Numerical simulation of a rare winter hailstorm event over Delhi, India on 17 January 2013

This study analyzes the cause of the rare occurrence of a winter hailstorm over New Delhi/NCR (National Capital Region), India. The absence of increased surface temperature or low level of moisture incursion during winter cannot generate the deep convection required for sustaining a hailstorm. Consequently, NCR shows very few cases of hailstorms in the months of December-January-February, making the winter hail formation a question of interest. For this study, a recent winter hailstorm event on 17 January 2013 (16:00–18:00 UTC) occurring over NCR is investigated. The storm is simulated using the Weather Research and Forecasting (WRF) model with the Goddard Cumulus Ensemble (GCE) microphysics scheme with two different options: hail and graupel. The aim of the study is to understand and describe the cause of hailstorm event during over NCR with a comparative analysis of the two options of GCE microphysics. Upon evaluating the model simulations, it is observed that the hail option shows a more similar precipitation intensity with the Tropical Rainfall Measuring Mission (TRMM) observation than the graupel option does, and it is able to simulate hail precipitation. Using the model-simulated output with the hail option; detailed investigation on understanding the dynamics of hailstorm is performed. The analysis based on a numerical simulation suggests that the deep instability in the atmospheric column led to the formation of hailstones as the cloud formation reached up to the glaciated zone promoting ice nucleation. In winters, such instability conditions rarely form due to low level available potential energy and moisture incursion along with upper level baroclinic instability due to the presence of a western disturbance (WD). Such rare positioning is found to be lowering the tropopause with increased temperature gradient, leading to winter hailstorm formation

Object storage: how chunky would you like your data?

In this study we examine object storage, a cutting-edge cloud-native technology specifically designed for efficiently managing large datasets. While object storage offers significant cost-effectiveness compared to disk storage, it requires data to be appropriately adapted to fully realise its benefits. Data retrieval from object storage is over HTTP in complete "objects," which are either entire files or file chunks. As this is relatively new technology, there is a clear lack of established tools and best-practice for converting various file types for optimal use with object storage, particularly for large gridded and N-dimensional datasets used in environmental and climate science. The performance and speed of object storage are contingent upon the data's structure, chunking, and the specific analysis requirements of the user. Consequently, a better understanding of these interactions is essential before widespread adoption. To address this need, our study conducted a series of experiments using gridded data with different chunking strategies, aiming to identify the most efficient approach for utilizing and accessing data stored in an object store. Our findings highlight the need for comprehensive understanding of object storage before its widespread adoption, and serve as a valuable resource for guiding future users in utilizing object storage effectively

Role of atmospheric horizontal resolution in simulating tropical and subtropical South American precipitation in HadGEM3-GC31

We assess the effect of increasing horizontal resolution on simulated precipitation over South America in a climate model. We use atmosphere-only simulations, performed with HadGEM3-GC31 at three horizontal resolutions: N96 (∼130 km; 1.88∘×1.25∘), N216 (∼60 km; 0.83∘×0.56∘), and N512 (∼25 km; 0.35∘×0.23∘). We show that all simulations have systematic biases in annual mean and seasonal mean precipitation over South America (e.g. too wet over the Amazon and too dry in the northeast). Increasing horizontal resolution improves simulated precipitation over the Andes and northeast Brazil. Over the Andes, improvements from horizontal resolution continue to ∼25 km, while over northeast Brazil, there are no improvements beyond ∼60 km resolution. These changes are primarily related to changes in atmospheric dynamics and moisture flux convergence. Over the Amazon Basin, precipitation variability increases at higher resolution. We show that some spatial and temporal features of daily South American precipitation are improved at high resolution, including the intensity spectra of rainfall. Spatial scales of daily precipitation features are also better simulated, suggesting that higher resolution may improve the representation of South American mesoscale convective systems

Predictability of South China Sea summer monsoon onset

Predicting monsoon onset is crucial for agriculture and socioeconomic planning in countries where millions rely on the timely arrival of monsoon rains for their livelihoods. In this study we demonstrate useful skill in predicting year to year variations in South China Sea summer monsoon onset at up to 3 months lead time using the GloSea5 seasonal forecasting system. The main source of predictability comes from skilful prediction of Pacific sea surface temperatures associated with El Niño and La Niña. The South China Sea summer monsoon onset is a known indicator of the broadscale seasonal transition that represents the first stage of the onset of the Asian summer monsoon as a whole. Subsequent development of rainfall across East Asia is influenced by sub-seasonal variability and synoptic events that reduce predictability, but interannual variability in the broadscale monsoon onset for East Asian summer monsoon still provides potentially useful information for users about possible delays or early occurrence of the onset of rainfall over East Asia

Projected changes in the East Asian hydrological cycle for different levels of future global warming

Recent decades have shown significant changes to the hydrological cycle over East Asia (EA), and further changes are expected due to future global warming. This study evaluates projected seasonal changes in the EA hydrological cycle using simulations that are 1.5 °C, 2.0 °C and 3.0 ∘C warmer than pre-industrial, from the Met Office Unified Model (MetUM) Global Ocean Mixed Layer model version 2.0 (GOML2.0), compared against present-day conditions. The moisture sources of the warming-induced precipitation changes are identified over five hydrologically unique regions within EA. Precipitation over EA increases with warming (except over southeastern EA in the spring and autumn) due to the intensified hydrological cycle. The projected seasonal changes in the hydrological cycle are usually nonlinear, with the rate of change between 1.5 ∘C and 2.0 ∘C larger than the rate of change between 2.0 ∘C and 3.0 ∘C of warming. The warming-induced precipitation increases are mainly associated with an increase in remote moisture convergence rather than local moisture recycling, except over the Tibetan Plateau. Decomposition of the changes in moisture sources by direction and flux component indicate that changes from the west are dominated by changes to moisture and changes from the north are more circulation driven. The changes from the south are moisture driven over southern EA and driven by moisture and circulation change over northern EA. Our results highlight the regionally and seasonally diverse projected changes to the EA hydrological cycle due to global warming, which will be useful for region-specific climate mitigation policies and the implementation of seasonally varying adaptation methods

Moisture sources for East Asian precipitation: mean seasonal cycle and interannual variability

This study investigates the moisture sources that supply East Asian (EA) precipitation and their interannual variability. Moisture sources are tracked using theWater Accounting Model-2layers (WAM-2layers), based on the Eulerian framework. WAM-2layers is applied to five subregions over EA, driven by the ERA-Interim reanalysis from 1979 to 2015. Due to differences in regional atmospheric circulation and in hydrological and topographic features, the mean moisture sources vary among EA subregions. The tropical oceanic source dominates southeastern EA, while the extratropical continental source dominates other EA subregions. The moisture sources experience large seasonal variations, due to the seasonal cycle of the EA monsoon, the freeze-thaw cycle of the Eurasian continent and local moisture recycling over the Tibetan Plateau. The interannual variability of moisture sources is linked to interannual modes of the coupled ocean-atmosphere system. The negative phase of the North Atlantic Oscillation increases moisture transport to northwestern EA in winter by driving a southward shift in the mid-latitude westerly jet over theMediterranean Sea, the Black Sea and the Caspian Sea. Atmospheric moisture lifetime is also reduced due to the enhanced westerlies. In summers following El Ni ˜nos, an anti-cyclonic anomaly over the western North Pacific increases moisture supplied from the South China Sea to the southeastern EA and shortens the travelling distance. A stronger Somali Jet in summer increases moisture to the Tibetan Plateau and therefore increases precipitation over the eastern Tibetan Plateau. The methods and findings in this study can be used to evaluate hydrological features in climate simulations

The Indian Easterly Jet During the pre-monsoon season in India

We identify for the first time an Indian Easterly Jet (IEJ) in the mid-troposphere during the pre-monsoon using reanalysis data. The IEJ is weaker and smaller than the African Easterly Jet over West Africa, with a climatological location of 10°N, 60–90°E, 700 hPa, and strength 6–7 m s−1 during March–May. The IEJ is a thermal wind associated with low-level meridional gradients in temperature (positive) and moisture (negative), resulting from equatorward moist convection and poleward dry convection. The IEJ is associated with a negative meridional potential vorticity gradient, therefore satisfying the Charney-Stern necessary condition for instability. However, no wave activity is detected, suggesting that the potential for combined barotropic-baroclinic instability is not often realized. IEJ strong (weak) years feature increased (reduced) near-surface temperatures and drier (wetter) conditions over India. This study provides an introduction to the IEJ's role in pre-monsoon dynamics, and a platform for further research

Forecast skill of the Indian monsoon and its onset in the ECMWF seasonal forecasting system 5 (SEAS5)

Accurate forecasting of variations in Indian monsoon precipitation and progression on seasonal timescales remains a challenge for prediction centres. We examine prediction skill for the seasonal-mean Indian summer monsoon and its onset in the European Centre for Medium Range Weather Forecasts (ECMWF) seasonal forecasting system 5 (SEAS5). We analyse summer hindcasts initialised on 1st of May, with 51 ensemble members, for the 36-year period of 1981—2016. We evaluate the hindcasts against the Global Precipitation Climatology Project (GPCP) precipitation observations and the ECMWF reanalysis 5 (ERA5). The model has significant skill at forecasting dynamical features of the large-scale monsoon and local-scale monsoon onset tercile category one month in advance. SEAS5 shows higher skill for monsoon features calculated using large-scale indices compared to those at smaller scales. Our results also highlight possible model deficiencies in forecasting the all-India monsoon rainfall

Forecasting annual maximum water level for the Negro river at Manaus using dynamical seasonal predictions

Early and skilful prediction of the Negro River maximum water levels at Manaus is critical for effective mitigation measures to safeguard lives and livelihoods. Using dynamical seasonal prediction hindcasts, from six prediction centres, we investigate extending the lead time of previously developed statistical models, which issue forecasts in March for Manaus. The original statistical forecast models used observed rainfall as the major predictor. We advance the capability to issue skilful forecasts earlier, in February. We develop ensemble forecasts by combining predictor data from observations and seasonal hindcasts. We compare those forecasts against the original statistical forecast models and forecasts using the observed climatology or persistence of predictors. The ensemble-mean forecasts, issued in February, using European Centre for Medium-Range Weather Forecasts (ECMWF) hindcast input, perform similarly as the original forecasts issued in March and gain one month of lead time. The ECMWF-based ensemble forecasts skilfully predict the likelihood of water levels exceeding the severe flood level of 29 m. Forecast performance reduces and ensemble spread increases with increasing lead time from February to January. We conclude that forecasts for Manaus maximum water levels can be produced using combined input from observations and real-time ECMWF forecasts

Forecasting annual maximum water level for the Negro River at Manaus using dynamical seasonal predictions

Early and skilful prediction of the Negro River maximum water levels at Manaus is critical for effective mitigation measures to safeguard lives and livelihoods. Using dynamical seasonal prediction hindcasts, from six prediction centres, we investigate extending the lead time of previously developed statistical models, which issue forecasts in March for Manaus. The original statistical forecast models used observed rainfall as the major predictor. We advance the capability to issue skilful forecasts earlier, in February. We develop ensemble forecasts by combining predictor data from observations and seasonal hindcasts. We compare those forecasts against the original statistical forecast models and forecasts using the observed climatology or persistence of predictors. The ensemble-mean forecasts, issued in February, using European Centre for Medium-Range Weather Forecasts (ECMWF) hindcast input, perform similarly as the original forecasts issued in March and gain one month of lead time. The ECMWF-based ensemble forecasts skilfully predict the likelihood of water levels exceeding the severe flood level of 29 m. Forecast performance reduces and ensemble spread increases with increasing lead time from February to January. We conclude that forecasts for Manaus maximum water levels can be produced using combined input from observations and real-time ECMWF forecasts