**7. Advantages of HRM and ARPS**

There are various NWP models available for providing short-range weather predictions over a specific site, however in the present study we have confined our description to HRM and ARPS for the following reasons:


ensemble model. In a broad perspective, it may be indeed a difficult task to achieve 100% accurate model, but intelligent human intervention to the model-derived forecast products would lead to generation of error statistics of individual models, which can help the mission

<sup>41</sup> Applications of Mesoscale Atmospheric Models

in Short-Range Weather Predictions During Satellite Launch Campaigns in India

We greatly acknowledge the support and inspiring guidance rendered by Dr. K. Krishna Moorthy, Director, SPL. One of the authors DBS is very much thankful to all the members of "Weather Forecasting Expert Team" constituted by Satish Dhawan Space Centre, Sriharikota for useful discussion during several of the PSLV and GSLV launches. Special thanks are also due to Ms. T. J. Anurose and S. Indira Rani for their contribution in HRM and ARPS simulations during the launch campaigns. The NCEP-FNL Reanalysis data for this study are from the Research Data Archive (RDA) which is maintained by the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NCAR). NCAR is sponsored by the National Science Foundation. The original data are available from the RDA (http://dss.ucar.edu) in dataset number ds083.2. KALPANA Satellite images over the Indian sub-continent are downloaded from the Indian Meteorological Department

Anthes, R. A. & Warner, T. T. (1974). Development of hydrodynamical models suitable for air

Bannoon, P. R. (1995). Hydrostatic adjustment: Lamb's problem, *J Atmos Sci* 52: 1743 – 1752. Case, J. L., Manobianco, J., Oram, T. D., Garner, T., Blottman, P. F. & Spratt, S. M. (2002). Local

Cox, R., Bauer, B. L. & Smith, T. (1998). A mesoscale model intercomparisona mesoscale model

Das, S. S., Sijikumar, S. & Uma, K. (2011). Further investigation on stratospheric air intrusion

Klein, G., Moon, B. & Hoffman, R. R. (2006). Making sense of sensemaking 2: A

Majewski, D. (2010). HRM-User's Guide: For Vrs. 2.8 or higher, Deutscher Wetterdienst,

Majewski, D., Liermann, D., Prohl, P., Ritter, B., Buchhold, M., Hanisch, T., Wergen, G. P. D. W.,

Pielke, R. A. (1984). *Mesoscale Meteorological Modeling*, Academic Press Inc,Orlando, Florida.

and MST radar observations, *Atmospheric Research* 101(4): 928 – 937. Doswell, C. A. (2004). Forecasters's forum: Weather forecasting by humans-heuristics and

pollution and other mesometeorological studies, *Mon Weather Rev* 106: 1045 – 1078.

data integration over East-Central Florida using the ARPS data analysis system,

into the troposphere during the episode of tropical cyclone: Numerical simulation

& Baumgardner, J. (2002). The Operational Global Icosahedral-Hexagonal Gridpoint Model GME: Description and High-Resolution Tests, *Monthly Weather Review* 130. Manobianco, J., Zack, W. & Taylor, G. E. (1996). Work station based real-time mesoscale

modeling designed for weather support to operations at the Kennedy Space Center and Cape Canaveral Air Station, *Bullen of American Meteorological Society* 77: 653 –

(http://www.imd.gov.in/) and we duly acknowledge their services.

intercomparison, *Bull. Amer. Meteor. Soc.* 79: 265 – 283.

decision making, *Weather and Forecasting* 19: 1115 – 1126.

macrocognitive model, *Intelligent Systems* 21(5): 88 – 92.

*Weather Forecast* 17: 3 – 26.

Germany, p. 121.

672.

team in decision making tasks.

**9. Acknowledgments**

**10. References**

Fig. 7. A typical comparison of ARPS model-simulated zonal and meridional winds in vertical with balloon-borne GPS Sonde measurements.

4. By choosing HRM and ARPS, we take care of the possible differences in forecasting due to hydrostatic and non-hydrostatic approach. Thus, we make use of both the models simultaneously to provide short-range weather predictions over SHAR.

#### **8. Scope for future work**

In the present chapter, we described the potential of two atmospheric models, namely HRM and ARPS in providing valuable information with respect to *launch commit criteria* for satellite launch missions. Among various regional atmospheric models, HRM has got a definite advantage as the initial conditions and lateral boundary conditions are derived through a tailored dataset of the GME global model, or else, one need to depend on the whole global sets, thereby spending excessive time. Thus, the HRM simulations can be made available to PSLV and GSLV mission teams with a reasonable lead time. The HRM has also got a good potential to capture low pressure systems over the Bay of Bengal well in advance (≈ 18 hours), thus severe weather threats can be provided at right time. Similarly, the potential of ARPS model is vastly exploited in simulations of mesoscale convective events, such as thunderstorms. It was also very useful in capturing the fine features in the vertical profiles of zonal and meridional winds to +9 hrs.

Having exploited the potential of these two models, one need to explore the possibility of assimilating the routine meteorological observations on a regular basis for continuous improvements in the initial and lateral boundary conditions of the model. Also there is need of generation of very good quality climatology of mesoscale convective events which are hazardous to the launch activities. In future, there may also be enough scope of validating a combination of different NWP models, which can lead to development of a statistical ensemble model. In a broad perspective, it may be indeed a difficult task to achieve 100% accurate model, but intelligent human intervention to the model-derived forecast products would lead to generation of error statistics of individual models, which can help the mission team in decision making tasks.

#### **9. Acknowledgments**

16 ATMOSPHERIC MODELS

Fig. 7. A typical comparison of ARPS model-simulated zonal and meridional winds in

simultaneously to provide short-range weather predictions over SHAR.

4. By choosing HRM and ARPS, we take care of the possible differences in forecasting due to hydrostatic and non-hydrostatic approach. Thus, we make use of both the models

In the present chapter, we described the potential of two atmospheric models, namely HRM and ARPS in providing valuable information with respect to *launch commit criteria* for satellite launch missions. Among various regional atmospheric models, HRM has got a definite advantage as the initial conditions and lateral boundary conditions are derived through a tailored dataset of the GME global model, or else, one need to depend on the whole global sets, thereby spending excessive time. Thus, the HRM simulations can be made available to PSLV and GSLV mission teams with a reasonable lead time. The HRM has also got a good potential to capture low pressure systems over the Bay of Bengal well in advance (≈ 18 hours), thus severe weather threats can be provided at right time. Similarly, the potential of ARPS model is vastly exploited in simulations of mesoscale convective events, such as thunderstorms. It was also very useful in capturing the fine features in the vertical profiles of zonal and meridional

Having exploited the potential of these two models, one need to explore the possibility of assimilating the routine meteorological observations on a regular basis for continuous improvements in the initial and lateral boundary conditions of the model. Also there is need of generation of very good quality climatology of mesoscale convective events which are hazardous to the launch activities. In future, there may also be enough scope of validating a combination of different NWP models, which can lead to development of a statistical

vertical with balloon-borne GPS Sonde measurements.

**8. Scope for future work**

winds to +9 hrs.

We greatly acknowledge the support and inspiring guidance rendered by Dr. K. Krishna Moorthy, Director, SPL. One of the authors DBS is very much thankful to all the members of "Weather Forecasting Expert Team" constituted by Satish Dhawan Space Centre, Sriharikota for useful discussion during several of the PSLV and GSLV launches. Special thanks are also due to Ms. T. J. Anurose and S. Indira Rani for their contribution in HRM and ARPS simulations during the launch campaigns. The NCEP-FNL Reanalysis data for this study are from the Research Data Archive (RDA) which is maintained by the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NCAR). NCAR is sponsored by the National Science Foundation. The original data are available from the RDA (http://dss.ucar.edu) in dataset number ds083.2. KALPANA Satellite images over the Indian sub-continent are downloaded from the Indian Meteorological Department (http://www.imd.gov.in/) and we duly acknowledge their services.

#### **10. References**


**3** 

Akiyoshi Wada

*Japan* 

*Meteorological Research Institute* 

**Numerical Study on the Effect** 

**of the Ocean on Tropical-Cyclone** 

The ocean is an indispensable source of energy for tropical cyclones (TCs). TCs enable extraction of heat and moisture from the sea surface through the transfer of turbulent heat energy in the atmospheric boundary layer. TCs are often generated where sea-surface temperature (SST) is higher than 26.5ºC (Palmén, 1948), and they intensify in areas that have high SST and deep oceanic mixed layer, thus having high upper-ocean heat content. Previous studies reported that TC intensity is related to 'tropical-cyclone heat potential', which is oceanic heat content integrated from the surface to the depth of the 26°C-isotherm (Wada & Usui, 2007; Wada, 2010). However, most of people believe that TCs intensify when

The oceans affect both the genesis and intensification of TCs, which in turn apply wind stresses to the ocean that induce sea-surface cooling (SSC) during their passages (Ginis, 1995). Numerous numerical modeling studies leave no doubt that SSC affects both the evolution of TCs and prediction of their intensities (Bender & Ginis, 2000; Wada et al., 2010). Nevertheless, uncertainties remain in the use of numerical models to predict TC intensity: these are related to initial atmospheric and oceanic conditions, the spatial and temporal resolution of the models, and the physical processes incorporated into the models (Wang & Wu, 2004; Wada, 2007). It may appear that a sophisticated model such as an atmospherewave-ocean coupled model can produce valid predictions of TC intensity when TC intensity predicted by that model matches best-track intensity derived from satellite observations. However, best-track intensity is not always valid, particularly in the western North Pacific where there is a lack of direct observation such as aircraft observations for measuring TC intensity directly. Furthermore, a coupled model that has successfully calculated TC intensities in one situation may provide erroneous results in another situation because of unexpected interactions among the specifications, physical processes, and initial and boundary conditions used in the model. One particular combination of model specifications

However, studies using a numerical model provide us scientific explanations on dynamical and physical processes associated with TC intensification. (Wada, 2009) explained the effects on TC intensification of atmospheric dynamics such as filamentation, the formation of

and parameters may thus not always be valid for other TC predictions.

**1. Introduction** 

SST is higher than 26-28ºC.

**Intensity and Structural Change** 

