Refine your search
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Prasad, V. S.
- Impact of Assimilation of Megha-Tropiques ROSA Radio Occultation Refractivity by Observing System Simulation Experiment
Abstract Views :189 |
PDF Views:85
Authors
C. J. Johny
1,
V. S. Prasad
1
Affiliations
1 National Centre for Medium Range Weather Forecasting, Noida 201 309, IN
1 National Centre for Medium Range Weather Forecasting, Noida 201 309, IN
Source
Current Science, Vol 106, No 9 (2014), Pagination: 1297-1305Abstract
Numerical weather prediction models are assimilating more Global Positioning System Radio Occultation (GPSRO) observations into their operational model in recent times as a result of significant positive impact with use of GPSRO data in assimilation system. The Megha-Tropiques satellite mission is aimed to provide large number of observations over the tropical region and carries payload ROSA for providing GPSRO observations. At present, the quality of processed GPSRO retrievals from Megha-Tropiques ROSA is not satisfactory. In order to assess the impact of assimilation of good-quality ROSA observations, an observing system simulation system experiment (OSSE) was conducted using NCMRWF T574 model. The experiment was conducted for a period of 15 days during September 2012 and refractivity operator was used for assimilation. Results show significant improvement in forecast skill for forecasts beyond 72 h with OSSE data.Keywords
Assimilation System, Forecast Skill, Numerical Models, Weather Prediction.Full Text
- Global Retrospective Analysis Using NGFS for the Period 2000-2011
Abstract Views :216 |
PDF Views:86
Authors
Affiliations
1 National Centre for Medium Range Weather Forecasting, A-50 Secto-62, Institutional Area, Noida 201 309, IN
1 National Centre for Medium Range Weather Forecasting, A-50 Secto-62, Institutional Area, Noida 201 309, IN
Source
Current Science, Vol 112, No 02 (2017), Pagination: 370-377Abstract
The National Centre for Medium Range Weather Forecasting (NCMRWF) conducted its first global data retrospective analysis (reanalysis) for the period 1 January 2000-31 March 2011 using its GFS based system (NGFS). This reanalysis is called NGFS-R and the main objectives of this effort are to address issues for studying decadal variability of the Indian summer monsoon, high-resolution global analysis fields to study the Indian monsoon and to provide short-term mean fields for its seasonal/long-term forecasts by ensemble methods. NGFS-R has been conducted with the T574L64 version of the Global Data Assimilation and Forecasting System of NCMRWF that is operational as of May 2015, and using CFS-reanalysis data dump. With this effort, a high-resolution global data analysis at 6 h intervals is made available for about 16 years (2000-2015) for various uses and applications.Keywords
Global Data Assimilation and Forecasting, Monsoon Season, Numerical Weather Prediction Models, Retrospective Analysis.Full Text
- Quality and Impact of GPSRO Observations from Megha-Tropique Satellite on NGFS Model
Abstract Views :261 |
PDF Views:77
Authors
C. J. Johny
1,
V. S. Prasad
1
Affiliations
1 National Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, A-50, Sector-62, Noida 201 309, IN
1 National Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, A-50, Sector-62, Noida 201 309, IN
Source
Current Science, Vol 114, No 05 (2018), Pagination: 1083-1088Abstract
Megha-Tropique satellite mission launched in 2011 was aimed at providing more observations in the tropical region. In the initial phase of the mission, it was found that the quality of global positioning system radio occultation (GPSRO) observations was not satisfactory. The Indian Space Research Organisation (ISRO) took remedial measures in this regard by modifying the data processing algorithm and releasing the new version of data. In 2012, an observing system simulation experiment (OSSE) was done at National Centre for Medium Range Weather Forecasting (NCMRWF) using simulated data at Megha- Tropiques ROSA observation location with Global Forecast System (GFS) based model. As an extension of the previous study, the quality of new version of GPSRO bending angle observations and impact of assimilation of these observations in NCMRWF GFS (NGFS) model were studied. It was found that with the use of a new data processing algorithm, quality of bending angle observations improved and comparable with other GPSRO missions in the pressure range between 500 hpa and 200 hpa. Impact study shows that the new observations improved forecasts in the middle and upper levels in the tropics.Keywords
Assimilation, Bending Angle, GPSRO, Megha-Tropiques, NGFS.Full Text
References
- Gettelman, A. and Birner, T., Insights into tropical tropopause layer processes using global models. J. Geophys. Res., 2007, 112, D23104.
- Huang, C. Y., Kuo, Y. H., Chen, S. Y., Rao, A. S. K. A. V. P. and Wang, C. J., The assimilation of GPS radio occultation data and its impact on rainfall prediction along the west coast of India during monsoon 2002. Pure Appl. Geophys., 2007, 164(8), 1577–1591.
- Jakowsk, N., Wilken, V. and Mayer, C., Space weather monitoring by GPS measurements on board CHAMP. Space Weather, 2007, 5, S08006.
- Schmidt, T., de la Torre, A. and Wickert, J., Global gravity wave activity in the tropopause region from CHAMP radio occultation data. Geophys. Res. Lett., 2008, 35, L16807.
- Wickert, J. et al., GPS radio occultation: results from CHAMP, GRACE and FORMOSAT-3/COSMIC. Terr. Atmos. Ocean. Sci., 2009, 20(1), 35–50.
- Pavelyev, A. G., Liou, Y. A., Wickert, J., Pavelyev, A. A. and Igarashi, K., New applications and advances of the GPS radio occultation technology as recovered by analysis of the FORMOSAT3/COSMIC and CHAMP database. In New Horizons in Occultation Research – Studies in Atmosphere and Climate (eds Steiner, A. et al.), Springer Berlin Heidelberg, 2009, pp. 163–176.
- Jin, S., Occhipinti, G. and Jin, R., GNSS ionospheric seismology: recent observation evidences and characteristics. Earth Sci. Rev., 2015, 147, 54–64.
- Steiner, A. K. et al., Quantification of structural uncertainty in climate data records from GPS radio occultation. Atmos. Chem. Phys. Discuss., 2013, 12, 26963–26994.
- Harnisch, F., Healy, S. B., Bauer, P. and English, S. J., Scaling of GNSS radio occultation impact with observation number using an ensemble of data assimilations. Mon. Weather Rev., 2013, 141, 4395–4413; doi:10.1029/2008GL035873.
- Healy, S. B. and Thepaut, J. N., Assimilation experiments with CHAMP GPS radio occultation measurements. Q. J. R. Meteorol. Soc., 2006, 132, 605–623.
- Aparicio, J. and Deblonde, G., Impact of the assimilation of CHAMP refractivity profiles in environment Canada global forecasts. Mon. Weather Rev., 2008, 136, 257–275.
- Poli, P., Healy, S., Rabier, F. and Pailleux, J., Preliminary assessment of the scalability of GPS radio occultations impact in numerical weather prediction. Geophys. Res. Lett., 2008, 35(23), L23 811.
- Cucurull, L., Improvement in the use of an operational constellation of GPS radio occultation receivers in weather forecasting. Weather Forecast., 2010, 25, 749–767.
- Cucurull, L., Kuo, Y. H., Barker, D. and Rizvi, S. R. H., Assessing the impact of simulated COSMIC GPS radio occultation data on weather analysis over the Antarctic: A case study. Mon. Weather Rev., 2006, 134, 3283–3296.
- Auligne, T., McNally, A. and Dee, D., Adaptive bias correction for satellite data in a numerical weather prediction system. Q. J. R. Meteorol. Soc., 2007, 133, 631–642.
- Cardinali, C. and Healy, S., Impact of GPS radio occultation measurements in the ECMWF system using adjoint-based diagnostics. Q. J. R. Meteorol. Soc., 2014, 140, 2315–2320.
- Cucurull, L. and Derber, J. C., Operational implementation of COSMIC observations into NCEP’s global data assimilation system. Weather Forecast., 2007, 23, 702–711.
- Johny, C. J. and Prasad, V. S., Impact of assimilation of MeghaTropiques ROSA radio occultation refractivity by observing system simulation experiment. Curr. Sci., 2014, 106(9), 1297–1305.
- Sokolovskiy, S., Effect of super-refraction on inversions of radio occultation signals in the lower troposphere. Radio Sci., 2003, 38, 1058; doi:10.1029/2002ES002728.
- Cucurull, L., Derber, J. C. and Purser, R. J., A bending angle forward operator for global positioning system radio occultation measurements. J. Geophys. Res. Atmos., 2013, 118, 14–28; 2012JD017782.
- Prasad, V. S., Saji, M., Gupta, M. D., Rajagopal, E. N. and Dutta, S. K., Implementation of upgraded global forecasting systems (T382L64 and T574L64) at NCMRWF. Technical Report. 2011, NCMR/TR/5/2011.
- Hu, M., Shao, H., Stark, D. and Newman, K., Gridpoint Statistical Interpolation (GSI; Version 3.3 User’s Guide). Developmental Testbed Centre, NCAR, NOAA, USA, 2014; http://www.dtcenter.org/com-GSI/users/index.php
- A Comparative Study of Expectation Against Actual Performance of Some Recently Constructed Residential Buildings in Calicut
Abstract Views :274 |
PDF Views:0
Authors
Affiliations
1 R. E. College, Calicut, IN
1 R. E. College, Calicut, IN
Source
Journal of the Association of Engineers, India, Vol 52, No 1 (1977), Pagination: S2-S2Abstract
The proof of the pudding is in the eating. Whatever may be the claims made by the architect or engineer regarding the conveniences and comfort of a building plan, or the durability and economy of the structure, these matters have to be finally decided on the basis of the satisfaction of the user and the performance of the building. A study was undertaken to compare the actual performance and the satisfaction to the user of a number of recently constructed residential buildings in Calicut with the hopes and expectations raised by the architects and engineers who designed and executed the work.
- A Comparative Study of Expectation Against Actual Performance of Some Recently Constructed Residential Buildings in Calicut
Abstract Views :291 |
PDF Views:0
Twenty-five residential buildings of various types covering high, middle and low income groups and situated partly in the R. E. C. Compus and surrounding ares and partly in Calicut city were selected for evaluation. The evaluation was based on a study of the plan and specifications of the building and its actual performance as revealed by a detailed personal inspection. Dialogue with the occupants was used as a means of obtaining the extent of satisfaction or dissatisfaction. Dialogue with the architects, engineers and contractors associated with the building were used to obtain information on any points not revealed by the plans and personal inspection.
The study has indicated that users are generally satisfied with the architectural design; their chief complaint is about the poor quality of construction arising from poor supervision leading to the use of poor quality materials and poor standard of workmanship. There may be scope for effecting economy by reducing foundations. R. C. Roofs can be so constructed as to be leakproof and dampproof even in a heavy rainfall area such as Calicut.
The study has proved useful in understanding the causes of greatest annoyance and irritation to occupants and in showing the way of eliminating or reducing them.
Authors
Affiliations
1 Department of Civil Engineering, Regional Engineering College, Calicut, Kerala State, IN
2 Regional Engineering College, Calicut, Kerala State, IN
1 Department of Civil Engineering, Regional Engineering College, Calicut, Kerala State, IN
2 Regional Engineering College, Calicut, Kerala State, IN
Source
Journal of the Association of Engineers, India, Vol 52, No 1 (1977), Pagination: 55-65Abstract
A study was undertaken to evaluate the actual performance and the satisfaction to the user of a number of recently constructed residential buildings in Calicut.Twenty-five residential buildings of various types covering high, middle and low income groups and situated partly in the R. E. C. Compus and surrounding ares and partly in Calicut city were selected for evaluation. The evaluation was based on a study of the plan and specifications of the building and its actual performance as revealed by a detailed personal inspection. Dialogue with the occupants was used as a means of obtaining the extent of satisfaction or dissatisfaction. Dialogue with the architects, engineers and contractors associated with the building were used to obtain information on any points not revealed by the plans and personal inspection.
The study has indicated that users are generally satisfied with the architectural design; their chief complaint is about the poor quality of construction arising from poor supervision leading to the use of poor quality materials and poor standard of workmanship. There may be scope for effecting economy by reducing foundations. R. C. Roofs can be so constructed as to be leakproof and dampproof even in a heavy rainfall area such as Calicut.
The study has proved useful in understanding the causes of greatest annoyance and irritation to occupants and in showing the way of eliminating or reducing them.
- INSAT-3DR-Rapid Scan Operations for Weather Monitoring Over India
Abstract Views :271 |
PDF Views:99
Authors
M. Mohapatra
1,
A. K. Mitra
1,
Virendra Singh
1,
S. K. Mukherjee
1,
Kavita Navria
2,
Vikram Prashar
1,
Ashish Tyagi
1,
Atul Kumar Verma
1,
Sunitha Devi
1,
V. S. Prasad
3,
Mudumba Ramesh
4,
Raj Kumar
5
Affiliations
1 National Meteorological Satellite Centre, India Meteorological Department, New Delhi 110 003, IN
2 National Meteorological Satellite Centre, India Meteorological Department, New Delhi 110 003ii
3 National Centre for Medium Range Weather Forecasting, Noida 201 309, IN
4 Master Control Facility, Indian Space Research Organisation, Hassan 573 201, IN
5 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
1 National Meteorological Satellite Centre, India Meteorological Department, New Delhi 110 003, IN
2 National Meteorological Satellite Centre, India Meteorological Department, New Delhi 110 003ii
3 National Centre for Medium Range Weather Forecasting, Noida 201 309, IN
4 Master Control Facility, Indian Space Research Organisation, Hassan 573 201, IN
5 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
Source
Current Science, Vol 120, No 6 (2021), Pagination: 1026-1034Abstract
In order to observe severe weather conditions during cyclones, thunderstorms, etc., IMAGER instruments on-board INSAT3D/3DR have been built with a flexible scanning feature known as ‘rapid scan mode’. Using this feature, the number of scan lines over a given coverage region and the number of repetitions of the selected region can be programmed for scanning. Therefore, to understand the physical processes involved in convective clouds associated with severe weather phenomena, rapid scan of INSAT3DR mode is attempted. It has very high temporal resolution of approximately 4 min and 30 sec. The present study will help in better understanding of the physical processes of severe weather phenomena and in nowcasting. It will also help to improve the accuracy in the NWP model forecast through assimilation of radiances and atmospheric motion wind determined using rapid scan mode.Keywords
Nowcasting, Physical Processes, Rapid Scan Operations, Severe Weather Conditions, Weather Monitoring.Full Text
References
- IMD, A technical report ‘INSAT-3D Data Products Catalog’, India Meteorological Department, New Delhi, January 2014.
- EUMETSAT, Meteosat-9 takes over rapid scanning service, 9 April 2013; http://www.eumetsat.int/Home/Main/News/Press_ Releases/831419?l=en
- Schmit, T. J. et al., Geostationary operational environmental satellite (GOES)-14 super rapid scan operations to prepare for GOES-R. J. Appl. Remote Sensing, 2013, 7(1), 073462.
- Bessho, K. et al., An introduction to Himawari‐8/9 – Japan’s new‐generation geostationary meteorological satellites. J. Meteorol. Soc. Jpn., 2016, 94(2), 151–183; https://doi.org/10.2151/ jmsj.2016‐009.
- Sawada, Y., Okamoto, K., Kunii, M. and Miyoshi, T., Assimilating every-10-minute Himawari‐8 infrared radiances to improve convective predictability. J. Geophys. Res.: Atmos., 2019, 124, 2546–2561; https://doi.org/10.1029/2018JD029643.
- Dvorak, V., Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Weather Rev., 1975, 103(5), 420– 430.
- Dvorak, V., Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. 1984, 11, 45; NOAA/NESDIS, Washington, DC, USA, 1984, p. 45.
- Ribeiro, B. Z., Machado, L. A. T., Huamán, Ch. J. H., Biscaro, T. S., Freitas, E. D., Goodman, S. J. and Mozer, K. W., An evaluation of the GOES-16 rapid scan for nowcasting in Southeastern Brazil: analysis of a severe hailstorm case. Weather Forecast., 2019, 34(6).
- Gairola, R. M, Mishra, A., Prakash, S. and Mahesh, C., Development of INSAT multi-spectral rainfall algorithm (IMSRA) for monitoring rainfall events over India using Kalpana-IR and TRMM-precipitation radar observations. Scientific Report, SAC/EPSA/AOSG/INSAT/SR-39/2010, 2010, p. 22.
- Karagiannidis, A., Lagouvardos, K., Kotroni, V. and Mazarakis, N., Investigation of isolated thunderstorms lightning activity over eastern Mediterranean using Meteosat rapid scan infrared imagery. Int. J. Remote Sensing, 2016, 37(20), 5001–5020; doi:10.1080/ 01431161.2016.12260000.
- RSMC, Report on cyclonic disturbances over North Indian Ocean during 2018. No. ESSO/IMD/CWD Report No-01 (2019)/09. India Meteorological Department, New Delhi and World Meteorological Organization, 2019.
- Goodman, S. J., Blakeslee, R. J., Koshak, W. J., Mach, D., Bailey, J. and Buechler, D. L., The GOES-R geostationary lightning Mapper (GLM). Atmos. Res., 2013, 125–126, 34–49.
- Velden, C. et al., Recent innovations in deriving tropospheric winds from meteorological satellites. Bull. Am. Meteorol. Soc., 2005, 86, 205–223.
- Gallucci, D. et al., Convective initiation proxies for nowcasting precipitation severity using the MSG-SEVIRI rapid scan. Remote Sensing, 2020, 12, 2562.
- Langland, R. H., Velden, C., Pauley, P. M. and Berger, H., Impact of satellite-derived rapid-scan wind observations on numerical model forecasts of Hurricane Katrina. Mon. Weather Rev., 2009, 137, 1615–1622; https://doi.org/10.1175/2008MWR2627.1.
- Li, J., Li, J., Velden, C., Wang, P., Schmit, T. J. and Sippel, J., Impact of rapid‐scan‐based dynamical information from GOES‐16 on HWRF hurricane forecasts. J. Geophys. Res.: Atmos., 2020, 125, e2019JD031647; https://doi.org/10.1029/2019JD031647.
- Hybrid Assimilation on a Parameter-Calibrated Model to Improve the Prediction of Heavy Rainfall Events during the Indian Summer Monsoon
Abstract Views :117 |
PDF Views:66
Authors
Affiliations
1 Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, IN
2 National Centre for Medium Range Weather Forecasting, A-50, Sector 62, Noida 201 309, IN
3 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
1 Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, IN
2 National Centre for Medium Range Weather Forecasting, A-50, Sector 62, Noida 201 309, IN
3 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
Source
Current Science, Vol 124, No 6 (2023), Pagination: 693-703Abstract
Heavy rainfall events during the Indian summer monsoon cause landslides and flash floods resulting in a significant loss of life and property every year. The exactness of the model physics representation and initial conditions is critical for accurately predicting these events using a numerical weather model. The values of parameters in the physics schemes influence the accuracy of model prediction; hence, these parameters are calibrated with respect to observation data. The present study examines the influence of hybrid data assimilation on a parameter-calibrated WRF model. Twelve events during the period 2018–2020 were simulated in this study. Hybrid assimilation on the WRF model significantly reduced the model prediction error of the variables: rainfall (18.04%), surface air temperature (7.91%), surface air pressure (5.90%) and wind speed at 10 m (27.65%) compared to simulations with default parameters without assimilation.Keywords
Heavy Rainfall Events, Hybrid Assimilation, Numerical Weather Model, Parameter Calibration, Summer Monsoon.Full Text
References
- Singh, D., Ghosh, S., Roxy, M. K. and McDermid, S., Indian summer monsoon: extreme events, historical changes, and role of anthropogenic forcings. Wiley Interdiscip. Rev.: Climate Change, 2019, 10(2), e571.
- Dash, S. K., Kulkarni, M. A., Mohanty, U. C. and Prasad, K., Changes in the characteristics of rain events in India. J. Geophys. Res.: Atmosp., 2009, 114(D10).
- Pattanaik, D. R. and Rajeevan, M., Variability of extreme rainfall events over India during southwest monsoon season. Meteorol. Appl., 2010, 17(1), 88–104.
- Bjerknes, V., Hesselberg, T. and Devik, O., Dynamic Meteorology and Hydrography2. Kinematics. Number v. 2 in Publication, Carnegie Inst., 1911.
- Stensrud, D. J., Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models, Cambridge University Press, 2009.
- Mukhopadhyay, P., Taraphdar, S., Goswami, B. N. and Krishna kumar, K., Indian summer monsoon precipitation climatology in a high-resolution regional climate model: impacts of convective parameterization on systematic biases. Weather Forecast., 2010, 25(2), 369–387.
- Srinivas, C. V., Hariprasad, D., Bhaskar Rao, D. V., Anjaneyulu, Y., Baskaran, R. and Venkatraman, B., Simulation of the Indian summer monsoon regional climate using advanced research WRF model. Int. J. Climatol., 2013, 33(5), 1195–1210.
- Attada Raju, Anant Parekh, Chowdary, J. S. and Gnanaseelan, C., Assessment of theIndian summer monsoon in the WRF regional climate model. Climate Dyn., 2015, 44(11–12), 3077–3100.
- Ratnam, J. V., Behera, S. K., Krishnan, R., Doi, T. and Ratna, S. B., Sensitivity of Indian summer monsoon simulation to physical parameterization schemes in the WRF model. Climate Res., 2017, 74(1), 43–66.
- Sandeep, C. P. R., Krishnamoorthy, C. and Balaji, C., Impact of cloud parameterization schemes on the simulation of cyclone Vardah using the WRF model. Curr. Sci., 2018, 115(6), 1143–1153.
- Park Sojung and Park, S. K., A micro-genetic algorithm (GA v1. 7.1 a) for combinatorial optimization of physics parameterizations in the Weather Research and Forecasting model (v4. 0.3) for quantitative precipitation forecast in Korea. Geosci. Model Dev., 2021, 14(10), 6241–6255.
- Baki, H., Chinta, S., Balaji, C. and Srinivasan, B., A sensitivity study of WRF model microphysics and cumulus parameterization schemes for the simulation of tropical cyclones using GPM radar data. J. Earth Syst. Sci., 2021, 130(4), 1–30.
- Di, Z. et al., Assessing WRF model parameter sensitivity: a case study with 5-day summer precipitation forecasting in the Greater Beijing area. Geophys. Res. Lett., 2015, 42(2), 579–587.
- Hourdin, F. et al., The art and science of climate model tuning. Bull. Am. Meteorol. Soc., 2017, 98(3), 589–602.
- Bellprat, O., Kotlarski, S., Lüthi, D. and Schär, C., Exploring perturbed physics ensembles in a regional climate model. J. Climate, 2012, 25(13), 4582–4599.
- Di, Z., Duan, Q., Wang, C., Ye, A., Miao, C. and Gong, W., Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area. Climate Dyn., 2018, 50(56), 1927–1948.
- Edwards, N. R., Cameron, D. and Rougier, J., Precalibrating an intermediate complexity climate model. Climate Dyn., 2011, 37(7–8), 1469–1482.
- Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P., Jackson, L. and Yamazaki, K., History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble. Climate Dyn., 2013, 41(7–8), 1703–1729.
- Quan, J. et al., An evaluation of parametric sensitivities of different meteorological variables simulated by the WRF model. Q. J. R. Meteorol. Soc., 2016, 142(700), 2925–2934.
- Hou, Z., Huang, M., Ruby Leung, L., Lin, G. and Ricciuto, D. M.. Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model. J. Geophys. Res.: Atmosp., 2012, 117(D15).
- Li, J. et al., Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis. Hydrol. Earth Syst. Sci., 2013, 17(8), 3279.
- Wang, C. et al., Assessing the sensitivity of land–atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon. Atmosp. Res., 2020, 234, 104738.
- Baki, H., Chinta, S., Balaji, C. and Srinivasan, B., Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning. Geosci. Model Dev., 2022, 15(5), 2133–2155.
- Williamson, D., Blaker, A. T., Hampton, C. and Salter, J., Identifying and removing structural biases in climate models with history matching. Climate Dyn., 2015, 45(5–6), 1299–1324.
- Duan, Q. et al., Automatic model calibration: a new way to improve numerical weather forecasting. Bull. Am. Meteorol. Soc., 2017, 98(5), 959–970.
- Yang, B. et al., Parametric and structural sensitivities of turbine–height wind speeds in the boundary layer parameterizations in the Weather Research and Forecasting model. J. Geophys. Res.: Atmosp., 2019, 124(12), 5951–5969.
- Baki, H., Chinta, S., Balaji, C. and Srinivasan, B., Parameter calibration to improve the prediction of tropical cyclones over the Bay of Bengal using machine learning-based multi-objective optimization. J. Appl. Meteorol. Climatol., 2022.
- Duan, Q., Sorooshian, S. and Gupta, V. K., Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol., 1994, 158(3–4), 265–284.
- Severijns, C. A. and Hazeleger, W., Optimizing parameters in an atmospheric general circulation model. J. Climate, 2005, 18(17), 3527–3535.
- Jackson, C. S., Sen, M. K., Huerta, G., Deng, Y. and Bowman, K. P., Error reduction and convergence in climate prediction. J. Climate, 2008, 21(24), 6698–6709.
- Ollinaho, P., Järvinen, H., Bauer, P., Laine, M., Bechtold, P., Susiluoto, J. and Haario, H., Optimization of NWP model closure parameters using total energy norm of forecast error as a target. Geoscientific Model Dev., 2014, 7(5), 1889–1900.
- Bannister, R. N., A review of operational methods of variational and ensemble variational data assimilation. Quart. J. R. Meteorol. Soc., 2017, 143(703), 607–633.
- Zhang, F., Meng, Z. and Aksoy, A., Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: Perfect model experiments. Mon. Weather Rev., 2006, 134(2), 722–736.
- Liu, H. and Xue, M., Prediction of convective initiation and storm evolution on 12 June 2002 during IHOP-2002. Part I: Control simulation and sensitivity experiments. Mon. Weather Rev., 2008, 136(7), 2261–2282.
- Parrish, D. F. and Derber, J. C., The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Weather Rev., 1992, 120(8), 1747–1763.
- Lorenc, A. C., Analysis methods for numerical weather prediction. Q. J. R. Meteorol. Soc., 1986, 112(474), 1177–1194.
- Evensen, G., Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res.: Oceans, 1994, 99(C5), 10143–10162.
- Hamill, T. M., Whitaker, J. S. and Snyder, C., Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Weather Rev., 2001, 129(11), 2776–2790.
- Hamill, T. M. and Snyder, C., A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Weather Rev., 2000, 128(8), 2905–2919.
- Lorenc, A. C., The potential of the ensemble Kalman filter for NWP – a comparison with 4D-Var. Q. J. R. Meteorol. Soc., 2003, 129(595), 3183–3203.
- Hamill, T. M., Whitaker, J. S., Fiorino, M. and Benjamin, S. G., Global ensemble predictions of 2009s tropical cyclones initialized with an ensemble Kalman filter. Mon. Weather Rev., 2011, 139(2), 668–688.
- Zhang, M. and Zhang, F., E4DVar: coupling an ensemble Kalman filter with four- dimensional variational data assimilation in a limited-area weather prediction model. Mon. Weather Rev., 2012, 140(2), 587–600.
- Kleist, D. T. and Ide, K., An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part I: system description and 3Dhybrid results. Mon. Weather Rev., 2015, 143(2), 433–451.
- Prasad, V. S., Johny, C. J. and Sodhi, J. S., Impact of 3DVar GSI-ENKF hybrid data assimilation system. J. Earth Syst. Sci., 2016, 125(8), 1509–1521.
- Singh, S. K. and Prasad, V. S., Evaluation of precipitation forecasts from 3DVar and hybrid GSI-based system during Indian summer monsoon 2015. Meteorol. Atmosp. Phys., 2019, 131(3), 455–465.
- Morris, M. D., Factorial sampling plans for preliminary computational experiments. Technometrics, 1991, 33(2), 161–174.
- Chinta, S., Yaswanth Sai, J. and Balaji, C., Assessment of WRF model parameter sensitivity for high-intensity precipitation events during the Indian summer monsoon. Earth Space Sci., 2021, 8(6), e2020EA001471.
- Wang, C., Duan, Q., Gong, W., Ye, A., Di, Z. and Miao, C., An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environ. Modell. Softw., 2014, 60, 167–179.
- Chinta, S. and Balaji, C., Calibration of WRF model parameters using multiobjective adaptive surrogate model-based optimization to improve the prediction of the Indian summer monsoon. Climate Dyn., 2020, 55(3), 631–650.
- Dhanya, M. and Chandrasekar, A., Multivariate background error covariances in the assimilation of SAPHIR radiances in the simulation of three tropical cyclones over the Bay of Bengal using the WRF model. Int. J. Remote Sensing, 2018, 39(1), 191–209.
- Baki, H., Balaji, C. and Srinivasan, B., Impact of data assimilation on a calibrated WRF model for the prediction of tropical cyclones over the Bay of Bengal. Curr. Sci., 2022, 122(5), 569–583.
- Cui, B., Toth, Z., Zhu, Y. and Hou, D., Bias correction for global ensemble forecast. Weather Forecast., 2012, 27(2), 396–410.
- Gao, J., Fu, C., Stensrud, D. J. and Kain, J. S., OSSEs for an ensemble 3DVAR data assimilation system with radar observations of convective storms. J. Atmosp. Sci., 2016, 73(6), 2403–2426.
- Wang, X., Parrish, D., Kleist, D. and Whitaker, J., GSI 3DVar-based ensemble-variational hybrid data assimilation for NCEP Global Forecast System: single-resolution experiments. Mon. Weather Rev., 2013, 141(11), 4098–4117.
- Skamarock, W. C. et al., A description of the advanced research WRF model version 4. National Center for Atmospheric Research, Boulder, CO, USA, 2019, p. 145.
- Kain, J. S., The Kain–Fritsch convective parameterization: an update. J. Appl. Meteorol., 2004, 43(1), 170–181.
- Hong, S.-Y. and Jade Lim, J.-O., The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pac. J. Atmosp. Sci., 2006, 42(2), 129–151.
- Dudhia, J., Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmosp. Sci., 1989, 46(20), 3077–3107.
- Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. and Clough, S. A., Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res.: Atmosp., 1997, 102(D14), 16663–16682.
- Dudhia, J., PSU/NCAR Mesoscale Modeling System, Tutorial Class Notes and Users’ Guide, MM5 Modeling System Version 3, 2005.
- Hong, S.-Y., Noh, Y. and Dudhia, J., A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev., 2006, 134(9), 2318–2341.
- Chen, F. and Dudhia, J., Coupling an advanced land surface–hydrology model with the Penn State-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon. Weather Rev., 2001, 129(4), 569–585.
- Rajeevan, M., Gadgil, S. and Bhate, J., Active and break spells of the Indian summer monsoon. J. Earth Syst. Sci., 2010, 119(3), 229–247.
- Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S. and Mukhopadhyay, B., Development of a new high spatial resolution (0.25 × 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam, 2014, 65(1), 1–18.
- NCSP, NCEP ADP global upper air and surface weather observations (prepbufr format), National Centers for Environmental Prediction, National Weather Service, NOAA, US Department of Commerce, 2008.
- NCEP, NCEP GDAS satellite data 2004 – continuing, National Centers for Environmental Prediction, National Weather Service, 2009.
- Ashrit, R. et al., IMDAA regional reanalysis: performance evaluation during Indian summer monsoon season. J. Geophys. Res.: Atmosp., 2020, e2019JD030973.
- Indira Rani, S. et al., IMDAA: high-resolution satellite-era reanalysis for the Indian monsoon region. J. Climate, 2021, 34(12), 5109–5133.