Feed aggregator

Variance component adaptive estimation algorithm for coseismic slip distribution inversion using interferometric synthetic aperture radar data

Journal of Geodesy - Thu, 06/20/2024 - 00:00

Abstract

When conducting coseismic slip distribution inversion with interferometric synthetic aperture radar (InSAR) data, there is no universal method to objectively determine the appropriate size of InSAR data. Currently, little is also known about the computing efficiency of variance component estimation implemented in the inversion. Therefore, we develop a variance component adaptive estimation algorithm to determine the optimal sampling number of InSAR data for the slip distribution inversion. We derived more concise variation formulae than conventional simplified formulae for the variance component estimation. Based on multiple sampling data sets with different sampling numbers, the proposed algorithm determines the optimal sampling number by the changing behaviors of variance component estimates themselves. In three simulation cases, four evaluation indicators at low levels corresponding to the obtained optimal sampling number validate the feasibility and effectiveness of the proposed algorithm. Compared with the conventional slip distribution inversion strategy with the standard downsampling algorithm, the simulation cases and practical applications of five earthquakes suggest that the developed algorithm is more flexible and robust to yield appropriate size of InSAR data, thus provide a reasonable estimate of slip distribution. Computation time analyses indicate that the computational advantage of variation formulae is dependent of the ratio of the number of data to the number of fault patches and can be effectively suitable for cases with the ratio smaller than five, facilitating the rapid estimation of coseismic slip distribution inversion.

The B-spline mapping function (BMF): representing anisotropic troposphere delays by a single self-consistent functional model

Journal of Geodesy - Thu, 06/20/2024 - 00:00

Abstract

Troposphere’s asymmetry can introduce errors ranging from centimeters to decimeters at low elevation angles, which cannot be ignored in high-precision positioning technology and meteorological research. The traditional two-axis gradient model, which strongly relies on an open-sky environment of the receiver, exhibits misfits at low elevation angles due to their simplistic nature. In response, we propose a directional mapping function based on cyclic B-splines named B-spline mapping function (BMF). This model replaces the conventional approach, which is based on estimating Zenith Wet Delay and gradient parameters, by estimating only four parameters which enable a continuous characterization of the troposphere delay across any directions. A simulation test, based on a numerical weather model, was conducted to validate the superiority of cyclic B-spline functions in representing tropospheric asymmetry. Based on an extensive analysis, the performance of BMF was assessed within precise point positioning using data from 45 International GNSS Service stations across Europe and Africa. It is revealed that BMF improves the coordinate repeatability by approximately \(10\%\) horizontally and about \(5\%\) vertically. Such improvements are particularly pronounced under heavy rainfall conditions, where the improvement of 3-dimensional root mean square error reaches up to \(13\%\) .

Assessment of ZWD field predictions using the dynamic mode decomposition method

GPS Solutions - Thu, 06/20/2024 - 00:00

Abstract

The existing water vapor present in the lower regions of the atmosphere plays a pivotal role in both weather forecasting and the propagation of signals in satellite-based observations. This parameter introduces a delay in GNSS observations, known as tropospheric wet delay. Accurately predicting the spatial distribution of this parameter can significantly enhance our ability to forecast rainfall and floods. It can also improve satellite-based positioning techniques. One mathematical technique that proves invaluable in modeling various temporal aspects of a signal is the Dynamic Mode Decomposition (DMD) method. To construct the necessary snapshot matrix in the DMD method, we have opted to employ B-spline coefficient time series, computed by assimilating GNSS-derived Zenith et Delay (ZWD) values into the GPT3w model as a reference, with the Ensemble Kalman Filter (EnKF) method serving as the core of the assimilation process. In the DMD procedure, we have utilized a dataset spanning approximately 30 consecutive days, with a temporal resolution of roughly 5 min, to predict B-spline coefficients representing the spatial distribution of ZWD values for a 24-h period ahead. This dataset comprises ZWD values collected from 241 GNSS stations located in Germany and nearby regions throughout the year 2018. Comparative analysis has been performed, including 10 excluded GNSS stations from the assimilation and DMD procedure and 10 existing radiosonde stations within the study region. The results of the analysis step demonstrate the superiority of the proposed method over the ERA5, GFS, and GPT3w models, showcasing the Root Mean Squared Error (RMSE) of approximately 0.8 cm. This performance marks a substantial improvement, being approximately 51%, 57%, and 74% lower than each respective model. In the prediction phase, the proposed method outperforms the ERA5 and GFS models up to the 6-h and 24-h prediction windows in comparison with the GPT3w model.

A reduction in aerolized ammonium in the rural USA and increased ammonia deposition near emission hotspots

Nature Geoscience - Thu, 06/20/2024 - 00:00

Nature Geoscience, Published online: 20 June 2024; doi:10.1038/s41561-024-01456-8

Chemical regimes of atmospheric secondary inorganic aerosol formation and nitrogen deposition in rural areas of the USA shifted from ammonia-sensitive to ammonia-insensitive between 2011 and 2020, according to analyses of long-term observations. These regime shifts led to a reduction in ammonium in aerosols and increased ammonia deposition near emission hotspots.

Regime shift in secondary inorganic aerosol formation and nitrogen deposition in the rural United States

Nature Geoscience - Thu, 06/20/2024 - 00:00

Nature Geoscience, Published online: 20 June 2024; doi:10.1038/s41561-024-01455-9

Chemical regimes of atmospheric secondary inorganic aerosol formation in rural areas of the United States shifted from NH3-sensitive to NH3-insensitive between 2011 and 2020, according to analyses of long-term observational data on aerosol composition and gaseous precursors.

Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States

JGR–Atmospheres - Wed, 06/19/2024 - 19:47

Abstract

Agricultural soils containing nitrogen-rich fertilizers are a substantial source of reactive nitrogen to the atmosphere with potential to impact air quality. One form of reactive nitrogen, nitrogen oxides (NOx = NO + NO2), are a harmful air pollutant and form secondary pollutants, including particulate matter (PM) and ozone (O3). Soil nitrogen oxide emissions (SNOx) are heavily influenced by environmental conditions, however the understanding of the influence of environmental drivers on the behavior of SNOx is limited. Here, we implement a modified soil moisture-dependent SNOx parameterization into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and investigate the impact on regional air quality in the central U.S. Evaluating against TROPOspheric Monitoring Instrument (TROPOMI) column NO2 observations, WRF-Chem columns better capture the TROPOMI column magnitudes earlier in the growing season when using the updated SNOx parametrization, with modeled column bias improved to −1.1% over the most heavily fertilized regions. Evaluating against Environmental Protection Agency (EPA) surface NO2 observations, the relationship between surface NO2 and soil moisture is better represented in agriculturally-dominant regions when using the updated parameterization, with greatest surface NO2 concentrations at moderate soil moisture and lower concentrations at wetter or drier soil conditions. In simulations, these SNOx lead to increased O3 in select urban regions, with more than double the occurrences of O3 exceeding the EPA 8-hr O3 standard of 70 ppb.