by Shantikumar S.Ningombam, Sethulakshmy E.S, SrideviJade, T.S.Shrungeshwara, Chiranjeevi G.Vivek, Dorje Angchuk, T.P.Prabhu and Tashi Tshering Mahay
The present work reports atmospheric opacity at 220 GHz over Indian Astronomical Observatory (IAO)-Hanle during 2006 to 2018. The opacity data were derived from the radiometric measurement of sky-brightness temperature as a function of zenith angle for every 10 min during day and night. Since the operating frequency range is not completely opaque during rainfall or cloudy atmosphere, observations are performed even during rainfall or cloudy sky conditions. The minimum opacities observed at the site varied from 0.06-0.07 at 1st quartile and 0.08-0.09 at 2nd quartile during December-January. Opacity at Hanle is linearly correlated with high temporal resolution GPS (Global Positioning System) Precipitable Water Vapor (PWV) with a correlation coefficient of about 0.8. The observed median opacity at Hanle has increased by 44% during the best observing months (October–March) and 24% over the annual data (October-September) with reference to the opacity observed during 2000–2003. Such increasing opacity trends are apparently due to effects of the dynamics of the regional and global hydrological cycle. In order to assess the impacts of the regional and global hydrological cycle, PWV obtained from AErosol RObotic NETwork (AERONET), satellite and reanalysis data were studied at nine high-altitude astronomical observatories spread spatially across the globe such as Atacama desert in Chile, Mauna Loa Observatory in Hawaii, Hanle and Merak in India, Ali and Yangbajing Observatories in Tibet, LLAMA in Argentina, and Dome A and Dome C in the Antarctic Plateau during 2003-2018. On an average, PWV trend over Atacama desert is increasing by 0.01 to 0.03 mm per year and at IAO-Hanle, Merak and Ali by 0.01 to 0.05 mm per year, indicating non uniform dynamic hydrological cycles. Interestingly, there is no noticeable PWV trend for the sites located in the Antarctic Plateau. During the four best observing months, PWV observed at Hanle, Merak and Ali sites have values lower than the Mauna Loa observatory by 0.14 mm and 0.56 mm at 1st and 2nd quartiles, respectively.
Source: https://doi.org/10.1016/j.jastp.2020.105404
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