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The states of coupling between clouds and surface or boundary layer have been investigated much more extensively for marine stratocumulus clouds than for continental low clouds, partly due to more complex thermodynamic structures over land. A manifestation is a lack of robust remote sensing methods to identify coupled and decoupled clouds over land. Following the idea for determining cloud coupling over the ocean, we have generalized the concept of coupling and decoupling to low clouds over land, based on potential temperature profiles. Furthermore, by using ample measurements from lidar and a suite of surface meteorological instruments at the U.S. Department of Energy's Atmospheric Radiation Measurement Program's Southern Great Plains site from 1998 to 2019, we have developed a method to simultaneously retrieve the planetary boundary layer (PBL) height (PBLH) and coupled states under cloudy conditions during the daytime. The new lidar-based method relies on the PBLH, the lifted condensation level, and the cloud base to diagnose the cloud coupling. The coupled states derived from this method are highly consistent with those derived from radiosondes. Retrieving the PBLH under cloudy conditions, which has been a persistent problem in lidar remote sensing, is resolved in this study. Our method can lead to high-quality retrievals of the PBLH under cloudy conditions and the determination of cloud coupling states. With the new method, we find that coupled clouds are sensitive to changes in the PBL with a strong diurnal cycle, whereas decoupled clouds and the PBL are weakly related. Since coupled and decoupled clouds have distinct features, our new method offers an advanced tool to separately investigate them in climate systems.

The increasing sophistication of wind turbine design and control generates a need for high-quality wind data. The relatively limited set of available measured wind data may be extended with computer generated data, for example, to make reliable statistical studies of energy production and mechanical loads. Here, a data-driven model for the generation of surrogate wind speeds is compared with two state-of-the-art time series models that can capture the probability distribution and the autocorrelation of the target wind data. The proposed model, based on the phase-randomised Fourier transform, can generate wind speed time series that possess the power spectral density of the target data and converge to their generally non-Gaussian probability distribution with an arbitrary, user-defined precision. The model performance is benchmarked in terms of probability distribution, power spectral density, autocorrelation, and nonstationarities such as the diurnal and seasonal variations of the target data. Comparisons show that the proposed model can outperform the selected models in reproducing the statistical descriptors of the input datasets and is able to capture the nonstationary diurnal and seasonal variations of the wind speed. This work was partly supported by the Research Foundation Flanders (FWO) [grant number 74213/K231719N].

Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR data, Gaussian process classification algorithm for SAR images is studied in this paper. In this paper, we introduce the basic principle of Gaussian process, briefly analyze the basic theory of classification and the characteristics of SAR images, provide the evaluation index system of image classification, and give the SAR classification model of Gaussian process. Taking Laplace approximation as an example, several classification algorithms are introduced directly. Based on the two classifications, we propose an indirect multipurpose classification method and a multifunction classification method for two-pair two-Gaussian processes. The SAR image algorithm based on the two categories is relatively simple and achieves certain results.

Accurate forecasting of runoff is necessary for water resources management. However, the runoff time series consists of complex nonlinear and non-stationary characteristics, which makes forecasting difficult. To contribute towards improved forecasting accuracy, a novel combination model based on complementary ensemble empirical mode decomposition (CEEMD) for runoff forecasting is proposed and applied in this paper. Firstly, the original runoff series is decomposed into a limited number of intrinsic mode functions (IMFs) and one residual based on CEEMD, which makes the runoff time series stationary. Then, approximate entropy is introduced to judge the complexity of each IMF and residual. According to the calculation results of approximate entropy, the high complexity components are predicted by Gaussian process regression (GPR), the medium complexity components are predicted by support vector machine (SVM), and the low complexity components are predicted by autoregressive integrated moving average model (ARIMA). The advantages of each forecasting model are used to forecast the appropriate components. In order to solve the problem that the forecasting performance of GPR and SVM is affected by their parameters, an improved fireworks algorithm (IFWA) is proposed to optimize the parameters of two models. Finally, the final forecasting result is obtained by adding the forecasted values of each component. The runoff data collected from the Manasi River, China is chosen as the research object. Compared with some state-of-the-art forecasting models, the comparison result curve between the forecasted value and actual value of runoff, the forecasting error, the histogram of the forecasting error distribution, the performance indicators and related statistical indicators show that the developed forecasting model has higher prediction accuracy and is able to reflect the change laws of runoff correctly.

The aim of this work is the evaluation of the influence of the rotation on the turbulence and on the entrainment within a buoyant plume. A series of laboratory experiments are performed in the rotating water tank of the Turlab in Torino (Italy) producing negatively buoyant plumes emitted from a finite circular source, with varying plume density and background rotations. Thanks to the particle image velocimetry technique, we obtain a detailed description of the velocity field within the plume. We present the results in term of mean velocity, turbulence and vorticity fields. The swirling strength analysis is also performed to identify the coherent structures. Finally, the velocity spectra are examined. We find that the rotation affects the dynamics of the plume increasing the turbulence and, in particular, the number of structures developed along the plume edges, which feeds the entrainment. The analysis of the mean velocity fields also allows us to quantify the turbulent entrainemnt of ambient fluid within the buoyant plume, and discuss the role of rotation in this process.

Recent study indicates that the non-instantaneous interaction of convection and circulation is essential for evolution of large-scale convective systems. It is incorporated into cumulus parameterization (CP) by relating cloud-base mass flux of shallow convection to a composite of subcloud moisture convergence in the past 6 h. Three pairs of 19-yr simulations with original and modified CP schemes are conducted in a tropical channel model to verify their ability to reproduce the Madden–Julian oscillation (MJO). More coherent tropical precipitation and improved eastward propagation signal are observed in the simulations with the modified CP schemes based on the non-instantaneous interaction. It is found that enhanced feedback between shallow convection and low-level moisture convergence results in amplified shallow convective heating, and then generates reinforced moisture convergence, which transports more moisture upward. The improved simulations of eastward propagation of the MJO are largely attributed to higher specific humidity below 600 hPa in the free troposphere to the east of maximum rainfall center, which is related to stronger boundary layer moisture convergence forced by shallow convection. Large-scale horizontal advection causes asymmetric moisture tendencies relative to rainfall center (positive to the east and negative to the west) and also gives rise to eastward propagation. The zonal advection, especially the advection of anomalous specific humidity by mean zonal wind, is found to dominate the difference of horizontal advection between each pair of simulations. The results indicate the vital importance of non-instantaneous feedback between shallow convection and moisture convergence for convection organization and the eastward MJO propagation.

El Niño and La Niña exhibit asymmetric evolution characteristics during their decay phases. The decay speed of El Niño is significantly greater than that of La Niña. This study systematically and quantitatively investigates the relative contributions of the equatorial western Pacific (WP) and central-eastern Pacific (CEP) wind stress anomalies to ENSO decay and its asymmetry through data analysis, numerical experiments, and dynamic and thermodynamic diagnoses. It is demonstrated that the sea surface temperature anomalies (SSTAs) forced by the wind stress anomalies in the equatorial CEP play a dominant role in ENSO decay and contribute to ENSO decay asymmetry, while the forcing by the equatorial WP wind stress anomalies has a small contribution. Diagnoses of the oceanic mixed layer heat budget indicate that anomalous zonal advection term and vertical advection term forced by the wind stress anomalies in the equatorial CEP are the most important dynamic terms contributed to ENSO decay. Both terms in El Niño decay phase are much larger than in La Niña decay phase, resulting in a larger decay speed in El Niño than in La Niña. The contributions of these two terms do not depend on the equatorial WP wind field, confirming that the equatorial WP wind stress anomalies do not act as a pivotal part in ENSO asymmetric decay. Moreover, it is demonstrated that within the equatorial CEP, dominant contribution comes from the wind stress anomalies in the equatorial central Pacific, in which those in the equatorial southern central Pacific play a major role. Significance Statement Previous studies proposed why wind fields in the equatorial western Pacific (WP) or central-eastern Pacific (CEP) are asymmetric and how the asymmetric wind fields affect ENSO decay and decay asymmetry. By using an oceanic general circulation model, we quantitatively estimate the relative contributions of the wind stress anomalies over the equatorial WP and CEP. It is demonstrated that the wind stress anomalies over the equatorial CEP and the associated ocean response play a dominant role in the asymmetric decay. Additionally, it is further illustrated the predominant role comes from the wind stress anomalies in the equatorial southern central Pacific within the equatorial CEP. Our study provides a physical explanation on the ENSO decay and its asymmetry.

Understanding the systematic characteristics of tropical cyclones (TCs) represented in global climate models (GCMs) is important for reliable climate change impact assessments. The atmospheric GCM (AGCM) and ocean wave models were coupled by incorporating the wave-dependent momentum flux. Systematic impacts of wave-dependent momentum flux on TC characteristics were estimated by analyzing 100 historical TCs that occurred in the western North Pacific Ocean. Wave-dependent momentum flux parameterization considering wind and wave direction misalignment was used for assessing the wave–atmosphere interaction. The larger the wave age and misalignment are, the larger the drag coefficient is. The drag coefficient at the left-hand side of the TC was enhanced by the wave condition. It was found that the wave-dependent momentum flux did not have any impact on peak TC intensity. On the other hand, the wave-dependent momentum flux showed a significant impact on TC development during the early development stage. Although systematic differences in TC intensity at most developed stages were not detected, systematic differences in TC tracks between experiments were observed. The TC tracks of the wave-coupled AGCM tend to pass in a relatively eastward direction in comparison with those from the uncoupled AGCM. This is because the wave-dependent momentum flux in the coupled AGCM altered the environmental steering flow and the smaller beta effect of smaller TC at the early developing stage. Systematic differences in TC tracks have significant impacts on climate change assessments, such as extreme sea level changes in coastal regions due to climate change.

Drift models are commonly used to study the transport of early life stages of fish and other marine organisms. Various approaches may be applied to examine the distribution and variability of ocean trajectory pathways. In the present study, we compare results using passive Eulerian tracers and Lagrangian float trajectories that are embedded in numerical models. We supplement this analysis by applying an offline model for drift computations. The contrasts in the results from the various configurations are mainly due to differences in drift depth. Simulations were performed using horizontal resolutions of 4 and 0.8 km. The higher-resolution experiment gives somewhat more realistic results for the drift time from Lofoten to the Tromsøflaket bank at the southwestern entrance of the Barents Sea. Furthermore, differences in results between simulation years are much larger than the differences that arise from the choice of model configuration. Climate variability at high latitudes on a multi-decadal time scale is dominated by large interannual variability superimposed on an underlying moderate warming trend. We conclude that a properly configured offline drift model using hourly or 2-hourly results from a simulation with a horizontal resolution of 1 km or finer is the best approach for investigations of trajectory pathways. The flexibility of an offline drift model is also highly advantageous in biological contexts, as it easily allows for a variety of ways in which behavioural characteristics can be parameterized, including descriptions that are defined after the ocean circulation simulation has been executed.

Jupiter’s atmosphere is one of the most turbulent places in the solar system. Whereas observations of lightning and thunderstorms point to moist convection as a small-scale energy source for Jupiter’s large-scale vortices and zonal jets, this has never been demonstrated due to the coarse resolution of pre-Juno measurements. The Juno spacecraft discovered that Jovian high latitudes host a cluster of large cyclones with diameter of around 5,000 km, each associated with intermediate- (roughly between 500 and 1,600 km) and smaller-scale vortices and filaments of around 100 km. Here, we analyse infrared images from Juno with a high resolution of 10 km. We unveil a dynamical regime associated with a significant energy source of convective origin that peaks at 100 km scales and in which energy gets subsequently transferred upscale to the large circumpolar and polar cyclones. Although this energy route has never been observed on another planet, it is surprisingly consistent with idealized studies of rapidly rotating Rayleigh–Bénard convection, lending theoretical support to our analyses. This energy route is expected to enhance the heat transfer from Jupiter’s hot interior to its troposphere and may also be relevant to the Earth’s atmosphere, helping us better understand the dynamics of our own planet.

THE PURPOSE. Determine the impact of the meteorological state of the atmosphere on the efficiency of the functioning of solar thermal and power plants. Modeling the molecular absorption of solar radiation by the atmosphere. Modeling the optical characteristics of the gaseous components of the atmosphere, atmospheric aerosol and clouds.METHODS. A method for numerical modeling of incoming solar radiation fluxes their functioning to determine the efficiency of solar thermal and power plants. The solar fluxes are calculated by stacking layers in a multi-stream approximation, taking into account the multi-tiered cloud cover and the probability of overlapping the sky with clouds. The absorption of radiation by the gaseous phase of the atmosphere is taken into account by the method of equivalent mass in an inhomogeneous atmosphere. The optical characteristics of the dispersed phase of the atmosphere are calculated using the Mie theory.RESULTS. An electronic database has been created on the optical characteristics of the gaseous components of the atmosphere, the optical characteristics of atmospheric aerosol and clouds. The effect of anthropogenic impact on the flux of solar radiation falling on the underlying surface is taken into account. The developed modeling takes into account the effect of humidity on the optical characteristics of atmospheric aerosol and its multicomponent composition, depending on the location of the power plant.CONCLUSION. The information necessary for numerical modeling of meteorological effects on the functioning of solar thermal and power plants is generalized. When calculating solar radiation fluxes, direct illumination of the light-receiving surface by solar radiation, scattered radiation by atmospheric aerosol and clouds are taken into account.

The goal of this study was to investigate the performance of a spectral-transformation wave retrieval algorithm and confirm the accuracy of wave retrieval from C-band Chinese Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) images. More than 200 GF-3 SAR images of the coastal China Sea and the Japan Sea for dates from January to July 2020 were acquired in the Quad-Polarization Strip (QPS) mode. The images had a swath of 30 km and a spatial resolution of 8 m pixel size. They were processed to retrieve Significant Wave Height (SWH), which is simulated from a numerical wave model called Simulating WAves Nearshore (SWAN). The first-guess spectrum is essential to the accuracy of Synthetic Aperture Radar (SAR) wave spectrum retrieval. Therefore, we proposed a wave retrieval scheme combining the theocratic-based Max Planck Institute Algorithm (MPI), a Semi-Parametric Retrieval Algorithm (SPRA), and the Parameterized First-guess Spectrum Method (PFSM), in which a full wave-number spectrum and a non-empirical ocean spectrum proposed by Elfouhaily are applied. The PFSM can be driven using the wind speed without calculating the dominant wave phase speed. Wind speeds were retrieved using a Vertical-Vertical (VV) polarized geophysical model function C-SARMOD2. The proposed algorithm was implemented for all collected SAR images. A comparison of SAR-derived wind speeds with European Center for Medium-Range Weather Forecasts (ECMWF) ERA-5 data showed a 1.95 m/s Root-Mean-Squared Error (RMSE). The comparison of retrieved SWH with SWAN-simulated results demonstrated a 0.47 m RMSE, which is less than the 0.68 m RMSE of SWH when using the PFSM algorithm.

Global differences of temperature and buoyancy flux at the ocean surface are responsible for small-scale convection at high latitudes, global overturning, and the top-to-bottom density difference in the oceans. With planetary rotation the convection also contributes to the large-scale horizontal, geostrophic circulation, and it crucially involves a 3D linkage between the geostrophic circulation and vertical overturning. The governing dynamics of such a surface-forced convective flow are fundamentally different from Rayleigh–Bénard convection, and the role of buoyancy forcing in the oceans is poorly understood. Geostrophic balance adds to the constraints on transport in horizontal convection, as illustrated by experiments, theoretical scaling, and turbulence-resolving simulations for closed (mid-latitude) basins and an annulus or reentrant zonal (circumpolar) channel. In these geometries, buoyancy drives either horizontal mid-latitude gyre recirculations or a strong Antarctic Circumpolar Current, respectively, in addition to overturning. At large Rayleigh numbers the release of available potential energy by convection leads to turbulent mixing with a mixing efficiency approaching unity. Turbulence-resolving models are also revealing the relative roles of wind stress and buoyancy when there is mixed forcing, and in future work they need to include the effects of turbulent mixing due to energy input from tides.

Energetic particle precipitation associated with pulsating aurora (PsA) can reach down to lower mesospheric altitudes and deplete ozone. It is well documented that pulsating aurora is a common phenomenon during substorm recovery phases. This indicates that using magnetic indices to model the chemistry induced by PsA electrons could underestimate the energy deposition in the atmosphere. Integrating satellite measurements of precipitating electrons in models is considered to be an alternative way to account for such an underestimation. One way to do this is to test and validate the existing ion chemistry models using integrated measurements from satellite and ground-based observations. By using satellite measurements, an average or typical spectrum of PsA electrons can be constructed and used as an input in models to study the effects of the energetic electrons in the atmosphere. In this study, we compare electron densities from the EISCAT (European Incoherent Scatter scientific radar system) radars with auroral ion chemistry and the energetics model by using pulsating aurora spectra derived from the Polar Operational Environmental Satellite (POES) as an energy input for the model. We found a good agreement between the model and EISCAT electron densities in the region dominated by patchy pulsating aurora. However, the magnitude of the observed electron densities suggests a significant difference in the flux of precipitating electrons for different pulsating aurora types (structures) observed.

Laboratory measurements at the AIDA cloud chamber and airborne in-situ observations suggest that the homogeneous freezing thresholds at low temperatures are possibly higher than expected from the so-called “Koop-line”. This finding is of importance, because the ice onset relative humidity affects the cirrus cloud coverage and, at the very low temperatures of the tropical tropopause layer, together with the number of ice crystals also the transport of water vapor into the stratosphere. Both, the appearance of cirrus clouds and the amount of stratospheric water feed back to the radiative budget of the atmosphere. In order to explore the enhanced ice onset humidities, we re-examine the entire homogeneous ice nucleation process, ice onset and nucleated crystal numbers, by means of a two-moment microphysics scheme embedded in the trajectory based model (CLaMS-Ice) as follows: the well-understood and described theoretical framework of homogeneous ice nucleation yet includes certain formulations of the water activity of the freezing aerosol particles and the saturation vapor pressure of water with respect to liquid water. However, different formulations are available for both parameters. Here, we present extensive sensitivity simulations testing the influence of three different formulations for the water activity and four for the water saturation on homogeneous ice nucleation. We found that the number of nucleated ice crystals is almost independent of these formulations but is instead sensitive to the size distribution of the freezing aerosol particles. The ice onset humidities, also depending on the particle size, are however significantly affected by the choices of the water activity and water saturation, in particular at cold temperatures  205 K. From the CLaMS-Ice sensitivity simulations, we here provide combinations of water saturation and water activity formulations suitable to reproduce the new, enhanced freezing line.

Tropical ideals are a class of ideals in the tropical polynomial semiring that combinatorially abstracts the possible collections of supports of all polynomials in an ideal over a field. We study zero-dimensional tropical ideals I with Boolean coefficients in which all underlying matroids are paving matroids, or equivalently, in which all polynomials of minimal support have support of size deg(I) or deg(I)+1 -- we call them paving tropical ideals. We show that paving tropical ideals of degree d+1 are in bijection with $\mathbb Z^n$-invariant d-partitions of $\mathbb Z^n$. This implies that zero-dimensional tropical ideals of degree 3 with Boolean coefficients are in bijection with $\mathbb Z^n$-invariant 2-partitions of quotient groups of the form $\mathbb Z^n/L$. We provide several applications of these techniques, including a construction of uncountably many zero-dimensional degree-3 tropical ideals in one variable with Boolean coefficients, and new examples of non-realizable zero-dimensional tropical ideals., Comment: 13 pages

Marine X-band radar has been suggested to be capable of monitoring significant wave heights in both offshore and open sea areas. In contrast to studies on offshore radar, significant wave height estimations from coastal radar images, which exhibit complicated radar backscattering features, have received little attention. This study proposes a method for retrieving the significant wave height from coastal areas that are often influenced by nononshore winds. The square root of the signal-to-noise ratio in radar images has been widely applied to estimate the significant wave height. However, nononshore wind cases show a poor correlation between the square root of the signal-to-noise ratio and the in situ significant wave height. In addition, the spectral shapes from radar images in nononshore wind cases are very different from those in onshore wind cases. To improve the significant wave height estimations from coastal radar images, we implement an artificial neural network algorithm. After training and testing the algorithm, we confirm that the estimated significant wave heights are more reliable for both onshore and nononshore wind cases if the square root of the signal-to-noise ratio, power from nearshore radar subimages, and in situ wind components are included in the input layer of the neural network.

Specified dynamics schemes are ubiquitous modeling tools for isolating the roles of dynamics and transport on chemical weather and climate. They typically constrain the circulation of a chemistry–climate model to the circulation in a reanalysis product through linear relaxation. However, recent studies suggest that these schemes create a divergence in chemical climate and the meridional circulation between models and do not accurately reproduce trends in the circulation. In this study we perform a systematic assessment of the specified dynamics scheme in the Community Earth System Model version 2, Whole Atmosphere Community Climate Model version 6 (CESM2 (WACCM6)), which proactively nudges the circulation toward the reference meteorology. Specified dynamics experiments are performed over a wide range of nudging timescales and reference meteorology frequencies, with the model's circulation nudged to its own free-running output – a clean test of the specified dynamics scheme. Errors in the circulation scale robustly and inversely with meteorology frequency and have little dependence on the nudging timescale. However, the circulation strength and errors in tracers, tracer transport, and convective mass flux scale robustly and inversely with the nudging timescale. A 12 to 24 h nudging timescale at the highest possible reference meteorology frequency minimizes errors in tracers, clouds, and the circulation, even up to the practical limit of one reference meteorology update every time step. The residual circulation and eddy mixing integrate tracer errors and accumulate them at the end of their characteristic transport pathways, leading to elevated error in the upper troposphere and lower stratosphere and in the polar stratosphere. Even in the most ideal case, there are non-negligible errors in tracers introduced by the nudging scheme. Future development of more sophisticated nudging schemes may be necessary for further progress.

Continuity, momentum, and energy equations have been solved to predict the natural convection heat transfer from an upward, downward, and sideward solid or hollow hemisphere either suspended in air...

Wind stress, wind waves, and turbulence are essential variables and play a critical role in regulating a series of physical and biogeochemical processes in large shallow lakes. However, the parameterization of these variables and simulation of their interactions in large shallow lakes have not been strictly evaluated owing to a lack of field observations of lake hydrodynamic processes. To address this problem, two process-based field observations were conducted to record the development of summer and winter wind-driven currents in Lake Taihu, a large shallow lake in China. Using these observations and numerical experiments, a Wave and Current Coupled Model (WCCM) is developed by rebuilding the wind drag coefficient expression, introducing wave-induced radiation stress, and adopting a simple turbulence scheme to simulate wind-driven currents in Lake Taihu. The results show that the WCCM can accurately simulate the upwelling process driven by wind-driven currents during the field observations. A comparison with a reference model indicates a 42.9 % increase of the WCCM-simulated current speed, which is mainly attributed to the new wind drag coefficient expression. The WCCM-simulated current direction and field are also improved owing to the introduction of wave-induced radiation stress. The use of the simple turbulent scheme in the WCCM improves the efficiency of the upwelling process simulation. The WCCM thus provides a sound basis for simulating shallow lake ecosystems.

Coherent microwave radar, which is a rapidly emerging tool to sense waves, usually utilizes the orbit velocities extracted from either temporal or spatial radar echoes. The distribution of energy in the wavenumber-frequency spectrum is changed by some nonlinear features, and correspondingly, this kind of method always provides a significantly overestimated wave period. To address this problem, a novel inversion algorithm, which utilizes the spatial and temporal returns collected with a recently developed coherent S-band radar, is proposed for retrieving wave parameters. A 2-D Fourier transform is applied to the spatial-temporal matrix of velocities to estimate the wavenumber-frequency spectrum. Then the wavenumber-frequency spectrum is integrated over the wavenumber domain to obtain the 1-D velocity spectrum. And the wave height spectrum is estimated from the 1-D velocity spectrum by the direct transform relationship between the 1-D velocity spectrum and the wave height spectrum. Later, significant wave heights and mean wave periods can be derived by the zeroth and first moments of the wave height spectra, respectively. The algorithm is validated using simulation and real data. An approximately four-day dataset collected with a shore-based coherent S-band radar deployed at Beishuang island during a typhoon period is reanalyzed and used to retrieve significant wave heights and mean wave periods. Comparisons between the measurements of radar and wave buoy are conducted, and radar-derived and buoy-measured wave parameters are in a reasonable agreement with a coherent coefficient over 0.9. The results indicate that the proposed method is effective for wave measurements using coherent S-band radar.

The landfall of Hurricane Michael (2018) at category-5 intensity occurred after rapid intensification (RI) spanning much of the storm’s lifetime. Four Hurricane Hunter aircraft missions observed the RI period with tail Doppler radar (TDR). Data from each of the 14 aircraft passes through the storm were quality controlled via a combination of interactive and machine-learning techniques. TDR data from each pass were synthesized using the Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI) variational wind retrieval technique to yield three-dimensional kinematic fields of the storm to examine inner-core processes during RI. Vorticity and angular momentum increased and concentrated in the eyewall region. A vorticity budget analysis indicates that the tendencies became more axisymmetric over time. In this study, we focus in particular on how the eyewall vorticity tower builds vertically into the upper levels. Horizontal vorticity associated with the vertical gradient of tangential wind was tilted into the vertical by the eyewall updraft to yield a positive vertical vorticity tendency inward atop the existing vorticity tower, which is further developed locally upward and outward along the sloped eyewall through advection and stretching. Observed maintenance of thermal wind balance from a thermodynamic retrieval shows evidence of a strengthening warm core, which aided in lowering surface pressure and further contributed to the efficient intensification in the latter stages of this RI event.

The poleward expansion of the Hadley cells is one of the most robust modeled responses to increasing greenhouse gas concentrations. There are many proposed mechanisms for expansion, and most are consistent with modeled changes in thermodynamics, dynamics, and clouds. The adjustment of the eddies and the mean flow to greenhouse gas forcings, and to one another, complicates any effort toward a deeper understanding. Here we modify the Gray Radiation and Moist Aquaplanet (GRANDMA) model to uncouple the eddy and mean flow responses to forcings. When eddy forcings are held constant, the purely axisymmetric response of the Hadley cell to a greenhouse gas–like forcing is an intensification and poleward tilting of the cell with height in response to an axisymmetric increase in angular momentum in the subtropics. The angular momentum increase drastically alters the circulation response compared to axisymmetric theories, which by nature neglect this adjustment. Model simulations and an eddy diffusivity framework demonstrate that the axisymmetric increase in subtropical angular momentum—the direct manifestation of the radiative–convective equilibrium temperature response—drives a poleward shift of the eddy stresses which leads to Hadley cell expansion. Prescribing the eddy response to the greenhouse gas–like forcing shows that eddies damp, rather than drive, changes in angular momentum, moist static energy transport, and momentum transport. Expansion is not driven by changes in baroclinic instability, as would otherwise be diagnosed from the fully coupled simulation. These modeling results caution any assessment of mechanisms for circulation change within the fully coupled wave–mean flow system.

The proliferation of synthetic aperture radar (SAR) imagery, its accessibility in open platforms like Google Earth Engine, and the development of automated classification and geophysical information extraction algorithms have prompted the need for understanding the role of incidence angle (IA) on radar scattering mechanism, backscatter intensity, and classification accuracy. This letter demonstrates the dependence of image texture parameters on SAR IA, using Arctic landfast sea ice samples extracted from C-band frequency Sentinel-1 SAR scenes collected during the winter period. Gray-level cooccurrence matrix (GLCM) derived texture parameters, and occurrence texture parameters, derived from undeformed first-year sea ice and multi-year sea ice, which are dominated by surface and volume scattering mechanisms, respectively, were analyzed. All GLCM texture parameters were found to be dependent on IA, highlighting the need for consideration of angular dependence in texture parameters, particularly in the development of image classification and inversion algorithms utilizing them. Occurrence texture parameters showed negligible influence by IA; however, feature discrimination capability was also lost. Some GLCM parameters had similar angular dependencies for both sea ice types, suggesting that, in some cases, global image normalization approaches for texture may be applied to account for the IA effect.

The initiation of the Madden–Julian oscillation over the Indian Ocean is examined through the use of a moisture budget that applies a version of the weak temperature gradient (WTG) approximation that does not neglect dry adiabatic vertical motions. Examination of this budget in ERA-Interim reveals that horizontal moisture advection and vertical advection by dry adiabatic lifting govern the moistening of the troposphere for both primary and successive MJO initiation events. For both types of initiation events, horizontal moisture advection peaks prior to the maximum moisture tendency, while dry adiabatic lifting peaks after the maximum moisture tendency. Once convection initiates, moisture is maintained by anomalous radiative and adiabatic lifting. The dry adiabatic lifting during successive MJO initiation is attributed to the return of the circumnavigating circulation from a previous MJO event, while in primary events the planetary-scale circulation appears to originate over South America. Examination of the same budget with data from the DYNAMO northern sounding array shows that adiabatic lifting contributes significantly to MJO maintenance, with a contribution that is comparable to that of surface heat fluxes. However, results from the DYNAMO data disagree with those from ERA-Interim over the importance of adiabatic lifting to the moistening of the troposphere prior to the onset of convection. In spite of these differences, the results from the two datasets show that small departures from WTG balance in the form of dry adiabatic motions cannot be neglected when considering MJO convective onset.

To mitigate uncertainties in wind resource assessments and to improve the estimation of energy production of a wind project, this work uses a decision tree machine learning model to assess the effectiveness of hub-height wind speed, rotor-equivalent wind speed, and lapse rate as variables in power prediction. Atmospheric data is used to train regression trees and correlate the power outputs to wind profiles and meteorological characteristics to be able to predict power responses according to physical patterns. The decision tree model was trained for four vertical wind profile classifications to showcase the need for multiple calculations of wind speed at various levels of the rotor layer. Results indicate that when compared to traditional power curve methods, the decision tree combining rotor-equivalent wind speed and lapse rate improves prediction accuracy by 22% for the given data-set, while also proving to be the most effective method in power prediction for all classified vertical wind profile types. Models incorporating lapse rate into predictions performed better than those without it, showing the importance of considering atmospheric criteria in wind power prediction analyses.

This study formulates three-dimensional (3D) residual flow, treating both stationary and transient waves. The zonal and meridional momentum equations contain four terms: the geostrophic wind tendency, Coriolis force for the residual horizontal flow, product of the geostrophic wind and potential vorticity other than the constant planetary vorticity, and friction. The thermodynamic equation contains three terms: the potential temperature tendency, advection of the basic potential temperature by the residual vertical flow, and diabatic heating. The zonal mean of the 3D residual flow equals the time mean of the residual flow of the transformed Eulerian-mean equations. The new residual flow is the sum of that derived by Plumb for transient waves and the quadratic terms of the time-mean fields, which correspond approximately to the Stokes correction due to stationary waves. The 3D residual flow and momentum equations are symmetric in the zonal and meridional directions, in contrast with those formulated by Kinoshita et al., which treat the time-mean zonal-mean zonal wind as the basic wind. The newly derived formulas are applied to the climatology of the 3D structure of the deep branch of the Brewer–Dobson circulation. In the Northern Hemisphere in December–February, the residual flows are directed inward toward the polar vortex strongly over east Siberia, where the downward flow is maximized, and weakly over the Atlantic; meanwhile, they are directed outward from the vortex over North America and Europe. A longitudinal dependence of the poleward flow is also observed in the Southern Hemisphere in June–August.

Time series forecasting plays a key role in many fields such as business, energy or environment. Traditionally, statistical or machine learning models for time series forecasting are trained with the historical values of the series to be forecast. Unfortunately, some time series are too short to suitably train a model. Motivated by this fact, this paper explores the use of data available in a pool or collection of time series to train a model that predicts an individual series. Concretely, we train a generalized regression neural network with the examples drawn from the historical values of a pool of series and then use the model to forecast individual series. In this sense several approaches are proposed, including to draw the examples from a pool of series related to the series to be forecast or the training of several models with mutually exclusive series and the combination of their forecasts. Experimental results in terms of forecasting accuracy using generalized regression neural networks are promising. Furthermore, the proposed approaches allow to forecast series that are too short to build a traditional generalized regression neural network model.

An Argo simulation system is used to provide synthetic Lagrangian trajectories based on the Estimating the Circulation and Climate of the Ocean Model, phase II (ECCO2). In combination with ambient Eulerian velocity at the reference layer (1000 m) from the model, quantitative metrics of the Lagrangian trajectory–derived velocities are computed. The result indicates that the biases induced by the derivation algorithm are strongly linked with ocean dynamics. In low latitudes, Ekman currents and vertically sheared geostrophic currents influence both the magnitude and the direction of the derivation velocity vectors. The maximal shear-induced biases exist near the equator with the amplitudes reaching up to about 1.2 cm s−1. The angles of the shear biases are pronounced in the low-latitude oceans, ranging from −8° to 8°. Specifically, the study shows an overlooked bias from the float drifting motions that mainly occurs in the western boundary current and Antarctic Circumpolar Current (ACC) regions. In these regions, a recently reported horizontal acceleration measured via Lagrangian floats is significantly associated with the strong eddy–jet interactions. The acceleration could induce an overestimation of Eulerian current velocity magnitudes. For the common Argo floats with a 9-day float parking period, the derivation speed biases induced by velocity acceleration would be as large as 3 cm s−1, approximately 12% of the ambient velocity. It might have implications to map the mean middepth ocean currents from Argo trajectories, as well as to understand the dynamics of eddy–jet interactions in the ocean.

This study investigates precipitation amounts and apparent heat sources, which are coupled with equatorial Kelvin waves and equatorial Rossby waves, using TRMM PR level 2 data products. The synoptic structures of wave disturbances are also studied using the ERA5 dataset. We define the wave phase of equatorial waves based on FFT-filtered brightness temperature and conduct composite analyses. Rossby waves show a vertically upright structure and their upright vortices induce large-amplitude column water vapor (CWV) anomalies. Precipitation activity is almost in phase with CWV, and thus is consistent with a moisture mode. Kelvin waves, on the other hand, indicate a nearly quadrature phase relationship between temperature and vertical velocity, like gravity wave structure. Specific humidity develops from near the surface to the middle troposphere as the Kelvin wave progresses. A clear negative CWV anomaly also does not exist despite the existence of negative precipitation anomalies. Convective activity corresponds well with its tilting structure of moisture and modulates the phase relationship between temperature and vertical motion. For both wave cases, apparent heat sources can amplify available potential energy despite the difference of coupling mechanisms of these two waves; precipitation is driven by CWV fluctuation for the Rossby wave case, and by buoyancy-based fluctuations for the Kelvin wave case. These can be observational evidence of actual coupling processes that is comparable to previous idealized studies. Significance Statement A coupling mechanism between equatorial waves and convective activity is a significant issue in tropical meteorology. While many previous idealized studies suggested some instability mechanisms, their true roles are not yet clear because detailed precipitation characteristics are not well investigated. We aim to quantify precipitation and synoptic-scale wave disturbances, and compare equatorial Rossby waves and equatorial Kelvin waves, which should have different instability coupling modes between each other, in order to shed light on a convectively coupling mechanism. We found that precipitation is actually driven by column moisture in Rossby waves and by dynamical fluctuation in Kelvin waves. Despite these competing mechanisms, similar top-heavy heating can maintain convectively coupled disturbances. Our observational results will support and improve theoretical studies.

The conversion of electricity to chemical energy is a key technology for absorbing the fluctuations of electricity and utilizing solar and wind-powered electricities as chemical fuels.

Noise radars can be understood in terms of a correlation coefficient which characterizes their detection performance. Although most results in the literature are stated in terms of the signal-to-noise ratio (SNR), we show that it is possible to carry out performance prediction in terms of the correlation coefficient. To this end, we derive the range dependence of the correlation coefficient. We then combine our result with a previously-derived expression for the receiver operating characteristic (ROC) curve of a coherent noise radar, showing that we can obtain ROC curves for varying ranges. A comparison with corresponding results for a conventional radar employing coherent integration shows that our results are sensible. The aim of our work is to show that the correlation coefficient is a viable adjunct to SNR in understanding radar performance., Comment: 5 pages, 3 figures; accepted for publication in IEEE Access

Ocean fronts can cause the accumulation of nutrients and affect the propagation of underwater sound, so high-precision ocean front detection is of great significance to the marine fishery and national defense fields. However, the current ocean front detection methods either have low detection accuracy or most can only detect the occurrence of ocean front by binary classification, rarely considering the differences of the characteristics of multiple ocean fronts in different sea areas. In order to solve the above problems, we propose a semantic segmentation network called location and seasonality enhanced network (LSENet) for multi-class ocean fronts detection at pixel level. In this network, we first design a channel supervision unit structure, which integrates the seasonal characteristics of the ocean front itself and the contextual information to improve the detection accuracy. We also introduce a location attention mechanism to adaptively assign attention weights to the fronts according to their frequently occurred sea area, which can further improve the accuracy of multi-class ocean front detection. Compared with other semantic segmentation methods and current representative ocean front detection method, the experimental results demonstrate convincingly that our method is more effective.

The Cross-track Infrared Sounder (CrIS) is a Michelson-type Fourier Transform Spectrometer. The CrIS flight module 2 instrument was launched into orbit on November 18, 2017, onboard the NOAA-20 satellite as part of the United States (US) Joint Polar Satellite System (JPSS). The CrIS instrument measures the top-of-atmosphere upwelling spectral radiance in the thermal infrared (IR) spectrum. These measurements provide critical information for medium-range weather forecasting, and the retrieval of atmospheric profiles of temperature, water vapor, and other trace gases. The instrument noise equivalent radiance differential (NEdN) estimates are used by the weather forecasting systems, the trace gas atmospheric retrieval algorithms, and for trending the health and stability of the instrument over time. The current operational NEdN estimate is calculated using instrument observations from the deep space (DS) view and the internal calibration target (ICT). Two alternative methodologies are described here based on the principal component analysis (PCA) of an ensemble of calibrated Earth scene (ES) spectra. The NEdN calculation methods show that the instrument meets the specifications with a margin for all 27 detectors with an exception of one midwave IR (MWIR) field-of-view (FOV) 9, which is borderline. The PCA analysis shows that warmer ES spectra have higher noise, known as scene shot, for the short-wave IR (SWIR) band. Using the PCA analysis, the NEdN for the long-wave IR FOV 5 is 30% higher than the NEdN calculated by the operational algorithm. Correlated noise is also found due to the effect of the instrument self-apodization correction.

In the present work, the dust pollutants from a fully mechanized mining face under different situation were investigated for a typical coal roadway. The Euler-Lagrange model was adopted for dust movement process. After the validation, the varieties of the air curtain, the spatial and temporal distribution of dust pollutants under various conditions were analyzed. The present study revealed that the air curtain jet flow develops with time and space and significantly influences the surrounding flow field. The dust concentration was controlled for the front part of the roadway. The higher initial supply air velocities (

The distribution of meteorological elements has always been an important factor in determining the horizontal and vertical distribution of particles in the atmosphere. To study the effect of meteorological elements on the three-dimensional distribution structure of particles, mobile vehicle lidar and fixed-location observations were collected in the western Guangdong–Hong Kong–Macao Greater Bay Area of China during September and October in 2019 and 2020. Vertical aerosol extinction coefficient, depolarization ratio, and wind and temperature profiles were measured using a micro pulse lidar, a Raman scattering lidar, and a Doppler wind profile lidar installed on a mobile monitoring vehicle. The mechanism of how wind and temperature in the boundary layer affects the horizontal and vertical distribution of particles was analysed. The results show that particles were mostly distributed in downstream areas on days with moderate wind speed in the boundary layer, whereas they were distributed homogeneously on days with weaker wind. There are three typical types of vertical distribution of particles in the western Guangdong–Hong Kong–Macao Greater Bay Area (GBA): surface single layer, elevated single layer, and double layer. Analysis of wind profiles and Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) backward trajectory reveals different sources of particles for the three types. Particles concentrating near the temperature inversion and multiple inversions could cause more than one peak in the extinction coefficient profile. There were two mechanisms affecting the distribution of particulate matter in the upper and lower boundary layers. Based on this observational study, a general model of meteorological elements affecting the vertical distribution of urban particulate matter is proposed.

One of the challenges in ocean surface current retrieval from synthetic aperture radar (SAR) data is the estimation and removal of the wave-induced Doppler centroid (DC). This article demonstrates empirically the relationship between the dc derived from spaceborne X-band InSAR data and the ocean surface wind and waves. In this study, we analyzed over 300 TanDEM-X image pairs. It is found that the general characteristics of the estimated dc follow the theoretically expected variation with incidence angle, wind speed, and wind direction. An empirical geophysical model function (GMF) is fit to the estimated dc and compared to existing models and previous experiments. Our GMF is in good agreement (within 0.2 m/s) with other models and data sets. It is found that the wind-induced Doppler velocity contributes to the total Doppler velocity with about 15% of the radial wind speed. This is much larger than the sum of the contributions from the Bragg waves (~0.2 m/s) and the wind-induced drift current (~3% of wind speed). This indicates a significant (dominant) contribution of the long wind waves to the SAR dc. Moreover, analysis of dual-polarized data shows that the backscatter polarization ratio (PR=σ⁰VV/σ⁰HH) and the dc polarization difference (PD=|dcVV|-|dcHH|) are systematically larger than 1 and smaller than 0 Hz, respectively, and both increase in magnitude with incidence angle. The estimated PR and PD are compared to other theoretical and empirical models. The Bragg scattering theory-based (pure Bragg and composite surface) models overestimate both PR and PD, suggesting that other scattering mechanisms, e.g., wave breaking, are involved. In general, it is found that empirical models are more consistent with both backscatter and Doppler data than theory-based models. This motivates a further improvement of SAR dc GMFs.

High-resolution synthetic aperture radar (SAR) satellites have become an important way to observe the earth. However, the geometric positioning accuracy of SAR satellite images across different times and spaces is an important factor affecting the realization of global remote sensing applications. In this study, a multimode hybrid geometric calibration method that incorporates an atmospheric propagation delay correction and that can be used to detect systematic errors affecting the geometric positioning accuracy of SAR satellites is described. The spatiotemporal variation reasons of geometric positioning error sources for spaceborne SAR are then analyzed. Finally, the stability of geometric positioning accuracy is evaluated using the described method on data extracted from Yaogan-13 (YG-13) SAR satellite images with long time series and multiple test areas in China. The results reveal that during the study period (2015-2017), the geometric positioning accuracy of the YG-13 SAR system was relatively stable and is better than 3 m regardless of the spatial distribution, after removal of systematic pulse-dependent slant range errors and atmospheric correction. Furthermore, the validation results provide a reference for the design of SAR satellite systems, the establishment of calibration periods, and quantitative remote sensing application.

The detailed study on convection heat transfer and flow structure characteristic of air-jet studied with impinging vertically on a hemisphere plate. The inner surface of the cylindrical-shaped plate is subject to constant heat flux boundary conditions. Hemispherical plate subjected to comparative computations with Reynolds number (Re) ranging from 3000, 6000, and 9000 for the round nozzle impinging jet.

Oceanic temperature has a great impact on global climate and worldwide ecosystems, as its anomalies have been shown to have a direct impact on atmospheric anomalies. The major parameter for measuring the thermal energy of oceans is the sea surface temperature (SST). SST prediction plays an essential role in climatology and ocean-related studies. However, SST prediction is challenging due to the involvement of complex and nonlinear sea thermodynamic factors. To address this challenge, we design a novel ensemble of two stacked deep neural networks (DNNs) that uses air temperature, in addition to water temperature, to improve the SST prediction accuracy. To train our model and compare its accuracy with the state-of-the-art, we employ two well-known datasets from the national oceanic and atmospheric administration as well as the international Argo project. Using DNNs, our proposed method is capable of automatically extracting required features from the input time series and utilizing them internally to provide a highly accurate SST prediction that outperforms state-of-the-art models.

The paper discusses the issues of the disturbances allocations in the ionosphere that are associated with large atmospheric events - tropical cyclones of the 4–5 categories of 2014. On the basis of the characteristic spatio-temporal characteristics of the acoustic-gravity waves from atmospheric disturbances, the method is proposed for the detection of relative disturbances of the electron concentration in the frequency band characteristic of the disturbances. The observation of electron density by Langmuir probes carried out on the ESA Swarm satellites were used as experimental data. In the paper we consider all large 2014 Tropical Cyclones (TC) and discuss the statistics of the identified disturbances from TC in the ionosphere against the background of regular changes in the electron density in quiet geo-heliomagnetic conditions. The obtained results show that the wave mechanism of disturbance transmission from an atmospheric source to ionospheric heights is not the only one.

Absorption level by riometer was estimated at high-latitudes during four moderate magnetic storms that occurred in 2018 and were similar by several features. The data of riometer absorption at 30 MHz was obtained from Sodankyla Geophysical Observatory in Finland (67.36°N, 26.63°E). Particle precipitations were estimated by POES satellite data. According to our results, the character of development of the magnetic disturbance and the similarity/difference of Space Weather parameter variations (IMF magnitude, solar wind parameters, Dst variation) did not played the leading role in the absorption peak value. The absorption level in the auroral oval during the considered similar moderate magnetic disturbances depended on the combination of factors: simultaneous electron bursts of particular energies, the number of precipitating particles in these bursts and the moment of the storm at which such precipitation bursts occurred. The highest absorption was caused by the large electron precipitations with energies of >40 keV and >130 keV that occurred just after the maximal storm development at the very beginning of the recovery phase of the storm.

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep learning techniques to be applied to off-the-grid spatio-temporal data. This model has a substantial advantage over existing downscaling methods in that the trained model can be used to generate multisite predictions at an arbitrary set of locations, regardless of the availability of training data. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies, and motivates further research into applications of deep learning techniques in statistical downscaling., 26 pages, 12 figures

The non-uniform beam filling bias in Doppler velocity measurements from a spaceborne precipitation radar is investigated. A simple derivation of the already-known formula that relates the bias to the along-track gradient of measured radar reflectivity in dBZ is shown. From the formula, the uncertainty of bias correction due to discretization is discussed. The error is shown to be proportional to the square of the spacing for discretization, and is related to the third moment of the distribution of the radar reflectivity convolved with the antenna pattern. The effectiveness of averaging is also shown from a simulation.

The atmosphere affects the propagation of radar signals by provoking unwanted signal phase changes. In interferometric applications, such as coherent change detection and displacement measurements, this effect may significantly degrade the system performances. Moreover, atmosphere-induced phase changes are both time and space variants, and therefore, they are not easy to be removed. This article proposes a novel method to remove atmospheric effects by using a parametric model of the refractive index, which is derived as an extension of the International Telecommunication Union—Radiocommunication model. The proposed algorithm has been tested on real data acquired by using a ground-based synthetic aperture radar system in conjunction with data collected by a weather station. Data have been acquired continuously for three consecutive days, approximatively every 5 min. Results have shown how the proposed method can effectively remove atmospheric effects and restore the signal phase.

This article reviews 3D porous biomass-derived carbon materials as microwave absorbers, including their biomass sources, the transformation from biomass to porous carbon, and their corresponding microwave absorption applications and mechanism.

A method is proposed for the formation of a numerical criterion, which is a generalized characteristic of weather forecast taking into account features of optical observations. The proposed approach on weather forecast parameterization is used for planning optical observation on some telescopes coordinated by KIAM RAS.

The two leading perturbations acting on satellites in the orbital decay region of low Earth orbit are gravitational nonsphericity of the Earth and atmospheric drag. A detailed understanding of thei...

This article presents a novel approach for the joint estimation of satellite attitude and size based on inverse synthetic aperture radar (ISAR) image interpretation and parametric optimization. The satellite's solar panel, which is segmented from the ISAR image by employing pix2pix generative adversarial network (Pix2pixGAN), is chosen for investigation in this article due to its unique rectangular structure. We innovatively use the principal component analysis (PCA) to explore the satellite solar panel's structural features in an ISAR imagery. The projection matrix is then established to link the extracted features and the satellite's absolute attitude and size. Parametric optimization is established based on the relationship between the extracted features and the satellite's absolute attitude and size. A Broyden-Fletcher-Goldfarb-Shanno (BFGS)-based fast iterative search algorithm is employed to search the satellite's absolute attitude and size simultaneously through an iterative approach. The simulation data are generated from actual satellite orbital parameters and computer-aided-design (CAD) models of the Aqua satellite in the experiments. Simulation experiments verify the effectiveness of the proposed method.

Global positioning system (GPS) and inertial measurement units (IMUs) are often combined to produce navigation systems for airborne imaging platforms. The current state-of-the-art radar technology allows for radars to pulse at very high rates. GPS and IMU update rates are not fast enough to accurately report the platform position for each radar pulse. Independent GPS and IMUs cannot provide positional accuracy for long term stability. Traditional techniques, such as the Kalman and particle filter, are used to fuse GPS and IMU measurements. The Kalman filter excels for linear and Gaussian systems whereas the particle filter excels at non-linear and non-Gaussian systems. Sensor fusion techniques are used to help correct for IMU errors and provide the positional accuracy required for synthetic aperture radar (SAR) imaging applications. However, SAR requires the fusion algorithms to provide faster update rates. This paper explores the use of an up-sampled particle filter (UPF) for SAR to provide highly accurate position estimates at sampling frequencies comparable to radar pulse rates and overcome the limitations of standard interpolation techniques. This up-sampled particle filter is proven through simulations and instrumentation with a NovAtel GPS and IMU. The UPF technique allows for the GPS/IMU sampling rate to be different from the radar pulse repetition frequency (PRF) while providing accurate position solutions for each radar pulse capable of compensating for the phase history required for focusing a SAR image. The algorithms are instrumented in a SAR system and the position estimates are further validated and demonstrated through captured SAR images.