Atmospheric Initial Conditions Research Articles

Quantifying the Influence of Cloud Seeding on Ice Particle Growth and Snowfall Through Idealized Microphysical Modeling

Cloud seeding is a weather modification technique for enhancing precipitation in arid and semi-arid regions, including the Western U.S. However, designing an optimal cloud seeding operation based on comprehensive evaluation metrics, such as seeding agent dispersion and atmospheric conditions, has yet to be thoroughly explored for this region. This study investigated the impacts of cloud seeding, particularly utilizing silver iodide, on ice particle growth within clouds through numerical modeling. By leveraging the Snow Growth Model for Rimed Snowfall (SGMR), the microphysical processes involved in cloud seeding across five distinct events were simulated. The events were in the Lake Tahoe region, nestled within the Sierra Nevada Mountain ranges in the Western U.S. This model was executed based on six primary variables, including cloud top height, cloud base height, cloud top temperature, cloud base temperature, liquid water content, and ice water content. This study incorporated datasets from the Modern-Era Retrospective Analysis for Research and Applications Version 2 and the Clouds and the Earth Radiant Energy System. The findings revealed the importance of ice nucleation, aggregation, diffusion, and riming processes and highlighted the effectiveness of cloud seeding in enhancing ice particle number concentration, ice water content, and snowfall rates. However, event-specific analyses indicated diverse precipitation responses to cloud seeding based on initial atmospheric conditions. The SGMR modeling hints at the importance of improving ice microphysical processes and provides insights for future cloud seeding research using regional and global climate models.

Role of Initial Conditions and Meteorological Drought in Soil Moisture Drought Propagation: An Event‐Based Causal Analysis Over South Asia

AbstractThe role of meteorological droughts and initial conditions (land and atmosphere) in soil moisture drought (SMD) propagation are not yet fully understood. This work uses a drought event‐based causal framework to investigate the relative importance of meteorological drought (MD) duration and intensity and initial conditions that result in surface and rootzone SMD, considering their event‐level propagation time (PT) over South Asia. Initially, spatial variability of drought propagation is assessed by the Propagation Ratio (PR) computed based on MD counts that trigger SMD at various lags. PR depicts 2–3 months slower rootzone propagation than at surface. The gradual decrease in PR with increasing regional aridity indicates faster propagation over humid regions. The causal impact of initial conditions and MD parameters on propagating SMD are evaluated using normalized mutual information and a newly proposed normalized conditional mutual information. We found greater importance of triggering MD parameters followed by initial soil moisture condition on propagating SMD. This behavior is more evident for the surface layer propagation at shorter PT. There is a confounding effect of initial atmospheric conditions on drought propagation through initial soil moisture, depicting the significance of land‐atmosphere interactions prior to propagation. In the rootzone propagation, initial soil moisture has a greater influence on propagation, especially at longer PT, indicating the significance of soil moisture persistence. Stronger causal links obtained through the joint influence of MD parameters on SMD suggest the importance of accounting for MD duration and intensity simultaneously, which are not considered in drought index‐based propagation studies.

Initial atmospheric conditions control transport of volcanic volatiles, forcing and impacts

Abstract. Volcanic eruptions impact the climate and environment. The volcanic forcing is determined by eruption source parameters, including the mass and composition of volcanic volatiles, eruption season, eruption latitude, and injection altitude. Moreover, initial atmospheric conditions of the climate system play an important role in shaping the volcanic forcing and response. However, our understanding of the combination of these factors, the distinctions between tropical and extratropical volcanic eruptions, and the co-injection of sulfur and halogens remains limited. Here, we perform ensemble simulations of volcanic eruptions at 15 and 64° N in January, injecting 17 Mt of SO2 together with HCl and HBr at 24 km altitude. Our findings reveal that initial atmospheric conditions control the transport of volcanic volatiles from the first month and modulate the subsequent latitudinal distribution of sulfate aerosols and halogens. This results in different volcanic forcing, surface temperature and ozone responses over the globe and Northern Hemisphere extratropics (NHET) among the model ensemble members with different initial atmospheric conditions. NH extratropical eruptions exhibit a larger NHET mean volcanic forcing, surface cooling and ozone depletion compared with tropical eruptions. However, tropical eruptions lead to more prolonged impacts compared with NH extratropical eruptions, both globally and in the NHET. The sensitivity of volcanic forcing to varying eruption source parameters and model dependency is discussed, emphasizing the need for future multi-model studies to consider the influence of initial conditions and eruption source parameters on volcanic forcing and subsequent impacts.

Quantifying sources of subseasonal prediction skill in CESM2

Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is evidence that predictability on subseasonal timescales comes from a combination of atmosphere, land, and ocean initial conditions. Predictability from the land is often attributed to slowly varying changes in soil moisture and snowpack, while predictability from the ocean is attributed to sources such as the El Niño Southern Oscillation. Here we use a set of subseasonal reforecast experiments with CESM2 to quantify the respective roles of atmosphere, land, and ocean initial conditions on subseasonal prediction skill over land. These reveal that the majority of prediction skill for global surface temperature in weeks 3–4 comes from the atmosphere, while ocean initial conditions become important after week 4, especially in the Tropics. In the CESM2 subseasonal prediction system, the land initial state does not contribute to surface temperature prediction skill in weeks 3–6 and climatological land conditions lead to higher skill, disagreeing with our current understanding. However, land-atmosphere coupling is important in week 1. Subseasonal precipitation prediction skill also comes primarily from the atmospheric initial condition, except for the Tropics, where after week 4 the ocean state is more important.

The impact of ENSO and NAO initial conditions and anomalies on the modeled response to Pinatubo-sized volcanic forcing

Abstract. Strong, strato-volcanic eruptions are a substantial, intermittent source of natural climate variability. Initial atmospheric and oceanic conditions, such as El Niño Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), also naturally impact climate on interannual timescales. We examine how initial conditions of ENSO and NAO contribute to the evolution of climate in the period following a Pinatubo-type eruption using a large (81-member) ensemble of model simulations in GISS model E2.1-G. Simulations are initialized from sampled conditions of ENSO and NAO using the protocol of the coordinated CMIP6 Volcanic Model Intercomparison Project (VolMIP) – where aerosols are forced with respect to time, latitude, and height. We analyze paired anomalous variations (perturbed – control) to understand changes in global and regional climate responses under positive, negative, and neutral ENSO and NAO conditions. In particular, we find that for paired anomalies there is a high probability of strong (∼1.5 ∘C) warming of northern Eurasia surface air temperature in the first winter after the volcanic eruption for negative NAO ensembles coincident with decreased lower stratospheric temperature at the poles, decreased geopotential height, and strengthening of the stratospheric polar vortex. Climate anomalies (relative to average conditions across the control period), however, show no mean warming and suggest that the strength of this response is impacted by conditions present in the selected period of the control run. Again using paired anomalies, we also observe that under both +ENSO and −ENSO ensembles sea surface temperature decreases in the first post-eruptive boreal winter coinciding with surface cooling from volcanic aerosols. Neutral ENSO ensembles, on the other hand, show variability in their response with no clear trend in post-eruptive warming or cooling. In general, paired anomalies from unperturbed simulations give insight into the evolution of the climate response to volcanic forcing; however, when compared with anomalies from climatological conditions, it is clear that paired anomalies are significantly affected by sampled initial conditions occurring at the time of the volcanic eruption.

Impact Point Dispersion Prediction for 300 mm R-Han 300 Artillery Rocket

The effectiveness of artillery rocket in battlefield is the determined by its impact point dispersion, which may occur due to manufacturing and measurement inaccuracy, initial launch perturbations and atmospheric conditions. Therefore, the objective of this study is to establish model that could predict the impact point dispersion of R-Han 300 rocket using Monte Carlo method. Generic rocket of 6 Degree-of-Freedom model was implemented to investigate the impact point. Initially two simulations with 1000 iterations were carried out, first to study the effect of value uncertainty of every parameter on the impact point dispersion at launch elevation angle 50 degrees, second to study the impact point dispersion caused by value uncertainty of all the parameters at launch elevation angles ranging from 30 to 70 degrees. The second simulation is then repeated with 10000 iterations. This study showed that the dispersion increases as the launch elevation angle increases, except around the optimal launch elevation angle that give the farthest range. Monte Carlo simulation with 10000 iterations showed a better normal distributed data then the simulation with 1000 iterations, but the maximum difference in value of CEP resulted from both simulations is very small, which is 3.16%.

The Interactions Between Ocean and Three Consecutive Typhoons Affecting Northeast Asia in 2020 From a Model Perspective

AbstractIn August 2020, the historical warmest upper‐ocean thermal condition occurred in the western North Pacific (WNP), along with three tropical cyclones (TCs, namely Bavi, Maysak, and Haishen) generated in the WNP and affected Northeast China and the Korea peninsular (KP), within an unprecedentedly short span of 12 days from late August to early September of 2020. Meanwhile, prior to the formation of Bavi, a series of extreme warm mesoscale oceanic eddies were also observed over the WNP, and clearly sea surface cooling was detected after the passage of the three TCs. However, it remains unknown whether the warmest WNP with extreme warm mesoscale oceanic eddies has a modulation effect on intensity change of the three TCs. The results of sensitive experiments with fixed atmospheric initial conditions revealed that the historically warmest upper‐ocean thermal condition in the WNP in August 2020 might have contributed to increasing the intensity of three TCs by 5.3–10.0 hPa compared to normal ocean thermal conditions. The simulation results indicated that the extreme warm mesoscale eddy conglomeration might assist in intensifying the three TCs when they passed over these warm mesoscale eddies. Whereas the cold wake over the East China Sea caused by preceding TCs, Bavi and Maysak, might partly aid in weakening subsequent TCs in the decaying period. Furthermore, the simulated bias in the intensity evolution of the three TCs and the possible cause of minor differences in the simulated tracks and large‐scale steering flow between the sensitive and control experiments were discussed.

What is the effect of atmospheric initial condition inconsistency between the hindcasts and real-time forecasts?

What is the effect of atmospheric initial condition inconsistency between the hindcasts and real-time forecasts?

Multiweek Prediction and Attribution of the Black Saturday Heatwave Event in Southeast Australia

Abstract Southeastern Australia experienced an extreme heatwave event from 27 January to 8 February 2009, which culminated in the devastating “Black Saturday” bushfires that led to hundreds of human casualties and major economic losses in the state of Victoria. This study investigates the causes of the heatwave event, its prediction, and the role of anthropogenic climate change using a dynamical subseasonal-to-seasonal (S2S) forecast system. We show that the intense positive temperature anomalies over southeastern Australia were associated with the persistent high pressure system over the Tasman Sea and a low pressure anomaly over southern Australia, which favored horizontal warm-air advection from the lower latitudes to the region. Enhanced convection over the tropical western Pacific and northern Australia due to weak La Niña conditions appear to have played a role in strengthening the high pressure anomalies over the Tasman Sea. The observed climate conditions are largely reproduced in the hindcast of the Australian Community Climate and Earth System Simulator–Seasonal prediction system version 1 (ACCESS-S1). The model skillfully predicts the spatial characteristics and relative intensity of the heatwave event at a 10-day lead time. A climate attribution forecast experiment with low atmospheric CO2 and counterfactual cold ocean–atmospheric initial conditions suggests that the enhanced greenhouse effect contributed about 3°C warming of the predicted event. This study provides an example of how a S2S prediction system can be used not only for multiweek prediction of an extreme event and its climate drivers, but also for the attribution to anthropogenic climate change.

Constraint of Air–Sea Interaction Significant to Skillful Predictions of North Pacific Climate Variations

Abstract The Pacific decadal oscillation (PDO) is the most dominant decadal climate variability over the North Pacific and has substantial global impacts. However, the interannual and decadal PDO prediction skills are not satisfactory, which may result from the failure of appropriately including the North Pacific midlatitude air–sea interaction (ASI) in the initialization for climate predictions. Here, we present a novel initialization method with a climate model to crack this nutshell and achieve successful PDO index predictions up to 10 years in advance. This approach incorporates oceanic observations under the constraint of ASI, thus obtaining atmospheric initial conditions (ICs) consistent with oceanic ICs. During predictions, positive atmospheric feedback to sea surface temperature changes and time-delayed negative ocean circulation feedback to the atmosphere over the North Pacific play essential roles in the high PDO index prediction skills. Our findings highlight a great potential of ASI constraints during initialization for skillful PDO predictions. Significance Statement The Pacific decadal oscillation is a prominent decadal climate variability over the North Pacific. However, accurately predicting the Pacific decadal oscillation remains a challenge. In this study, we use an advanced initialization method where the oceanic observations are incorporated into a climate model constrained by air–sea interactions. We can successfully predict the Pacific decadal oscillation up to 10 years in advance, which is hardly achieved by the state-of-the-art climate prediction systems. Our results suggest that the constraint of air–sea interaction during initialization is important to skillful predictions of the climate variability on decadal time scales.

Ensemble Spread Behavior in Coupled Climate Models: Insights From the Energy Exascale Earth System Model Version 1 Large Ensemble

AbstractAssessing uncertainty in future climate projections requires understanding both internal climate variability and external forcing. For this reason, single‐model initial condition large ensembles (SMILEs) run with Earth System Models (ESMs) have recently become popular. Here we present a new 20‐member SMILE with the Energy Exascale Earth System Model version 1 (E3SMv1‐LE), which uses a “macro” initialization strategy choosing coupled atmosphere/ocean states based on inter‐basin contrasts in ocean heat content (OHC). The E3SMv1‐LE simulates tropical climate variability well, albeit with a muted warming trend over the twentieth century due to overly strong aerosol forcing. The E3SMv1‐LE's initial climate spread is comparable to other (larger) SMILEs, suggesting that maximizing inter‐basin ocean heat contrasts may be an efficient method of generating ensemble spread. We also compare different ensemble spread across multiple SMILEs, using surface air temperature and OHC. The Community Earth system Model version 1, the only ensemble which utilizes a “micro” initialization approach perturbing only atmospheric initial conditions, yields lower spread in the first ∼30 years. The E3SMv1‐LE exhibits a relatively large spread, with some evidence for anthropogenic forcing influencing spread in the late twentieth century. However, systematic effects of differing “macro” initialization strategies are difficult to detect, possibly resulting from differing model physics or responses to external forcing. Notably, the method of standardizing results affects ensemble spread: control simulations for most models have either large background trends or multi‐centennial variability in OHC. This spurious disequlibrium behavior is a substantial roadblock to understanding both internal climate variability and its response to forcing.

A new approach for seasonal prediction using the coupled model CFSv2 with special emphasis on Indian Summer Monsoon

AbstractPredicting Indian Summer Monsoon (ISM) is a challenging task due to its complexity and nonlinear interactions. Any improvement in the seasonal prediction skill of models would highly benefit a large population and the economy. In this context, three sets of hindcast experiments are carried out for 9 months each, for the period 1993–2019, using the National Centers for Environmental Prediction‐Climate Forecast System version 2 (NCEP‐CFSv2). The experiments differ from each other in the way they are initialized: one is initialized in February (FebIC), and the second in May (MayIC), whereas in the third one (Exp), ocean is initialized in February and allowed to evolve but the atmosphere reinitialized every month up to May. So, hindcasts of Exp are from the pre‐evolved ocean with realistic atmospheric initial conditions of May. The representation of mean tropical Indo‐Pacific Sea surface temperature (SST), Walker circulation, mean monsoon circulation and moisture transport to Indian landmass are better represented in Exp. The ISM rainfall prediction (interannual) skill improved in Exp as compared to FebIC and MayIC over central India, Indian land mass and extended monsoon region. The initialization strategy adopted in Exp reduces the model initial shocks especially in the upper ocean heat content and SST over the Indo‐Pacific region, thereby offering a cost‐effective alternative approach for reduction of the initial shock. The well‐known cold tongue SST bias over the equatorial Pacific in MayIC is reduced significantly in Exp with improved monsoon teleconnections and the overdependence of ISM rainfall on El Niño Southern Oscillation in MayIC is also reduced in Exp.

Improved forecast of 2015/16 El Niño event in an experimental coupled seasonal ensemble forecasting system

To improve NOAA’s seasonal forecasting capabilities, a new coupled system within the Unified Forecast System (UFS) framework is being developed through a community-wide effort led by NOAA’s Environmental Modeling Center targeting the configuration of a future operational Seasonal Forecast System (SFS v1). An experimental version of this ensemble seasonal forecasting system is tested on forecasting the strong El Niño of 2015/16. The then-operational systems and NCEP real-time seasonal forecasts (CFSv2) underestimated its strength towards the end of 2015 and beginning of 2016. In addition to perturbing the atmospheric initial conditions, run-time stochastic physics-based perturbations are applied in both atmosphere and ocean components of this new coupled system to represent the model uncertainties. The UFS ensembles are initialized on June 1st, 2015 and run through a 9-month period. Compared to CFSv2, the forecast of Niño 3.4 SST and intra-seasonal zonal windstress for the 2015/16 El Niño in the UFS system are improved, as is the ensemble spread. A cold SST forecast error develops in the central equatorial Pacific, likely from excess evaporative cooling, shallower thermocline, and an excessively strong vertical current shear driven cooling. Near the eastern equatorial Pacific coast, on the other hand, warm surface and cool subsurface errors persist from initialization until the end of the forecast. The results suggest that further improvement in the seasonal forecast may be achieved by a combination of factors, including, but not limited to, improving the coupled system initialization, along with the atmospheric physics.

Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability

Abstract Two distinct features of anthropogenic climate change, warming in the tropical upper troposphere and warming at the Arctic surface, have competing effects on the midlatitude jet stream’s latitudinal position, often referred to as a “tug-of-war.” Studies that investigate the jet’s response to these thermal forcings show that it is sensitive to model type, season, initial atmospheric conditions, and the shape and magnitude of the forcing. Much of this past work focuses on studying a simulation’s response to external manipulation. In contrast, we explore the potential to train a convolutional neural network (CNN) on internal variability alone and then use it to examine possible nonlinear responses of the jet to tropospheric thermal forcing that more closely resemble anthropogenic climate change. Our approach leverages the idea behind the fluctuation–dissipation theorem, which relates the internal variability of a system to its forced response but so far has been only used to quantify linear responses. We train a CNN on data from a long control run of the CESM dry dynamical core and show that it is able to skillfully predict the nonlinear response of the jet to sustained external forcing. The trained CNN provides a quick method for exploring the jet stream sensitivity to a wide range of tropospheric temperature tendencies and, considering that this method can likely be applied to any model with a long control run, could be useful for early-stage experiment design.

Exoplanet atmosphere evolution: emulation with neural networks

ABSTRACT Atmospheric mass-loss is known to play a leading role in sculpting the demographics of small, close-in exoplanets. Knowledge of how such planets evolve allows one to ‘rewind the clock’ to infer the conditions in which they formed. Here, we explore the relationship between a planet’s core mass and its atmospheric mass after protoplanetary disc dispersal by exploiting XUV photoevaporation as an evolutionary process. Historically, this inference problem would be computationally infeasible due to the large number of planet models required; however, we use a novel atmospheric evolution emulator which utilizes neural networks to provide three orders of magnitude in speedup. First, we provide a proof of concept for this emulator on a real problem by inferring the initial atmospheric conditions of the TOI-270 multi-planet system. Using the emulator, we find near-indistinguishable results when compared to the original model. We then apply the emulator to the more complex inference problem, which aims to find the initial conditions for a sample of Kepler, K2, and TESS planets with well-constrained masses and radii. We demonstrate that there is a relationship between core masses and the atmospheric mass they retain after disc dispersal. This trend is consistent with the ‘boil-off’ scenario, in which close-in planets undergo dramatic atmospheric escape during disc dispersal. Thus, it appears that the exoplanet population is consistent with the idea that close-in exoplanets initially acquired large massive atmospheres, the majority of which is lost during disc dispersal, before the final population is sculpted by atmospheric loss over 100 Myr to Gyr time-scales.

Influence of ASCAT soil moisture on prediction of track and intensity of landfall tropical cyclones

ABSTRACT The current study attempted to evaluate the impact of Soil Moisture (SM) initial conditions in a regional NWP system on the simulation of land falling cyclones over North Indian Ocean region. SM initial conditions used in this study are created using a simplified EKF based LDAS, in which satellite derived SM information is also used. Here, two sets of numerical experiments using regional NWP system are performed with two different sets of SM initial conditions. The CTL (assimilated only surface level observations in LDAS) and ASCAT (assimilated satellite derived SM plus observations used in CTL) experiments are performed to understand the impact of SM initial conditions in NCUM-R NWP system. The atmospheric initial conditions for both experiments are prepared through 4DVAR assimilation technique. This study shows that high resolution regional model initialized with improved SM initial conditions created using satellite observations could improve structure, intensity, track and evolution of the storms. The positive values of humidity Jacobians are noticed over the regions affected by storms mainly in ASCAT. The evolution of vorticity tendency and convergence profiles is well differentiated in ASCAT as the system moved from higher to lower intensity stage or vice versa, but that pattern is not observed in CTL. The value of static stability indices is varying with strength of cyclones, which are well represented in ASCAT and are also well matched with observations. The evolution of magnitude of diabatic heating in and around the storm core region is more realistic in ASCAT than CTL. The moisture budget terms are improved in ASCAT, the contribution of moisture convergence (evaporation) to the precipitation is more over the oceanic (land) region before and after landfall of storm. The study deduced that accurate SM initial conditions are essential for better prediction of structure and intensity of high impact weather events.

Large‐eddy simulation of soil moisture heterogeneity‐induced secondary circulation with ambient winds

AbstractLand surface heterogeneity in conjunction with ambient winds influences the convective atmospheric boundary layer by affecting the distribution of incoming solar radiation and forming secondary circulations. This study performed coupled large‐eddy simulation (ICON‐LEM) with a land surface model (TERRA‐ML) over a flat river corridor mimicked by soil moisture heterogeneity to investigate the impact of ambient winds on secondary circulations. The coupled model employed double‐periodic boundary conditions with a spatial scale of 4.8 km. All simulations used the same idealized initial atmospheric conditions with constant incident radiation of 700 W⋅m−2 and various ambient winds with different speeds (0 to 16 m⋅s−1) and directions (e.g., cross‐river, parallel‐river, and mixed). The atmospheric states are decomposed into ensemble‐averaged, mesoscale, and turbulence. The results show that the secondary circulation structure persists under the parallel‐river wind conditions independently of the wind speed but is destroyed when the cross‐river wind is stronger than 2 m⋅s−1. The soil moisture and wind speed determine the influence on the surface energy distribution independent of the wind direction. However, secondary circulations increase advection and dispersive heat flux while decreasing turbulent energy flux. The vertical profiles of the wind variance reflect the secondary circulation, and the maximum value of the mesoscale vertical wind variance indicates the secondary circulation strength. The secondary circulation strength positively scales with the Bowen ratio, stability parameter (−Zi/L), and thermal heterogeneity parameter under cross‐river wind and mixed wind conditions. The proposed similarity analyses and scaling approach provide a new quantitative perspective on the impact of the ambient wind under heteronomous soil moisture conditions on secondary circulation.

Application of Empirical Orthogonal Function Analysis to 1 km Ensemble Simulations and Himawari–8 Observation in the Intensification Phase of Typhoon Hagibis (2019)

An empirical orthogonal function (EOF) analysis was performed for the inner core of Typhoon Hagibis (2019) in the intensification phase. The Himawari–8 geostationary infrared (IR) brightness temperature (BT) collocated at the Hagibis’s center was combined with the IR BT simulated by a radiative transfer model, with 1 km ensemble simulations conducted by an atmosphere model and the coupled atmosphere–wave–ocean model. The ensemble simulations were conducted under one control atmospheric initial condition and the 26 perturbed ones with two different oceanic initial conditions. The first four EOF modes showed symmetric and asymmetric patterns such as a curved band, cloud dense overcast, and eye pattern used in the classification of the Dvorak technique. The influence of ocean coupling on the modes appeared only in the early intensification phase but was relatively small compared to the difference from the Himawari–8 observations. While ocean coupling and different oceanic initial condition quantitatively affected the IR BT, the normalized amplitude for the first EOF mode did not become close to that of the Himawari–8 observation in the late intensification phase. The intensification rate in the late intensification phase was inconsistent between the simulation results and the estimate from the Himawari–8 normalized amplitude by multiple linear regression analysis.

Improving the prediction of monsoon depressions by assimilating ASCAT soil moisture in NCUM-R modeling system

The present study investigates the impact of assimilation of satellite derived soil wetness data from Advanced Scatterometer (ASCAT) on simulation of inland moving monsoon depressions (MDs) over Indian region using high resolution NCUM-R forecasting system. The simplified Extended Kalman Filter (sEKF) based Land Surface Data Assimilation (LSDA) system is used to create land surface soil moisture initial conditions for NCUM-R by assimilating ASCAT soil moisture (SM) along with screen level observations. Two numerical experiments, namely CTL (incorporating only screen level observations in LSDA) and ASCAT (assimilating both ASCAT SM and screen level observations in LSDA) are carried out to update the model soil moisture initial conditions. Identical atmospheric initial condition prepared considering the 4DVAR data assimilation technique is used in both experiments.The results of this study show that the assimilation of remotely sensed SM has a beneficial impact on MD simulation, its movement, structure and associated precipitation. The analysis increment of fluxes from ASCAT experiment clearly shows properly coupled positive feedback between land and atmosphere. SM in the vicinity of MD is considerably improved in the ASCAT analysis as compared to the CTL analysis and is well correlated with the observed rainfall over the regions. The root mean square error of top layer SM is decreased by 5 to 15% in ASCAT analyses compared to CTL analyses. The land-atmosphere coupling feedback is investigated by using Dirmeyer's indices, it is noticed that the precipitation modulated by the latent heat flux is higher in ASCAT simulation than in the CTL simulation. This improvement is due to the proper representation of surface conditions by assimilating the satellite SM observations into LSDA. The contribution of individual terms of vorticity equation and total vorticity tendency is considerably well represented in ASCAT experiment as compared to the CTL experiment during the various phases of MD. Overall, this study indicates that the use of ASCAT SM in the preparation of soil moisture initial conditions helps to improve the forecast of MDs by the regional NWP system.

Interactions between a Tropical Cyclone and Upper-Tropospheric Cold-Core Lows Simulated by an Atmosphere-Wave-Ocean Coupled Model: A Case Study of Typhoon Jongdari (2018)

Typhoon Jongdari (2018) took an unusual track along the circumference of an upper-tropospheric cold low (UTCL) before making landfall in Japan on 29 July. To investigate the effects of atmosphere–ocean interactions and interactions between the UTCL and Jongdari on the storm's track, numerical simulations were conducted with a 3-km-mesh nonhydrostatic atmosphere model and an atmosphere–wave–ocean coupled model, using different initial conditions created by adopting different start times of numerical integration. The UTCL was characterized by high potential vorticity (PV), low pressure, and low relative humidity on the 355-K isotherm surface. While the UTCL moved southwestward north of Jongdari from 25 to 27 July, simulation results indicate that Jongdari traveled counterclockwise along the circumference of the UTCL. After Jongdari moved westward, the coupled model clearly simulated sea surface cooling along the track. Jongdari weakened after making landfall while the UTCL also weakened south of Japan. In particular, latent heat flux from the sea and the resulting humidification of the upper troposphere through the convection affected the UTCL. When Jongdari redeveloped over the ocean south of Kyushu, some simulations showed that Jongdari merged with the UTCL there as a result of high PV in Jongdari and relatively low upper-tropospheric PV near the UTCL. Ocean coupling helped sustain the uppertropospheric PV near the UTCL and weakened the column of elevated PV associated with Jongdari, which affected the location of the tropopause folding transformed from the UTCL by lowering the PV column of Jongdari and weakening the upper-tropospheric outflow from the center. Because the steering flow of Jongdari was affected by the geostrophic-balanced cyclonic circulation created by the UTCL, a larger difference of the atmospheric initial conditions between the initial times had a stronger influence on track and intensity simulations of both Jongdari and UTCL than ocean coupling.