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The present paper discusses development of a hybrid model for predicting near surface air temperature (2 m) over the city scale (∼100 m). The model is based on the premise that temperature at each place within the city is correlated with its mean temperature. The proposed model estimates the mean temperature of a city, using the forecast generated (5 km × 5 km resolution) by regional weather prediction model viz., Weather and Research Forecast (WRF) and downscale it to city scale, using the correlations estimated from the satellite based high-resolution (1 km × 1 km) surface characteristics i.e. Land Surface Temperature (LST). The downscaling coefficients at each grid (1 km x1 km) are generated by formulating the statistical relationship between the LST over the grid to the mean temperature of a city. LST from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on-board Aqua satellite during the Indian summer season (April–June) of the year 2017–2018 has been employed for this purpose. The relationship between LST and air temperature is established using the collocated satellite estimated LST and monitored air temperature observations. As a case study, the model is demonstrated for the Indian city Ahmedabad, but the same procedure can be extended for any other cities. The existence of strong urban heat island during night hours over Ahmedabad city with a temperature gradient of 4-5 °C has been found in the study. During day hours the different parts of the city depicts different temperature based on their surface characteristics. The results have been validated using the in-situ data based on the ground based observational network temporally installed at different locations of the city for the year 2022. The developed model shows that it has a potential to be used in operational mode for generating location specific extreme temperature or heat wave warning within a city. • Development of a hybrid model for predicting near surface air temperature over the city scale. • Model involves downscaling numerical model forecast using LST and air temperature. • The developed model shows the potential for predicting location specific extreme temperature. [ABSTRACT FROM AUTHOR]

The intraseasonal reversal of the temperature over East Asia and the sudden stratospheric warming (SSW) are two abrupt events during the winter of 2020/21. The characteristics of these two events and their relationship are the focused issues in this paper. In winter 2020/21, there was a transition from cold to warm over East Asia. The surface air temperature (SAT) in East Asia suddenly changed from cold to warm with long duration. The cold period lasted from 21 November 2020 to 11 January 2021, while the warm period lasted from 19 January to 28 February 2021. And the transition of the cold to the warm was from 12 to 18 January 2021.There were significant differences of the atmospheric circulations between the cold and warm periods in the upper and lower troposphere. During the cold period, both of Ural Mountain High(UH) and Siberian High(SH) were stronger than normal, the Westerly Jet (WJ) was weakened and located to the area farther southerly than usual, the northerly wind anomalies and the cold advection occurred in East Asian; vice versa during the warm period. The mutations of the atmospheric circulations in the upper and lower levels were obvious during the transition period, especially for UH and SH. A strong SSW event occurred in the stratosphere at the beginning of 2021. As a result, the stratospheric polar vortex was largely weakened and deviated to the areas over Ural Mountains and nearby, the polar westerly wind turned to easterly wind and the temperature gradient reversed. The abnormal changes of the temperature and the geopotential height over the Ural Mountains area in the stratosphere were closely related to the SSW. These abnormal signals caused by the SSW gradually transmitted from stratosphere to troposphere over the Ural Mountains and the polar region, leading to the weakening and collapse of tropospheric UH and further caused the transition from cold to warm in East Asia. The SSW may be the most critical factor leading to the turning from cold to warm over East Asia during the winter of 2020/21. • Focuses on two newest and unexpected events during the winter 2020/21. • The intraseasonal reversal of the temperature over East Asia and the sudden stratospheric warming (SSW). • The characteristics of these two events and their relationship are examined.Apart from SSW. • Other factors of affecting the intraseasonal turning are also pointed out and discussed in this paper. [ABSTRACT FROM AUTHOR]

The main advantage of classifying weather types is its systematic use with pressure gridded values. In this paper, an extensive database covering 74 years was used to determine weather types and their temperature fields in the Iberian Peninsula. Moreover, two 37-year periods were compared to investigate climate change in this region. Lamb weather types were determined. The most frequent was the anticyclonic type (21%) followed by the unclassified type (16.5%), with the distribution of these weather types being nearly similar over time. The pressure field was quite uniform in the region and pressure increased 0.7 hPa between the two periods. Moreover, pressure of easterly flows was greater than the pressure of westerly flows, with their difference being around 7 hPa, and the centres of cyclonic types were closer to the Iberian Peninsula than the centres of the anticyclonic types. Isotherms follow geographic parallels, although the meridian gradient was greater over the continental surface than over the ocean. Northern Africa proved to be prominent, since temperatures were highest and specific flows might favour heat waves over the peninsula. The temperature increase between the two 37-year periods was 0.5 °C. In addition, the contrast between the meridian gradients in the east and west of the region evolved towards a lower contrast of these meridian gradients throughout the whole of the region. • Westerly flows were linked with lower pressure than easterly flows. • Cyclonic centres are closer to the Iberian Peninsula than anticyclonic centres. • The significant temperature gradient in North Africa favours heat waves. • Weather-type distribution was nearly similar over time. • Contrasts of the periods analysed showed increases of 0.7 hPa and 0.5 °C. [ABSTRACT FROM AUTHOR]