Attempts are made to study the seasonal climatology of the Arabian Peninsula, including the regional to station level information for Saudi Arabia for the period 1979–2009. The wet (November to ...April) and dry (June to September) season rainfall and temperature climatology are obtained from various data sources, namely, surface observations, CPC Merged Analysis of Precipitation (CMAP), Climatic Research Unit (CRU) and Tropical Rainfall Measuring Mission (TRMM). These gridded datasets detect the dry zone over the Rub Al-Khali, the world's largest sand desert, during the wet season. In this season, large rain belts exist north of 30°N and south of 15°N. During the dry season, the Arabian Peninsula is almost entirely dry north of 15°N but rain belts exist below this latitudinal boundary. Irrespective of the season or dataset used, a relatively heavy-rain area is obtained for the southwest of the Peninsula. The wet (dry) season temperature is highest over the western (middle to the northern) parts of the Peninsula.
Surface observations indicate that, irrespective of season, rainfall insignificantly increased in the first period (1979–1993), and then significantly decreased in the second period (1994–2009). The decrease rate is 35.1mm (5.5mm) per decade during the wet (dry) season. The temperature over Saudi Arabia has increased significantly, and the increase rate is faster (0.72°C per decade) in the dry season compared to the wet season (0.51°C per decade).
► The Arabian Peninsula’s seasonal climate is studied using observational and gridded data. ► Rainfall has decreased significantly in Saudi Arabia. ► Temperature has increased significantly in Saudi Arabia. ► Rainfall has statistically decreased significantly in Saudi Arabia.
A new version of the Community Land Model (CLM) was introduced to the Saudi King Abdulaziz University Atmospheric Global Climate Model (Saudi-KAU AGCM) for better land surface component ...representation, and so to enhance climate simulation. CLM replaced the original land surface model (LSM) in Saudi-KAU AGCM, with the aim of simulating more accurate land surface fluxes globally, but especially over the Arabian Peninsula. To evaluate the performance of Saudi-KAU AGCM, simulations were completed with CLM and LSM for the period 1981–2010. In comparison with LSM, CLM generates surface air temperature values that are closer to National Centre for Environmental Prediction (NCEP) observations. The global annual averages of land surface air temperature are 9.51, 9.52, and 9.57°C for NCEP, CLM, and LSM respectively, although the same atmospheric radiative and surface forcing from Saudi-KAU AGCM are provided to both LSM and CLM at every time step. The better temperature simulations when using CLM can be attributed to the more comprehensive plant functional type and hierarchical tile approach to the land cover type in CLM, along with better parameterization of upward land surface fluxes compared to LSM. At global scale, CLM exhibits smaller annual and seasonal mean biases of temperature with respect to NCEP data. Moreover, at regional scale, CLM demonstrates reasonable seasonal and annual mean temperature over the Arabian Peninsula as compared to the Climatic Research Unit (CRU) data. Finally, CLM generated better matches to single point-wise observations of surface air temperature and surface fluxes for some case studies.
•State of the art Community Land Model (CLM) is implemented in KAU-AGCM.•Surface flux parameterizations are updated in CLM.•KAU-AGCM simulation with CLM has less temperature bias.•CLM improves the global temperature climatology.•Arabian Peninsula temperature climatology is better in CLM simulation.
The performance of a regional climate model RegCM4.3.4 (RegCM4) in simulating the climate characteristics of the Middle East and North Africa (MENA) region has been evaluated. The simulations carried ...out in this study contribute to the joint effort by the international regional downscaling community called Coordinated Regional climate Downscaling Experiment (CORDEX). The model has been forced with the boundary conditions obtained from the global reanalysis dataset ERA-Interim for the period 1979–2010. An east–west cold bias is found in the northern part of the MENA domain in RegCM4 that is absent in the ERA-Interim driving forcings, whereas a large warm bias is found over the southern Arabian Peninsula (Yemen/Oman) for both RegCM4 and ERA-Interim. The possible causes leading to the warm bias over Yemen/Oman in the RegCM4 are discussed. The model performed well in capturing the salient features of precipitation which includes ITCZ, Mediterranean cyclones as well as precipitation minima over the deserts. Moreover, the annual cycles of precipitation and mean temperature over the prominent river basins of the region have been ably captured by the model. Temperature-precipitation relationship revealed that the ERA-Interim driving forcings stay closer to the observations; however, RegCM4 remains competent for most of the Koppen-Geiger climate classification types. Performance of the model in capturing the near surface winds and specific humidity is also presented. Based on the results of this study, it is concluded that RegCM4 is well suited to conduct long-term high-resolution climate change projection for the future period over the CORDEX-MENA/Arab domain.
Background
A new coupled global climate model (CGCM) has been developed at the Center of Excellence for Climate Change Research (CECCR), King Abdulaziz University (KAU), known as Saudi-KAU CGCM.
...Purpose
The main aim of the model development is to generate seasonal to subseasonal forecasting and long-term climate simulations.
Methods
The Saudi-KAU CGCM currently includes two atmospheric dynamical cores, two land components, three ocean components, and multiple physical parameterization options. The component modules and parameterization schemes have been adopted from different sources, and some have undergone modifications at CECCR. The model is characterized by its versatility, ease of use, and the physical fidelity of its climate simulations, in both idealized and realistic configurations. A description of the model, its component packages, and parameterizations is provided.
Results
Results from selected configurations demonstrate the model’s ability to reasonably simulate the climate on different time scales. The coupled model simulates El Niño-Southern Oscillation (ENSO) variability, which is fundamental for seasonal forecasting. It also simulates Madden-Julian Oscillation (MJO)-like disturbances with features similar to observations, although slightly weaker.
Conclusions
The Saudi-KAU CGCM ability to simulate the ENSO and the MJO suggests that it is capable of making useful predictions on subseasonal to seasonal timescales.
Human faecal sludge contains diverse harmful microorganisms, making it hazardous to the environment and public health if it is discharged untreated. Faecal sludge is one of the major sources of
that ...can produce extended-spectrum β-lactamases (ESBLs).
This study aimed to investigate the prevalence and molecular characterization of ESBL-producing
in faecal sludge samples collected from faecal sludge treatment plants (FSTPs) in Rohingya camps, Bangladesh.
ESBL producing
were screened by cultural as well as molecular methods and further characterized for their major ESBL genes, plasmid profiles, pathotypes, antibiotic resistance patterns, conjugation ability, and genetic similarity.
Of 296 isolates, 180 were phenotypically positive for ESBL. All the isolates, except one, contained at least one ESBL gene that was tested (
,
,
,
,
,
,
, and
). From plasmid profiling, it was observed that plasmids of 1-211 MDa were found in 84% (151/180) of the isolates. Besides, 13% (24/180) of the isolates possessed diarrhoeagenic virulence genes. From the remaining isolates, around 51% (79/156) harbored at least one virulence gene that is associated with the extraintestinal pathogenicity of
. Moreover, 4% (3/156) of the isolates were detected to be potential extraintestinal pathogenic
(ExPEC) strains. Additionally, all the diarrhoeagenic and ExPEC strains showed resistance to three or more antibiotic groups which indicate their multidrug-resistant potential. ERIC-PCR differentiated these pathogenic isolates into seven clusters. In addition to this, 16 out of 35 tested isolates transferred plasmids of 32-112 MDa to
J53 recipient strain.
The present study implies that the faecal sludge samples examined here could be a potential origin for spreading MDR pathogenic ESBL-producing
. The exposure of Rohingya individuals, living in overcrowded camps, to these organisms poses a severe threat to their health.
In this study, combined Dark Target and Deep Blue (DTB) aerosol optical depth at 550 nm (AOD550 nm) data the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra and Aqua ...satellites during the years 2003–2020 are used as a reference to assess the performance of the Copernicus Atmosphere Monitoring Services (CAMS) and the second version of Modern-Era Retrospective analysis for Research and Applications (MERRA-2) AOD over Bangladesh. The study also investigates long-term spatiotemporal variations and trends in AOD, and determines the relative contributions from different aerosol species (black carbon: BC, dust, organic carbon: OC, sea salt: SS, and sulfate) and anthropogenic emissions to the total AOD. As the evaluations suggest higher accuracy for CAMS than for MERRA-2, CAMS is used for further analysis of AOD over Bangladesh. The annual mean AOD from both CAMS and MODIS DTB is high (>0.60) over most parts of Bangladesh except for the eastern areas of Chattogram and Sylhet. Higher AOD is observed in spring and winter than in summer and autumn, which is mainly due to higher local anthropogenic emissions during the winter to spring season. Annual trends from 2003–2020 show a significant increase in AOD (by 0.006–0.014 year−1) over Bangladesh, and this increase in AOD was more evident in winter and spring than in summer and autumn. The increasing total AOD is caused by rising anthropogenic emissions and accompanied by changes in aerosol species (with increased OC, sulfate, and BC). Overall, this study improves understanding of aerosol pollution in Bangladesh and can be considered as a supportive document for Bangladesh to improve air quality by reducing anthropogenic emissions.
Display omitted
•Both satellite and reanalysis data show significantly higher AOD in Bangladesh.•Higher AOD was found in spring and winter than in summer and autumn.•Total AOD is significantly dominated by organic carbon, sulfate, and black carbon.•Significant increasing trends in AOD were observed during 2003–2020.•Increasing in anthropogenic emissions are responsible for AOD increasing trends.
High Temperature Data Converters in Silicon Carbide CMOS Rahman, Ashfaqur; Caley, Landon; Roy, Sajib ...
I.E.E.E. transactions on electron devices/IEEE transactions on electron devices,
2017-April, 2017-4-00, Volume:
64, Issue:
4
Journal Article
Peer reviewed
Open access
This paper presents an 8-b digital to analog converter (DAC) and 8-b analog to digital converter (ADC) for high-temperature applications. The pair of data converters were designed in a 1.2- - m ...silicon carbide CMOS process and have been tested from 25 °C to 400 °C. At 400 °C, the DAC has a maximum differential nonlinearity (DNL) and integral nonlinearity (INL) error of 1.2 least significant bit (LSB) and 2.7 LSB, respectively, while the offset and the gain error are 5.9 and 2.7 LSB. The ADC has a maximum DNL and INL error of 3.6 and -3 LSB, respectively, while the offset error is -7 LSB and the gain error is 2.6 LSB. The ADC has an SNDR = 32.15 dB and effective number of bits = 5.05 b at 300 °C. The DAC is the first of its kind in silicon carbide CMOS, while the ADC is the first reported at temperatures over 300 °C.
The northeast region of Bangladesh is highly vulnerable to flash floods which often damage the only crop of that region, the boro rice. Developing an efficient and real-time flash flood forecasting ...system for that region is found essential. The traditional model-centric approach requires plenty of time and manpower to effectively model flash floods. This paper discusses an open-source data-centric integrated flash flood modelling approach using a platform named Delft-FEWS. It is based on three inter-dependent models, e.g., meteorological, hydrologic, and hydrodynamic. The model was calibrated and validated with the flash flood data of 2010-12 and 2013-15, respectively. The performance of the model in real-time was assessed in the context of the 2021 flash flood season. In both cases, it performed satisfactorily. The entire system helps the practitioners to work in a flexible environment to generate flash flood forecasting and early warnings.
•We developed a customizable integrated flash flood forecasting system using Delft-FEWS.•This system coupled weather forecast , hydrological, & hydrodynamic model for a data-poor complex transboundary river basin.•The model provides satisfactory results in most of the stations (R2 > 0.7; NSE> 0.8) after calibration and validation.•The model performed satisfactorily (R2 > 0.7; NSE>0.8; PBIAS within ±1%; RMSE<0.1) in real-time water level forecasting during the 2021 flash flood season.
Despite ongoing reduction in genotyping costs, genomic studies involving large numbers of species with low economic value (such as Black Tiger prawns) remain cost prohibitive. In this scenario DNA ...pooling is an attractive option to reduce genotyping costs. However, genotyping of pooled samples comprising DNA from many individuals is challenging due to the presence of errors that exceed the allele frequency quantisation size and therefore cannot be simply corrected by clustering techniques. The solution to the calibration problem is a correction to the allele frequency to mitigate errors incurred in the measurement process. We highlight the limitations of the existing calibration solutions such as the fact they impose assumptions on the variation between allele frequencies 0, 0.5, and 1.0, and address a limited set of error types. We propose a novel machine learning method to address the limitations identified.
The approach is tested on SNPs genotyped with the Sequenom iPLEX platform and compared to existing state of the art calibration methods. The new method is capable of reducing the mean square error in allele frequency to half that achievable with existing approaches. Furthermore for the first time we demonstrate the importance of carefully considering the choice of training data when using calibration approaches built from pooled data.
This paper demonstrates that improvements in pooled allele frequency estimates result if the genotyping platform is characterised at allele frequencies other than the homozygous and heterozygous cases. Techniques capable of incorporating such information are described along with aspects of implementation.
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