A long-term regional variability analysis of wintertime temperature and its deep learning aspects
Tóm tắt
In present study, the variability in wintertime maximum (Tmax) and minimum (Tmin) temperature patterns over India using observed and deep learning techniques have been assessed. The analysis has been caried out for the period 1979–2018 during the months from November to February. The month of February depicted strongest variability in Tmax and Tmin over Northwest India (NWI) with significant + ve trend for upper half of the country. Wintertime temperature variability was seen to be dominant in the Indo-Gangetic plain area covering some parts of NWI and Northeast India (NEI) for Tmax and Tmin. Also, a gradual increase in the spatial coverage, engulfing majority of South Peninsular India (SPI) and Central India (CI) of the rising Diurnal Temperature Range (DTR) was found from November to January. Decreasing DTR was observed only for January extending along Indo-Gangetic plains. The model Random Forest (RF) performed quite well relative to Long Short-Term Memory model (LSTM) in predicting the winter temperatures (especially for Tmax) during all the considered months. The RF made a robust Tmax forecast during NDJF over all India (RMSE – 0.51, MAPE – 1.4). However, its performance is not up to the mark during the month of February over NEI (RMSE – 1.63, MAPE – 4.5). The maximum fluctuating patterns of temperature have been found during the month of February. The study emphasizes on algorithm-based approaches to study the temperature, so that better understanding could be developed for the meteorological sub-divisions over India.
Tài liệu tham khảo
Acharya N, Kar SC et al (2011) Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India. J Earth Syst Sci 120:795–805
Agrawal S, Chakraborty A et al (2019) Effects of winter and summer-time irrigation over Gangetic Plain on the mean and intra-seasonal variability of Indian summer monsoon. Clim Dyn 53:3147–66
Amato F, Federico F et al (2020) A novel framework for spatio-temporal prediction of environmental data using deep learning. Sci Rep 10(1):22243
Apaydin H, Feizi H et al (2020) Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting. Water 12:1500. https://doi.org/10.3390/w12051500
Bandara K, Bergmeir C et al (2017) Forecasting across time series databases using long short-term memory networks on groups of similar series. arXiv preprint arXiv [online], 1710.03222. https://arxiv.org/abs/1710.03222.
Bhardwaj R, Kumar A et al (2007) Bias free rainfall forecast and temperature trend based temperature forecast based upon T-170 model during monsoon season. Meteorol Appl 14(4):351–360
Bhatla R, Tabassum S et al (2016a) Trend analysis and extreme events of temperature during post monsoon and winter seasons over Varanasi, Uttar Pradesh. India J-IGU 20(1):123–127
Bhatla R, Tripathi A et al (2016b) Study of trend analysis and extreme events of temperature over Varanasi During Summer monsoon season. Mausam 67(2):463–474
Bhutiyani MR, Kale VS et al (2007) Long-term trends in maximum, minimum and mean annual air temperatures across the Northwestern Himalaya during the twentieth century. Clim Change 85(1–2):159–77
Bolton T, Zanna L (2019) Applications of deep learning to ocean data inference and subgrid parameterization. J Adv Model Earth Syst 11:376–399. https://doi.org/10.1029/2018MS001472
Chakraborty TK (2006) Prediction of winter minimum temperature of Kolkata using statistical model. Mausam 57(3):451–458
Chen Y, Zhan W et al (2014) Disaggregation of Remotely sensed land surface temperature: A generalized paradigm. IEEE Trans Geosci Remote Sens 52:5952–5965
Chevuturi A, Dimri AP (2015) Inter-comparison of physical processes associated with winter and non-winter hailstorms using the Weather Research and Forecasting (WRF) model. Model Earth Syst Environ 1:1–9
Chowdary JS, Gnanaseelan C (2007) Basin-wide warming of the Indian Ocean during El Ni˜no and Indian Ocean dipole years. Int J Climatol 27:1421–1438
Chowdary JS, John N et al (2014) Interannual variability of surface air-temperature over India: impact of ENSO and Indian Ocean Sea surface temperature. Int J Climatol 34(2):416–429
Cifuentes J, Marulanda G et al (2020) Air temperature forecasting using machine learning techniques: a review. Energies 13:4215. https://doi.org/10.3390/en13164215
Dash SK, Hunt JC (2007) Variability of climate change in India. Curr Sci 25:782–8
Dash Y, Mishra SK et al (2019) Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques. Environmetrics. 30(4):e2533
De US, Rao GSP et al (2001) Visibility over Indian airports during winter season. Mausam 52(4):717–726
Dueben PD, Bauer P (2018) Challenges and design choices for global weather and climate models based on machine learning. Geosci Model Dev. 11(10):3999–4009
Dimri AP (2004) Models to improve winter minimum surface temperature forecasts, Delhi, India. Meteorol Appl 11:129–139. https://doi.org/10.1017/S1350482704001215
Dimri AP, Chevuturi, (2014) A Model sensitivity analysis study for western disturbances over the Himalayas. Meteorol Atmos 123:155–80
Dimri AP, Ganju A (2007) Wintertime seasonal scale simulation over Western Himalaya using RegCM3. Pure Appl Geophys 164(8–9):1733–46
Dimri AP, Niyogi D (2013) Regional climate model application at subgrid scale on Indian winter monsoon over the western Himalayas. Int J Climatol 33(9):2185–205
Dimri AP, Mohanty UC et al (2002) Statistical model-based forecast of minimum and maximum temperatures at Manali. Curr Sci 82:997–1003
Dimri AP, Niyogi D et al (2015) Western disturbances: a review. Rev Geophys 53(2):225–246
Gers FA, Schmidhuber J et al (1999) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451–2471
Gupta A, Dhaka SK et al (2013) AIRS observations of seasonal variability in meridional temperature gradient over Indian region at 100 hPa. J Earth Syst Sci 122(1):201–213
Hart KA, Steenburgh WJ et al (2004) An evaluation of mesoscale-model-based model output statistics (MOS) during the 2002 Olympic and Paralympic winter games. Weather Forecast 19:200–218
Hewage P, Trovati M et al (2021) Deep learning-based effective fine-grained weather forecasting model. Pattern Anal Appl 24:343–366. https://doi.org/10.1007/s10044-020-00898-1
Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Horii T, Masumoto Y et al (2009) Mixed layer temperature balance in the eastern Indian Ocean during the 2006 Indian Ocean dipole. J Geophys Res 114:C07011. https://doi.org/10.1029/2008JC00518010.1029/2004GL022201
IPCC (2013) Climate Change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G –K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, pp 1535
IPCC Climate Change (2007) The Fourth Assessment Report of the IPCC. Cambridge University Press, Cambridge, UK
Jach L, Schwitalla T, Branch O, Warrach-Sagi K, Wulfmeyer V (2022) Sensitivity of land–atmosphere coupling strength to changing atmospheric temperature and moisture over Europe. Earth System Dynamics 13(1):109–132
Jaeger EB, Seneviratne SI (2011) Impact of soil moisture–atmosphere coupling on European climate extremes and trends in a regional climate model. Clim Dyn 36(9–10):1919–1939
Jaswal AK (2010) Recent winter warming over India-spatial and temporal characteristics of monthly maximum and minimum temperature trends for January to March. Mausam 61(2):163–174
Jeganathan C, Hamm NAS et al (2011) Evaluating a thermal image sharpening model over a mixed agricultural landscape in India. Int J Appl Earth Obs 13:178–191
Karl TR, Kukla G et al (1991) Global warming: evidence for asymmetric diurnal temperature change. Geophys Res Lett 18:182253–182256. https://doi.org/10.1029/91GL02900
Karl TR, Knight RW et al (2000) The record breaking global temperatures of 1997 and 1998: Evidence for an increase in the rate of global warming? Geophys Res Lett 27(5):719–722
Kedia S, Khakare SP et al (2021) Estimates of change in surface meteorology and urban heat island over northwest India: Impact of urbanization. Urban Climate 36:100782. https://doi.org/10.1016/j.uclim.2021.100782
Kendall MG (1975) Rank correlation methods. Griffin, London
Kothawale DR, Rupa KK (2005) On the recent changes in surface temperature trends over India. Geophys Res Lett 32:L18714. https://doi.org/10.1029/2005GL023528
Krishnamurti TN, Sanjay J et al (2004) Determination of forecast errors arising from different components of model physics and dynamics. Mon Weather Rev 132(11):2570–2594
Le QV (2013) Building high-level features using large scale unsupervised learning. In: 2013 IEEE international conference on acoustics, speech and signal processing May 26. IEEE, pp 8595–8598
Lewis SC, King AD (2017) Evolution of mean, variance and extremes in 21st century temperatures. Weather Clim Extremes 15:1–10. https://doi.org/10.1016/j.wace.2016.11.002
Midhuna TM, Kumar P, Dimri AP (2020) A new Western disturbance index for the Indian winter monsoon. J Earth Syst Sci 129:1–4
Madhuri R, Sistla S et al (2021) Application of machine learning algorithms for flood susceptibility assessment and risk management. J Water Clim Change. https://doi.org/10.2166/wcc.2021.051
Malik P, Bhardwaj P et al (2020) Distribution of cold wave mortalities over India: 1978–2014. Int J Disaster Risk Reduct 51:101841
Mall RK, Singh R et al (2006) Impact of climate change on Indian agriculture: a review. Clim Change 78(2–4):445–478
Mann HB (1945) Nonparametric tests against trend. Econometrica: Journal of the Econometric society 245–259
Mishra AK, Dubey AK, Das S (2002) Identifying the changes in winter monsoon characteristics over the Indian subcontinent due to Arabian Sea warming. Atmos Res 273:106162
Mohanty UC, Dimri AP (2004) Location-specific prediction of the probability of occurrence and quantity of precipitation over the Western Himalayas. Weather Forecast 19(3):520–33
Peraudeau S, Lafarge T et al (2015) Effect of carbohydrates and night temperature on night respiration in rice. JXB 66:3931–3944
Riehl H (1962) Jet streams of the atmosphere. Department of Atmospheric Science, Colorado State University. Tech Rep 32
Roy S (2008) Impact of aerosol optical depth on seasonal temperatures in India: a spatio-temporal analysis. Int J Remote Sens 29(3):727–740
Roy S, Balling R (2005) Analysis of trends in maximum and minimum temperature, diurnal temperature range, and cloud cover over India. Geophys Res Lett 32:L12702
Sadeghfam S, Khatibi R et al (2021) Statistical downscaling of precipitation using inclusive multiple modeling (IMM) at two levels. J Water Clim Change. https://doi.org/10.2166/wcc.2021.106
Saha M, Chakraborty A et al (2016) Predictor-year subspace clustering based ensemble prediction of Indian summer monsoon. Adv Meteorol. https://doi.org/10.1155/2016/9031625
Salehipour H, Peltier WR (2019) Deep learning of mixing by two ‘atoms’ of stratified turbulence. J Fluid 861. https://doi.org/10.1017/jfm.2018.980
Scheitlin KN, Dixon PG (2010) Diurnal temperature range variability due to land cover and airmass types in the Southeast. J Appl Meteorol Climatol 49(5):879–888
Shao Q, Li W et al (2021) A deep learning model for forecasting sea surface height anomalies and temperatures in the South China Sea. J Geophys Res: Oceans 126(7):e2021JC017515
Sharma S, Mujumdar PP (2019) On the relationship of daily rainfall extremes and local mean temperature. J Hydrol 572:179–191. https://doi.org/10.1016/j.jhydrol.2019.02.048
Socher R, Lin CC, Manning C, Ng AY (2011) Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 129–136
Srivastava AK et al (2009) Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos Sci Let 10(4):249–54
Sterl A, Severijns C et al (2008) When can we expect extremely high surface temperatures? Geophys Res Lett 35(14)
Sun X, Zhou F et al (2017) Encoding spectral and spatial context information for hyperspectral image classification. IEEE Geosci Remote Sens Lett. 14(12):2250–4
Sunoj VSJ, Shroyer KJ et al (2016) Diurnal temperature amplitude alters physiological and growth response of maize (Zea mays L.) during the vegetative stage. JXB 130:113–121. https://doi.org/10.1016/j.envexpbot.2016.04.007
Tiwari PR, Kar SC et al (2014) Dynamical downscaling approach for wintertime seasonal-scale simulation over the Western Himalayas. Acta Geophys. 62:930–52
Trenberth KE, Caron JM et al (2002) Evolution of El Nino-Southern Oscillation and global atmospheric surface temperatures. J Geophys Res 107(D8):4065. https://doi.org/10.1029/2000JD000298
Vose RS, Easterling DR et al (2005) Maximum and minimum temperature trends for the globe: an update through 2004. Geophys Res Lett 32:L23822. https://doi.org/10.1175/JAMC-D-13-0248.1
Wang F, Zhang C et al (2014) Diurnal temperature range variation and its causes in a semiarid region from 1957 to 2006. Int J Climatol 34:343–354. https://doi.org/10.1002/joc.3690
Wang ZL, Lai CG et al (2015) Flood hazard risk assessment model based on random forest. J Hydrol 527:1130–1141
Wen-Jian H, Hai-Shan C (2013) Impacts of regional-scale land use/land cover change on diurnal temperature range. Adv Clim Change Res 4(3):166–172