The prediction of the weather at subseasonal-to-seasonal (S2S) timescales is dependent on both initial and boundary conditions. An open question is how to best initialize a relatively small-sized ensemble of numerical model integrations to produce re
liable forecasts at these timescales. Reliability in this case means that the statistical properties of the ensemble forecast are consistent with the actual uncertainties about the future state of the geophysical system under investigation. In the present work, a method is introduced to construct initial conditions that produce reliable ensemble forecasts by projecting onto the eigenfunctions of the Koopman or the Perron-Frobenius operators, which describe the time-evolution of observables and probability distributions of the system dynamics, respectively. These eigenfunctions can be approximated from data by using the Dynamic Mode Decomposition (DMD) algorithm. The effectiveness of this approach is illustrated in the framework of a low-order ocean-atmosphere model exhibiting multiple characteristic timescales, and is compared to other ensemble initialization methods based on the Empirical Orthogonal Functions (EOFs) of the model trajectory and on the backward and covariant Lyapunov vectors of the model dynamics. Projecting initial conditions onto a subset of the Koopman or Perron-Frobenius eigenfunctions that are characterized by time scales with fast-decaying oscillations is found to produce highly reliable forecasts at all lead times investigated, ranging from one week to two months. Reliable forecasts are also obtained with the adjoint covariant Lyapunov vectors, which are the eigenfunctions of the Koopman operator in the tangent space. The advantages of these different methods are discussed.
Drying is an important agricultural process, particularly for crops, and shriveled products are used all over the world. The performance of drying green chili was also tested in this article, which created an alternate way of drying agricultural prod
ucts. The goal of this study is to provide a solar biomass hybrid dryer with improved design, construction, and performance testing. During most hours of the trial, the temperature within the solar collector and dryer was sufficiently higher than the ambient temperature, according to the results obtained during the test period. The temperature of the ambient air at the collector intake ranged from 30 to 35 degrees Celsius. The temperature of the air at the collectors outlet ranged from 54 to 64 degrees Celsius, while the temperature of the drying chamber ranged from 51 to 60 degrees Celsius, making it suitable for drying green chili and a variety of other agricultural products. The collector was found to be 46.54 percent efficient. The findings revealed that the alteration of the collector, which produces turbulent air flow and improves chamber wall insulation, affects drying. Based on the results of this study, the created solar biomass hybrid drier is cost-effective for small-scale crop growers in rural areas of developing countries.
A simple method for adding uncertainty to neural network regression tasks via estimation of a general probability distribution is described. The methodology supports estimation of heteroscedastic, asymmetric uncertainties by a simple modification of
the network output and loss function. Method performance is demonstrated with a simple one dimensional data set and then applied to a more complex regression task using synthetic climate data.
Pollutant emissions have been a topic of interest in the last decades. Not only environmentalists but also governments are taking rapid action to reduce emissions. As one of the main contributors, the transport sector is being subjected to strict scr
utiny to ensure it complies with the short and long-term regulations. The measures imposed by the governments clearly involve, all the stakeholders in the logistics sector, from road authorities and logistic operators to truck manufacturers. Improvement of traffic conditions is one of the perspectives in which the reduction of emissions is being addressed. Optimization of traffic flow, avoidance of unnecessary stops, control of the cruise speed, and coordination of trips in an energy-efficient way are necessary steps to remain compliant with the upcoming regulations. In this study, we have measured the $CO_2$ and $NO_x$ emissions in heavy-duty vehicles while traversing signalized intersections and we examined the differences between various scenarios. We found that avoiding a stop can reduce $CO_2$ and $NO_x$ emissions on 0.32 kg and 1.8 g, respectively. These results put traffic control in the main scene as a yet unexplored dimension to control pollutant emissions, enabling the authorities to more accurately estimate cost-benefit plans for traffic control system investments.
We have analyzed the teleconnection of total cloud fraction (TCF) with global sea surface temperature (SST) in multi-model ensembles (MME) of the fifth and sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6). CMIP6-MME has a more robust an
d realistic teleconnection (TCF and global SST) pattern over the extra-tropics (R ~0.43) and North Atlantic (R ~0.39) region, which in turn resulted in improvement of rainfall bias over the Asian summer monsoon (ASM) region. CMIP6-MME can better reproduce the mean TCF and have reduced dry (wet) rainfall bias on land (ocean) over the ASM region. CMIP6-MME has improved the biases of seasonal mean rainfall, TCF, and outgoing longwave radiation (OLR) over the Indian Summer Monsoon (ISM) region by ~40%, ~45%, and ~31%, respectively, than CMIP5-MME and demonstrates better spatial correlation with observation/reanalysis. Results establish the credibility of the CMIP6 models and provide a scientific basis for improving the seasonal prediction of ISM.
We present a field study of snow settling dynamics based on simultaneous measurements of the atmospheric flow field and snow particle trajectories. Specifically, a super-large-scale particle image velocimetry (SLPIV) system using natural snow particl
es as tracers is deployed to quantify the velocity field and identify vortex structures in a 22 m $times$ 39 m field of view centered 18 m above the ground. Simultaneously, we track individual snow particles in a 3 m $times$ 5 m sample area within the SLPIV using particle tracking velocimetry (PTV). The results reveal the direct linkage among vortex structures in atmospheric turbulence, the spatial distribution of snow particle concentration, and their settling dynamics. In particular, with snow turbulence interaction at near-critical Stokes number, the settling velocity enhancement of snow particles is multifold, and larger than what has been observed in previous field studies. SLPIV measurements show higher concentration of snow particles preferentially located on the downward side of the vortices identified in the atmospheric flow field. PTV, performed on high resolution images around the reconstructed vortices, confirms the latter trend and provides statistical evidence of the acceleration of snow particles, as they move toward the downward side of vortices. Overall, the simultaneous multi-scale particle imaging presented here enables us to directly quantify the salient features of preferential sweeping, supporting it as an underlying mechanism of snow settling enhancement in the atmospheric surface layer.
Quantifying the impact of climate change on future air quality is a challenging subject in air quality studies. An ANN model is employed to simulate hourly O3 concentrations. The model is developed based on hourly monitored values of temperature, sol
ar radiation, nitrogen monoxide, and nitrogen dioxide which are monitored during summers (June, July, and August) of 2009-2012 at urban air quality stations in Tehran, Iran. Climate projections by HadCM3 GCM over the study area, driven by IPCC SRES A1B, A2, and B1 emission scenarios, are downscaled by LARS-WG5 model over the periods of 2015-2039 and 2040-2064. The projections are calculated by assuming that current emissions conditions of O3 precursors remain constant in the future. The employed O3 metrics include the number of days exceeding one-hour (1-hr) (120 ppb) and eight-hour (8-hr) (75 ppb) O3 standards and the number of days exceeding 8-hr Air Quality Index (AQI). The projected increases in solar radiation and decreases in precipitation in future summers along with summertime daily maximum temperature rise of about 1.2 and 3 celsius in the first and second climate periods respectively are some indications of more favorable conditions for O3 formation over the study area in the future. Based on pollution conditions of the violation-free summer of 2012, the summertime exceedance days of 8-hr O3 standard are projected to increase in the future by about 4.2 days in the short term and about 12.3 days in the mid-term. Similarly, based on pollution conditions of the polluted summer of 2010 with 58 O3 exceedance days, this metric is projected to increase about 4.5 days in the short term and about 14.1 days in the mid-term. Moreover, the number of Unhealthy and Very Unhealthy days in 8-hr AQI is also projected to increase based on pollution conditions of both summers.
In order to investigate the scope of uncertainty in projections of GCMs for Tehran province, a multi-model projection composed of 15 models is employed. The projected changes in minimum temperature, maximum temperature, precipitation, and solar radia
tion under the A1B scenario for Tehran province are investigated for 2011-2030, 2046-2065, and 2080-2099. GCM projections for the study region are downscaled by the LARS-WG5 model. Uncertainty among the projections is evaluated from three perspectives: large-scale climate scenarios downscaled values, and mean decadal changes. 15 GCMs unanimously project an increasing trend in the temperature for the study region. Also, uncertainty in the projections for the summer months is greater than projection uncertainty for other months. The mean absolute surface temperature increase for the three periods is projected to be about 0.8{deg}C, 2.4{deg}C, and 3.8{deg}C in the summers, respectively. The uncertainty of the multi-model projections for precipitation in summer seasons, and the radiation in the springs and falls is higher than other seasons for the study region. Model projections indicate that for the three future periods and relative to their baseline period, springtime precipitation will decrease about 5%, 10%, and 20%, and springtime radiation will increase about 0.5%, 1.5%, and 3%, respectively. The projected mean decadal changes indicate an increase in temperature and radiation and a decrease in precipitation. Furthermore, the performance of the GCMs in simulating the baseline climate by the MOTP method does not indicate any distinct pattern among the GCMs for the study region.
Multi-model projections in climate studies are performed to quantify uncertainty and improve reliability in climate projections. The challenging issue is that there is no unique way to obtain performance metrics, nor is there any consensus about whic
h method would be the best method of combining models. The goal of this study was to investigate whether combining climate model projections by artificial neural network (ANN) approach could improve climate projections and therefore reduce the range of uncertainty. The equally-weighted model averaging (the mean model) and single climate model projections (the best model) were also considered as references for the ANN combination approach. Simulations of present-day climate and future projections from 15 General Circulation Models (GCMs) for temperature and precipitation were employed. Results indicated that combining GCM projections by the ANN combination approach significantly improved the simulations of present-day temperature and precipitation than the best model and the mean model. The identity of the best model changed between the two variables and among stations. Therefore, there was not a unique model which could represent the best model for all variables and/or stations over the study region. The mean model was also not skillful in giving a reliable projection of historical climate. Simulation of temperature indicated that the ANN approach had the best skill at simulating present-day monthly means than other approaches in all stations. Simulation of present-day precipitation, however, indicated that the ANN approach was not the best approach in all stations although it performed better than the mean model. Multi-model projections of future climate conditions performed by the ANN approach projected an increase in temperature and reduction in precipitation in all stations and for all scenarios.
Large eddy simulations (LES) are employed to investigate the role of time-varying currents on the form drag and vortex dynamics of submerged 3D topography in a stratified rotating environment. The current is of the form $U_c+U_t sin(2pi f_t t)$, wher
e $U_c$ is the mean, $U_t$ is the tidal component and $f_t$ is its frequency. A conical obstacle is considered in the regime of low Froude number. When tides are absent, eddies are shed at the natural shedding frequency $f_{s,c}$. The relative frequency $f^*=f_{s,c}/f_t$ is varied in a parametric study which reveals states of high time-averaged form drag coefficient. There is a two-fold amplification of the form drag coefficient relative to the no-tide ($U_t=0$) case when $f^*$ lies between 0.5 and 1. The spatial organization of the near-wake vortices in the high drag states is different from a Karman vortex street. For instance, the vortex shedding from the obstacle is symmetric when $f^*=5/12$ and strongly asymmetric when $f^*=5/6$. The increase in form drag with increasing $f^*$ stems from bottom intensification of the pressure in the obstacle lee which is linked to changes in flow separation and near-wake vortices.
