The diurnal cycle of precipitation over the Maritime Continent (MC) has been examined for a 15-year period from 2006 to 2020 using the CMORPH integrated high-resolution satellite estimates (Xie et al. 2017). Hourly fields of precipitation are computed on a 0.10^o lat/lon grid over the MC region [80^oE-160^oE; 15^oS-15^oN]. Composite mean hourly precipitation is constructed for each grid box and for each season using the CMORPH data for the 15-year period. Diurnal cycle of precipitation and its evolution are examined relative to the topography and the phase of the MJO.  Our initial investigation focuses on the precipitation over the Sumatra Island and found the following:   1. Precipitation over the MC exhibits very strong diurnal cycle, with peak observed in late afternoon and early morning, respectively, over islands and adjacent oceans; 2. Over Sumatra, the diurnal cycle propagates from the mountain ridge towards the ocean over both the eastern and western slopes, reaches the maximum over the island in later afternoon and then continues into the adjacent ocean; 3. Over the eastern side, convection over ocean develops and moves back westward toward the island and converge with convection coming down from the mountain over the eastern coastal regions in the afternoon; and4. Both the evolution pattern and the amplitude of precipitation diurnal cycle is modulated by the MJO, with enhanced / suppressed during phases 2-3 / 5-6, respectively. Further work is underway to expand the investigation of diurnal evolution to other major islands of the maritime Continent and to examine the association of the diurnal cycle variations with large-scale / local circulations and topographic effects. Updated results will be reported at the AOGS conference.

This study analyzes wind speed and surface latent heat flux anomalies from the Cyclone Global Navigation Satellite System (CYGNSS), aiming to understand the physical mechanisms regulating intraseasonal convection, particularly associated with the Madden-Julian oscillation (MJO). An advantage of CYGNSS compared to other space-based datasets is that its surface wind speed retrievals have reduced attenuation by precipitation, thus providing improved information about the importance of wind-induced surface fluxes for the maintenance of convection. Consistent with previous studies from buoys, CYGNSS shows that enhanced MJO precipitation is associated with enhanced wind speeds in the oceans bracketing the Maritime Continent, and that associated surface heat fluxes anomalies have a magnitude about 7%-12% of precipitation anomalies. Thus, latent heat flux anomalies are an important maintenance mechanism for MJO convection through the column moist static energy budget. We also show that enhanced fluxes support MJO convection as it transits between the Maritime Continent and northern Australia. A composite analysis during boreal summer over the eastern north Pacific supports the idea that wind-induced surface flux is important for MJO maintenance there. We also show the wind-induced surface fluxes help moisten the atmosphere in advance of diurnal convective disturbances that propagate offshore from the Colombian Coast during boreal summer, helping to sustain such convection.  

Sea surface temperature anomaly (SSTA) is a key factor controlling the tropical cyclone (TC) activity over the western North Pacific (WNP). Significant impacts of the warm SSTA over the Pacific subtropical SSTA on the WNP TC activity in the boreal summer 2018 had been pointed out. In 2018, Asian summer monsoon was active with enhanced lower tropospheric westerlies, and the TC activities are above normal. Here we discuss how the regional Pacific SSTA affects the WNP TC and TC precursor activities by sensitivity experiments using a global cloud-system-resolving model, NICAM. The impacts of subtropical/tropical Pacific SSTA were examined by comparing the simulations forced by all SSTA and removing the subtropical/tropical Pacific SSTA. It was found that (1) the warm SSTA in the subtropical Pacific in 2018 was responsible for the enhanced TC and precursor activities over the central Pacific with the anomalous upward motion of Walker circulation and zonal convergence in the lower troposphere, while (2) the strong monsoon westerlies in 2018 were critical to the enhanced TC and precursor activities in the western Pacific (<150E), which was maintained by warm SSTA in the tropical Pacific. The responses of boreal summer Intraseasonal Oscillation (ISO), which is known to affect subseasonal activity of the WNP TCs, are also discussed.

Long term trends in the intensity of tropical intraseasonal oscillations, like Madden and Julian oscillations (MJO), tropical depressions and convectively coupled equatorial waves namely Kelvin, Rossby and mixed Rossby gravity waves are presented for the period of 40 years from 1979 to 2018. Daily satellite observed outgoing longwave radiation data is used to quantify these waves. From the spatial distribution of waves during different seasons it is seen that there is large heterogeneity in space and time. Most of the waves are observed to have over all increasing trends in DJF season, in particular over northern hemisphere. Kelvin wave shows the most homogeneous behavior with significant increasing trends throughout the tropics in DJF season. In case of MJO, over the period of consideration, spatio-temporal distribution of intensity tends to become more homogeneous with regions of climatological peak having decreasing trends and vice versa. Spatially, MJO and ER waves have decreasing intensities whereas Kelvin and MT have increasing intensities in almost all the seasons over central Pacific. Comparison with long term trends in SST, Vertical shear of zonal wind and TCWP show that SST and TCWV explain observed trends in waves only in limited cases since these decrease over longer period of time over central Pacific. However vertical shear shows more significant influence on wave tendencies and seem to be more contributing factor in long term variations of waves.

Convection scheme is responsible for simulating tropical convection and thus its performance affects model’s fidelity for convection related variability. To improve the variability (including Madden-Julian oscillation, MJO), a spectral cumulus parameterization (spectral scheme) has been developed (Baba 2019, Clim. Dyn.), which exhibits better performance for simulating the variability in different GCMs, as well as diurnal cycle over the Maritime Continent, and global tropical cyclone activity. The scheme was implemented in a coupled GCM which was used as a seasonal prediction system to improve the atmospheric variability during the initialization and prediction.  The evaluation for intraseasonal variability was performed for the initialization of the atmosphere. The results show that the spectral scheme outperformed the original convection scheme (Tiedtke scheme) of the GCM, in terms of MJO from various perspectives (e.g., qualitative and statistical views). The cause of superiority was analyzed regarding the organized convective structure. The analysis indicated that the spectral scheme could simulate shallower convection preceding to the organized convection better than the original scheme, which resulted in amplifying moisture anomaly from low to upper altitude, and titled moist static energy anomaly observed in the observation. The present results imply that the spectral scheme is useful to statistically simulate intraseasonal variability, and thus is expected to improve seasonal variability whose evolution might be influenced by the intraseasonal variability. 

In Vietnam, research on drought is predominantly focused on investigating drought hazard. However, monitoring and evaluation of drought hazard itself would be insufficient for understanding its impact on population, which in turn brings little benefits for drought management. Therefore, it is necessary to conduct more comprehensive drought risk assessment which considers the hazard as well as the socio-economic conditions of a particular region where drought occurs. Vulnerability is the starting point to identify the susceptibility of the exposed population or assets to drought impact. This approach to drought risk assessment based on three independent determinants - hazard, exposure and vulnerability – has been widely applied as an effective tool for drought warning service, at both global and national scale. Using this approach, we assessed drought risk for 27 provinces from four administrative areas of Vietnam: South Central Coast, Central Highlands, South East, and Mekong River Delta. Drought hazard index was calculated combining the Standard Precipitation Index (SPI) and the Vegetation Health Index (VHI) which describe meteorological and agricultural drought, respectively. Exposure and vulnerability indices were calculated using statistical data of land use and socio-economic characteristics obtained from Vietnam’s statistical yearbooks. Using ArcGIS tool, drought risk assessment was conducted calculating drought hazard, exposure, vulnerability, and then deriving a combined drought risk index. The results demonstrated that the highest at-risk provinces were in the Mekong River Delta which is the agricultural production center of Vietnam, especially Vinh Long, Hau Giang, and Soc Trang. By contrast, the South East regions were less impacted by drought compared to other regions. In summary, this research presents a comprehensive approach to drought risk assessment in Vietnam and it has potential to assist agriculture sector with improving preparedness for drought.

The Covid-19 outbreak sweeps the world and largely changes emissions of air pollutants, and subsequently affects air quality. In this study, we studied the long-term trends in concentration of air pollutants quality at two selected cities, i.e., Haikou and Sanya, in a tropical island province of China (Hainan), with particular attentions to the influences of Covid-19 outbreak in different phase periods. To gain the true effect of changing emissions of air pollutants on air quality, we applied boosted regression trees (BRTs) and random forest (RF) algorithm to decouple the impacts of meteorological conditions on air quality. We find that, 1) during the lockdown period in Haikou, the time-slot average concentrations of CO, NO₂, PM_2.5 and PM₁₀ decreased by 9-16%, 26-42%, 17-41% and 14-35%, respectively, comparing to the same periods in 2018 and 2019. In reverse, the average of O₃ concentration increased by 29-46% and SO₂ concentration increased 19% compare to 2019. The similar results can be found in Sanya, i.e., CO, NO₂, PM_2.5 and PM₁₀ concentrations decreased by 18-27%, 15-47%, 20-39% and 11-30%, respectively, with 15-46% increases in SO₂, and 3% increases in O₃ compare to 2019; 2) using the normalization meteorologic conditions, the time-slot average concentrations of CO, NO₂, PM_2.5 and PM₁₀ still showed a decreasing trend both in Haikou and Sanya. However, the deceasing percentages were 4-9%, 0.3-26%, 1-31% and 6-30% in Haikou, 14-20%, 3-26%, 12-28% and 7-22% in Sanya, respectively. The average concentration of O₃ in Haikou showed 25-32% increase while that showed a stable trend in Sanya. In Haikou and Sanya, SO₂ concentration increased by 2-11% and 7-54%; 3) after lockdown period, CO and PM₁₀ concentrations remained steady and the average concentration of NO₂ showed increases with 55% and 60% while O₃ concentration decreased 8% and 12% in Haikou and Sanya, respectively.

Almost all the debates on air quality in India are (often) limited to big cities (like Delhi, Mumbai, and Kolkata), even though most of India’s population lives in Tier-2, Tier-3, and smaller towns. This is primarily driven by the lack of enough surface measurements for ground truthing and lack of an official emissions inventory for modeling. The Air Pollution knowledge Assessment (APnA) city program, launched in 2017, is an attempt to fill this lacuna of information, with an objective to create a baseline database for air pollution in Indian cities, to not only support long-term strategy planning process, but also build a short-term air quality forecasting system for public dissemination. In this program, we integrated satellite feeds on open fires, dust events, and lightning, 3D meteorological feeds processed through WRF model linked to adjust temporal emissions (for example resuspended dust), traffic speed maps to build spatial and temporal profiles for transport sector, power demand and consumption rates from the load dispatch centres, and annual/seasonal reports and resource material from various sectors listed at https://www.urbanemissions.info. This presentation will cover an overview of the methodologies and available in assessing emissions and pollution and how we are integrating these databases to study extreme events like open fires in Oct/Nov over Northern India, Diwali fire crackers, and changes observed during the COVID-19 lockdown periods.

Quantifying how much primary organic carbons (PriOC) and secondary organic carbons (SOC), dominant components of urban PM_2.5, originating from local and transported sources can provide evidential basis to strategize control policies and assess associated health implications. This research work employed synergized methods to study 4545 hourly PM_2.5 in Singapore, a city state constantly affected by local and transboundary influences. Source apportionment was first conducted via positive matrix factorization (PMF) with 30 input species, including selected gaseous species (e.g., O₃, CO, NO_x, isoprene and methyl vinyl ketone). The resolved seven sources were then coupled with analyses of an EC-tracer method to determine PriOC and SOC therein.  Referenced against concentrations during stagnant atmospheric conditions when SOC contribution of total OC (SOC/OC) is >50%, SOC/OC increases to 71–85% under more substantial transported influences, especially substantial influx of transported peat-forest burning smoke during the southwest monsoon season. Compared to locally borne OC, transported OC (1.0–5.2 µg/m³) responsible for >30% of total quantified OC in hourly PM_2.5 are substantially “processed”, evidenced by large fractions (86–92%) of SOC therein. Although a substantial component of OC, SOC concentrations attributed to long-range transport vary depending on seasons (months) of the year. Characteristics and effects of transported OC during southwest versus northeast monsoon on urban SOC may complement results obtained from chemical transport modelling.

Understandings of the combustion conditions at smoke sources and resultant chemical features (e.g., in-situ acidity) of emitted biomass burning smoke are needed and useful to evaluate associated atmospheric chemical reactions of transported smoke and health implications. More than 150 daily samples of urban PM_2.5 predominantly affected by transboundary peat-forest (PF) smoke (a.k.a. smoke-dominant PM_2.5) in 2011–2013, 2015 and 2019 are investigated. We demonstrate the concentration ratio of char-EC/soot-EC as an indicator of smoldering- or flaming-dominated emissions from burning sources. Under predominant influences of transported PF smoke, the mean concentration ratio of char-EC to soot-EC of PM_2.5 at our study site (an urban receptor location) decreased by >70% from 8.2 in 2011 to 2.3 in 2015 but increased from 2015 to 3.8 in 2019 (p < 0.05). The reversed trend with a 65% increase from 2015 to 2019 indicates stronger smoldering relative to flaming, indicating a higher level of soil moisture at smoke origins, possibly associated with rewetting and revegetating peatlands since 2016 and an indication of economical characterization of smoke emissions through distant studies. Concurring with the most flaming-dominant burning conditions in 2015, mean concentrations of sulfates and ammonium in smoke-dominant PM_2.5 reached the highest in 2015, more than double that in 2011, 2012, 2013 and 2019. PM_2.5-bound liquid water content is also the highest in 2015, 1.2–3.5 folds of other years affected by the PF smoke. Even though annual mean concentrations of ammonia gas remain largely below 3.5 µg/m³, an exceptionally high mean concentration of 15.6 µg/m³ was detected during the episodic smoke in June 2013, corresponding to the lowest aerosol acidity with an in-situ pH of 2.6, more alkaline compared to other smoke-dominant PM_2.5 with an in-situ pH ranging from 1.8–2.0.   

Ammonia emitted from the agricultural and livestock sector accounts for 79% of the total ammonia emissions in Korea, and 43% of the agricultural ammonia are determined from pig facilities. Ammonia emission from livestock has required continuous management in view of the environmental issue. This study monitored ammonia emissions in mechanically-ventilated pig houses, which are the general type of pig farming in Korea, and analyzed the effects of various conditions, such as the number of heads, pigs’ age, temperature and relative humidity inside pig farming, on the ammonia emission. The results of correlation analysis showed high relevance   R²=0.8 between the independent variables and the ammonia emission. The regression model was statistically significant with F-value=29.22, p<0.001. Among the independent variables, relative humidity (p-value=3.81e-05) and breeding heads (p-value:9.96e-07) were significantly correlated with ammonia emission of pig houses. Ammonia emissions increased as the relative humidity (B=4.275e+03) and the breeding heads (B=2.22e+02) increased. Among the two independent variables, the number of breeding heads had more influence on ammonia emission. 

Nitrogen dioxide (NO₂) is an important trace gas in the atmosphere, which has important impacts on the surface air quality, atmospheric environment, climate change, and human life. Current available surface NO₂ datasets have overall low accuracy with numerous missing values at coarse spatial resolutions due to their inherent limitations from the input data sources and traditional models. Therefore, the missing forest method is first employed to fill in the missing values of Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) tropospheric NO₂ products caused by cloud contaminations. Then we developed a spatiotemporal machine learning method to derive the full-coverage ground-level NO₂ data at a high spatial resolution of 5 km in China incorporated with the ERA5 meteorological data, surface condition and change, model simulations, and other auxiliary data. Our ground-level NO₂ retrievals are reliable with an average cross-validation coefficient of determination (CV-R²) of 0.88 and root-mean-square error (RMSE) of 6.12 µg/m³, and are superior to previous related studies. This high-resolution, high-quality, and full-coverage NO₂ dataset in China (ChinaHighNO₂) allows us to investigate the NO₂ exposure and variations from small-scale areas and short-time periods. High-polluted risks are mainly concentrated in the North China Plain and typical urban agglomerations which is closely related to the population distribution. Significant NO₂ differences are observed between urban and rural areas, especially in the provincial capital cities of China. Furthermore, we also observed the significant holiday effects of NO₂ during the Spring Festival and National Day in China. Lastly, the rapid decrease of NO₂ concentrations was seen due to the impacts of the lockdowns during the COVID-19 pandemic in China, and then gradually returned to the normal level since about the 72^th days after the Lunar New Year. The ChinaHighNO₂ dataset is valuable for studies on air pollution and environmental health in China.

With the importance of aerosols in Earth’s climate, cloud microphysics, and air quality, aerosol optical properties have been observed extensively from many satellites. Diverse algorithms have been implemented with the use of different sophisticated techniques. The Yonsei AErosol Retrieval (YAER) algorithm can retrieve aerosol properties only over dark surfaces, so it is important to mask pixels with bright surfaces. In contrast to GOCI, AHI is equipped with 3 shortwave and 9 IR channels, which is advantageous for bright pixel masking. In addition, multiple visible and near-IR channels provide a great advantage in aerosol property retrieval from GOCI and AHI. Also, by retrieving the aerosol optical properties through the YAER algorithm at 10-minute of AHI or 1-hour interval of GOCI in 6 km x 6 km resolution, we can observe diurnal variations and transport of aerosols, which has not been possible from LEO satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), and bias correction based on normalized difference vegetation indexes; and (2) compilation of the fused product using ensemble-mean and maximum-likelihood estimation methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error, median bias error than the retrieved result for each product.

The tropopause is the boundary between the troposphere and stratosphere and is normally defined by the temperature lapse rate. Previous studies have noted that synoptic-scale and planetary-scale disturbances bring about lapse-rate-tropopause (LRT) height fluctuations on time scales from several days to several years. In the present study, a diagnostic expression for the tendency of LRT height is derived by assuming that the LRT can be characterized as a discontinuity in the vertical gradient of the potential temperature. In addition, the contribution from each term in the thermodynamic equation to the LRT height is quantified. Using the derived equation, the factors of the seasonal cycle of the tropical tropopause height will be discussed quantitatively.

It has been argued that part of the North Atlantic Oscillation (NAO) variability may be attributed to the 11-year solar UV irradiance cycle, through a top-down effect originating in the stratosphere. Several studies have shown that maximum surface pressure response to the irradiance cycle occurs 2-4 years after solar maximum and that atmosphere-ocean feedbacks might explain the multi-year lag. Alternatively, another top-down effect from particle precipitation (in particular medium-to-high energy electrons or MEE), which maximizes in the declining phase of the solar cycle has been suggested as a potential cause of this lag. Yet these combined effects from irradiance and MEEs have never been modelled in a coupled (ocean-atmosphere) framework. In this study, we use the coupled NorCPM model along with a state-of-the-art MEE forcing dataset to explore the roles of irradiance and particle precipitation on the winter NAO. Three decadal ensemble experiments have been conducted over solar cycle 23 in an idealized setting: a control experiment without an 11-year solar cycle; one experiment with varying spectral solar irradiance and one with MEE in addition.  We find a positive NAO signal from increased irradiance under solar maximum conditions in winter, but without lag.  This lack of multi-annual lag is likely due to the solar-signals imprinted in temperatures below the oceanic mixed-layer tending to re-emerge in the subsequent winters equatorward of the region where large sea surface temperature gradients anchor strong ocean-atmosphere feedbacks. The role of enhanced MEE precipitation in some years is discussed. In summary, our results indicate that the combination of irradiance and MEE precipitation induce overall a very weak modulation of the NAO. However, the signal-to-noise ratios remain very small indicating the predominance of internal NAO variability, a manifestation of the signal-to-noise paradox in climate model responses to external forcings.

In this paper, we use a set of climate sensitivity simulations with the abrupt quadrupling of the atmospheric CO2 mixing ratio (4xCO2) experiment to investigate the impact of interactive chemistry processes in place of prescribed space-time distributions of trace gases on surface climate in Japan Meteorological Agency-Meteorological Research Institute (JMA-MRI) earth system model. Here a diagnostic metric of equilibrium climate sensitivity (ECS) is used, which is defined as the change in the global-mean surface air temperature due to the abrupt 4xCO2 in a standard climate change experiment, in order to assess the impact of interactive chemistry processes with a new graphical method developed by Jain et al. (2021). While using interactive chemistry as compared to the prescribed-value run, statistically significant amplification of global warming is noted in the southern hemisphere whereas significant reduction of global warming in the northern hemispheres, although there is no significant impact on the global-mean surface temperature change. That is, the ECS remains almost unchanged. However, the climate forcing and climate feedbacks are noted to change by ~10% when the chemistry is interactive as compared to the prescribed-value run. We also find that using interactive chemistry in place of prescribed values leads to additional cooling of ~2K near the tropical tropopause, but large warming in the stratosphere in this model. Water vapor is also noted to be reduced in the stratosphere in the interactive chemistry experiment as compared to the prescribed one. This could be explained by enhanced freezing and drying mechanism due to the additional cooling around the tropical tropopause. There are also changes in ozone concentration near the surface, corresponding to the temperature changes in each hemisphere with opposite sign, together with reduction in the tropical upper-troposphere lower-stratosphere region.

Effects of realistic propagation of gravity waves (GWs) on distribution of GW pseudomomentum fluxes are explored using a global ray-tracing model for the 2009 sudden stratospheric warming (SSW) event. Four-dimensional (4D; x–z and t) and two-dimensional (2D; z and t) results are compared for various parameterized pseudomomentum fluxes. In ray-tracing equations, refraction due to horizontal wind shear and curvature effects are found important and comparable to one another in magnitude. In the 4D, westward pseudomomentum fluxes are enhanced in the upper troposphere and northern stratosphere due to refraction and curvature effects around fluctuating jet flows. In the northern polar upper mesosphere and lower thermosphere, eastward pseudomomentum fluxes are increased in the 4D. GWs are found to propagate more to the upper atmosphere in the 4D, since horizontal propagation and change in wave numbers due to refraction and curvature effects can make it more possible that GWs elude critical level filtering and saturation in the lower atmosphere. GW focusing effects occur around jet cores, and ray-tube effects appear where the polar stratospheric jets vary substantially in space and time. Enhancement of the structure of zonal wave number 2 in pseudomomentum fluxes in the middle stratosphere begins from the early stage of the SSW evolution. An increase in pseudomomentum fluxes in the upper atmosphere is present even after the onset in the 4D. Significantly enhanced pseudomomentum fluxes, when the polar vortex is disturbed, are related to GWs with small intrinsic group velocity (wave capture), and they would change nonlocally nearby large-scale vortex structures without substantially changing local mean flows.

Changes in the stratospheric circulation associated with sudden stratospheric warmings (SSWs), such as deformation, displacement, breakup, and temporary disappearance of the polar vortex, cause changes in the propagation path and critical level of atmospheric gravity waves (GWs) that originate in the troposphere and propagate upward to the mesosphere and lower thermosphere (MLT), resulting in a substantial modulation of GW activity in the MLT. While combining multiple satellite measurements might capture the daily modulation of MLT GW activity that accompanies the rapidly developing SSW with global coverage, the complementary approach using high-resolution atmospheric general circulation model (GCM) simulations is promising to depict this process vividly. We have developed a four-dimensional whole neutral atmosphere data assimilation system [Koshin et al. 2020; Koshin et al. submitted], which assimilates stratospheric and mesospheric satellite data (MLS, SABER, SSMIS, etc.) in addition to conventional observation data. Based on a long-term analysis product created with the data assimilation system, we have conducted global GW simulations using a 20 km-mesh high vertical resolution T639L340-GCM to study the modulation of MLT GW activity in various SSW cases and the role of Rossby waves and GWs in the time evolution of MLT large-scale fields [e.g., Okui et al. submitted]. In this study, we focus on the polar vortex splitting SSW that occurred on February 11, 2018 and present the three-dimensional propagation of GWs, which dramatically evolved with the development of SSW.

After several recent stratospheric sudden warming (SSW) events, the stratopause disappeared and reformed at a higher altitude, forming an elevated stratopause (ES). The relative roles of atmospheric waves in the mechanism of ES formation are still not fully understood. We performed a hindcast of the 2018/19 SSW event using a gravity-wave (GW) permitting general circulation model containing the mesosphere and lower thermosphere (MLT), and analyzed dynamical phenomena throughout the entire middle atmosphere. An ES formed after the major warming on 1 January 2019. There was a marked temperature maximum in the polar upper mesosphere around 28 December 2018 prior to the disappearance of the descending stratopause associated with the SSW. This temperature structure with two maxima in the vertical is referred to as a double stratopause (DS). We showed that adiabatic heating from the residual circulation driven by GW forcing (GWF) causes barotropic and/or baroclinic instability before DS formation, causing in situ generation of planetary waves (PWs). These PWs propagate into the MLT and exert negative forcing, which contributes to DS formation. Both negative GWF and PWF above the recovered eastward jet play crucial roles in ES formation. The altitude of the recovered eastward jet, which regulates GWF and PWF height, is likely affected by the DS structure. Simple vertical propagation from the lower atmosphere is insufficient to explain the presence of the GWs observed in this event.

The February 2016 disruption of the QBO (quasi-biennial oscillation) westerly winds was an unprecedented event, which was prominently caused by equatorward propagating extratropical Rossby waves. Further studies (e.g. Kang et al. 2020) showed that equatorial planetary waves and small-scale gravity waves play an equally important role. Gravity waves, albeit known for their small horizontal and vertical characteristic wavelengths, can transport large amounts of energy and momentum from their tropospheric sources deep into the middle and upper atmosphere. Observations of gravity waves in general remain challenging. This is especially true for distinct events like the 2016 QBO disruption. In this presentation we will show results from remote-sensing satellite observations. We use observational data from NASA’s Atmospheric Infrared Sounder (AIRS) satellite instrument for our analysis. Variances of the 4.3 μm CO₂ band brightness temperatures are analysed together with level 3 temperature retrievals to investigate the connection of gravity waves with the QBO disruption event. Spatial and temporal variations of gravity wave activity in the middle atmosphere are analysed for seven different regions in the tropics. The results show an increase in gravity wave variances during the QBO westerly phase prior the disruption with highest variances found in the pacific region. However, the Latin America region shows more prominent gravity wave activity in January and February 2016. We will further discuss potential implications of these spatial gravity wave distributions to the development of the QBO disruption.

In ocean flows estimation, initial condition and physical parameters in dynamics model generally need to be retrieved via 4D-Var method from remote sensing data for reliable prediction. Due to model parameterization, some underlying physical process of fluids flows evolution will not be well preserved. In this scenario, we introduce a fluid dynamics features-driven sparsity regularization strategy into the framework of 4D-Var. Introducing the adaptive sparse representation of rich spatial variations of flows helps overcome magnitude underestimation and staircase artifacts caused by classical gradient regularization-based 4D-Var methods. In addition, inspired by avoiding the manual computation of the adjoint model, we proposed a deep least-squared variational model based on neural network. The promising performance on 2D fluid flows estimation in real test cases using observation image from Grenoble Coriolis platform demonstrates the efficiency of our approach.

An ensemble-based forecast sensitivity to observations (EFSO) diagnosis has been implemented in an atmospheric general circulation model--ensemble Kalman filter data assimilation system to estimate the impacts of specific observations from the quasi-operational global observing system on weekly short- and medium-range forecasts. It was examined whether EFSO reasonably estimate the impacts of a subset of observations from specific geographical locations in 6-hour to 7-day forecasts, with the reference obtained from 12 data denial experiments in each of which a subset of three radiosonde observations launched from a geographical location was excluded. The 12 locations were selected from three latitudinal bands comprising (i) four Arctic regions, (ii) four midlatitude regions in the Northern Hemisphere, and (iii) four tropical regions during the Northern Hemisphere winter of 2015/16. The estimated winter-averaged EFSO-derived observation impacts well corresponded to the 6-hour observation impacts obtained by the data denials and EFSO could reasonably estimate the observation impacts by the data denials on short-range (6-hour to 2-day) forecasts. Furthermore, during the medium-range (4-day to 7-day) forecasts, it was found that the Arctic observations tend to seed the broadest impacts and their short-range observation impacts could be projected to beneficial impacts in Arctic and midlatitude North American areas. The midlatitude area located just downstream of dynamical propagation from the Arctic toward the midlatitudes.

The computation of error covariance is essential to the performance of the Kalman filter. Its high computational load can make an algorithm intractable for many high dimensional models in data assimilation. Toward the effort of estimating unknown error covariance, we prove that a matrix upper bound of Kalman filter’s error covariance can be approximated based on its quantitative characteristics, including (i) the peak value and its location in the symmetric matrix, (ii) the decay rate from peak to bottom, and (iii) inequality constraints of the elements in error covariance. These quantitative characteristics unveil interconnections between error covariance and system models, which are new discoveries in the literature of Kalman filters. Although the computation of error covariance is generally intractable for high dimensional systems, computational methods are developed that can numerically compute the quantitative characteristics with a limited computational load. We demonstrate some potential applications of the discoveries through three examples. In the first example, we sketch the outline shape of error covariance using its peak and bottom value as well as the curve of a decay function. The method's goal is not about replacing the need to estimate the error covariance in a process, such as EnKF or 4DVar. Instead, the outline shape provides information that helps to validate an estimated error covariance if the full matrix is intractable or if its estimation is based on a relatively small ensemble size. In the second example, the quantitative characteristics of error covariance are used as an indicator of the upper bound for the localization radius of an EnKF. In the third example, the results proved for linear systems are tested using a nonlinear model, the shallow water equations.

The diurnal cycle is the most prominent mode of rainfall variability in the Tropics, governed mainly by the strong solar heating and land-sea interactions which trigger convection. Over the western Maritime Continent, complex orographic and coastal effects can also play an important role. Weather and climate models often struggle to represent these physical processes, resulting in substantial model biases in simulations over the region. For numerical weather prediction, these biases manifest themselves in the initial conditions, leading to phase and amplitude errors in the diurnal cycle of precipitation. Using a tropical convective-scale data assimilation system, we assimilate 3-hourly radiosonde data from the pilot field campaign of the Years of Maritime Continent, in addition to existing available observations, to diagnose the model biases and assess the relative impacts of additional wind, temperature and moisture information on the simulated diurnal cycle of precipitation over the western coast of Sumatra. We show how assimilating such high frequency in-situ observations can improve the simulated diurnal cycle, verified against satellite-derived precipitation, radar-derived precipitation and rain gauge data. The improvements are due to a better representation of the sea breeze and increased available moisture in the lowest 4 km prior to peak convection. Assimilating wind information alone was sufficient to improve the simulations. We also highlight weaknesses in the data assimilation system that can negatively impact the simulations. These weaknesses suggest that even with the availability of additional high frequency observations, effort must be made to ensure they are assimilated optimally.

Advances of high-performance computing technologies have enabled us to study ensemble data assimilation with ensemble sizes larger than 1000. Earlier studies (Miyoshi et al. 2014; 2015, Kondo and Miyoshi 2016) demonstrated the advantage of using large ensemble sizes up to 10240. However, the advantage is still unclear in the real situation, where imperfections are included in the observation and model, and in representing the observation errors. This study examines the advantage of a large ensemble of 1000 members in the local ensemble transform Kalman filter (LETKF; Hunt et al. 2007) in a real case with conventional observations in the Japan region in summer 2020. We used the SCALE-LETKF system (Lien et al. 2017) composed of the 18-km resolution SCALE regional model (Nishizawa et al. 2015) and the LETKF. The conventional observation dataset known as PREPBUFR used in NCEP GDAS is assimilated every 6 hours. For the lateral and surface boundary conditions, NCEP GFS data is used. In this study, we run the SCALE-LETKF with ensemble sizes up to 1000. The covariance inflation and localization parameters are fixed regardless of the ensemble size, so that we focus on the qualitative impact on the analysis. For each experiment, the data assimilation cycle is initialized at 0000 UTC 1 August 2020 using the NCEP GFS data with random perturbations. The analyses at 0000 UTC 3 August are investigated. The analysis RMSE and first-guess RMSE relative to the observations are significantly reduced for most variables and vertical levels with larger ensemble sizes. However, analysis biases for surface pressure and humidity are degraded. The treatment of systematic errors in the model and observations would be important to achieve the full potential of the LETKF with a large ensemble size. The results of further experiments will be discussed in the presentation.

The Kalman filter (KF) is derived under the assumption of time-independent (white) observation noise. Although this assumption can be reasonable in many ocean and atmospheric applications, the recent increase in sensors coverage such as the launching of new constellations of satellites with global spatio-temporal coverage will provide high density of oceanic and atmospheric observations that are expected to be time-dependent (colored). In this situation, the KF update has been shown to generally provide overconfident probability estimates, which may degrade the filter performance. Different KF-based schemes accounting for time-correlated observation noise were proposed for small systems by modeling the colored noise as a first-order autoregressive model driven by white Gaussian noise. This work introduces new ensemble Kalman filters (EnKFs) that account for colored observational noises for efficient data assimilation into large-scale oceanic and atmospheric applications. More specifically, we follow the standard and the one-step-ahead smoothing formulations of the Bayesian filtering problem with colored observational noise, modeled as an autoregressive model, to derive two (deterministic) EnKFs. We demonstrate the relevance of the colored observational noise-aware EnKFs and analyze their performances through extensive numerical experiments conducted with the Lorenz-96 model.

Oil spills at sea pose a serious threat to the coastal environment. To control and limit unreported spills, it is essential to identify pollution sources, and satellite imagery can be an effective tool for this purpose. We present in this work a Bayesian inference approach to identify the source parameters of a spill from contours of oil slicks detected by satellite images. The approach adopts an observation error model based on a non-local measure of the dissimilarity between the predicted and observed contours. A Markov chain Monte Carlo (MCMC) technique is then employed to sample the posterior distribution of five parameters of interest: the x and y coordinates of the source of release, the time and duration of the spill, and the quantity of oil released. To make the estimation of the posterior distribution computationaly feasible, a Polynomial Chaos-based surrogate of the oil spill model is used within MCMC. To that end, a feature-based object localization method based on image moments is proposed to approximate contours, or binary images, in the form of integral quantities, for which surrogate models can be built. Two synthetic experiments of a spill released from a fixed point source are investigated, where a contour is completely observed in the first case, while two contours are partially observed at different times in the second case. In both experiments, the proposed framework is able to provide good estimates of the source parameters along with a level of confidence reflected by the uncertainties within. In the case of partial observations, the estimated parameters can be used to reconstruct the missing parts of an observed slick from which an oil spill model can be initiated to better forecast the spread of oil.

The Japan’s Big Data Assimilation (BDA) project started in October 2013 and ended its 5.5-year period in March 2019. Here, we developed a novel numerical weather prediction (NWP) system at 100-m resolution updated every 30 seconds for precise prediction of individual convective clouds. This system was designed to fully take advantage of the phased array weather radar (PAWR) which observes reflectivity and Doppler velocity at 30-second frequency for 100 elevation angles at 100-m range resolution. By the end of the 5.5-year project period, we achieved less than 30-second computational time using the Japan’s flagship K computer for past cases with all input data such as boundary conditions and observation data being ready to use. The direct follow-on project started in April 2019. We continued the development to achieve real-time operations of this novel 30-second-update NWP system for demonstration at the time of the Tokyo 2020 Olympic and Paralympic games. The games were postponed, but the project achieved successful real-time demonstration of the 30-second-update NWP system at 500-m resolution during July 31 and August 7, 2020 using a powerful supercomputer called Oakforest-PACS operated jointly by the Tsukuba University and the University of Tokyo. This presentation will summarize the real-time demonstration in 2020 and plans for 2021.

Convection initiation (CI) and the subsequent upscale convective growth (UCG) at the coast of South China in a warm-sector heavy rainfall event are shown to be closely linked to a varying marine boundary-layer jet (MBLJ) over the northern South China Sea (NSCS). Compared to radar observations, the simulations capture CI locations and the following southwest–northeast-oriented convection development. The nocturnal MBLJ peaks at 950 hPa and significantly intensifies with turning from southwesterly to nearly southerly by inertial oscillation. The strengthened MBLJ promotes mesoscale ascent on its northwestern edge and terminus where enhanced convergence zones occur. Located directly downstream of the MBLJ, the coastal CI and UCG are dynamically supported by mesoscale ascent. Through a series of numerical sensitivity simulations, we further investigated the effects of the terrain, coastline, and cold pools on CI and UCG during this case. CI occurred at the vertex of the coastal concave mountain geometry as a combined result of coastal convergence, orographic lifting and mesoscale ascent driven by the terminus of a marine boundary-layer jet (MBLJ). In numerical simulations with the coastline or terrain of South China removed, the coastal CI does not occur or becomes weaker as the MBLJ extends farther north, suggesting that the coastline and terrain play a role in CI. In addition, local small-scale terrain can modulate the detailed location and timing of CI and UCG. When the coastal concave terrain and coastline near the CI are artificially removed or filled by additional mountains, the orographic lifting and the local convergence along the coast correspondingly change, which strongly affects the CI and UCG. Furthermore, new convection is continuously generated by the lifting of low-level moist southerlies at the leading edges of cold pools that tend to move southeastward because of the blocking coastal mountains. 

The detection and attribution of precipitation changes are fundamental for adaptation and mitigation planning. Based on high-quality observations, we determined the detectability of the trends of multiple precipitation characteristics across China using a field significance test. Furthermore, the timing at which spatially-aggregated changes become significant and do not reflect random internal variability was also estimated. The results show that the significant increases in the annual total precipitation (PRCPTOT) and simple precipitation intensity (SDII) and a significant decrease in the wet days (WD) are detectable from 1961 to 2017. Namely, the percentage of stations showing these significant trends exceeds that expected by chance. The time of the trend emergence from the mimicked range of internal variability is around 2000 for SDII and WD, while the PRCPTOT trend can only be detected for recent years. The analysis on precipitation of various intensity levels unearths that the significant increases in the amount and frequency of extreme heavy precipitation emerged around 2014, while a significant decreasing trend in light precipitation might be detected as early as 2000. Global warming is expected to affect the detection of precipitation trends because the timing at which global warming signal in trend of precipitation emerges is consistent with the time at which the trends become significant. In general, significant changes in the PRCPTOT, SDII, and WD occur more frequently in winter than in summer.

In this study, we investigate rainfall characteristics, such as rainfall intensity, rainfall coverage, and the location of heavy rain, associated with tropical cyclones (TCs) that made landfall over China during 2005–2014, using the observations of Tropical Rainfall Measuring Mission and environmental fields. Results show that before landfall, the stronger the TC itself is, the stronger the TC rainfall intensity is in both eyewall and inner rainband regions. However, there is no obvious difference in rainfall intensity in the outer core region for the TCs with different intensity. The coverage of heavy rainfall is also found to be correlated with increasing TC intensity before landfall. It is found that the weak TC with heavy rainfall is usually under strong westward vertical wind shear (VWS), resulting from the impact of enhanced upper-level easterlies associated with a significant northward-shifted South Asia high. In particular, when the TC is located under the right-hand side of the entrance of the upper-level easterly jet, the updraft over the downshear side of the TC circulation is further enhanced under the combined influence of VWS and anomalous upper-tropospheric divergence caused by the jet. The precipitation thus manifests a clear asymmetric feature with severe rainfall over the downshear sector of the weak TC. Our results suggest that the interaction between a weak TC and synoptic systems deserves as much attention as that between a strong TC and surrounding systems in landfalling TC forecasting and research.

The synoptic- and mesoscale processes leading to the generation of three extreme rainfall episodes with hourly rates of greater than 100mm/h over the southern, middle, and northern portions of the eastern foothills of Mt. Taihang in North China on 19–20 July 2016 is examined. The extreme rainfall episodes took place over the 200–600-m elevation zones in the southern and northern portions but also over the lower elevations in the middle portion of the target region, sequentially during late morning, early evening, and midnight hours. Echo training accounted for the development of a linear convective system in the southern region after the warm and moist air carried by a southeasterly low-level jet (LLJ) was lifted to condensation as moving across Mt. Yuntai. In contrast, two isolated circular-shaped convective clusters, with more robust convective cores in its leading segment, developed in the northern region through steep topographical lifting of moist northeasterly airflow, albeit conditionally less unstable. Extreme rainfall in the middle region developed from the convergence of a moist easterly LLJ with a northerly colder airflow associated with an extratropical cyclogenesis. Results reveal that the LLJs and associated moisture transport, the intensifying cyclone interacting with a southwest vortex and its subsequent northeastward movement, and the slope and orientation of local topography with respect to and the stability of the approaching airflows played different roles in determining the timing and location, the extreme rainfall rates, and convective organizations along the eastern foothills of Mt. Taihang.

Monsoon coastal cities often suffer from extreme rain-induced flooding and severe hazard. However, the associated physical mechanisms and detailed storm structures are poorly understood due to the lack of high-resolution data. This study presents an analysis of a thunderstorm that produce extreme hourly rainfall (EXHR) of 219 mm over Guangzhou megacity in the southern coast of China using integrated multi-platform observations and a four-dimensional variational Doppler radar analysis system. Results indicate that weak environmental flows and convectively generated weak cold pool facilitate the formation of a quasi-stationary storm, while onshore warm and moist flows in the boundary layer (BL) provide the needed moisture supply. The 219 mm-EXHR is attendant by a shallow meso-γ-scale vortex due to stretching of intense latent heating-induced convergence, which in turn helps organize convective updrafts into its core region. Lightning and dual-polarization radar observations reveal active warm-rain (but weak mixed-phase) microphysical processes, with raindrop size distribution (RSD) closer to marine convection. In contrast, another storm develops about 4 hours earlier and only 35 km to the northwest, but with more lightning, higher cloud tops, more graupel and supercooled liquid water content, more continental RSD, little evidence of rotation, and much less rainfall; they are attributable to the presence of larger convective available potential energy resulting from the urban heat island effects and less moisture supply in the BL. These results highlight the importance of using multi-source remote sensing datasets in understanding the microphysical and kinematic structures of EXHR-producing storms.

Precipitation accumulations, integrated over rainfall events, are investigated using hourly data across continental China during the warm season (May–October) from 1980 to 2015. Physically, the probability of precipitation accumulations drops slowly with event size up to an approximately exponential cutoff scale s_L where probability drops much faster. Hence s_L can be used as an indicator of high accumulation percentiles (i.e., extreme precipitation accumulations). Overall, the climatology of s_L over continental China is about 54 mm. In terms of cutoff changes, the current warming stage (1980–2015) is divided into two periods, 1980–97 and 1998–2015. We find that the cutoff in 1998–2015 increases about 5.6% compared with that of 1980–97, with an average station increase of 4.7%. Regionally, s_L increases are observed over East China (10.9% ± 1.5%), Northwest China (9.7% ± 2.5%), South China (9.4% ± 1.4%), southern Southwest China (5.6% ± 1.2%), and Central China (5.3% ± 1.0%), with decreases over North China (−10.3% ± 1.3%), Northeast China (−4.9% ± 1.5%), and northern Southwest China (−3.9% ± 1.8%). The conditional risk ratios for five subregions with increased cutoff s_L are all greater than 1.0, indicating an increased risk of large precipitation accumulations in the most recent period. For high precipitation accumulations larger than the 99th percentile of accumulation s₉₉, the risk of extreme precipitation over these regions can increase above 20% except for South China. These increases of extreme accumulations can be largely explained by the extended duration of extreme accumulation events, especially for “extremely extreme” precipitation greater than s₉₉.

The controlling factors of interannual variability of extreme precipitation (EP) are examined on a global scale. We quantify the moisture (thermodynamic) and vertical motion (dynamic) contributions to the interannual variability of EP based on a physical scaling of EP. The thermodynamic contribution is small and the dynamic contribution dominants the interannual variability of EP. We further apply a quasi-geostrophic diagnosis on extreme precipitation to decompose the vertical motion and its variability into components due to the large-scale adiabatic perturbations (dry dynamic) and due to diabatic heating feedback (moist dynamic). The variability of vertical motion associated with large-scale synoptic perturbations is about twice that of the diabatic heating feedback. These results highlight the dominant role of the large-scale synoptic perturbations on the year-to-year variability of EP, in contrast to the responses of extreme precipitation to global warming, in which both the large-scale adiabatic perturbations and diabatic heating feedback are important.

This paper studies the impacts of El Niño-Southern Oscillation (ENSO) on wintertime extreme precipitation in China from 1961 to 2017, and possibly different influences of two El Niño types are also examined. We find that ENSO poses profound influences on extreme precipitation in many portions of China. El Niño (La Niña) intensifies (weakens) the precipitation extremes in southeastern China (SEC) and slightly weakens (intensifies) that in central-north China, mainly by changing the frequency rather than the intensity of extreme precipitation. Further analysis suggests that the East Asian winter monsoon tends to be weaker during El Niño winters, suppressing the southward invasion of cold dry air. Via the weakened Walker circulation, El Niño also triggers anomalously descending motion and anticyclone over the western North Pacific (WNP), which exhibits southwesterly anomalies over SEC and is thus conducive to the transport of sufficient moisture into that area. The changes over WNP are also accompanied by ascending motions over East Asia via a local meridional circulation alike the Pacific-Japan pattern. Meanwhile, El Niño induces a southward-displaced East Asian jet stream, as characterized by intensified westerly over southern China, corresponding to anomalously ascending motion there. Together, these changes facilitate the updrafts of circulations and the condensation of water vapor, thus increasing the occurrence of extreme precipitation in SEC. Moreover, we find that different Niño types exhibit distinct subregional influences. In particular, the canonical eastern Pacific El Niño significantly increases the frequency of extreme precipitation in South China, whereas the Modoki central Pacific El Niño mainly impacts that in East China.

The present study objectively classify the convective cloud objects detected by the space-borne CloudSat radar over the Asian-Australian monsoon region using the hierarchical agglomerative clustering algorithm. Based on key properties representing the morphological features and convective intensity of the systems, five distinct convective cloud regimes are derived. The unique Coastal-Intense regime exhibits the most expansive horizontal scales (> 1000 km), high convective strength, the strongest cloud radiative effects, the highest probability of extreme rainfall, and a significant coupling with the sharp onset of the Asian summer monsoon circulation. Secondly, the Coastal regime illustrates smaller but also highly organized coastal convections, with the strongest convective strength. Less than 10% of the systems in the CI and Coastal regimes overlap with the tropical cyclones. The rest three regimes mark the less organized convection at various life cycle stages mainly over the land areas, with small seasonal variation in their occurrence.

The impacts of cross equatorial northerly surge (CENS) towards the diurnal cycle of rainfall (DCR) over Java Island during boreal winter (December-January-February) has been studied. High resolution rainfall estimate dataset from the Integrated Multi-Satellite Retrievals for GPM (IMERG) is used for the climatological analysis from 2000-2020. This study takes step as the groundwork to explain the mechanism of how CENS modified the DCR over Java Island. It is known that some of severe flood over Jakarta (north western coast of Java Island) that occurred during boreal winter are associated with CENS events. This study confirms previous studies that CENS enhance precipitation over north western coast of Java Island. The daily mean of rainfall along the northern coast of Java Island increases significantly during CENS in the twenty boreal winter seasons. Moreover, a distinguished feature is shown in the mountainous region of western Java and eastern Java, where the daily mean precipitation shows negative anomaly. It is indicating that these regions have less amount of rainfall during CENS events. Therefore, this study shows that the impact of the CENS varies regionally and also indicates there may be a role of topography in the initiation and development of convective systems. Impact of CENS on the daily rainfall propagation in the western, central, and eastern Java lead to change of DCR for each region. A significant change on rainfall propagation during CENS is shown in the western Java. The southerly rainfall propagation, that is predominate in this region, weakened and the strong rainfall system tend to develop in the evening over the north coastal land of western Java during CENS. As a result, the late afternoon peak that is predominate in this region shifted to early morning during CENS.

This study demonstrates the potential of the Integrated Multi-satellitE Retrievals for GPM (IMERG) from the Global Precipitation Measurement (GPM) mission as a powerful observational tool to advance our understanding of the diurnal cycle of precipitation and assist in improving the representation of precipitation in global models. Using the constellation of passive microwave and geosynchronous infrared satellites from the GPM mission, IMERG provides observational estimates of global precipitation at a high resolution of 0.1° every half-hour globally over two decades (2000–present). The improvements in the algorithm of the latest version (V06B) and evaluation over the U.S. confirm IMERG’s ability to accurately represent diurnal cycles with only a slight lag in the diurnal phase over land, although with more variability in the diurnal amplitude. 
Applying IMERG to study the diurnal cycle of precipitation in the Maritime Continent, we demonstrate the ability of IMERG to capture the intricate progression in the diurnal phase of precipitation over both land and ocean. Its high resolution allows us to quantify the diurnal cycle over small regions such as Singapore. We explore how the diurnal cycle depends on the large-scale environment under different modes of variability such as the monsoon and the Madden-Julian Oscillation. We also investigate the degree to which the diurnal cycle over the ocean as in IMERG data exhibits a bimodal behavior—a morning peak under convective conditions and an afternoon peak under suppressed conditions—as observed in TOGA COARE. With the ability to accurately reveal the global diurnal cycle of precipitation, IMERG is a valuable tool in understanding the sub-daily dynamics associated with precipitation and improving the representation of such processes in weather and climate models.

Past studies have shown that the diurnal cycle of solar radiation and topography may inhibit propagation of the Madden-Julian Oscillation (MJO) across the Maritime Continent (MC). However, to what extent the interactions between the diurnal cycle and topography influence the MJO is not known. This question for an MJO event from April 2009 is addressed using a set of cloud-permitting simulations that include: Control (CTL), no-diurnal cycle (NO_DC), no-topography (No_Topo), and no-diurnal cycle plus no topography (No_DC_Topo). In the CTL simulation, the MJO signal is weakened when it enters the MC, with major convection stalling over Sumatra and Borneo islands. In No_DC, No_Topo, and No_DC_Topo experiments, the MJO propagation signal is intensified. We use a factor separation method from set theory to quantify the role of the interactions between the diurnal cycle and topography on the MJO.

Diurnal rainfall variability in the tropics is closely linked to the diurnally varying solar insolation, directly through destabilisation of surface parcels as well as indirectly through initiation of local circulations. Moisture availability, both at the surface and throughout the atmospheric column also has a direct impact on tropical rainfall by determining the moist adiabat of surface parcels and controlling the total water availability for precipitation. However, over complex topography in the tropics, the relative contribution of these factors to overall diurnal rainfall variability is unclear. This is of particular importance because of the interplay of both solar insolation and moisture availability with intraseasonal variability.  In this study, we attempt to separate the influence of surface moisture, solar insolation and column moisture on diurnal rainfall variability in the tropics using in-situ observations, re-analysis data, satellite observations and convection-permitting simulations. We demonstrate a weak positive relationship between integrated morning solar insolation and afternoon rainfall amount, and no relationship between afternoon rainfall and surface or column relative humidity, indicating the difficulty of fully characterising, modelling or predicting the tropical diurnal rainfall cycle. Moreover, we show a high degree of infidelity in these relationships in convection permitting simulations, which is attributed to multi-scale errors in both the cloudiness and radiative forcing, as well as the model response to surface fluxes in terms of convective initiation. We conjecture that the remaining variability in diurnally forced rainfall is attributable to local flows. Finally, preliminary numerical studies over an idealised domain with prescribed diurnal variation of surface fluxes and background moisture will be presented. These simulations will help elucidate the relative roles of surface fluxes, surface and column moisture and model cloudiness in dictating the diurnal precipitation cycle.

The unified parameterization (UP) is a framework that physically adjusts the precipitation partition between the parameterized convection and the grid-scale processes. The generalization is based on the convective updraft fraction. In this study, we investigate the effects of the UP on the diurnal cycle of precipitation over land in the Maritime Continent using an atmospheric general circulation model at the spatial resolution of 15 km. Three experiments with different methods to represent deep moist convection are carried out, which are parameterizing the cumulative effects (relaxed Arakawa-Schubert scheme, RAS), explicitly resolving the grid motion (no deep convection scheme, NDC), and utilizing the UP that generalizes the two (unified-RAS, URAS). Each experiment consists of 45 short-term hindcasts spanning an active MJO event. The result shows that the URAS outperforms the other experiments in terms of the diurnal amplitude and phase. The UP effects exhibit a clear diurnal cycle due to the diurnal variation of convective instability; therefore, the parameterized convection in the URAS is weaker than the RAS, especially in the daytime. The weaker parameterized convection leads to the wetter mid-to-low level troposphere. More cloud condensates are formed when convection occurs. Consequently, the URAS has a colder surface temperature, stronger anomalous high-pressure near the surface, and weaker diurnal amplitude than the RAS. The grid-scale precipitation contributes to the continuation of precipitation after the diurnal peak time in the URAS over the inland area.

This study examines the variations in rainfall over Darwin and its vicinity during Australian summer under different large-scale regimes by utilizing the in-situ and C-band polarimetric (CPOL) radar rainfall data at hourly resolution. The result shows that the large-scale forcing starts to build up from phase 4 of the Madden-Julian Oscillation (MJO) as defined by Wheeler and Hendon (2004) by the reversal of low- to mid-level easterly winds to moist westerly winds, reaching a maximum in phase 5 and weakening through phases 6 to 7. The region experiences widespread rainfall during the MJO active (phases 4-6), but with distinct spatial and temporal structures. During these phases, Darwin and adjacent coastal areas receive more rainfall in the early morning (0200 – 0400 LT) due to the expansion of rainfall from the Beagle Gulf, explaining the occurrence of a secondary diurnal rainfall peak over Darwin. In contrast, local-scale mechanisms (sea breezes) reinvigorate from phase 8, further strengthening through phases 1 – 3, when low-level easterly winds become established over Darwin producing rainfall predominately over land and island locations during the afternoon. During these phases, below average rainfall is observed over most of the study domain, except over the Tiwi Islands in phase 2. How well these characteristics of rainfall are represented in the latest version of high-resolution Australian Community Climate and Earth System Simulator (ACCESS-CM2) model is under investigation. 

The cool elongated pool of sea surface temperature (SST) over the South China Sea (SCS), known as the cold tongue (CT) is a distinct climatological feature in boreal winter. The anomalously strong CT is associated with cyclonic wind anomalies, which are forced by various tropical remote forcings, including diabatic heating anomalies over the warm western Pacific SST anomalies induced by La Niña. However, how local SST anomalies during strong CT events influence the atmosphere over the SCS has yet to be investigated. For this reason, two experiments are conducted with the Singapore Variable Resolution (SINGV) atmospheric model to compare the atmospheric impacts from the local and remote SSTs on the SCS during strong CT events. The results show that the local SSTs have negligible atmospheric impact, while remote western Pacific SSTs excite a Gill response with an anomalous cyclone over Philippines that is accompanied by northeasterly wind anomalies over the SCS.