Surface Meteorological Parameters Research Articles

Modeling of Zenith Tropospheric Delay using ERA5 data over East African region

The Zenith Tropospheric Delay (ZTD) is a crucial parameter in meteorology and climate research, often estimated from surface meteorological parameters and Global Navigation Satellite Systems (GNSS) observations. In East Africa, the lack of reliable surface meteorological data and gaps in GNSS observations compromise the accuracy and reliability of ZTD data. To address this issue, site-specific ZTD models were developed using ERA5 data from 2013 to 2017, employing Empirical Orthogonal Function (EOF) analysis. The accuracy of the proposed EOF models was validated using the tropospheric product from the Nevada Geodetic Laboratory (NGL) as a reference and compared to the Global Pressure and Temperature 3 (GPT3) ZTD model. The results of the study show that the EOF ZTD models significantly outperformed the GPT3 model, reducing Mean bias (MnB) by 72.3% and Root Mean Square Error (RMSE) by 3.0%. EOF models performed particularly well for stations near the equator (latitudes 4°S and 4°N) and between latitudes 12° S and 4° S in terms of MnB and RMSE, respectively. Seasonally, EOF models surpassed the GPT3 model in MnB and RMSE across most seasons near the equator, except during the September-October-November (SON) period, where GPT3 showed an 85.5% better performance in MnB. For stations between latitudes 12° S and 4° S, GPT3 generally performed better in terms of RMSE, except during the March-April-May (MAM) period, where the EOF model excelled. However, the EOF model consistently showed better (reduced) MnB in this region. This study demonstrates that the EOF method is a viable alternative for estimating ZTD in areas with limited surface meteorological data and GNSS observation gaps. The site-specific ZTD models developed using the EOF method can significantly improve the accuracy and reliability of ZTD data, with broad applications in geodesy, atmospheric science, and navigation among others.

A novel method to estimate the 3D chlorophyll a distribution in the South China Sea surface waters using hydrometeorological parameters

Chlorophyll a (Chl-a) is a key indicator of marine ecosystems, and certain hydro-meteorological parameters (HMPs) are highly correlated with its fluctuations. Here, relevant and accessible HMPs were used as inputs, combined with machine learning (ML) algorithms for estimating 3D Chl-a in the South China Sea (SCS). With the inputs of temperature, salinity, depth, wind speed, wind direction, sea surface pressure, and relative humidity, the LightGBM-based model performed well, achieving high R2 values of 0.985 and 0.789 in validation and testing sets, respectively. Based on a large number of in situ measurements, this model enables the estimation of the 3D distribution of summer Chl-a in the SCS over the past fifteen years using a 3D hydrographic dataset combined with surface meteorological parameters. The results show that the 3D distribution of the model estimated Chl-a is characterized similarly to the previous studies and can capture the effect of hydro-meteorological conditions on Chl-a distribution. The environmental variables affecting Chl-a were considered more comprehensively in this study, and the methodological framework has the potential to be applied to the low-cost monitoring of the remaining water quality parameters.

Contribution of congested traffic flow condition to air pollution at intersections in Addis Ababa, Ethiopia

Currently, one of the serious problems in the world is transport-related air pollution. Air pollution from vehicles was not considered properly in the plan, design, and management system of the roads in Addis Ababa city, to solve the problem of traffic congestion. It is influenced by different factors such as road geometry, road surface type, traffic congestion, traffic characteristics, fuel type, and meteorological parameters. This study was aimed to investigate the contribution of congested traffic flow conditions on air pollution at intersection points in Addis Ababa city. To carry out the study, seven intersection points were selected in the city randomly. For the measurement technique of carbon monoxide (CO) and sulfur dioxide (SO2) gases, a portable Aeroqual series 500 instrument was exercised, and for the particular matter (PM2.5 and PM10), Light Amplification by Stimulated Emission of Radiation (LASER) was adapted. To count the number of traffic on the site, a video camera method was utilized. For the data analysis techniques, a multiple linear regression method was practiced, which is very powerful for multiple independent variables with a single dependent one. The study revealed that the level of traffic congestion at each selected intersection is at a severe level. During the congested time, as a base of the steady condition, the pollutants concentration of CO, SO2, PM2.5, and PM10 were increased by an average of 19.10, 51.61, 33.83, and 29.07 % respectively. Based on the analyzed results, when the concentration of pollutants increases, the traffic volume, percentage of heavy vehicles, green time, and approach grade are also increased. On the other hand, the concentration of pollutants decreased when the lane width and wind speed increased. Therefore, any concerned bodies in and around Addis Ababa city should take remedial measures to decrease the emission of the concentration of the pollutants. This investigation is very important for policymakers, municipalities, and planners to do any activities in the city. Researchers can also use it as a reference, for further investigations and remedial measurements.

Phosphate Influx and Dust Deposition Create Zonal and Meridional Biogeochemical Gradients in Trichodesmium Abundance

AbstractTrichodesmium plays a key role in the biogeochemical cycling of carbon, nitrogen and phosphorus in the Tropical Atlantic Ocean. A complex interplay of physicochemical factors control the growth of Trichodesmium. However, owing to the large spatial and temporal variability, the relative influence of these factors in controlling Trichodesmium distribution and abundance remains unclear. In this study, we examined the basin‐scale distribution pattern of Trichodesmium in the upper 200 m water column of the Atlantic Ocean (25°N–30°S and 70°W–20°E) using a large data set (n = 33,235) and tried to constrain the distribution based on various physicochemical parameters. We suggest that the combined effect of warm temperatures and phosphate (PO43−) availability determines the zonal spatial extent and the abundance of Trichodesmium in the Tropical North Atlantic Ocean. However, the availability of dissolved iron, along with high sea surface temperatures and meteorological parameters such as the wind direction and precipitation, likely govern the meridional distribution of Trichodesmium across the Atlantic Ocean. Excess PO43− at the surface rules out the possibility of PO43− limitation in regulating the meridional distribution of the Trichodesmium. Depth‐integrated nitrogen fixation rates, based on a multiple linear regression, vary from 0.07 to 306 μmol N m−2 d−1. The presence of Trichodesmium colonies down to a depth of 200 m and the depth‐integrated nitrogen fixation rates reflect the pivotal role of Trichodesmium in the nitrogen budget of this region.

Relationship between monthly surface water derived from Sentinel-2 imagery and meteorological data (precipitation and evaporation) at Baghdad, Iraq

ABSTRACT This study was carried out using two main monthly datasets: satellite images and some meteorological parameters (like precipitation and evaporation) for 2 years (2018 and 2021). Using the fine spatial resolution images acquired from Sentinel-2, digital maps of surface water areas were produced by calculating the Modified Normalized Difference Water Index for all months in QGIS and then classifying them into two main categories (water and non-water bodies). The results show that the largest areas were in January 2021 (77.2 km2) and 40.7 km2 in November 2018, while the lowest (18.7 km2) were in June 2021 and July 2018. Monthly mean values of cumulative precipitation and evaporation were used to find their relationships with surface water areas. The results show that the year 2021 was a severe dry year, whereas the lowest precipitation (25 mm) in that year was associated with higher evaporation losses (3,021.8 mm) during this year. When combined with surface water areas and meteorological parameters, a non-linear relationship between water areas and evaporation was found for both years, and a linear relationship between precipitation and water areas for the wet year of 2018.

Global zenith wet delay modeling with surface meteorological data and machine learning

The tropospheric delay is a major error source for space geodetic techniques, and the performance of its modeling is significantly limited due to the high spatiotemporal variability of the moisture in the lower atmosphere. In this study, global modeling of the tropospheric zenith wet delay (ZWD) was realized based on surface meteorological data obtained from radiosondes and Global Positioning System (GPS) radio occultation (RO) measurements through the random forest (RF) and backpropagation neural network (BPNN) regression analysis. The modeling performance was further validated based on two kinds of global atmospheric profiles for the year 2020. Our results show that the ZWD modeling accuracy gained by two machine learning regression approaches is significantly improved by taking into account surface meteorological parameters, especially the surface water vapor pressure when compared to the Global Pressure and Temperature 3 (GPT3) model. When surface meteorological data are available, the RF-B model yields ZWD estimations with an overall agreement of 3.1 cm in comparison with the sounding profiles and 2.4 cm in contrast to the GPS RO atmospheric profiles. The RF-B is superior to other models based on surface meteorological parameters for ZWD calculation, e.g., the accuracy improves by 21.8–23.8% against the approach by Saastamoinen and 7–12.2% against the formula by Askne and Nordius.

Mesoscale Convective Systems in Central Africa: Characteristics of the Associated Seasonal and Diurnal Cycle of Observed Surface Meteorological Parameters

Mesoscale Convective Systems in Central Africa: Characteristics of the Associated Seasonal and Diurnal Cycle of Observed Surface Meteorological Parameters

Some Features of Black Carbon Aerosols Connected with Regional Climate Over Pristine Environment

The authors report the results of aethalometer black carbon (BC) aerosol measurements carried out over a rural (pristine) site, Panchgaon, Haryana State, India during the winter months of 2021–2022 and 2022–2023. They are compared with collocated and concurrent observations from the Air Quality Monitoring Station (AQMS), which provides synchronous air pollution and surface meteorological parameters. Secular variations in BC mass concentration are studied and explained with variations in local meteorological parameters. The biomass burning fire count retrievals from NASA-NOAA VIIRS satellite, and backward airmass trajectories from NOAA-ERL HYSPLIT Model analysis have also been utilized to explain the findings. They reveal that the north-west Indian region contributes maximum to the BC mass concentration over the study site during the study period. Moreover, the observed BC mass concentrations corroborate the synchronous fire count, primary and secondary pollutant concentrations. The results were found to aid the development of mitigation methods to achieve a sustainable climate system.

Indigenization of Buoy Components Using Additive Manufacturing Technique

The Ocean Observation Systems (OOS) group of the National Institute of Ocean Technology (NIOT) is involved in the design, development and sustenance of moored data buoys in the Indian Seas. The moored buoy systems deployed in the Northern Indian Ocean provide real-time, continuous observation of surface meteorological and oceanographic parameters which help in monitoring extreme weather events and natural disasters such as cyclones and tsunamis. Buoy components are of different sizes and shapes and are made of various materials, including metals and plastics. However, due to unique and critical design requirements, the development of deep-sea components faces hurdles caused by manufacturing limitations. The advent of additive manufacturing (AM) has met the demand for quickly producing parts. Due to the high pressure and low temperature conditions, it is extremely difficult to design and develop deep sea components. Consequently, High Impact Polystyrene (HIPS) material has been selected for the subsurface floats. The float is manufactured using the Fused Deposition Modeling (FDM) additive manufacturing technique in the Fabheads 1K FDM printer with pellet based extrusion method. These subsurface floats are used at a water depth of 500 m in NIOT buoy systems, with a working pressure of approximately 50 bar. Taking a factor of safety of two into account, the part is designed to withstand 100 bar. To assess the component's performance under deep-sea hydrostatic conditions, it underwent testing in the hyperbaric chamber test facility at NIOT. During the qualification process, the component successfully withstood the design pressure of 100 bar and imploded at 102 bar. This study is part of NIOT's ongoing efforts to indigenize deep-sea components using AM and assess its future prospects.

Model simulation of carbonaceous fine particulate matter using SAFAR emission inventory and comparison with EDGAR-HTAP simulations

In this study, the evaluation of carbonaceous fine particulate matter (C-PM) was conducted using the SAFAR-India regional model. Two emission inventories were utilized: the newly developed SAFAR inventory and the global EDGAR-HTAP inventory, both for the year 2018. The simulation aimed to capture the seasonal and spatial patterns of black carbon (BC) and organic carbon (OC) concentrations. To validate model results, surface meteorological parameters, and C-PM concentrations were compared with the MERRA reanalysis data for the Indian region. Model simulated surface C-PM concentration with SAFAR emission inventory is found to be slightly overestimated (1.10), whereas simulated results with EDGAR emission inventory is significantly underestimated as compared to MERRA data. Model-simulated meteorological parameters showed a better correlation with MERRA reanalysis. Simulated geographical patterns of seasonal mean C-PM with SAFAR emission inventory exhibit quite a better comparison with MERRA reanalysis as against EDGAR simulated results. However, some differences in the present results are visible, particularly over the IGP region, as compared to MERRA data, but they are mainly attributed to a significant difference in the special resolution of present results (much finer) as compared to coarser MERRA data. In the Indo-Gangetic Plain (IGP) region, the concentration of BCSF and OCSF (BC and OC with SAFAR emission, respectively) show the peak during the winter, followed by the post-monsoon season. Although the correlation coefficients of hourly time series of surface BCSF and OCSF concentrations with MERRA over India are high (0.92 and 0.88), the poor RMSE, is attributed mainly to different scales of resolution. The model simulated BC, and OC concentrations with SAFAR emission input capture the pattern of spatial distribution reasonably well. The present evolution of BC and OC will help to better quantify their impact on climate and atmospheric conditions over the Indian region.

High spatial resolution long-term temperature profiling to inform near-shore atmospheric sound propagation

Atmospheric sound propagation depends on factors such as surface characteristics and meteorological parameters. Effective numerical modeling of atmospheric sound propagation requires reliable and realistic input parameters. One of the most influential factors is the air temperature profile. This work examines the application of experimentally determined air temperature profiles in acoustic modeling of littoral environments. Long term measurements of near-surface air temperature profiles over water and marsh grass are presented. Data have been recorded using temperature loggers affixed to two permanent 7 m masts. The loggers were positioned with a 1 m vertical spacing. A common approach to characterize the near-surface boundary layer uses Monin–Obukhov Similarity Theory (MOST). This work compares high spatial resolution air temperature measurements with measurement-informed profiles based on MOST. The development of a more experimentally grounded set of assumptions will ultimately contribute to the improvement of atmospheric acoustic transmission loss models in near shore environments.

Hourly global solar radiation prediction based on seasonal and stochastic feature

Accurate and detailed solar radiation data play a crucial role in the simulation of building thermal and photovoltaic systems. However, developing a highly precise and dependable solar radiation model using cost-effective data has proven challenging. This work proposes a new attenuation solar radiation model formed by conducting a comprehensive analysis of existing models and gaining new insights into solar radiation's seasonal and stochastic properties. Meanwhile, the model is constructed using easily obtainable surface meteorological parameters. The results demonstrate that the proposed model exhibits good performance in terms of prediction accuracy. Moreover, the majority of existing hourly solar radiation models have been primarily developed for clear-sky conditions. However, there is a growing demand for solar radiation hourly estimations that can uphold a high level of accuracy and reliability even in different weather state. Conversely, the proposed model is developed and validated by more than twenty year's meteorological data encompassing various weather conditions in Japan. It effectively captures the stochastic nature of solar radiation by utilizing turbidity parameters, even on cloudy and rainy days. Additionally, the inclusion of interaction variables significantly enhances its interpretability.

The Aerosol Optical Characteristics in Different Dust Events Based on a 532 nm and 355 nm Polarization Lidar in Beijing

Extreme weather events are happening more frequently as a result of global climate change. Dust storms broke out in the spring of 2017 in China and drastically impacted the local air quality. In this study, a variety of data, including aerosol vertical profiles, surface particle concentration, meteorological parameters, and MODIS–derived aerosol optical depth, as well as backward trajectory analysis, were employed to analyze two dust events from April to May in Beijing. The dust plumes were mainly concentrated below 0.8 km, with peak PM10 values of 1000 μg·m−3 and 300 μg·m−3 in the two cases. The aerosols showed different vertical distribution characteristics. The pure dust in case 1 from 4 to 5 May 2017 had a longer duration (2 days) and presented a larger aerosol extinction coefficient (2.27 km−1 at 355 nm and 1.25 km−1 at 532 nm) than that of the mixed dust in case 2 on 17 April 2017 (2.01 km−1 at 355 nm and 1.33 km−1 at 532 nm). The particle depolarization ratio (PDR) remained constant (0.24 ± 0.03 in case 1) from the surface to 0.8 km in height. In contrast, the PDR profile in the mixed dust (case 2) layer was split into two regions—large values exceeding 0.15 above 0.6 km and small values of 0.11 ± 0.03 below 0.6 km. The influence of meteorological information on aerosol distribution was also investigated, and wind was predominant through the observing period. The pure dust in case 1 was mainly from Mongolia, with strong northwest winds, while the near-surface mixed pollution was caused by the combination of long-transported sand and local emission. Furthermore, lidar-derived profiles of dust mass concentrations in the two cases were presented. This study reveals the vertical characteristics of dust aerosols in the production and dissipation of localized dust events and confirms the efficacy of thorough observations with multiple approaches from the ground to space to monitor dust events in real time.

Regional Tropospheric Correction Model from GNSS–Saastamoinen–GPT2w Data for Zhejiang Province

Tropospheric delay models based on GNSS observations are essential for studying tropospheric changes. However, the uneven distribution of GNSS stations reduces the accuracy of GNSS tropospheric delay models in remote areas. Moreover, the accuracy of the tropospheric delay calculated by traditional models, which rely on meteorological parameters, is lower compared to the accuracy achieved by GNSS tropospheric models. At present, there are sufficient surface meteorological observation facilities around the world that can obtain surface meteorological parameters in real time. It is of great importance to make full use of the measured meteorological parameters to establish tropospheric correction models. Moreover, the empirical tropospheric models use free and open data, and one can obtain tropospheric parameters through the model without requiring any auxiliary information. We established a provincial real-time regional tropospheric fusion model using ground-based GNSS observations, a meteorological model, and empirical model data. Results showed that the tropospheric delay observations of the three models can be fused to establish a real-time tropospheric delay model with better accuracy and higher spatiotemporal resolution. The accuracy of Zenith Total Delay (ZTD) estimated by the fusion model reached 0.96/1.04/3.11 cm during the tropospheric quiet/active/typhoon period.

A Numerical-Hierarchical Framework for Predicting Volume Changes in Expansive Soils under Variable Surface and Weather Conditions

This research developed a numerical-hierarchical framework that captured surface conditions and climate parameters. Volume changes under distinct scenarios of surface boundary, antecedent moisture, and meteorological parameters were predicted using a coupled seepage-deformation model. Risk was hierarchically based on expert judgment for surface scenarios (Stage-I indices) and normal distribution for antecedent moisture and atmospheric parameters scenarios (Stage-II indices). Results indicated seasonal volumetric changes with minor variations of −5 mm from January to April, a steady settlement of −17 mm by June, and a gradual heave of +8 mm by December. All Stage-I indices showed similar trends such that the fluctuations were highest for vegetation, followed by slope, then by cover, and lowest for loading. Volume changes gradually reduced with depth and diminished at 3.1 m. Similar seasonal and profile trends were generally found for most Stage-II indices. Nonetheless, different trends under wet and dry conditions were observed for initial water content, precipitation, and air temperature. For the datum scenario, risk was non-existent till February, increased to 2.3 by June, diminished by October, and rose back to 1.0 by December. Similar values of cyclic variations in risk were found in most urban facilities. Volume changes were found to be two times higher in parks, insignificant for roads, half for five story buildings, and one-fourth for pipes under roads. Among the Stage-II indices, risk for the initial water content inhibited seasonal variations whereas that for precipitation was about half with a wider distribution; all the other indices showed about one-third the values. Under a higher occurrence probability of 0.129, a magnified risk was observed for all the indices such that the most critical were the initial water content and precipitation.

Assessment of the impact of atmospheric aerosols and meteorological data assimilation on simulation of the weather over India during summer 2015

This study investigates the sensitivity of climate model-predicted surface radiation and surface meteorological parameters to atmospheric aerosols and meteorological data assimilation (meteorological initial and boundary conditions) utilizing the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For this purpose, three sets of simulations including WRFChemCntrl (WRFChem without meteorological data assimilation), WRFChemDA (WRFChem with meteorological data assimilation) and WRFDA (WRF only with meteorological data assimilation) were performed over the South Asian domain. A 12-hourly cyclic 3-dimensional variational (3DVAR) meteorological data assimilation (DA) of in-situ meteorological observations was used to generate WRFChemDA and WRFDA reanalyses, during summer (March–May) 2015. The Carbon Bond Mechanism-Z (CBMZ) gas-phase chemistry with Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) was selected for the chemistry simulations. A reduction in incoming shortwave radiation (∼10–60 W/m2) over the Indian landmass and a slight increase (∼10–20 W/m2) over the surrounding oceanic region due to the influence of aerosols in WRF-Chem simulations was observed, suggesting that aerosols effects, were simulated by the model successfully. These effects of aerosols were further evident in the model output of other parameters such as outgoing longwave radiation at the surface, 2-m temperature (T2), 2-m relative humidity (RH2), and planetary boundary layer height (PBLH). Our results also show that the inclusion of aerosols in the WRFDA simulations (i.e WRFChemDA) improved the prediction of incoming shortwave radiation but deteriorated some of the meteorological parameters. However, an improved agreement between model simulated radiation, T2, RH2 and PBLH and observations was evident in WRFChemDA output compared to WRFChemCntrl output, reinforcing DA induced improvements in meteorological parameters for a given model set-up.

A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution

Ozone pollution is harmful to human health and ecosystem, which occurs in ecosystems and has occurred frequently in China in recent years, especially during the warm seasons. Meteorological conditions are among the important factors affecting the occurrence of ozone pollution. In this study, a classification method for meteorological conditions of ozone pollution levels based on a back propagation (BP) neural network was proposed to reflect the impact of meteorological conditions on the occurrence of ozone pollution. Ozone pollution was divided into three levels according to surface hourly ozone (O3) concentrations and thus into three groups of meteorological conditions. The input physical parameters for the BP neural network were determined by evaluating the relationship between surface O3 concentrations and meteorological parameters and precursors, including relative humidity, temperature, mixing layer height, precipitation, and nitrogen dioxide (NO2) concentrations. The study area focused on 21 cities in Sichuan Province in southwestern China, which was divided into 12 BP classifiers according to the urban geographical location and sample number of each city, and a single BP classifier was trained for 21 cities. The classification results of the trained BP classifiers were verified by comparison to the observations. With 12 individual BP classifiers, the classification accuracy of all 21 cities was more than 60%, of which 18 cities were more than 70%, and 9 cities were more than 80%. With the single BP classifier, the classification accuracy of 20 cities was more than 60%, of which 18 cities were more than 70%, and 14 cities were more than 80%. Overall, the classification performance of the trained single model was better than trained 12 individual models. The classification method can comprehensively reflect the impact of meteorological conditions on the occurrence of ozone pollution.

A New Approach for Improving GNSS Geodetic Position by Reducing Residual Tropospheric Error (RTE) Based on Surface Meteorological Data

Positioning error components related to tropospheric and ionospheric delays are caused by the atmosphere in positioning determined by global navigation satellite systems (GNSS). Depending on the user’s requirements, the position error caused by tropospheric influences, which is commonly referred to as zenith tropospheric delay (ZTD), must be estimated during position determination or determined later by external tropospheric corrections. In this study, a new approach was adopted based on the reduction of residual tropospheric error (RTE), i.e., the unmodeled part of the tropospheric error that remains included in the total geodetic position error, along with other unmodeled systematic and random errors. The study was performed based on Global Navigation Satellite System (GLONASS) positioning solutions and accompanying meteorological parameters in a defined and harmonized temporal-spatial frame of three locations in the Republic of Croatia. A multidisciplinary approach-based analysis from a navigational science aspect was applied. The residual amount of satellite positioning signal tropospheric delay was quantitatively reduced by employing statistical analysis methods. The result of statistical regression is a model which correlates surface meteorological parameters with RTE. Considering the input data, the model has a regional character, and it is based on the Saastamoinen model of zenith tropospheric delay. The verification results show that the model reduces the RTE and thus increases the geodetic accuracy of the observed GNSS stations (with horizontal components of position accuracy of up to 3.8% and vertical components of position of up to 4.37%, respectively). To obtain these results, the Root Mean Square Error (RMSE) was used as the fundamental parameter for position accuracy evaluation. Although developed based on GLONASS data, the proposed model also shows a considerable degree of success in the verification of geodetic positions based on Global Positioning System (GPS). The purpose of the research, and one of its scientific contributions, is that the proposed method can be used to quantitatively monitor the dynamics of changes in deviations of X, Y, and Z coordinate values along coordinate axes. The results show that there is a distinct interdependence of the dynamics of Y and Z coordinate changes (with almost mirror symmetry), which has not been investigated and published so far. The resultant position of the coordinates is created by deviations of the coordinates along the Y and Z axes—in the vertical plane of space, the deviations of the coordinate X (horizontal plane) are mostly uniform and independent of deviations along the Y and Z axes. The proposed model shows the realized state of the statistical position equilibrium of the selected GNSS stations which were observed using RTE values. Although of regional character, the model is suitable for application in larger areas with similar climatological profiles and for users who do not require a maximum level of geodetic accuracy achieved by using Satellite-Based Augmentation Systems (SBAS) or other more advanced, time-consuming, and equipment-consuming positioning techniques.

Remapping of Temperature Profile Measurements in OMNI Buoy Systems

Abstract The OMNI (Ocean Moored Buoy Network for northern Indian Ocean) buoy network comprises 12 buoy systems that measure surface meteorological parameters along with temperature and salinity profile measurements at discrete levels up to 500 m. All the OMNI buoy systems are deployed with slack-line moorings, which respond more to wind, wave, and current forcing compared to taut-line mooring. Subsurface temperature measurements are subject to change depending on both environmental condition and mooring design. The standard sensor fit of the OMNI buoy systems has only one pressure sensor fixed at 500 m, which shows significant depth variability. In order to see the spatial and seasonal variability in the vertical movement of the mooring line and the associated temperature variability, four deployments with additional pressure measurements at 200 m are analyzed. It is observed that the depth/temperature variability exhibits significant seasonality with maximum variability during pre-monsoon season. Also, the effect of this movement in the shallower depths is analyzed with four more pressure sensors in the mooring line for a 1-year period in the central Bay of Bengal. The analysis shows that the maximum value of average and root mean square (RMS) temperature deviations is 0.38 °C and 0.48 °C in the deepest interpolated depth at 400 m where the mooring line experiences a greater range of motion and the actual temperature variability in shallower depths is negligible particularly up to 75 m (<0.01°C). The study reveals the necessity of additional pressure measurements for better remapping of temperature profile measurements.

Evaluation of surface meteorology parameters and heat fluxes from CFSR and ERA5 over the Pacific Arctic Region

Evaluation of surface meteorology parameters and heat fluxes from CFSR and ERA5 over the Pacific Arctic Region