Modern-Era Retrospective Analysis For Research And Applications Research Articles

Sensitivity of horizontal resolution and land surface model in operational WRF forecast for Online Nuclear Emergency Response System (ONERS)

Accurate Meteorological forecasts are crucial for the assessment of plume dispersion and dose prediction in nuclear power plant (NPP) sites. In this work the forecast sensitivity of the Weather Research and Forecasting (WRF) model is tested by running a series of forecast simulations for horizontal resolution, and land surface models (LSM) in the context of Online Nuclear Emergency Response System (ONERS) for Indian NPP sites. 72 h forecast simulations are made for three seasons viz. summer, southeast and northeast monsoon using the Global Forecast data. Three simulation experiments, namely 2 km-NOAH, 3 km-NOAH and 3 km-NOAHMP are conducted using two different nested domain configurations (18–6–2 km and 9–3 km) and two LSM schemes (NOAH and NOAH-MP) and tested at four different NPP sites. Forecast comparison of surface winds, relative humidity, temperature, heat fluxes and planetary boundary layer heights with data from meteorological tower, radiosonde and the Modern-Era Retrospective analysis for Research and Applications 2 (MERRA-2) shows 3 km-NOAH is equally capable in predicting surface parameters as well as vertical profiles compared to 2 km-NOAH with marginal differences. 3 km-NOAHMP shows less mean bias and better correlation for boundary layer height and heat fluxes. Comparison of spatial flow-field with 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data shows synoptic scale seasonal winds, sea level pressure systems and temperature hot-spots are better captured by 3 km-NOAHMP compared to 6 km coarse domain in the 18–6–2 km configuration. The daily accumulated rainfall by all simulations is overestimated compared to ERA5 data. The predictions by 3 km-NOAHMP better agree with Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM-IMERGE) data whereas 2 km-NOAH predicts delayed rainfall occurrence. Dispersion simulations of hypothetical plume release from a coastal NPP site with all three forecasts properly show the influence of local scale diurnal land-sea breeze and seasonal winds on the plume movement. Therefore the 9–3 km domain with NOAHMP LSM is found to be a suitable choice for operational weather forecast in ONERS for Indian NPP sites.

Global changes in aerosol single scattering albedo during COVID-19

A comprehensive global investigation on the impact of reduction (changes) in aerosol emissions due to Coronavirus disease-2019 (COVID-19) lockdowns on aerosol single scattering albedo (SSA) utilizing satellite observations and model simulations is conducted for the first time. The absolute change in Ozone Monitoring Instrument (OMI) retrieved, and two highly-spatially resolved models (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) simulated SSA is <4% (<0.04–0.05) globally during COVID (2020) compared to normal (2015–2019) period. Change in SSA during COVID is not significantly different from long-term and year-to-year variability in SSA. A small change in SSA indicates that significant reduction in anthropogenic aerosol emissions during COVID-19 induced lockdowns has a negligible effect in changing the net contribution of aerosol scattering and/or absorption to total aerosol extinction. The changes in species-wise aerosol optical depth (AOD) are examined in detail to explain the observed changes in SSA. Model simulations show that total AOD decreased during COVID-19 lockdowns, consistent with satellite observations. The respective contributions of sulfate and black carbon (BC) to total AOD increased, which resulted in a negligible change in SSA during the spring and summer seasons of COVID over South Asia. Europe and North America experience a small increase in SSA (<2%) during the summer season of COVID due to a decrease in BC contribution. The change in SSA (2%) is the same for a small change in BC AOD contribution (3%), and for a significant change in sulfate AOD contribution (20%) to total AOD. Since, BC SSA is 5-times lower (higher absorption) than that of sulfate SSA, the change in SSA remains the same. For a significant change in SSA to occur, the BC AOD contribution needs to be changed significantly (4–5 times) compared to other aerosol species. A sensitivity analysis reveals that change in aerosol radiative forcing during COVID is primarily dependent on change in AOD rather than SSA. These quantitative findings can be useful to devise more suitable future global and regional mitigation strategies aimed at regulating aerosol emissions to reduce environmental impacts, air pollution, and public health risks.

Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1

Abstract. Isoprene, a key biogenic volatile organic compound, plays a pivotal role in atmospheric chemistry. Due to its high reactivity, this compound contributes significantly to the production of tropospheric ozone in polluted areas and to the formation of secondary organic aerosols. The assessment of biogenic emissions is of great importance for regional and global air quality evaluation. In this study, we implemented the biogenic emission model MEGANv2.1 (Model of Emissions of Gases and Aerosols from Nature, version 2.1) in the surface model SURFEXv8.1 (SURface EXternalisée in French, version 8.1). This coupling aims to improve the estimation of biogenic emissions using the detailed vegetation-type-dependent treatment included in the SURFEX vegetation ISBA (Interaction between Soil Biosphere and Atmosphere) scheme. This scheme provides vegetation-dependent parameters such as leaf area index and soil moisture to MEGAN. This approach enables a more accurate estimation of biogenic fluxes compared to the stand-alone MEGAN model, which relies on average input values for all vegetation types. The present study focuses on the assessment of the SURFEX–MEGAN model isoprene emissions. An evaluation of the coupled SURFEX–MEGAN model results was carried out by conducting a global isoprene emission simulation in 2019 and by comparing the simulation results with other MEGAN-based isoprene inventories. The coupled model estimates a total global isoprene emission of 443 Tg in 2019. The estimated isoprene is within the range of results obtained with other MEGAN-based isoprene inventories, ranging from 311 to 637 Tg. The spatial distribution of SURFEX–MEGAN isoprene is consistent with other studies, with some differences located in low-isoprene-emission regions. Several sensitivity tests were conducted to quantify the impact of different model inputs and configurations on isoprene emissions. Using different meteorological forcings resulted in a ±5 % change in isoprene emissions using MERRA (Modern-Era Retrospective analysis for Research and Applications) and IFS (Integrated Forecasting System) compared with ERA5. The impact of using different emission factor data was also investigated. The use of PFT (plant functional type) spatial coverage and PFT-dependent emission potential data resulted in a 12 % reduction compared to using the isoprene emission potential gridded map. A significant reduction of around 38 % in global isoprene emissions was observed in the third sensitivity analysis, which applied a parameterization of soil moisture deficit, particularly in certain regions of Australia, Africa, and South America. The significance of coupling the SURFEX and MEGAN models lies particularly in the ability of the coupled model to be forced with meteorological data from any period. This means, for instance, that this system can be used to predict biogenic emissions in the future. This aspect of our work is significant given the changes that biogenic organic compounds are expected to undergo as a result of changes in their climatic factors.

Analysis of short-term wind speed variation, trends and prediction: A case study of Tamil Nadu, India

Abstract Purpose The purpose of this research article is to analyze the short-term wind speed and develop a framework model to overcome the challenges in the wind power industry. Design/Methodology/Approach Real data with a case study of wind speed is presented to illustrate the advantages of this new wind speed analytical framework. Hourly measurements of wind speed are observed, and the experiments are conducted using tools such as ANOVA, control charts, trend analysis, and predictive models. The August month data for over 13 years from modern era retrospective-analysis for research and applications (MERRA) National aeronautics and space administration (NASA) for Coimbatore and Erode locations in Tamil Nadu, India, have been used. The results were considered for the study to understand the wind speed data and the implementation of new wind power projects in India. Findings The essence of the proposed wind speed analytical framework is its flexible approach, which enables the effective integration of wind firms’ individual requirements by developing tailor-made analytical evaluations. Originality/Value This article derives the wind speed analytical framework with the application of statistical tools and machine learning algorithms.

Optical and physical characteristics of aerosols over Asia: AERONET, MERRA-2 and CAMS

A comprehensive study on the regional and spatial distributions of aerosol columnar optical and physical characteristics utilizing high-quality Aerosol Robotic Network (AERONET) datasets over Asia (Central, South, South-East, and East Asia), along with spatiotemporal collocated validation of two highly-spatially resolved models (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) simulated aerosol optical depth (AOD) and Ångström exponent (AE) on annual and seasonal scales, is conducted for the first time. AOD is the highest over South Asia in each season, followed by South-East, East, and Central Asia. Annual regional average AOD at 0.50 μm over South Asia is 0.61. Combined influence of both fine mode anthropogenic aerosol emissions from fossil fuel combustion and biomass burning, and coarse mode dust aerosols from seasonal transport lead to higher AOD and total volume concentration (TVC), and exhibit significant spatiotemporal variations. Coarse volume fraction (CVF) and effective radius (Reff) are higher over Central Asia due to the dominance of coarse dust aerosols. East and South-East Asia are dominated by fine mode aerosols and result in higher fine volume concentration (FVC), AE, fine mode fraction (FMF), and lower Reff. Compared to the other regions of the globe (except Africa), AOD is higher over Asia, with higher spatiotemporal variabilities in AOD, AE, FMF, and TVC. Over Asia, the agreement between AERONET and CAMS AOD is better than that of AERONET and MERRA-2 AOD. For high AOD conditions, underestimation in model AODs is higher, and lower fraction of model AODs satisfy the Global Climate Observing System (GCOS) requirement over all regions, and these are more pronounced over Asia. Biases in model AODs are higher over Asia compared to the other regions of the globe, and lower over North America, Europe, and Australia. Higher bias in model AEs compared to AODs over all the regions shows substantial challenges in simulating spectral AOD and appropriate contributions of fine and coarse mode aerosols. These findings over a global aerosol hotspot region, Asia, along with other regions of the globe are crucial for accurate simulation and fine-tuning of aerosol characteristics by regional and global models, and for reducing uncertainties in the assessment of radiative and climate impact of aerosols.

Improved estimates of smoke exposure during Australia fire seasons: importance of quantifying plume injection heights

Abstract. Wildfires can have a significant impact on air quality in Australia during severe burning seasons, but incomplete knowledge of the injection heights of smoke plumes poses a challenge for quantifying smoke exposure. In this study, we use two approaches to quantify the fractions of fire emissions injected above the planetary boundary layer (PBL), and we further investigate the impact of plume injection fractions on daily mean surface concentrations of fine particulate matter (PM2.5) from wildfire smoke in key cities over northern and southeastern Australia from 2009 to 2020. For the first method, we rely on climatological, monthly mean vertical profiles of smoke emissions from the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) together with assimilated PBL heights from NASA Modern-Era Retrospective Analysis for Research and Application (MERRA) version 2. For the second method, we develop a novel approach based on the Multi-angle Imaging SpectroRadiometer (MISR) observations and a random forest, machine learning model that allows us to directly predict the daily plume injection fractions above the PBL in each grid cell. We apply the resulting plume injection fractions quantified by the two methods to smoke PM2.5 concentrations simulated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in target cities. We find that characterization of the plume injection heights greatly affects estimates of surface daily smoke PM2.5, especially during severe wildfire seasons, when intense heat from fires can loft smoke high in the troposphere. However, using climatological injection profiles cannot capture well the spatiotemporal variability in plume injection fractions, resulting in a 63 % underestimation of daily fire emission fluxes injected above the PBL in comparison with those fluxes derived from MISR injection fractions. Our random forest model successfully reproduces the daily injected fire emission fluxes against MISR observations (R2=0.88, normalized mean bias = 10 %) and predicts that 27 % and 45 % of total fire emissions rise above the PBL in northern and southeastern Australia, respectively, from 2009 to 2020. Using the plume behavior predicted by the random forest method also leads to better model agreement with observed surface PM2.5 in several key cities near the wildfire source regions, with smoke PM2.5 accounting for 5 %–52 % of total PM2.5 during fire seasons from 2009 to 2020.

Analysis of Atmospheric Elements in Near Space Based on Meteorological-Rocket Soundings over the East China Sea

As an important means of in situ detection in near space, meteorological rockets can provide a high-precision distribution analysis of atmospheric elements. However, there are currently few studies on the principles of meteorological-rocket detection and the application of rocket-sounding data. The purpose of this paper is to fill this gap by providing a detailed introduction to the detection principle of a meteorological rocket launched in the East China Sea in November 2022. Moreover, empirical models, satellite data, and reanalysis data were selected for comparison and verification with the rocket-sounding data. Furthermore, the accuracy of these widely used datasets was studied based on the rocket-sounding data in the near space over the East China Sea. Additionally, gravity-wave power–frequency spectra were extracted using the maximum entropy method from both the rocket-sounding data and the remote-sensing data. Furthermore, the relationship between gravity waves and Kelvin–Helmholtz instability (KHI) was investigated by analyzing the gravity-wave energy and the Richardson number. The research findings indicate that among the remote-sensing data describing the atmospheric environment over the launch site, the COSMIC occultation data is more accurate compared with the SABER data. The wind-field distribution derived from rocket detection is consistent with the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis data, while also providing a more detailed description of the wind field. The main wavelengths of gravity waves extracted from rocket-sounding data are consistently smaller than those obtained from satellite remote-sensing data, indicating that rocket sounding is capable of capturing more intricate structures of gravity waves. The good correspondence between the peaks of gravity-wave energy and the regions where KHI occurs indicates that there is a strong interaction between gravity waves and KHI in the middle atmosphere.

Convectively coupled Rossby–Gravity waves in a field campaign: How they are captured in reanalysis products

AbstractConvectively coupled equatorial waves are a significant source of atmospheric variability in the tropics. Current numerical models continue to struggle in simulating the coupled diabatic heating fields that are responsible for the development and maintenance of these waves. This study investigates how the diabatic fields associated with Mixed Rossby–Gravity waves (MRGs) are represented in four reanalysis products by using a unique observational dataset from the TRMM‐KWAJEX (Tropical Rainfall Measuring Mission‐Kwajalein Experiment) field campaign. These reanalyses include ERA5, Japanese 55‐year Reanalysis (JRA‐55), Climate Forecast System Reanalysis (CFSR), and Modern‐Era Retrospective Analysis for Research and Applications (MERRA). We found that all four reanalyses captured the MRG structures in winds and temperature, and to a lesser degree in the humidity field except in the boundary layer. However, only the ERA5 and MERRA reanalyses captured the gradual rise and succession of the diabatic heating from boundary layer turbulence, shallow convection, cumulus congestus, and deep convection within the waves. ERA5 is the only product that also captured the gradual rise of the subgrid‐scale vertical transport of moist static energy. All reanalysis products underestimated the diabatic heating from cumulus congestus. Results provide observational basis on what aspects of MRG can be trusted and what cannot in the reanalysis products.

Comparative analysis of gridded rainfall datasets over the Bagmati river basin, India

Abstract In this study, three high-resolution gridded rainfall datasets, viz., Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) have been collected, analyzed, and compared against the ground-based observed rain gauge datasets of Indian Meteorological Department (IMD) of the Bagmati river basin from 2001 to 2014 in the Bihar State of India. Comparison analyses were performed at daily, monthly, seasonal, and annual time scales. Various statistical parameters, contingency tests, trend analysis, and rainfall anomaly index were used for comparison of datasets. Though MERRA-2 had the highest probability of detection (POD) and lowest false alarm ratio (FAR), analysis showed that IMERG data were closely matching with the observed data, whereas MERRA-2 and PERSIANN underestimated the extreme values. For the monthly scale, again IMERG had the most optimal Coefficient of Determination (R2) and Nash-Sutcliffe Efficiency (NSE) values. IMERG also performed well in detecting rainfall trends and identifying wet and dry years. Overall, IMERG was the most suitable dataset at all time scales for future studies in the basin.

Heat wave monitoring over West African cities: uncertainties, characterization and recent trends

Abstract. Heat waves can be one of the most dangerous climatic hazards affecting the planet, having dramatic impacts on the health of humans and natural ecosystems as well as on anthropogenic activities, infrastructures and economy. Based on climatic conditions in West Africa, the urban centres of the region appear to be vulnerable to heat waves. The goals of this work are firstly to assess the potential uncertainties encountered in heat wave detection and secondly to analyse their recent trend in West Africa cities during the period 1993–2020. This is done using two state-of-the-art reanalysis products, namely the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) and Modern-Era Retrospective analysis for Research and Applications (MERRA), as well as two local station datasets, namely Dakar–Yoff in Senegal and Aéroport Félix Houphouët-Boigny, Abidjan, in Côte d'Ivoire. An estimate of station data from reanalyses is processed using an interpolation technique: the nearest neighbour to the station with a land sea mask ≥0.5. The interpolated temperatures from local stations in Dakar and Abidjan show slightly better correlation with ERA5 than with MERRA. Three types of uncertainty are discussed: the first type of uncertainty is related to the reanalyses themselves, the second is related to the sensitivity of heat wave frequency and duration to the threshold values used to monitor them, and the last one is linked to the choice of indicators and the methodology used to define heat waves. Three sorts of heat wave have been analysed, namely those occurring during daytime, nighttime, and both daytime and nighttime concomitantly. Four indicators have been used to analyse heat waves based on 2 m temperature, humidity, 10 m wind or a combination of these. We found that humidity plays an important role in nighttime events; concomitant events detected with wet-bulb temperature are more frequent and located over the northern Sahel. Strong and more persistent heat waves are found in the continental (CONT) region. For all indicators, we identified 6 years with a significantly higher frequency of events (1998, 2005, 2010, 2016, 2019 and 2020), possibly due to higher sea surface temperatures in the equatorial Atlantic Ocean corresponding to El Niño events for some years. A significant increase in the frequency, duration and intensity of heat waves in the cities has been observed during the last decade (2012–2020); this is thought to be a consequence of climate change acting on extreme events.

Domestic Wind Energy Planning for Deprived Communities in the Tropics: A Case Study of Nigeria

Despite the notable inventions in solar energy, it is still too high for standalone users from developing countries. For example, it cost $2200 to provide power for a two-bedroom apartment while the average citizen lives below the country’s poverty line of $381.75 per year. The use of fossil fuel generators remains cheaper, except there is an affordable energy option for the average populace. The objective of this study is to investigate the wind energy potential for domestic or standalone use in Nigeria. It is proposed that the domestic wind turbine will be relatively cheap for adoption. Hence, there is the need to wholistic examine the prospects of wind energy generation in Nigeria. Though previous studies had been carried out, none has been wholistic as presented in this research work. Forty years wind speed and wind direction dataset, i.e., 1980-2020, was obtained from the Modern-Era Retrospective analysis for Research and Applications (MERRA). The analysis of the wind energy potential across the research locations was considered using five sampling techniques, i.e., considering the general statistics of the forty years dataset; considering ten years in an evenly distributed pattern and accruable wind energy across the nation. It was observed that the early wet season (MAM) is the most unstable among the seasons. Also, sudden multi-directionality of the wind vectorization within forty years was observed. This event is ascribed to evidence of climate change to wind energy generation. Wind energy generation prospect was seen to be generally sustainable and reliable with SON, MAM, DJF and JJA having energy distribution of 325-950 kWh, 539-1700 kWh, 161-650 kWh and 761-3650 kWh respectively. Despite the variation of energy generation over the years within all seasons over Nigeria, it was found that it is predictable and can be optimized using various technological solutions.

Signatures of Eurasian heat waves in global Rossby wave spectra

Abstract. This paper investigates systematic changes in the global atmospheric circulation statistics during Eurasian heat waves (HWs). The investigation of Rossby wave energy anomalies during HWs is based on the time series of Hough expansion coefficients representing Rossby waves with the troposphere–barotropic structures through the extended boreal summer in the European ERA5, ERA-Interim, Japanese 55-year Reanalysis (JRA-55) and Modern-Era Retrospective analysis for Research and Applications (MERRA). The climatological Rossby wave energy distribution is shown to follow a χ2 distribution with skewness dependent on the zonal scale. The applied multivariate decomposition reveals signatures of the Eurasian HWs in the probability density functions (PDFs) of the Rossby wave energy across scales. Changes in the PDFs are consistent with changes in the intramonthly variance during HWs. For the zonal-mean state (the zonal wavenumber k=0), a decrease in skewness is found, although it is not statistically significant. A reduction in skewness hints to an increase in the number of active degrees of freedom, indicating more independent modes involved in the circulation. A shift in the spectral distribution of the k=0 intramonthly variance is shown to describe a weakening of the mean westerlies near their core at 45∘ N and their strengthening at high latitudes. At planetary scales (k= 1–3), the skewness in the troposphere–barotropic Rossby wave energy significantly increases during HWs. This coincides with a reduction of intramonthly variance, in particular at k=3, and persistent large-scale circulation anomalies. Based on the χ2 skewness, we estimate a reduction of the active degrees of freedom for the planetary-scale Rossby waves of about 25 % compared to climatology. At synoptic scales (k= 4–10), no change in skewness is detected for the Eurasian HWs. However, synoptic waves k= 7–8 are characterised by a statistically significant increase in intramonthly variance of about 5 % with respect to the climatology. In addition, a shift of the entire Rossby wave energy distribution at synoptic scales, along with amplification, is observed during HWs.

Aerosol characteristics over Indo-Gangetic Plain from ground-based AERONET and MERRA-2/CAMS model simulations

A comprehensive investigation of physical, optical, and chemical characteristics of columnar aerosols over two locations with distinct environmental settings in the Indo-Gangetic Plain (IGP) region, namely, Kanpur (urban and industrial area) and Gandhi College (rural area), is conducted using high-quality aerosol datasets obtained from ground-based Aerosol Robotic Network (AERONET) observations during the recent five year period (2015–2019). This study utilizes all the crucial columnar aerosol parameters necessary for accurately estimating aerosol radiative forcing. Quantification of contribution by different aerosol species originating from natural and anthropogenic sources to the total aerosol optical depth (AOD) and single scattering albedo (SSA) is important to understand the specific mechanisms that influence the aerosol composition, thereby reducing the uncertainty in aerosol radiative forcing. For the first time, two highly spatially resolved models' (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) simulated absorbing species-wise (black carbon (BC), dust, and brown carbon (BrC)) AOD, and absorption AOD (AAOD) are compared and contrasted against the AERONET observations over the IGP region in a systematic manner. MERRA-2 AODs are mostly lower, whereas CAMS AODs are consistently higher than the AERONET AODs. A comparison of collocated time and space observations with models clearly suggests that improvements in emission inventories on a seasonal scale are essential. MERRA-2 SSA is noted lower than the AERONET SSA during the winter season due to overestimation in BC AOD. During winter in >70% of MERRA-2 simulated SSA the difference is higher than ±0.03 (the uncertainty range of AERONET SSA) whereas during pre-monsoon and monsoon seasons >60% of MERRA-2 SSA lies within the uncertainty range of AERONET SSA. Both models show a gradient in AODDust decreasing from west to east in the IGP. However, observations do not often exhibit the gradient in dust, which is validated by air mass back trajectory analyses as air masses travel through different pathways to IGP and reverse the west to east gradient in AODDust. This quantitative and comparative collocated analysis of observed aerosol characteristics with models on a seasonal scale will enable a better estimation of aerosol radiative forcing, and can help improve aerosol processes and parameterizations in models.

Geothermal Well Exploration in Nigeria Using Remote Sensing and Modified Thermal Equations

Based on the preliminary evidence of volcanoes, hot springs, and Geysers in parts of Nigeria, it is proposed that deep geothermal wells can be found in Nigeria. This research uses thermal anomalies zones to identify types of geothermal wells in Nigeria, i.e., using remote sensing and modified thermal equations. The remote sensing dataset includes the ground heat flux (GHF) dataset from Modern-Era Retrospective analysis for Research and Applications (MERRA) of 28 years; sediment thickness dataset from EarthData; and surface geology from LANDSAT. The thermal transport model was used to narrow potential locations across Nigeria using the ground heat flux and sediment thickness, while the surface geology was used to confirm the deep geothermal zones. Four GHF patterns were discovered in Nigeria. The research shows that the deep geothermal wells might be located in Plateau, Bauchi, Gombe, Taraba, Ekiti, Kogi, Benue, Nassarawa, and Taraba. Also, the medium-depth geothermal wells may be located in Sokoto, Zamfara, Kastina, Kwara, Oyo, and Jigawa States. It was revealed that the southern parts of Nigeria have lots of shallow geothermal wells. The deep geothermal wells can be found in the Chad Basin and Benue trough, while the medium-depth geothermal wells can be found in the Sokoto basin, Bida basin, and parts of the lower Benue trough. It is recommended that further ground trotting exploration be carried out in the identified geographical locations.

An assessment of reprocessed GPS/MET observations spanning 1995–1997

Abstract. We have performed an analysis of reprocessed GPS/MET data spanning 1995–1997 generated by the COSMIC Data Analysis and Archive Center (CDAAC) in 2007. CDAAC developed modified dual-frequency processing methods for the encrypted data (anti-spoofing (AS)-on) during 1995–1997. We compared the CDAAC data set to the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) reanalysis, separately for AS-on and AS-off, focusing on the altitude range 10–30 km. MERRA-2 did not assimilate GPS/MET data in the period 1995–1997. To gain insight into the CDAAC data set, we developed a single-frequency data set for GPS/MET, which is unaffected by the presence of encryption. We find excellent agreement between the more limited single-frequency data set and the CDAAC data set: the bias between these two data sets is consistently less than 0.25 % in refractivity, whether or not AS is on. Given the different techniques applied between the CDAAC and the new data set presented here (designated JPL), agreement suggests that the CDAAC AS-on processing and the single-frequency processing are not biased in an aggregate sense greater than 0.25 % in refractivity, which corresponds approximately to a temperature bias less than 0.5 K. Since the profiles contained in the new single-frequency data set are not a subset of the CDAAC profiles, the combination of the CDAAC data set, consisting of 9579 profiles, and the new single-frequency data set, consisting of 4729 profiles, yields a total number of 11 531 unique profiles from combining the JPL and CDAAC data sets. All numbers are after quality control has been applied by the respective processing activities.

Effect of Sea Salt AOT on Precipitation Processes Associated with Red Sea Moisture Flux on Iraq, a Case Study

Sea salt acquires importance in the process of precipitation formation through its hygroscopic nature. and its role in accelerating the droplet formation process within a moisture saturation less than if it was not present. In this work, the maps of precipitation distribution with sea salt aerosol optical thickness (AOT) as well as moisture flux and wind at an altitude of 50 meters were compared. The data of Three rainy cases, characterized by heavy rains and moisture feeding from the Red Sea were selected. They are respectively 13 to 15 November 2018, 1 December 2018, and 27 to 29 January 2019. Cloud top temperature (CTT) data were obtained from Meteosat satellites (Meteosat 9, and 10). Total Precipitable water (TPW) and sea salt (AOT) were obtained from Model projects: a modern-era retrospective analysis for research and applications 2 (MERRA-2). the correlation coefficient (R) was used to test the relation between the variables like Total Precipitable water (TPW), Cloud top temperature (CTT), precipitation, and Sea Salt AOT. The results showed that there is a good correlation between the sea salt AOT and the amount of precipitation. The work also showed the roles that other variables play in the precipitation process. Where the relationships showed values of correlation coefficients about 0,6 to 0,7 between the variables, leading to the enhancement of precipitation.

The Effect of Varying Aerosol Concentrations on Visibility and Particle Size Distribution in Urban Atmosphere: Validating OPAC Results using MERRA Satellite Aerosol Data

Well validated simulation software and models are meant to add value through a more precise representation of different atmospheric features and climate characteristics that are physically out of reach. In this work, comparison and validation was carried out using the Modern Era Retrospective Analysis for Research and Applications (MERRA) averaged Angstrom Exponents (α) and averaged visibilities for ten urban countries against simulated Optical Properties of Aerosols and Clouds (OPAC) urban components. The simulations of OPAC were performed at 0%, 50%, 70%, 80%, 90%, 95%, 98% and 99% relative humidities (RH) while the MERRA satellite data were extracted at an average of 78% relative humidity. results based on α values showed that OPAC was only able to simulate well the insoluble and soot aerosol particle size distributions of India’s urban atmosphere such that it had approximately the same insoluble and soot aerosol particle size distributions as India. For the rest of the nine countries (USA, Brazil, Indonesia, China, Japan, Mexico, Nigeria, Pakistan and Ethiopia), OPAC was seen to either overestimate or underestimate water soluble, insoluble and soot aerosol particle size distributions. For visibility and water soluble or insoluble aerosol concentrations, OPAC showed good simulated values that were approximately the same as those found within Ethiopia’s and Japan’s urban atmosphere. OPAC also had visibility value and insoluble aerosol concentration that were approximately the same as those found within China’s atmosphere. For all other countries (India, USA, Brazil, Indonesia, Mexico, Nigeria and Pakistan) OPAC either overestimated their visibilities and underestimated their water soluble, insoluble and soot aerosol concentrations or vice versa. With both OPAC and MERRA, the presence of fine mode aerosol particles in an urban atmosphere was established with α values &gt; 1. But MERRA also showed otherwise for three countries (Nigeria, Ethiopia and Pakistan) that had α values &lt; 1 which implied the presence of coarse mode aerosol particles. The relationship between visibility and α satisfied the direct power law for OPAC, for MERRA the relationship approximately satisfied the power law for most of the countries.

Mesoscale/Microscale and CFD Modeling for Wind Resource Assessment: Application to the Andaman Coast of Southern Thailand

Situated in the southern part and on the western coast of Thailand, the Andaman Coast covers the provinces of Ranong, Phangnga, Phuket, Krabi, Trang and Satun. Using a coupled mesoscale atmospheric model and a microscale wind flow model, along with computational fluid dynamics (CFD) modeling, this paper presents a detailed assessment of the wind energy potential for power generation along the Andaman Coast of Thailand. The climatic data are obtained from the Modern Era Retrospective analysis for Research and Applications (MERRA), along with a high-resolution topography database and Land Use Land Cover digital data. The results are compared to the equivalent wind speeds obtained with the Weather Research and Forecasting (WRF) atmospheric model. The results showed that, at 120 m above ground level (agl), the predicted wind speeds from the models proposed were 20% lower for the mesoscale model and 10% lower for the microscale model when compared to the equivalent wind speeds obtained from the WRF model. A CFD wind flow model was then used to investigate 3D wind fields at 120–125 m agl over five potential sites offering promising wind resources. The annual energy productions (AEP) and the capacity factors under three different wake loss models and for five wind turbine generator technologies were optimized for 10-MW wind power plants, as per Thailand’s energy policies. With capacity factors ranging from 20 to 40%, it was found that the AEPs of the best sites were in the range of 18–36 GWh/year, with a total AEP in the vicinity of 135 GWh/year when using a single wind turbine model for the five sites studied. The combined energy productions by these wind power plants, once operational, could avoid GHG emissions of more than 80 ktons of CO2eq/year.

How Do Different Reanalysis Radiation Datasets Perform in West Qilian Mountains?

Solar radiation plays an important role in the cryospheric water cycle, especially in alpine regions. This study presents an evaluation of the Modern-Era Retrospective analysis for Research and Applications 2 (MERRA2), ERA5, High Asia Refined analysis version 2 (HAR v2), JRA-55, and National Centers for Environmental Prediction/Climate Forecast System Reanalysis datasets at different time scales by comparing observed datasets from July 2010 to December 2015 at 4,550 m in the Laohugou Basin. In terms of shortwave radiation, ERA5 performs significantly better than the other reanalysis radiation datasets. For downward shortwave radiation, HAR v2 performs better than ERA5 on only two timescales, 3 months and half-year, with mean absolute errors (MAEs) of 13.28 and 7.96 w/m2. The upward shortwave radiation, ERA5, outperforms the other reanalysis datasets on all 12 timescales. For downward longwave radiation, ERA5 also performs significantly better, with only MERRA2 outperforming ERA5 on the daily scale and annual scale, with R2, bias, root mean square error, and MAE of 0.6, 0.95, −9.51 w/m2, −9.41 w/m2, 34.98 w/m2, 9.46 w/m2, and 27.52 w/m2, 9.41 w/m2, respectively. In the upward longwave radiation, HAR v2 performs better than the other reanalysis datasets on all timescales, except for ERA5, which has a better R2 of 0.92 on the annual scale. All the reanalysis datasets can show the variation trend of the four radiation parameters in different seasons and achieve a better performance in winter. Therefore, ERA5 is recommended for regions without shortwave radiation observations, and HAR v2 and ERA5 are recommended for longwave radiation simulations. Although there are obvious shortcomings in the reanalysis radiation datasets, they still provide important supplementary information for research in high-altitude areas, where the observed datasets are too sparse.

Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands

Reducing the uncertainties in carbon balance assessment is essential for better pastureland management in arid areas. Climate forcing data are some of the major uncertainty sources. In this study, a modified Biome-BGC grazing model was driven by an ensemble of reanalysis data of the Climate Forecast System Reanalysis data (CFSR), the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), to study the effect of climate change and grazing on the net ecosystem exchange (NEE) of the pasturelands in Central Asia. Afterwards, we evaluated the performance of corresponding climate datasets over four major pastureland types, and quantified the modeling uncertainty induced by climate forcing data. Our results suggest that (1) a significant positive trend in temperature and a negative trend in precipitation were obtained from the three climate datasets. The average precipitation is apparently higher in the CFSR and MERRA data, showing the highest temperature value among the data sets; (2) pasturelands in Central Asia released 2.10 ± 1.60 Pg C in the past 36 years. The highest values were obtained with the CFSR (−1.53 Pg C) and the lowest with the MERRA (−2.35 Pg C) data set; (3) without grazing effects, pasturelands in Central Asia assimilated 0.13 ± 0.06 Pg C from 1981–2014. Grazing activities dominated carbon release (100%), whereas climate changes dominated carbon assimilation (offset 6.22% of all the carbon release). This study offered possible implications to the policy makers and local herdsmen of sustainable management of pastureland and the adaptation of climate change in Central Asia.