Evaluation of subseasonal to seasonal forecasts over India for renewable energy applications
Aheli Das
CORRESPONDING AUTHOR
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New
Delhi, 110016, India
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New
Delhi, 110016, India
Related authors
No articles found.
K. Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
EGUsphere, https://doi.org/10.5194/egusphere-2024-1431, https://doi.org/10.5194/egusphere-2024-1431, 2024
Short summary
Short summary
The study aimed to improve the representation of spring wheat and rice in the CLM5. The modified CLM5 model performed significantly better than the default model in simulating crop phenology, yield, carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific parameters for accurately simulating vegetation processes and land surface processes.
Kangari Narender Reddy, Shilpa Gahlot, Somnath Baidya Roy, Gudimetla Venkateswara Varma, Vinay Kumar Sehgal, and Gayatri Vangala
Earth Syst. Dynam., 14, 915–930, https://doi.org/10.5194/esd-14-915-2023, https://doi.org/10.5194/esd-14-915-2023, 2023
Short summary
Short summary
Carbon fluxes from agroecosystems change the carbon cycle and the amount of CO2 in the air. Using the Integrated Science Assessment Model (ISAM), we looked at the carbon cycle in areas where spring wheat is grown. The results showed that fluxes vary a lot between regions, mostly because planting times are different. According to our investigation into which variables have the greatest impact on the carbon cycle, nitrogen fertilizers added to crops have the greatest impact on carbon uptake.
Axel Kleidon, Gabriele Messori, Somnath Baidya Roy, Ira Didenkulova, and Ning Zeng
Earth Syst. Dynam., 14, 241–242, https://doi.org/10.5194/esd-14-241-2023, https://doi.org/10.5194/esd-14-241-2023, 2023
Tanvi Gupta and Somnath Baidya Roy
Adv. Geosci., 56, 129–139, https://doi.org/10.5194/adgeo-56-129-2021, https://doi.org/10.5194/adgeo-56-129-2021, 2021
Short summary
Short summary
In this paper we study how the momentum extracted by wind turbines get replenished so that the wind farm can continue to function. We use a numerical model to simulate the dynamics of a hypothetical coastal wind farm in the Arabian Sea under sea breeze conditions. Results show that vertical turbulent eddies can replenish more than half of the lost momentum, but horizontal advection also plays a role near the wind farm edges especially in sparsely packed wind farms.
Tanvi Gupta and Somnath Baidya Roy
Wind Energ. Sci., 6, 1089–1106, https://doi.org/10.5194/wes-6-1089-2021, https://doi.org/10.5194/wes-6-1089-2021, 2021
Short summary
Short summary
Wind turbines extract momentum from atmospheric flow and convert that to electricity. This study explores recovery processes in wind farms that replenish the momentum so that wind farms can continue to function. Experiments with a numerical model show that momentum transport by turbulent eddies from above the wind turbines is the major contributor to recovery except for strong wind conditions and low wind turbine density, where horizontal advection can also play a major role.
Narender Kangari Reddy and Somnath Baidya Roy
Adv. Geosci., 54, 79–87, https://doi.org/10.5194/adgeo-54-79-2020, https://doi.org/10.5194/adgeo-54-79-2020, 2020
Short summary
Short summary
In this study, we apply the Genetic Algorithm technique that mimics the natural selection process observed in nature to design optimal layouts for massive wind farms off the southeastern coast of India using real wind data. Our results show that layout optimization leads to large improvements in power generation (up to 28 %), efficiency (up to 34 %), and cost (up to 25 %) due to the reduction in wake losses.
Shilpa Gahlot, Tzu-Shun Lin, Atul K. Jain, Somnath Baidya Roy, Vinay K. Sehgal, and Rajkumar Dhakar
Earth Syst. Dynam., 11, 641–652, https://doi.org/10.5194/esd-11-641-2020, https://doi.org/10.5194/esd-11-641-2020, 2020
Short summary
Short summary
Spring wheat, a staple for millions of people in India and the world, is vulnerable to changing environmental and management factors. Using a new spring wheat model, we find that over the 1980–2016 period elevated CO2 levels, irrigation, and nitrogen fertilizers led to an increase of 30 %, 12 %, and 15 % in countrywide production, respectively. In contrast, rising temperatures have reduced production by 18 %. These effects vary across the country, thereby affecting production at regional scales.
Cited articles
Alduchov, O. and Eskridge, R.: Improved Magnus Form Approximation of
Saturation Vapor Pressure, J. Appl. Meteorol., 35, 601–609,
https://doi.org/10.1175/1520-0450(1996)035<0601:imfaos>2.0.co;2,
1996.
Bell, R. and Kirtman, B.: Seasonal Forecasting of Wind and Waves in the
North Atlantic Using a Grand Multimodel Ensemble, Weather Forecast., 34,
31–59, https://doi.org/10.1175/waf-d-18-0099.1, 2019.
Bloomfield, H. C., Brayshaw, D. J., Gonzalez, P. L. M., and Charlton-Perez, A.: Sub-seasonal forecasts of demand and wind power and solar power generation for 28 European countries, Earth Syst. Sci. Data, 13, 2259–2274, https://doi.org/10.5194/essd-13-2259-2021, 2021.
CDS: Seasonal forecast monthly statistics on single levels, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-monthly-single-levels?tab=form, last access: 6 March 2021.
Chen, M., Wang, W., and Kumar, A.: Prediction of Monthly-Mean Temperature:
The Roles of Atmospheric and Land Initial Conditions and Sea Surface
Temperature, J. Climate, 23, 717–725, https://doi.org/10.1175/2009jcli3090.1, 2010.
De Felice, M., Soares, M., Alessandri, A., and Troccoli, A.: Scoping the
potential usefulness of seasonal climate forecasts for solar power
management, Renew. Energ., 142, 215–223, https://doi.org/10.1016/j.renene.2019.03.134,
2019.
Doblas-Reyes, F., García-Serrano, J., Lienert, F., Biescas, A., and
Rodrigues, L.: Seasonal climate predictability and forecasting: status and
prospects, WIREs Clim. Change, 4, 245–268, https://doi.org/10.1002/wcc.217, 2013.
Dunning, C., Turner, A., and Brayshaw, D.: The impact of monsoon
intraseasonal variability on renewable power generation in India, Environ.
Res. Lett., 10, 064002, https://doi.org/10.1088/1748-9326/10/6/064002, 2015.
Ferro, C., Richardson, D., and Weigel, A.: On the effect of ensemble size on
the discrete and continuous ranked probability scores, Meteorol. Appl.,
15, 19–24, https://doi.org/10.1002/met.45, 2008.
Fröhlich, K., Dobrynin, M., Isensee, K., Gessner, C., Paxian, A.,
Pohlmann, H., Haak, H., Brune, S., Früh, B., and Baehr, J.: The German
Climate Forecast System: GCFS, J. Adv. Model. Earth Sy., 13, e2020MS002101,
https://doi.org/10.1029/2020ms002101, 2021.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2019.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
IEA – International Electricity Agency: Global Energy Review 2021, 36 pp.,
Paris, available at: https://www.iea.org/reports/global-energy-review-2021,
last access: 18 September 2021.
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and Monge-Sanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019.
Kothawale, D. and Rupa Kumar, K.: On the recent changes in surface
temperature trends over India, Geophys. Res. Lett., 32, L18714,
https://doi.org/10.1029/2005gl023528, 2005.
Lledó, L., Torralba, V., Soret, A., Ramon, J., and Doblas-Reyes, F.:
Seasonal forecasts of wind power generation, Renew. Energ., 143, 91–100,
https://doi.org/10.1016/j.renene.2019.04.135, 2019.
Lynch, K., Brayshaw, D., and Charlton-Perez, A.: Verification of European
Subseasonal Wind Speed Forecasts, Mon. Weather Rev., 142, 2978–2990,
https://doi.org/10.1175/mwr-d-13-00341.1, 2014.
MacLachlan, C., Arribas, A., Peterson, K., Maidens, A., Fereday, D., Scaife,
A., Gordon, M., Vellinga, M., Williams, A., Comer, R., Camp, J., Xavier, P.,
and Madec, G.: Global Seasonal forecast system version 5 (GloSea5): a
high-resolution seasonal forecast system, Q. J. Roy. Meteor. Soc., 141,
1072–1084, https://doi.org/10.1002/qj.2396, 2014.
Marcos, R., González-Reviriego, N., Torralba, V., Soret, A., and
Doblas-Reyes, F.: Characterization of the near surface wind speed
distribution at global scale: ERA-Interim reanalysis and ECMWF seasonal
forecasting system 4, Clim. Dynam., 52, 3307–3319,
https://doi.org/10.1007/s00382-018-4338-5, 2018.
Meteo-France: Documentation of the METEO-FRANCE Pre-Operational seasonal
forecasting system, Copernicus Climate Change Service, available at:
http://www.umr-cnrm.fr/IMG/pdf/system6-technical.pdf (last access: 18 June
2021), 2017.
MNRE – Ministry of New and Renewable Energy: Monthly Summary for the
Cabinet for the month of February, 2021, Govt. of India, 5 pp., available at:
https://mnre.gov.in/img/documents/uploads/file_f-1615785529839.pdf, last access: 18 June 2021.
Orlov, A., Sillmann, J., and Vigo, I.: Better seasonal forecasts for the
renewable energy industry, Nat. Energy, 5, 108–110,
https://doi.org/10.1038/s41560-020-0561-5, 2020.
Pechlivanidis, I., Crochemore, L., Soret, A., Lledó, L., Manrique-Sunñén,
A., Brayshaw, D., Gonzlez, P., Charlton Perez, A., Bloomfield, H., Catalano,
F., and Cionni, I.: Benchmarking skill assessment of current subseasonal and
seasonal forecast systems for users' selected case studies, S2S4E –
Subseasonal to seasonal climate predictions for energy, available at:
https://s2s4e.eu/sites/default/files/2020-06/s2s4e_d41.pdf
(last access: 18 June 2021), 2019.
PIB – Press Information Bureau: Low-carbon, green and climate resilient
urban infrastructure is the need of the hour: Vice President, Govt. of
India, 3 pp., available at:
https://pib.gov.in/PressReleseDetail.aspx?PRID=1570611 (last access: 18
June 2021), 2019.
POSOCO – Power System Operation Corporation Limited: Analysing the
Electricity Demand Pattern, 1 pp., available at:
https://www.iitk.ac.in/npsc/Papers/NPSC2016/1570293957.pdf (last access: 18
June 2021), 2016.
Prodhomme, C., Materia, S., Ardilouze, C., White, R., Batté, L., Guemas,
V., Fragkoulidis, G., and García-Serrano, J.: Seasonal prediction of
European summer heatwaves, Clim. Dynam., online first, https://doi.org/10.1007/s00382-021-05828-3,
2021.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer,
D., Hou, Y., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez,
M., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP
Climate Forecast System Version 2, J. Climate, 27, 2185–2208,
https://doi.org/10.1175/jcli-d-12-00823.1, 2014.
Sanna, A., Borrelli, A., Athanasiadis, P., Materia, S., Storto, A., Navarra,
A., Tibaldi, S., and Gualdi, S.: RP0285 – CMCC-SPS3: The CMCC Seasonal
Prediction System 3, CMCC, available at:
https://www.cmcc.it/it/publications/rp0285-cmcc-sps3-the-cmcc-seasonal-prediction-system-3
(last access: 18 June 2021), 2017.
Siegert, S.: Package “SpecsVerification”, Cran.r-project.org, available at:
https://cran.r-project.org/web/packages/SpecsVerification/SpecsVerification.pdf
(last access: 18 June 2021), 2020.
Soret, A., Torralba, V., Cortesi, N., Christel, I., Palma, L.,
Manrique-Suñén, A., Lledó, L., González-Reviriego, N., and
Doblas-Reyes, F.: Sub-seasonal to seasonal climate predictions for wind
energy forecasting, J. Phys. Conf. Ser., 1222, 012009,
https://doi.org/10.1088/1742-6596/1222/1/012009, 2019.
Vitart, F. and Takaya, Y.: Lagged ensembles in sub-seasonal predictions, Q.
J. Roy. Meteor. Soc., 147, 3227–3242, https://doi.org/10.1002/qj.4125, 2021.
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C.,
Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H.,
Hodgson, J., Kang, H., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P.,
Mallas, I., Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean, P.,
Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M.,
Robertson, A., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F.,
Waliser, D., Woolnough, S., Wu, T., Won, D., Xiao, H., Zaripov, R., and
Zhang, L.: The Subseasonal to Seasonal (S2S) Prediction Project Database, B.
Am. Meteorol. Soc., 98, 163–173, https://doi.org/10.1175/bams-d-16-0017.1, 2017.
White, C., Carlsen, H., Robertson, A., Klein, R., Lazo, J., Kumar, A.,
Vitart, F., Coughlan de Perez, E., Ray, A., Murray, V., Bharwani, S.,
MacLeod, D., James, R., Fleming, L., Morse, A., Eggen, B., Graham, R.,
Kjellström, E., Becker, E., Pegion, K., Holbrook, N., McEvoy, D.,
Depledge, M., Perkins-Kirkpatrick, S., Brown, T., Street, R., Jones, L.,
Remenyi, T., Hodgson-Johnston, I., Buontempo, C., Lamb, R., Meinke, H.,
Arheimer, B., and Zebiak, S.: Potential applications of
subseasonal-to-seasonal (S2S) predictions, Meteorol. Appl., 24, 315–325,
https://doi.org/10.1002/met.1654, 2017.
Wilks, D. S.: Statistical methods in the atmospheric sciences, Elsevier,
Amsterdam, 2019.
Short summary
In this study we evaluated subseasonal-seasonal scale forecasts of solar radiation, wind speed, temperature and relative humidity over India from 6 global models by comparing against observations. Results show that the overall quality of the forecasts are not good. However, they demonstrate enough skill suggesting that further improvement through calibration may make then useful for the renewable energy sector.
In this study we evaluated subseasonal-seasonal scale forecasts of solar radiation, wind speed,...