Satellite Estimates Research Articles

Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?

Abstract. NOx is an important primary air pollutant of major environmental concern which is predominantly produced by anthropogenic combustion activities. NOx needs to be accounted for in national emission inventories, according to international treaties. Constructing accurate inventories requires substantial time and effort, resulting in reporting delays of 1 to 5 years. In addition to this, difficulties can arise from temporal and country-specific legislative and protocol differences. To address these issues, satellite-based atmospheric composition measurements offer a unique opportunity for the independent and large-scale estimation of emissions in a consistent, transparent, and comprehensible manner. Here we test the multi-source plume method (MSPM) to assess the NOx emissions over Germany in the COVID-19 period from 2019–2021. For the years where reporting is available, the differences between satellite estimates and inventory totals were within 75–100 kt (NO2) NOx (<10 % of inventory values). The large reduction in the NOx emissions (∼15 %) concurrent with the COVID-19 lockdowns was observed in both the inventory and satellite-derived emissions. The recent projections for the inventory emissions of 2021 pointed to a recovery of the 2021 emissions towards pre-COVID-19 levels. In the satellite-derived emissions, however, such an increase was not observed. While emissions from the larger power plants did rebound to pre-COVID-19 levels, other sectors such as road transport did not, and the change in emissions is likely due to a reduction in the number of heavier transport trucks compared to the pre-COVID-19 numbers. This again illustrates the value of having a consistent satellite-based methodology for faster emission estimates to guide and check the conventional emission inventory reporting. The method described in this work also meets the demand for independent verification of the official emission inventories, which will enable inventory compilers to detect potentially problematic reporting issues, bolstering transparency and comparability, which are two key values for emission reporting.

Initial orbit determination via artificial intelligence for too-short arcs

In the framework of Space Traffic Management (STM) the growing number of space debris and the consequent increased risk of collisions have become a topic of great interest. Moreover, the difficulty of detecting and characterizing a Resident Space Object (RSO) in terms of orbital parameters makes this research field very challenging. In most cases the available observations have a very short duration and an accurate estimation of the orbit is very difficult. This paper introduces the Initial Orbit Determination via Artificial intelligence (IODA) algorithm, a reliable and accurate orbit estimator combining metaheuristic and artificial intelligence frameworks to perform precise orbit determination from Very Short Arcs (VSAs). Starting from an initial orbit estimate derived through an Artificial Bee Colony (ABC) algorithm, a Physics Informed Extreme Learning Machine is employed to increase the accuracy of the predicted orbit. Results on simulated data have shown the robustness of the algorithm to random parameters initialization and a percentage of success of 89% of retrieving the reference orbit with an error under 1 km and 0.01 km/s on the initial target’s state.

A Retrospective Hydrological Uncertainty Analysis Using Precipitation Estimation Ensembles for a Poorly Gauged Basin in High Mountain Asia

Abstract We study the impact of uncertain precipitation estimates on simulated streamflows for the poorly gauged Yarlung Tsangpo basin (YTB), high mountain Asia (HMA). A process-based hydrological model at 0.5-km resolution is driven by an ensemble of precipitation estimation products (PEPs), including analyzed ground observations, high-resolution precipitation estimates, climate data records, and reanalyses over the 2008–15 control period. The model is then forced retrospectively from 1983 onward to obtain seamless discharge estimates till 2007, a period for which there is very sparse flow data coverage. Whereas temperature forcing is considered deterministic, precipitation is sampled from the predictive distribution, which is obtained through processing PEPs by means of a probabilistic processor of uncertainty. The employed Bayesian processor combines the PEPs and outputs the predictive densities of daily precipitation depth accumulation as well as the probability of precipitation occurrence, from which random precipitation fields for probabilistic model forcing are sampled. The predictive density of precipitation is conditional on the precipitation estimation predictors that are bias corrected and variance adjusted. For the selected HMA study site, discharges simulated from reanalysis and climate data records score lowest against observations at three flow gauging points, whereas high-resolution satellite estimates perform better, but are still outperformed by precipitation fields obtained from analyzed observed precipitation and merged products, which were corrected against ground observations. The applied methodology indicates how missing flows for poorly gauged sites can be retrieved and is further extendable to hydrological projections of climate. Significance Statement We show how to use different precipitation estimates, like computer simulations of weather or satellite observations, in conjunction with all available ground measurements in regions with generally poor meteorological and flow measurement infrastructure. We demonstrate how it is possible to retrieve past unobserved river flows using these estimates in combination with a hydrological computer model for streamflow simulations. The method can help us to better understand the hydrology of poorly gauged regions that play an important role in the distribution of water resources and can be affected by future changes. We applied the method to a large transboundary river basin in China. This basin holds water needed by large, densely populated regions of India that may become water constrained by warmer climate.

Frequency analysis of extreme rainfall over the Japanese archipelago by leveraging gauge-adjusted radar and satellite estimates

With the increasing frequency of extreme rainfall and difficulty in capturing their spatial variability by rain gauges, especially over regions with complex topographic and climatic features like Japan, there is a growing interest in using radar and satellite-derived precipitation estimates for building intensity–duration–frequency (IDF) curves. Such a statistical method provides references for studying extreme rainfall statistics at varying spatiotemporal scales. Yet, a comparative evaluation of the efficiency of these datasets to derive reliable IDFs over the Japanese archipelago is lacking. This study compares IDFs developed from four rain gauge-adjusted remotely sensed datasets (RSDs) derived from weather radars (R/A) and satellites (GsMAP_G, CMORPH_G, and IMERG_G) with those created from long-term rain gauge records (i.e., reference IDFs) for evaluating their reliabilities, particularly for 10- and 100-year return periods. A Bayesian Generalized Extreme Value model was adopted to estimate the IDFs. The results show that the four RSDs exhibited varying performances in deriving reliable IDFs within the 5–95% confidence bounds of the reference IDFs depending on rainfall duration and climate settings. While R/A performed better for one-hour rainfall return levels, particularly in the cold climate with warm summers, satellite-driven RSDs, especially IMERG_G, showed better performances for long-duration rainfall return levels. The performance discrepancies were primarily linked to the differences in rainfall estimation and bias adjustment schemes rather than the differences in record lengths and spatial resolutions among the tested RSDs. Thus, it is crucial to consider the rainfall duration and climate settings when using the derived IDFs for diverse practical applications.

Impacts of inter-satellite links on the ECOM model performance for BDS-3 MEO satellites

Inter-satellite link (ISL) plays an essential role in current and future Global Navigation Satellite System (GNSS). In this study, we investigate the impact of ISL observations on precise orbit determination for BeiDou-3 Navigation Satellite System (BDS-3) Medium Earth Orbit (MEO) satellites based on different Extended CODE Orbit Models (ECOM). Thanks to the better observation geometry of the Ka-band ISL data compared to the L-band data for BDS-3 MEO satellites, the ISL solution substantially reduces Orbit Boundary Discontinuity (OBD) errors, except for C30, which suffers from unstable Ka-band hardware delay. From the external quality analysis, ISL significantly enhances the reliability of the orbit of MEO satellites manufactured by the China Academy of Space Technology (CAST). The standard deviation (STD) of the satellite laser ranging (SLR) residuals is approximately 2.5 cm, and the root mean square (RMS) is reduced by 10–23% compared to L-band solutions. Besides, the Sun-elongation angle dependent systematic error in SLR residuals nearly vanishes based on the reduced 5-parameter ECOM (ECOM1) or extended 7-parameter ECOM (ECOM2) with ISL data. This is because the ISL reduces the correlation between state parameters and solar radiation pressure (SRP) parameters as well as those among SRP parameters, leading to a more accurate estimation of both orbit and SRP perturbations, particularly those along B direction. This confirms that the deficiency of the SRP models for BDS-3 CAST satellites can be compensated by using better observation geometry from ISL data. On the other hand, for the satellite manufactured by Shanghai Engineering Center for Microsatellites (SECM), the ISL allows for a more accurate estimation of the Bc1 parameter in the ECOM1 model. This only reduces linear systematic error, possibly because the impact generated by the satellite bus cannot be entirely absorbed by the B-direction parameters.

Application of commercial microwave links (CMLs) attenuation for quantitative estimation of precipitation

AbstractPrecipitation estimation models are typically sourced by rain gauges, weather radars and satellite observations. A relatively new technique of precipitation estimation relies on the network of Commercial Microwave Links (CMLs) employed for cellular communication networks: the rain‐inducted attenuation in the links enables the precipitation estimation. In the paper, it is analysed to what extent the precipitation derived from CML attenuation data is useful in estimation of the precipitation field with the high temporal and spatial resolution required in nowcasting models. Two methods of determination of precipitation along CMLs from attenuation of signal with several frequencies were proposed. Then, in order to generate precipitation field, three approaches for assigning appropriate precipitation values to a specific point or set of pixels along the link are developed and tested. The CML‐based estimates are compared with point observations from manual rain gauges and multi‐source precipitation fields using daily and half‐hourly accumulations. It was found that the CML‐based precipitation fields are much worse than radar‐derived estimates. At the same time, they had slightly poorer reliability than spatially interpolated telemetric rain gauge data and significantly higher reliability than satellite estimates. Furthermore, the impact of link characteristics, such as length and frequency, on the reliability of CML‐based precipitation estimates is analysed.

Multi-scale analysis of satellite, reanalysis and muti-source precipitation estimates over the Tibetan Plateau

Due to the important hydrological and meteorological significance, the performance of different kinds of precipitation products for the Tibetan Plateau (TP) has been evaluated in previous studies. However, the performance of the products on a high spatial-temporal scale has not been clearly shown. Based on the Chinese Meteorological Administration (CMA) ground rain gauge observations during 2017–2018 over the TP, this paper analyzes the multi-scale performance of four satellite-based datasets (IMERG_Uncal, IMERG_Cal, GSMaP_MVK, and GSMaP_Gauge), two reanalysis datasets (ERA5 and ERA5-L), and one multi-source merged dataset (CMFD) for different precipitation intensities with a special attention to the characterization of spatiotemporal variability of heavy precipitation. Firstly, this study examines how the quality of precipitation products varies with time scale and precipitation intensity. CMFD generally shows the best quality and has the most stable quality at all temporal scales. Satellite products perform better in summer while reanalysis products show better results in cold seasons. Another significant issue is the impact of gauge-based calibration. GSMaP_Gauge with daily-scale gauge calibration is often considered to outperform other products. However, our study finds that GSMaP_Gauge is problematic at smaller spatiotemporal scales, especially in capturing heavy precipitation. The long-term performance of multi spatiotemporal scale comparison of different precipitation products is the focus of this study, which can help with their application and improvement.

Performance of high-resolution MUR satellite sea surface temperature data as a proxy for near-surface in situ temperatures on neotropical reefs

As ocean temperatures increase, so does the need for accurate, regular, and widespread temperature data. This is especially important for researchers studying corals, which can be pushed over their bleaching thresholds by small, sustained changes in water temperature. Satellite data products offer longitudinal estimates of sea surface temperature (SST) taken at regular intervals without the ongoing costs and geospatial limits associated with underwater temperature loggers. However, previous work indicates satellite data are sometimes characterized by significant temperature biases, particularly in stratified coastal waters. Here, we compared the foundation sea surface temperature (fSST) estimated from a highly processed, gridded satellite data set (GHRSST MUR L4 Global SST) to in situ temperature (IST) data collected from coral reef habitats in the top 20 m of the water column at 19 sites in Panama. Comparisons between Pacific and Caribbean coasts and between upwelling and non-upwelling seasons provide a robust test of the utility of this product to estimate subsurface temperatures. The mean differences between fSST and IST show that MUR fSST accurately reflects temperatures at shallow (<9 m) depths on the Caribbean coast (mean bias ≤0.55°C ± standard error (SE) 0.01°C) and during the non-upwelling season on the Pacific coast (mean bias ≤0.26°C ± SE 0.03°C). MUR fSST poorly reflects IST during the Pacific upwelling season, especially at sites 12 m and deeper. In such cases, 66% of satellite estimates deviated from ISTs by >1°C. Our findings underscore how highly processed SST data products should be ground-truthed before being used as a substitute for IST, especially in upwelling areas.

Validation of TRMM and GPM Satellite Data Using Daily Precipitation Observations

Accurate precipitation data holds immense significance in hydrological analysis. A common challenge in this field often stems from the lack of comprehensive data availability. High-resolution satellite-based precipitation measurements covering large areas offer a potential solution. However, disparities in the resolution of observed rainfall data can impact data accuracy. The main goal of this study is to evaluate the accuracy of rainfall data obtained from the TRMM and GPM satellites in the Kuranji watershed. The evaluation was conducted on the performance of the GPM IMERG-F from the Integrated Multi-satellite Retrievals for the GPM mission and the TRMM 3B42RT on a daily scale spanning from 2015 to 2019 over the Kuranji watershed. The daily precipitation measurements were validated using three widely used statistical metrics (R, RMSE, and RB). The precipitation detection capability (POD, FAR, and CSI) was also considered in this assessment. The findings demonstrate that both satellite estimations exhibit a substantial correlation coefficient (0.68 for GPM, 0.62 for TRMM) with the measurements obtained from gauges, along with an inclination to overestimate precipitation. GPM IMERG-F and TRMM 3B42RT manifest a consistent spatial pattern in daily precipitation distribution, effectively representing the observed precipitation distribution. The greater probability of detection (POD), critical success index (CSI), and lower false alarm ratio (FAR) exhibited by GPM IMERG-F at varying rainfall intensities suggests its superior performance in accurately identifying observed precipitations. This finding supports the preference for GPM IMERG-F data over TRMM 3B42RT data across various applications, hydrology, and related disciplines in the future.

What Is the Contribution of Convergence Zones to Global Precipitation? Assessing Observations and Climate Models Biases

AbstractConvergence zones (CZs) are known drivers of precipitation regimes from regional to planetary scales. However, there is a scarcity of accounts of the contribution of CZs to the global precipitation. In this study, we build upon a recently developed Lagrangian diagnostic to attribute precipitation to CZ events in observations and simulations submitted to the Coupled Model Intercomparison Project 6 (CMIP6). Observed CZs are identified using ERA5 reanalysis wind and attributed precipitation from observational products based on satellite estimates and rain gauges. We estimate that approximately 54% (51%–59%, depending on the precipitation product) of global precipitation falls over CZs; in some regions, such as the Intertropical Convergence Zone (ITCZ) and subtropical monsoon regions, this proportion is greater than 60%. All CMIP6 simulations analyzed here attribute about 10% more precipitation to CZ events than what the observations suggest. To investigate this overestimation, we decompose the precipitation error in terms of frequency and intensity of CZ precipitation and find that all models present a substantial positive bias in the frequency of CZ precipitation, suggesting that climate models trigger precipitation too easily in regions of airmass confluence; such positive frequency biases in CZ precipitation help explaining well‐known biases in climate models, such as the double‐ITCZ in the Pacific. We also find that models with better mass conservation present an apportionment of CZ precipitation closest to the observational estimates, demonstrating the relevance of mass conservation in advection schemes.

Associations between satellite-derived estimates of PM2.5 species concentrations for organic carbon, elemental carbon, nitrate, and sulfate with birth weight and preterm birth in California during 2005-2014.

Characterizing the spatial distribution of PM2.5 species concentrations is challenging due to the geographic sparsity of the stationary monitoring network. Recent advances have enabled valid estimation of PM2.5 species concentrations using satellite remote sensing data for use in epidemiologic studies. In this study, we used satellite-based estimates of ambient PM2.5 species concentrations to estimate associations with birth weight and preterm birth in California. Daily 24 h averaged ground-level PM2.5 species concentrations of organic carbon, elemental carbon, nitrate, and sulfate were estimated during 2005-2014 in California at 1 km resolution. Birth records were linked to ambient pollutant exposures based on maternal residential zip code. Linear regression and Cox regression were conducted to estimate the effect of 1 µg/m3 increases in PM2.5 species concentrations on birth weight and preterm birth. Analyses included 4.7 million live singleton births having a median 28 days with exposure measurements per pregnancy. In single pollutant models, the observed changes in mean birth weight (per 1 µg/m3 increase in speciated PM2.5 concentrations) were: organic carbon -3.12 g (CI: -4.71, -1.52), elemental carbon -14.20 g (CI: -18.76, -9.63), nitrate -5.51 g (CI: -6.79, -4.23), and sulfate 9.26 g (CI: 7.03, 11.49). Results from multipollutant models were less precise due to high correlation between pollutants. Associations with preterm birth were null, save for a negative association between sulfate and preterm birth (Hazard Ratio per 1 µg/m3 increase: 0.973 CI: 0.958, 0.987).

Evaluation of particulate organic carbon from MODIS-Aqua in a marine-coastal water body.

La Paz Bay (LPB) in Mexico is one of the largest marine-coastal bodies of water in the Gulf of California (GC) and is ecologically important for the feeding, reproduction, and refuge of marine species. Although particulate organic carbon (POC) is an important reservoir of oceanic carbon and an indicator of productivity in the euphotic zone, studies in this region are scarce. This study evaluates the performance of satellite-derived POC in LPB from January 2003 to December 2020. The metrics obtained for COP ( ; y ), Chla-a ( ; y ), and SST ( ; y ) establish that although in some cases there was a slight over/underestimation, the satellite estimates consistently represent the variability and average values measured in situ. On the other hand, the spatio-temporal analysis of the POC allowed us to identify two seasons with their respective transition periods and five subregions in which the POC is characterized by having its maximum variability; two of these coincide with the locations of the eddies reported for the winter and summer seasons in the LPB, while the following three are located: one in the coastal zone and in the two areas in which the LPB interacts with the GC. The associations, variability nodes, and multiple linear regression analysis suggest that POC fluctuations in the LPB respond mainly to biological processes and, to some extent, to the seasonality of SST and wind. Finally, our results justify the use of the MODIS-Aqua satellite POC for studies in marine-coastal water bodies with similar characteristics to the LPB and suggest that this water body can be considered a reservoir for the marine region of northwestern Mexico.

Orbit Determination for Impulsively Maneuvering Spacecraft Using Modified State Transition Tensor

This paper proposes a method to accurately resolve orbit determination for a spacecraft with unknown impulsive maneuvers. The proposed method handles the unknown impulsive maneuver by incorporating the magnitude, direction, and time of the impulsive maneuver into the estimation parameter vector. First, a modified state transition tensor (STT) is proposed via orbit division and segment connection, allowing the orbit to be directly propagated under the effects of impulsive maneuver uncertainties. Then, based on the modified STT, a second-order measurement model is established with the estimation parameter vector as the input. Combining the second-order measurement model with observations, a second-order optimal solution is derived to correct the estimation parameters. The spacecraft orbit, together with the magnitude, direction, and time of the impulsive maneuver, are simultaneously estimated in an iterative framework. The performance of the proposed method is validated in a low-Earth-orbit case and a high-Earth-orbit case. Simulations show that the proposed method outperforms its linear version in terms of convergence, accuracy, and uncertainty quantification capacity. Its maneuver reconstruction and orbit estimation errors are one order of magnitude less than those of competitive methods. Moreover, the proposed method can handle severe conditions and is robust to initial guesses.

Satellite-Derived Estimate of City-Level Methane Emissions from Calgary, Alberta, Canada

Cities are important sources of anthropogenic methane emissions. Municipal governments can play a role in reducing those emissions to support climate change mitigation, but they need information on the emission rate to contextualize mitigation actions and track progress. Herein, we examine the application of satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) to estimate city-level methane emission rates in a case study of the City of Calgary, Alberta, Canada. Due to low and variable annual observational coverage, we integrated valid TROPOMI observations over three years (2020–2022) and used mass balance modeling to derive a long-term mean estimate of the emission rate. The resulting column-mean dry-air mole fraction (XCH4) enhancement over Calgary was small (4.7 ppb), but within the city boundaries, we identified local hot spots in the vicinity of known emission sources (wastewater treatment facilities and landfills). The city-level emission estimate from mass balance was 215.4 ± 132.8 t CH4/d. This estimate is approximately four times larger than estimates from Canada’s gridded National Inventory Report of anthropogenic CH4 emissions and six times larger than the Emissions Database for Global Atmospheric Research (EDGAR v8.0). We note that valid TROPOMI observations are more common in warmer months and occur during a narrow daily overpass time slot over Calgary. The limited valid observations in combination with the constrained temporal observational coverage may bias the emission estimate. Overall, the findings from this case study highlight an approach to derive a screening-level estimate of city-level methane emission rates using TROPOMI data in settings with low observational coverage.

The Effectiveness of Environmental Provisions in Regional Trade Agreements

Abstract Trade liberalization can spur environmental degradation. Concerns over these adverse impacts have led to a debate over the need for environmental provisions in regional trade agreements (RTAs); however, the effectiveness of such provisions is unknown. In this paper, we provide new plausibly causal evidence that environmental provisions are effective in limiting deforestation following the entry into force of RTAs. We exploit high resolution, satellite-derived estimates of deforestation, and identify the content of RTAs using a new dataset with detailed information on individual provisions. Accounting for the potential endogeneity of environmental provisions in RTAs, we find that the inclusion of specific provisions aimed at protecting forests and/or biodiversity almost entirely offsets the net increases in forest loss observed in similar RTAs without such provisions. The effects are particularly strong in tropical, developing countries with greater biodiversity. The inclusion of these provisions limits agricultural expansion and agricultural trade.

A Global Climatology of Diabatic Heating in Tropical Easterly Waves

Abstract Tropical easterly waves (TEWs) play a critical role in regulating convection and precipitation across the global tropics. TEWs act as seed disturbances for tropical cyclogenesis, serve as an essential component in monsoon precipitation, and produce large amounts of rainfall and diabatic heating that can strongly affect the large-scale circulation. To help improve our knowledge of a more elusive type of tropical wave, we use satellite and reanalysis estimates of the diabatic heating associated with TEWs that are identified by a tracking algorithm based on low-level curvature vorticity. This study uses the Tropical Rainfall Measuring Mission (TRMM) version 6 convective–stratiform heating (CSH) and spectral latent heating (SLH) orbital products to create a global climatology (1998–2015) of TEW diabatic heating. TEW-specific composites for the satellite-observed vertical structure of diabatic heating are compared to similar terms from MERRA-2 across a variety of tropical regions. There are striking differences between the reanalysis and satellite heating with MERRA-2 having much stronger background heating, especially at low levels. Both the satellite-observed and reanalysis heating profiles show stronger midlevel heating associated with TEWs relative to the unconditional background. Similar patterns of mid- and bottom-heaviness emerge in two-dimensional composites of TEW latent heating as stronger heating rates and percent contributions to the background are generally higher at 500 hPa than at 850 hPa. Although TEWs only represent a few percent of the background heating across the global tropics, they comprise 30%–50% of the heating in the prominent TEW tracks over the northeastern Atlantic and Pacific Oceans.

Pliocene Weakening of Gradients in Temperature but Not in Productivity in the Eastern Equatorial Pacific

AbstractThe modern eastern equatorial Pacific Ocean (EEP) exhibits strong upwelling, producing pronounced gradients in sea surface temperature (SST), nutrient concentration, and biological productivity between 80° and 140°W. During the globally warmer late Pliocene (3.0–3.6 Ma), the EEP may have experienced permanent El Niño‐like conditions, supported by a reduced SST gradient across the equatorial Pacific. However, the weakened east‐west SST gradient has been controversial, with disparate results depending on the proxy used to monitor Western Warm Pool SSTs. We present new Pliocene alkenone‐based SST and paleoproductivity records from four Ocean Drilling Program (ODP) cores spanning an east‐west transect across the EEP, which present an internally consistent picture of SST and productivity gradients in the modern cold tongue, resolved at orbital‐scale variability. Strong agreement between core top reconstructions and satellite estimates indicates that alkenone paleotemperature and paleoproductivity proxies are appropriate for reconstructing Pliocene EEP conditions. The average SST gradient between 90° and 120°W was reduced from the modern 1.8°C gradient to 0.9°C in the late Pliocene. Despite the weakened SST gradient, the surface productivity gradient was stronger during the late Pliocene compared to modern, based on calibrated X‐ray fluorescence biogenic opal and alkenone average accumulation rates. Contrary to modern El Niño SST and productivity patterns, reduced Pliocene surface productivity did not accompany the weakened SST gradient. Instead, strong Pliocene biogenic opal and alkenone concentration accumulation gradients in the eastern EEP suggest that subsurface tilting of the nutricline and thermocline persisted to supply vigorous upwelling of warm but nutrient‐rich subsurface waters in a warmer climate.

Local mitigation of higher-order ionospheric effects in DFMC SBAS and system performance evaluation

Dual-frequency multi-constellation (DFMC) satellite-based augmentation system (SBAS) is a new SBAS standard for aeronautical navigation systems. It supports aircraft navigation from the enroute to approach phases via the L1 and L5 frequencies (1575.42 and 1176.45 MHz). Although the ionosphere-free (IF) combination in the DFMC SBAS operation removes the first-order ionospheric delays in the pseudorange measurement, remaining terms including the satellite-clock offset errors and higher-order ionospheric (HOI) delays are still unaccounted for. The DFMC SBAS accuracy and integrity can be affected by the HOI effects, especially during severe ionospheric disturbances. In this work, we present the local DFMC SBAS corrections with and without the mitigation of HOI delays. We first estimate the HOI delay terms using the received pseudorange followed by separate satellite and receiver bias estimations based on the minimum sum-variance technique. The integrity terms can then be obtained. The performances of DFMC SBAS using the global navigation satellite system (GNSS) data including GPS, Galileo, and QZSS are evaluated using obtained GNSS data at stations in Thailand on the ionospheric quiet and disturbed days. The results show that with the HOI mitigation, the vertical positioning errors (VPE) on the quiet and disturbed days can be improved by 12% and 9%, whereas the vertical protection levels (VPL) are improved by 16% and 21%, respectively. In addition, we perform a preliminary assessment of DFMC SBAS based on the International Civil Aviation Organization (ICAO) requirements of two categories: Localizer Performance with Vertical guidance (LPV-200) and Category I precision approach (CAT-I) showing promising results.

Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing

In light of recent extreme weather events, it is imperative to explore innovative methodologies for promptly and accurately measuring various meteorological parameters. The high spatial and temporal variability in precipitation often surpasses the resolution capabilities of traditional rain gauge measurements and satellite estimation algorithms. Therefore, exploring alternative methods to capture this variability is crucial. Research on the correlation between signal attenuation and precipitation could offer valuable insights into these alternative approaches. This study investigates (a) the feasibility of the classification of precipitation rate using signal power measurements in cellular terminals and (b) the impact of atmospheric humidity as well as other meteorological parameters on the signal. Specifically, signal power data were collected remotely through a specialized Android application designed for this research. During the time of analysis, the power data were processed alongside meteorological parameters obtained from the meteorological station of the Physics Department at the University of Ioannina gathered over one semester. Having in mind the radio refractivity of the air as a fascinating concept affecting the way radio waves travel through the atmosphere, the processed results revealed a correlation with signal attenuation, while a correlation between the latter and absolute humidity was also observed. Moreover, a precipitation rate classification was attained with an overall accuracy exceeding 88%.

Linking optical data and nitrates in the Lower Mississippi River to enable satellite‐based monitoring of nutrient reduction goals

AbstractHypoxic zones and associated nitrate pollution from farms, cities and industrial facilities is driving declines in water quality that affect ecosystems, economies and human health in major rivers and coastal areas worldwide. In the Mississippi River, the United States Environmental Protection Agency set a goal of reducing nitrogen loading 20% by 2025, but estimating progress towards this goal is difficult because data from in‐stream gauges and laboratory samples are too sparse. Satellites have the potential to provide sufficient data across the Mississippi River, if a key methodological challenge can be overcome. Satellites provide data from visible light, but nitrates are only observable with ultraviolet light. We address this methodological challenge by using a two‐step surrogate modelling procedure to link optical data and nitrates in the Lower Mississippi River. First, we correlate in situ nitrate measurements to common water quality parameters, particularly turbidity and chlorophyll, using data from water sensors installed at Baton Rouge, Louisiana, USA, and a long‐term dataset from Louisiana State University. Second, we correlate these water quality data to satellite estimates of water quality parameters. We found a correlation between these water quality parameters and nitrate concentrations, as indicated by a coefficient of determination, when the relationship was viewed in nonlinear parameter space. The spatial extent of the correlation was tested with an upstream nitrate sensor 140 km north of the estimation location. These results provide proof of concept that we can develop models that use satellite data to provide large‐scale monitoring of nitrates across the Mississippi River Basin and other impaired rivers, globally.