Comparison of the Performance of Hybrid ETKF-3DVAR and 3DVAR Data Assimilation Systems on Short-Range Forecasts during Indian Summer Monsoon Season in a Limited-Area Model
by Rekha Bharali Gogoi, Govindan Kutty, V. Rakesh & Arup Borogain
The impact of deploying a flow-dependent ensemble error covariance in Weather Research and Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation (DA) system is examined for short-range rainfall forecasts during an Indian summer monsoon season. The flow-dependent background error covariance (BEC) is generated using a 50-member ensemble, which is further updated using the ensemble transform Kalman filter (ETKF). Assimilation is performed using a Hybrid variational-ensemble (“Hybrid”) and traditional 3DVAR DA system during the 4 weeks of July 2013. The forecasted wind, temperature, and rainfall from the assimilation experiments are verified against corresponding observations. The results indicate that the flow-dependent ensemble background error covariance in 3DVAR has systematically improved the forecasted wind and temperature when compared to the traditional 3DVAR. Similarly, rainfall forecast skill is superior in the Hybrid experiments relative to that of 3DVAR. Convection-permitting resolution rainfall forecast is validated against 746 telemetric rain gauge observations over the state of Karnataka. The Hybrid experiments show higher quantitative precipitation forecast skill than 3DVAR, particularly towards the later stages of data assimilation cycling. Spatially, the 3DVAR experiment shows a dry bias over the upper peninsular regions and a slight wet bias over the central and the northern Indian regions, while the magnitude of such wet and dry biases is smaller in forecasts from Hybrid analysis. Additionally, the westerly wind over the peninsular Indian landmass analyzed by 3DVAR is considerably weaker than that analyzed by the Hybrid experiments. This is proposed as a possible reason for the reduced dry bias in rainfall forecasts over the Indian landmass in Hybrid versus 3DVAR experiments.
Source: https://link.springer.com/article/10.1007/s00024-020-02537-6
Comparison of the performance of HYBRID ETKF-3DVAR and 3DVAR data assimilation scheme on the forecast of tropical cyclones formed over the Bay of Bengal
by Govindan Kutty, Rekha Gogoi, V Rakesh & M Pateria
This study compares the performance of hybrid ensemble transform Kalman filter – three dimensional variational data assimilation (HYBRID) system and three dimensional variational (3DVAR) data assimilation system in Weather Research and Forecasting Model (WRF) in simulating tropical cyclones (TC) formed over the Bay of Bengal. An Ensemble Transform Kalman Filter (ETKF) system updates the ensemble system that provides flow-evolving background error covariance for HYBRID data assimilation system. Results indicate that use of flow-evolving ensemble error covariance in 3DVAR system has systematically reduced the TC position and intensity errors in the analysis; however, adding more weights to the ensemble error covariance term in 3DVAR cost function has not made any significant impact. The 3DVAR analysis depicts a stronger TC vortex with a well pronounced warm core structure as compared to that in HYBRID analysis. The forecasts from HYBRID analysis outperform that from 3DVAR in reducing TC track forecast error. The relative improvement in TC landfall position is 43% and 49% for variously configured HYBRID experiments. The forecasts initiated from HYBRID analysis has higher skill in quantitative precipitation forecasts during TC landfall compared to 3DVAR, which may be attributed to improved track prediction in the HYBRID experiments.
Source: https://link.springer.com/article/10.1007/s12040-020-01497-8
Climate variability and warming in Coastal Odisha: assessing interannual temperature trends and impacts
by Bishnupada Sethi, Sarat Chandra Sahu, Krushna Chandra Gouda, Roshan Beuria, Manas Kumar Mallick, Satish Kumar Samal, Amrutanshu Panigrahi & Abhilash Pati
Coastal Odisha, strategically positioned between the Bay of Bengal and a vast inland landmass, is highly susceptible to climate change and variability impacts. This study comprehensively examines interannual temperature variability and its relationship with mean seasonal temperature using 54 years of homogenised surface meteorological data (1969–2022) from seven WMO-standard meteorological stations. To assess inter-annual variability in association with mean seasonal temperature and precipitation, a difference of standard deviations (SD) of two periods (e.g. 1969–1996 and 1995–2022) along with the relation between running 28-year values of SD and means of seasonal temperature for the period from 1969 to 2022, F-ratio test along with slope and correlation values have been conducted. By comparing two distinct periods (1969–1996 and 1995–2022), the analysis reveals significant climatic shifts, including rising mean temperatures and increased interannual variability at most stations, but some of the stations show an increase in mean temperature with decreased or fluctuating variability. These trends are linked to a heightened frequency of extreme weather events, cascading impacts on agriculture, water resources, health, and infrastructure. Notably, the findings identify localised patterns where warming is accompanied by increased or reduced variability, offering nuanced insights into the region’s complex dynamics. Explicitly noted the limitations of prior studies (e.g. low station density, short temporal scales, lack of focus on interannual variability),this study contributes novel insights to climate research in Odisha by employing long-term, high-resolution station-level data and advanced homogenisation techniques. The relationship between temperature and interannual variability is unique and the inverse relationship between the mean temperature and the variance of temperature is disrupted at most of the stations in coastal Odisha. Its findings hold critical implications for future research as a foundation for regional climate modelling, extreme event prediction, and studying climate variability’s socio-economic impacts. Actionable recommendations, including targeted disaster risk reduction measures, adaptive agricultural practices, and resilient infrastructure planning, underscore policy relevance. By addressing gaps in regional climate knowledge, this work provides a roadmap for integrating localised climate insights into state-level and national climate adaptation frameworks, ensuring preparedness for the evolving climate challenges in coastal Odisha.
Characteristics of pre-monsoon convective systems over south peninsular India and neighborhood using tropical rainfall measuring mission's precipitation radar
by Geeta Agnihotri, K. C. Gouda & Someshwar Das
Ten-year climatology of physical properties of convective echoes during pre-monsoon season over south peninsular India and neighborhood are studied using Precipitation Radar dataset onboard Tropical Rainfall Measuring Mission satellite. Attenuation corrected radar reflectivity (Ze) is used to define an intense convective echo (ICE) which is a group of two or more contiguous convective pixels with Ze exceeding 30 dBZ. Height distribution of ICE is right skewed, single modal with mean and median as 7.8 and 7.3 kms. The ICE with area in range of more than 102 to 103 km2 (C) are most frequent (58.5%) followed by smallest scale (D) having area ≤ 102 km2 (37.2%). Large ICE’s (B/C scale) are less frequent (4.2%). Mean areas of ICE’s in D, C and B/C scales are 72.7, 279 and 1932 km2, respectively. The relation between height and area is linear indicating that taller ICE’s are broader. The mean top heights of D, C and B/C scales are 5.5, 8.7 and 14.2 km, respectively. Frequency distributions of the height of 30 and 40 dBZ show single peaks at 5.5 and 4.75 km. Mean heights of 30 dBZ and 40 dBZ are 5.7 and 4.8 km while median heights are 5.5 and 4.7 km. Their cumulative frequency distribution shows that 6 and 3% of ICE cross 10 km height. Reflectivity structures of ICE show that systems over land are intense compared to that over ocean.
Source: https://link.springer.com/article/10.1007/s00703-020-00740-7
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