Impact of Kalpana-1 Retrieved Multispectral AMVs on Mahasen

Tropical Cyclone Forecast

Inderpreet Kaur1, Prashant Kumar1, S. K. Deb1, C. M. Kishtawal1,

P. K. Pal1 and Raj Kumar1

1. Atmospheric and Oceanic Sciences Group, EPSA, Space Applications Centre (ISRO),

Ahmedabad-380015, India

Published in : Natural Hazards , Volume 77, Issue 1, pp 205-222

Corresponding author’s address:

Prashant Kumar

Atmospheric Sciences Division,

Atmospheric and Oceanic Sciences Group,

Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area,

Space Applications Centre (ISRO), Ahmedabad-380015, India

E-mail: kam3545@gmail.com, prashant22@sac.isro.gov.in

Phone: +91-79-26916052

FAX: +91-79-26916075

http://link.springer.com/journal/11069mailto:prashant22@sac.isro.gov.inmailto:kam3545@gmail.com

Abstract

The Atmospheric Motion Vectors (AMVs) retrieved from geostationary satellites are

recognized as one of the important input for numerical weather prediction (NWP) models to

improve the tropical cyclone (TC) forecast. In this study, the Weather Research and Forecasting

(WRF) model, WRF 3-dimensional variational (3D-Var) data assimilation system, WRF Tangent

linear and Adjoint model are used to investigate the impact of multispectral Kalpana-1 AMVs on

the simulation of Mahasen tropical cyclone over the Indian Ocean. Three different sets of

experiments are performed to evaluate the impact of Kalpana-1 AMVs. First, the impacts of

Kalpana-1 AMVs are evaluated for different forecast lengths. The assimilation of Kalpana-1

AMVs improves the cyclone track prediction compared to control experiment. However, all the

experiments are unable to capture the deep re-curvature of the TC. Next set of experiments are

performed to evaluate the impact of Kalpana-1 AMVs derived from different multispectral

channels (viz. visible, infrared and water vapor channels). More improvement is observed in TC

track forecast when AMVs from Water Vapor (WV) channel are used for assimilation compared

to Infrared (IR) channel. Results also show degradation in short-range forecast when less-strict

quality control is used for AMVs assimilation, but a considerable improvement is observed in

long-range forecasts. Finally, the WRF tangent linear and adjoint model are used to compute the

forecast sensitivity to Kalpana-1 AMVs observations. Upper and lower level circulation

information provided by the Kalpana-1 AMVs influences the TC steering flow and a positive

influence on the track prediction is observed.

Keywords: Atmospheric Motion Vectors, Data Assimilation, Forecast Sensitivity, Tropical Cyclone.

1. Introduction

Over the last two decades, numerical weather prediction (NWP) models have improved

considerably to provide tropical cyclone (TC) track and intensity forecast (Goerss et al. 2004).

The accurate TC track and intensity forecast have significant societal and economic benefits and

are recognized as the most challenging tasks in the NWP models. An accurate representation of

the initial conditions in NWP models is the foremost requirement for a reliable forecast. Over the

last few years, with the availability of the improved assimilation techniques and the NWP models

formulation, much emphasis have been placed on improving the initial conditions through the

use of enhanced observations from space based observing systems. Conventional observations

are scarce in the vicinity of a tropical cyclone; hence, the information available from satellite

based observing systems is indispensable.

Atmospheric motion vectors (AMVs) retrieved from a constellation of geostationary satellites

provide global coverage of tropospheric wind information and are recognized as an important

input to most of the global and regional model’s data assimilation systems (Bouttier and Kelly,

2001; Soden et al. 2001). Many studies have highlighted the positive impact of the AMVs

towards improving the forecast skill over mesoscale forecasts especially during extreme weather

events like TCs. Soden et al. (2001) demonstrated the positive impact of GOES-8 AMVs in

Geophysical Fluid Dynamics Laboratory (GFDL) hurricane prediction system. Observing

systems experiments performed by Zapotocny et al. (2008) showed that assimilation of AMVs

has a significant impact on the Pacific basin tropical cyclones. The assimilation of rapid-scan

AMVs in the simulation of TC Katrina showed a reduction in the track error by almost 12%

(Langland et al. 2009). The positive impact of four dimensional Variational (4D-Var) data

assimilation of AMVs over track prediction has been demonstrated by Bennett et al. (1996) and

Berger et al. (2011). In the past, AMVs derived from Indian geostationary satellite Kalpana-1

have also been utilized for improving the cyclone track prediction over the Indian Ocean. Deb et

al. (2010) highlighted the positive impact of the Water Vapor (WV) winds in the simulation of

the initial position errors and subsequently track and intensity forecast for the tropical cyclones

Sidr and Nargis. It also showed that impact of Kalpana-1 WV winds and Meteosat-7 WV winds

is comparable over the Indian Ocean cyclones. The assimilation of Kalpana-1 AMVs derived

from both Infrared (IR) and WV channels has also shown a positive impact on the initial position

errors and track forecasts (Deb et al. 2011). Due to its capability of capturing near storm and

environmental flows in the upper and the lower troposphere, assimilating AMVs can influence

the TC steering flow and therefore improve the track prediction.

With the implementation of improved quality control techniques (Deb et al. 2013) and height

assignment techniques (Deb et al. 2014), the operational generation of Kalpana-1 AMVs has

evolved significantly over time (Kishtawal et al. 2009). The significant improvement is noticed

due to change in height assignment schemes. It is reported in an analysis using one and half years

of data that root mean square vector difference (RMSVD) for high, mid and low-level IR AMVs

have reduced from 7.9, 10.4 and 7.6 m s-1 for the older empirical height assignment to 6.8, 7.3

and 5.5 m s-1 respectively for the improved height assignment (Deb et al. 2012). Similarly, for

WV AMVs the RMSVD has reduced from 8.2 m s-1 to 7.5 m s-1 due to change in height

assignment. Some positive impact is also noticed for the improved quality control technique as

well, for example, the RMSVD for high, mid and low-levels IR AMVs have reduced from 6.8,

7.3 and 5.5 m s-1 to 6.6, 6.6 and 5.0 m s-1 respectively for the new quality control method. The

corresponding RMSVD for WV AMVs also decreased from 7.5 m s-1 to 6.7 m s-1 .

To assess the impact of these improved AMVs for the simulation of track and intensity forecast

of tropical cyclone, a case study with the TC Mahasen is attempted here. The main objective of

this study is to assess the impact of the assimilation of both Kalpana-1 IR and WV AMVs

observations upon the track forecasting performance of the Weather Research and Forecasting

(WRF) model. Over TC, most of the AMV observations are available at the upper levels as low

level clouds are largely obscured by the upper level clouds. In the present study, various

experiments are performed to emphasize the importance of the upper level AMVs in TC track

and intensity prediction. Firstly, the impacts of the AMVs observations over various forecast

lengths are studied. The sensitivity of the AMVs derived from different spectral channels of

Kalpana-1 satellite is also investigated in the next set of experiments. Lastly, the forecast

sensitivity to observations (here Kalpana-1 AMVs) using adjoint model (Baker and Daley, 2000)

is used to evaluate the impact of Kalpana-1 AMVs over short range forecasts.

For optimal usage of AMVs in global community and monitor the impact of AMVs in the NWP

models a large set of model impact studies are required. This is not an efficient way

(computationally expensive) to evaluate the forecast sensitivities. An alternative efficient

approach of performing impact studies for the purposes of assessing forecast sensitivity is to

perform a single integration of the adjoint of the NWP model to evaluate the sensitivity of a

specific forecast feature to the full initial state vector (Langland and Baker 2004). Over the last

few years, adjoint derived observational impact is used to quantify the contribution of

observations on the analysis and the subsequent forecast (Baker and Daley, 2000; Langland and

Baker, 2004; Zhu and Gelaro, 2008 ; Cardinali, 2009; Jung et al. 2013; Ota et al. 2013).

Observation impact is evaluated by computing the adjoint sensitivity gradients of a forecast error

cost function. The adjoint technique is a computationally efficient method to determine the

sensitivity of a forecast’s response to model initial conditions. It can be used to locate high-

sensitivity regions (e.g. centre of cyclone), where small perturbations can have relatively large

effects on forecast characteristics. Such assessments are important for testing the impact of

satellite data on numerical model. The section 2 of this study provides a brief description of the

TC Mahasen, while the section 3 gives a brief description of the data used. The section 4 briefly

describes the WRF model and assimilation technique used for this study. The section 5 presents

the results and the discussions and the section 6 concludes the study.

2. Tropical cyclone Mahasen

A depression over the southeast Bay of Bengal was reported by India Meteorological

Department (IMD) on 10 May 2013 at 0900 UTC and over a few hours it was intensified into a

deep depression. On 11 May 2013, the deep depression moved north-westward and intensified

into a cyclonic storm, Mahasen. Due to anti-cyclonic circulation to its east, the TC altered its

track from north-westerly to northerly on 13 May 2013, and progressed further towards north-

north easterly direction. On 15 May 2013, approximately around 77oE, the influence of the mid-

latitude westerly trough enhanced the north-northeastward movement of the cyclonic storm. The

north-northeastward speed increased up to 12.5 - 14 m s-1, as the TC approached the trough. The

TC made landfall around 0900 UTC of 16 May 2013 on the Bangladesh coast (91.4oE, 22.8oN).

After landfall, the storm continued its north-northeastwards movement but weakened due to

surface interaction. The TC maximum sustained wind speed of around 23.6 – 26.3 m s -1 and the

minimum sea level pressure (MSLP) was reported around 990 hPa just before landfall.

3. Data used

Kalpana-1 is one of the Indian geostationary satellites built for meteorological

applications. It carries a Very High Resolution Radiometer (VHRR), which scans the earth in

three spectral channels: visible (VIS; 0.55-0.75 μm); thermal infrared window (IR; 10.5–12.5

μm) and water vapor (WV; 5.6–7.2 μm). The IR, WV and VIS images from Kalpana-1 are used

to retrieve AMVs operationally every 30 minutes at Space Applications Centre (SAC), Indian

Space Research Organisation (ISRO; Kishtawal et al. 2009) and are disseminated operationally

for global use. The current operational algorithm utilizes a sequence of nine images to derive the

motion vectors (Deb et al. 2013) instead of traditional three images. The cloud/moisture features

are tracked in each pair of the nine images to create a wind buffer. Each wind vector stored in the

wind buffer is assessed for its quality on the basis of wind speed, wind direction and height

consistency with its spatial and temporal neighbors (Deb et al. 2013), and the vectors which pass

the quality control (QC) are retained as the final derived vectors. During QC, each vector is

assigned with a quality indicator (QI) value based on its consistency with the neighbors. The

value of the QI ranges between 0 – 1.0, and vectors with QI close to 1.0 are considered as good

quality vectors. The height assignment of each vector is assigned through IR-window technique,

H2O intercept method, cloud base method according to the spectral radiance measurements (Deb

et al. 2013b). For the present study, Kalpana-1 AMVs derived from only IR and WV images, for

the period 11 May 2013 to 14 May 2013, are used for data assimilation. Since all experiments are

performed at 0000 UTC hence, the non-availability of VIS AMV data during night restricts the

utilization of AMVs derived from Kalpana-1 VIS images. A typical example of satellite-derived

high-level (i.e. between 100 - 500 hPa) winds (combination of both IR and WV AMVs) from

Kalpana-1 valid at 0000 UTC of 13 May 2013 is shown in Figure 1. As evident from Figure 1,

the AMVs capture the large-scale and synoptic-scale features and also provide a vast coverage of

the atmospheric circulation in the upper atmosphere

In the present study, before assimilating Kalpana-1 AMVs into the WRF model, the wind

observations are screened on the basis of quality flag, defined according to the QI value and

wind speed and direction consistency check. The AMVs with QI value greater than 0.5 is

considered for assimilation. In addition to the QI check, all observations with wind speed and

wind direction difference greater than 20 m s-1 and 60o respectively, against the National Centers

for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analysis are

also rejected. Regular validation of Kalpana-1 AMVs against radiosonde and NCEP GDAS

analyzed winds has shown that this wind speed and direction consistency check helps in

screening out the observations associated with large height assignment errors. It filters out

around 5-7% of the total available observations. In addition to the Kalpana-1 AMVs, the

conventional observations available from upper-air (e.g. Radiosondes, dropsonde, Global

Positioning System (GPS) sonde, etc.), ground stations, aircrafts, buoys (drifted and moored) and

ships observations are also assimilated in this study.

In the present study, Joint Typhoon Warning Center’s (JTWC) best-track analyses are utilized to

compare the track forecasts. JTWC uses several tools and techniques including subjective

Dvorak estimates, objective fix data, and observations. In addition, JTWC uses six baro-clinic

dynamical models and one baro-tropic model. JTWC provides an estimate of the maximum

sustained wind speed, however, no data for the MSLP is available. Hence, for the cyclone

forecast intensity verification, the MSLP and maximum sustained wind speed available from

Indian Meteorological Department (IMD) are used.

4. Assimilation methodology and Experimental Designs

4.1 Methodology

The Weather Research and Forecasting (WRF; Skamarock et al. 2008) model version 3.4

is used for the present study. The WRF model provides a state-of-the-art atmospheric simulation

system. It incorporates the multiple re-locatable nesting schemes and improved physics options

for both operational forecast and atmospheric research. Details about various model physics used

here can be found in Kumar et al. 2013. Data assimilation is recognized as a useful way to obtain

optimum initial conditions, which represent the true state of the atmosphere. The variational data

assimilation method is one of the most effective methods of data assimilation. The WRF three-

dimensional variational (3D-Var) data assimilation system is used in the present study. 3D-Var

aims at producing an optimal estimate of the true atmospheric conditions by minimizing the cost

function defined by:

(1)

Here, x is the analysis state composing of atmospheric and surface variables and bx is the first

guess comprising of values from previous forecast. The conventional and satellite observations

are represented by . B and E represent the background and observation error covariance

matrices respectively. Observation operator H transforms the analysis to observation space

)(xHy . The inaccuracies introduced on account of observation operator are contained in

representative error covariance matrix F . A detailed description of the 3D-Var system can be

found in Barker et al. (2004). Additionally, WRF tangent linear and adjoint model are used to

perform the forecast sensitivity to observations.

The WRF adjoint and tangent linear model are utilized to evaluate the observational impact of

the Kalpana-1 AMVs. The nonlinear forecast model is integrated with full physics packages

including moist sub-grid processes, the adjoint model are integrated with only dry physics.

Kalpana-1 AMVs observations from IR and WV channels are used in this study to perform the

adjoint-based Forecast Sensitivity to Observations (FSO; figure 7 from Xiao et al., 2008)

y

techniques developed for the Weather Research and Forecasting (WRF) model. In the first step,

3D-Var data assimilation scheme is used to provide an optimal analysis )( ax and forecast )( fx

for an atmospheric state )(x . In the present experiment, the model is integrated upto 12 hours

from initial conditions starting at 0600 UTC of 13 May 2013. Differences between the 12 hour

forecast )( fx and the NCEP analysis )( tx valid at the same time are used to compute the

forecast error. The FSO (Aulign 2010) is used to assess the sensitivity of the forecast errors to the

actual observations.

4.2 Design of Numerical experiments

Three sets of experiments are performed for the simulation of tropical cyclone Mahasen.

In the first set, eight experiments (Table 1) are performed for different forecast lengths to assess

the impact of the Kalpana-1 winds. Conventional observations and Atmopsheric Infrared

Sounder (AIRS) temperature and humidity profiles are assimilated in all the experiments. Four

assimilation experiments (KAL1, KAL2, KAL3 and KAL4 initialized from 0000 UTC of 11, 12,

13 and 14 May 2013 respectively) based on different initial conditions are performed to

assimilate the Kalpana-1 AMVs. The next four control experiments (CNT1, CNT2, CNT3 and

CNT4 initialized from 0000 UTC of 11, 12, 13 and 14 May 2013 respectively) are performed

with an identical setup but without using Kalpana-1 AMVs. In all these eight experiments, the

model is integrated upto 1200 UTC of 16 May 2013.

In the second set of experiments, seven experiments (Table 1) are performed to assess the impact

of AMVs retrieved from different spectral channels on the sensitivity of the TC track and

intensity prediction. Out of four different initial conditions in first set of experiments, the initial

condition starting at 0000 UTC of 13 May 2013 is used for further assimilation experiments.

Similar to first set, the model is integrated upto 1200 UTC of 16 May 2013 for these

experiments. Out of these seven experiments, first one is CNT experiment (without Kalpana-1

AMVs assimilation), three experiments (viz. IR_WF, WV_WF and ALL_WF represent

exclusively for IR, exclusively for WV and combination of both IR and WV AMVs respectively)

are performed with AMVs observations screened according to the quality flag. In the present

setup quality flag defined as, all the wind observations with QI value less than 0.5 and wind

speed and direction difference greater than 20 m s-1 and 60o respectively with respect to NCEP

GDAS analysis is used to screen out the observations. In the remaining three experiments (viz.

IR_NF, WV_NF and ALL_NF represent exclusively for IR, exclusively for WV and combination

of both IR and WV AMVs respectively), all AMVs observations irrespective of the quality flag

are considered for assimilation. Since TCs are associated with large variability of the winds and

many wind observations near the TC environmental flow are flagged out in QC, three less-strict

QC experiments (IR_NF, WV_NF and ALL_NF) are performed to assess the potential impact of

screened out AMVs. NCEP GDAS analysis (1o × 1o spatial resolution) is used to prepare the

lateral boundary conditions for all the experiments. The 6 hr WRF model forecast valid at the

time of initialization, is used as the first guess (FG). All experiments in first and second sets are

conducted in the two-way nested domains, with a horizontal resolution of 30 km and 10 km in

the outer and the inner domain respectively. The outer domain extends from 30.15oS – 49.31oN,

35.74oE - 124.25oE while the inner domain extends from 3.82oS – 26.28oN, 73.43oE - 105.45oE in

330 x 330 and 358 x 370 grid points respectively. The model has 36 vertical levels, extending

upto 10 hPa. Data assimilation is performed in outer domain only in all the experiments to

prepare the model analysis. In this study, the impact of the AMVs observations is analyzed for the

inner high resolution domain only.

In the third set, three different (KAL_IR, KAL_WV and KAL_ALL; Table 1) combinations of

Kalpana-1 AMVs retrieved from IR, WV and both channels together are performed to assess the

impact of AMVs. The observational sensitivities are computed for 12 hour forecast from the

WRF model analysis and first guess at 0600 UTC, 13 May 2013. The target domain is focused in

the Bay of Bengal, and configured with 61 x 61 horizontal grids with 25 km grid spacing and 36

vertical levels with model top at 10 hPa. Three analyses )( ax are prepared assimilating AMVs

from IR, WV and both channels together. The observation impact in 12 hour forecasts is

evaluated from 0600 UTC 13 May 2013, with the variant formula of third-order approximation

of forecast error variation. To highlight the performance of the Kalpana-1 AMVs over the NCEP

analysis, no additional satellite data or conventional observations are used in this procedure.

These experiments will give an important feedback where additional satellite observations from

Indian Geo-stationary satellite can be included in operational analysis.

5. Results and Discussions

5.1. Impact of Forecast Length

5.1.1. Initial Position and Intensity Error

The impact of Kalpana-1 AMVs assimilation on initial condition is assessed by analyzing

the initial position errors and departure error in maximum surface wind speed and MSLP. The

cyclone center’s position and intensity are compared with the JTWC best-track analysis. JTWC

and IMD track analysis are mostly comparable but IMD lags in predicting the landfall correctly.

Hence, JTWC best track analysis is used for comparing the track forecasts. Figure 2(a) shows

the synoptic scale wind field at 850 hPa for the CNT3 experiment starting at 0000 UTC of 13

May 2013 and Figure 2(b) illustrates the differences in the wind fields between CNT3 and

KAL3 experiments. The cyclonic vortex in the low level flow is clearly highlighted in the Figure

2(a). The maximum wind speed in the cyclonic circulation is observed to be ~34 m s -1 in the

CNT3 experiment. The assimilation of the Kalpana-1 AMVs does not have a large impact on the

maximum wind speed due to unavailability of sufficient low level winds information. Though,

noteworthy changes are observed in the periphery of the cyclonic-vortex. Assimilation of the

Kalpana-1 AMVs has maximum influence on the analyzed wind field at 200 hPa (Figure 2c-d);

where maximum AMV observations are assimilated (Figure 1). For the initial condition starting

at 0000 UTC of 11 May 2013, the cyclone centre is observed at (91.62oE, 7.20oN) and (91.57oE,

7.16oN) for CNT1 and KAL1 experiments respectively. The assimilation of Kalpana-1 winds

improves the initial track position error from ~72 km to ~66 km when compared with JTWC best

track analysis. The positive impact of Kalpana-1 AMVs in improving the vortex positioning is

observed for initial conditions starting at 0000 UTC of 11 May2013 and 12 May 2013 as well.

All the experiments are not able to simulate the observed maximum surface wind speed and

MSLP.

5.1.2 Track Forecasts

For all the experiments, 6-hourly cyclone track forecasts are compared with the JTWC

observed best-track analysis. The observed and predicted tracks for different initial conditions

are illustrated in figure 3. All the experiments are able to predict the re-curving track of the

Mahasen TC but none of these experiments are able to capture the deep curve around 13o N.

Before landfall on 16 May 2013, the cyclone moved rapidly towards the Bangladesh coast but

none of the experiments is able to capture the landfall accurately. The model predicts the landfall

position more toward the Myanmar border instead of the actual landfall position near Chittagong,

Bangladesh. The forecast start from 0000 UTC of 14 May 2013 matches well with the best track

analyses around the deep curve, but the landfall prediction is observed to be at a lag of 6-hours.

Initialization of the model closer to the landfall time improves the track errors due to more

realistic boundary conditions. Shorter forecast lengths limit the divergence of the model from the

boundary conditions, which in turn provides a better estimate of process.

The landfall is predicted earlier around 12-24 hours in advance for all the CNT

experiments. Assimilation of the Kalpana-1 AMVs shows only a slight improvement in the

landfall errors. For a quantitative evaluation of the improvement in the accuracy, the track errors

for CNT and KAL experiments with respect to the JTWC best track are determined. The

differences between track errors for CNT and KAL experiments are plotted in Figure 4 for each

6-hours forecast. In parenthesis, it also shows the cumulative percentage improvement with

respect to CNT experiment for every 6-hour forecast. Cumulative percentage improvement is

defined as the average of the percentage track error (between CNT and KAL) for all four initial

conditions at each forecast time step (6 hours). Percentage track error is the ratio of the forecast

track errors of KAL to the forecast track errors of CNT. Since the forecast lengths for each initial

condition are different, the cumulative improvement is computed using the ratios available at

each forecast time step. The sample size decreases with the increasing forecast lengths. For

forecast hours upto 60 hours, the averaged ratios include contribution from all four KAL and

CNT experiments. For forecast hours between 66 and 84 hours, the calculations are based on

KAL1,2,3 and CNT1,2,3 experiments, while for 90 and 96 hours the ratios are averaged only

over the first two initial conditions.

The impact of the Kalpana-1 AMVs is mostly positive over the 96 hour forecast, for all

initial conditions. In initial 18 hours forecast, the difference in the track errors is less than 25 km,

while for longer forecast lengths, the error difference increases, indicating a positive impact of

the Kalpana-1 AMVs. Maximum positive impact of the Kalpana-1 AMVs is observed for the

KAL2 and KAL3. In KAL1 experiment, the analysis of the cross section of the wind circulation

patterns at upper level shows that the anti-cyclonic features were not fully developed by 0000

UTC; hence, this might be the reason for the relatively low improvements in the track error for

the initial condition starting at 0000 UTC of 11 May 2013. Longer forecast lengths also provide

sufficient time for the development of the large scale model errors, which in turn limit the

accuracy of the forecast.

5.1.3 Intensity Forecasts

The temporal variation of the observed and predicted central MSLP and maximum

surface wind speeds are compared to assess the impact of Kalpana-1 AMVs in the intensity

forecast. The intensity forecasts are compared against intensity data available from IMD as

JTWC does not provide the MSLP data. Figure 5 illustrates the temporal variation of the

observed and predicted MSLP for all four initial conditions. Over the Bay of Bengal region,

Mahasen remained a very slow moving cyclone. As per the observation data available from IMD,

the MSLP dropped to only 994 hPa on 11 May 2013, 1200 UTC and this value was sustained

over its entire lifetime. Just before landfall, the MSLP further dropped to 990 hPa. The predicted

MSLP is over-estimated in all the experiments. The initial intensity of all the experiments is

comparable to the observations, but the errors increase with the forecast lead times, and the

model forecasts provide much lower values of MSLP in comparison to the observations. For

initial condition starting at 0000 UTC of 11 May 2013 (i.e. CNT1, KAL1), the assimilation of

Kalpana-1 AMVs, tends to correct the over estimation of MSLP when integrated up to 36 hours,

but over larger forecast lengths, CNT gives a better accuracy. For the second initial condition

(i.e. CNT2, KAL2), the errors in the MSLP are comparable for both CNT and KAL experiment.

The assimilation of AMVs, at 0000 UTC of 13 May 2013 (i.e. CNT3, KAL3), show a substantial

positive impact on the CNT experiment in 60 hour forecast. The experiments start from 0000

UTC of 14 May 2013 (i.e. CNT4, KAL4) does not show much variation from the CNT run and is

substantially over estimated over the entire integration time.

Figure 6 shows the temporal variation of the observed and the predicted maximum surface wind

speed. The maximum surface wind speed was quite stable for the entire cyclone, and the

maximum value was observed to be around 50 m s-1, dropping to only 45 m s -1 at few time steps.

The maximum surface wind speed over short range forecast for all initial conditions is quite

weak in comparison to observations, but after integration time of around 36-42 hours, the

maximum surface wind speed becomes comparable to the observations for all the experiments.

The maximum surface wind speed increases to 50 m s-1 in the forecast for initial condition

starting at 0000 UTC of 12 May 2013 after 48 hours of integration time. Addition of the

Kalpana-1 AMVs helps in strengthening the maximum surface wind speed, though the effect is

seen only at longer forecast lengths.

The assimilation of the AMVs does not show a considerable effect on the intensity forecast. The

possible reason may be that the most of the AMV observations are available at upper levels

(Figure 1), which do help in improving the vortex position of the cyclone, but due to

unavailability of near surface and low level winds, no considerable impact is observed in

maximum wind speed and minimum sea level pressure. Unavailability of sufficient number of

low level observations (Kaur et al. 2014) also restricts the interaction with the surface.

Assimilation of upper level winds show a positive impact at the upper levels, but limited

interaction of the upper level wind information with the surface does not contribute much

towards a positive impact. Combined effect of AMVs with low level winds information (e.g.

scatterometer etc.) will be beneficial to improve the intensity of cyclone forecast. Poor intensity

forecasts can also be attributed to the inability of the AMVs to represent the local features.

Kalpana-1 AMVs have a resolution of 120 km x 120 km, and they best represent the large scale

circulation features. These large scale circulation features control the TC track, but the intensity

strongly depends both on the large and local scale features (Kumar et al 2011). The poor

intensity forecast of this north-eastward moving cyclone also corroborates the observation by

Srinivas et al 2013. Srinivas et al 2013 studied 21 tropical cyclones over Bay of Bengal using

WRF model and observed that northward/northwestward moving TCs forming at high latitudes

are best represented by the model dynamics as the increasing Coriolis force with higher latitudes

provides a relatively stronger contribution to the steering flow. But for the west and

northeastward moving cyclones (as in the present case), the model dynamics are faced with

deficiencies to balance the forces due to low Coriolis force magnitude. It should be noted that

this study did not provide any statistical significance for the results based on the track direction

due to limited number of samples, but our findings seem to be in agreement with the observation.

5.2 Impact of IR and WV AMVs

Both IR and WV channels provide upper level wind information, hence to investigate the

sensitivity of the winds from IR and WV channels to TC track and intensity forecast, these

AMVs are assimilated separately (Set 2 in table 1). In addition to these two experiments, to

account for the large variability of the TCs, all available wind observations without any quality

flag are also considered. The results for the track and intensity forecast for these experiments are

discussed in the subsequent sections.

5.2.1 Track and Intensity error

Out of the seven experiments (Table 1) conducted to assess the sensitivity of the track

and intensity forecast to the multispectral wind observations and quality flag, none of the

experiments could accurately predict the landfall location and the re-curvature of the TC, but a

substantial change in the track error forecast is observed in these experiments. Figure 7

illustrates the track error for different experiment against the JTWC best track analysis. No major

changes in track errors occur when quality flag Kalpana-1 winds are used for assimilation in

ALL_WF, IR_WF and WV_WF experiments. Assimilation of only WV winds, improves the

accuracy over CNT run, but in comparison to the ALL_WF, the impact is slightly less over short

range forecasts. For forecast after 36 hours, the track errors improve by almost 2-7 % over

ALL_WF, highlighting the importance of upper tropospheric wind information provided by WV

winds. The corresponding improvement over the CNT run is found to be around 4-22 %.

Assimilation of the wind observations without quality flag increases the total number of ingested

observation by almost 15-25%. Figure 8 shows the JTWC observed and IR_NF, WV_NF,

ALL_NF predicted track at every 6 hour forecast. Assimilation of the all the Kalpana-1 AMVs,

without any QC, also fails in predicting the re-curving of the TC. No clear differentiations in the

forecast errors are observed when IR winds with and without QC are assimilated, indicating a

smaller contribution from removing IR AMVs with bad quality. Height assignment issues and

rapidly changing cloud patterns affect the accuracy of the winds over the TC, and most of these

low accuracy wind observations are screened out in the inherent model quality check. In both the

experiments, the landfall is predicted around 12 hours in advance and the landfall error is 356 km

and 331 km for IR_WF and IR_NF respectively. Assimilation of without quality flag WV winds

(WV_NF) has a slightly negative impact on the short range forecast, but large positive impact is

seen over long range forecasts (Figure 7). The landfall error reduces with the utilization of un-

flagged WV winds. The landfall error is observed to be 201 km in WV_NF experiment in

comparison to 330 km for WV_WF and 327 km for ALL_WF respectively. Improvement over

long range forecast suggests that the information from the flagged WV winds is important over

longer time scales, but tends to degrade the model performance for shorter forecast lengths. The

performance of all the experiments is similar in the intensity forecasts. The model tends to

intensify the cyclone intensity and none of the experiments is able to capture the MSLP of 994

hPa, but the model is able to capture the maximum wind speed at long range forecasts. The effect

of assimilating multispectral observations separately and assimilating un-flagged observations

does not cause much variation in the maximum wind speed.

5.3 Observational Impact

From the last two sections it is evident that Kalpana-1 AMVs shows a considerable

impact in improving the track error, while very low impact on the intensity forecast is observed.

Hence, to assess the influence of Kalpana-1 AMVs on the initial condition uncertainties on the

Mahasen TC forecast, WRF adjoint model is used to evaluate forecast sensitivities. These

experiments (Set 3 in table 1) evaluate the relative impact of IR and WV AMVs. Figure 9 shows

the AMVs observation impact using the WRF model forecast. Kalpana-1 AMVs show the

positive impact in reducing 12 hour forecast error in this modeling system framework. The

observation sensitivity increases when WV AMVs observations are used in comparison to IR

AMVs. The performance of the KAL_ALL is better than the performance from assimilating IR

and WV AMVs separately (KAL_IR and KAL_WV)., and the analysis sensitivity gradients are

large in amplitude. Figure 9 shows that the observation sensitivity is considerably weaker for

individual channels winds product. The large observation sensitivities suggest that dense AMVs

observations with quality flag have a greater potential to change the forecast aspect than

individual channel AMVs observations.

6. Conclusion

In this study, an attempt is made to demonstrate the impact of Kalpana-1 AMVs on the

track and intensity of the Bay of Bengal TC, Mahasen. To investigate the impact of the Kalpana-

1 AMVs, different experiments are performed using WRF 3D-Var. In the first set of experiments,

the impact of Kalpana-1 AMVs on TC track and intensity forecast on different forecast lengths is

studied. For all four initial conditions, good track but poor intensity simulation is observed. The

mean track error forecast of KAL run from all four initial conditions is observed to be smaller

than the CNT experiment. For all initial conditions, the model predicts the landfall positions

towards the south-east of Chittagong. The forecast of landfall time is nearly 24 hours earlier

when model is integrated for long period (132 hours from 0000 UTC of 11 May 2013) but the

landfall error decreases for shorter forecast lengths. Since all the four experiments are integrated

up to the same time, later initialisation provides better initial conditions from the accumulated

impacts through cycled data assimilation. Therefore, over shorter forecast lengths, more accurate

forecast is observed. For the present study, the model fails in capturing the true intensity of the

TC. The lack of the local level information in AMVs affects the intensity simulation, which is

strongly dependent upon both local and large scale conditions. The lack of the low level

information in Kalpana-1 AMVs also restricts the interaction with the surface and causes

deficiencies in the simulation of the TC intensity.

In the present study, the impact of multispectral AMVs is also analyzed separately.

Overall the impact of WV winds is observed to be stronger than IR winds. The impact of the

flagged WV wind observation is found to be positive over long range but is negative over short

range forecast, indicating that the importance of flagged observations over longer times scales.

Among IR and WV AMVs observation sensitivity experiments, KAL_ALL experiment show the

largest impact in reducing 12 hour forecast error in this modeling system framework compare to

KAL_IR and KAL_WV experiments separately. Though, this study is conducted for only one

cyclone, the limited number of experiments shows that even in the absence of sufficient number

of low level wind observations, the impact of the upper level AMVs is positive and this

information can be used in conjunction with other low level wind observation sources to improve

the TC track and intensity prediction.

Due to the capability of the Kalpana-1 AMVs to capture the near-storm and

environmental flows in the upper and the lower troposphere, assimilation of the AMVs

influences the TC steering flow and therefore improves the track prediction. This study also

highlights the impact from assimilating AMVs derived from individual multispectral channels.

More impact studies with the recently launched INSAT-3D and Kalpana-1 AMVs would be

attempted in future. In future, the derived products such as the local divergence, vorticity, and

deep-layer atmospheric shear estimated via AMVs can be utilized to improve the understanding

of TC track and intensity forecast.

Acknowledgments

The authors would like to thank Mr. A. S. Kiran Kumar, Director, SAC for his constant

encouragement and guidance. Authors are thankful to National Center for Atmospheric Research

(NCAR) for WRF model. The global analyzed data provided by National Centers for

Environmental Prediction (NCEP) are acknowledged with sincere thanks. Authors are thankful to

the two anonymous reviewers for their valuable comments and suggestions.

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Table 1: Description of the three set of assimilation experiments. “WF” refers to experiments with quality flag, while “NF” represents no quality flag. In set 1 and set 2 experiments, 6 hr WRF model forecast is used as first guess.

Experiments Data Used For Assimilation

Forecast Initial Time

Forecast hours

Set1

CNT1 Conventional and AIRS data

0000UTC 11 May 2013

132

CNT2 Conventional and AIRS data

0000UTC 12 May 2013

108

CNT3 Conventional and AIRS data

0000UTC 13 May 2013

84

CNT4 Conventional and AIRS data

0000UTC 14 May 2013

60

KAL1

Conventional, AIRS data and Kalpana-1 AMVs

0000UTC 11 May 2013

132

KAL2

Conventional, AIRS data and Kalpana-1 AMVs

0000UTC 12 May 2013

108

KAL3

Conventional, AIRS data and Kalpana-1 AMVs

0000UTC 13 May 2013

84

KAL4

Conventional, AIRS data and Kalpana-1 AMVs

0000UTC 14 May 2013

60

Set2

CNT Conventional and AIRS data 0000UTC 13 May 2013

84

IR_WF

Conventional, AIRS data and quality flag Kalpana-1 IR AMVs

0000UTC 13 May 2013

84

WV_WF

Conventional, AIRS data and quality flag Kalpana-1 WV AMVs

0000UTC 13 May 2013

84

ALL_WF

Conventional, AIRS data and quality flag Kalpana-1 IR and WV AMVs

0000UTC 13 May 84

2013IR_NF

Conventional, AIRS data and without quality flag Kalpana-1 IR AMVs

0000UTC 13 May 2013

84

WV_NF

Conventional, AIRS data and without quality flag Kalpana-1 WV AMVs

0000UTC 13 May 2013

84

ALL_NF

Conventional, AIRS data and without quality flag Kalpana-1 IR and WV AMVs

0000UTC 13 May 2013

84

Set3

KAL_IR With quality control Kalpana-1 IR AMVs

0600UTC 13 May 2013

12

KAL_WV With quality control Kalpana-1 WV AMVs

0600UTC 13 May 2013

12

KAL_ALL

With quality control Kalpana-1 IR and WV AMVs

0600UTC 13 May 2013

12

Figure 1: A typical example of satellite-derived high-level winds (combination of both infrared and water vapor winds) from Kalpana-1 valid at 0000 UTC of 13 May 2013. The box denotes theouter domain of the model integration, where AMVs are assimilated.

Figure 2: (a) The 850 hPa wind field (m s-1) for control analysis of 13 May 2013, 0000 UTC and (b) the difference between CNT3 and KAL3 analyzed 850 hPa wind fields. (c) The 200 hPa windfield (m s-1) for control analysis of 13 May 2013, 0000 UTC and (d) the difference between CNT3 and KAL3 analyzed upper level wind fields for the same initial condition.

Figure 3: The JTWC best track and WRF model predicted tracks for TC Mahasen for the initial condition starting from 0000 UTC of 11 May 2013 (CNT1, KAL1), 12 May 2013 (CNT2, KAL2), 13 May 2013 (CNT3, KAL3), 14 May 2013 (CNT4, KAL4).

Figure 4: The differences of the track errors between CNT and KAL experiments for all four initial conditions for every 6 hour integration time. The numbers in parentheses represent the cumulative percentage improvement for the each time step.

Figure 5: IMD best track and WRF model predicted MSLP forecast.

Figure 6: IMD best track and WRF model predicted maximum surface wind speed forecast.

Figure 7: The CNT, ALL_WF, IR_WF, WV_WF, IR_NF, WV_NF, and ALL_NF predicted trackerror as function of forecast time in 6 hour interval.

Figure 8: The JTWC best track and WRF model predicted tracks for the 78 hour period starting from 000 UTC of 13 May 2013 for the experiments where all the available AMVs are assimilated.

Figure 9: The forecast error contribution in total dry energy (J kg-1) for KAL_IR, KAL_WV and KAL_ALL experiments. Negative (positive) values correspond to an improvement (degradation) in forecast error. The “cross” sign represents the total number of observations assimilated in eachexperiment.

Kaur, I., Kumar, P., Deb, S.K., Kishtawal, C.M., Pal, P.K., Kumar, R., 2014. Impact of Kalpana-1 retrieved atmospheric motion vectors on mesoscale model forecast during summer monsoon. Theo. App. Climato. DOI 10.1007/s00704-014-1197-9. Published online 18 June 2014.