Akhilesh Nair

1. Nair, A. S., Counillon, F., & Keenlyside, N. (2024). Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation. Climate Dynamics.
2. Janardhanan, S., Nair A.S., Indu, J. Pagendam, D. (2023). Estimation of groundwater storage loss for the Indian Ganga Basin using multiple lines of
   evidence. Scientific Reports 13, 1797.
3. Pradhan, A., Nair, A. S., Indu, J., Makarieva, O., & Nesterova, N. (2023).Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. Remote Sensing, 15(6), 1532.
4. Nair, A. S., Soman, M. K., Girish, P., Karmakar, S., & Indu, J. (2022). Evaluating SWOT water level information using a large-scale hydrology simulator: A case study over India. Advances in Space Research, 70(5), 1362-1374.
5. Nair, A. S., Verma, K., Karmakar, S., Ghosh, S., & Indu, J. (2022). Exploring the potential of SWOT mission for reservoir monitoring in Mahanadi basin. Advances in Space Research, 69(3), 1481-1493.
6. Krishnan, S., Nair, A. S., Indu, J., Jove-Casulleras, R., & Gonzalvez-Foguet,R. (2022). Brightness temperature and emissivity retrieved from a portable L-band radiometer. International Journal of Remote Sensing, 43(7), 2503-2516.
7. Nair A.S., Indu, J., (2021). Assessment of groundwater sustainability and identifying factors inducing groundwater depletion in India, Geophysical Research Letters, 48, e2020GL087255.
8. Pradhan A., Nair A.S., J. Indu and Pierre Emmanuel Kirstetter (2021), Impact of Sampling of GPM orbital data on Streamflow simulations, Journal of hydrology, 593, 125798.
9. Nair, A. S., Kumar, N., Indu, J., & Vivek, B. (2021). Monitoring lake levels from space: preliminary analysis with SWOT. Frontiers in Water, 3,717852.
10. Verma, K., Nair, A. S., Jayaluxmi, I., Karmakar, S., & Calmant, S. (2021). Satellite altimetry for Indian reservoirs. Water Science and Engineering, 14(4), 277-285.
11. Nair A.S., Rohit M, Thiruvengadam P, J. Indu (2020), Remote sensing Data Assimilation in Hydrometeorology, Hydrological Sciences, 10.1080/02626667.2020.1761021.
12. Nair A.S. and J. Indu (2020), Changing groundwater storage trend of India post severe drought, International Journal of Remote Sensing,7565-7584,41(19).
13. Sooraj Krishnan, Nair A.S., J. Indu (2020), Simulation of Passive Microwave Data towards Efficient Assimilation over Indian Sub-Continent, IEEE Geoscience and Remote Sensing Letters, 10.1109/LGRS.2020.2986011.
14. Mohit Prakash Mohanty, S.Nithya, Nair A.S., J.Indu, Subimal Ghosh, Chandra Mohan Bhatt. Goru Srinivasa Rao, Subhankar Karmakar (2020), Sensitivity of various topographic data in flood management: Implications on inundation mapping over large data-scarce regions, Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2020.125523.
15. Nair, A. S., and Indu, J., (2019) Improvement of land surface model simulations over India via data assimilation of satellite-based soil moisture products, Journal of Hydrology, 573, 406-421 
16. Aan S, Nair A.S., and J. Indu (2019), Role of precipitation forcing on the uncertainty of land surface model simulated soil moisture estimates, Journal of Hydrology, Vol. 580, doi: 10.1016/j.jhydrol.2019.124264.
17. Nair, A. S., and Indu, J., (2018) A Coupled Land Surface and Radiative Transfer Models Based on Relief Correction for a Reliable Land Data Assimilation Over Mountainous Terrain, IEEE Geoscience and Remote Sensing Letters, 15 (11), 1657-1661. Doi: 10.1109/LGRS.2018.2854908.
18. Nair, A. S., and Indu, J., (2017), Performance Assessment of Multi Source Weighted Ensemble Precipitation (MSWEP) product over India, Climate, 5, 2; Doi:10.3390/cli5010002
19. Nair, A. S., and Indu, J., (2016), Enhancing Noah Land Surface Model Prediction skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture, Remote Sensing, 8, 976; Doi: 10.3390/rs8120976

• Moreno, Roberto Suarez; Svendsen, Lea; Counillon, Francois Stephane; Bilge, Tarkan Aslan; Nair, Akhilesh & Bethke, Ingo (2026). Prediction skill of ENSO and ENSO teleconnections in NorCPM: A comparative analysis with C3S. Weather and forecasting. ISSN 0882-8156. 41(3), p. 465–488. doi: 10.1175/waf-d-25-0028.1. Full text in Research Archive Show summary

  In recent decades, high demand from stakeholders and policymakers has intensified research efforts toward operational seasonal forecasts. This is particularly relevant when it comes to El Niño-Southern Oscillation (ENSO), the primary driver of year-to-year climate variability with global socioeconomic impacts. While various state-of-the-art systems effectively target ENSO forecasting, the relative influence of different modeling approaches on prediction skill remains incompletely understood. Here, we evaluate an upgraded version of the Norwegian Climate Prediction Model (NorCPM), which employs anomaly initialization and ocean-only data assimilation (DA), against Copernicus Climate Change Service (C3S) systems using full-field DA in both atmosphere and ocean components. Through systematic comparison, we examine the spatio-temporal prediction skill of sea surface temperatures (SST) in the tropical Pacific and associated teleconnection patterns. Despite its ocean-only constraint and lower resolution, NorCPM demonstrates competitive performance for ENSO’s SST signature. However, the system exhibits systematic amplitude errors in representing mid-latitude atmospheric circulation and precipitation patterns. Our comparative analysis reveals three key findings: (1) ocean-only initialization achieves comparable skill to comprehensive approaches during the mature phase of ENSO; (2) model resolution impacts forecast quality in the eastern Pacific boundary region, where fine-scale ocean-atmosphere coupling processes are crucial, particularly during transition seasons; and (3) initialization strategy more strongly influences the representation of global teleconnection patterns than local ENSO dynamics. While targeted experiments are encouraged to establish causal links between specific model features and prediction capabilities, these results provide practical insights for optimizing prediction systems, particularly for applications requiring accurate precipitation predictions in regions affected by ENSO.

• Nair, Akhilesh Sivaraman; Counillon, Francois Stephane & Keenlyside, Noel Sebastian (2024). Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation. Climate Dynamics. ISSN 0930-7575. 62, p. 10483–10502. doi: 10.1007/s00382-024-07444-3. Full text in Research Archive Show summary

  This study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.

• Janardhanan, Sreekanth; Nair, Akhilesh Sivaraman; Indu, J.; Pagendam, Dan & Kaushika, G.S. (2023). Estimation of groundwater storage loss for the Indian Ganga Basin using multiple lines of evidence. Scientific Reports. 13. doi: 10.1038/s41598-023-28615-y. Full text in Research Archive Show summary

  We used remote sensing data, field observations and numerical groundwater modelling to investigate long-term groundwater storage losses in the regional aquifer of the Ganga Basin in India. This comprised trend analysis for groundwater level observations from 2851 monitoring bores, groundwater storage anomaly estimation using GRACE and Global Land Data Assimilation System (GLDAS) data sets and numerical modelling of long-term groundwater storage changes underpinned by over 50,000 groundwater level observations and uncertainty analysis. Three analyses based on different methods consistently informed that groundwater storage in the aquifer is declining at a significant rate. Groundwater level trend indicated storage loss in the range − 1.1 to − 3.3 cm year−1 (median − 2.6 cm year−1) while the modelling and GRACE storage anomaly methods indicated the storage loss in the range of − 2.1 to − 4.5 cm year−1 (median − 3.2 cm year−1) and − 1.0 to − 4.2 cm year−1 (median − 1.7 cm year−1) respectively. Probabilistic modelling analysis also indicated that the average groundwater storage is declining in all the major basin states, the highest declining trend being in the western states of Rajasthan, Haryana and Delhi. While smaller compared to the western states, average groundwater storage in states further towards east—Uttar Pradesh, Bihar and West Bengal within the basin are also declining. Time series of storage anomalies obtained from the three methods showed similar trends. Probabilistic storage analysis using the numerical model vetted by observed trend analysis and GRACE data provides the opportunity for predictive analysis of storage changes for future climate and other scenarios.

• Pradhan, Ankita; Nair, Akhilesh Sivaraman; Indu, J.; Makarieva, Olga & Nesterova, Nataliia (2023). Leveraging Soil Moisture Assimilation in Permafrost Affected Regions. Remote Sensing. 15(6). doi: 10.3390/rs15061532. Full text in Research Archive Show summary

  The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study’s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance.

• Krishnan, Sooraj; Nair, Akhilesh Sivaraman; Indu, J.; Jove-Casulleras, R. & Gonzalvez-Foguet, R. (2022). Brightness temperature and emissivity retrieved from a portable L-band radiometer. International Journal of Remote Sensing. ISSN 0143-1161. 43(7), p. 2503–2516. doi: 10.1080/01431161.2022.2063039. Full text in Research Archive

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• Wang, Yiguo; Bethke, Ingo; Counillon, Francois Stephane; Williams, Nicholas; Nair, Akhilesh & Chiu, Ping-Gin [Show all 8 contributors for this article] (2026). Development of NorCPM2 for CMIP7 DCPP: Ocean-Sea ice Initialisation Strategy. Full text in Research Archive
• Nair, Akhilesh S.; Tallaksen, Lena M.; Bjørbæk, Torkel A. & Collenteur, Raoul (2025). Gap-filling groundwater level time series of irregular temporal resolution using physical modeling (Pastas) and simple statistical scaling. doi: 10.5194/egusphere-egu25-10107. Full text in Research Archive Show summary

  Groundwater level (GWL) monitoring datasets are essential for effective groundwater resource management and understanding the potential impacts of climate change. However, these datasets frequently contain gaps and irregular measurement intervals, posing challenges for time series analyses that depend on consistent sampling. As a result, GWL datasets with substantial gaps are frequently excluded from further analysis, leading to a loss of temporal and spatial coverage, regional representativity, and potentially valuable insights. Addressing this issue requires effective and interpretable imputation techniques to fill missing values while preserving the physical realism of the reconstructed data. Traditional statistical imputation methods and advanced machine learning algorithms, such as missForest, have been used to address data gaps. While these approaches often yield effective imputations, they lack physical interpretability, particularly for extreme events, which is crucial for understanding the variability and resilience of groundwater systems under changing environmental conditions. This study proposes a novel hybrid imputation approach that combines physical modeling with statistical adjustments. First, GWL data are simulated using Pastas, an open-source framework that leverages hydrometeorological variables and impulse response functions to model GWL time series. These simulations serve as a physically consistent basis for imputing missing values. In the second step, a linear scaling approach is applied to scale the simulated GWL to match the observed start and end point of each gap, ensuring consistency with observations. The hybrid method was tested on data from 213 monitoring wells across Sweden, encompassing diverse temporal resolution and gap characteristics. This process generated continuous daily time series spanning 34 years (1990–2023), enabling the evaluation of long-term groundwater dynamics across Sweden (future work). Validation focused on the ability to capture extreme GWL events. While Pastas-only simulations performed well in reproducing seasonal GWL variability, they failed to accurately capture extremes. The hybrid technique demonstrated significant improvements in representing extreme variability, offering a robust solution for handling irregular and incomplete datasets. Additionally, the study provides insights into regional data characteristics, such as variations in gap patterns and hydrometeorological drivers, offering valuable information for groundwater modeling and analysis. The proposed method not only enhances the reliability of GWL datasets but also supports better decision-making in groundwater resource management. The work is a contribution to the Water4All GroundedExtremes project.

• Nair, Akhilesh Sivaraman; Counillon, Francois Stephane & Keenlyside, Noel Sebastian (2024). Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation. Full text in Research Archive
• Nair, Akhilesh Sivaraman; Counillon, Francois Stephane & Keenlyside, Noel Sebastian (2023). Improving subseasonal prediction with land surface initialisation in NorCPM. Full text in Research Archive
• Nair, Akhilesh Sivaraman; Counillon, Francois Stephane & Keenlyside, Noel Sebastian (2023). Improving subseasonal prediction with land data assimilation in NorCPM. Full text in Research Archive
• Counillon, Francois Stephane; Wang, Yiguo; Bethke, Ingo; Nair, Akhilesh Sivaraman; Keenlyside, Noel Sebastian & Garcia, Lilian (2023). Long coupled Earth System reanalysis with a focus on ocean and sea ice . Full text in Research Archive
• Bethke, Ingo; Counillon, Francois Stephane; Wang, Yiguo; Keenlyside, Noel Sebastian; Barthelemy, Sebastien Jean-Claude & Chiu, Ping-Gin [Show all 11 contributors for this article] (2023). NorCPM – status and plans for CMIP7. Full text in Research Archive
• Nair, Akhilesh Sivaraman; Counillon, Francois Stephane & Keenlyside, Noel Sebastian (2022). Improving sub-seasonal prediction skill of NorCPM using soil moisture assimilation. Full text in Research Archive

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