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The interferometric radar altimeter (IRA) is an innovative remote sensing sensor that enables the observation of mesoscale and submesoscale (meso–submesoscale) ocean dynamic phenomena. The charge-neutral atmosphere introduces path delay and bending in signal propagation. In this study, three types of sea surface height (SSH) errors caused by charge-neutral atmosphere propagation path for IRA were identified: differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Among them, DDE exhibits a proportionality to the negative zenith neutral delay (ZND) and demonstrates a significant increase with the incident angle; PDE is solely reliant on the ZND; PBE is like DDE in trend and magnitude resembling a ramp. Intriguingly, PBE exhibits insensitivity to variations in the charge-neutral atmosphere, behaving more like a systematic error. Theoretically, PBE leads to an increase in the SSH error of about 1.2 cm at far range for SWOT. The ZND spectrum fitted from the Jason-3 zenith delay correction data is additionally utilized to simulate the spatial distribution of ZND anomaly within the SWOT observation swaths. Then, the impact of PDE anomaly (PDEA), PBE, and DDE anomaly (DDEA) on the observation performance of SWOT is also considered in conjunction with SSH data provided by HYCOM. The findings indicate that both PDEA and PBE significantly reduce IRA's performance in oceanic phenomena, while the impact of DDEA can be disregarded. The PBE can distort the sea surface trend and increases the mean sea level within the range, requiring further attention. Significance Statement: This paper focuses on how the signal path affects the accuracy of measuring sea surface height (SSH) using the interferometric radar altimeter (IRA) in the charge-neutral atmosphere. The present paper defines three types of SSH errors caused by propagation path to IRA, namely, differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Three types of SSH errors will make a significant impact on the altimetry performance of IRA. Among them, PDE spatial anomaly and PBE will reduce the expected observation performance of the meso–submesoscale ocean phenomena in IRA's swath, while PBE has not received enough attention in previous studies. Therefore, the research will provide a theoretical basis for IRA to correct the above SSH errors. [ABSTRACT FROM AUTHOR]

Regional weather networks—also referred to as mesonets—are imperative for filling in the spatial and temporal data gaps between nationally supported weather stations. The North Carolina Environment and Climate Observing Network (ECONet) fills this regional role; it is a mesoscale network of 44 (as of 2023) automated stations collecting 12 environmental variables every minute across North Carolina. Measured variables include air temperature, precipitation, relative humidity, barometric pressure, wind speed, wind direction, total solar radiation, photosynthetically active solar radiation, soil temperature, soil moisture, leaf wetness index, and black globe temperature. All data undergo quality control procedures and are made freely available to the public via data portals hosted by the State Climate Office of North Carolina at North Carolina State University. This paper provides a technical overview of ECONet, including a description of the siting criteria, station maintenance procedures, data quality control procedures, and data availability. We also summarize unique aspects of ECONet data collection as well as innovative research and applications that rely on ECONet data. ECONet data are used by many sectors including, but not limited to, emergency management, natural resources management, public health, agriculture, forestry, science education, outdoor recreation, and research. ECONet data and data-powered applications offer valuable insights to local, regional, and federal partners, yet opportunities to expand ECONet research and applications remain. Significance Statement: This paper explains the ongoing and emerging impacts of a statewide weather station network called the North Carolina Environment and Climate Observing Network (ECONet). ECONet consists of 44 (as of 2023) automated stations located across the state. Each station collects 12 environmental variables every minute. ECONet data and data-powered applications offer valuable insights to local, regional, and federal partners. There are many opportunities to expand ECONet-based research and applications. [ABSTRACT FROM AUTHOR]

Estimation of uncertainties (random error statistics) of radio occultation (RO) observations is important for their effective assimilation in numerical weather prediction (NWP) models. Average uncertainties can be estimated for large samples of RO observations and these statistics may be used for specifying the observation errors in NWP data assimilation. However, the uncertainties of individual RO observations vary, and so using average uncertainty estimates will overestimate the uncertainties of some observations and underestimate those of others, reducing their overall effectiveness in the assimilation. Several parameters associated with RO observations or their atmospheric environments have been proposed to estimate individual RO errors. These include the standard deviation of bending angle (BA) departures from either climatology in the upper stratosphere and lower mesosphere (STDV) or the sample mean between 40 and 60 km (STD4060), the local spectral width (LSW), and the magnitude of the horizontal gradient of refractivity (|∇HN|). In this paper we show how the uncertainties of two RO datasets, COSMIC-2 and Spire BA, as well as their combination, vary with these parameters. We find that the uncertainties are highly correlated with STDV and STD4060 in the stratosphere, and with LSW and |∇HN| in the lower troposphere. These results suggest a hybrid error model for individual BA observations that uses an average statistical model of RO errors modified by STDV or STD4060 above 30 km, and LSW or |∇HN| below 8 km. Significance Statement: These results contribute to the understanding of the sources of uncertainties in radio occultation observations. They could be used to improve the effectiveness of these observations in their assimilation into numerical weather prediction and reanalysis models by improving the estimation of their observational errors. [ABSTRACT FROM AUTHOR]

We evaluate two stochastic subcolumn generators used in GCMs to emulate subgrid cloud variability enabling comparisons with satellite observations and simulations of certain physical processes. Our evaluation necessitated the creation of a reference observational dataset that resolves horizontal and vertical cloud variability. The dataset combines two CloudSat cloud products that resolve two-dimensional cloud optical depth variability of liquid, ice, and mixed-phase clouds when blended at ∼200 m vertical and ∼2 km horizontal scales. Upon segmenting the dataset to individual "scenes," mean profiles of the cloud fields are passed as input to generators that produce scene-level cloud subgrid variability. The assessment of generator performance at the scale of individual scenes and in a mean sense is largely based on inferred joint histograms that partition cloud fraction within predetermined combinations of cloud-top pressure–cloud optical thickness ranges. Our main finding is that both generators tend to underestimate optically thin clouds, while one of them also tends to overestimate some cloud types of moderate and high optical thickness. Associated radiative flux errors are also calculated by applying a simple transformation to the cloud fraction histogram errors, and are found to approach values almost as high as 3 W m−2 for the cloud radiative effect in the shortwave part of the spectrum. Significance Statement: The purpose of the paper is to assess the realism of relatively simple ways of producing fine-scale cloud variability in global models from coarsely resolved cloud properties. The assessment is achieved via comparisons to observed cloud fields where the fine-scale variability is known in both the horizontal and vertical directions. Our results show that while the generators have considerable skill, they still suffer from consistent deficiencies that need to be addressed with further development guided by appropriate observations. [ABSTRACT FROM AUTHOR]

The image-based visibility detection methods have been one of the active research issues in surface meteorological observation. The visual feature extraction is the basis of these methods, and its effectiveness has become a key factor in accurately estimating visibility. In this study, we compare and analyze the effectiveness of various visual features in visibility detection from three aspects, namely, visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene, including three traditional visual features such as local binary patterns (LBP), histograms of oriented gradients (HOG), and contrast as well as three deep learned features extracted from the Neural Image Assessment (NIMA) and VGG-16 networks. Then the support vector regression (SVR) models, which are used to map visual features to visibility, are also trained, respectively based on the region of interest (ROI) and the whole image of each scene. The experiment results show that compared to traditional visual features, deep learned features exhibit better performance in both feature analysis and model training. In particular, NIMA, with lower dimensionality, achieves the best fitting effect and therefore is found to show good application prospects in visibility detection. Significance Statement: The visual feature extraction is a basic step for image-based visibility detection and significantly affects the detection performance. In this paper, we compare six candidate visual features, including traditional and deep learned features, from visibility sensitivity, environmental variables robustness, and object depth sensitivity in multiscene. Then the SVR models are also trained to construct the mapping relations between different kinds of features and the visibility of each scene. The experiment results show that the deep learned features exhibit better performance in both feature analysis and model training, especially NIMA achieves the best fitting performance with fewer feature dimensions. [ABSTRACT FROM AUTHOR]

Deep-sea sediments hold evolutionary records of the oceanic environment—records of great significance for scientific fields investigating marine sedimentary processes, structural evolution, and seabed mineral resource exploration. However, the acquisition of original samples from deep-sea sediments is completely dependent on advanced seabed sediment collection methods and technical equipment. In this paper, a novel sampler is proposed to obtain intact sediment samples at full ocean depth. It mainly consists of a sampling device, pressure-retaining device, pressure-compensating device, and sample transfer device. The sampler can collect samples at full ocean depth (11 000 m) with a maximum core diameter of 54 mm and core length of 350 mm, and samples can be maintained at near–in situ pressures during recovery. The sampler can be installed on a remote-operated vehicle or human-occupied vehicle, and it can be operated with a single mechanical arm to collect pressure-retained samples. The experimental test showed that the novel sampler had good pressure-retaining performance and suitability with a mechanical arm, and can be applied to pressure-retaining sampling of seabed sediments at depth of 11 000 m. [ABSTRACT FROM AUTHOR]

The NASA Goddard Space Flight Center's (GSFC's) W-band (94 GHz) Cloud Radar System (CRS) has been comprehensively updated to modern solid-state and digital technology. This W-band (94 GHz) radar flies in nadir-pointing mode on the NASA ER-2 high-altitude aircraft, providing polarimetric reflectivity and Doppler measurements of clouds and precipitation. This paper describes the design and signal processing of the upgraded CRS. It includes details on the hardware upgrades [solid-state power amplifier (SSPA) transmitter, antenna, and digital receiver] including a new reflectarray antenna and solid-state transmitter. It also includes algorithms, including internal loop-back calibration, external calibration using a direct relationship between volume reflectivity and the range-integrated backscatter of the ocean, and a modified staggered–pulse repetition frequency (PRF) Doppler algorithm that is highly resistant to unfolding errors. Data samples obtained by upgraded CRS through recent NASA airborne science missions are provided. [ABSTRACT FROM AUTHOR]

The sampling error caused by the uneven distribution of radio occultation (RO) profiles in both space and time domains is an important error source of RO climatologies. In this paper, the sampling error RO temperature climatologies is investigated using the 4-yr (2007-10) data from the Constellation Observing System for Meteorology, Ionosphere, and Climate mission. The error is divided into three parts, including local time component (LTC), temporal component (TC), and spatial component (SC). The characteristics of the three components are investigated. Results show the following: 1) The LTC part of the total sampling error is characterized by a pattern of periodic positive and negative deviations, with a full cycle of about four months. The most significant LTC values are found in the area around 608N/S and the polar regions. 2) The TC part is mainly associated with the extent of day-to-day temperature variability and the daily number of RO profiles observed in each month. The most pronounced TC part is shown in high-latitude areas in wintertime, where the day-to-day temperature variability is high. 3) The SC part shows distinct features in different altitude ranges. It is characterized by a systemic error in the lower troposphere (2-8 km) but exhibits a seasonal trend at the altitude range from 8 to 40 km. 4) The total sampling error is dominated by the TC and SC parts in the troposphere and lower stratosphere, whereas in the upper stratosphere it is dominated by the LTC part. [ABSTRACT FROM AUTHOR]

Continuous monitoring of the earth radiation budget (ERB) is critical to the understanding of Earth's climate and its variability with time. The Clouds and the Earth's Radiant Energy System (CERES) instrument is able to provide a long record of ERB for such scientific studies. This manuscript, which is the first of a two-part paper, describes the new CERES algorithm for improving the clear/cloudy scene classification without the use of coincident cloud imager data. This new CERES algorithm is based on a subset of the modern artificial intelligence (AI) paradigm called machine learning (ML) algorithms. This paper describes the development and application of the ML algorithm known as random forests (RF), which is used to classify CERES broadband footprint measurements into clear and cloudy scenes. Results from the RF analysis carried using the CERES Single Scanner Footprint (SSF) data for January and July are presented in the manuscript. The daytime RF misclassification rate (MCR) shows relatively large values (>30%) for snow, sea ice, and bright desert surface types, while lower values (<10%) for the forest surface type. MCR values observed for the nighttime data in general show relatively larger values for most of the surface types compared to the daytime MCR values. The modified MCR values show lower values (<4%) for most surface types after thin cloud data are excluded from the analysis. Sensitivity analysis shows that the number of input variables and decision trees used in the RF analysis has a substantial influence on determining the classification error. [ABSTRACT FROM AUTHOR]

The evaporation duct is an abnormal refractive phenomenon with wide distribution and frequency occurrence at the boundary between the atmosphere and the ocean, which directly affects electromagnetic wave propagation. In recent years, the use of meteorological and hydrological data to predict the evaporation duct height has become an emerging and promising approach. There are some evaporation duct models that have been proposed based on the Monin–Obukhov similarity theory. However, each model adopts different stability functions and roughness length parameterization methods, so the prediction accuracies are different under different environmental conditions. To improve the prediction accuracy of the evaporation duct under different environmental conditions, a model selection optimization method (MSOM) of the evaporation duct model is proposed based on sensitivity analysis. According to the sensitivity of each model to input parameters analyzed by the sensor observation accuracy, curve graph, and Sobol sensitivity, the model input parameters are divided into several intervals. Then the optimization model is selected in different intervals. The model was established using numerical simulation data from local areas in the South China Sea, and its accuracy was verified by the observational data from the offshore observation platform located in the South China Sea. The results show that the MSOM can effectively improve the prediction accuracy of the evaporation duct height. Under unstable conditions, the maximum relative error is reduced by 7.1%, and under stable conditions, the relative error is reduced by 10.7%. Significance Statement: The evaporation duct height has a significant effect on marine radar or wireless apparatus applications. To obtain the evaporation duct height, there are some evaporation duct models that have been proposed. However, different evaporation duct models are applicable to different meteorological and hydrological environments. A single model cannot achieve accurate evaporation duct height predictions in all environments. We propose a model selection optimization method of the evaporation duct model based on sensitivity analysis. This method can dynamically select the optimal model according to different meteorological and hydrological environment, and improve the prediction accuracy of the evaporation duct height. Under unstable conditions, the maximum relative error is reduced by 7.1%, and under stable conditions, the relative error is reduced by 10.7%. [ABSTRACT FROM AUTHOR]

This paper generalizes a previous formulation of signal separation problem for dynamic wind turbine clutter mitigation at weather radar systems. In this modified formulation, we use nonconvex regularizers together with multichannel overlapping group shrinkage (MOGS) to penalize weather signals and adopt multidimensional processing. We show the restored weather signals in plan position indicator (PPI) format and, to demonstrate the improvement, compare them with the ones produced by the previous method in reflectivity, spectral width, and Doppler velocity estimates of weather data. The improvement results from a better characterization of the sparsities of the weather radar returns. During the course of experiments, we observe that the proposed method successfully mitigates the wind turbine clutter and dramatically increases the signal-to-clutter ratio, even for different weather and wind turbine signatures. In addition, when the wind turbine clutter is weak in the mixture, our algorithm manages to attenuate the ground clutters and produces clutter-free weather signals favorable for further processing. [ABSTRACT FROM AUTHOR]

In case of a release of a hazardous material (e.g., a chemical or a biological agent) in the atmosphere, estimation of the source from concentration observations (provided by a network of sensors) is a challenging inverse problem known as the atmospheric source term estimation (STE) problem. This study emphasizes a method, known in the literature as the renormalization inversion technique, for addressing this problem. This method provides a solution that has been interpreted as a weighted minimal norm solution and can be computed in terms of a generalized inverse of the sensitivity matrix of the sensors. This inverse is constructed by using an appropriate diagonal weight matrix whose components fulfill the so-called renormalizing conditions. The main contribution of this paper is that it proposes a new compact algorithm (it requires less than 15 lines of MATLAB code) to obtain, in a fast and efficient way, those optimal weights. To show that the algorithm, based on the properties of the resolution matrix, matches the requirements of emergency situations, analysis of the computational complexity and memory requirements is included. Some numerical experiments are also reported to show the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Atmospheric gravity waves are a kind of mesoscale disturbance, commonly found in the atmospheric system, that plays a key role in a series of mesospheric dynamic processes. When propagating to the upper atmosphere, the gravity waves will disturb the local temperature and density, and then modulate the intensity of the surrounding airglow radiation. As a result, the presence of gravity waves on a moonless night can usually cause the airglow to reveal ripple features in low-light images. In this paper we have applied a two-dimensional Stockwell transform technique (2DST) to airglow measurements from nighttime low-light images of the day–night band on the Suomi National Polar-Orbiting Partnership. To our knowledge this study is the first to measure localized mesospheric gravity wave brightness amplitudes, horizontal wavelengths, and propagation directions using such a method and data. We find that the method can characterize the general shape and amplitude of concentric gravity wave patterns, capturing the dominant features and directions with a good degree of accuracy. The key strength of our 2DST application is that our approach could be tuned and then automated in the future to process tens of thousands of low-light images, globally characterizing gravity wave parameters in this historically poorly studied layer of the atmosphere. [ABSTRACT FROM AUTHOR]

Simulation of radar beam propagation is an important component of numerous radar applications in meteorology, including height assignment, quality control, and especially the so-called radar forward operator. Although beam propagation in the atmosphere depends on the refractive index and its vertical variation, which themselves depend on the actual state of the atmosphere, the most common method is to apply the 4/3 earth radius model, based on climatological standard conditions. Serious deviations from the climatological value can occur under so-called ducting conditions, where radar beams at low elevations can be trapped or propagate in a waveguide-like fashion, such that this model is unsuitable in this case. To account for the actual atmospheric conditions, sophisticated methods have been developed in literature. However, concerning the practical implementation of these methods, it was determined that the description in the literature is not always complete with respect to possible pitfalls for practical implementations. In this paper, a revised version of an existing method (one example for the above-mentioned 'pitfall' statement) is introduced that exploits Snell's law for spherically stratified media. From Snell's law, the correct sign of the local elevation is a priori ambiguous, and the revised method explicitly applies (i) a total reflection criterion and (ii) another ad hoc criterion to solve the problem. Additionally, a new method, based on an ordinary differential equation with respect to range, is proposed in this paper that has no ambiguity. Sensitivity experiments are conducted to investigate the properties of these three methods. The results show that both the revised and new methods are robust under nonstandard conditions. But considering the need to catch an elevation sign ambiguity in the revised method (which cannot be excluded to fail in rare instances), the new method is regarded as more robust and unproblematic, for example, for applications in radar forward operators. [ABSTRACT FROM AUTHOR]

Lagrangian air parcel tracking is a powerful tool for estimating vapor source locations, particularly for isotope hydrology applications. Identified vapor source regions may be sensitive to the distribution of altitudes at which back trajectories are initiated. Ideally, those initial altitudes should reflect the altitudes where precipitation forms. This paper introduces a novel method for estimating these heights from reanalysis data and an air parcel lofting routine, which is referred to as the 'Reanalysis' method. Using Barrow, Alaska (now known as Utqiaġvik), as a test site, the study compares the distribution of air parcel initiation heights and vapor source conditions from back trajectories initiated at 1) heights determined by the Reanalysis method and 2) heights acquired from 35-GHz vertically resolved cloud radar, termed the 'Cloud Radar' method. Only 2 of the 70 events failed to produce condensation at any elevation. The distribution of air parcels generated by each method was compared on the basis of the median height and the median-adjusted overlap, and yielded excellent ( n = 28), good ( n = 20), fair ( n = 6), and poor ( n = 14) matches between the two methods. The excellent and good category events tended to produce condensation profiles with lower median heights, which translated to more similar vapor source characteristics. Poorly matched events tended to result from rain events where the Reanalysis method yielded much higher median heights than the Cloud Radar method. [ABSTRACT FROM AUTHOR]

Typhoon rainfall predictions provide critical information that can be used for flood control and advanced disaster prevention preparations. However, total rainfall nowcasts (i.e., several days ahead) are not available in Taiwan when typhoons are distant. This paper proposes a long-distance total rainfall forecast (LTRF) model and presents a real-time forecasting process that can use the LTRF model to determine the formation and possible approach of typhoons in the future. The LTRF model was formulated using two designed climate scenarios. Scenario 1 considered El Niño-Southern Oscillation (ENSO) effects, whereas scenario 2 did not. Various raw sensor data, comprising climatological characteristics, sea surface temperature, satellite brightness temperatures, and total rainfall, were collected; moreover, attributes of the ENSO indices, including the Southern Oscillation index and the Niño-3.4 sea surface temperature anomaly, were reviewed. The scenario models were constructed using the C4.5 and random forest tree-based algorithms. Typhoon events occurring during 2001-13 and 2014-15 (specifically, Typhoons Matmo and Fung-Wong in 2014 and Soudelor and Dujuan in 2015) were examined for training and testing purposes, respectively. The Hualien Weather Station in Taiwan was selected as a study site, and the forecasting horizon was set at 6 h. Finally, the model simulations, observations, and Central Weather Bureau (Taiwan) nowcasts were compared. The simulation results showed that the proposed LTRF model, when ENSO effects were accounted for, can efficiently forecast total typhoon rainfall when typhoons are distant from Taiwan. [ABSTRACT FROM AUTHOR]

Heliotropes are passive solar hot-air balloons that are capable of achieving nearly level flight within the lower stratosphere for several hours. These inexpensive flight platforms enable stratospheric sensing with high cadence enabled by the low cost to manufacture, but their performance has not yet been assessed systematically. During July–September of 2021, 29 heliotropes were successfully launched from Oklahoma and achieved float altitude as part of the Balloon-based Acoustic Seismology Study (BASS). All of the heliotrope envelopes were nearly identical with only minor variations to the flight line throughout the campaign. Flight data collected during this campaign comprise a large sample to characterize the typical heliotrope flight behavior during launch, ascent, float, and descent. Each flight stage is characterized, dependence on various parameters is quantified, and a discussion of nominal and anomalous flights is provided. [ABSTRACT FROM AUTHOR]

It is increasingly recognized that metadata can significantly improve the quality of scientific analyses and that the availability of metadata is particularly important for the study of climate variability. The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) contains in situ observations frequently used in climate studies, and this paper describes the ship metadata that are available to complement ICOADS. This paper highlights the metadata available in World Meteorological Organization Publication No. 47 that include information on measurement methods and observation heights. Changing measurement methods and heights are known to be a cause of spurious change in the climate record. Here the authors focus on identifying measurement heights for air temperature and wind speed and also give information on SST measurement depths. [ABSTRACT FROM AUTHOR]

Satellite observations in the shortwave infrared (SWIR) part of spectrum between 3.5 and 4.0 μm deliver critically important information for many applications. The satellite signal in this spectral band consists of solar-reflected radiation and thermal radiation emitted by surface, clouds, and atmosphere. Accurate retrievals require precise knowledge of solar irradiance values within a channel's bandwidth. The magnitudes of solar irradiance for shortwave infrared channels (3.7–3.9 μm) for the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration-7 (NOAA-7) to NOAA-18 satellites and the Geostationary Operational Environmental Satellite-8 (GOES-8) to GOES-12 are considered in this paper. Four recent solar reference spectra [those of Kurucz, Gueymard, the American Society for Testing and Materials (ASTM), and Wehrli] are analyzed to determine uncertainties in the knowledge of solar irradiance values for SWIR channels of the listed sensors. Because thermal radiation is frequently converted to effective blackbody temperature for analysis, computations, and calibration purposes, it is proposed here to express band-limited solar irradiance values in terms of brightness temperature as well. It is shown that band-limited solar irradiance for AVHRR radiometers expressed in terms of blackbody equivalent brightness temperature correspond to the range 355–360 K, and vary around 345 K for the SWIR channels of the GOES imagers. The values of band-limited solar irradiance and brightness temperatures are provided for various reference solar spectra. The relative differences in band-limited solar irradiance computed for the considered reference solar spectra are between 0% and 2.5%. Differences expressed in terms of brightness temperatures may reach 0.8 K. The results for the ASTM and the Kurucz reference spectra agree within 0.1% relative difference. Parameters of linear fits relating effective brightness temperatures and spectral radiance equivalent temperatures are also determined for all sensors. They are required for precise radiance–temperature and temperature–radiance conversion through Planck's functions in the case of the finite spectral response of real sensors. [ABSTRACT FROM AUTHOR]

During the intensive observing period of the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment, a large number of collocated rawinsonde and profiler wind observations were taken at six Integrated Sounding System (ISS) sites and Biak, Indonesia. To mitigate limitations in the rawinsonde dataset due to missing and bad wind observations, a procedure was developed to combine profiler and sonde winds to produce an integrated, high-quality, upper-air sounding dataset. In addition to improving the overall quality of the winds, this procedure eliminates several data gaps in the sonde dataset. For example, below 800 hPa the amount of bad and missing wind data is reduced from about 45% to 20% at the land-based ISS sites. This paper describes the procedure for combining sonde and profiler winds into a single, coherent merged dataset. Examining the impact of this merger upon various atmospheric analyses, the authors find that inclusion of profiler winds results in some substantial changes in the analyses, particularly on daily to weekly timescales. To assess whether these changes are an improvement, budget-derived rainfall estimates from analyses with and without profiler winds are compared to Special Sensor Microwave/Imager satellite-based estimates. Overall, this comparison shows that inclusion of profiler winds into the sonde dataset has a beneficial impact upon the analyses. Information for accessing the merged datasets for the seven sites considered in this paper via the Internet is described. [ABSTRACT FROM AUTHOR]

Superrefraction at the top of the atmospheric boundary layer introduces problems for assimilation of radio occultation data in weather models. A method of detection of superrefraction by spectral analysis of deep radio occultation signals introduced earlier has been tested using 2 years of COSMIC-2/FORMOSAT-7 radio occultation data. Our analysis shows a significant dependence of the probability of detection of superrefraction on the signal-to-noise ratio, which results in a certain sampling nonuniformity. Despite this nonuniformity, the results are consistent with the known global distribution of superrefraction (mainly over the subtropical oceans) and show some additional features and seasonal variations. Comparisons to the European Centre for Medium-Range Weather Forecasts analyses and limited set of radiosondes show reasonable agreement. Being an independent measurement, detection of superrefraction from deep radio occultation signals is complementary to its prediction by atmospheric models and thus should be useful for assimilation of radio occultation data in the atmospheric boundary layer. [ABSTRACT FROM AUTHOR]

Only a climate model that is able to simulate well the historical atmospheric temperature trend can be used for estimating the future atmospheric temperature trends on different emission scenarios. Satellite-based Microwave Sounding Unit (MSU) brightness temperature in the middle troposphere ( T2) is an important analog of midtropospheric atmospheric temperature. So, there is the need to compare the atmospheric temperature trend simulated by the fifth phase of the Coupled Model Intercomparison Project (CMIP5) historical realizations and the observed MSU T2. There are two approaches for estimating modeled MSU T2: apply a global-mean static weighting function to generate the weighted average of the modeled temperature at all atmospheric layers and simulate satellite-view MSU T2 using the model's output as input into a radiative transfer model (RTM). In this paper, the two approaches for estimating modeled MSU T2 are evaluated. For each CMIP5, it is shown that there exists a model-simulated static weighting function, such that the MSU T2 trend using the weighting function is equivalent to that calculated by RTM. The effect of modeled cloud liquid water on MSU T2 trends in CMIP5 simulations is investigated by comparing the modeled cloud liquid water vertical profile and the weighting function. Moreover, it is found that warming trends of MSU T2 for CMIP5 simulations calculated by the RTM are about 15% less than those using the two traditional static weighting functions. By comparing the model-derived weighting function with the two traditional weighting functions, the reason for the systematical biases is revealed. [ABSTRACT FROM AUTHOR]

The Indian Space Research Organization (ISRO)'s Vikram Sarabhai Space Centre (VSSC) has developed a new GPS radiosonde, called Pisharoty sonde, with its ground station for atmospheric research and operational meteorology. The latest version of this radiosonde weighs 125 g and uses a bead thermistor, a capacitive humidity sensor, and a GPS receiver module. It computes geopotential heights and pressures using the temperature and GPS altitude profile. This paper describes the radiosonde and its associated ground system, summarizes its different versions, and discusses balloon tests of the newly developed version, including comparisons with internationally accepted high-quality radiosondes. [ABSTRACT FROM AUTHOR]

Radar returns from the ground, known as ground clutter, can contaminate weather signals, often resulting in severely biased meteorological estimates. If not removed, these contaminants may artificially inflate quantitative precipitation estimates and obscure polarimetric and Doppler signatures of weather. A ground-clutter filter is typically employed to mitigate this contamination and provide less biased meteorological-variable estimates. This paper introduces a novel adaptive filter based on the autocorrelation spectral density, which is capable of mitigating the adverse effects of ground clutter without unnecessarily degrading the quality of the meteorological data. The so-called Clutter Environment Analysis using Adaptive Processing (CLEAN-AP) filter adjusts its suppression characteristics in real time to match dynamic atmospheric environments and meets Next Generation Weather Radar (NEXRAD) clutter-suppression requirements. [ABSTRACT FROM AUTHOR]

Profiles of atmospheric cloud and aerosol extinction coefficients are retrieved on a global scale from measurements made by the lidar on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO) mission since mid-June 2006. This paper presents an analysis of how the uncertainties in the inputs to the extinction retrieval algorithm propagate as the retrieval proceeds downward to lower levels of the atmosphere. The mathematical analyses, which are being used to calculate the uncertainties reported in the current (version 3) data release, are supported by figures illustrating the retrieval uncertainties in both simulated and actual data. Equations are also derived that describe the sensitivity of the extinction retrieval algorithm to errors in profile calibration and in the lidar ratios used in the retrievals. Biases that could potentially result from low signal-to-noise ratios in the data are also examined. Using simulated data, the propagation of bias errors resulting from errors in profile calibration and lidar ratios is illustrated. [ABSTRACT FROM AUTHOR]

This paper presents the design and evaluates the performance of a double-walled electrically aspirated radiation shield for thermometers measuring air temperature and its gradients in the atmospheric surface layer. Tests were performed to quantify its solar radiation error and wake production, and to characterize the observer effect of the forced aspiration on vertical temperature gradients in the calm and stable boundary layer. Construction requirements were to design a unit that uses inexpensive off-the-shelf components, to assemble easily, to facilitate reconfiguration to accommodate various sensors, and to reduce power consumption with the goal of reducing costs and enabling use in sensor networks in remote locations. The custom-aspirated shield was evaluated in reference to a triple-walled commercially available model and subjected to rigorous testing in a wind tunnel and field experiments. The relative radiation error of air temperature measurements in the custom-aspirated shield was equal to or smaller than that in the reference shield within ±0.08 K for solar irradiances ≥1000 W m−2 and calm winds. At night, thermal imagery revealed no significant differences in surface temperatures of both shields and the air temperature. Both shields produced significant wake within a ±30° sector of incident flow. Even for weak flows ≤0.7 m s−1, higher-order moments were increased by a factor of 3, while the mean airflow speed was reduced by up to 30% compared to uncontaminated directions. Careful inspection of the spatiotemporal dynamics of air temperatures in a vertical profile showed negligible impact of the forced aspiration on the finescale structure of the observations for the nocturnal and transitional calm surface layers. [ABSTRACT FROM AUTHOR]

This paper presents the development of a multisodar mode from a five-beam sodar whereby a wind speed vector is found for sets of three acoustic beams. If the vertical beam is used, this mode enables the profiling of four wind speed measurements around the sodar. These multisodars can be used to investigate the spatial and temporal nature of winds in both flat and complex terrain. The spatial, temporal, and signal-to-noise qualities of the sodar signal in this mode are analyzed, and a metric for understanding the inhomogeneity of the atmosphere through cross-correlating opposing multisodars is developed. A driving factor for this work is developing the use of such sodar systems in complex terrain, where experimental measurements are traditionally difficult and expensive. [ABSTRACT FROM AUTHOR]

Atmospheric winds in the troposphere have been observed routinely for many years with wind profiling (VHF and UHF) radars using the Doppler beam swinging (DBS) technique. Accuracy of wind estimates using wind profiling radars with different beam configurations has its limitations due to both the system of observation and atmospheric conditions. This paper presents a quantitative analysis and evaluation of horizontal wind estimation in different beam configurations up to an altitude of 18 km using the mesosphere–stratosphere–troposphere (MST) radar located in Gadanki, India. Horizontal wind velocities are derived in three different ways using two-, three-, and four-beam configurations. To know the performance of each configuration, radar-derived winds have been compared with the winds obtained by simultaneous GPS sonde balloon measurements, which are considered to be a standard reference by default. Results show that horizontal winds measured using three different beam configurations are comparable in general but discrepancy varies from one beam configuration to the other. It is observed that horizontal winds measured using four-beam configuration (east, west, north, and south) have better estimates than the other two-beam configurations. The standard deviation was found to be varying from 1.4 to 2.5 m s-1 and percentage error is about 9.68%–12.73% in four-beam configuration, whereas in other beam configurations the standard deviation is about 1.65–3.9 m s-1 and the percentage error is about 11.29%–15.16% with reference to GPS sonde balloon–measured winds. [ABSTRACT FROM AUTHOR]

During the African Monsoon Multidisciplinary Analyses (AMMA) program, which included a special observing period that took place over West Africa in 2006, a major effort was devoted to monitor the atmosphere and its water cycle. The radiosonde network was upgraded and enhanced, and GPS receivers deployed. Among all sondes released in the atmosphere, a significant number were Vaisala RS80-A sondes, which revealed a significant dry bias relative to Vaisala RS92 (a maximum of 14% in the lower atmosphere, reaching 20% in the upper levels). This paper makes use of a simple but robust statistical approach to correct the bias. Comparisons against independent GPS data show that the bias is almost removed at night, whereas for daytime conditions, a weak dry bias (5%) still remains. The correction enhances CAPE by a factor of about 4 and, thus, becomes much more in line with expected values over the region. [ABSTRACT FROM AUTHOR]

A multifrequency phased-array Doppler sodar system has been installed recently at the National Atmospheric Research Laboratory (NARL) for the continuous observation of the lower atmosphere from near ground to the atmospheric boundary layer (ABL). The NARL sodar, developed in technical collaboration with the Society for Applied Microwave Electronics Engineering and Research (SAMEER), was built using piezoceramic tweeters, which are capable of generating 100-W acoustic power. In favorable atmospheric conditions, the sodar gives wind profiles up to 1 km. The performance evaluation is one of the most important aspects for quality assurance of sodar operations. This paper presents the first results of experimental observations of the NARL sodar system and its scientific validation. The NARL sodar has been validated using the simultaneous observation of another sodar system (Scintec model MFAS64). Various physical parameters of the atmosphere are derived using the results obtained from both of the systems. Comparison of simultaneous measurements by both of the sodars, located about 100 m apart, shows good agreement on wind speed, wind direction, and vertical wind variance. The correlation coefficient of more than 0.80 in wind speed and direction between the sodars shows the usefulness of the system for observing the atmosphere and deriving physical parameters below the ABL. [ABSTRACT FROM AUTHOR]

Mixing in the free atmosphere above the planetary boundary layer is of great importance to the fate of trace gases and pollutants. However, direct measurements of the turbulent dissipation rate by in situ probes are very scarce and radar measurements are fraught with uncertainties. In this paper, turbulence scaling concepts, developed over the past decades for application to oceanic mixing, are used to suggest an alternative technique for retrieving turbulence properties in the free atmosphere from high-resolution soundings. This technique enables high-resolution radiosondes, which have become quite standard in the past few years, to be used not only to monitor turbulence in the free atmosphere in near–real time, but also to study its spatiotemporal characteristics from the abundant archives of high-resolution soundings from around the world. Examples from several locations are shown, as well as comparisons with radar-based estimations and a typical Richardson number–based parameterization. [ABSTRACT FROM AUTHOR]

This paper explains and develops a correction algorithm for measurement of cloud particle size distributions with the Stratton Park Engineering Company, Inc., Cloud Particle Imager (CPI). Cloud particle sizes, when inferred from images taken with the CPI, will be oversized relative to their “true” size. Furthermore, particles will cease to be “accepted” in the image frame if they lie a distance greater than the depth of field from the object plane. By considering elements of the scalar theory for diffraction of light by an opaque circular disc, a calibration method is devised to overcome these two problems. The method reduces the error in inferring particle size from the CPI data and also enables the determination of the particles distance from the object plane and hence their depth of field. These two quantities are vital to enable quantitative measurements of cloud particle size distributions (histograms of particle size that are scaled to the total number concentration of particles) in the atmosphere with the CPI. By using both glass calibration beads and novel ice crystal analogs, these two problems for liquid drops and ice particles can be quantified. Analysis of the calibration method shows that 1) it reduces the oversizing of 15-μm beads (from 24.3 to 14.9 μm for the sample mean), 40-μm beads (from 50.0 to 41.4 μm for the sample mean), and 99.4-μm beads (from 103.7 to 99.8 μm for the sample mean); and 2) it accurately predicts the particles distance from the object plane (the relationship between measured and predicted distance shows strong positive correlation and gives an almost one-to-one relationship). Realistic ice crystal analogs were also used to assess the errors in sampling ice clouds and found that size and distance from the object plane could be accurately predicted for ice crystals by use of the particle roundness parameter (defined as the ratio of the projected area of the particle to the area of a circle with the same maximum length). While the results here are not directly applicable to every CPI, the methods are, as data taken from three separate CPIs fit the calibration model well (not shown). [ABSTRACT FROM AUTHOR]

The very high frequency (VHF) middle and upper atmosphere radar radio acoustic sounding system (MU-RASS) in Shigaraki, Japan, is able to provide tropospheric virtual temperature data with high temporal resolution on the order of a few minutes. The objective of this paper is to test the usefulness of MU-RASS as a tool for examining high-frequency changes in atmospheric stability and its effects on wave and energy propagation. For this study, temperature and wind data below 8-km altitude during a 2-day campaign period in October 2001 were used. A long-lasting inversion layer at 3.5-km altitude dominated the observation period. Large vertical wind perturbations with periods of less than 30 min were observed inside this inversion layer. Wavelet analysis was used to identify the dominant wave period for calculating the wind and temperature variances. The temperature variance characteristics exhibited a combination of the horizontal and vertical wind variance characteristics. In conclusion, the high temporal resolution of the MU-RASS enabled the study of short time-scale wind and temperature perturbations. These perturbations were related to the atmospheric stability, wave propagation, and energy in the troposphere, demonstrating the usefulness of the MU-RASS for this kind of study. [ABSTRACT FROM AUTHOR]

The present work investigates the possibility of retrieving humidity using the bending angle data obtained from radio occultation of GPS signals without additional external information. In particular, with the proposed approach, the dry pressure profiles are obtained by fitting the bending angles of the outer-troposphere layers (from h = h250K up to the stratopause) using the Hopfield dry atmosphere model. The ground pressure and temperature are the parameters of the model to be estimated. In the second step the humidity profiles are extracted by subtracting the contribution resulting from the dry atmosphere from the measured bending angles. Such derivation implies a complex mathematical treatment of the relationship between the bending angle and the refractivity, which is fully explained herein. Furthermore, the method was applied on Challenging Minisatellite Payload (CHAMP) profiles. The CHAMP profiles are achieved by applying heuristic retrieval algorithms based on the canonical transform. The algorithms are applied to minimize the negative refractivity bias that is observed for low-latitude GNSS RO. Thus, the results are shown and discussed in the second part of the paper. Finally, it is widely discussed how the proposed method is able to retrieve refractivity profiles without using the Abel inversion. [ABSTRACT FROM AUTHOR]

In this paper a system for in situ measurement of H216O/H218O in air based on tunable diode laser (TDL) absorption spectroscopy is described. Laboratory tests showed that its 60-min precision (one standard deviation) was 0.21‰ at a water vapor volume mixing ratio of 2.67 mmol mol-1 (dewpoint temperature -10.8°C at sea level) and improved to 0.09 at 15.3 mmol mol-1 (dewpoint temperature 13.4°C). The TDL measurement of the vapor generated by a dewpoint generator differed from the equilibrium prediction by -0.11 ± 0.43‰ (mean ± one standard deviation). Its measurement of the ambient water vapor differed from the cold-trap/mass spectrometer method by -0.36 ± 1.43‰. The larger noise of the latter comparison was caused primarily by the difficulty in extracting vapor from air without altering its isotope content. In a 1-week test in Logan, Utah, in August 2003, the isotope ratio of water vapor in ambient air was positively correlated with the water vapor mixing ratio and also responded to wetting events (rain and irrigation) in an expected manner. This system has been in continuous operation in New Haven, Connecticut, since December 2003. It is suggested that such uninterrupted measurement may open a new window on the hydrologic cycle, particularly processes involving phase changes of water, and can increase the power of the isotope method in ecological applications. [ABSTRACT FROM AUTHOR]

The amount of solar radiation emerging from the top of the atmosphere is strongly influenced by the reflectance of the underlying surface. For this reason, some information about the magnitude and the spectral variability of the surface reflectance typically has to be included in the retrieval of atmospheric parameters from reflected solar radiation measurements. Sufficient information about the surface reflectance properties is rarely available, and the integration of this effect in the retrieval might turn out to be a challenge, especially for broadband instruments. In this paper the focus is on the Measurements of Pollution in the Troposphere (MOPITT) remote sensing instrument. Theoretical studies are performed to investigate how a spectrally varying surface reflectance might impact the retrieval of the total column amount of methane from MOPITT radiance measurements, and the current findings are compared to observed biases. However, the findings present herein might be valuable and applicable for other remote sensing instruments that are sensitive to the amount of solar radiation reflected from the earth’s surface. [ABSTRACT FROM AUTHOR]

The Clouds and Earth’s Radiant Energy System (CERES) provides coincident global cloud and aerosol properties together with reflected solar, emitted terrestrial longwave, and infrared window radiative fluxes. These data are needed to improve the understanding and modeling of the interaction between clouds, aerosols, and radiation at the top of the atmosphere, surface, and within the atmosphere. This paper describes the approach used to estimate top-of-atmosphere (TOA) radiative fluxes from instantaneous CERES radiance measurements on the Terra satellite. A key component involves the development of empirical angular distribution models (ADMs) that account for the angular dependence of the earth’s radiation field at the TOA. The CERES Terra ADMs are developed using 24 months of CERES radiances, coincident cloud and aerosol retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological parameters from the Global Modeling and Assimilation Office (GMAO)’s Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) V4.0.3 product. Scene information for the ADMs is from MODIS retrievals and GEOS DAS V4.0.3 properties over the ocean, land, desert, and snow for both clear and cloudy conditions. Because the CERES Terra ADMs are global, and far more CERES data are available on Terra than were available from CERES on the Tropical Rainfall Measuring Mission (TRMM), the methodology used to define CERES Terra ADMs is different in many respects from that used to develop CERES TRMM ADMs, particularly over snow/sea ice, under cloudy conditions, and for clear scenes over land and desert. [ABSTRACT FROM AUTHOR]

Many characteristics of radar echoes from ground targets vary with time as the properties of the atmosphere in which the radar waves propagate evolve. For example, if the phase of a target varies with time and the target is known to be stationary, that phase variation is related to changes in the refractive index of air between the radar and that target. These changes are themselves caused by variations in the pressure, temperature, and especially the humidity of air. The changing phase and intensity of ground targets are hence records of evolving atmospheric conditions and could therefore in theory be used to retrieve parameters of meteorological value. In this paper, the various ways meteorological conditions can affect radar returns from ground targets are explored and quantified. In particular, the extent with which the changes in the phase of ground targets can be used to extract weather-related information is investigated. While wind, precipitation, and the vertical structure of temperature and humidity all affect ground echo appearance, it is demonstrated that the most promising quantity to be obtained is the refractive index of air near the ground. The process and the uncertainty of refractive index measurements by radar are then described. Comparisons with surface stations indicate that near-surface refractive index can be obtained reasonably accurately by radar in very flat terrain. In more complex topography, sensitivity analyses show that radar-based refractive index measurements will be strongly affected by the vertical profile of refractive index. [ABSTRACT FROM AUTHOR]

The Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) mission will measure Earth's emission at wavelengths ranging from 3 to 54 μm. The prelaunch clear-sky retrieval algorithm, evaluated with simulated test data, indicates that PREFIRE measurements will be valuable for retrieving atmospheric water vapor and temperature profiles. Far infrared measurements provide unique retrieval information, indicated by the high ranking of select FIR channels as primary contributors to the total degrees of freedom for signal (DFS). In utilizing all the PREFIRE channels, the average total DFS of 4 test regions ranges from 1.90 to 4.71. The information content increases with higher column water vapor and in the presence of near-surface temperature inversions. Using the DFS profiles for guidance, the retrieval concentrates information into 7 distinct layers to reduce the retrieval uncertainty per layer. Sensitivity tests indicate forward model error due to surface emissivity uncertainty results in about a 9% increase in column water vapor uncertainty. The clear-sky retrieval is sensitive to the presence of undetected ice clouds, especially those with optical depths larger than 0.2. Hence, in addition to a separate PREFIRE cloud mask, optimal estimation retrieval metrics are explored as possible indicators of cloudy scenes. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s−1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets. [ABSTRACT FROM AUTHOR]

Atmospheric electricity measurements made from small unmanned aircraft systems (UAS) are rare but are of increasing interest to the atmospheric science community due to the information that they can provide about aerosol and turbulence characteristics of the atmospheric boundary layer (ABL). Here we present the first analysis of a new dataset of space charge and meteorology measurements made from the small, electric, fixed-wing UAS model MASC-3. Two distinct experiments are discussed: 1) Flights past a 99 m metal tower to test the response of the charge sensor to a fixed distortion of the electric field caused by the geometry of the tower. Excellent agreement is found between the charge sensor response from the MASC-3 and modeled electric field around the tower. 2) Vertical profiles up to an altitude of 2500 m to study the evolution of the ABL with the time of day. These flights demonstrated close agreement between the space charge profiles and temperature, relative humidity, and turbulence parameters, as would be expected on a fair-weather day with summertime convection. Maximum values of space charge measured were of order 70 pC m−3, comparable with other measurements in the literature from balloon platforms. These measurements demonstrate the suitability of small UAS for atmospheric electrical measurements, provided that care is taken over the choice of aircraft platform, sensor placement, minimization of electrical interference, and careful choice of the flight path. Such aircraft are typically more cost-effective than manned aircraft and are being increasingly used for atmospheric science purposes. [ABSTRACT FROM AUTHOR]

The virtual ionosphere reflection height variation is investigated temporally and spatially, with specific reference to the Met Office's lightning detection network, the Arrival Time Difference Network (ATDnet). Data from this network, operating at 13.7 kHz, and a propagation model built by the Met Office based upon published theory were used to investigate this variation, specifically with regard to diurnal, seasonal, and 11-yr solar cycle variation. Variation over these temporal scales is chosen, since they correspond with variation in solar irradiance upon the earth's atmosphere, something known to drive ionosphere height variation. The virtual ionosphere reflection height is found to vary diurnally from ~65 km for the period 1000-1600 UTC to ~80 km for the period 2200-0400 UTC, from 1 June to 31 August 2013 inclusive. A similar magnitude of variation is also observed seasonally, with the ionosphere height for daytime in August 2013 being ~64 km and for December 2013 being ~76 km. No significant variation is observed between the minimum and maximum of the last solar cycle, with a difference in ionosphere height of ~1 km at most. The potential impacts of these results upon a very low-frequency (VLF) lightning detection network such as ATDnet are discussed, with solutions such as subnetting and wave-mode dominance analysis examined. [ABSTRACT FROM AUTHOR]

Simulated weather time series are often used in engineering and research practice to assess radar systems behavior and/or to evaluate the performance of novel techniques. There are two main approaches to simulating weather time series. One is based on summing individual returns from a large number of distributed weather particles to create a cumulative return. The other is aimed at creating simulated random signals based on the predetermined values of radar observables and is of interest herein. So far, several methods to simulate weather time series, using the latter approach, have been proposed. All of these methods are based on applying the inverse discrete Fourier transform to the spectral model with added random fluctuations. To meet the desired simulation accuracy, such an approach typically requires generating the number of samples that is larger than the base sample number due to the discrete Fourier transform properties. In that regard, a novel method to determine simulation length is proposed. It is based on a detailed theoretical development that demonstrates the exact source of errors incurred by this approach. Furthermore, a simple method for time series simulation that is based on the autocorrelation matrix exists. This method neither involves manipulations in the spectral domain nor requires generating the number of samples larger than the base sample number. Herein, this method is suggested for weather time series simulation and its accuracy and efficiency are analyzed and compared to the spectral-based approach. Significance Statement: All research articles published so far on the topic of weather time series simulation propose the use of inverse discrete Fourier transform (IDFT) when based on the desired Doppler moment values. Herein, a detailed theoretical development that demonstrates the exact source of errors incurred by this approach is presented. Also, a novel method to determine the simulation length that is based on the theoretical error computation is proposed. As an alternative, a computationally efficient general method (not using IDFT) previously developed for the simulation of sequences with desired properties is suggested for weather time series simulation. It is demonstrated that the latter method produces accurate results within overall shorter computational times. Moreover, it is shown that the use of graphics processing unit (GPU), ubiquitous in modern computers, significantly reduces computational times compared to the sole use of central processing unit (CPU) for all simulation-related calculations. [ABSTRACT FROM AUTHOR]

Monitoring atmospheric conditions that lead to severe weather events is critical to their timely and accurate prediction and can help prevent of large economic losses. Bulgaria, located in southeastern Europe, has the highest mean number of thunderstorms and hailstorms. These events generally occur between April and September with a peak in July. In this study, both radio occultation (RO) and ground-based observations from the Global Navigation Satellite Systems (GNSS) were used to study two severe hailstorms that occurred in 2014 and 2019. In both storms, a cold upper-air pool was detected in addition to a large specific humidity anomaly between 2 and 6 km. In the hailstorm that occurred in July 2014, there was an RO temperature anomaly between 10 and 14 km as well as a positive specific humidity anomaly between 4 and 6 km. The integrated vapor transport (IVT) reanalysis from ERA5, indicated that the high specific humidity over the Mediterranean could be tracked to an atmospheric river over the North Atlantic, which was connected to a tropical cyclone. In the hailstorm that occurred in May 2019, elevated IVT values were observed before the storm. During this storm, a negative temperature anomaly peak was observed in the RO profile at 11.3 km as well as a positive specific humidity anomaly between 2 and 4.5 km. The WRF Model and the ERA5 dataset could reproduce the temperature profiles for both storms relatively well; however, they tended to underestimate specific humidity. The RO profiles were complemented by ground-based GNSS tropospheric delays with high temporal resolution. The evaluation of the WRF with ground-based GNSS tropospheric products revealed a time delay between the modeled and observed developments of both hailstorms. [ABSTRACT FROM AUTHOR]

High-frequency radars (HFR) are traditionally used in coastal environments to observe ocean current and wave characteristics. With an HFR forward model, HFR adjoint model, and the Simulating Waves Nearshore model, HFR Doppler spectra observations were used to estimate near-surface winds in the Southern California Bight in October 2017. The HFR 10-m wind retrievals were assimilated into the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) with the COAMPS four-dimensional variational (4DVar) assimilation system to integrate the HFR wind retrievals into the initial conditions. Impact of the HFR-derived winds on the forecast are evaluated in terms of adjointderived forecast sensitivity observation impact (FSOI), and by an observing system experiment that compared forecasts from simulations that assimilated the HFR wind retrievals to a control simulation that excluded HFR winds. The addition of the HFR-estimated wind observations reduced the error in the forecasted dry energy norm in the lowest model level and also contributed to small improvements in the 10-m wind field over a 25-day experiment. The potential benefit of this new method to estimate near-surface ocean winds near the coast for data assimilation and improved numerical weather prediction is an exciting advancement in remote sensing of coastal winds and expands the benefit of existing HFR networks beyond their intended use. More importantly, wind fields retrieved from HFR have the potential to fill an observation gap near the shoreline where ship and buoy observations are sparse and scatterometer observations are unavailable due to land contamination. [ABSTRACT FROM AUTHOR]

A vast amount of ice crystal imagery exists from a variety of field campaign initiatives that can be utilized for cloud microphysical research. Here, nine convolutional neural networks are used to classify particles into nine regimes on over 10 million images from the Cloud Particle Imager probe, including liquid and frozen states and particles with evidence of riming. A transfer learning approach proves that the Visual Geometry Group (VGG-16) network best classifies imagery with respect to multiple performance metrics. Classification accuracies on a validation dataset reach 97% and surpass traditional automated classification. Furthermore, after initial model training and preprocessing, 10 000 images can be classified in approximately 35 s using 20 central processing unit cores and two graphics processing units, which reaches real-time classification capabilities. Statistical analysis of the classified images indicates that a large portion (57%) of the dataset is unusable, meaning the images are too blurry or represent indistinguishable small fragments. In addition, 19% of the dataset is classified as liquid drops. After removal of fragments, blurry images, and cloud drops, 38% of the remaining ice particles are largely intersecting the image border ($10% cutoff) and therefore are considered unusable because of the inability to properly classify and dimensionalize. After this filtering, an unprecedented database of 1 560 364 images across all campaigns is available for parameter extraction and bulk statistics on specific particle types in a wide variety of storm systems, which can act to improve the current state of microphysical parameterizations. [ABSTRACT FROM AUTHOR]

An instrumentation package for wind and turbulence observations in the atmospheric boundary layer on an unmanned aerial vehicle (UAV) called BOREAL has been developed. BOREAL is a fixed-wing UAV built by BOREAL company, which weighs up to 25 kg (5 kg of payload) and has a wingspan of 4.2 m. With a light payload and optimal weather conditions, it has a flight endurance of 9 h. The instrumental payload was designed in order to measure every parameter required for the computation of the three wind components, at a rate of 100 s−1, which is fast enough to capture turbulence fluctuations: a GPS–inertial measurement unit (IMU) platform measures the three components of the groundspeed a well as the attitude angles; the airplane nose has been replaced by a five-hole probe in order to measure the angles of attack and sideslip, according to the so-called radome technique. This probe was calibrated using computational fluid dynamics (CFD) simulations and wind tunnel tests. The remaining instruments are a Pitot tube for static and dynamic pressure measurement and temperature/humidity sensors in dedicated housings. The optimal airspeed at which the vibrations are significantly reduced to an acceptable level was defined from qualification flights. With appropriate flight patterns, the reliability of the mean wind estimates, through self-consistency and comparison with observations performed at 60 m on an instrumented tower could be assessed. Promising first observations of turbulence up to frequencies around 10 Hz and corresponding to a spatial resolution to the order of 3 m are hereby presented. [ABSTRACT FROM AUTHOR]

Hyperspectral infrared satellite observations from geostationary platforms allow for the retrieval of temperature and water vapor measurements with higher temporal and vertical resolution than was previously available. The Chinese satellite Fengyun-4A (FY-4A) includes the Geostationary Interferometric Infrared Sounder (GIIRS), which has the ability to measure vertical profiles of temperature and water vapor from space at times when ground-based upper-air soundings are not available and can fill an important need in short-range weather prediction. In this study, convective available potential energy (CAPE) and lifted index (LI), which are used for forecasting atmospheric instability, were computed using the SHARPpy algorithm used by the NWS Storm Prediction Center. However, remote infrared and microwave sensing is lacking detailed information in the boundary layer, so the addition of the NOAA Meteorological Assimilation and Data Ingest System (MADIS) surface data may be necessary in order to get accurate temperature and moisture measurement near the surface. This study uses 10–16 May 2019 in the coastal region near Hong Kong for evaluating the use of hourly surface observations combined with satellite soundings from FY-4A GIIRS at 2-h intervals. The GIIRS plus MADIS surface-based CAPE and LI estimates are compared to estimates derived from low-Earth-orbiting (LEO) SNPP and NOAA-20 from NOAA, MetOp from EUMETSAT, NWP reanalysis, and local radiosondes. In the case study, the 2-h sampling interval of the GIIRS geostationary sounder was able to capture the rapid transition (16 h) from a stable to an unstable atmosphere in both CAPE and LI. The use of surface observations with satellite soundings gave mixed results, possibly due to the complex terrain near Hong Kong. [ABSTRACT FROM AUTHOR]

Coincident Geostationary Lightning Mapper (GLM) and National Lightning Detection Network (NLDN) observations are used to build a generator of realistic lightning optical signal in the perspective to simulate Lightning Imager (LI) signal from European NLDN-like observations. Characteristics of GLM and NLDN flashes are used to train different machine-learning (ML) models, which predict simulated pseudo-GLM flash extent, flash duration, and event number per flash (targets) from several NLDN flash characteristics. Comparing statistics of observed GLM targets and simulated pseudo-GLM targets, the most suitable ML-based target generators are identified. The simulated targets are then further processed to obtain pseudo-GLM events and flash-scale products. In the perspective of lightning data assimilation, flash extent density (FED) is derived from both observed and simulated GLM data. The best generators simulate accumulated hourly FED sums with a bias of 2% to the observation while cumulated absolute differences remain of about 22%. A visual comparison reveals that hourly simulated FED features local maxima at the similar geolocations as the FED derived from GLM observations. However, the simulated FED often exceeds the observed FED in regions of convective cores and high flash rates. The accumulated hourly area with FED > 0 flashes per 5 km × 5 km pixel simulated by some pseudo-GLM generators differs by only 7%–8% from the observed values. The recommended generator uses a linear support vector regressor (linSVR) to create pseudo-GLM FED. It provides the best balance between target simulation, hourly FED sum, and hourly electrified area. [ABSTRACT FROM AUTHOR]

Meteorological measurements in the upper troposphere and lower stratosphere were carried out in the past by several high-flying airplanes. It is a common practice to use the temperature spectrum of the time series obtained from these measurements to compute an averaged value (over the flight path) of the temperature structure constant CT2. In this paper time-frequency techniques are used to obtain an estimate for the local spectra of the temperature, which is utilized to obtain local estimates for CT2. [ABSTRACT FROM AUTHOR]