Improved Prediction of Bay of Bengal Tropical Cyclones Through Assimilation of Doppler Weather Radar Observations

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Improved Prediction of Bay of Bengal Tropical Cyclones through Assimilation of Doppler Weather Radar Observations

KRISHNA K. OSURI AND U. C. MOHANTY School of Earth, Ocean and Climate Sciences, Indian Institute of Technology, Bhubaneswar, India

A. ROUTRAY National Centre for Medium Range Weather Forecasting, Noida, India

DEV NIYOGI Purdue University, West Lafayette, Indiana

(Manuscript received 27 November 2013, in ﬁnal form 16 March 2015)

ABSTRACT

The impact on tropical cyclone (TC) prediction from assimilating Doppler weather radar (DWR) observations obtained from the TC inner core and environment over the Bay of Bengal (BoB) is studied. A set of three operationally relevant numerical experiments were conducted for 24 forecast cases involving 5 unique severe/very severe BoB cyclones: Sidr (2007), Aila (2009), Laila (2010), Jal (2010), and Thane (2011). The first experiment (CNTL) used the NCEP FNL analyses for model initial and boundary conditions. In the second experiment [Global Telecommunication System (GTS)], the GTS observations were assimilated into the model initial condition while the third experiment (DWR) used DWR with GTS observations. Assimilation of the TC environment from DWR improved track prediction by 32%–53% for the 12–72-h forecast over the CNTL run and by 5%–25% over GTS and was consistently skillful. More gains were seen in intensity, track, and structure by assimilating inner-core DWR observations as they provided more realistic initial organization/ asymmetry and strength of the TC vortex. Additional experiments were conducted to assess the role of warm- rain and ice-phase microphysics to assimilate DWR reflectivity observations. Results indicate that the ice- phase microphysics has a dominant impact on inner-core reflectivity assimilation and in modifying the intensity evolution, hydrometeors, and warm core structure, leading to improved rainfall prediction. This study helps provide a baseline for the credibility of an observational network and assist with the transfer of research to operations over the India monsoon region.

1. Introduction represent the realistic position, strength, and structure of TC vortices. Osuri et al. (2013) analyzed forecasts of 100 Tropical cyclones (TCs) are one of the most hazard- TC cases over the NIO, and concluded that the average ous weather events affecting the Indian monsoon region. initial position and initial intensity errors are about The devastation from TCs is particularly notable over 2 57 km and 8–10 m s 1, respectively. These errors prop- heavily populated, long, and low-lying coastal regions of agate and increase with time and limit the predictive skill the Bay of Bengal (BoB). of high-resolution mesoscale models for TC movement, Conventional observations are scarce over the north intensiﬁcation, and decay (Elsberry et al. 2007; Osuri Indian Ocean (NIO) where the BoB TCs form and et al. 2013). As a result, a number of recent efforts over evolve over the deep ocean/sea. Initial conditions de- the NIO basin have been directed to assess the impact of rived from coarser-resolution global analyses cannot assimilating remotely sensed data to improve the position and structure of the initial vortex and consequently the forecast of TCs (Mandal and Mohanty 2006; Singh Corresponding author address: U. C. Mohanty, School of Earth, Ocean and Climate Sciences, Indian Institute of Technology, et al. 2008; Xavier et al. 2008; Osuri et al. 2012a). Toshali Bhavan, Satya Nagar, Bhubaneswar, 751007, India. Responding to the need for severe weather monitor- E-mail: [email protected] ing and prediction associated with a landfalling TC, the

DOI: 10.1175/MWR-D-13-00381.1

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India Meteorological Department (IMD) extended the automated weather stations (AWS), satellite-derived winds, Doppler Weather Radar (DWR) network along the east and aircraft observations} on the intensity and spatial coast. The DWRs can provide a high-resolution, spa- rainfall distribution of landfalling TC. These studies over tiotemporal perspective of landfalling TCs (Marks the Indian monsoon domain have focused primarily on 2003). The DWR-based high-resolution reflectivity and assimilating DWR observations only within the inner- radial velocity fields are important for weather analysis core region, and the impact of assimilating the DWR and forecasting at meso- and microscales. When the TC observations of the TC environment and its interaction is within the DWR’s range, the reflectivity and velocity with TC evolution has not been addressed. fields can provide significant details of the TC eyewall, In this study, the objective is to assess how important inner-core winds, and hydrometeor structures (Marks are radar observations in defining (or correcting) TC en- and Shay 1998; Marks 2003). When the TC is out of the vironment, and the TC inner-core region, and in turn DWR range, it can still provide potentially useful in- impacting the TC forecasts. The DWR observations formation about the TC environment. include both radar reflectivity and radial wind impact, Past studies, principally over the United States and for while the TC environment refers to a region where the landfalling TCs in the Atlantic basin (e.g., Xiao and Sun TC center is away from the radar coverage and the TC 2007; Zhao and Xue 2009), have shown that high- outer bands are detected. resolution radar inner-core observations have an over- In general, S-band weather radar observes hydrome- all positive impact on analysis and prediction. Studies teors including rain, hail, and snow. Hail in particular such as Gao et al. (1999), Xiao et al. (2005), and Zhao can have a very strong reflectivity signal. By assuming and Jin (2008) used the three-dimensional variational warm-rain processes in reflectivity assimilation, the data assimilation (3DVAR) systems in the framework of contribution of hail and snow, which exist in tropical various regional/mesoscale models, such as the ARPS cyclone systems, is ignored but can have a large impact model, the Coupled Ocean–Atmosphere Mesoscale on TC intensity forecast (Houze 2010; Zhu and Zhang Prediction System (COAMPS), and the fifth-generation 2006). Therefore, in addition to the above objective, the Pennsylvania State University–National Center for At- assimilation of reflectivity observations in the TC inner- mospheric Research Mesoscale Model (MM5), and core with different (warm rain and ice phase) micro- showed improvement in the prediction of hurricane physics parameterization schemes is also investigated. track, intensity, and structure. These studies demon- The study investigates the necessity and potential con- strated that radial velocity assimilation can improve tribution of DWR observations for consistent and im- track forecasts, while the assimilation of reflectivity data proved prediction of TC track, intensity, and structure can help improve intensity forecasts. The assimilation of over the Bay of Bengal. radial velocity and reflectivity combined help improve track and intensity predictions as compared to the in- 2. Methodology and numerical experiments dividual data alone. The assimilation of DWR observations (both ground and airborne based) using the ensemble The Advanced Research version of the Weather Re- Kalman filter (EnKF) is a recent effort (although com- search and Forecasting (WRF) Model (ARW) V3.3 putationally expensive) to improve the predictions of with a single domain covering 38–288N and 758–1058E, at hurricanes (Aksoy et al. 2012, 2013) and severe con- 9-km horizontal resolution and 51 vertical levels was vection (Dowell et al. 2011). used for this study. The model followed Arakawa C-grid Over the Indian monsoon region, studies with DWR staggering and the model integration time step was 30 s. data assimilation are still evolving. Kiran Prasad et al. The model physics used include the Kain–Fritsch con- (2014) showed the positive impact of assimilating DWR vection scheme, the Yonsei University (YSU) planetary reflectivity and radial wind observations on the simula- boundary layer (PBL) scheme, the WRF single-moment tion of severe thunderstorms over northeastern parts of 3-class (WSM3) microphysics scheme, the Monin– India. Routray et al. (2010, 2013) noted that the assim- Obukhov similarity scheme, the thermal diffusion land ilation of DWR radial velocity and reflectivity helped surface scheme, and the Rapid Radiative Transfer improve predictions associated with monsoon depres- Model (RRTM) for longwave and Goddard for short- sion forecasts for the Indian region. Govindankutty wave atmospheric radiation schemes. Osuri et al. (2012b) et al. (2010) demonstrated the positive impact of DWR identified the above parameterization choices to be better radial wind and the Global Telecommunication System for TC track and intensity predictions for the Bay of (GTS) data {which includes radiosonde atmospheric Bengal domain. The details of the model physics, dy- profiles [the radiosonde/radiowind (RS/RW) and the pilot namics, and description of the model equations can be balloon (pibal)], surface synoptic observations (SYNOP), found in Skamarock et al. (2005).

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A 3DVAR is available within the ARW modeling Note that all of the strong cyclones (.48 kt) during system (WRF-Var). This variational assimilation system 2007–11 that were monitored by the DWR are consid- blends in the observations with the global model analysis ered in this study; however, some of the marginal cy- through an iterative solution of prescribed cost function clones (,47 kt) are not included. The synoptic situations [see details in Barker et al. (2004)]. The cost function and detailed characteristics of each cyclone were ob- J(x) is described as tained from the Regional Specialized Meteorological Centre reports (Regional Specialized Meteorological 1 2 Centre 2007, 2009, 2010, 2011) from IMD, New Delhi, J(x) 5 Jb 1 Jo 5 (x 2 xb)TB 1(x 2 xb) 2 India. Each cyclone was initialized at different times 1 2 based on the availability of DWR data and a summary of 1 (y 2 yo)T(E 1 F) 1(y 2 yo), (1) 2 DWR data counts at 1.5-km height (;850 hPa) is given in Table 1. Multiple simulations at 12-h intervals were where Jb and Jo are the background and observation cost used to examine the evolution of each storm (4, 4, 5, 3, functions, respectively, x is the state vector, xb is the first and 8 for Sidr, Alia, Laila, Jal, and Thane, respectively) guess (also known as background), B is the background (Table 1). Three categories of numerical experiments error covariance, y is the model state projected into were conducted to assess 1) the sensitivity of the indi- observational space (y 5 Hx), H is the nonlinear forward vidual and combined impact of DWR observations, operator, yo is the observation, E is the observational or 2) the assimilation of DWR observations in the TC en- instrumental error covariances, and F is the repre- vironment and inner core and the impact on TC pre- sentivity error covariances. The background B was dictions, and 3) the sensitivity of microphysics in prepared for the same 9-km grid domain using one- assimilating TC inner-core reflectivity. The complete month ARW model forecasts from 15 October to details on the numerical experiments are given in 15 November 2007 employing the National Meteoro- Table 2. Figure 1 shows the data distribution of GTS logical Center (NMC, now known as NCEP) method observations for case 1 (i.e., 0000 UTC 13 November (Parrish and Derber 1992). In the standard NMC 2007) and radial velocity distribution for case 1 (Sidr), method, the perturbation is calculated by the difference case 5 (Aila), case 12 (Laila), and case 14 (Jal). The between 24- and 12-h forecasts verifying at the same assimilation experiments (viz., GTS and DWR) were time. The average of such perturbations over a period of carried out with a 6-hourly assimilation cycle using data time is considered as the climatological estimates of B. cutoff 63 h. The first case of each TC is a cold start as To find an optimal horizontal component of background DWR observations for previous dates to start cyclic runs error covariance in the WRF-Var system, it is important were not available. The 6-h forecast obtained from the to tune the B. Empirical multiplicative tuning factors are previous cycle is used as the first guess in the next cycle. applied to the length scales calculated via the NMC Sidr and Aila were observed by the DWR located in method. The scale lengths used in the horizontal back- Kolkata (22.578N, 88.358E), Laila and Jal were under the ground error covariances via recursive filter are tuned coverage of DWR, Chennai (13.078N, 80.288E), and for the control variables such as streamfunction, velocity Thane was covered by the DWR at Machilipatnam potential, unbalanced pressure, and specific humidity. (16.188N, 81.158E). All DWR data were available at The WRF-Var system did not consider the multiple 0.5-km spatial and 15-min temporal resolution up to a scales in applying the recursive filter in an internal maximum range of 500 km. However, in our analysis, we minimization loop, and has multiple external loop have limited the data ingestion up to 300 km. The quality functions. Therefore, implementation of the different control procedure removes the radial velocities and re- tuning factors to B for the different external loops in the flectivity having absolute values outside the range of 2– 2 WRF-Var system reproduces the multiple scales in the 45 m s 1 and 10–60 dBZ. The Nyquist velocity for these 2 recursive filter. Sensitivity experiments with tuning DWRs is 647.04 m s 1 and the range of reflectivity factors of the scale lengths in WRF-Var indicate that is 231.5 and 95.5 dBZ. The specified standard deviations tuning factors of 1.0 for all control variables give a rea- of observation error for radial velocity and reflectivity 2 sonably good spread in the WRF-Var analyses (Routray are 3 m s 1 and 2 dBZ. The DWR data preprocessor et al. 2014). module was useful in thinning out the voluminous data In this study, 24 forecast cases involving 5 unique by specifying a desired grid box (a 9-km grid box was severe/very severe cyclones over the BoB are analyzed. used in this study) and number of (maximum of 50) ra- The five cyclones are: Sidr (11–16 November 2007), Aila dar observations having a maximum number of vertical (23–26 May 2009), Laila (17–22 May 2010), Jal (3– levels in each grid box from the left-lower corner. De- 8 November 2010), and Thane (25–30 December 2011). tails on preprocessor software and quality control and

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TABLE 1. Number of initial conditions (cases) considered for each cyclone.

DWR data density at Cyclone name Case No. Initial conditions Forecast length (h) 1.5-km height (;850 hPa) Radar coverage SIDR (very severe cyclone) 1 0000 UTC 13 Nov 2007 72 1911 Kolkata DWR 2 1200 UTC 13 Nov 2007 60 1868 3 0000 UTC 14 Nov 2007 48 2702 4 1200 UTC 14 Nov 2007 36 2657 Aila (severe cyclone) 5 0000 UTC 23 May 2009 60 1995 Kolkata DWR 6 1200 UTC 23 May 2009 48 2467 7 0000 UTC 24 May 2009 36 1937 8 1200 UTC 24 May 2009 24 1869 Laila (severe cyclone) 9 1200 UTC 17 May 2010 72 1800 Chennai DWR 10 0000 UTC 18 May 2010 60 2556 11 1200 UTC 18 May 2010 48 2532 12 0000 UTC 19 May 2010 36 2300 13 1200 UTC 19 May 2010 24 2433 Jal (severe cyclone) 14 0000 UTC 6 Nov 2010 48 1250 Chennai DWR 15 1200 UTC 6 Nov 2010 36 2364 16 0000 UTC 7 Nov 2010 24 1950 Thane (very severe cyclone) 17 1200 UTC 25 Dec 2011 108 2117 Machilipatnam DWR 18 0000 UTC 26 Dec 2011 96 1916 19 1200 UTC 26 Dec 2011 84 1964 20 0000 UTC 27 Dec 2011 72 2213 21 1200 UTC 27 Dec 2011 60 1981 22 0000 UTC 28 Dec 2011 48 2351 23 1200 UTC 28 Dec 2011 36 2290 24 0000 UTC 29 Dec 2011 24 2038 thinning of DWR data can be found in Routray et al. mainly reflectivity and radial velocity (for the vertical (2010). velocity component), within the ARW-Var analysis The radial velocity is assimilated into the analysis system. The observation operator for radar reflectivity is through the Richardson’s balance equation that con- shown in Eq. (3). When rainwater (from reflectivity) tains the information of vertical velocity increments. entered into the minimization iteration procedure, the Vertical velocity plays an important role for initiation of forward and backward adjoint of the warm-rain process convective activities and the observation operator of distributes this information to the increments of other radial velocity considered is as in Eq. (2): variables and updates the model states of qc and qr:

u(x 2 x ) y(y 2 y ) (w 2 y )(z 2 z ) Z 5 43:1 1 17:5 log(rq ), (3) 5 i 1 i 1 t i r Vr , (2) ri ri ri where Z is the reflectivity (dBZ) and qr is the rainwater where (u, y, w) are the wind components, and mixing ratio. (x, y, z) and (xi, yi, zi) are the radar and observation locations. The variable r is the distance between the i 3. Results and discussion radar and the observation; yt is the terminal velocity y 5 : 0:125 represented by t 5 40aqr ; and a is the correction The impact of assimilating the Indian DWR data on 0:4 factor as (p0/p) , where p0 is the pressure at the surface TC simulations was studied. Representative cases (case and p is the base-state pressure. 1 of Sidr, case 5 of Aila, case 12 of Laila, case 14 of Jal, In the reflectivity assimilation, the total water mixing and case 20 of Thane) are chosen to reflect the impact of ratio is used as a control variable and partitioning of DWR data assimilation on different forecast lead times water vapor and water hydrometeor increments fol- (i.e., 72–36-h lead time) (Table 1). The impact of addi- lowed the warm-rain scheme of Dudhia (1989). The tional data on initial conditions, intensity evolution in frequency of DWRs is 3 GHz and is sensitive to rain, terms of minimum sea level pressure (MSLP), 10-m hail, and snow. The inclusion of vertical velocity and maximum winds (10-m winds), track predictions, struc- hydrometeors in the model analysis is a difficult task ture changes during peak intensity, atmospheric profiles (Sun and Crook 1997, 1998). Xiao et al. (2005) provided of dynamics and thermodynamic fields, and finally the an efficient methodology for DWR data assimilation, distribution of model-simulated rainfall and reflectivity

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TABLE 2. Numerical experiments conducted in the study.

No. Category of expt Name of expt Data used in assimilation 1 Individual impact of Rwind Assimilation of radial velocity DWR observations Ref Assimilation of reflectivity Both Assimilation of both radial velocity and reflectivity 2 Impact of DWR observations CNTL No assimilation [Initial and boundary conditions from the National Centers for in TC environment and TC Environmental Prediction (NCEP) Final (FNL) analyses (18318 resolution)] inner core GTS Assimilation of GTS observations (RS/RW, SYNOP, pibal, AWS, buoy/ship, satellite winds, etc.) DWR Assimilation of DWR (reflectivity and radial velocity) and GTS observations 3 Assimilation of reflectivity Warm-Cntl As in CNTL run, but using warm-rain microphysics (Kessler scheme; Kessler 1969) observations sensitivity Ice-Cntl As in CNTL experiments, but using the ice-phase microphysics (WSM6; Hong and to microphysics Lim 2006) Warm-Refle As in Warm-Cntl, but assimilation of only TC inner-core reflectivity observations Ice-Refle As in Ice-Cntl, but assimilation of only TC inner-core reflectivity observations were also analyzed. The model-simulated track and in- combined (Both) on TC simulation. Tropical Cyclones tensity (MSLP and 10-m winds) were compared with Sidr, Laila, and Thane at different initial conditions (as observations from the IMD. Following Osuri et al. shown in Table 1) were considered. The model config- (2013) the TC position has been identified using the uration and physics were identical for these experiments automated tracking scheme that considers seven pa- and no GTS data were used in the assimilation. Table 3 rameters related to minimum MSLP, relative vorticity, provides statistics for the mean departure of background geopotential height, and wind speed. The model-simulated (O 2 B) and analysis (O 2 A) for reflectivity and radial rainfall was compared with Tropical Rainfall Measuring wind, where O, B, and A represent observation, back- Mission (TRMM) rainfall data (3B42V6) to validate the ground field, and analysis, respectively. These inno- spatial distribution of rainfall. vations are from the assimilation of individual DWR observations. Results suggest that the root-mean-square a. Individual impacts of DWR observations error (RMSE) difference between observations and Experiments were also conducted to delineate the model states is reduced more for radial wind than re- individual impact of reflectivity alone (Ref), radial flectivity. Assimilation of reflectivity alone had a grea- wind alone (Rwind), and reflectivity and radial wind ter impact on thermodynamic fields (moisture and

FIG. 1. Data distribution and number of SYNOP, aircraft report (AIREP), sounding data (SOUND), METAR, QuikSCAT, SSM/I, and Kolkata DWR, respectively, for case 1 (TC Sidr) at 0000 UTC 13 Nov 2007.

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TABLE 3. Mean departure of background and analysis from the at 925 hPa for the representative cases of five cyclones observations of each cyclone for radial velocity and reflectivity; was also analyzed. The bottom panels of Fig. 1 provide 2 2 O B is observation minus background, O A is observation the radial wind distribution. The negative values (blue, minus analysis. purple) indicate the wind coming toward the DWR and Radial velocity Reflectivity positive values (green and yellow) indicate wind going O 2 BO2 AO2 BO2 A away from the DWR. Figures 2a,d,g,j,m show the wind Sidr 2.60 1.48 1.84 1.84 magnitude of the NCEP Final Analysis (FNL) back- Aila 2.09 1.74 2.85 2.85 ground field. In the figures, the shaded region shows Laila 2.90 1.82 2.80 2.77 positive increments and contours indicate negative in- Jal 2.80 1.66 2.74 2.70 crements. It is clear that the strength of the TC vortex is Thane 2.97 2.89 2.67 1.26 relatively weak compared to the observation in the model background for most cases. After the assimilation of GTS and DWR data, significant positive wind speed temperature) with a secondary impact on dynamical increments were observed. In the DWR improved fields (zonal and meridional wind). Similarly, the radial analysis, the DWR data impact was prominently seen at wind influenced dynamical fields more than the ther- and around the DWR stations (DWR stations are shown modynamical fields. When both reflectivity and radial with different symbols in Figs. 2c,f,i,l,o). For example, in wind were assimilated, the analyses response was no- the case of TC Sidr (Fig. 2c, first cycle), where the TC is ticeably high on both the dynamical and thermody- away from the DWR station, analysis increments are namical fields. A similar result was noted in previous noticeable in the outer regions of the TC. The GTS data studies by Zhao and Jin (2008), Xiao and Sun (2007), show negative increments over the same region (Fig. 2b). and Routray et al. (2010). The track simulation was also In case 12 of Laila and case 16 of Jal (Figs. 2g–i and 2j–l), improved when reflectivity and radial wind were used where the TC is under DWR coverage, positive wind 2 together as compared to the assimilation experiments increments ($3ms 1) are seen around the DWR site. In with reflectivity alone and radial wind data alone. The the case of Thane (case 20) at 0000 UTC 27 December 2 24-, 48-, and 72-h forecast errors for the (Rwind and 2011, the intensity of the system is 21 m s 1 while the 2 Ref) experiments are (140 and 185) km, (203 and 326) km, CNTL analyses results in intensity of more than 30 m s 1. and (301 and 454) km, while the corresponding forecast This overprediction of the wind field in the CNTL errors for the Both run are 119, 186, and 257 km, re- analysis is effectively corrected by the DWR data and spectively. Based on these results and previous studies, anticyclonic wind increments can be seen in the DWR- both observations are assimilated in the DWR experi- CNTL field (Fig. 2o). Note that some of the cases shown ments (see the next section). here are cold start while others are warm start. For the warm start cases, the improvements are not from a single b. Improvement in the model initial conditions DA analysis alone but from the combination of analysis The mean background field departure (O 2 B)of and the previous cycle’s forecast. 2 2 zonal (u;ms 1) and meridional (y;ms 1) winds, tem- The vertical cross section of the relative vorticity and 2 perature (T; K), and humidity (q;gkg 1) were reduced temperature anomaly for the representative cases of five in the GTS data-assimilated analyses (Table 4). This cyclones was also analyzed and is shown in Figs. 3a–e for confirms that the assimilation of GTS data also produces CNTL, Figs. 3f–i for GTS, and Figs. 3k–o for DWR improved analyses compared with low-resolution global analysis. The latitude–height cross sections for TC Sidr analyses. and Aila averaged between 878 and 908E are shown in The spatial distribution of analysis increments (A–B; Figs. 3a,f,k and Figs. 3b,g,l; longitude–height cross sec- here A is either GTS or DWR analyses and B is back- tions averaged between 108 and 158N and are shown for ground or first-guess field) in wind vectors and magnitude Laila and Jal in Figs. 3c,h,m and Figs. 3d,i,n; and the

TABLE 4. As in Table 3, but for GTS data (U wind, V wind, temperature, humidity, and wind speed).

2 2 2 2 U wind (m s 1) V wind (m s 1) Temperature (K) Humidity (g kg 1) Wind speed (m s 1) O 2 BO2 AO2 BO2 AO2 BO2 AO2 BO2 AO2 BO2 A Sidr 1.98 1.82 2.04 1.82 1.76 1.08 1.76 1.35 2.33 1.15 Aila 2.46 2.17 2.34 2.05 1.76 1.20 2.29 1.44 2.99 1.33 Laila 3.52 3.07 3.80 3.51 1.62 1.18 1.82 1.45 2.40 1.41 Jal 3.34 2.89 2.85 2.55 1.83 1.34 1.95 1.59 1.51 0.94

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21 FIG. 2. Initial 925-hPa wind (m s ) from the CNTL experiment for the representative cases: (a) case 1, (d) case 5, (g) case 12, (j) case 14, and (m) case 20; (b),(e),(h),(k),(n) the wind increment of GTS analyses [i.e., difference with reference to the CNTL analyses (1ve shaded, 2ve contours)]; and (c),(f),(i),(l),(o) as in (b),(e),(h),(k),(n), but with DWR analyses. Maxi- mum radial wind (Vr) from DWR is showed in the third column. The solid circle in (c),(f); the solid circle with cross hair in (i),(l); and the ﬁlled square box in (o) represent the location of Kolkata, Chennai, and Machilipatnam DWR stations, respectively.

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26 21 FIG. 3. Vertical cross section of relative vorticity (310 s , shaded 1ve) and temperature anomaly (contours) from CNTL for representative cases (a) case 1 of Sidr, (b) case 5 of Aila, (c) case 12 of Laila, (d) case 14 of Jal, and (e) case 20 of Thane. (f)–(j),(k)–(o) As in (a)–(e), but for GTS and DWR analysis, respectively. averaged fields between 118 and 178N for Thane are Table 5 provides the vortex position error and in- presented in Figs. 3e,j,o, respectively. For case 1 of TC tensity error at initial time for all TC cases. There was Sidr, there is positive vorticity circulation between the improvement in the initial position and strength of the latitudes 178 and 208N(;head BoB) extended in the vortex. The mean initial position error from CNTL, middle atmosphere in the DWR experiment, unlike GTS, and DWR is 62, 54, and 37 km and the mean in- 2 CNTL and GTS. The atmospheric column of the TC tensity was 27, 27, and 25ms 1, respectively (negative environment is warmer along the same latitudes by 1 K sign indicates underestimation). A significance test over a larger region as compared to that of the GTS was conducted for these errors as summarized in analysis. In the case of Aila (second column), there is a Table 5. The initial position error showed a 99.9% significant increase in the upper-level warmer core (2 K statistical significance level (with the t value more around 300 hPa) and midlevel relative vorticity along than the critical value of 3.77 for the CNTL, GTS, and the latitudes ;158–198N in the DWR run. For CNTL DWR runs). The intensity error showed a 95% sig- (Fig. 3b) and GTS (Fig. 3g), there is little difference in nificance level for DWR and GTS runs (with a t value the temperature anomalies, but the relative vorticity is more than the critical value of 2.07) and 99% signif- stronger in the GTS run. In Laila (Figs. 3c,h,m) and Jal icance for the CNTL with a t value just above the (Figs. 3d,i,n), the positive vorticity is stronger around the critical value (2.82). DWR station (80.38E) in the BoB in the DWR run. In c. Impacts of DWR observations in the TC the case of Laila, the warming is more in the mid- to environment and the inner core upper levels in the DWR analysis and indicates positive improvement around the radar station when the system The 3-h forecast of composite reflectivity in Fig. 4 is under radar coverage. In TC Jal, the negative tem- shows spiral bands in the outer region of Sidr (case 1, perature anomaly and relative vorticity regions are im- where the TC is away from radar coverage) in the case of proved with DWR data as compared to the GTS and the DWR run (Fig. 4c). The spiral band structure is less CNTL analysis. In case 20 of TC Thane (Figs. 3e,j,o) apparent in the GTS and CNTL runs (Figs. 4a,b). At similar results are noticed with a much warmer core 0000 UTC 13 November 2007, Sidr was at the very se- region in the upper levels. vere cyclonic storm (VSCS) stage and the outer bands

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TABLE 5. Initial vortex position and intensity errors for each cyclone. Intensity error is calculated as (observed 2 model). Positive/ negative values indicate overestimation/underestimation of intensity. Against mean error, RMSE is given for intensity and a simple average is given for position error. The value in the parentheses in intensity error columns represents the ‘‘observed intensity’’ at different initial times of each cyclone.

2 Initial vortex position error (km) Intensity error (observed model in m s 1) Cyclone name Case No. CNTL GTS DWR CNTL GTS DWR SIDR (very severe cyclone) 1 61 55 42 34 (46) 34 31 2 46 46 45 32 (46) 32 28 3 83 83 77 32 (46) 32 27 4 115 91 47 30 (46) 30 25 Aila (severe cyclone) 5 90 92 61 22 (12) 0 22 690734224 (13) 23 23 749373822 (13) 22 22 8 113 70 6 2 (18) 2 0 Laila (severe cyclone) 9 20 20 20 3 (15) 3 3 10 65 55 28 5 (18) 4 4 11 9 18 8 9 (23) 9 7 12 33 26 12 10 (23) 8 5 13 65 55 34 13 (28) 11 7 Jal (severe cyclone) 14 48 48 27 6 (28) 2 3 15 83 81 39 5 (31) 3 1 16 61 55 42 6 (31) 4 1 Thane (very severe cyclone) 17 45 45 36 24 (13) 23 23 18 36 36 36 24 (15) 23 23 19 50 39 35 27 (15) 27 27 20 46 45 19 23 (20) 22 22 21 66 60 57 27 (20) 27 22 22 77 71 48 1 (23) 221 23 77 65 59 10 (33) 7 3 24 70 41 37 12 (36) 7 5 Mean RMS error 68 58 41 14 14 12 Statistical significance in % (t value) 99.9 (11.6) 99.9 (12.8) 99.9 (10.7) 99 (2.89) 95 (2.62) 95 (2.43) extended up to 208N (at the head of BoB), as is also According to Regional Specialized Meteorological noted in the DWR spiral bands [see Fig. 2.12.1c valid Centre (2007), Sidr moved in a northward direction for at 0600 UTC 13 November in Regional Specialized 60 h and then recurved to the northeast. The CNTL Meteorological Centre (2007),p.84].Figures 4d–f give experiment with initial and boundary conditions from an example of case 12 (0000 UTC 19 May 2010) for TC FNL analyses could not simulate this trajectory and Laila and highlight the spiral band structure produced speed of the system and hence produced an erroneous in the DWR run when the TC is completely under its landfall prediction in case 1, case 2, and case 3. However, coverage. There is a clear organization of spiral bands in case 4, a better track prediction as well as landfall in the storm area resulting in a well-defined eyewall location was achieved. The GTS experiment simulated a around the TC center in the DWR run (Fig. 4f). The better track compared to the CNTL experiment al- DWR run, unlike CNTL or GTS, was again successful in though it failed to correctly predict landfall. Unlike reproducing the observed mesoscale region of maxi- CNTL and GTS, the DWR experiment predicted re- mum reflectivity in the left forward and left backward alistic movement and landfall of the system in all cases. sector of the TC center [image not shown here but see By considering the results in case 1, the impact of ad- Fig. 2.1.9 in Regional Specialized Meteorological ditional data (with and without DWR) is clearly illus- Centre (2010), p.48, valid at 0246 UTC 19 May 2010]. trated. In case 1 of Sidr, the track was displayed in 6-h intervals and up to 0000 UTC 16 November 2007 for the 1) IMPACTS OF DWR OBSERVATIONS FROM THE CNTL and GTS experiments and up to 1800 UTC TC ENVIRONMENT ON TRACK PREDICTION 15 November for the DWR experiment. This is because The model-simulated cyclonic tracks of Sidr, Aila, for the DWR simulated case, the cyclone dissipated Laila, Jal, and Thane from the three experiments are rapidly after landfall. All remaining tracks are in 6-h shown in Fig. 5. The IMD observed best tracks are also intervals and plotted up to the same time displayed with shown for reference. the observed track.

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FIG. 4. Composite reﬂectivity (dBZ) valid for 3-h forecast from (a) CNTL, (b) GTS, and (c) DWR for the ﬁrst cycle of case 1 of Sidr. (d)–(f) As in (a)–(c), but for case 11 of Laila. The TC symbol represents observed TC position, the solid circle in (c) is Kolkata DWR station, and the solid circle with a crisscross in (f) is Chennai DWR station.

To understand the possible reasons for better track Gray (1982) demonstrated the significance of environ- prediction with the DWR experiment, the deep layer mental flow for TC movement by advecting vorticity to (850–200 hPa) steering flow (Fig. 6) and the latitudinal the front of the TC. In the DWR experiment, where (128–218N) averaged time–longitude section of upper- the three-dimensional winds were being assimilated, the air divergence and wind patterns between 300 and steering flow was noticeably stronger as compared to the 100 hPa (see Fig. 8) were analyzed for the three exper- others, and makes the system to move along the flow. iments. The corresponding mid- to upper-level steering Chan and Gray (1982) and Elsberry et al. (1987) con- flow at different layers and the upper-air divergence clude that if the steering flow is weak, TCs tend to move field between 300 and 150 hPa as observed from Mete- in a climatological path (i.e., a poleward/west/north- orological Satellite-7 (Meteosat-7) are provided in Fig. 7. westward direction). The time–longitude section of the 2 2 The TC symbol in Fig. 6 represents the (MSLP) position 300–100-hPa divergence field (310 5 s 1) along with the of the TC at that particular time. The GTS and DWR wind field also clearly depicts the same for the DWR panels in Fig. 6 provide the wind vector difference from experiment (Fig. 8c). The upper-air divergence field the CNTL run. The CNTL and GTS experiments show a shows a preferential westward movement with time in northwestward large-scale circulation in the vicinity of the CNTL (Fig. 8a) and GTS (Fig. 8b) simulations, the system for the first 48-h forecast. In the DWR ex- which steered the system westward with the forecast periment, there is a clear difference in the westerly flow time. In the DWR simulation, the divergence field was (trough) in the north side of the system at the initial time simulated along the same longitude up to 1200 UTC (0 h), a northward flow at the 24-h forecast, and a 14 November 2007 and then shifted eastward for the northeast current at the 48-h forecast, which caused the remaining forecast period, consistent with the Meteosat-7 system to move north and northeastward. Chan and observations (Fig. 7) and shows improved track forecast.

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FIG. 5. Model-simulated tracks of all TCs from CNTL, GTS, and DWR along with IMD best track (a) cases 1–4 of Sidr, (b) cases 5–8 of Aila, (c) cases 9–13 of Laila, (d) cases 14–16 of Jal, and (e) cases 17, 18, 20, 22, and 24 of Thane.

2 2 The upper-level divergence ﬁeld was also stronger in the (310 5 s 1) at the 850-hPa level also showed the same DWR experiments unlike the other runs. From Figs. 8d–f, pattern as in the DWR experiment (Fig. 8f)andcould the latitudinal-averaged (128–218N) model-simulated capture the maximum vorticity from the initial time in the time–longitude section of the relative vorticity ﬁeld correct direction. The CNTL and GTS experiments

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FIG. 6. (a)–(c) Deep-layer (850–200 hPa) wind circulation pattern from CNTL experiment, and (d)–(f) difference vectors between GTS minus CNTL and (g)–(i) difference between DWR minus CNTL run for TC Sidr at initial time: (a),(d),(g) 0000 UTC 13 Nov 2007; (b),(e),(h) 0000 UTC 14 Nov 2013 (i.e., 24-h forecast); and (c),(f),(i) at 0000 UTC 15 Nov 2013 (i.e., 48-h forecast).

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FIG. 7. Mid- to upper-level Meteosat-7 satellite winds valid for (a) 0000 UTC 13 Nov, (b) 0000 UTC 14 Nov, and (c) 0000 UTC 15 Nov 2007 [500–351 hPa (green), 350–251 hPa (yellow), and 250–100 hPa (blue)], and (d)–(f) upper-air divergence at 300–150 hPa valid for 0000 UTC 13 Nov, 0000 UTC 14 Nov, and 0000 UTC 16 Nov 2007, respectively. simulated stronger vorticity from 0000 UTC 14 No- the CNTL and GTS experiments could not simulate vember 2007 (after 24 h) in the northwest direction. It is the long path of the cyclone over land; however, the known that TC motion is linked to the positive potential DWR performed relatively better in case 7 and case 8. vorticity (PV) tendency (Wu and Wang 2000). The Again, the CNTL experiment failed in predicting the 2 2 2 model-simulated 24-h PV tendency (10 11 m2 s 2 Kkg 1) movement and landfall of TC Laila in all the cases at the 850-hPa level is shown in Figs. 8d–f.IntheDWR (Fig. 5c). According to the observations, Laila re- run, the positive PV tendency was distributed to the north curved after making landfall, and the CNTL experi- and northeast direction, while it was to the northwest in ment predicted the recurvature back to sea but failed the CNTL and GTS runs. Comparing the PV advection to predict landfall and decay. The simulations with and diabatic heating terms, it can be concluded that GTS data improved the track and landfall to some the positive PV tendency is primarily due to higher di- extent, and the DWR run produced the best results in abatic heating, which facilitated an increase in convec- terms of the evolution of Laila’s track (Fig. 5c), in- tion. All of these reasons combined are responsible for tensiﬁcation, and decay (see Figs. 10e,f). In the case of better simulation of track and intensity in the DWR Jal (Fig. 5d), all experiments were able to simulate a experiments. good track and landfall. The translation speed of the Similar to Sidr, the tracks of Aila, Laila, Jal, and system was particularly well captured by the DWR Thane were also well predicted by the GTS and DWR runs and, hence, landfall position and time were close data assimilation experiments (Figs. 5b–e). The CNTL to the observed as compared to the other cases. For experiment failed to simulate a realistic track in the Thane (Fig. 5d), although the three experiments could majority of the cases. In the case of Aila (Fig. 5b), capture the peculiarity of the storm motion (i.e., initially

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25 21 FIG. 8. Mean latitudinal (128–218N) model-simulated time–longitude cross section of divergence ﬁeld (10 s ) between 300- and 2 2 100-hPa layer for (a) CNTL, (b) GTS, and (c) DWR experiments. (d)–(f) As in (a)–(c), but for relative vorticity (10 5 s 1). (g)–(i) The PV 2 2 2 tendency (10 11 m2 s 2 Kkg 1) at 850 hPa in contour intervals of 2 (range from 24 to 6).

northward and then westward), they show differences in forecast. The VDEs of the GTS experiment ranged up to predicting the speed of the system. The CNTL run simu- 350 km while the CNTL errors were even higher (up to lated the landfall more northward compared to the ob- 480 km). The gain in skill of the DWR and GTS exper- served, while the landfall position was noticeably improved iments with respect to the CNTL experiment clearly in the DWR runs followed by the GTS experiment. demonstrates the advantage of DWR data assimilation. To quantify these results, the mean vector displace- The gain in skill of the DWR experiment over CNTL ment errors (VDEs) of all cyclones for the three ex- runs ranged from 32% to 53% from the 12–72-h fore- periments were calculated (Fig. 9a). The mean VDEs cast, while in comparison, it was about 5%–25% for (of all 24 cases) were less for the DWR-simulated tracks, GTS data assimilation alone. The skill of DWR over which varied from 70 to 225 km from the 12–72-h GTS varies from 30% to 35% from the 12–72-h forecast.

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also reduced in the DWR experiment in most cases as compared to the others. The mean location error at landfall (irrespective of forecast length/lead time) of CNTL (16 cases), GTS (18 cases), and DWR (22 cases) is 104, 80, and 54 km, respectively. Considering the same 16 cases where all experiments (CNTL, GTS, and DWR) showed landfall, the DWR showed a mean error of 49 km.

2) IMPACTS OF DWR OBSERVATIONS FROM THE TC ENVIRONMENT ON STRUCTURE The vertical cross section through the storm center for vorticity, Qrain and Qcloud, horizontal and vertical

winds and equivalent potential temperature ue at peak intensity of the system from the three experiments was studied for all the cases. Results for case 1 (at 0300 UTC 15 November 2007) of Sidr are shown in Fig. 10. From Fig. 10a, the maximum vorticity of more than 2 2 160 3 10 5 s 1 extending from the surface to 700 hPa was observed in each of the three experiments. The vorticity fields indicate a narrow and compact eyewall in the DWR experiment. Further, as observed by the In- FIG. 9. (a) Histograms for mean vector displacement errors dian satellite Kalpana-1 [image not shown here, avail- (VDEs, km) of each experiment and line plots for the gain in skill able in Regional Specialized Meteorological Centre (%) of the DWR and GTS experiments with respect to the CNTL. (2007)], there was more convection to the east of the eye (b) Mean gain in skill (%) of the DWR experiment in predicting westward- and northward-moving TCs over the CNTL experiment. and this feature was reproduced in the DWR experiment with positive vorticity at 500 hPa between 918 and 928E. Corresponding to this strong convection simulated 2 This improvement may be a result of capturing a more in the DWR experiment, the rainwater (0.4 g kg 1) and 2 realistic three-dimensional environmental field even cloud mixing ratios (3.0 g kg 1)werealsohigh(Fig. 10b). when the system was away from the radar location The CNTL and GTS experiments simulated the large (Marks and Shay 1998). Figure 9b shows the mean gain values of rainwater mixing ratios but could not accu- in skill of the DWR experiment over the CNTL exper- rately predict the cloud water mixing ratios. In Fig. 10c, iment separately for the northward- and westward- in contrast to the observed maximum convection east of moving systems. There was clear improvement of the center, CNTL and GTS simulated the maximum about 25% at all forecast lengths for both types of sys- winds to the left of its center. However, GTS does sim- tems in the DWR experiment. The impact of the DWR ulate an improved storm structure relative to CNTL. data was significantly greater for westward-moving sys- The assimilation of DWR data thus improved the tems compared to northward-moving systems (with the structure and also helped predict the peak winds in exception of the 72-h forecast, in which it was relatively the correct sector off the center. During 15 and 16 similar). In general, there is greater variability in trans- November 2007, TC Sidr was influenced by the strong lation speed of TCs moving northward or recurving southwesterly wind in the upper troposphere as a result eastward and is expected to have larger forecast errors of an upper-tropospheric westerly trough to the left of as compared to those TCs moving westward (climato- the storm and an anticyclonic circulation to its right logical movement). Further, the ARW Model is skillful (Regional Specialized Meteorological Centre 2007). in predicting westward-moving TCs (or TC with clima- This was again simulated correctly in the DWR experi- tological path) than recurving or northward-moving TCs ment (Fig. 10c) with unbroken strong westerlies at the (Osuri et al. 2013). Table 6 summarizes the landfall er- 200-hPa level. Consistent to maximum convection and rors of each case for the three experiments. Out of the 24 strong horizontal winds, the DWR simulation captured 2 landfalling TC cases, CNTL and GTS could predict the the strong updrafts (;2ms 1) on the right side of the landfall in 16 and 18 cases, respectively, while the DWR storm center. These features were not simulated in the experiment succeeded in 22 cases with the smallest track other two experiments (Fig. 10d). In the DWR experi- errors. The errors in simulating the time of landfall were ment, the improved large-scale updrafts in and around

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TABLE 6. Landfall position (time) errors from each experiment for all 24 cases. The 1/2 sign represents the ahead/delay in time. NL represents no landfall.

Cyclone (landfall time) Case No. (forecast length) CNTL GTS DWR Sidr (landfall at 1500 UTC 15 Nov 2007) 1 NL NL 128 (15) 2 NL NL 234 (26) 3 NL NL 104 (26) 485(23) 113 (23) 70 (23) Aila (landfall at 0900 UTC 25 May 2009) 5 110 (26) 74 (29) 32 (26) 649(26) 26 (29) 33 (29) 766(26) 51 (29) 37 (23) 895(26) 85 (29) 41 (23) Laila (landfall at 1200 UTC 20 May 2010) 9 NL NL NL 10 NL NL NL 11 NL NL 64 (29) 12 NL 67 (212) 26 (212) 13 NL 21 (212) 40 (23) Jal (landfall at 1600 UTC 7 Nov 2010) 14 92 (25) 75 (25) 33 (22) 15 89 (25) 95 (22) 67 (11) 16 36 (14) 28 (14) 22 (0) Thane (landfall at 0300 UTC 30 Dec 2011) 17 207 (118) 146 (115) 122 (115) 18 218 (23) 186 (23) 92 (23) 19 177 (23) 122 (23) 92 (23) 20 208 (23) 185 (23) 74 (0) 21 92 (23) 80 (23) 70 (0) 22 68 (0) 38 (0) 16 (0) 23 39 (0) 33 (23) 25 (0) 24 36 (23) 21 (13) 14 (13)

the eyewall helped the incursion of moisture fluxes from upper-level divergence flow improved the TC move- the surface to the boundary layer that play an important ment in the DWR experiment. role in storm intensification (Gopalakrishnan et al. 3) IMPACTS OF DWR OBSERVATIONS FROM THE 2011). The analysis of the temperature anomaly (cal- TC ENVIRONMENT ON INTENSITY PREDICTION culated with respect to the 1083108 area-averaged temperatures at initial time) shows that the DWR run The intensity evolution in the three set of experiments produced a warmer-core region (6 K) around the 400– for MSLP and 10-m winds for the representative cases is 300-hPa level (Fig. 10e). Zhang and Chen (2012) showed shown in Fig. 12. There was again a clear improvement that the upper-level warm core contributes more than in intensity prediction in the DWR experiments. The twice as much as the lower warm core to the pressure CNTL and GTS simulations typically showed compa- drop during peak intensity. The TC environment on rable intensity evolution. For example, considering case either side of the center is also warmer in the middle 1 for Sidr (Figs. 12a,b), even though none of the exper- atmosphere in the DWR run. The warm core in the case iments could predict the maximum intensity (944 hPa 2 of CNTL and GTS is modest compared to DWR. and 59 m s 1), the DWR experiment captured the evo- In addition to the inner-core structure changes for the lution better compared to the CNTL and GTS experi- three experiments, Fig. 11 provides a view of the larger ments. The improvement from the DWR data on modifications in wind field, temperature, and relative intensity forecast is less in the case of Sidr and is mainly humidity profiles of three inland stations, Bhubaneswar due to the lack of inner-core region data since the station (20.258N, 85.838E), Dhaka station (23.768N, storm’s center was away from radar coverage. The 90.388E), and Guwahati station (26.108N, 91.588E). CNTL and GTS experiments did not simulate the landfall There are noteworthy changes in the simulated profiles and predicted continuous intensification of the system of wind and relative humidity in the boundary layer throughout the simulation. The DWR-simulated TC (below the 700-hPa level) and upper levels of the at- made landfall as a very severe cyclonic storm as ob- mosphere (200-hPa level) before and after the assimi- served, but the landfall was early by 6 h. All the ex- lation of additional data, in particular, the DWR data. periments predicted the intensity evolution of cyclone However, there was minimal change in the temperature Aila relatively well (Figs. 12c,d). Figures 12e and 12f fields. These positive changes in the steering flow and show the intensity of Laila when it was under DWR

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25 21 FIG. 10. Vertical cross sections of (a) vorticity (10 s ), (b) rainwater mixing ratio (shaded) and cloud water mixing ratio (contour) 2 2 2 (g kg 1), (c) horizontal wind speed (m s 1), (d) vertical velocity (cm s 1), and (e) temperature anomaly (K, thick and dashed lines represent positive and negative values), through the center of TC Sidr (case 1) at observed peak intensity time from (left) CNTL, (middle) GTS, and (right) DWR.

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21 21 FIG. 11. Vertical proﬁles of (a) zonal wind (m s ), (b) meridional wind (m s ), (c) air temperature (8C), and (d) relative humidity (%) for case 1 at 0000 UTC 15 Nov 2007 for the Bhubaneswar station. (e)–(h) As in (a)–(d), but at 1200 UTC 15 Nov 2007 for the Dhaka station. (i)–(l) As in (a)–(d), but at 0000 UTC 16 Nov 2007 for the Gauhati station. coverage and the results are described in next section. In landfall. This decay before landfall could not be cap- the case of Jal (Figs. 12g,h), observations indicated an tured by any of the experiments. Based on 24 forecast unusual storm intensity change from a severe cyclonic cases, the DWR run showed a mean intensity improve- stage to a deep depression stage over the BoB before ment of about 27%, 30%, 38%, and 28% over the CNTL

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21 FIG. 12. Simulated intensity (a) MSLP (hPa) and (b) 10-m wind (m s ) from CNTL, GTS, and DWR experiments for TC Sidr. (c),(d);(e),(f);(g),(h) As in (a),(b) but for case 5 (Aila), case 12 (Laila), and case 14 (Jal), respectively. The vertical arrow on the x axis indicates the time of landfall. run and 10%, 22%, 22%, and 14% over GTS runs at the section, the evolution of TC Laila (from case 12), when it 12-, 24-, 48-, and 72-h forecast. comes under radar coverage (see Fig. 4 for coverage), and the corresponding changes in intensity, track, and 4) IMPACTS OF DWR OBSERVATIONS FROM THE structure are discussed (Fig. 13). From Figs. 12e and 12f, INNER CORE ON TC PREDICTION it is clear that the CNTL run simulated a very strong Previous sections highlight the positive impact of system with continuous deepening of the storm core reﬂectivity and radial wind data on TC evolution when unlike the observed evolution of intensity of TC Laila. the TC center is outside of the DWR coverage. In this On the other hand, there is clear improvement in the

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21 FIG. 13. Simulated vertical cross sections of horizontal wind (m s ) averaged for 24–30-h forecast through the storm latitude for (a) CNTL, (b) GTS, and (c) DWR runs of case 12 of TC Laila. (d)–(f) As in (a)–(c), but for temperature anomaly (K, shaded) and vertical 2 2 velocity (contour, cm s 1). Simulated rain rate (mm h 1) swath of (h) CNTL, (i) GTS, and (j) DWR, along with (g) TRMM observed rain rate.

2 initial strength of the TC (by 4 m s 1) in the DWR run valid for 0000–0600 UTC 20 May 2010) of the horizontal (Fig. 10f). The DWR run captured the realistic change in wind and temperature anomaly with vertical velocity the storm intensity both in terms of MSLP and maxi- contours through the TC center are given in Figs. 13a–c mum wind speed consistent with IMD observations and and 13d–f, respectively. The DWR run produced a exhibited the maximum gain in intensity prediction tighter inner-core structure with height and the wind 2 when the TC was under DWR coverage. The maximum speed is more than 20 m s 1 on either side of the eye. The 2 intensity of Laila in the DWR run (30 m s 1) is closer to vertical tilting of the eyewall on the west side is reduced 2 the observed intensity (28 m s 1). The assimilation of to a large extent with the assimilation of DWR data. The conventional observations in the GTS run could not updraft in the DWR experiment is increased by 35– 2 2 improve the intensity evolution by much; however, it did 40 cm s 1 over the GTS run and 60–70 cm s 1 over the show the maximum intensification, and weakening of CNTL run. There is an upper-level warm-core ($4K) the system toward the end of the forecast, which could structure around 400–300 hPa in the eyewall region be attributed to land interaction. The CNTL run showed (Fig. 13f). This warm core helps increase the low-level gradual intensification of the system and achieved peak convergence and storm intensification. Unlike the DWR 2 intensity (35 m s 1) at the end of integration as the sys- run, the eyewall is tilted to the west in CNTL and GTS tem stayed over the BoB without making landfall. experiments and suppresses warming in the eyewall re- To review the changes in vertical structure during the gion as the released latent heat is distributed over the observed peak intensity time of TC Laila (case 12), larger region. There was a drop of 18–19 hPa in MSLP vertical cross sections (averaged for 24–30 h of forecast (difference from initial MSLP) in the DWR run, while in

Unauthenticated | Downloaded 09/28/21 03:35 PM UTC NOVEMBER 2015 O S U R I E T A L . 4553 the case of CNTL and GTS, it was 5 and 8 hPa. The surface processes not captured in this study (Chang et al. DWR run shows that upper-air warming increases the 2009; Niyogi et al. 2006, 2010; Kishtawal et al. 2012). MSLP drop leading to intensification, and these results Considering the overall model performance, all the ex- support the findings by Zhang and Chen (2012). The periments overestimated rainfall when compared to the model-simulated rain swath rate overlapped with sim- TRMM rainfall fields. Note that the TRMM satellite ulated tracks from CNTL, GTS, and DWR runs is shown rainfall analysis smooths the maximum rainfall peaks in Figs. 13h–j along with the TRMM-analyzed rain and over a particular region due to spatial and temporal at- IMD best track in Fig. 13g. The track with the TC tenuation (Vrieling et al. 2009). Mohanty et al. (2010) symbol in Figs. 13h–j represents the DWR-derived TC demonstrated that TRMM underestimates rainfall by center up to 2100 UTC 19 May and after that DWR about 20–30 mm over the Indian region compared to the could not identify the TC center due to the weakening IMD rain gauge network. Considering this uncertainty stage of Laila. The DWR-simulated track is closer to the in the TRMM rainfall amounts, the rainfall prediction observed track compared to the GTS and CNTL runs. can be considered comparable to the observations. Therefore, the DWR run could be able to reproduce The model-simulated reflectivity at landfall from the similar patterns of observed rain rate patterns in the three experiments is presented in Fig. 15 for (Fig. 15a) coastal region. The rainfall intensity is more realistic in case 1 of Sidr (valid at 1800 UTC 15 November 2007), 2 the DWR run (.10 mm h 1) while CNTL and GTS do (Fig. 15b) case 5 of Aila (1200 UTC 25 May 2009), and not show any rain over the land that was observed. (Fig. 15c) case 20 of Thane (0000 UTC 30 December These results highlight the significant gain in track, in- 2011). With the better track and intensity prediction, the tensity, and structure from simulations conducted using spatial distribution of model reflectivity was also im- the DWR data when the TC is under radar coverage. proved in Sidr (a cell over east Bangladesh was well reproduced) with the DWR experiment (Fig. 15a). For d. Rainfall and reflectivity prediction at landfall Aila (Fig. 15b), only DWR simulated the realistic spatial The model-simulated 24-h accumulated rainfall from distribution of reflectivity echoes over land compared to the three experiments along with the TRMM-observed that of the CNTL and GTS experiments. The reflectivity rainfall was analyzed for each case. It is noted that the structure of Thane during landfall was well captured by position and structure of rainbands was improved and the DWR run. Thane crossed land as a severe cyclone the rainfall intensity was corrected in the DWR exper- with peak convection to the north of its center, which is iment for all cases. [Figure 15 presents the summary of predicted in the DWR run (Fig. 15c). Unlike observa- model-simulated rainfall during the day of landfall for tions, the maximum reflectivity from CNTL and GTS Sidr valid for the 72-h forecast: Aila (72-h forecast), Jal was over the BoB. Similar results are noted in the case of (48-h forecast), and Thane (72-h forecast).] The CNTL Laila and Jal and are not shown here. and GTS experiments failed to predict rainfall in the e. Impacts of cloud microphysics on assimilation of coastal areas as they could not simulate landfall for cy- TC inner-core reflectivity data clone Sidr. However, in the DWR experiment for Sidr, predicted rainfall was close to the observed rainfall In this section, the emphasis is on the assimilation of patterns for the landfall day. Similar results were noted reflectivity observations in the TC inner core with dif- for cyclone Aila (Fig. 14b). Rainfall over the eastern ferent [warm-rain physics (Kessler) and ice-phase phys- parts of Bangladesh due to Aila could be captured only ics (WSM6)] microphysical parameterization schemes. in the DWR experiment. In the case of Jal (Fig. 14d), the Tropical Cyclone Laila was taken as an example. Simula- convective clouds were sheared to the west causing more tions were performed for 6-h cyclic assimilation of reflec- rainfall inland than in the coastal regions (Regional tivity observations from 0000 to 1800 UTC 18 May 2010 to Specialized Meteorological Centre 2010). The rainfall obtain model-generated (cycled) initial conditions for over the interior regions (near 158N, 768E) was not 0000 UTC 19 May 2010 (case 12 in Table 1). Following captured by either the CNTL or GTS experiment. The this initialization, reflectivity data were assimilated in DWR experiment predicted a rainfall zone of 40– the 6-h forecast of the previous cycle (1800 UTC 120 mm in agreement with the TRMM-observed rainfall 18 May) to have updated analysis and the model was over the same region. The rainfall due to TC Thane was then run for 54 h (details are provided in Table 2). Re- well corrected by the DWR experiments (Fig. 14e)as sults indicate that microphysics had little impact on the compared to other runs. A much localized rainfall zone track (not shown) and intensity (Fig. 16a) of Laila when along 128N in TRMM was correctly reproduced in the reflectivity was not assimilated. Intercomparing both DWR run. The discrepancies between observed and runs, it is noted that the impact of ice-phase physics simulated rainfall patterns could be a feedback of land without data assimilation is apparently weaker than

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FIG. 14. The 24-h accumulated model rainfall from three experiments along with TRMM observed rainfall [(from left to right) TRMM, CNTL, GTS, and DWR] for (a) case 1 of Sidr valid for a 72-h forecast, (b) case 5 of Aila valid for a 72-h forecast, (c) case 14 of Jal valid for a 48-h forecast, and (d) case 20 of Thane valid for a 72-h forecast. Note the different scale used for the bottom row (Thane) for clarity. warm rain at some instances. In these no-assimilation consistent with the observations (Regional Specialized runs, the cyclone skirted the coast without making Meteorological Centre 2010). The assimilation of DWR landfall for both microphysics options. Of the two mi- reflectivity with ice-phase physics improved the intensity 2 crophysics runs, the track from ice physics is relatively prediction (35 m s 1) bringing it closer to the observa- closer to the IMD observation. tions (Fig. 16a). This wind improvement may have The initial vortex as well as the simulated TC is benefited from the previous assimilation cycles of re- stronger with the warm-rain physics in the experiments flectivity assimilation, either from the direct impact on with and without reflectivity assimilation. In ice-phase the wind or through the model adjustment during DA runs (with and without assimilation), both strength and cycles [see, e.g., the results of sensitivity experiments in structure showed improvement in the southern and section 4, as well as Zhao and Jin (2008); Xiao and Sun southwestern part of the vortex (not shown), which is (2007)]. Further, this reduction in intensity of Laila

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FIG. 15. Simulated reflectivity (dBZ) from three experiments along with observed reflectivity [(from left to right) observed, CNTL, GTS, and DWR] for (a) case 1 of Sidr (valid at 1800 UTC 15 Nov 2007), (b) case 5 of Aila (valid at 1200 UTC 25 May 2009), and (c) case 20 of Thane (valid at 0000 UTC 30 Dec 2011). when ice-phase microphysics is used, is consistent with Though this difference is less during warm cycles, warm- previous studies (Wang 2002; Zhu and Zhang 2006; rain physics always showed increments at the higher side Yamasaki 2013). The intensity evolution during pre- than that of the ice phase. vious assimilation cycles (0000 UTC 18 May, 0600 UTC The vertical structure of rainwater mixing ratio and 18 May, 1200 UTC 18 May, and 1800 UTC 18 May 2010) cloud water mixing ratio averaged between 1800 UTC has been analyzed. The intensity difference between 19 May and 0600 UTC 20 May 2010 over a latitudinal warm-rain and ice-phase physics (Warm-Refle minus circle 14.58–16.58N, was analyzed and is presented in Ice-Refle run) is positive in all the cycles up to first 12 h Figs. 16b–e. Thick dashed lines represent the 08C iso- of the forecast. Further analysis reveals that, in the first therm. The cloud water mixing ratio was concentrated in cycle (cold start at 0000 UTC 18 May), the increment in the region of strong updrafts (not shown) and extended intensity (3DVAR minus CNTL) with warm-rain to upper levels (200 hPa) showing higher values of 2 2 physics is 8 m s 1 in 6-h forecast and 3 m s 1 in the ice- rainwater mixing ratio throughout the entire column phase physics. In the remaining cycles (warm starts), the both with and without the reflectivity assimilation runs wind increment with warm-rain physics ranges between (Figs. 16b,c). Similarly, in the case of experiments with 2 7 and 11 m s 1 in the 6-h forecast and is in between 3 and ice-phase microphysics (Figs. 16d,e), the cloud ice, snow, 2 6ms 1 with the ice-phase physics. Similar evolution is and graupel were concentrated in the upper levels noted for rainwater mixing ratio from both the physics. (;200 hPa), with the cloud water mixing ratio field ex- In the cold start, the difference of rainwater increment tending up to ;400 hPa. The cloud water changes into 2 between these two physics (warm minus ice) is 1.8 g kg 1. rainwater when falling through the 08C isotherm level

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21 FIG. 16. (a) The 3-hourly simulated 10-m wind (m s ) of TC Laila from warm-rain and ice-phase microphysics along with IMD 2 observations with and without reflectivity assimilation. Vertical cross section of rain (shaded) and cloud (contours) mixing ratio (g kg 1) from (b) Warm-Cntl, (c) Warm-Refle, (d) Ice-Cntl, and (e) Ice-Refle. (f)–(i) As in (b)–(e), but for temperature anomaly. The thick dashed line in (b)–(e) represents the 08C isotherm.

(;500 hPa). A similar distribution of hydrometeors for (with warm-rain and with ice-phase physics) resulted warm-rain and ice-phase microphysics was found in in a negative temperature anomaly below ;600 hPa Wang (2002). There are some boundary layer clouds when compared to that of no-assimilation runs. This outside the eyewall region in all the runs. Overall, there cooling in the subcloud layer is mainly due to evapora- are notable changes in the structure of mixing ratios and tion of falling rainwater. temperature anomaly fields (Figs. 16f–i) with and with- The distribution and intensity of 24-h accumulated out inner-core reflectivity assimilation helping to im- rainfall (between 0300 UTC 20 and 21 May 2010) with prove intensity prediction. With warm-rain physics, a and without assimilation of reflectivity using warm-rain warmer core (58C) was extended from ;850 to 200 hPa, and ice-phase microphysics is shown in Figs. 17b–e. The while the warm core is confined to the middle tropo- corresponding TRMM 3B42 observed rainfall fields sphere with ice-phase physics. It is known that the were also plotted (Fig. 17a). From this figure it is quite stronger the warm core, the stronger the TC (Zhu and clear that the rainfall prediction is intimately tied to Zhang 2006). The assimilation of inner-core reflectivity accurate prediction of track and intensity of the TC. As

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FIG. 17. The 24-h accumulated rainfall (cm) valid for 0300 UTC 20–21 May 2010 (day 2) from (b) Warm-Cntl, (c) Warm-Refle, (d) Ice-Cntl, and (e) Ice-Refle along with (a) the TRMM observation. the track of Laila is improved with ice-phase physics in assimilation of both data together improves TC simu- assimilation and no-assimilation runs, rainfall prediction lations and are consistent with the previous studies is closer to TRMM observations, whereas rainfall pre- (Zhao and Jin 2008; Zhao and Xue 2009). Tropical cy- diction is displaced to higher latitudes with warm-rain clone track prediction was enhanced when both re- physics. The warm-rain scheme overestimated rainfall flectivity and radial wind were assimilated together probably due to excess moisture obtained through as- compared to runs using only reflectivity or radial wind. similation cycles and a stronger warmer core. The The improvement with ‘‘both data together’’ and ‘‘radial overall results suggest that the choice of microphysics is wind alone’’ was about 35% and 14% in 24-h forecasts, critical for intensity as well as rainfall predictions. Ad- 42% and 8% in 48-h forecasts, and 43% and 15% in 72-h ditionally, the assimilation of inner-core reflectivity led forecasts, respectively. The broad conclusion of this to improved rainfall intensity and distribution compared study is that the DWR experiment did improve the track to the no-assimilation run. Of the Warm-Refle and Ice- significantly by ;32%–53% for 12–72-h forecast lengths Refle results, the rainfall intensity and distribution is as compared with CNTL runs, and ;5%–25% as com- more realistic with the ice-phase microphysics. Results pared with GTS runs. support the notion that the DWR reflectivity assimila- It was also noted that when the TC is farther away tion, and thereby the TC prediction, are sensitive to ice- from the radar, the intensity improvements are meager, phase microphysics. but the tracks forecast still showed consistent improvement. These positive impacts on track simulations can be mainly attributed to the fact that the DWR provides a 4. Conclusions better representation of the TC environment. For ex- Based on the experiments conducted, it is clear that ample, in the case of TC Sidr, the DWR experiment assimilation of available GTS and DWR data is critical improved the deep layer steering flow during 15 and in improving TC simulations over the Bay of Bengal. 16 November 2007. The assimilation of DWR data Numerical experiments to assess the relative impact of helped reproduce the maximum convection to the east radial velocity, reflectivity, and/or both, show that the of the cyclone as was observed in the Kalpana satellite

Unauthenticated | Downloaded 09/28/21 03:35 PM UTC 4558 MONTHLY WEATHER REVIEW VOLUME 143 imagery. The improved track prediction enhanced the Ensemble Data Assimilation System (HEDAS) for high- spatial patterns and structure of 24-h accumulated resolution data: The impact of airborne Doppler radar rainfall. When a TC is within radar range, the initial observations in an OSSE. Mon. Wea. Rev., 140, 1843– 1862, doi:10.1175/MWR-D-11-00212.1. organization, asymmetry and strength of the TC vortex ——, S. D. Aberson, T. Vukicevic, K. J. Sellwood, S. Lorsolo, and show significant improvement. As a result, there are X. Zhang, 2013: Assimilation of high-resolution tropical cy- considerable gains not only in the track simulation but clone observations with an ensemble Kalman filter using also in the intensity simulation. The DWR run produced NOAA/AOML/HRD’s HEDAS: Evaluation of the 2008–11 a tighter inner-core structure and corrected the shear vortex-scale analyses. Mon. Wea. 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