mean (bias) and (b) the standard deviation of the
difference between the simulated and measured brightness temperature. Statistics
performed for the NOAA-6 satellite from 11/01/2004 to 20/01/2004.
3.1 Accuracy of temperature profiles
The vertical accuracy statistics based on satellite and NCEP reanalysis were computed from the
collocations for NOAA-16 satellite data for the period of January and July 2004. The statistics
were made from total collocations, which include the types of land and sea, and clear and cloudy
conditions. The atmospheric profiles retrieved by ICI are within the sensor specifications, which
foresee over all errors of up to 1.5 K for the temperature profiles and 1.5 g kg-1 for the moisture
(Lavanant et al., 1999b). The results illustrated in Figure 3 show the bias and RMS error of
temperature profiles retrieved using ICI over land and sea for January and July 2004. All of them
had similar RMS error in the lower troposphere (below 850 hPa) in clear and cloudy conditions
with values about 3.2 K over land and about 2.0 K over sea. The bias and RMS errors are small
over the sea compared to that of the land in both situations and for both months. This is because
of constant emissivity over the sea compared to highly variable emissivity over land.
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Retrieval of ATOVS data using ICI system and its application in MM5
(a)
150
200
250
Pressure (hPa)
300
(a)
NOAA-16 LAND
10
20
30
50
70
100
Clear
Cloudy
Clear
−5717
Cloudy
−13767
150
200
250
Period
Jan, 2004
300
(b)
NOAA-16 SEA
10
20
30
50
70
100
Clear
Cloudy
Clear
−1619
Cloudy
−2751
150
200
250
Period
Jan, 2004
300
(b)
NOAA-16 LAND
10
20
30
50
70
100
Clear
Cloudy
Clear
−368
Cloudy
−1378
150
200
250
Period
Jul, 2004
300
400
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1000
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4
NOAA-16 SEA
10
20
30
50
70
100
Clear
Cloudy
Clear
−17
Cloudy
−418
Period
Jul, 2004
−4
−2
0
2
4
Temperature (k)
Figs. 3. Vertical error statistics concerning the bias and the RMS error of the difference
between the temperature and moisture profiles using ICI scheme computed from
NCEP reanalysis data, for (a) January 2004 and (b) July 2004.
In practice an important factor that contributes to the retrieval accuracy is the correct selection
of the spectral channels used in the inversion process. Channels with large noise, and those which
cannot be properly simulated by the forward model, generally downgrade the quality of the
solutions. However, the mere exclusion of these channels may also eliminate useful information.
In practice, these errors are caused by various sources such as forward models approximations
and measurement errors, among others (Uddstrom and Mcmillin, 1993). As expected, the error
levels vary considerably from channel to channel. The accuracy also depends on the surface type
(sea and land). In general, surface channels yield the worst result due to their high sensitivity to
surface parameters such as the emissivity and the surface temperature. The channels associated
with the water vapor absorption band (moisture channels) also show a larger error (std. dev). We
can also see that the values of standard deviation are higher over land, especially in the AMSU
channels. This occurs because the surface parameters have lower variability and are easier to be
handled in the inversion process.
3.2 Accuracy of moisture profiles
Figure 4 depicts the bias and RMS error for specific humidity retrievals over land and sea for
January and July 2004. As in the case of temperature retrieval, we found that at the lower surface
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levels (up to 850 hPa), the temperature estimates are less accurate over land than over sea. Also, the
error levels found for different cloud coverage were similar. Overall, the best results were obtained
over sea compared to land in all situations. Differently from the temperature retrieval cases, the
impact caused by the moisture inversion process in the mean error level decrease is small for
almost all situations. However, a qualitative analysis of individual horizontal fields shows that the
ICI inversion system identifies properly the structure of the moisture field. The areas with higher
and lower water vapor contents are correctly represented, mainly over the sea. On the other hand,
there are some regions where the error became significant, especially in soundings over land. In
addition to the problems already mentioned for retrieving the temperature profiles, the moisture
inversion process involves additional difficulties. The moisture fields usually are subject to a
large spatial and temporal variability, especially in tropical regions, which makes it difficult for
the comparison with observed data, necessary for validation purposes. Moreover the water vapor
channels present a larger noise level.
(a)
4
NOAA-16 LAND
300
Clear
−5717
Cloudy
−13767
400
400
Period
Jan, 2004
Period
Jan, 2004
Pressure (hPa)
Clear
−1619
Cloudy
−2551
5
NOAA-16 LAND
300
Clear
Cloudy
Clear
Cloudy
400
(b)
NOAA-16 SEA
300
Clear
Cloudy
Clear
−368
Cloudy
−1378
Period
Jul, 2004
400
500
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1000
−4
−2
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4
NOAA-16 SEA
300
Clear
Cloudy
Clear
−17
Cloudy
−418
Period
Jul, 2004
−4
−2
0
2
4
Sph. humidity (gm/kg)
Figs. 4. Vertical error statistics concerning the bias and the RMS error of the difference
between the temperature and moisture profiles using ICI scheme computed from
NCEP reanalysis data, for (a) January 2004, and (b) July 2004.
A number of retrieval approaches have been investigated, including empirical techniques,
coupling with numerical weather prediction models, and data analysis techniques such as regression.
In a direct comparison of this technique with results from the operational advanced television and
Retrieval of ATOVS data using ICI system and its application in MM5
201
infrared observation satellite operational vertical sounder (ATOVS) retrievals, it was found that
the ICI scheme retrievals had smaller errors than the ATOVS retrievals using statistical regression
scheme.
3.3 Model simulation
The objective of this simulation study is to incorporate AMSU and HIRS temperature and moisture
profiles in the initial analysis for the MM5 forecast so that the inadequacies in the initial analysis
due to the lack of conventional observations over the oceanic area could be alleviated. As the
tropical cyclones form over the oceanic region, the lack of conventional meteorological data would
result in insufficient initial analysis for the numerical prediction, which in turn would result in an
inadequate forecast. An earlier study (Sandeep et al., 2006) showed that the modification of the
initial condition by the assimilation of AMSU temperature and moisture profiles resulted in the
simulation of a better thermodynamic structure, minimum sea level pressure (SLP) and maximum
wind speed (MWS) patterns. In the present study the AMSU and HIRS data obtained from the
ICI retrieval method is assimilated in the MM5 model and the model forecast is compared with
observation wherever available.
Figure 5 shows the time series variation in minimum SLP and MWS of model simulation and
observation from 24 November 18UTC to 26 November 00UTC. The observed minimum SLP and
MWS remained constant during this period. The observed values of minimum SLP and MWS are
estimations based on satellite observations (Mishra and Gupta, 1976). However the model forecasted
minimum SLP and MWS varied with time. From this figure, it is very clear that the minimum SLP
simulated by the FDDA experiment remained closer to the observations throughout the forecasting
period. It can be noted from this figure that the minimum SLP simulated by the FDDA experiment is
lower by 1 to 2.5 hPa when compared to the minimum SLP simulated by the CTRL experiment. Further,
the lowest SLP value attained in the FDDA experiment is 1002.5 hPa, which is closer to the observed
999.0 when compared with the lowest SLP of 1004.5 attained in the CTRL experiment. The observed
MWS remained constant at 17 m s-1 during 24 November 18UTC to 26 November 00UTC. The MWS
simulated by the FDDA experiment varies between a minimum value of 14 and a maximum value of
21 m s-1 while that of CTRL varies between 12 and 18 m s-1.
Figure 6 depicts the longitude-height structure of the horizontal wind speed across the center of the
cyclone at 24 November 2002 18UTC and at 12 and 24 hours of forecast (FCST). It can be observed
from this figure that though the simulated system was weaker at the initial time, it became stronger
at later times. It can be observed that at 12 hours of FCST, the asymmetric structure of the cyclone is
very well produced in the FDDA experiment, which is also very strong in terms of wind speed when
compared with the corresponding FCST of CTRL experiment. At 24 hours of FCST, both CTRL and
FDDA simulations produced the asymmetric structure of the system. However the FDDA is stronger
in teRMS of wind speed. Figure 7 illustrates the longitude-height structure of vertical velocity and
potential temperature close to the cyclone center. It is expected that the assimilation of AMSU
temperature and moisture profiles will improve the simulation of thermodynamic structure of the
202
D. Singh et al.
cyclone. This is supported by the fact that the thermodynamic structure of the system as simulated
in the FDDA experiment is stronger than that simulated by the CTRL experiment throughout
the FCST period. From this figure, it is very clear that the middle to upper tropospheric warming as
manifested by the presence of the warm core is stronger in the FDDA simulations. Furthermore, the
vertical velocity is also stronger in the FDDA simulations for the entire FCST period. The above
results are in general agreement with the previous study of Sandeep et al. (2006).
(a)
1009
CTRL
FDDA
OBS
1008
Pressure (hPa)
1007
1006
1005
1004
1003
1002
1001
1000
999
998
18Z
24 NOV
2002
Wind speed (m/s)
22
21
00Z
25 NOV
06Z
00Z
06Z
12Z
18Z
00Z
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12Z
18Z
00Z
26NOV
Time
(b)
20
19
18
17
16
15
14
13
12
11
10
18Z
Time
Fig. 5. Time series of (a) minimum sea level pressure (hPa) and (b) maximum
wind speed (m s-1) simulated by CTRL and FDDA runs and observed values for
November 2002 cyclone from 24 November 18UTC to 26 November 00UTC.
Figure 8 depicts the SLP patterns at 24 November 18UTC and 25 November 2002 06UTC and
18UTC, respectively. It is very clear from this figure that the FDDA simulation produced a stronger
cyclonic system in teRMS of SLP except for the initial time. At 12 and 24 hours of FCST the FDDA
simulated a minimum SLP of 1003 hPa while that of CTRL is 1006 hPa for the corresponding
FCST period. Figure 9 depicts the lower tropospheric (σ = 0.995) winds at 24 November 18UTC
and 25 November 2002 06UTC and 18UTC, respectively. This figure show that both CTRL and
FDDA simulations could produce cyclonic type of circulation throughout the model integration
period. However the wind produced by FDDA simulation is stronger.
Figure 10 shows the 24 hour accumulated precipitation produced by CTRL and FDDA
simulations and the Tropical Rainfall Measuring Mission (TRMM) estimated 24 hour accumulated
Retrieval of ATOVS data using ICI system and its application in MM5
203
precipitation at 25 November 2002 18UTC. Both CTRL and FDDA runs overestimated the amount
of rainfall. The maximum accumulated rainfall amount for the 24 hour period from 24 November
18UTC to 25 November 18UTC estimated from TRMM satellite observations is 15 cm, while
the CTRL and FDDA simulations produced greater than 30 cm of rainfall for the corresponding
period. However, it can be observed that the pattern of the rainfall produced by the FDDA run has
a close resemblance with the TRMM observed rainfall pattern. Table I shows the average track
error in km of CTRL and FDDA simulations with respect to observed track for the November 2002
cyclone. It can be noted that in the FDDA simulation, the track error is reduced by 37 and 137.5
km at 12 and 18 hours of forecast. However, the track error of FDDA simulation has increased at
24 and 30 hours of forecast.
In order to draw useful and broad conclusions, it would be better to obtain results of the impacts
of assimilation of AMSU observations for more than one case. Towards this end, this study has
extended its investigations to include an additional tropical cyclone case over the Bay of Bengal
region and the results of the second cyclone are being presented below briefly. Figure 11a and b
depicts the time series of minimum SLP and MWS for the May 2004 cyclone. It can be observed
that both the CTRL and FDDA simulations could produce the observed pattern of intensity variation
of the cyclone with time. It should be noted that FDDA simulation is not showing consistent
improvement as compared to the CTRL simulation throughout the integration period. Figure
12 shows the longitude-height cross section of vertical velocity and potential temperature for
May 2004 cyclone. Consistent with the previous case, the FDDA simulation produced a stronger
thermodynamic structure associated with the tropical cyclone. Figure 13a, b and c shows the
24 hour accumulated precipitation produced by CTRL and FDDA simulations and the TRMM
estimated 24 hour precipitation at 16 May 2004 18UTC. In this case also, both the CTRL and the
FDDA runs overestimated the rainfall amount. The rainfall pattern produced by both CTRL and
FDDA runs are also similar to one another and are not in very good agreement with the TRMM
observed precipitation pattern. Table II shows the average track error in km of CTRL and FDDA
simulations with respect to observed track for the May 2004 cyclone. It is to be noted that the
FDDA simulation could not improve the track forecast in this case.
Table I. Track errors for November 2002
cyclone with respect to the observed track.
Time of forecast
Track error (km)
(hours)
CTRL
FDDA
06
70.5
190.0
12
198.0
161.0
18
213.5
76.0
24
183.0
258.0
30
135.5
335.5
Table II. Track errors for May 2004 cyclone with
respect to the observed track.
Time of forecast
Track error (km)
(hours)
CTRL
FDDA
06
231.0
476.0
12
41.0
148.0
18
95.0
203.5
24 87.5
218.5
30
127.0
232.0
18 UTC
18
18 UTC
UTC
18
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UTC
(a) 1400018 UTC
Height
(m)
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Height
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(m)(m)
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D. Singh et al.
24 NOV2002 CTRL
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NOV2002 CTRL
CTRL
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(d)
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+ 12 HRS
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(e)
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+ 12
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FCST
+
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(°)
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(°)
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structureLongitude
of horizontal
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91
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Fig. 6. Longitude-height
winds (m s-1) simulated
by CTRL and FDDA runs across the cyclone center for November
2002 cyclone, at 18UTC of the 24th, and 12 and 24 h later.
92
92
92
92
92
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92
205
Retrieval of ATOVS data using ICI system and its application in MM5
18 UTC
UTC 24 NOV2002
NOV2002 CTRL
18
18 UTC 24
24 NOV2002 CTRL
CTRL
UTC 24 NOV2002 CTRL
FDDA
FDDA
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(d)
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Fig. 7. Longitude-height structure of vertical velocity (m s-1) and potential
temperature (K) simulated by CTRL and FDDA runs across the cyclone
center for November 2002 cyclone.
92
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D. Singh et al.
LongitudeNN
Longitude
18UTC
UTC 24
24NOV2002
NOV2002CTRL
CTRL
FDDA
18 UTC
24 18
NOV2002
CTRL
FDDA
FDDA
(d)
(a)
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UTC
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(d)(d)
(a)(a) 24 NOV2002
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UTC
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NOV2002
CTRL
FDDA
(d)FDDA
(a)
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UTC CTRL
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FDDA
18
UTC
24 NOV2002
CTRL
(d)
(a)
18
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FDDA
18 UTC
UTC
24 NOV2002
NOV2002
18
CTRL
24 NOV2002
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CTRL
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FDDA FDDA
(d)
(d)
(a)
(a) UTC CTRL
(d)
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21N
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21N
21N
1010
1010
1011
1011
21N
21N
1010
1011
21N
21N
21N
21N
1010
1011
1010
1011
21N
1010
21N
21N
21N
1011
20N
21N
20N
20N
21N
20N
20N
20N
1010
1010
21N
21N
21N
21N
1011
1011
1010
1011
20N
20N
1010
1010
20N
1011
1011
20N
20N
1010
20N
1010
1010
20N
20N
20N
20N
20N
19N
20N
19N
19N
1010
19N
1010
19N
20N
20N
19N
20N
20N
1010
1013
1013
19N
19N
1010
1010
19N
1013
19N
1010
19N
19N
1013
1010
1010
19N
1013
19N
19N
19N
18N
18N
18N
19N
1013
18N
19N
18N
18N
19N
19N
19N
19N
1013
1013
1013
1013
18N1012
1013
18N
18N
18N
1013
1013
1013
18N
18N
1012
1013
1012
18N
18N
18N
17N
1013
18N
17N
17N
18N
17N
18N
1013
1012
17N
17N
1012
18N
18N
18N
18N
1012
1013
17N1012
17N1013
1013
17N
17N
17N
1012
17N
1013
1013
1012
17N
17N
1012
1012
17N
17N
16N
17N
16N
16N
17N
16N
16N
16N
17N
17N
17N
17N
16N
16N
16N
16N
16N
16N
16N
16N
16N
16N
1012
15N
16N
1012
15N
15N
16N
15N
1012
15N
15N
16N
16N
16N
16N
1012
15N
15N
1012 15N
15N
15N
1010
1012
15N
1010
1010
1012
15N
1012
15N
14N
15N
15N
14N
14N
14N
1012
15N
15N
1010
14N
14N
1010
1012
1012
15N
15N
15N
15N
1010
14N
14N
14N
14N
1010
1010
14N
14N
1010
1010
1010
14N
14N
14N
14N
14N
13N
14N
13N
13N
13N
13N
13N
14N
14N
14N
14N
13N
13N
13N
13N
13N
13N
13N
13N
13N
13N
12N
13N
12N
12N
13N
12N
12N
12N
13N
13N
13N
13N
12N
12N
12N
12N
12N
12N
12N
12N
11N
12N
12N
11N
11N
11N
12N
12N
1013 11N
11N
11N
12N
12N
12N 1013
12N1013
11N
11N
1013
11N
11N
11N
1013
1013
1010
11N
1010
11N
11N
11N 1013
11N
10N
11N
10N
10N
1013
10N
1010
10N
11N
11N
10N
11N 1013
11N
1010
101310N
10N
1010
1010
10N
10N 1013
10N
10N
1010
1010
1010
10N
10N
10N
10N
9N
9N
9N
10N
9N
10N
1010
1010
9N
9N
10N
10N
10N
10N
9N
1012
9N
1012
9N
9N
1012
9N
9N
1012
9N
9N
9N
9N
1012
8N
9N
8N
8N
9N
8N
1012
8N9N 1012 8N 1012
8N9N
9N
9N
1012
8N
1011
1011
8N 1012
8N
8N 1012 8N
8N
1011
8N
1011
7N
8N
8N
7N
8N
8N
7N7N
7N7N
1011
8N
8N
8N
8N
7N 1011
7N 82 84 92
1011
7N
7N
1011
84 8692
90 9292 9494 9696
82
90 92921011
80 7N
88 9096
80 7N
88
96
84
84
86 8894
86 8894
78 8086
78 8086
1011
1011
78 80 82 7N
78 80 827N
8490
8490
8288
8288
9096
9494
78
78
7N
92
84
84
82 94
90
82 94
90 9692
80 8692
88
94 7N
96 78 80 827N
80 92
88
94 96
86
86
78
78
7N
7N
7N78 80 82 7N
7N
84
84
90
90
88
96
88
96
86
86
92
92
84
84
82
90
82
90
80
88
94
96
80
88
86
86
78
78
92
92
84
84
82
90
82
90
84 96
84 96
82 94
90 96
82 94
90 96
80
88
80
88
94
96
80 92
88 94
80 92
88 94
86
86
78
78
86 92
86 92
78 90
78 90
84
80
86
78
92
92
92
92
84
84
84
84
82
82
90
90
82 84
82
90
90
80 82
80 88
88
88
96
94
96 94 9678
80 82
80 88
88
88
96
94
96 94 96
86
86 94
86
86 94
78 FCST
78
78 80
78 86
FCST
+
12
HRS
+
12
HRS
FCST
+
12
HRS
(e)
(e)
(b)
(b)
FCST
+
12
HRS
(e)
(b) + 12 HRS
FCST
FCST +
+ 12
12FCST
HRS+ 12 HRS
(e)
(b)
(e)
(b)
FCST
HRS
(e)
(b)+
FCST
12
FCST
+21N
12 HRS
HRS
FCST
+ 12
HRS
(e)
(e)
(b)
(b)
(e)
(b)
21N
(e)21N
(e)
(b)21N
(b)
21N
21N
21N
21N
21N
21N
21N
21N
21N
21N
21N
21N
20N
21N
20N
20N
21N
20N
20N
20N
21N
21N
21N
21N
20N
20N
20N
20N
20N
20N
20N
20N
20N
20N
20N
19N
20N
19N
19N
19N
1011
1011
19N
20N
20N
19N
20N
20N
1011
1011
1011
19N
19N
1011
1011
19N
19N
1011
19N
19N
1011
1011
1011
19N
19N
1011
19N
18N
19N
18N
1011
19N
1011
18N
18N
19N
1011
18N
18N
19N
19N
1011
1011
19N
19N
1011
1011
1011
18N
18N
18N
1011
1011
18N
18N
18N
18N
18N
17N
18N
18N
17N
17N
18N
17N
18N
17N
17N
18N
18N
18N
18N
17N
17N
17N
17N
17N
17N
17N
17N
17N
17N
16N
17N
16N
16N
17N
16N
16N
16N
17N
17N
17N
17N
16N
16N
16N
16N
16N
16N
16N
16N
16N
16N
15N
16N
15N
15N
16N
15N
15N
15N
16N
16N
16N
16N
15N
15N
15N
15N
15N
15N
15N
15N
14N
14N
15N
15N
14N
15N
14N
15N
14N
14N
15N
15N
15N
15N
14N
14N
14N
14N
14N
14N
14N
14N
14N
14N
14N
13N
13N
14N
13N
13N
13N
14N
14N
13N
14N
14N
13N
13N
13N
13N
13N
13N
13N
13N
13N
13N
12N
13N
12N
12N
13N
12N
12N
13N
13N
12N
13N
13N
12N
12N
12N
12N
12N
12N
12N
12N
11N
12N
11N
12N
11N
12N
11N
12N
11N
11N
12N
12N
12N
12N
11N
11N
11N
11N
11N
11N
11N
11N
11N
11N
11N
10N
11N
10N
10N
10N
10N
11N
11N
1011
10N
11N
11N
1011
1011
10N
10N
10N 1011
10N
10N
10N
10N
10N 1011
10N
9N 1011
10N
9N 1011
10N
9N
9N
10N
1011
9N
9N
1010
10N
10N
1010
10N
10N
1010
9N
1011 9N 1011 1010
9N 1011
9N
9N
1010
9N
1010
9N
9N
9N
9N
9N
8N
9N
8N
1010
1010
1010
8N8N
1010
9N
9N
8N8N
1010
9N
9N
1010
8N
8N
1010
1010
8N
1010
1010
8N
1010
8N
8N
1010
1010
1010
7N
8N
8N
7N
7N
8N
8N
7N
8N
1010
8N
1010
1010
7N
7N
1010
1010
1010
8N
8N
8N
8N
7N
1010
1010
7N
7N
7N
1010
1010
7N
7N
1010
84 8692
82 8490
90 9292 9494 9696
82 8490
84 8692
90 9292 9494 9696
1010
1010 94
80 7N
88 9096
80 7N
88 9096
84
86
78 80
86 8894
78 8086
86
78 80 827N
78 80 82 7N
7N
8288
8288
7884
78
7N
7N 78
92 94 7N
92 94 96
84
82 88
82 94
84
80 92
88
96 78 80 82 7N
80 92
88
86
78
86
78
90
84
90
80 82 7N84
88
94
96
88
96
86
92 90
86
92 90
84
82
90
82
84
90
80
88
80
88
86
78
86
78
92
84
82
90
92
84 96
82 94
90 96
82
84
90
80
88
94
96
82 94
84 96
90 96
80 92
88 94
80
88
94
96
86
78
80 92
88 94
86 92
78 90
86
78
86 92
78 90
84
80
86
78
92
92
92
92
84
84
82
82
90
90
82 84
84
82
90
84
90
80 82
80 88
88
88
96
94
96 94 9678
80 82
80 88
88
88
96
94
96 94 96
86
86 94
78 FCST
78
86
86 94
78 80
78 86
FCST
+
24
HRS
+
24
HRS
FCST
+
24
HRS
(f)
(f)
(c )
(f)
FCST
+
24
HRS
(c(c
) )+ 24 HRS
FCST
(f)
FCST
+
24
HRS
(cFCST
)
(f)
(c )
(f)
(c
)
+
24
HRS
FCST
+
24
HRS
FCST
+
24
HRS
(f) 21N
(f)
(c
(cFCST
)
(f)
FCST
+ 24 HRS
(c
(f) 21N
(c ))21N
(c )21N
)+ 24 HRS
21N
1013
1013 (f)
1013
1013 21N
21N
21N
21N
21N
21N 1013
21N 1013
1013
1013
1013 20N
21N
21N
1013 20N
1013
21N
21N
20N
21N
20N
1013
20N
21N
20N
21N
21N
1013
1013
21N
21N
1013
1013
1013
1013
20N
20N
20N
1013 20N
1013
20N
1013 20N
1013
20N
20N
20N
20N
20N
19N
20N
19N
19N
19N
19N
20N
20N
19N
20N
20N
19N
19N
19N
19N
19N
19N
1012
1012
1012
1012
19N
19N
1012
19N
18N
19N
18N
19N
18N
1012
18N
19N
1010
18N
1012 1010
18N
19N
19N
1010
19N
19N
1012
1012
1012
18N
1012
18N
1012
1010 18N
18N
18N
18N
1012
10121010
1010
1012
1012
1012
1012
18N
18N
17N
18N
18N
17N
18N
1012
1012
1010
17N
1010 17N
18N
1010
1012
1012
17N
17N
18N
18N
18N
18N
1010
1010
17N
17N
17N
17N
17N
17N
17N
17N
17N
17N
16N
17N
16N
16N
17N
16N
16N
16N
17N
17N
17N
17N
16N
16N
16N
16N
16N
16N
16N
16N
16N
16N
15N
16N
15N
15N
16N
15N
15N
15N
16N
16N
16N
16N
15N
15N
15N
15N
15N
15N
15N
15N
14N
14N
15N
15N
14N
15N
14N
15N
14N
14N
15N
15N
15N
15N
14N
14N
14N
14N
14N
14N
14N
14N
14N
14N
14N
13N
13N
14N
13N
13N
13N
14N
14N
13N
14N
14N
13N
13N
13N
13N
13N
1010
13N
1010
1010
13N
13N
13N
13N
12N
13N
12N
12N
13N
12N
1010
12N
13N
13N
1010
12N
13N
13N
1010
12N
12N
12N
1010
12N
1010
12N
1010
12N
1010
1010
12N
12N
11N
12N
11N
12N 1010
11N
12N
11N
12N
11N
11N
12N
12N
12N
12N
1010
11N
11N
1010
11N
11N
11N
11N
1010
1010
11N
11N
1010
11N
11N
11N
10N
11N
10N
10N
10N
10N
11N
11N
1010
10N
1010
11N
11N
10N
10N 1010
1010
1010
10N
10N
1011
10N
1011
10N
1011
1011
10N
10N
10N
9N
10N 1011
1011
9N
10N
9N
1011
9N
10N
9N
1011
9N
1011
10N
10N
1011
10N
10N
9N
1011
9N 1011
9N
1011
1011
9N
9N
1011
9N
1011
1011
1011
1011
1011
9N
9N
9N
9N
8N
9N
8N
8N
9N
1011
1011
8N
8N9N
9N
8N9N
9N
8N
8N
8N
8N
8N
8N
7N
8N
8N
7N
7N
8N
8N
7N
8N
8N
7N8N
7N8N
8N
8N
7N
7N
7N
7N
7N84
78 8086
82 8490
84 8692
90 9292 9494 9696
78 80 827N7884
80 7N
88 9096
86 8894
82 8490
84 8692
90 9292 9494 9696
80 7N
88 9096
86 8894
78 80 827N
7N
8288
8288
7878 8086
7N
7N 78 80 827N
7N84
92
78
82 94
84
80 92
94 7N
96 78
92
86
82 94
84
80 92
94 96
78
86
90
96
92 90
78 86
80 82 7N84
88
96
82
84
90 88
86
80 88
88
94
96
92 90
86
82 90
84
90 88
80
88
94
96
78
86
92
92
78
82
84
90
78
82
84
90
80
88
94
96
80
88
94
96
92
86
82
84
90
86
92
82 94
84 96
90 96
78
80
88
82
84
90
80 92
88 94
78
86
78 90
86 92
84
80
88
78
86
Longitude
W 82
Longitude
78 80
78 86
82 90
84 92
90 92
80 82 84
80 88
88
94
88 96
96
94 96 WLongitude
92
92
86
86 94
82
84
82
90
84
90
80
80
88
94
88
96
94
96 94 96
78 W
78
86
86
Longitude W
Longitude N
Longitude
Longitude
Longitude
Longitude
NNN N
Longitude
Longitude
Longitude
Longitude
NNNN
206
Longitude W Longitude
W
W
W
Longitude
Longitude W
W Longitude
LongitudeLongitude
W
1003
1003
1003
1003
1003
1004
1003
1004
1003
1003
1004
1004
1003
1004
1005
1004
1003
1005
1004
1004
1003
1005
1004
1005
1006
1005
1005
1004
1006
1005
1004
1005
1006
1006
1005
1006
1007
1006
1005
1007
1006
1006
1005
1007
1007
1006
1008
1007
1007
1006
1008
1007
1007
1006
1008
1008
1007
1008
1009
1008
1007
1009
1008
1008
1007
1009
1008
1009
Fig. 8. Sea level pressure (hPa) pattern simulated by
CTRL and FDDA runs for November 2002 cyclone.
1009
1009
1008
1009
1008
1009
1009
1009
1009
207
Retrieval of ATOVS data using ICI system and its application in MM5
Longitude
Longitude
Longitude
Longitude
Longitude
Longitude
Longitude
NLongitude
Longitude
Longitude
N
N
NNN NNN
Longitude
Longitude
Longitude
Longitude
Longitude
Longitude
Longitude
Longitude
N
N
N
NN
N
NN
N
Longitude
N
Longitude N
Longitude N
18
18 UTC
UTC 24
24 NOV2002
NOV2002 CTRL
CTRL
18
18 UTC
UTC 24
24 NOV2002
NOV2002 CTRL
CTRL
(a)
18 UTC 24 NOV2002 CTRL
(a)
(a)
(a)
UTC 24 CTRL
NOV2002 CTRL
18 UTC
2418
NOV2002
FDDA
21N
(d)
(a)
21N
18
(a)
18 UTC
UTC 24
24 NOV2002
NOV2002 CTRL
CTRL
21N
21N
(a)
20N
18
UTC
24
NOV2002
CTRL
(d)
20N
(a)
21N
18
UTC
24
NOV2002
CTRL
(a)
21N
(d) 21N
18
UTC
24 24
NOV2002
(a)
18
UTC
NOV2002
CTRL
20N
20N
(a)
1010 CTRL
1011
(d)
18
CTRL
21N
(d)
18 UTC
UTC 24
24 NOV2002
NOV2002
CTRL
19N
(a)
21N
20N
19N
21N
20N
(a)
20N
(d)21N
21N
21N
(a)
21N
(a)
19N
19N
(a)
20N
21N
21N
(d)
21N
(a)
1010
18N
20N
20N
21N
20N
19N
18N
(d)
20N
19N
20N
19N
(d)
21N
21N
20N
(d)
21N21N
18N
18N
(d)
1013
20N
19N
20N
20N
21N
21N
17N
19N
20N 18 UTC 24 NOV2002 CTRL
19N
(d)
18N
17N
19N
19N
(d)
18N
18N
20N
21N
19N
20N
21N
20N20N
19N
(d)
21N
17N
19N
19N
(d)
17N
20N
1013
(d)
18N
21N
20N
18N
20N
(d)
1012
19N
16N
(d)
18N
19N
18N
21N
17N
18N
20N
17N
19N
17N
16N
(a)
21N
18N
20N
19N
20N
18N
18N
18N
19N19N
20N
21N
16N
16N
17N
17N
19N
19N
21N21N
19N
21N
17N
18N
15N
20N
18N
18N
17N
19N
21N
16N
20N
16N
17N
15N
21N
19N
16N
17N
17N
17N
19N
18N
17N
19N
18N18N
20N
16N
15N
18N
15N
20N20N
18N
16N
20N
16N
18N
17N
17N
14N
19N
18N
20N
18N
19N
20N
101215N
15N
16N
16N
15N
17N
16N
18N
14N
16N
(d)
18N
17N
16N
19N
17N
15N
17N17N
19N19N
16N
14N
14N
15N
15N
19N
1010
16N
17N
17N
18N
19N
17N
13N
18N
16N
15N
17N
14N18N
15N
15N
14N
16N
19N
14N
13N
15N
17N
14N
18N
16N
15N
18N
21N
16N
15N
14N
13N
18N
13N
16N16N
15N
17N
16N
14N
18N
16N
16N
17N
14N
14N
16N
15N
16N
12N
13N
16N
13N
14N
17N
13N
18N
15N
13N
15N
14N
12N
17N17N
20N
14N
15N
13N
17N
14N
14N
15N15N
16N
15N
12N
17N
12N
16N
15N
13N
15N
13N
13N
15N
15N
14N
15N
11N
16N
14N
16N
12N
12N
12N
13N
14N
16N
17N
19N
13N
12N
11N
16N
12N
13N
14N
13N
14N
15N
13N
16N
14N
14N14N
15N
11N
11N
14N
12N
12N
14N
12N
14N
15N
14N
11N
13N
13N
10N
11N15N
15N
11N 1013
18N
13N
12N
11N
15N
10N
11N
12N
16N
14N
12N
13N
12N
15N
14N
13N
13N
13N
13N
11N
12N
11N
13N
10N
10N
13N
14N
11N
13N
12N
10N
14N14N
12N
1010
17N
9N
10N
14N
10N
10N
12N
11N
11N
13N
15N
12N
14N
10N
9N
11N
13N
12N
11N
10N
12N
12N
10N
11N12N
12N
9N
12N
9N
13N
11N
13N
9N
10N
12N
13N
16N
13N
12N
9N
8N
9N
11N
9N
11N
10N
12N
13N
10N
11N
12N
14N
9N
8N
10N
11N
11N
9N
9N
10N
1012
11N
11N
10N
12N
11N11N
12N
10N
8N
8N
8N
12N
15N
9N
12N
8N
11N
11N
9N
7N
10N
8N
10N
8N
12N
11N
10N
10N
9N
7N
13N
8N
10N
8N
8N
9N
9N
10N
11N
7N
10N
9N
1011
10N 80
11N11N
9N78
14N
10N
7N
7N
7N
11N
94
82
84
90
92
86
88
96
78
8N
8N
10N
9N
10N
11N
7N
7N
94
82
84
90
9N
92
80 82
88 90
94
84 86
10N
92 94
86
80
88
96
9N
86 88
78 80
82 84
90 92
78
7N
7N
9N
9N
8N
7N
9N10N
8N
12N
10N
94
80
88
84
92
94 96
8N
84
90 92
80 82
88 90
94
78
84 86
92 94
82
90
86
8N
80 82
88
96
86 88
78 80
82 84
90
78
10N
13N
78
9N
9N 80
8N
9N84
10N
92 88
92
90 86
7N
88 84
94 90
96 92
94
86 82
86
78 80(b)
78
9N
9N
8N
10N
9N
8N
(b) 827N
9N
94
82
84
90
92
80
88
94
84
92
86
80
88
96
8N
86
78
82
90
8N
78
7N
8N
8N
7N
9N
7N
9N
11N
(b)
9N
(b)
12N
7N
8N
9N
94
82
84
90
92
80
88
94
84
92
86
88
96
86
78
7N
82
90
8N 80
7N
8N78
8N
9N
7N
8N
8N
7N
++ 12
HRS
7N
94
90
92
80
88
94
84
92
80
88
96
86
(b)21N
82
90
78
8N
7N
94
82
84
90
21N
92
80
88
94
84
92
86
80
88
86
78
82
78
8N
94
FCST
1284
HRS86
82
84
90
92
80
88
94
84
92
86
80 82
88
96
86
78
82
90
FCST
+10N
12
HRS
78 FCST
(e)
11N
8N 8N78
7N
94 96
82
84
90
92
80
88 90
94
84
92
86
80
88
96
86
78
82
90
7N
8N
21N
(e)
(b)21N
7N
(e)
++ 12
HRS
7N FCST
FCST
12 84
HRS86
(b)
7N 78
7N 78
20N
94
90
92
80
88
94
84
92
80
88
86
82
(e)
(e)8N
(b)
94 96
80
88 90
94
20N7N
84
92
7N
86
80 82
88
96
86
82
90
78 FCST
7N 78
(b)
7N78
82
84
90
21N
92
7N
(b)
+
12
HRS
10N
7N
94
82
84
90
92
80
88
84
92
(b)
86
80
88
96
86
78
82
90
78
21N
94
82
84
90
20N
92
80
88
9N
84
92
86
20N
94 96
82 84
84
90 92
92 94
80 82
88 90
94
84 86
92 94
86
80 8082
88 8890
96 96 (e)7N
86 8688
78 7880
82 8284
90 9092
78 78
21N
94
21N
86
80
88
78
78
19N
FCST
+
12
HRS
21N
(b)
94
82
84
90
92
80
88
94
84
92
86
80
88
96
86
78
82
90
21N
78
19N
(b)20N
21N
21N
21N
(e)9N
21N
(b)
FCST
+
12
HRS
21N
19N
19N
(b)8N
20N
(b)
21N
(e)
20N
18N
FCST
+
12
HRS
(b)
(e)
20N
21N
(e)
(b)
20N
18N
20N
(e)
21N
20N
19N
20N
20N
8N
FCST
+
12
HRS
21N
20N
21N
20N
18N
18N
(e)
FCST
+
12
HRS
20N
21N
7N
17N
21N
19N
19N
(e)
FCST
+
HRS
21N
FCST
+ 12
HRS
21N
20N
21N
21N
17N
19N
(e)
+ 12
12
HRS
21N
20N
18N
19N
(e)
7N
19N
21N
19N
(e)
20N 78 FCST
21N
19N
FCST
17N
19N
17N
19N
(e)
19N
20N
1011
94
82+ 1284HRS
90
92
80 1011
88
94
84
92
86
80
88
96
86
78
82
90
19N
20N
(e)
16N
18N
20N20N
18N
21N
20N
21N
20N
16N
20N
18N
17N
19N
19N
18N
20N
20N
18N
18N
18N
21N
20N
16N
16N
18N
19N
21N21N
18N
18N
(b)
18N
21N
19N
17N
15N
19N
19N
19N
21N
20N
17N
15N
19N
18N
20N
16N
17N
19N
18N
17N
19N
17N
19N
18N
17N
17N
15N
17N
20N
15N
19N
20N
20N
17N
19N
17N
14N
18N
17N
16N
20N
18N
18N18N
21N
14N
FCST + 12 HRS
20N
17N
18N
19N
16N
15N
16N
18N
18N
16N
17N
(e)
19N
16N
14N
17N
14N
18N
16N
16N
16N
18N
16N
17N
19N
13N
18N
15N
16N
17N17N
16N
19N19N
20N
17N
13N
17N
19N
16N
14N
15N
17N
19N
18N
15N
16N
15N
18N
17N
13N
15N
13N
16N
21N
17N
15N
15N
15N
15N
16N
14N
17N
12N
18N
16N16N
17N
19N
18N18N
16N
15N
12N
15N
15N
16N
18N
14N
13N
16N
14N
15N
18N
14N
17N
14N
12N
12N
16N
17N
14N
20N
14N
15N
16N
14N
11N
13N
14N
15N
15N15N
16N
18N
17N
11N
16N
17N17N
14N
14N
15N
12N
14N
13N
17N
14N
13N
15N
11N
11N
13N
17N
16N
13N
13N
14N
16N
14N
12N
10N
15N
14N
13N
13N
14N
17N
19N
13N
15N
15N
10N
16N
14N
11N
13N
12N
16N16N
15N
13N
13N
13N
12N
16N
14N
10N
12N
10N
16N
15N
12N
13N
11N
12N
9N
13N
16N
14N
15N
12N
18N
12N
12N
13N
14N
9N 13N
13N
11N
14N
10N
12N
15N
11N
12N
14N
15N15N
12N
11N
12N
9N
9N
13N
15N
11N
12N
14N
10N
8N
15N
12N
15N
11N
11N
14N
12N
13N
8N 12N
11N
17N
12N
11N
9N
11N
13N
10N
12N
11N
13N
10N
14N
8N
10N
8N
13N
14N14N
11N
11N
10N
11N
7N
12N
11N
14N
9N
11N
14N
14N
7N
11N
13N
10N
10N
12N
8N
13N
10N
9N
10N
11N
16N
12N
11N
9N
10N
1011
7N
12N
9N
10N
7N
13N
12N
9N10N
13N13N
10N
10N
8N
10N
10N
13N
13N
11N
10N
13N
7N
12N
9N
9N
8N
9N
10N
12N
9N
9N
11N
8N
15N
9N
11N
10N
8N
1010
11N
8N
12N
7N
9N
11N
12N12N
9N 9N
9N
12N
9N
12N
9N
10N
7N
8N
12N
9N
7N
11N
8N
8N
8N
7N
8N
11N
10N
1010
14N
9N
7N
8N
10N
8N
10N
11N
11N
8N 8N 1010
10N
11N11N
8N
8N
7N
9N
11N
8N
10N
7N
11N
7N
7N
7N
9N
10N
7N
13N
9N
8N
7N 7N
7N
9N
10N
10N
10N 80 82
9N78
10N
7N
7N
78
84
90
86
88
8N
10N
92 88
92
90 86
88 84
94 90
96 92
94
86 82
78 80 827N 84
7878 80
94 96
84
82
90
92 94
10N
86
80
88
96
78 80
94
9N
82 84
84 86
90 92
92 94
86 88
80 82
88 90
9N
8N
12N
9N 78
8N
7N
94 96
84
82 84
90 92
88
92 94
86
80 82
88
96
90
78 80
94
86
82 84
84
90 92
92 94
88
90
8N
86
80 82
88
86
78
9N
9N 80
8N
9N78
9N
7N
9N
94
84
82
90
92
86
88
96
78 80
94
82
84
90
92
86
80
88
8N
78
8N
8N
7N
FCST + 2478HRS
7N
7N
94
84
82
90
92
86
80
88
96
8N
(f) 11N
94
82
84
90
92
86
80
88
8N
78
(c )
7N
8N
FCST
+
24
HRS
8N
94
84
82
90
92
86
80
88
96
FCST
+
24 84
HRS 86
78
94
82
84
90
92
86
80
88
78
(c
))
8N
94
90
92
80
88
96
78
94
82
84
90
92
86
80
88
7N
94
84
82
90
92
86
80
88
96
78
94
84
90
92
86
80
88
78
7N 78
(c7N
(f)
94
84
8224
90
92
FCST
+82
24 HRS
HRS
86
80 +
88
96
78 FCST
94
82
84
90
92
86
80 82
88
78
(f) 10N
7N
(c
(c ))
7N 7N 80
94
84
82
90
92
86
80
88
96
21N
78
94
84
90
92
86
88
94
84
8224 HRS
90
92
(f)
86
80 +
88
96
(f)21N7N
78 FCST
94
82
84
90
92
86
80 82
88
781013
7N 78
1013 (c21N
94
84
82
90
92
)
86
80
88
96
78
94
82
84
90
92
94
84
82
90
92
86
80
88
86
80
88
96
9N
78
78
82
84
90
92
86
80
88
94
84
82
90
92
78
86
80
88
96
21N
78
94
82
84
90
92
86
80
88
78
FCST
+
24
HRS
94
84
82
90
92
(f)20N
86
80
88
96
78
94
82
84
90
92
21N
86
80
88
21N 78
94
84
82
90
92
86
80
88
96
78
94
(c21N
)
82
84
90
92
86
80
88
20N
78
FCST
+
24
HRS
21N
21N
21N
(f) 20N
(c
))
FCST
+
24
HRS
(c
)
8N
20N
(c
(f)
(c
)
(f)
21N
20N
20N
21N
(f)
FCST
+
24
HRS
19N
19N
(f)
FCST
+
24
HRS
20N
20N
20N
20N
(c
))
21N
21N
(c
FCST
+
HRS
FCST
+ 24
HRS
(f)
19N
19N
7N
(f)
FCST
+ 24
24
HRS
(c
))) (c )
21N
1012
21N
20N
19N
(c
21N
1012
20N
19N
FCST
+
24
HRS
21N
21N
18N
(f)
21N
18N
(c
(f)
21N
(f)
21N
19N
(c
) 78 80
19N
19N
19N
94
84
82
90
92
86 1010
88
96
(f)
94
84
90
92
20N
86
80 82
88
78
20N
(f)18N
21N
18N
21N
21N
20N
20N
21N
19N
20N
18N
18N
19N
20N
17N
17N
20N
20N
21N
21N
20N
21N
20N
21N21N
21N
18N
18N
18N
18N
19N
21N
19N
21N
21N
17N
21N
20N
17N
20N
19N
20N
20N
19N
17N
18N
19N
18N
17N
16N
19N
16N
19N
19N
20N
20N
19N
17N
20N20N
19N
17N
20N20N FCST + 24 HRS
17N
17N
20N
18N
18N
20N
(c
)
20N
20N
19N
16N
16N
19N
18N
18N
19N
17N
(f)
19N
18N
16N
17N
18N
15N
16N
15N
18N
18N
19N
19N
18N
18N
19N19N
19N19N
16N
16N
16N
16N
17N
19N
17N
19N
19N
19N
18N
18N
15N
15N
17N
18N
17N
14N
17N
21N
14N
18N
16N
16N
15N
21N
17N
15N
17N
17N
18N
18N
17N
17N
18N18N
18N18N
15N
15N
15N
15N
18N
16N
16N
17N
18N
18N
14N
17N
14N
17N
16N
17N
16N
13N
20N
13N
14N
15N
20N
16N
14N
16N
15N
16N
17N
16N
17N
17N17N
16N
16N
17N17N
14N
14N
14N
14N
17N
17N
15N
15N
1010
17N
17N
13N
13N
16N
16N
16N
16N
15N
12N
15N
19N
12N
13N
14N
13N
14N
19N
15N
15N
15N
15N
16N
16N
16N
15N
16N16N
16N
15N
13N
13N
13N
13N
16N
14N
14N
16N
12N
16N
12N
15N
15N
11N
14N
15N
14N
11N
12N
15N
13N
13N
18N
18N
12N
14N
14N
14N
14N
1010
15N
15N
15N
14N
12N
14N
15N
12N
15N
12N
15N
12N
13N
13N
15N
15N
15N
11N
15N
14N
11N
14N
13N
10N
13N
14N
14N
12N
17N
10N
12N
11N
13N
13N
17N
11N
13N
13N
14N
14N
13N
13N
14N14N
14N14N
11N
11N
11N
11N
12N
1011
12N
14N
14N
13N
13N
1011
14N
14N
10N
10N
12N
9N
13N
13N
12N
9N
12N
11N
16N
12N
11N
16N
10N
10N
12N
12N
13N
13N
12N
12N
13N13N
13N13N
10N
10N
10N
10N
13N
11N
13N
11N
12N
13N
13N
9N
12N
9N
12N
8N
8N
11N
11N
12N
9N
11N
10N
10N
15N
11N
15N
9N 12N
11N
12N
11N
12N
12N12N
11N
11N
12N
9N
9N
9N
12N
9N
12N
10N
10N
12N
12N
7N
11N
8N
11N
7N
8N
11N
10N
11N
10N
9N
14N
14N
8N 11N
10N
10N
8N 11N
9N
10N
10N
11N
11N
10N
10N
11N
11N
8N
8N
8N
8N
9N
11N
9N
92
78 80 827N
11N
84
90
88
94
96
86
78 80 82 84 86 88 90 92 94
11N
10N
10N
7N
9N
10N
10N
9N
13N
13N
9N
8N
7N
8N
7N
9N
9N
9N
10N
10N
10N 80
10N 80 82
9N78
10N
10N
9N78
7N
7N
7N
7N
94
82
84
90
92
86
88
96
10N
8N
10N
8N
94
84 86
90 92
92 94
9N
10N
86 88
10N
80 82 84
88 90
78
94
82
84
90
Longitude
W
92
9N
86
88
96
78 80
9N78
8N
12N
8N
12N
7N
8N
7N
8N
9N
90
94
86
84
82 84
90 92
92 94
88
86
80 82
88
94
82
84
90
92
94
86
82
84
90
80
88
96
92
86
80
88
96
78
8N
78
8N
9N
9N 80
9N
8N
9N78
8N
−1
9N 9N
9N
7N
7N
9N
m
s
−1
94
84
90
92
9N78
86
80 82
88
94
82 m
84s−1
90
92
86
88
96
9N
78 80
8N
8N
8N
7N
8N
7N
11N
−1
11N
7N
7N
7N
20
7N
94
84
90
92
m
86
88
m
8N
94
82
84ss 86
90
92
8N
88
96Longitude
8N 80 82
7N
7N
8N78
8N788N 80
Longitude W
W
20
8N
8N
94
84
82
90
92
86
80
88
78
94
82
84
90
92
86
80
88
96
78
94
84
82
90
92
8N
86
80
88
8N
Longitude W
94
82
90
92
W
80
88
96
7N
7N
20
94
84
90
m84
s−1 86
92
86
80
88
78
94
82
84
90
92
86
80 20
88
96
78
7N 78
7N 78
94
84
82 1008
90
92
86
80 82
88
10N
78
94 1006
82 1004
84
90
92
86
88 1005
96Longitude10N
78
7N
7N
7N 80 20
1003
1007
1009
7N 80
s−1−1
7N
86
88
Longitude W
7N
78
94
84
90
92
88
96
94
84
90
92
−1 86
7N
7N
94
84
82
90
92
86
80 82
88
78
94
82 m
84
90
92
86
80 2082
88
96Longitude
78 80
7N 78
−1
7N
m
s
W
84
82
90
−1
92
86
80
88
78
94
82
84
90
92
86
80
88
96
78
9N
8688
8890
96
94
84 8486
82 8284
90 9092
92 9294
86 8688
80 8082
88 8890
9N 78
78 7880
94 9496
82 82m
84s84
90 9092
92 9294
86
80 8020
88
96
78 7880
94
78
82
84
86
−1
Longitude
W
94
82 m
84s−1
90
92
86
88
96 Longitude
78 80 20
−1
W
20
−1
Longitude8N
W 78 80 82 84 86 88 90 92 94
20
−1
m
ssm
−1 s−1
8N
m
−1
Longitude
W
20
−1
m
ss
Longitude
W
-1
20
m
Longitude
W
20
Longitude
W
20
20
Longitude7N
W
20
7N
Longitude
W
20
94
84
90
92
86
80 82
88
78
94
82
84
90
92
86
88
96
78 80
Fig. 9. Lower tropospheric (σ = 0.995) winds (m s ) simulated
by CTRL and FDDA runs for November 2002 cyclone.
20
m s−1
Longitude W
96
96
96
96
96
96
96
96
96
96
96
96
96
9496
96 96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
9496
96 96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
96
9496
96 96
96
96
96
208
D. Singh et al.
18UTC 25 Nov. 2002 CTRL
25
FDDA
TRMM
15
10
5
80
75
85
90
95
100 75
80
85
90
95
100
75
80
85
90
95
Longitude W
0.2
4
2
8
12
15
20
30
cm
Fig. 10. Twenty four hours accumulated precipitation (cm) valid at 18UTC 25
November 2002 simulated by CTRL and FDDA runs and TRMM observations.
(a)
1004
1003
CTRL
FDDA
OBS
1002
Pressure (hPa)
1001
1000
999
998
997
996
995
994
993
992
991
990
18Z
15 MAY
2004
00Z
16 MAY
06Z
00Z
16 MAY
06Z
12Z
18Z
00Z
17 MAY
12Z
18Z
00Z
17 MAY
Time
(b)
34
32
30
Wind speed (m/s)
Latitude N
20
28
26
24
22
20
18
16
14
12
10
18Z
15 MAY
2004
Time
Fig. 11. Time series of (a) minimum sea level pressure (hPa) and (b) maximum
wind speed (m s-1) simulated by CTRL and FDDA runs and observed values
for May 2004 cyclone from 15 May 18UTC to 17 May 00UTC.
100
209
Retrieval of ATOVS data using ICI system and its application in MM5
18 UTC
UTC
15MAY2004
CTRL
18 UTC
15MAY2004
CTRL
15MAY2004
CTRL
(a) (a) 18
18
(a)
(a)
18 UTC
UTC 15MAY2004
15MAY2004 CTRL
CTRL
(a)
(a)
(a)(a)
0.1 0.1
0.1
0.1
0.1
Heigh (m)
Heigh
(m)
Heigh
Heigh
Heigh
Heigh
Heigh
(m)
(m)
(m)
(m)
(m)
Heigh (m)
0.5
0.6 0.6
0.6
0.5
0.5
0.6
0.6
0.6
0.7 0.7
0.60.7
0.6
0.7 310
310 310
0.7
310
310
0.7
0.8
0.7
0.7 0.8310
0.8
0.8
310
310
0.8
0.8
0.9 0.9
0.80.9
0.8
0.9
0.9
0.9
0.90.9
89 89
90 90
91 91
89
90
91
89
90
91
89
90
91
89
90
91
8989 FCST
90
90
91
91
+ 12
12
HRS
FCST
+
12
HRS
FCST
+
HRS
(b)
(b)
FCST
+
12
HRS
(b)
(b)
FCST
+
12
HRS
(b)
FCST + 12 HRS
(b) FCST
FCST
++
1212
HRS
HRS
(b)(b)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2 0.2345
345 345
0.2
0.2
0.2 345
345
340 340
340
0.2
345
0.3
340
0.3
340
0.3
0.2
0.2 0.3
345
345
0.3
340
0.3 340
0.4 0.4340
0.30.4
0.3
0.4
0.4
0.4
0.5
0.4
0.4 0.5
0.5
0.5
0.5
0.5
0.6 0.6
0.6
0.5
0.5
0.6
0.6
0.6
0.7 0.7
0.60.7
0.6
0.7
0.7
0.7
0.8
0.7
0.7 0.8
0.8
0.8
0.8
0.8
0.9 0.9
0.80.9
0.8
0.9
0.9
0.9
0.90.9 89
89 89
89
89
(d)
(d)(d)
355 355
355
355
355
350 350
355350
350
355
355350
345 345
345
345
350
345
350
350
340 340
340
345340
345
345340
335
335
340
335 335
335
340
340
330
330
330
335
330
330
335
335325
325
325
330325
330
330325
320 320
325
320
320
325
325
320
315320
315
315
315
320
320
315
315
315
315
310 310
310
310
310
310
310
310
305 305
305
305
305
0.1
0.1
0.1
0.2 0.2
0.2
0.2
0.2 340
340 340
0.2 0.3340
340
0.3
0.3
0.3
0.20.3
0.2 340
340
0.3 340
0.4
0.4
0.30.4
0.3
0.4
0.4
0.4
0.5
0.4
0.4 0.5
0.5
0.5
0.5
305
305
305
92 92
92
92
92
92
9292
FDDA
FDDA
FDDA
FDDA
FDDA
FDDA
FDDA
FDDA
355 355
355
355
(d) (d)
(d)
(d)
(d)
18 UTC 15MAY2004 CTRL
1818
UTC
UTC 15MAY2004
15MAY2004
CTRL
CTRL
355
355350
350
350
350
355
355
350
345
345
345
350
345
345
350
350
340 340
340
345340
340
345
345
335
335
335 335340
335
340
340
330
330
335330
330
330
335
335325 325
325
330325
325
330
330
320325
320
320
320
325
325
320
315
320315
315
315
320
320
315
315
310 310
315
315310
310
310
310
310
310
305 305
305
305
305
305
305
305
93 93
93
93
93
93
9393
94 94 89
89 89
94
94
89
94
89
94
89
9494
8989
(e)
(e)
(e) (e)
(e)
90 90
90
90
90
90
9090
91 91
91
91
91
91
9191
(e)
(e)(e)
355 355
355
355
355
355350
350 350
355
355350
350
345 345
345
345
350
345
350
350
340 340
340
345
340
340335
345
345
335
335
335
340
335
340
340
335
330
330
330
330
335
335
330
325 325
330
325
325
330
330
325
320 320
325
320
320
325
325
320
320315
315
315
315
320
320
315
315
310
310
310
315
315
310
310
310
305 305
310
310 305
305
305
93 93
93
93
93
89 FCST
90
93
91
92
FCST
+
24
HRS
FCST
+
24
HRS
+
24
HRS
FCST90
+9024
24 HRS
HRS 9191
(c)
89
89 (c)
9393
9292
(c)
FCST
+
(c)
(c) FCST + 24 HRS
(c) FCST
FCST
++
2424
HRS
HRS
0.1
355 355
(c)(c)
0.1
0.1
355
0.1
355
0.1
355
350 350
350 350
0.1 350
355 350
350
350
0.1
0.1
350
355
355
350
0.2
0.2
0.2
0.2
350
0.2 350
345 345
345
350
350
350
350
345
0.2
340
0.3
345
340
0.3
340
0.3
340
0.3
0.2
0.2
340335
0.3
345
335
345
345
335
340335
0.3
0.4 0.4
330 330
340335
0.30.4
0.3
330
0.4
330 340
335
0.4
330
335
335
0.4
330
325
325
0.5
325
0.4
0.4 0.5
0.5
330
330
325
0.5
325
0.5
320 320
320
320
325
0.5
320
0.6 0.6
325
325
0.6
0.5
0.5
0.6
320
0.6
315 315
320
320
315
0.6
315
315
0.7 0.7
0.60.7
0.6
0.7
0.7
315
310
310
310
315
315
310
0.7
310
0.8
0.7
0.7 0.8
0.8
0.8
310
305 305
305 305
0.8 305
305
305
305
310
310
305
305
0.8
0.9 0.9
0.80.9
0.8
0.9 305
305
0.9
305
305
305
305
0.9
0.90.9 89 89
90 90
93 93
91
92
91
92
89
90
93
91
92
89
90
93
91
92
89
90
93
91
92
89
90
93
91
92
8989
9090
9393
9191
9292
-0.4-0.4
-0.4
-0.4
-0.4
-0.4
-0.4
-0.4
91 91
91
91
91
-0.2-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
92 92
92
92
92
0.5 0.5
0.5
0.5
0.5
0.5
0.50.5
88 88
94 94 88
94
88
94
88
94
88
94
(f)88(f)
88
9494
(f)
(f)
(f)
(f)
(f) (f)
94 94
94
94
94
94
9494
350 350
350
350
350345
345
345
345
350
345
350
350
340 340
345
340
340
345
345
340
335 335
335
335
340
335
340
340
330
330
330
335330
330
335
335
325
325
325
325330
325
330
330
320
320
325320
320
320
325
325
320315
315
315
315
320
320
315
315310
310 310
310
315
315 310
310
310
310
305 305
305
305
305
305
305
305
89 89
89
89
89
89
8989
90 90
90
90
90
90
9090
91 91
91
91
91
91
9191
92 92
92
92
92
92
9292
93 93
93
93
93
93
9393
355 355
355
355
355
350 350355
350
350
350
355
355
345 345
345
345
350
345
350
350
340 340345
340
340
345
340 345
335
335
335
335
340
335
340
330 330 340
330
330
335
330
335
335
325 325
325
325
330
325
330
330
320 320
325 320
320
325
325 320
315
315320
315
315
315 320
320
315
310 310
315
315
310
310
310
310
310
310
305 305
305
305
305
305
305
305
310 310
310
310
310
310
310
310
305 305
305
305
305
305
305
305
94 94 88
88 88
94
94
88
94
88
Longitude
W
Longitude
94
Longitude
W
88 W
Longitude
W
Longitude
W
9494
88
88
Longitude W
Longitude
Longitude
W
W
0.7 0.7
1 1
0.7
11
0.7
0.7
1
0.7
1
0.70.7
1 1
93 93
93
93
93
93
9393
355 355
355
355
355
355
355
355
350 350
350
350
350
345 345
345
345
350
345
350
350
345
345
345
335 335
335
335
335330
330
330
330
335
330
325
325
335
335
325
325
330
325
330
330
325
325
325
305
305
305
90 90
90
90
90
92 92
92
92
92
92
9292
89 89
89
89
89
89
8989
1.7 1.7
1.7
1.7
1.7
1.7
1.71.7
90 90
90
90
90
90
9090
91 91
91
91
91
91
9191
2.4 2.4
2.4
2.4
2.4
2.4
2.42.4
92 92
92
92
92
92
9292
3.1 3.1
3.1
3.1
3.1
3.1
3.13.1
Fig. 12. Longitude-height structure of vertical velocity (m s-1) and potential
temperature (K) simulated by CTRL and FDDA runs for May 2004 cyclone.
93 93
93
93
93
93
9393
210
D. Singh et al.
18UTC 16 May 2004
CTRL
FDDA
TRMM
20
15
10
5
75
80
85
0.2
90
2
95
100
4
75
80
8
85
12
90
95
15
100
20
75
80
85
90
95
100
30
Fig. 13. Twenty four hours accumulated precipitation (cm) valid at 18UTC
16 May 2004 simulated by CTRL and FDDA runs, and TRMM observations.
4. Conclusions
In this paper we investigated the problem of retrieving atmospheric temperature and moisture
profiles from satellite data. Emphasis was given in the analysis for different surface type (sea and
land) and atmospheric conditions (clear and cloud sky) for summer and winter conditions. The
results showed that it is easier to retrieve temperature and moisture profiles over the sea than over
land. This occurs because over the land, the forward model is less accurate due to the difficulty
of estimating surface parameters such as the emissivity and the surface temperature. Surprisingly,
we found the atmospheric conditions do not affect significantly the accuracy of the inversion
process. Also, even without most of the infrared channels (cloudy sky situations) it is possible to
obtain accurate retrievals. The numerical simulation with AMSU temperature and moisture profile
data is in close agreement with observations. Though the simulated wind speed was higher than
the observed, the trend shown by simulated wind speed is similar to that of observed. The MM5
simulation with AMSU data could produce strong vertical velocity as well as simulate a warm core
in the middle and upper troposphere. The overall thermodynamic structure simulated by the model
with AMSU data resembles observed thermodynamic structure of tropical cyclones as revealed
by a previous study (Kidder et al., 2000). The improvement in the thermodynamic structure is
prominent and consistent in both November 2002 and May 2004 cyclone cases. Though the amount
of the accumulated precipitation simulated by the model for the 24 hours of forecast is not in good
agreement with TRMM estimated precipitation, the spatial pattern of the precipitation was in
good agreement with the observed pattern of TRMM rainfall for the November 2002 cyclone case.
There was no marked improvement in the track forecast except for the 12 and 18 hours of forecast
in the November 2002 cyclone case. The comparisons of FDDA and CTRL simulations with each
other as well as with observations brought out the positive impact of ingestion and assimilation
of AMSU and HIRS temperature and moisture for the prediction of a tropical cyclone using the
MM5 model.
Retrieval of ATOVS data using ICI system and its application in MM5
211
Acknowledgements
Thanks are due to EUMETSAT for providing the AAPP4.0 and ICI3.0 software. We are very much
thankful to Dr. Lydie Lavanant, Meteo-France, for helping in the installation of ICI software and
useful suggestion from time to time. MM5 model is obtained from NCAR. NCEP-FNL analysis is
obtained from NCEP. The authors acknowledge IMD, NOAA and NASA for radiosonde/rawinsonde
and surface observations, AMSU data and TRMM data. One of the authors (A. C.) thanks SAC,
ISRO, India and DST, India for project support. S. Sandeep thanks the Council of Scientific and
Industrial Research (CSIR), India for a research fellowship. The authors thank the anonymous
reviewers for their comments and suggestions, which have greatly improved the paper.
References
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J. R. Eyre, 2000. A comparison of the impact of TOVS and ATOVS satellite sounding data on
the accuracy of numerical weather forecast. Quart. Jour. R. Met. Soc. 126, 2911-2931.
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