Improving Madden–Julian Oscillation Simulation in Atmospheric General Circulation Models by Coupling with Snow–Ice–Thermocline One-dimensional Ocean Model

A one-column turbulent kinetic energy–type ocean mixed-layer model Snow–Ice–Thermocline (SIT) when coupled with three atmospheric general circulation models (AGCMs) to yielded superior Madden–Julian Oscillation (MJO) simulation. SIT is designed to have fine layers similar to those observed near the ocean surface and therefore can realistically simulate the diurnal warm layer and cool skin. This refined discretization of the near ocean surface in SIT provides accurate sea surface temperature (SST) simulation, thus facilitating realistic air–sea interaction. Coupling SIT with European Centre Hamburg Model, Version 5 (ECHAM5); Community Atmosphere Model, Version 5 (CAM5); and High Resolution Atmospheric Model (HiRAM) significantly improved MJO simulation in three coupled AGCMs compared with the AGCM driven with prescribed SST. This study suggests two major improvements to the coupling process. First, during the preconditioning phase of MJO over Maritime Continent (MC), the over underestimated surface latent heat bias in AGCMs can be corrected. Second, during the phase of strongest convection over MC, the change of the intraseasonal circulation in the meridional circulation is the dominant factor in the coupled simulations relative to the uncoupled experiments. The study results indicate that a fine vertical resolution near the surface, which better captures temperature variations in the upper few meters of the ocean, considerably improves different models with different configurations and physical parameterization schemes; this could be an essential factor for accurate MJO simulation.

Detection of Martian dust storms using mask regional convolutional neural networks

Martian dust plays a crucial role in the meteorology and climate of the Martian atmosphere. It heats the atmosphere, enhances the atmospheric general circulation, and affects spacecraft instruments and operations. Compliant with that, studying dust is also essential for future human exploration. In this work, we present a method for the deep-learning-based detection of the areal extent of dust storms in Mars satellite imagery. We use a mask regional convolutional neural network, consisting of a regional-proposal network and a mask network. We apply the detection method to Mars daily global maps of the Mars global surveyor, Mars orbiter camera. We use center coordinates of dust storms from the eight-year Mars dust activity database as ground-truth to train and validate the method. The performance of the regional network is evaluated by the average precision score with $$50\%$$ 50 % overlap ($$mAP_{50}$$ m A P 50 ), which is around $$62.1\%$$ 62.1 % .

Simulation of winter and summer climates with PRL Atmospheric General Circulation Model

A global, spectral Atmospheric General Circulation Model (AGCM) has been developed indigenously at Physical Research Laboratory (PRL) for climate studies. The model has six a levels in the vertical and has horizontal resolution of 21 waves with rhomboidal truncation. The model includes smooth topography, planetary boundary layer, deep convection, large scale condensation, interactive hydrology, radiation with interactive clouds and diurnal cycle. Sea surface temperature and sea ice values were fixed based on climatological data for different calender months. The model was integrated for six years starting with an isothermal atmosphere (2400K), zero winds initial conditions and forcing from incoming solar radiation. After one year the model stabilizes. The seasonal averages of various fields of the last five years are discussed in this paper. It is found that the model reproduces reasonably well the seasonal features of atmospheric circulation, seasonal variability and hemispheric differences.

On improving the ability of a high-resolution atmospheric general circulation model for dynamical seasonal prediction of the extreme seasons of the Indian summer monsoon

The paper is devoted to examine the ability of a high-resolution National Center for Environmental Prediction (NCEP) T170/L42 Atmospheric General Circulation Model (AGCM), for exploring its utility for long-range dynamical prediction of seasonal Indian summer monsoon rainfall (ISMR) based on 5-members ensemble for the hindcast mode 20-year (1985-2004) period with observed global sea surface temperatures (SSTs) as boundary condition and 6-year (2005-2010) period in the forecast-mode with NCEP Coupled Forecast System (CFS) SSTs as boundary condition. ISMR simulations are examined on five day (pentad) rainfall average basis. It is shown that the model simulated ISMR, based on 5-members ensemble average basis had limited skill in simulating extreme ISMR seasons (drought/excess ISMR). However, if the ensemble averaging is restricted to similar ensemble members either in the overall run of pentad-wise below (B) and above (A) normal rainfall events, as determined by the departure for thethreshold value given by coefficient of variability (CV) for the respective pentads based on IMD observed climatology, or during the season as a whole on the basis of percentage anomaly of ISMR from the seasonal climatology, the foreshadowing of drought/excess monsoon seasons improved considerably. Our strategy of improving dynamical seasonal prediction of ISMR was based on the premise that the intra-seasonal variability (ISV) and intra-annual variability (IAV) are intimately connected and characterized by large scale perturbations westward moving (10-20 day) and northward moving (30-60 day) modes of monsoon ISV during the summer monsoon season. As such the cumulative excess of B events in the simulated season would correspond to drought season and vice-versa. The paper also examines El Niño-Monsoon connections of the simulated ISMR series and they appear to have improved considerably in the proposed methodology. This strategy was particularly found to improve for foreshadowing of droughts. Based on results of the study a strategy is proposed for using the matched signal for simulated ISMR based on excess B over A events and vice-versa for drought or excess ISMR category. The probability distribution for the forecast seasonal ISMR on category basis is also proposed to be based on the relative ratio of similar ensemble members and total ensembles on percentage basis. The paper also discusses that extreme monsoon season are produced by the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) modes in a combined manner and hence stresses to improve prediction of IOD mode in ocean-atmosphere coupled model just as it has happened for the prediction ENSO mode six to nine months in advance.

Detection of Martian Dust Storms Using Mask-Regional Convolutional Neural Networks

Martian dust plays a crucial role in the meteorology and climate of the Martian atmosphere. It heats the atmosphere, enhances the atmospheric general circulation, and affects spacecraft instruments and operations. Compliant with that, studying dust is also essential for future human exploration. In this work, we present a method for the deep-learning-based detection of the areal extent of dust storms in Mars satellite imagery. We use a mask regional convolutional neural network (R-CNN), consisting of a regional-proposal network (RPN) and a mask network. We apply the detection method to Mars Daily Global Maps (MDGMs) of the Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC). We use center coordinates of dust storms from the eight-year Mars Dust Activity Database (MDAD) as ground-truth to train and validate the method. The performance of the regional network is evaluated by the average precision score with 50% overlap (mAP50), which is around 62.1%.