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Southeast Asia is a region dominated by intense convection and characterised by the high-impact weather associated with synoptic scale tropical depressions, typhoons, or tropical cyclones (TCs). However, more localised convection such as mesoscale convective systems (MCSs) can also produce intense precipitation which can be a major risk for loss of lives and property for the communities in the region. Due to these high-impact weather features, its complex orography, and the significant impact of large-scale weather features on its meteorological variability, predicting weather in Southeast Asia is of great importance and scientific interest, but is a challenge. We aim to characterise the distribution of MCSs in the region and capture how the systems are modulated by the Madden-Julian Oscillation (MJO) and equatorial waves. MCSs in Southeast Asia between 2015 and 2020 were tracked using Himawari satellite data, their associated rainfall estimated using IMERG, and classified by lifetime and propagation speed. TC-related rainfall was also deduced using data from IBTrACS to identify certain cloud clusters as associated with TCs. Between 10S and 10N, MCSs account for 45-70% of the precipitation between November and April, and over most of the region, the fractional MCS contribution to rainfall is higher than average on extreme wet days (>55%). Long-lived (>12 hours) MCSs contribute disproportionately, providing 84% of the rainfall despite comprising only 34% of all MCSs.The MJO modulates MCS rainfall in a similar way to total rainfall, contributing >50% of the total rainfall anomaly, with the number of MCSs being greater in convectively active phases. However, in the West part of the region there are more fast-moving MCSs in the active MJO phases and more slow-moving MCSs in the inactive phases, resulting in fast-moving MCSs having a greater impact on the MJO-associated variation in MCS rainfall. This variation in MCS rainfall is larger in the West part of the region than the East. Meanwhile, variation in the area-mean rainfall rate within the storms, and sizes of storms were less well correlated with MCS rainfall in different phases; when areas were large, area-mean rainfall rate was generally low, and vice versa, providing compensating effects.In the low-level convergence phase of an equatorial Kelvin wave, MCS rainfall and non-organized rainfall both increase, accounting for 20-50% of local rainfall anomalies, a pattern which is again enhanced in the West of the region. By contrast, Westward-propagating Mixed Rossby-Gravity waves, and Rossby-1 waves, do not strongly modulate MCS rainfall, and instead their rainfall anomalies are dominated by TC-related rainfall.These relationships between MCSs and the MJO and Kelvin waves provide useful insight into forecasting MCSs in Southeast Asia by utilising knowledge of the synoptic weather regimes that are or will be affecting the region.

Regional orography around India exerts a profound control on weather and climate, both in summer and winter as part of the diurnal cycle of convection, as well as in extreme events. This poster summarizes the key results of the Indo-UK IMPROVE project (Indian Monsoon Precipitation over Orography: Verification and Enhancement of understanding). IMPROVE considers two focal regions. The Western Ghats intercept the monsoon flow across the Arabian Sea and receive some of the most frequent and heaviest summer rainfall, including being subject to extremes such as the 2018 Kerala floods. Meanwhile, the Himalayas play a vital role in separating dry midlatitude flows from tropical airmasses in summer, while suffering extremes in winter due to western disturbances - cyclonic storms propagating on the subtropical westerly jet. We examine the impact of orography on the observed convective diurnal cycle and assess its simulation in models at a range of resolutions including convection-permitting scales. MetUM and WRF model experiments, in addition to DWR retrievals, are used to identify key mechanisms between forcing at the large scale from the BSISO and newly identified regimes of on- and offshore convection near the Western Ghats. An additional aspect to this work is consideration of a novel Froude number approach for understanding the convective regimes. Secondly, the role of orography in extreme events is considered, including its interactions between passing tropical depressions or western disturbances. Finally, land-atmosphere interactions occurring during the diurnal cycle of precipitation in the Western Ghats and Himalayas regions are discussed. IMPROVE works towards a deeper understanding of orographic rainfall and its extremes over India and uncovering why such mechanisms may be poorly represented in models.

Operational ground-based weather radar data provide a unique opportunity to evaluate simulations of convection in high-resolution models. This is especially advantageous for tropical regions such as India where convection is frequent and interacts with the large-scale circulation. Here, storms derived from 15 Indian operational radars are directly compared to modelled storms at two convection-permitting resolutions, 1.5 and 4.4 km, for a period of 3 weeks during the peak 2016 monsoon season. We objectively identify different morphological properties of storms for 6 regions of India, that is to say their heights, sizes, and intensities. Both model resolutions are found to simulate too much shallow convection compared to radars for all 6 regions. Modelled convection is also frequently too wide and intense, but the 1.5 km model performs noticeably better. Modelled storms also exhibit a maximum area around the freezing level, higher than observed by radars, especially at 4.4 km resolution. We discuss various potential microphysical and dynamical reasons for the major differences seen, thus demonstrating the power of radar-based evaluation of monsoon convection for the Indian region.

Rainfall information is essential for many applications in developing countries, and yet, continually updated information at fine temporal and spatial scales is lacking. In Africa, rainfall monitoring is particularly important given the close relationship between climate and livelihoods. To address this information gap, this paper describes two versions (v2.0 and v3.0) of the TAMSAT daily rainfall dataset based on high-resolution thermal-infrared observations, available from 1983 to the present. The datasets are based on the disaggregation of 10-day (v2.0) and 5-day (v3.0) total TAMSAT rainfall estimates to a daily time-step using daily cold cloud duration. This approach provides temporally consistent historic and near-real time daily rainfall information for all of Africa. The estimates have been evaluated using ground-based observations from five countries with contrasting rainfall climates (Mozambique, Niger, Nigeria, Uganda, and Zambia) and compared to other satellite-based rainfall estimates. The results indicate that both versions of the TAMSAT daily estimates reliably detects rainy days, but have less skill in capturing rainfall amount—results that are comparable to the other datasets.

Rainfall information is essential for many applications in developing countries, and yet, continually updated information at fine temporal and spatial scales is lacking. In Africa, rainfall monitoring is particularly important given the close relationship between climate and livelihoods. To address this information gap, this paper describes two versions (v2.0 and v3.0) of the TAMSAT daily rainfall dataset based on high-resolution thermal-infrared observations, available from 1983 to the present. The datasets are based on the disaggregation of 10-day (v2.0) and 5-day (v3.0) total TAMSAT rainfall estimates to a daily time-step using daily cold cloud duration. This approach provides temporally consistent historic and near-real time daily rainfall information for all of Africa. The estimates have been evaluated using ground-based observations from five countries with contrasting rainfall climates (Mozambique, Niger, Nigeria, Uganda, and Zambia) and compared to other satellite-based rainfall estimates. The results indicate that both versions of the TAMSAT daily estimates reliably detects rainy days, but have less skill in capturing rainfall amount-results that are comparable to the other datasets.