Climatology of blizzards over Schirmacher Oasis, east Antarctica

Weather in Antarctica is subject to frequent and sudden changes. Strong winds and blizzards dominate Antarctic weather. A combination of blowing snow, gale force wind and very low visibility is normally defined as blizzard. Meteorological data recorded at Indian Antarctic Station Maitri, in respect of blizzards recorded during the period 1990-2005 has been studied to find out climatological features of blizzards affecting Schirmacher Oasis. At Maitri the blizzard is mostly associated with extra-tropical storms and is normally preceded by precipitation. On average during the year about 21 blizzards affects the station for 45 days during the year. During the month of April to August 3 to 4 blizzards affects the station. Maximum number of blizzards occurs in the month of August with about 7 blizzard days. Average wind speed recorded during the blizzard is about 52 kt but it exceeded 100 kt on several occasions. The duration may vary from hours to days with average of 25 hours. Longest duration of 168 hours was recorded in June 1997. There are about 12 such occasions when blizzard lasted more than 72 hours. No correlation has been found between maximum wind speed and temperature rise during blizzard and the speed is also not correlated with pressure departure during the period.

Transport of Water Vapor from Tropical Cyclones to the Upper Troposphere

This paper investigates the influence of tropical cyclones on water vapor concentrations in the upper atmosphere above these storms. We use independent data sets of tropical storm intensity, water vapor and lightning activity to investigate this relationship. Water vapor in the upper troposphere is a key greenhouse gas, with direct impacts on surface temperatures. Both the amount and altitude of water vapor impact the radiative balance and the greenhouse effect of the atmosphere. The water vapor enters the upper troposphere through deep convective storms, often associated with lightning activity. The intensity of the lightning activity represents the intensity of the convection in these storms, and hence the amount of water vapor transported aloft. In this paper, we investigate the role of tropical cyclones on the contribution of water vapor to the upper atmosphere moistening. Tropical cyclones are the largest most intense storms on Earth and can last for up to two weeks at a time. There is also evidence that the intensity of tropical cyclones is increasing, and will continue to increase, due to global warming. In this study we find that the maximum moistening of the upper atmosphere occurs at the 200 hPa level (~12 km altitude), with a lag of 1–2 days after the maximum sustained winds in the tropical cyclone. While the water vapor peaks after the maximum of the storm intensity, the lightning activity peaks before the maximum intensity of the storms, as shown previously. We show here that the absolute amount of water vapor in the upper troposphere above tropical storms increases linearly with the intensity of the storms. For every 10 hPa decrease in the minimum pressure of tropical storms, the specific humidity increases around 0.2 g/kg at the 200 hPa level.

Development of Tropical Cyclone over the Bay of Bengal during South West Monsoon

South West Monsoon (SW Monsoon) and Tropical Cyclone (TC) are two important weather systems for Bangladesh. During SW Monsoon i.e. during rainy season Bangladesh gets 70% to 85% of her annual rainfall. TC accompanied with strong gale winds, tornadoes, torrential rains and storm surges is considered as a deadly natural disaster. TC’s are mostly formed during pre-monsoon and post-monsoon season, but not uncommon during SW monsoon. This study consults the best track data (cyclone e-atlas) of India Meteorological Department (IMD) containing tracks of cyclones and depression over North Indian Ocean (NIO) for the years 1891 to 2020 i.e. of 130 years. In these 130 years, among total 1219 storms, 608 had formed during SW monsoon. If only Cyclonic Storms (CS) and Severe Cyclonic Storms (SCS) are considered then, 150 storms formed during SW Monsoon. This paper studied two cyclogenesis factors; vertical wind shear and upper level anticyclone for eight cases of tropical storms formed during SW Monsoon. Besides cyclogenesis factors, influence of Madden Julian Oscillation (MJO) is also studied. Threat analysis associated with tropical storms during SW Monsoon has been done which might help in planning of National Disaster Management Program. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 10(1), 2021, P 57-65

Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds

Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitates a comparison of the predicting performances of different models. The analytical results indicate that the autoregressive integrated moving average (ARIMA) model outperforms the other models in the one-step ahead prediction and presents the least forecasting errors in both the mean and maximum wind speeds. However, the support vector regression (SVR) model has the worst performance on the selected dataset. When it comes to the multi-step ahead forecasting, the prediction error of each model increases as the number of steps expands. Although each model shows an insufficient ability to capture the variation of future wind speed, the ARIMA model still appears to have the least forecasting errors. Hence, the ARIMA model can offer effective short-term forecasting of tropical-storm winds in both one-step and multi-step scenarios.

Assessing the Potential of a Long-Term Climate Forecast for Cuba Using the WRF Model

Seasonal climatic prediction studies are a matter of wide debate all over the world. Cuba, a mainly agricultural nation, should greatly benefit from the knowledge, which is available months in advance of the precipitation regime and allows for the proper management of water resources. In this work, a series of six experiments were made with a mesoscale model WRF (Weather Research and Forecasting Model) that produced a 15-month forecast for each month of cumulative precipitation starting at two dates, and for three non-consecutive years with different meteorological characteristics: one dry year (2004), one year that started dry and turned rainy (2005), and one year where several tropical storms occurred (2008). ERA-Interim reanalysis data were used for the initial and border conditions and experiments started 1 month before the beginning of the rainy and the dry seasons, respectively. In a general sense, the experience of using WRF indicated that it was a valid resource for seasonal forecast, since the results obtained were in the same range as those reported by the literature for similar cases. Several limitations were revealed by the results: the forecasts underestimated the monthly cumulative precipitation figures, tropical storms entering through the borders sometimes followed courses different from the real courses inside the working domain, storms that developed inside the domain were not reproduced by WRF, and differences in initial conditions led to significantly different forecasts for the corresponding time steps (nonlinearity). Changing the model parameterizations and initial conditions of the ensemble forecast experiments was recommended.

Economic damages due to extreme precipitation during tropical storms: evidence from Jamaica

This study investigates economic damage risk due to extreme rainfall during tropical storms in Jamaica. To this end, remote sensing precipitation data are linked to regional damage data for five storms. Extreme value modelling of precipitation is combined with an estimated damage function and satellite-derived nightlight intensity to estimate local risk in monetary terms. The results show that variation in maximum rainfall during a storm significantly contributes to parish level damages even after controlling for local wind speed. For instance, the damage risk for a 20 year rainfall event in Jamaica is estimated to be at least 238 million USD, i.e. about 1.5% of Jamaica’s yearly GDP.