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The multifractal fusion method has proved to be an effective algorithm to mitigate the noise of the sea surface salinity (SSS) of Soil Moisture Ocean Salinity (SMOS) mission. However, the traditional nonparametric weight function used in this method is unable to fully capture the dynamic evolution of the oceanic environment. Considering the multiscale, nonuniform, anisotropic, and flow-dependent nature of the ocean, a prototype with the so-called flexible circle (FLC) weight function or flexible ellipse (FLE) weight function with a set of predefined parameters is proposed in this paper. The improved weight functions could draw dynamic information from the sea surface temperature, Rossby radius of deformation, and surface geostrophic flow to improve the quality of the remotely sensed SSS. The validation against the in situ data indicates that the improved weight functions perform better than the traditional one with a reduced root-mean-square (RMS) and standard deviation (STD) of the differences with respect to EN 4.2.0 profiles (from 0.50 and 0.46 to 0.42 and 0.38 for FLC and 0.39 and 0.36 for FLE in the global ocean). In particular, the FLE scheme could highlight the variation of the strong currents without affecting the computational efficiency. Furthermore, this paper discusses the influences of the error distribution on the fusion results and underlines the importance of error-based adaptions for further improvements. [ABSTRACT FROM AUTHOR]

A practical method for instrumental calibration and aerosol optical properties retrieval based on coherent Doppler lidar (CDL) and sun photometer is presented in this paper. To verify its feasibility and accuracy, this method is applied into three field experiments in 2019 and 2020. In this method, multiwavelength (440, 670, 870, and 1020 nm) aerosol optical depth (AOD) from sun-photometer measurements are used to estimate AOD at 1550 nm and calibrate integrated CDL backscatter signal. Then it is validated by comparing the retrieved calibrated AOD at 1550 nm from CDL signal and that from sun-photometer measurements. Good agreement between them with the correlation of 0.96, the RMSE of 0.0085, and the mean relative error of 22% is found. From the comparison results of these three experiments, sun photometer is verified to be an effective reference instrument for the calibration of CDL return signal and the aerosol optical properties measurement with CDL is feasible. It is expected to promote the study on the aerosol flux and transport mechanism in the planetary boundary layer with the widely deployed CDLs. Significance Statement: A practical method to calibrate and retrieve the vertical profiles of aerosol optical properties with coherent Doppler lidar (CDL) is proposed. Besides the wind field measurement ability, the extended capability of CDL in observing the aerosol optical properties is developed. In this method, a collocated sun photometer is operated as the reference instrument to calibrate and validate the aerosol optical properties from CDL. Since sun photometers and CDLs are world-widely deployed, this method has significant practical value. Additionally, once a CDL is well calibrated, it is promising to mount it on various platforms for simultaneous observations of aerosol optical properties and wind fields. Consequently, the CDL has the potential to measure the aerosol fluxes and vertical transport within the planetary boundary layers. [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]

This paper describes a technique for estimating the liquid water content (LWC) and a characteristic particle diameter in stratocumulus clouds using radar and lidar observations. The uncertainty in LWC estimate from radar and lidar measurements is significantly reduced once the characteristic particle diameter is known. The technique is independent of the drop size distribution. It is applicable for a broad range of W-band reflectivity Z between −30 and 0 dBZ and all values of lidar backscatter β observations. No partitioning of cloud or drizzle is required on the basis of an arbitrary threshold of Z as in prior studies. A method for estimating droplet diameter and LWC was derived from the electromagnetic simulations of radar and lidar observations. In situ stratocumulus cloud and drizzle probe spectra were input to the electromagnetic simulation. The retrieved droplet diameter and LWC were validated using in situ measurements from the southeastern Pacific Ocean. The retrieval method was applied to radar and lidar measurements from the northeastern Pacific. Uncertainty in the retrieved droplet diameter and LWC that are due to the measurement errors in radar and lidar backscatter measurements are 7% and 14%, respectively. The retrieved LWC was validated using the concurrent G-band radiometer estimates of the liquid water path. [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 ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02ms-1, respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds,Aerosols, and Precipitation in theMarineBoundary Layer(CAP-MBL) campaign at the Azores in 2009-10. Two cases corresponding to light andmoderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 gm-2 with the benchmark microwave method. [ABSTRACT FROM AUTHOR]

The Program of the Antarctic Syowa Mesosphere–Stratosphere–Troposphere/Incoherent Scatter (PANSY) radar is a large atmospheric radar located at the Antarctic Syowa Station (69.01°S, 39.59°E). The PANSY radar performed the first incoherent scatter (IS) measurements in the Antarctic region in 2015. Several specific observations were undertaken in 2017 including a 24-h observation of the ionosphere using a peripheral antenna array to suppress interference from the field-aligned irregularities (FAIs). This paper presents the preliminary results derived from the IS measurements using the PANSY radar and the adaptive signal processing techniques to suppress FAIs. The norm-constrained and directionally constrained minimization of power (NC-DCMP) algorithm was applied to the 24-h ionosphere observations by the PANSY radar with a weighting applied to the directional constraint based on the gain differences of the subarrays. When compared with the conventional nonadaptive approach, the number of usable power profiles was increased by about 24% by the gain-weighted NC-DCMP algorithm, suggesting its effectiveness for FAI clutter suppression in ionosphere observations. Furthermore, detection of FAIs using the dedicated antenna array was found valuable in assessing the reliability of estimations of electron density based on VHF-band IS radar data. [ABSTRACT FROM AUTHOR]

Red–green–blue (RGB) composites are increasingly used by operational forecasters to interpret vast amounts of satellite imagery by reducing several bands into a single, easily understood product which identifies important atmospheric features with unique colors. Limb effects, a result of an increase in optical pathlength of the absorbing atmosphere between the satellite and Earth as viewing zenith angle increases, adversely affects RGB composite interpretation by causing anomalous reductions in brightness temperature, thus changing the color interpretation of the RGB composites. In a previous paper, Elmer et al. demonstrated a limb correction technique that effectively removes limb effects from polar-orbiting infrared channels in both clear and cloudy regions using latitudinally and seasonally varying correction coefficients. This study applies the Elmer et al. limb correction to Air Mass RGB composites derived from geostationary sensors and compares the limb-corrected geostationary imagery to limb-corrected polar-orbiter imagery and satellite-derived atmospheric profiles. A statistical comparison in overlapping regions shows that the limb correction reduces the absolute mean brightness temperature difference from 4–12 K to 0–2 K for all infrared bands, demonstrating that the Elmer et al. limb correction algorithm is a robust method of removing limb effects from infrared imagery derived from both geostationary and polar-orbiting sensors. The limb-corrected RGB composites derived from geostationary sensors present several advantages, including the improved depiction of atmospheric features and enabling wider use of imagery from overlapping geostationary coverage regions where viewing zenith angles are large for both geostationary sensors. [ABSTRACT FROM AUTHOR]

Modern polar-orbiting meteorological satellites provide both imaging and sounding observations simultaneously. Most imagers, however, do not have H2O and CO2 absorption bands and therefore struggle to accurately estimate the height of optically thin cirrus clouds. Sounders provide these needed observations, but at a spatial resolution that is too coarse to resolve many important cloud structures. This paper presents a technique to merge sounder and imager observations with the goal of maintaining the details offered by the imager's high spatial resolution and the accuracy offered by the sounder's spectral information. The technique involves deriving cloud temperatures from the sounder observations, interpolating the sounder temperatures to the imager pixels, and using the sounder temperatures as an additional constraint in the imager cloud height optimal estimation approach. This technique is demonstrated using collocated VIIRS and Cross-track Infrared Sounder (CrIS) observations with the impact of the sounder observations validated using coincident CALIPSO/CALIOP cloud heights These comparisons show significant improvement in the cloud heights for optically thin cirrus. The technique should be generally applicable to other imager/sounder pairs. [ABSTRACT FROM AUTHOR]

Doppler sodar is a technology used for acoustic-based remote sensing of the lower planetary boundary layer. Sodars are often used to measure wind profiles; however, they suffer from problems caused by noise (both acoustic and electrical) and echoes from fixed objects, which can bias radial velocity estimates. An experimental bistatic sodar was developed with 64 independent channels. The device enables flexible beamforming; beams can be tilted at the same angle irrelevant of frequency, a limitation in most commercial devices. This paper presents an alternative sodar signal-processing algorithm for wind profiling using a multifrequency stepped-chirp pulse. A noncoherent matched filter was used to analyze returned signals. The noncoherent matched filter combines radial velocity estimates from multiple frequencies into a single optimization. To identify and separate sources of backscatter, noise, and fixed echoes, a stochastic pattern-recognition technique, Gaussian mixture modeling, was used to postprocess the noncoherent matched filter data. This method allowed the identification and separation of different stochastic processes. After identification, noise and fixed echo components were removed and a clean wind profile was produced. This technique was compared with traditional spectrum-based radial velocity estimation methods, and an improvement in the rejection of fixed echo components was demonstrated; this is one of the major limitations of sodar performance when located in complex terrain and urban environments. [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]

Accurate and automated dealiasing of radar data is important for data interpretation and downstream applications such as numerical weather prediction (NWP) models. In this paper an improved radial velocity dealiasing scheme is presented and evaluated using observations from several S-band radars under the severe weather conditions of typhoons and hurricanes. This scheme, named Automated Dealiasing for Typhoon and Hurricane (ADTH), is a further development of the China New Generation Doppler Weather Radar (CINRAD) improved dealiasing algorithm (CIDA). The upgraded algorithm ADTH includes three modules designed to select the first radial from which the dealiasing process starts, to conduct a two-way multipass dealiasing, and to perform an error check for a final local dealiasing. The dealiasing algorithm is applied to two typhoon hurricane cases and four typhoon cases observed with radars from CINRAD, NEXRAD of the United States, and the Taiwan radar network for a continuous period of 12 h for each of the selected cases. The results show that ADTH outperforms CIDA for all of the test cases. [ABSTRACT FROM AUTHOR]

The scattering of microwaves at frequencies between 50 and 200 GHz by ice particles in the atmosphere is an essential element in the retrieval of instantaneous surface precipitation from spaceborne passive radiometers. This paper explores how the variable distribution of solid and liquid hydrometeors in the atmospheric column over land surfaces affects the brightness temperature (TB) measured by GMI at 89 GHz through the analysis of Dual-Frequency Precipitation Radar (DPR) reflectivity profiles along the 89-GHz beam. The objective is to refine the statistical relations between observed TBs and surface precipitation over land and to define their limits. As GMI is scanning with a 53° Earth incident angle, the observed atmospheric volume is actually not a vertical column, which may lead to very heterogeneous and seemingly inconsistent distributions of the hydrometeors inside the beam. It is found that the 89-GHz TB is mostly sensitive to the presence of ice hydrometeors several kilometers above the 0°C isotherm, up to 10 km above the 0°C isotherm for the deepest convective systems, but is a modest predictor of the surface precipitation rate. To perform a precise mapping of atmospheric ice, the altitude of the individual ice clusters must be known. Indeed, if variations in the altitude of ice are not accounted for, then the high incident angle of GMI causes a horizontal shift (parallax shift) between the estimated position of the ice clusters and their actual position. We show here that the altitude of ice clusters can be derived from the 89-GHz TB itself, allowing for correction of the parallax shift. [ABSTRACT FROM AUTHOR]

The air-sea flux of greenhouse gases [e.g., carbon dioxide (CO2)] is a critical part of the climate system and a major factor in the biogeochemical development of the oceans. More accurate and higher-resolution calculations of these gas fluxes are required if researchers are to fully understand and predict future climate. Satellite Earth observation is able to provide large spatial-scale datasets that can be used to study gas fluxes. However, the large storage requirements needed to host such data can restrict its use by the scientific community. Fortunately, the development of cloud computing can provide a solution. This paper describes an open-source air-sea CO2 flux processing toolbox called the 'FluxEngine,' designed for use on a cloud-computing infrastructure. The toolbox allows users to easily generate global and regional air-sea CO2 flux data from model, in situ, and Earth observation data, and its air-sea gas flux calculation is user configurable. Its current installation on the Nephalae Cloud allows users to easily exploit more than 8 TB of climate-quality Earth observation data for the derivation of gas fluxes. The resultant netCDF data output files contain >20 data layers containing the various stages of the flux calculation along with process indicator layers to aid interpretation of the data. This paper describes the toolbox design, which verifies the air-sea CO2 flux calculations; demonstrates the use of the tools for studying global and shelf sea air-sea fluxes; and describes future developments. [ABSTRACT FROM AUTHOR]

The CSU-CHILL radar is a dual-wavelength, dual-polarization weather radar system operating at S and X bands with coaxial beams. One of the capabilities of this radar system is the possibility of developing and/or validating algorithms across dual wavelengths and dual polarizations. This paper presents one such instance, showing how the rainfall field can be estimated either from the S- and X-band reflectivities or from the differential propagation phase at X band. To do so, the paper first presents a dual-wavelength attenuation correction method that uses the reflectivity measured at S band, as the constraint for the correction of the reflectivity measured at X band, and it describes how Mie scattering regions at X band may be detected from the retrieved path-integrated attenuation field. Then, the paper describes how the resulting specific attenuation field relates to rainfall and specific phase at X band, which can be obtained from dual-polarization data at a single wavelength as well, and shows examples. Finally, the paper looks at the relation between attenuation and the differential phase as a function of elevation angle for a few cases, which may be related to the drop size distribution and mean diameter, as well as temperature. [ABSTRACT FROM AUTHOR]

One pivotal factor affecting the accuracy of HF radar current measurements is the direction of arrival (DOA) estimation performance of the current signal. The beamforming technology or superresolution algorithm cannot always perform best in practical applications because of the phase errors existing in array channels. These phase errors, which cause uncertain estimation of DOA, lead to confused values in radial current maps. To solve this problem, this paper is focused on discussing the performances of two autocalibration methods using sea echoes for multifrequency high-frequency (MHF) radar current measurements. These two array calibration methods, based on maximum likelihood (ML) and multiple signal classification (MU), first seek single-DOA sea echoes and then gather them for array calibration using different cost functions. The ML and MU methods provide approximate mean phases, while the standard phase errors of the MU method are smaller. After array calibration using these two methods, the results show significant improvements in current retrievals. Comparisons between the MHF radar and ADCPs reveal that array calibration using the ML and MU methods also improves the estimation of radial currents clearly, with correlation coefficients over 0.93 and rms differences of 0.09-0.18 m s−1 at different operating frequencies and sampling locations. The performance of the bearing offset is also improved. Only small bearing offsets less than 10° exist in radial current measurements. Therefore, this paper demonstrates that array calibration is a crucial part for current measurements, especially for direction-finding HF radar. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of mean wind measurements from a coordinated system of long-range WindScanners. From individual scan patterns the mean wind field was reconstructed over a large area, and hence it highlights the spatial variability. From comparison with sonic anemometers, the quality of the WindScanner data is high, although the fidelity of the estimated vertical velocity component is significantly limited by the elevation angles of the scanner heads. The system of long-range WindScanners presented in this paper is close to being fully operational, with the pilot study herein serving not only as a proof of concept but also verifying expectations of reliable wind measurements over arbitrary three-dimensional volumes, in future sustained meteorological campaigns. [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]

The National Aeronautics and Space Administration (NASA) has always included data reprocessing as a major component of every science mission. A final reprocessing is typically a part of mission closeout (known as phase F). The Tropical Rainfall Measuring Mission (TRMM) is currently in phase F, and NASA is preparing for the last reprocessing of all the TRMM precipitation data as part of the closeout. This reprocessing includes improvements in calibration of both the TRMM Microwave Imager (TMI) and the TRMM Precipitation Radar (PR). An initial step in the version 8 reprocessing is the improvement of geolocation. The PR calibration is being updated by the Japan Aerospace Exploration Agency (JAXA) using data collected as part of the calibration of the Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory. JAXA undertook a major effort to ensure TRMM PR and GPM Ku-band calibration is consistent. A major component of the TRMM version 8 reprocessing is to create consistent retrievals with the GPM version 05 (V05) retrievals. To this end, the TRMM version 8 reprocessing uses retrieval algorithms based on the GPM V05 algorithms. This approach ensures consistent retrievals from December 1997 (the beginning of TRMM) through the current ongoing GPM retrievals. An outcome of this reprocessing is the incorporation of TRMM data products into the GPM data suite. Incorporation also means that GPM file naming conventions and reprocessed TRMM data carry the V05 data product version. This paper describes the TRMM version 8 reprocessing, focusing on the improvements in TMI level 1 products. [ABSTRACT FROM AUTHOR]

Carbon flux phenology is widely used to understand carbon flux dynamics and surface exchange processes. Vegetation phenology has been widely evaluated by remote sensors; however, very few studies have evaluated the use of vegetation phenology for identifying carbon flux phenology. Currently available techniques to derive net ecosystem exchange (NEE) from a satellite image use a single generic modeling subgroup for agricultural crops. But, carbon flux phenological processes vary highly with crop types and land management practices; this paper reexamines this assumption. presented here are an evaluation of ground-truth remotely sensed vegetation indices with in situ NEE measurements and an identification of vegetation indices for estimating carbon flux phenology metrics by crop type. Results show that the performance of different vegetation indices as an indicator of phenology varies with crop type, particularly when identifying the start of a season and the peak of a season. Maize fields require vegetation indices that make use of the near-infrared and red reflectance bands, while soybean fields require those making use of the shortwave infrared (IR) and near-IR bands. In summary, the study identifies how to best utilize remote sensing technology as a crop-specific measurement tool. [ABSTRACT FROM AUTHOR]

The Broadband Radiometer (BBR) is one of a suite of instruments to be flown on the Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) space mission. Its role is to make broadband measurements of Earth radiance in terms of reflected solar radiation and emitted thermal infrared radiation for use with the other EarthCARE instruments for the study of Earth atmosphere processes. The Broadband Radiometer has its design based on the principles and heritage of previous instruments for studying the earth radiation budget (ERB). The radiometer has common features with those instruments: two measurement bands-shortwave (solar energy of 0.25-4 µm) and total wave (0.25 to >50 µm)-with a longwave band (thermal emission of 4 to >50 µm) being obtained by subtraction of the two measured bands. Multiple simultaneous views of Earth at three different view angles are used to account for angular variations in radiance. The radiometer requires an accuracy of 1% in each band, similar to those of the previous instruments, and detailed calibration measurements on ground and in orbit. This paper describes the instrument calibration algorithms and the corresponding requirements on the ground calibration of the flight instrument prior to launch. It includes a description of the main methods to be used and the error sources to be controlled, based on the design heritage and analysis results of the previous ERB instruments. [ABSTRACT FROM AUTHOR]

In Doppler weather radars, the presence of unfolding errors or outliers is a well-known quality issue for radial velocity fields estimated using the dual-pulse repetition frequency (PRF) technique. Postprocessing methods have been developed to correct dual-PRF outliers, but these need prior application of a dealiasing algorithm for an adequate correction. This paper presents an alternative procedure based on circular statistics that corrects dual-PRF errors in the presence of extended Nyquist aliasing. The correction potential of the proposed method is quantitatively tested by means of velocity field simulations and is exemplified in the application to real cases, including severe storm events. The comparison with two other existing correction methods indicates an improved performance in the correction of clustered outliers. The technique proposed is well suited for real-time applications requiring high-quality Doppler radar velocity fields, such as wind shear and mesocyclone detection algorithms, or assimilation in numerical weather prediction models. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for the automatic determination of the diagonal-loading level for robust adaptive beamforming on radar wind profilers. This method balances the degradation of the signal-to-interference ratio with that of the signal-to-noise ratio to maximize the detectability of the backscattered signals. Because radial wind velocities are usually estimated from the first moment of the spectrum of backscattered echoes, both the residual ground clutter and any increase in noise level degrade the detectability of atmospheric echoes. The proposed algorithm evaluates the power spectral density of the residual clutter and increased noise to determine the optimal diagonal-loading level by balancing these two factors. The results of numerical simulation show that, without the need to specify any user parameters, the proposed algorithm is stable and more effective at maximizing the signal-to-interference ratio than the conventional norm-constrained diagonal-loading approach. The stability and clutter suppression capability of the proposed algorithm are examined using data from the Program of the Antarctic Syowa Mesosphere-Stratosphere-Troposphere/Incoherent Scatter Radar. [ABSTRACT FROM AUTHOR]

The refractivity from clutter (RFC) technique has been proved to be an effective way to estimate atmospheric duct structure. An important issue for RFC is how to make the estimate more robust, especially in range-dependent ducting conditions. Traditionally, statistical inversion methods need a large number of forward propagation model runs to obtain an acceptable result. Especially when the parameter search space is multidimensional, these methods are prone to being trapped into local optimal solutions. Recently published results (Zhao and Huang) indicate that the adjoint parabolic equation (PE) method holds promise for real-time estimation of one-dimensional refractive index structure from radar sea clutter returns. This paper is aimed at extending the adjoint PE method to range-dependent evaporation duct cases, with a log-linear relationship describing duct structures. Numerical simulations are used to test the performance of this method and the results are compared with that retrieved using a genetic algorithm. Both noise-free and 3-dB additive Gaussian noise clutter simulations are considered, as well as linearly and nonlinearly varying duct height with range. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive scan strategy concept for phased array weather radars (PAWR) with the objective of increasing the scan speed and capturing features of the storm system while maintaining the measurement accuracy. The adaptive scan strategy is developed based on the space-time variability of the storm under observation. Quickly evolving regions are scanned more often and the spatial sampling resolution is matched to the spatial scale. A model that includes the interaction between space and time is used to extract spatial and temporal scales of the medium and to define scanning regions. The temporal scale constrains the radar revisit time, while the measurement accuracy controls the radar's dwell time. These conditions are employed in a task scheduler that works on a ray-by-ray basis and is designed to balance task priority and radar resources. The scheduler algorithm also includes an optimization procedure for minimizing radar scan time. The model and the scan strategy are demonstrated using simulation data. The results show that the proposed scan strategy can reduce the scan time significantly without compromising data quality. [ABSTRACT FROM AUTHOR]

Rain evaporation, while significantly contributing to moisture and heat cloud budgets, is a still poorly understood process with few measurements presently available. Multiwavelength lidars, widely employed in aerosols and clouds studies, can also provide useful information on the microphysical characteristics of light precipitation, for example, drizzle and virga. In this paper, lidar measurements of the median volume raindrop diameter and rain evaporation rate profiles are compared with a model analytical solution. The intercomparison reveals good agreement between the model and observations, with a correlation between the profiles up to 65% and a root-mean-square error up to 22% with a 5% bias. Larger discrepancies are due to radiosonde soundings different air masses and model assumptions no more valid along the profile as nonsteady atmosphere and/or appearance of collision-coalescence processes. Nevertheless, this study shares valuable information to better characterize the rain evaporation processes. [ABSTRACT FROM AUTHOR]

This paper presents a novel, simple method to correct reflectivity measurements of weather radars that operate in attenuation-influenced frequency bands using observations from less attenuated radar systems. In recent years radar systems operating in the X-band frequency range have been developed to provide precipitation fields for areas of special interest in high temporal (≤1 min) and spatial (≤250 m) resolution in complement to nationwide radar networks. However, X-band radars are highly influenced by attenuation. C- and S-band radars typically have coarser resolution (250 m-1 km and 5 min) but are less affected by attenuation. Correcting for attenuation effects in simple (non-Doppler) single-polarized X-band radars remains challenging and is often dependent on restriction parameters, for example, those derived from mountain returns. Therefore, these algorithms are applicable only in limited areas. The method proposed here uses measurements from C-band radars and hence can be applied in all regions covered by nationwide C- (or S-) band radar networks. First, a single scan of X-band radar measurements is used exemplary to identify advantages and disadvantages of the novel algorithm compared to a standard single radar algorithm. The performance of the correction algorithms in different types of precipitation is examined in nine case studies. The proposed method provides very promising results for each type of precipitation. Additionally, it is evaluated in a 5-month comparison with Micro Rain Radar (MRR) observations. The bias between uncorrected X-band radar and MRR data is nearly eliminated by the attenuation correction algorithm, and the RMSE is reduced by 20% while the correlation of ~0.9 between both systems remains nearly constant. [ABSTRACT FROM AUTHOR]

The 35-yr NOAA Advanced Very High Resolution Radiometer (AVHRR) observation record offers an excellent opportunity to study decadal climate variability, provided that all participating AVHRR instruments are calibrated on a consistent radiometric scale. Because of the lack of onboard calibration systems, the solar imaging channels of the AVHRR must be vicariously calibrated using invariant Earth targets as a calibrated reference source. The greatest challenge in calibrating the AVHRR dataset is the orbit degradation of the NOAA satellites, which eventually drift into a terminator orbit several years after launch. Therefore, the invariant targets must be characterized over the full range of solar zenith angles (SZAs) sampled by the satellite instrument. This study outlines a multiple invariant Earth target calibration approach specifically designed to account for the degrading NOAA orbits. The desert, polar ice, and deep convective cloud (DCC) invariant targets are characterized over all observed SZAs using NOAA-16 AVHRR measurements, which are referenced to the Aqua MODIS Collection 6 calibration via direct transfer of the MODIS calibration to the NOAA-16 AVHRR instrument using simultaneous nadir overpass (SNO) observations over the North Pole. The multiple invariant target calibrations are combined using the inverse of their temporal variance to optimize the resulting calibration stability. The NOAA-18 AVHRR gains derived using the desert, polar ice, and DCC targets, as well as from SNO, were found consistent within 1%, thereby validating that the Aqua MODIS calibration is effectively transferred to the reference calibration targets. The companion paper, Part II, applies the methodology across the AVHRR record to derive the sensor-specific calibration coefficients. [ABSTRACT FROM AUTHOR]

In this paper, the operational Global Precipitation Measurement (GPM) mission combined radar-radiometer algorithm is thoroughly described. The operational combined algorithm is designed to reduce uncertainties in GPM Core Observatory precipitation estimates by effectively integrating complementary information from the GPM Dual-Frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) into an optimal, physically consistent precipitation product. Although similar in many respects to previously developed combined algorithms, the GPM combined algorithm has several unique features that are specifically designed to meet the GPM objectives of deriving, based on GPM Core Observatory information, accurate and physically consistent precipitation estimates from multiple spaceborne instruments, and ancillary environmental data from reanalyses. The algorithm features an optimal estimation framework based on a statistical formulation of the Gauss-Newton method, a parameterization for the nonuniform distribution of precipitation within the radar fields of view, a methodology to detect and account for multiple scattering in Ka-band DPR observations, and a statistical deconvolution technique that allows for an efficient sequential incorporation of radiometer information into DPR precipitation retrievals. [ABSTRACT FROM AUTHOR]

Vertical mixing is thought to play an essential role in phytoplankton blooms, yet measurements of mixing properties are very sparse. This paper presents a methodology to estimate profiles of the upper-ocean vertical mixing from satellite color observations, using a coupled turbulence-phytoplankton model and data assimilation-based calibration techniques. The method is tested at a location in the eastern North Atlantic for which an integrated set of observations (vertical mixing, phytoplankton, nutrients) is available. Results of identical twin experiments show that the method is very robust and achieves accurate turbulence model parameter calibrations even with noisy or sparsely sampled observations. The application of surface chlorophyll- a (Chl a) concentration to MODIS Aqua satellite observations leads two independent cases (data for the years 2009 and 2011) to a calibration of the model parameterization that produces weaker winter mixing compared to the standard configuration. As a consequence of the weaker mixing, the timing and intensity of increased surface Chl a satellite observations in spring and summer was reproduced by the model. Moreover, the weaker mixing resembles the in situ observations of vertical mixing better than the stronger mixing based on the standard configuration. This shows that the new calibration indeed improves the performance of the turbulence model. [ABSTRACT FROM AUTHOR]

This study provides the first comprehensive quantitative detection efficiency (DE) estimation of the Met Office long-range very low-frequency (VLF) lightning location system, the Arrival Time Difference Network ATDnet, with a focus on cloud flash detection. ATDnet uses sensors in and around Europe to detect lightning in Europe, northern Africa, and northern parts of the Atlantic. In the present paper, ATDnet is validated against the Hydrological Cycle in the Mediterranean Experiment (HyMeX) Lightning Mapping Array (HyLMA) deployed in the south of France in autumn 2012 as a part of the HyMeX project Special Observation Period 1 (SOP1). Three storms (on 5, 11, and 25 September 2012) were selected for this study, as their activity was sufficient to provide good statistics but also low enough to avoid ambiguous situations due to temporally overlapping flashes. The overall ATDnet flash DE was approximately 89% for the 281 ground flashes [cloud to ground (CG)] and 24% for the 1324 cloud flashes [inter/intracloud (IC)] during the storms. It was also demonstrated that variations in cloud flash characteristics (e.g., flash vertical extent) have a significant impact on DE between and during the studied storms. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) algorithms consist of modules. This paper describes version 4 (V4) of GPM DPR level 2 (L2) classification (CSF) modules, which consist of two single-frequency (SF) modules-that is, Ku-only and Ka-only modules-and a dual-frequency (DF) module. Each CSF module detects bright band (BB) and classifies rain into three major types, that is, stratiform, convective, and other. The Ku-only and Ka-only CSF modules use algorithms that are similar to the Tropical Rainfall Measuring Mission (TRMM) rain type classification algorithm 2A23. The DF CSF module uses a new method called the measured dual-frequency ratio (DFRm) method for the rain type classification and the detection of BB. It is shown that the Ku-only CSF module and the DF CSF module produce almost indistinguishable rain type counts in a statistical sense. It is also shown that the DFRm method in the DF CSF module improves the detection of BB. [ABSTRACT FROM AUTHOR]

This paper describes a cloud type radiance record derived from NOAA polar-orbiting weather satellites using cloud properties retrieved from the Advanced Very High Resolution Radiometer (AVHRR) and spectral brightness temperatures ( T b) observed by the High Resolution Infrared Radiation Sounder (HIRS). The authors seek to produce a seamless, global-scale, long-term record of cloud type and T b statistics intended to better characterize clouds from seasonal to decadal time scales. Herein, the methodology is described in which the cloud type statistics retrieved from AVHRR are interpolated onto each HIRS footprint using two cloud classification methods. This approach is tested over the northeast tropical and subtropical Pacific Ocean region, which contains a wide variety of cloud types during a significant ENSO variation from 2008 to 2009. It is shown that the T b histograms sorted by cloud type are realistic for all HIRS channels. The magnitude of T b biases among spatially coincident satellite intersections over the northeast Pacific is a function of cloud type and wavelength. While the sign of the bias can change, the magnitudes are generally small for NOAA-18 and NOAA-19, and NOAA-19 and MetOp-A intersections. The authors further show that the differences between calculated standard deviations of cloud-typed T b well exceed intersatellite calibration uncertainties. The authors argue that consideration of higher-order statistical moments determined from spectral infrared observations may serve as a useful long-term measure of small-scale spatial changes, in particular cloud types over the HIRS-AVHRR observing record. [ABSTRACT FROM AUTHOR]

The rain/no-rain classification for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) fails to detect rain over coasts, where the microwave footprint encompasses a mixture of radiometrically cold ocean and radiometrically warm land. A static land-ocean-coast mask is used to determine the surface type of each satellite footprint. The coast mask is conservatively wide to account for the largest footprints, preventing use of the more appropriate ocean or land algorithm for coastal regions. The purpose of this paper is to develop a classification whereby the smallest region possible is defined as coast. In this endeavor, two major improvements are applied to the land-ocean-coast classification. First, the surface classification based on microwave footprints of the high frequency actually used in rain detection is employed. Second, the footprint area of the surface classification is established using an effective field-of-view size and scan geometry of the TMI. These improvements are applied to the Global Satellite Mapping of Precipitation TMI algorithm. The classification result is validated using the TRMM precipitation radar. The validation shows that these improvements lead to better rain detection in the coastal region. [ABSTRACT FROM AUTHOR]

High-frequency radar (HFR) surface current data are an increasingly utilized tool for capturing complex dynamics of coastal ocean systems worldwide. The radar is uniquely capable of sampling relevant temporal and spatial scales of nearshore processes that impact event response activities and basic coastal ocean research. HFR is a shore-based remote sensing system and is therefore subject to data gaps, which are predominately due to environmental effects, like increased external noise or low signal due to ocean surface conditions. Many applications of these surface current data require that these gaps be filled, such as Lagrangian numerical models, to estimate material transport and dispersion. This study introduces a new penalized least squares regression method based on a three-dimensional discrete cosine transform method to reconstruct hourly HFR surface current data with a horizontal resolution of 6 km. The method explicitly uses both time and space variability to predict the missing value. Furthermore, the method is fast, robust, and requires relatively low computer memory storage. This paper evaluates the method against two scenarios of common data gaps found in HFR networks currently deployed around the world. The validation is based on observed surface current maps along the mid-Atlantic coast of the United States with specific vectors removed to replicate these common gap scenarios. The evaluation shows that the new method is robust and particularly well suited to fill a more common scenario with complete data coverage surrounding an isolated data gap. It is shown that the real-time application of the method is suitable for filling data gaps in large oceanography datasets with high accuracy. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has enabled remarkable progress in the ocean, earth, atmospheric, and environmental sciences through its ability to provide global coverage with ever-increasing spatial resolution. While exceptions exist for geostationary ocean color satellites, the temporal coverage of low-Earth-orbiting satellites is not optimal for oceanographic processes that evolve over time scales of hours to days. In hydrology, time scales can range from hours for flash floods, to days for snowfall, to months for the snowmelt into river systems. On even smaller scales, remote sensing of the built environment requires a building-resolving resolution of a few meters or better. For this broad range of phenomena, satellite data need to be supplemented with higher-resolution airborne data that are not tied to the strict schedule of a satellite orbit. To address some of these needs, a novel, portable, high-resolution airborne topographic lidar with video, infrared, and hyperspectral imaging systems was integrated. The system is coupled to a highly accurate GPS-aided inertial measurement unit (GPS IMU), permitting airborne measurements of the sea surface displacement, temperature, and kinematics with swath widths of up to 800 m under the aircraft, and horizontal spatial resolution as low as 0.2 m. These data are used to measure ocean waves, currents, Stokes drift, sea surface height (SSH), ocean transport and dispersion, and biological activity. Hydrological and terrestrial applications include measurements of snow cover and the built environment. This paper describes the system, its performance, and present results from recent oceanographic, hydrological, and terrestrial measurements. [ABSTRACT FROM AUTHOR]

As atmospheric reflectance dominates top-of-the-atmosphere radiance over ocean, atmospheric correction is a critical component of ocean color retrievals. This paper explores the operational Sea-viewing Wide Field-of-view Sensor (SeaWiFS) algorithm atmospheric correction with ~13 000 coincident surface-based aerosol measurements. Aerosol optical depth at 440 nm (AOD440) is overestimated for AOD below ~0.1-0.15 and is increasingly underestimated at higher AOD; also, single-scattering albedo (SSA) appears overestimated when the actual value <~0.96. AOD440 and its spectral slope tend to be overestimated preferentially for coarse-mode particles. Sensitivity analysis shows that changes in these factors lead to systematic differences in derived ocean water-leaving reflectance ( Rrs) at 440 nm. The standard SeaWiFS algorithm compensates for AOD anomalies in the presence of nonabsorbing, medium-size-dominated aerosols. However, at low AOD and with absorbing aerosols, in situ observations and previous case studies demonstrate that retrieved Rrs is sensitive to spectral AOD and possibly also SSA anomalies. Stratifying the dataset by aerosol-type proxies shows the dependence of the AOD anomaly and resulting Rrs patterns on aerosol type, though the correlation with the SSA anomaly is too subtle to be quantified with these data. Retrieved chlorophyll- a concentrations (Chl) are affected in a complex way by Rrs differences, and these effects occur preferentially at high and low Chl values. Absorbing aerosol effects are likely to be most important over biologically productive waters near coasts and along major aerosol transport pathways. These results suggest that future ocean color spacecraft missions aiming to cover the range of naturally occurring and anthropogenic aerosols, especially at wavelengths shorter than 440 nm, will require better aerosol amount and type constraints. [ABSTRACT FROM AUTHOR]

Many quantitative uses of the nighttime imagery provided by low-light sensors, such as the day-night band (DNB) on board the Suomi-National Polar-Orbiting Partnership ( SNPP), have emerged recently. Owing to the low nighttime radiance, low-light calibration at night must be investigated in detail. Traditional vicarious calibration methods are based on some targets with nearly invariant surface properties under lunar illumination. However, the relatively stable light emissions may also be used to realize the radiometric calibration under low light. This paper presents a low-light calibration method based on bridge lights, and Visible Infrared Imaging Radiometer Suite (VIIRS) DNB data are used to assess the proposed method. A comparison of DNB high-gain-stage (HGS) radiances over a 2-yr period from August 2012 to July 2014 demonstrates that the predictions are consistent with the observations, and the agreement between the predictions and the observations is on the order of −2.9% with an uncertainty of 9.3% (1 σ) for the Hangzhou Bay Bridge and −3.9% with an uncertainty of 7.2% (1 σ) for the Donghai Bridge. Such a calibration method based on stable light emissions has a wide application prospect for the calibration of low-light sensors at night. [ABSTRACT FROM AUTHOR]

The measurement range of a coherent wind Doppler lidar (CWDL) along a laser beam is the maximum distance from the lidar where wind speed data are accurately retrieved. It means that, at this distance, a sufficient number of emitted laser photons are backscattered and received by the lidar. Understanding of the propagation of the laser through the atmosphere, and particularly the backscattering and extinction processes from aerosols, is therefore important to estimate the metrological performances of a CWDL instrument. The range is directly related to specific instrument characteristics and atmospheric content, such as the aerosols type, size, and density distributions. Associated with the measurement range is the notion of data availability, which can be defined, at a given range and over a time period, as the percentage number of data retrieved correctly by the CWDL over the total number of measurement attempts. This paper proposes a new approach to predict the CWDL data availability and range of measurement using both instrumental simulation and atmospheric observations of aerosol optical properties from weather stations and simulations. This method is applied in several CWDL measurement campaigns during which estimated data availabilities and ranges are compared with the observations. It is shown that it is fairly possible to anticipate the data availability and the range coverage of CWDL technology at any site of interest where atmospheric data are available. The method also offers an additional way to diagnose the operation of the instrument and will help in the design of future instruments. [ABSTRACT FROM AUTHOR]

This paper documents the study of the radar signature of the sea surface on the images collected by an X-band weather radar and its application to remote sensing of the coastal zone. The main radar parameters considered here are the reflectivity factor [which was converted into a normalized radar cross section (NRCS)], the differential reflectivity, and the mean radial velocity. Measurements of the NRCS in the vicinity of an instrumented offshore buoy allowed for identifying its variations with wind speed and relative direction, which are found to be consistent with the Georgia Institute of Technology (GIT) model and other measurements. A more accurate empirical model for the NRCS is developed. For moderate to strong winds, the mean radial velocity is linearly related to the radial component of the wind speed, showing the potentiality of weather radars to map the radial surface wind speed at the scale of a basin. The influence of surface currents is weak here but should be likely taken into account in other places with stronger currents. Measurements of the differential reflectivity are also consistent with other measurements made elsewhere for the polarization ratio. The analysis of radar data over the entire coverage could also be performed using surface wind estimates from a meteorological model. Similar but less accurate results are obtained for the NRCS and the mean radial velocity. The extra spatial variability not due to wind speed variations is identified. An inversion procedure is proposed to recover the wind vector field from radar measurements after calibration of the NRCS values. Results are promising and pave the way to the concept of weather radar for ocean remote sensing. [ABSTRACT FROM AUTHOR]

EUMETSAT has been deriving atmospheric motion vectors (AMV) operationally from the EUMETSAT Polar System satellite MetOp over polar regions since 2011. The launch of MetOp-B in 2012 permitted doubling the frequency of extracting AMVs using AVHRR imagery data of both MetOp-A and -B satellites. The tandem configuration with two satellites on the same orbital plane, but with a phase difference, provided an interesting opportunity to create global AMVs from the satellites with a significant overlap in imagery data. EUMETSAT has therefore developed a new global AVHRR winds product derived from a pair of MetOp-A and MetOp-B images that has been operational since January 2015. The temporal gap between the two images used for tracking clouds is about 50 min. The global coverage of this new wind product allows for a homogeneous retrieval of wind product over the whole globe, including the polar regions. This clearly helps fill the gaps between 55° and 70° latitude north and south, where only few wind observations are currently available for assimilation into numerical weather prediction models. The new global AVHRR wind product can be directly compared with AMVs derived from geostationary satellites. This paper describes the scientific concept of wind extraction using dual MetOp satellites. It highlights the performance of the new global AVHRR wind product by comparing with collocated AMVs extracted from the EUMETSAT geostationary satellites Meteosat-7 and Meteosat-10. [ABSTRACT FROM AUTHOR]

Two new algorithms for hydrometeor classification using polarimetric radar observations are developed based on prototypes derived by applying clustering techniques (Part I of this two-part paper). Each prototype is defined as a probability distribution of the polarimetric variables and ambient temperature corresponding to a hydrometeor type. The first algorithm is a maximum prototype likelihood classifier that uses all prototypes attributed to the different hydrometeor types in Part I. The hydrometeor type is assigned as the prototype with the highest likelihood when comparing the polarimetric variables and temperature with each prototype. The second algorithm is a Bayesian classifier that uses the probability density functions (PDFs) as derived from the prototype set associated with the identical hydrometeor type. The posteriori probability in the Bayesian method is calculated from a combination of the PDFs and the prior probability, the maximum of which corresponds to the most likely hydrometeor type. The respective merits of the two techniques are discussed. The two classifiers are applied to CP-2 S-band radar observations of two hailstorms that occurred between 16 and 20 November 2008, including the so-called Gap storm, which produced a devastating microburst and large hail at the ground. Results from the classifiers are compared with those derived using the well-established National Center for Atmospheric Research fuzzy logic classifier. In general, good agreement is found, yielding overall confidence in the robustness of the new classifiers. However, large differences are found for the melting ice and ice crystal categories, which will need to be studied further. [ABSTRACT FROM AUTHOR]

Conventional altimetry measures a one-dimensional profile of sea surface height (SSH) along the satellite track. Two-dimensional SSH can be reconstructed using mapping techniques; however, the spatial resolution is quite coarse even when data from several altimeters are analyzed. A new satellite mission based on radar interferometry is scheduled to be launched in 2020. This mission, called Surface Water and Ocean Topography (SWOT), will measure SSH at high resolution along a wide swath, thus providing two-dimensional images of the ocean surface topography. This new capability will provide a large amount of data even though they are contaminated with instrument noise and geophysical errors. This paper presents a tool that simulates synthetic observations of SSH from the future SWOT mission using SSH from any ocean general circulation model (OGCM). SWOT-like data have been generated from a high-resolution model and analyzed to investigate the sampling and accuracy characteristics of the future SWOT data. This tool will help explore new ideas and methods for optimizing the retrieval of information from future SWOT missions. [ABSTRACT FROM AUTHOR]

Low-frequency passive microwave observations allow for oceanic remote sensing of surface wind speed and rain rate from spaceborne and airborne platforms. For most instruments, the modeling of contributions of rain absorption and reemission in a particular field of view is simplified by the observing geometry. However, the simplifying assumptions that can be applied in most applications are not always valid for the scenes that the airborne Hurricane Imaging Radiometer (HIRAD) regularly observes. Collocated Stepped Frequency Microwave Radiometer (SFMR) and HIRAD observations of Hurricane Earl (2010) indicate that retrieval algorithms based on the usual simplified model, referred to here as the decoupled-pixel model (DPM), are not able to resolve two neighboring rainbands at the edge of HIRAD's swath. The DPM does not allow for the possibility that a single column of atmosphere can affect the observations at multiple cross-track positions. This motivates the development of a coupled-pixel model (CPM) that is developed and tested in this paper. Simulated observations as well as HIRAD's observations of Hurricane Earl (2010) are used to test the CPM algorithm. Key to the performance of the CPM algorithm is its ability to deconvolve the cross-track scene, as well as unscramble the signatures of surface wind speed and rain rate in HIRAD's observations. While the CPM approach was developed specifically for HIRAD, other sensors could employ this method in similar complicated observing scenarios. [ABSTRACT FROM AUTHOR]

A multifrequency high-frequency (MHF) radar system was designed and developed by Wuhan University in 2007. This system can simultaneously operate at four frequencies mainly in the 7.5-25-MHz band. This paper focuses on discussing the performances of an MHF radar system deployed along the coast of the East China Sea based on comparisons with multidepth ADCP datasets, which were obtained from ADCPs deployed at different locations in August 2010 during a small storm. The comparisons illustrate that radar-derived radial currents are correlated with ADCP data at mainly a 2-4-m depth with correlation coefficients over 0.95 and RMS differences less than 0.12 m s−1 for both operating frequencies. Bearing offsets at points A, C, and D are computed for different operating frequencies with magnitudes of 0°-11°. The capability of MHF radar to measure currents at different depths is explored. The results indicate that the effective depth of current measurements by MHF radar increases with decreasing operating frequency. A linear regression (with a regression coefficient of 0.0576) of the responses in the mean effective depth on the predictors in radio wavelength is obtained. The dominant semidiurnal and diurnal constituents are also analyzed. The radial current amplitudes of the M2 and K1 constituents are strong in this area during this experiment. The residual currents vary with wind speed, with a correlation coefficient of 0.52. A correlation coefficient of 0.79 between nontidal currents and the radial wind speed after a clockwise rotation of the wind vector by about 50° was obtained. [ABSTRACT FROM AUTHOR]

This study investigates the potential of rotary wing unmanned aerial vehicles (UAVs) to monitor the surfzone. This paper shows that these UAVs are extremely flexible surveying platforms that can gather near-continuous moderate spatial resolution and high temporal resolution imagery from a fixed position high above a study site. The rotary wing UAVs used in this study can fly for ~12 min with a mean loiter radius of 1-3.5 m and a mean loiter error of 0.75-4.5 m. These numbers depend on the environmental conditions, flying style, battery type, and vehicle type. The images obtained from the UAVs, and in combination with surveyed ground control points (GCPs), can be georectified to a pixel resolution between 0.01 and 1 m, and a reprojection error-that is, the difference between the surveyed GPS location of a GCP and the location of the GCP obtained from the georectified image-of O(1 m). The flexibility of rotary wing UAVs provides moderate spatial resolution and high temporal resolution imagery, which are highly suitable to quickly obtain surfzone and beach characteristics in response to storms or for day-to-day beach safety information, as well as scientific pursuits of surfzone kinematics on different spatial and temporal scales, and dispersion and advection estimates of pollutants. [ABSTRACT FROM AUTHOR]

The Airborne Remote Optical Spotlight System (AROSS) family of sensors consists of airborne imaging systems that provide passive, high-dynamic range, time series image data and has been used successfully to characterize currents and bathymetry of nearshore ocean, tidal flat, and riverine environments. AROSS-multispectral polarimeter (AROSS-MSP) is a 12-camera system that extends this time series capability to simultaneous color and polarization measurements for the full linear polarization of the imaged scene in red, green, and blue, and near-infrared (RGB-NIR) wavelength bands. Color and polarimetry provide unique information for retrieving dynamic environmental parameters over a larger area (square kilometers) than is possible with typical in situ measurements. This particular field of optical remote sensing is developing rapidly, and simultaneous color and polarimetric data are expected to enable the development of a number of additional important environmental data products, such as the improved ability to image the subsurface water column or maximizing wave contrast to improve oceanographic parameter retrievals of wave spectra and wave heights. One of the main obstacles to providing good-quality polarimetric image data from a multicamera system is the ability to accurately merge imagery from the cameras to a subpixel level. This study shows that the imagery from AROSS-MSP can be merged to an accuracy better than one-twentieth of a pixel, comparing two different automated algorithmic techniques. This paper describes the architecture of AROSS-MSP, the approach for providing simultaneous color and polarization imagery in space and time, an error analysis to characterize the measurements, and example color and polarization data products from ocean wave imagery. [ABSTRACT FROM AUTHOR]

This paper analyzes the sea surface height dataset from the TOPEX, Jason-1, and Jason-2 satellites of a 19-yr time series in order to extract the tide harmonic constituents for the region limited by latitude 5°N-35°S and longitude 55°-20°W. The harmonic analysis results implemented here were compared with the tidal constituents estimated by three classical tidal models [i.e., TOPEX/Poseidon Global Inverse Solution 7.2 (TPXO7.2), Global Ocean Tide 4.7 (GOT4.7), and Finite Element Solution 2102 (FES2102)] and also with those extracted from in situ measurements. The Courtier criterion was used to define the tide regimes and regionally they are classified as semidiurnal between the latitude range from approximately 5°N to 22°S, semidiurnal with diurnal inequality from 22° to about 29°S, and mixed southward of latitude 22°S. The comparison results among all tide approaches were done by analyzing the root-sum-square misfit (RSSmisfit) value. Generally, the RSSmisfit difference values are not higher than 12 cm among them in deep-water regions. On the other hand, in shallow water, all models have presented quite similar performance, and the RSSmisfit values have presented higher variance than the previous region, as expected. The major discrepancy results were particularly noted for two tide gauges located in the latitude range from 5°N to 2°S. The latter was investigated and conclusions have mainly pointed to the influence of the mouth of the Amazon River and the considerable distance between tide measurements and the satellite reference point, which make it quite hard to compare those results. In summary, the results have showed that all models presently generate quite reliable results for deep water; however, further study should done in order to improve them in shallow-water regions too. [ABSTRACT FROM AUTHOR]

The Visible and Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-Orbiting Partnership satellite brings new opportunities for improving scientists' understanding of deep convective cloud (DCC) radiometry with multiple bands in the visible (VIS), near-infrared (NIR), and longwave infrared (LWIR) spectrum. This paper investigated the radiometric sensitivity of DCC reflectance to spatial resolution, brightness temperature of the LWIR band centered at ~11 μm (TB11), TB11 calibration bias, and cluster size using VIIRS VIS (M5), NIR (M7 and I2), and LWIR (M15 and I5) observations at 375- and 750-m spatial resolutions. The mean and mode of the monthly probability distribution functions of DCC reflectance are used as two important indices in using DCC for calibration, and the results show that the onboard radiometric calibration of M5, M7, and I2 are stable during May 2013-April 2014 despite severe instrument responsivity degradations. The standard deviations of the mean and mode of monthly DCC reflectance are 0.5% and 0.2%, respectively, for all bands. It was found that a TB11 calibration bias on the order of 0.5 K has minimal impact on monthly DCC reflectance, especially when the mode method is used. The mean and mode of VIS and NIR DCC reflectance are functions of spatial resolution, TB11 threshold, and DCC cluster size in all seasons. However, the mode of DCC reflectance is more stable than the mean in terms of all three factors. Therefore, the mode is more suitable as an indicator of calibration stability for individual VIS and NIR bands. [ABSTRACT FROM AUTHOR]

Two-dimensional water vapor fields were retrieved by simulated measurements from multiple ground-based microwave radiometers using a tomographic approach. The goal of this paper was to investigate how the various aspects of the instrument setup (number and spacing of elevation angles and of instruments, number of frequencies, etc.) affected the quality of the retrieved field. This was done for two simulated atmospheric water vapor fields: 1) an exaggerated turbulent boundary layer and 2) a simplified water vapor front. An optimal estimation algorithm was used to obtain the tomographic field from the microwave radiometers and to evaluate the fidelity and information content of this retrieved field. While the retrieval of the simplified front was reasonably successful, the retrieval could not reproduce the details of the turbulent boundary layer field even using up to nine instruments and 25 elevation angles. In addition, the vertical profile of the variability of the water vapor field could not be captured. An additional set of tests was performed using simulated data from a Raman lidar. Even with the detailed lidar measurements, the retrieval did not succeed except when the lidar data were used to define the a priori covariance matrix. This suggests that the main limitation to obtaining fine structures in a retrieved field using tomographic retrievals is the definition of the a priori covariance matrix. [ABSTRACT FROM AUTHOR]