Temperature Profile Data Research Articles

Cold Surge Impacts on the Structure, Energy Budget, and Turbulence of the South China Sea Boundary Layer

Abstract Episodic cold surges in the East Asia winter monsoon can penetrate deep into the South China Sea (SCS), enhance consequent tropical rainfall, and further strengthen the East Asia meridional overturning circulation. These cold surges can promote strong surface fluxes and lead to a deeper marine boundary layer (MBL). However, there is a lack of boundary layer studies over the SCS, unlike many other well-studied regions such as the north Atlantic Ocean and the central-eastern Pacific Ocean. In this study, we use high resolution radiosonde data of temperature and humidity profiles over Dongsha Island (116.69E, 20.70N) to identify the inversion layer, mixed layer, cloud base, cloud top, and factors controlling low cloud cover for the period of December-January-February from 2010 to 2020. We perform an energy budget analysis with ERA-5 meteorological variables and surface fluxes. Here we show a strong turbulent flux convergence of both heat and moisture within the SCS MBL during cold surges, which leads to a lifting of the mixed layer to ~1.0 km and inversion layer to ~2.0 km and associated cloud development over Dongsha Island. The cold and dry horizontal advection is balanced by this vertical turbulent flux convergence in the energy budget. Overall, cold surges over the SCS enhance lower branch of winter monsoon meridional overturning circulation with stronger inversion and higher low cloud covers.

System Parameters Sensitivity Analysis of Ocean Thermal Energy Conversion

Ocean Thermal Energy Conversion (OTEC) is a technology to harvest the solar energy stored in the ocean by utilizing the temperature difference between warm surface and cold deep seawater. Considering that the OTEC system works in a low-temperature range, the present paper assessed the technical resources comprehensively by acquiring in-situ thermocline data and conducting a sensitivity analysis of the system parameters. The in-situ temperature profile data were measured in the waters of North Bali, Indonesia. The temperature gradient data based on field measurements were then compared with the HYCOM consortium model. The data were then used as input in the OTEC power and efficiency estimation through a single-stage ranking cycle. The analysis was conducted by varying the type of working fluid, the performance of the heat exchanger, and the location to investigate how the system parameters influenced the power produced. Using an unusual combination of parameters made it difficult to analyze the resulting data multiple times. However, with reference-based analysis and the formulation of calculations, the sensitivity of each parameter could be assessed at both locations. As a result, the ammonia working fluid provided the highest net power output of the system but had the lowest efficiency of all working fluids. The heat exchanger performance in terms of net power and efficiency cannot be separated from the seawater mass flow requirement. This referred to the results where the heat exchanger with a temperature difference of 3°C before and after the seawater passed through the heat exchanger and produced the highest net power and efficiency. Additionally, the net power output reached its convergence level at a water depth of 400m for the Bungkulan site and 450m for Celukan Bawang, which was proportional to the thermocline tendency. Doi: 10.28991/ESJ-2024-08-02-04 Full Text: PDF

Unlocking geothermal energy for sustainable greenhouse farming in arid regions: a remote-sensed assessment in Egypt’s New Delta

This study introduces a novel approach for assessing geothermal potential in arid regions, specifically Egypt’s New Delta Agriculture Mega Project area. The challenge of limited sub-soil temperature profile data was addressed by integrating Global Land Data Assimilation System (GLDAS) weather data. Using the Earth-to-Air Heat Exchanger (EAHE) model, the extracted air and sub-soil temperature profiles the potential for geothermal energy production was estimated. We modeled the annual sinusoidal soil surface periodic heating pattern by utilizing GLDAS ambient air temperature (AAT) and land surface temperature (LST). Using either AAT or LST yielded a Root-Mean-Square Error (RSME) of 0.2°C. The generated sub-soil profiles for the New Delta region showed a temperature variation of no more than 1.5°C at a 4-m depth, making it an optimal depth for EAHE installation. One-pipe EAHE demonstrated a cooling/heating capacity ranging from 400 W (cooling) to −300 W (heating). The study highlights the New Delta region’s strong geothermal potential for greenhouse cooling and heating, underlining its suitability as a sustainable energy source in arid areas. It also offers a practical guide for the EAHE application and it emphasizes the global potential for geothermal energy exploration, using innovative GLDAS data to expand sub-soil temperature profile accessibility.

Modeling the First Hydrogen Direct Reduction Pilot Reactor for Ironmaking in the USA Using Finite Element Analysis and Its Validation Using Pilot Plant Trial Data

Direct reduction of hematite pellets with hydrogen (H2) was used to produce directly reduced iron (DRI) in a pilot scale reactor at a pellet feed rate of 21.4 kg/h. At a steady state, operational parameters of the pilot plant (gas recycling rate and inlet temperature) along with key reactor output parameters, the pellet metallization, and the internal temperature profile of the reactor were reported for two scenarios with high recycle and low recycle rate of H2. Scenario 1, with a high recycle rate of 400 L/min H2 along with external heating of 870 °C, gave an average metallization of 91.8%, while Scenario 2, with low recycle rate of 100 L/min H2 and external heating of 850 °C gave a metallization of 67.8% due to the higher moles of H2 available for reduction and the external energy required for the endothermic reduction reaction in Scenario 1 as compared with Scenario 2. Finite element analysis was used to build a model of the shaft reactor, which was validated against the metallization and internal temperature profile data. The average metallization values predicted by the model were very close to the metallization values obtained from the pilot plant samples, with 90.9% average metallization for Scenario 1 and 65.6% average metallization for Scenario 2. The internal temperature profiles in the lower region of the reactor obtained from the model were very close to these pilot plant data, with a maximum difference of 52.7 °C and 67.6 °C for Scenarios 1 and 2, respectively. The pilot plant reactor model was used extensively in the commissioning of the pilot plant and to predict the startup outcomes for a given set of operating parameters.

Application of GOES-16 Atmospheric Temperature-Profile Data Assimilation in a Hurricane Forecast

This paper selects the case of the Atlantic hurricane “Michael” in 2018 to evaluate the accuracy of the GOES-16 atmospheric temperature profile during the hurricane and its effect on forecasting. Based on the weather research and forecasting (WRF) model, the assimilation of GOES-16 atmospheric temperature-profile products was achieved by using three-dimensional variational (3DVar) and the ensemble transform Kalman filter/three-dimensional variational (ETKF/3DVAR) hybrid system (Hybrid) systems. And the impact of geostationary satellite GOES-16 atmospheric temperature-profile data assimilation on a hurricane forecast is evaluated. The results show that, during the hurricane, the root mean square errors of the GOES-16 atmospheric temperature profile are all within 2 k at the height of 200–1000 hPa, and the quality of the data is generally good. Assimilating the GOES-16 atmospheric temperature-profile data can indeed effectively improve the analysis increment and improve the prediction results. The assimilation increment obtained by the hybrid system has obvious “flow-dependent” characteristics, which can reasonably improve the initial field of the model. Its temperature increment has an obvious spiral structure, which is in line with the characteristics of the hurricane, and the adjustment of the wind field and geopotential height field is also more beneficial to the development of the hurricane. It has a positive impact on the forecast of track, intensity, and precipitation, and the hybrid system is improved more obviously. In addition, from the RMSE of the analysis field and the forecast field relative to the observation data of different elements, the hybrid system is superior to the 3DVar system.

A new real-time maximum power point tracking scheme for PV-BASED microgrid STABILITY using online DEEP ridge extreme learning machine algorithm

A new deep representation-based Maximum Power Point Tracking (MPPT) controller is proposed in this paper for accurate Control references calculation in Photovoltaic (PV) based Micro Grid (MG) operation. The deep representation is obtained by two-step estimation: Data dimension reduction and MPPT Tracker towards optimal computation. The considered deep learning architecture is targeted for N number (large scale) of PV-based DGs, connected locally in the distribution system (DC link extended to AC utility). The collected data of solar irradiation (in W/m2) and PV panel temperature (in oC) profiles of local DGs are subjected to data dimensions using Extreme Learning Machine (ELM) based on Moore-Penrose inverse technique. The compressed represented PV-DG data is further communicated to the Tertiary Control side MPPT Tracker, where Ridge Regression-based ELM is presented for estimating Maximum Power Point Power (PMPP) and Voltage (VMPP) values for kth instant. The initial randomness present in the proposed Deep Representation based Ridge Regression Extreme Learning Machine (DR-RRELM) is further minimized by adopting Huber's characteristic distribution-based likelihood estimator. The proposed MPPT scheme is effectively implemented for accurate control reference in DC-DC and DC-AC converters in the MG. The proposed controller is also suitable for stability improvement at point of common coupling (PCC). Three different case studies such as past data verification, stability analysis under various operating conditions, irradiant change and source power variation. The efficacy of the proposed deep representation-based MPPT scheme is evidenced in MATLAB-based simulation. The proposed technique provides better tracking ability, faster learning and effective reference generation. The case study with irradiation change is validated in TMS320C6713 (32-bit) based Hardware-in-Loop (HIL) validation.

An Automatic Internal Wave Recognition Algorithm Based on CNN Applicable to an Ocean Data Buoy System

The application of internal wave recognition to the buoy system is of great significance to enhance the understanding of the ocean internal wave phenomenon and provide more accurate data and information support. This article proposes an automatic internal wave recognition algorithm based on convolutional neural networks (CNN), which is used in the tight-profile intelligent buoy system. The sea profile temperature data were collected using the Bailong buoy system in the Andaman Sea in 2018. The CNN network structure is applied to feature compression of ocean temperature profile data, reducing the input feature amount of the feature recognition network, thereby reducing the overall algorithm parameters and computational complexity. By adjusting the number of convolution kernels and the length of convolution steps, the original data features in the time domain and the space domain are compressed, respectively. The experimental results show that the identification accuracy and robustness of this method are clearly superior to those of other methods. Additionally, the parameter number and calculation amount of this algorithm are very tiny, which greatly improves the possibility of its deployment in the buoy system.

Estimation of downwelling surface longwave radiation for cloudy skies by considering the radiation effect from the entire cloud layers

Cloud base height (CBH) is a key parameter to characterize the cloud radiation effect. However, the CBH used in downwelling surface longwave radiation (DSLR) estimation is generally obtained indirectly through cloud top parameters retrieved by passive optical remote sensing instruments, which is of high uncertainty. At the same time, it is unreasonable to replace the effective radiation of the entire cloud layers by only using the cloud base radiation with single layer cloud model. This study proposes a new method to estimate cloudy-sky DSLR, which considers the radiation effect of the entire cloud layers from the cloud base to top. First, the CBH estimation model is established by the genetic algorithm-artificial neural network (GA-ANN) algorithm. The cloud top height and cloud attribute parameters (cloud optical depth, cloud water path, and cloud phase) from the passive remote sensing are used as the input features, and meanwhile CBH data from the active remote sensing are output features in the training and testing process of the model. Then, the cloud base temperature (CBT) is estimated based on the CBH combined with the temperature profile data in the EAR5 reanalysis data. Finally, the effective temperature of the entire cloud layers is calculated by using CBT and cloud top temperature. The verification results of CBH estimation showed that R2 is 0.83, the bias and root mean square error (RMSE) are 0.02 km and 1.56 km, respectively, which indicates a comparable accuracy and higher stability compared with the previous studies. The ground-based measurements in the SURFRAD network are used to validate the newly proposed DSLR estimation method, and the results showed that the bias and RMSE are 5.27 W/m2 and 28.48 W/m2, respectively. Additionally, this study found that although the effective temperature of the entire cloud layers has a weaker linear correlation with DSLR, the radiation contribution generated by cloud still occupies a certain weight, and the maximum ratio of cloud radiation in DSLR estimation can account for 30%. Therefore, the cloud radiation effect must be taken into account in the estimation of cloudy-sky DSLR.

Estimation of Temperature Profiles using Low-Frequency Distributed Acoustic Sensing from In-Well Measurements

Summary Distributed fiber-optic sensing for in-well measurements is primarily used for monitoring purposes. Distributed acoustic sensing (DAS) is used to record acoustic disturbances and is sensitive to changes in strain, pressure, and temperature. Distributed temperature sensing (DTS) is used to measure temperature along the fiber. Here, we compare temperature changes measured by DAS and DTS in wells over different time periods. We affirm the linear dependency between DAS’s phase change and temperature, with the derived strain rate being proportional to the time derivative of the temperature response. Given that low-frequency (LF) DAS is sensitive to strain, pressure, and temperature effects, one must choose quiet periods in the well or condition the data to only analyze the effect of temperature on the fiber. We show that LF-DAS data can be used to track temperature changes over several weeks. We then propose a method, using liquid column movements, to invert LF-DAS data for absolute temperature profiles. The temperature profile in a well can be measured using DTS. However, DTS data are not always available, and conventional Raman scattering DTS is not used in subsea wells with long lead-in lengths. Hence, it would be desirable to acquire the temperature response from LF-DAS data to use as a multipurpose tool for in-well monitoring. Here, we show that when purely investigating the response to an initial displacement of the fluid column (i.e., from rest), LF-DAS can be used along with reference sensors, such as the wellhead and downhole temperature gauge data to estimate the depth variations in temperature in production and injection wells.

Spire RO Thermal Profiles for Climate Studies: Initial Comparisons of the Measurements from Spire, NOAA-20 ATMS, Radiosonde, and COSMIC-2

Global Navigation Satellite System (GNSS) Radio Occultation (RO) data play an essential role in improving numerical weather prediction (NWP) and monitoring climate change. The NOAA Commercial RO Purchase Program (CDP) purchased RO data provided by Spire Global Inc. To ensure the data quality from Spire Global Inc. is consistent with other RO missions, we need to quantify their accuracy and retrieval uncertainty carefully. In this work, Spire Wet Profile (wet temperature profile) data from 7 September 2021 to 31 October 2022, processed by the University Corporation for Atmospheric Research (UCAR), and COSMIC-2 (Constellation Observing System for Meteorology, Ionosphere, and Climate-2/Formosa Satellite Mission 7) data are evaluated through comparison with NOAA-20 Advanced Technology Microwave Sounder (ATMS) microwave sounder measurements and collocated RS41 radiosonde measurements. Through the Community Radiative Transfer Model (CRTM) simulation, we convert the Spire and COSMIC-2 RO retrievals to ATMS brightness temperature (BT) at sounding channels CH07 to CH14 (temperature channels), with weighting function peak heights from 8 km to 35 km, and CH19 to CH22 (water vapor channels), with weighting function peak heights ranging from 3.2 km to 6.7 km, and compare the simulations with the collocated NOAA-20 ATMS measurements over ocean. Using ATMS observations as references, Spire and COSMIC-2 BTs agree well with ATMS within 0.07 K for CH07-14 and 0.20 K for CH19-22. The trends between Spire and COSMIC-2 are consistent within 0.07 K/year over the oceans for ATMS CH07-CH13 and CH19-22, indicating that Spire/COSMIC-2 wet profiles are, in general, compatible with each other over oceans. The RO retrievals and RS41 radiosonde observation (RAOB) comparison shows that above 0.2 km altitude, RS41 RAOB matches Spire/COSMIC-2 temperature profiles well with a temperature difference of <0.13 K, and the trends between Spire and COSMIC-2 are consistent within 0.08 K/year over land, indicating that Spire/COSMIC-2 wet profiles are overall compatible with each other through RS41 RAOB measurements over land. In addition, the consistency of Spire and COSMIC-2 based on different latitude intervals, local times, and signal-to-noise ratios (SNRs) through ATMS was evaluated. The results show that the performance of Spire is comparable to COSMIC-2, even though COSMIC-2 has a higher SNR. The high quality of RO profiles from Spire is expected to improve short- and medium-range global numerical weather predictions and help construct consistent climate temperature records.

The Fusion of ERA5 and MERRA-2 Atmospheric Temperature Profiles with Enhanced Spatial Resolution and Accuracy

Accurate high-resolution atmospheric temperature profiles are essential for precisely characterizing the evolution of the atmosphere and developing numerical forecasts. Atmospheric datasets, such as ERA5 (the fifth-generation ECMWF Reanalysis) and MERRA-2 (the Modern-Era Retrospective Analysis for Research and Applications, Version 2), provide global and continuous temperature profiles, with fine vertical distribution and horizontal resolution. RAOB (Radiosonde Observation) sounding data have high confidence and representativeness and are usually used for data accuracy verification. Due to the difficulty of updating existing products, and the scarcity of research on mesospheric temperature profiles, this work maximizes the high observation accuracy of RAOB data, combines the benefits of ERA5’s horizontal resolution and MERRA-2’s vertical distribution, and employs the optimal interpolation method to combine the data, in order to produce a fused result with high spatial resolution. After converting all of the data to the same spatial distribution, the optimal interpolation method was used to combine the two datasets from separate places and different pressure layers in order to produce the fused results, which had a vertical distribution of 45 layers and a spatial resolution of 0.25°. The fused data’s RMSE and MAE were 6.0 K and 5.0 K lower than those of the MERRA-2 temperature profile data, respectively, and 0.3 K and 0.4 K lower than those of the ERA5 temperature profile data, respectively. The validation, using data from 2019, showed that the fused data exhibits better correlation and data accuracy than the other two datasets, which demonstrated that the fused algorithm can potentially be used to generate reliable datasets for future meteorological research.

Mesospheric and Upper Stratospheric Temperatures From OMPS‐LP

AbstractWe report the development of a temperature profile data set from the Ozone Mapping and Profiler Suite Limb Profiler (OMPS‐LP) instrument on the Suomi NPP satellite. The data set covers a roughly 10 year period from 2012 to 2022, and temperatures are provided in the altitude range between 35 and 70 km. The algorithm uses daytime Rayleigh scattered radiation near 350 nm to estimate atmospheric density profiles, which are vertically integrated using the hydrostatic equation to estimate atmospheric pressure. Temperature profiles are then derived using the ideal gas law. Spectral structures in the OMPS‐LP radiances are a source of systematic errors that limit the absolute accuracy to ±2 K. However, since the systematic errors do not change significantly over time, the relative accuracy is better than ±1 K. Our temperature data set has been designed to supplement global temperature maps produced by assimilation of data from traditional meteorological sensors, including GNSS radio occultation sensors, that provide high quality atmospheric temperature profiles up to 40–55 km. We show comparisons to other co‐located temperature datasets to validate our product.

Tidal creeks mediate micro-climate within artificial mangroves at their northmost boundary in China

One of the primary constraints for mangrove restoration is the low temperature in relatively high latitude regions. Yueqing Bay, at a latitude of 28.20°N, is the northmost region for the artificial mangrove forests in China. It is hypothesized that in this region, the micro-climate nearby tidal creeks can benefit the growth of mangroves, as the inundation process can mediate air temperature and soil temperature. To test this hypothesis, field observation was conducted to measure mangrove characteristics, hydrology and air/soil temperature within a channelised mangrove forest and compare variations between a tidal creek and its adjacent platform. LiDAR data and field measurement both indicated a height decrease of mangrove trees (Kandelia obovata) from the tidal creek edge to the platform. This pattern suggested that the presence of tidal creeks was able to promote the growth of mangrove in their northmost boundary and the mediation of microclimatic condition should be further investigated. The temperature profile data revealed that the key zone of temperature affected by tidal inundation was within a range of 5 cm and -15 cm referring to bed levels. Temperature monitoring in the winter revealed that compared with the platform, a mean increase of 0.6 °C in both air temperature and soil temperature were found in the tidal creek. However, spring tides and neap tides showed different effects on temperature mediation by the presence of tidal creeks. The greatest temperature increase was observed to occur in neap tides, reaching maximum of 9 °C in air and 3 °C in soil. Thus, the warming effects of tidal creeks in this region was most important in neap tides. In the northmost boundary of mangrove distribution in China, winter low temperature was considered to be the key restraint for mangrove restoration. Field survey found the growth of mangroves reached a maximal along the tidal creek edge in this region. This pattern was explained by the fact that the presence of tidal creeks increased winter temperature of mangroves and channelisation was found to be a potential technique to promote the growth of artificial mangroves in the relatively high latitude regions.

Temporarily summer-stratified lakes are common: profile data from 436 lakes in lowland Denmark

ABSTRACT Lakes that undergo temporary stratification in summer do not fit into the classic categorisation of polymictic or dimictic lakes, but how common are they and what are the effects of stratification on the development of anoxia? We used monthly and bimonthly (twice per month) temperature and oxygen profile data from 436 Danish lakes (area range 1–3954 ha, maximum depth range 1.3–45 m) and defined a stratification indicator based on the temperature difference between the upper and lower 2 m of the water column. The stratification indicator had values between −1.6 and 17.7 °C and was significantly and strongly related to lake maximum depth and significantly but less strongly related to lake area. The indicator was highly variable, especially in lakes with maximum depths between 4 and 10 m, where intermediate indicator values suggest one or several mixing events during summer. The dissolved oxygen concentration in summer at the bottom was often <1 mg/L, even when the difference between top and bottom temperature was as low as 0.5–1.0 °C. Temporarily stratifying lakes with frequent mixing events over the summer are probably common in temperate lowland areas but are easily overlooked in routine monitoring programs. Temporary stratification has pronounced implications for the oxygen concentrations and potentially also for the biota and interactions between sediment and water.

Recent Advances and Challenges in the Inverse Identification of Thermal Diffusivity of Natural Ice in China

The ice thermal parameters are the key to reasonably simulating ice phenology, distribution, and thickness, but they have always been a “vulnerable group” in ice research. Technically, it may seem simple to obtain accurate ice thermal property parameters, but in reality, there are numerous impact factors, requiring a rigorous research process. In the 1980s, the thermal conductivity of ice was explored in the field and laboratory, after which there has been no significant progress in China. In this century, mathematics is introduced, after which the inversion identification and analysis with the time-series data of the vertical temperature profiles of ice layers by in situ testing are carried out. The in situ thermal diffusivities of different natural ices were obtained and cross-validated with the inversion identification results. Both natural freshwater ice and sea ice exhibited differences in the thermal diffusivity of the pure ice chosen for the current simulations due to impurities within the unfrozen water among the ice crystals, but the trends are consistent with the results of a small number of laboratory tests on different types of saltwater frozen ice. In this paper, the inversion identification results of the thermal diffusivity of typical ice were selected, and the factors constraining the thermal diffusivities were analyzed. The importance of parameterizing the thermal diffusivity in the phase transition zone of ice under the trend of global warming was illustrated. Future research ideas on the physical mechanism, application value, and parameterization scheme of the thermal diffusivity of natural ice were envisaged.

Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica

The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve the snow temperature profiles by combining microwave and infrared brightness temperatures. This paper proposes a conjoint inversion algorithm to retrieve snow temperature profiles by combining multi-frequency microwave brightness temperature (BT) with infrared BT, in which different weight functions of microwave BT at different frequencies are adopted, and the atmosphere influence has also been corrected. The snow temperature profile data are retrieved based on AMSR2 microwave BT data and MODIS infrared BT data in 2017 and 2018, which are evaluated by comparing with the measured snow temperature at Dome-C station. The results confirm that the inverted snow temperature profiles are consistent with the field observation data from the Dome-C station. Multi-frequency microwave brightness temperature can be used to invert the snow temperature profiles; however, the inverted snow surface temperature is more accurate by combining the infrared BT with the microwave BT in the conjoint inversion algorithm.

Experimental investigation on energy and mass transport at steady-state evaporating interface in liquid methane storage tanks

In large-scale energy storage and transportation applications, a comprehensive understanding of the evaporation characteristics of liquid methane will benefit the extension of liquid methane storage duration and the improvement in energy storage efficiency. The complexities of achieving cryogenic steady-state evaporation and submillimeter visualization make experimental studies of liquid methane interfacial heat and mass transport difficult and insufficient. In this study, a cryogenic evaporation testing rig was constructed to obtain the interfacial temperature distributions of liquid methane with a spatial resolution of 50 µm. The evaporation coefficient was obtained directly from the measured temperature profile data near the liquid/vapor methane interface. The coupled effects of thermal conduction and Marangoni convection at the liquid-methane interface were revealed and quantitatively compared to those of liquid oxygen and water. A new high-precision dimensionless correlation for the evaporation coefficient was proposed for liquid methane, which explained the consistent dependence of the evaporation coefficient on the liquid-side temperature and wall interface height. The results help to enhance the simulation stability and accuracy and benefit the design of liquid methane or liquefied natural gas storage tanks.

Benchmarking of automatic quality control checks for ocean temperature profiles and recommendations for optimal sets

Millions of in situ ocean temperature profiles have been collected historically using various instrument types with varying sensor accuracy and then assembled into global databases. These are essential to our current understanding of the changing state of the oceans, sea level, Earth’s climate, marine ecosystems and fisheries, and for constraining model projections of future change that underpin mitigation and adaptation solutions. Profiles distributed shortly after collection are also widely used in operational applications such as real-time monitoring and forecasting of the ocean state and weather prediction. Before use in scientific or societal service applications, quality control (QC) procedures need to be applied to flag and ultimately remove erroneous data. Automatic QC (AQC) checks are vital to the timeliness of operational applications and for reducing the volume of dubious data which later require QC processing by a human for delayed mode applications. Despite the large suite of evolving AQC checks developed by institutions worldwide, the most effective set of AQC checks was not known. We have developed a framework to assess the performance of AQC checks, under the auspices of the International Quality Controlled Ocean Database (IQuOD) project. The IQuOD-AQC framework is an open-source collaborative software infrastructure built in Python (available from https://github.com/IQuOD). Sixty AQC checks have been implemented in this framework. Their performance was benchmarked against three reference datasets which contained a spectrum of instrument types and error modes flagged in their profiles. One of these (a subset of the Quality-controlled Ocean Temperature Archive (QuOTA) dataset that had been manually inspected for quality issues by its creators) was also used to identify optimal sets of AQC checks. Results suggest that the AQC checks are effective for most historical data, but less so in the case of data from Mechanical Bathythermographs (MBTs), and much less effective for Argo data. The optimal AQC sets will be applied to generate quality flags for the next release of the IQuOD dataset. This will further elevate the quality and historical value of millions of temperature profile data which have already been improved by IQuOD intelligent metadata and observational uncertainty information (https://doi.org/10.7289/v51r6nsf).

Numerical simulation of stand-alone photovoltaic integrated with earth to air heat exchanger for space heating/cooling of a residential building

Earth-to-air heat exchanger is one of the efficient energy-saving approaches to meet the heating/cooling requirement of residential buildings/greenhouses. In this work, a comprehensive numerical and statistical approach using the response surface method aiming at the integration of stand-alone photovoltaics with earth-to-air heat exchangers is discussed for the coordinates of Delhi. For parametric optimization of the earth-to-air heat exchanger, statistical tool, and response surface methodology is used by taking ground temperature profile and weather data into account. Among the primary parameters, the diameter of the pipe is found to be the most significant parameter. Optimized pipe diameter of 0.2 m, pipe length of 70 m, and air velocity of 7 m/s have yielded a total energy gain of 8116.7 kWh/year. Evaluation of herein designed hybrid system has estimated CO2 mitigation of 16.18 tons/year which in turn has evinced the carbon credit value to US$ 336.86/year. Calculations exhibit a simple payback period of approx. 5 and 10 years for one day and 2 days autonomy respectively when compared with the electricity cost of diesel generators for rural areas. With 24 h grid availability, the simple payback period with a photovoltaic system gets reduced to 4–5 years in urban areas as it doesn't require any battery bank. This kind of self-sufficient hybrid system is an eco-friendly and sustainable energy solution for altogether rural and urban areas. This work provides an effective blueprint of commercially viable photovoltaic integrated with earth-to-air heat exchanger systems for households.

Seasonal Variations in the Vertical Wavenumber Spectra of Stratospheric Gravity Waves in the Asian Monsoon Region Derived from COSMIC-2 Data

We used the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) dry temperature profile data from December 2019 to November 2021 to study the vertical wavenumber spectra of the potential energy of stratospheric gravity waves (GWs Ep) in the Asian monsoon region (15–45°N, 70–150°E). The GW Ep decreases with increasing vertical wavenumber, and the spectral slope varies with wavenumber. The spectral slope becomes smaller over a wavenumber range of 0.1–0.45 km−1, and larger from 0.45–1 km−1, with increasing wavenumber. The energy density distribution at middle and low latitudes shows seasonal variations. Over a wavenumber range of 0.05–0.5 km−1, the energy density in winter is higher at middle latitudes than at low latitudes, and the opposite is observed in summer over a wavenumber range from 0.1 to 1 km−1. Both the spectral amplitude and characteristic wavelength exhibit band-like patterns, and the large-value bands and their centers vary significantly with the season. In winter, the middle latitude spectral amplitude is larger than that at low latitudes, and the significant large-value band-like distribution is at ~40°N. In summer, the distribution is opposite, with large-value band regions over the Bay of Bengal and Indo-China Peninsula. The large-value region of the middle latitude spectral amplitude corresponds to a longer characteristic wavelength, while the large-value region of the low latitude spectral amplitude corresponds to a shorter characteristic wavelength. There is also significant seasonal variation in the distribution of spectral slopes. Over a wavenumber range of 0.1 to 0.5 km−1, the slope is smaller at middle latitudes and larger at low latitudes in winter; the opposite is observed in summer. There is a significant annual cycle of spectral amplitude at middle and low latitudes, and a 4.8 month cycle at middle latitudes.