The sensitivity of simulated aerosol climatic impact to domain size using regional model (WRF-Chem v3.6)

Domain size can have significant impact on regional modeling results, but few studies examined the sensitivities of simulated aerosol impact to regional domain size. This study investigates the regional modeling sensitivities of aerosol impact on the East Asian summer monsoon (EASM) to domain size. The simulations with two different domain sizes demonstrate consistently that aerosols induce the cooling of the lower troposphere that leads to the anticyclone circulation anomalies and thus the weakening of EASM moisture transport. The aerosol-induced adjustment of monsoonal circulation results in an alternate increase and decrease pattern of precipitation over China. Domain size has a great influence on the simulated meteorological fields. For example, the simulation with larger domain size produces weaker EASM circulation, which also affects aerosol distributions significantly. This leads to the difference of simulated strength and area extent of aerosol-induced changes of lower-tropospheric temperature and pressure, which further results in different distributions of circulation and precipitation anomalies over China. For example, over southeastern China, aerosols induce the increase (decrease) of precipitation from the smaller-domain (larger-domain) simulation. Different domain sizes consistently simulate an aerosol-induced increase in precipitation around 30∘ N over eastern China. This study highlights the important influence of domain size on regional modeling results of aerosol impact on circulation and precipitation, which may not be limited to East Asia. More generally, this study also implies that proper modeling of meteorological fields with appropriate domain size is one of the keys to simulating robust aerosol climatic impact.

Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

Robustness of simulating aerosol climatic impacts using regional model (WRF-Chem v3.6): the sensitivity to domain size

Domain size can have significant impacts on regional modeling results, but few studies examining the sensitivities of regional modeling results of aerosol impacts to domain size. This study investigates the regional modeling sensitivities of aerosol impacts on East Asian summer monsoon (EASM) to domain size. The simulations with two different domain sizes demonstrate consistently that aerosols induce the cooling of lower troposphere that leads to the anti-cyclone circulation anomalies and thus the weakening of EASM moisture transport. The aerosol-induced adjustment of monsoonal circulation results in a spatial pattern of “+-+-+” for precipitation change over the continent of China. Domain size has a great influence on the simulated meteorological fields. For example, the simulation with increasing domain size produces weaker EASM circulation, which also affect aerosol distributions significantly. This leads to the difference of simulated strength and area extent of aerosol-induced changes of lower-tropospheric temperature and pressure, which further results in different locations of circulation and precipitation anomalies over the continent of China. For example, over Southeast China, aerosols induce the increase (decrease) of precipitation from the smaller-domain (larger-domain) simulation. Different domain sizes simulate consistently aerosol-induced increase of precipitation around 30° N over East China. This study highlights the important impacts of domain size on regional modeling results of aerosol impacts on circulation and precipitation, which may not be limited to East Asia. More generally, this study also implies that proper modeling of meteorological fields with appropriate domain size is one of the keys to simulate robust aerosol climatic impacts.

RETURNING TO THE RATE OF SPREAD OF EARLY FARMING IN EUROPE: COMMENT ON THE ARTICLE BY MANEN ET AL. (2019) IN RADIOCARBON

ABSTRACTThe aim of the comment is to address the misrepresentations of our work on the Neolithic transitions that are found in a recent article by Manen and coauthors in Radiocarbon. There are a fair number of them as indicated in the comment. The purpose of the comment is (1) to set the record straight, (2) to clarify several misconceptions that have persisted in the literature for some time, and (3) to comment briefly on the convergence between our own recent regional modeling of the spread of early farming along the north coast of the West Mediterranean and the position currently held by Manen and coauthors.

Reducing dairy data inconsistency through Regional Modeling Approach (RMA): a case from North-Western part of Bangladesh

In Bangladesh, the transformation of dairy farming from livelihood-oriented to enterprise-driven farming system might require deeper understanding on the regional differences in terms of regional potential for further dairy development. This, however, entails detailed data on dairy farm at regional level. Since the data are relatively very scarce in one hand and on the other hand, even available, are contradicting among various sources in terms of data accuracy and precision, the application of the regional modeling on the data and extrapolates to the national data and vice-versa is one of the ways to identify the possible options to improve the data availability and quality. Considering this, the current study was undertaken to assess the data inconsistency by comparing the dairy herd structure and its milk production at regional level and propose a validation tool to arrive at the national data by using the regional findings. The International Farm Comparison Network (IFCN) Regional Modeling Approach (RMA) along with the locally developed Integrated Dairy Research Network (IDRN) farm model was used. The primary data was collected from three divisions (9 districts) from the North-Western part of the country. The results revealed that proportion of household farm dominates over family and business farm while considering the total dairy cow as unit for defining the farm type. The share of the cross bred cows to the local cows is 74.6% and 24.4%, respectively. However, the proportion of lactating cows over dry cows and heifer seems to be higher in local cows (48.8%) than cross breed cows (34.2%). The average milk production for all regions is 4.49 lit/day/cow while that for cross breed is 6.23 lit and local 1.71 lit/day/cow. Using regional model and its coefficient on average milk production, herd composition, proportion of lactating cows on total milk production of DLS and IDRN revealed that IDRN new model estimates 36.5% lower milk than the DLS in 2019 and 33.5% lower in 2018. The IDRN version 1.0 and 2.0 model difference was found to 15.4% and 18.3% lower for 2018 and 2019, respectively. The model setup, calibration and validation are time-demanding and challenging tasks for these large set of data, given the scale intensive data requirements, and the need to ensure the reliability data from multiple regions. This study concludes that regional modeling is quite useful for validating the regional share of the milk production and national milk production. However, this study would recommend for using standardized for data collection, validation and thus conducting further study on the other regions and finally including all regions of the country. Bang. J. Anim. Sci. 2020. 49 (2): 128-141