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Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center. Significance Statement: This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster's ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center. [ABSTRACT FROM AUTHOR]

The polar-orbiting meteorological satellites operating in an observing system composed of early morning (EM), midmorning (AM), and afternoon (PM) orbits are extremely important to global numerical weather prediction (NWP). Following the proposal of World Meteorological Organization (WMO), the China Meteorological Administration (CMA) launched Fengyun-3E (FY-3E), the world's first EM-orbit meteorological satellite for civil use, on 5 July 2021. With 11 scientific instruments on board, FY-3E is capable of providing atmospheric sounding, low-light imaging, sea surface wind detection, and space weather monitoring. Six months after launch, we have finished the postlaunch test for all the payloads. This paper presents the FY-3E data obtained during the 6-month test period, their performance, and key geophysical products driven ready for downstream applications. Experiments have been conducted to better disseminate the sounding data within the 6-h NWP assimilation window. Further efforts have been made to benefit data application for severe weather monitoring, diurnal cycle of Earth data, quasi-continuous sun monitoring for space weather, and climate research. [ABSTRACT FROM AUTHOR]

Two multispectral satellite imagery products are presented that were developed for use within the fire management community. These products, which take the form of false color red–green–blue composites, were designed to aid fire detection and characterization, and for assessment of the environment surrounding a fire. The first, named the Fire Temperature RGB, uses spectral channels near 1.6, 2.2, and 3.9 μm for fire detection and rapid assessment of the range of fire intensity through intuitive coloration. The second, named the Day Fire RGB, uses spectral channels near 0.64, 0.86, and 3.9 μm for rapid scene assessment. The 0.64 μm channel provides information on smoke, the 0.86 μm channel provides information on vegetation health and burn scars, and the 3.9 μm channel provides active fire detections. Examples of these red–green–blue composite images developed from observations collected by three operational satellite imagers (VIIRS on the polar-orbiting platform and the Advanced Baseline Imager and Advanced Himawari Imager on the geostationary platform) demonstrate that both red–green–blue composites are useful for fire detection and contain valuable information that is not present within operational fire detection algorithms. In particular, it is shown that Fire Temperature RGB and Day Fire RGB images from VIIRS have similar utility for fire detection as the operational VIIRS Active Fire products, with the added benefit that the imagery provides context for more than just the fires themselves. Significance Statement: The current generation of operational polar-orbiting weather satellites that began with the launch of Suomi NPP offers new capabilities with regard to fire detection and monitoring. In particular, false color red–green–blue composite imagery is now being used by fire managers, incident meteorologists, and others in the fire management community to visualize a fire's behavior and the context in which it occurs. This paper outlines two of these red–green–blue composites that have gained widespread use throughout the U.S. National Weather Service and the Alaska Fire Service. These red–green–blue composites have been applied to the current generation of geostationary and polar-orbiting satellites to great effect and have changed how incident management teams respond to wildland fires. [ABSTRACT FROM AUTHOR]

In this paper, a novel technique is proposed to mitigate the so-called blind range on radars that use pulse compression. It is well known that the blind range is caused by the strong leak through into the receiver during the transmission cycle. The proposed technique is called progressive pulse compression (PPC) and is based on partial decoding. PPC uses a portion of the uncontaminated received signal in conjunction with pulse compression to estimate the echoes from the incomplete signal. The technique does not require the use of a fill pulse or any hardware modifications. PPC can be divided into three steps. The first step is to discard all the received signals during the transmit cycle and apply a smooth taper for continuous transition from zero to one. The second step is to perform the pulse compression using matched filter. The combination of these two steps is equivalent to performing pulse compression using a progressively changing template to partially extract the uncontaminated received signal for compression. The third step is to compensate for the progressively changing template so that proper reflectivity values can be recovered. This technique has been tested on the PX-1000 and will be implemented on PX-10k in the near future. These two radars are designed and operated by the Advanced Radar Research Center at the University of Oklahoma and are both X-band software-defined solid-state systems. The results presented in this paper are collected using the PX-1000 radar. [ABSTRACT FROM AUTHOR]

In a previous work, a weather radar algorithm with low computational cost was developed to estimate the background noise power from the data collected at each radial. The algorithm consists of a sequence of steps designed to identify signal-free range volumes that are subsequently used to estimate the noise power. In this paper, we derive compact, closed-form expressions to replace the numerical formulations used in the first two steps of the algorithm proposed in the original paper. The goal is to facilitate efficient implementation of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new objective typhoon positioning algorithm was proposed. The algorithm uses L1 12-channel far-infrared data of the FY-4A geostationary meteorological satellite for objective positioning, verified against best path data provided by the Tropical Cyclone Data Center of the China Meteorological Administration (CMA). By calculating the tangential and radial perturbation values of infrared brightness temperature images, the perturbation factor can be obtained. By adopting the position of the maximum perturbation factor as the center of a circle and considering a radius of no more than 20 km, the position of the minimum perturbation factor was determined; this value represents the central position of the typhoon. Tropical cyclones in 2019 and 2020 were selected for objective positioning, and the objective positioning results were verified against the corresponding time in the best path dataset. The results included centralized verification results for 29 typhoons and optimal path data in 2019. The maximum average error reached 54.67 km, with an annual average typhoon positioning error of 16.15 km. The centralization verification results for 23 typhoons and optimal path data in 2020 indicated a minimum average error of 12.71 km, a maximum average error of 18.56 km, and an annual average typhoon positioning error of 14.82 km. The positioning results for these two years suggest that the longitude obtained with the perturbation factor positioning method is satisfactory, exhibiting an error of less than 20 km. Significance Statement: The purpose of this study is to help researchers make scientific discoveries and help the development of typhoon center location technology in the future. This is important because accurate positioning of typhoon center can provide effective help for typhoon path prediction and typhoon intensity determination. [ABSTRACT FROM AUTHOR]

While storm identification and tracking algorithms are used both operationally and in research, there exists no single standard technique to objectively determine performance of such algorithms. Thus, a comparative skill score is developed herein that consists of four parameters, three of which constitute the quantification of storm attributes—size consistency, linearity of tracks, and mean track duration—and the fourth that correlates performance to an optimal postevent reanalysis. The skill score is a cumulative sum of each of the parameters normalized from zero to one among the compared algorithms, such that a maximum skill score of four can be obtained. The skill score is intended to favor algorithms that are efficient at severe storm detection, i.e., high-scoring algorithms should detect storms that have higher current or future severe threat and minimize detection of weaker, short-lived storms with low severe potential. The skill score is shown to be capable of successfully ranking a large number of algorithms, both between varying settings within the same base algorithm and between distinct base algorithms. Through a comparison with manually created user datasets, high-scoring algorithms are verified to match well with hand analyses, demonstrating appropriate calibration of skill score parameters. Significance Statement: With the growing number of options for storm identification and tracking techniques, it is necessary to devise an objective approach to quantify performance of different techniques. This study introduces a comparative skill score that assesses size consistency, linearity of tracks, mean track duration, and correlation to an optimal postevent reanalysis to rank diverse algorithms. This paper will show the capability of the skill score at highlighting algorithms that are efficient at detecting storms with higher severe potential, as well as those that closely resemble human-perceived storms through a comparison with manually created user datasets. The novel methodology will be useful in improving systems that rely on such algorithms, for both operational and research purposes focusing on severe storm detection. [ABSTRACT FROM AUTHOR]

Climate change (CC) is a topical issue of profound social interest. This paper aims to analyze the sentiments expressed in Twitter interactions in relation to CC. The study is performed considering the geographical and gender perspectives as well as different user typologies (individual users or companies). A total of 92 474 Twitter messages were utilized for the study. These are characterized by analyzing sentiment polarity and identifying the underlying topics related to climate change. Polarity is examined utilizing different commercial algorithms such as Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob, in conjunction with a procedure that uses word embedding and clustering techniques in an unsupervised machine learning approach. In addition, hypothesis testing is applied to inspect whether a gender independence exists or not. The topics are identified using latent Dirichlet allocation (LDA) and the usage of n-grams is explored. The topics identified are (in descending order of importance) CC activism, biodiversity, CC evidence, sustainability, CC awareness, pandemic, net zero, CC policies and finances, government action, and climate emergency. Moreover, globally speaking, it is found that the interactions on all topics are predominantly negative, and they are maintained as such for both men and women. If the polarity by topic and country is considered, it is also negative in most countries, although there are several notable exceptions. Finally, the presence of organizations and their perspective is studied, and results suggest that organizations post with more frequency when addressing topics such as sustainability, CC awareness, and net zero topics. Significance Statement: The purpose of this research is to gain a better understanding of the perception of Twitter users in relation to climate change. To do so, Twitter interactions are characterized by analyzing polarity (positive or negative sentiment) and identifying underlying topics that, with greater or lesser intensity, were discussed during the period analyzed. Then, to contextualize the information retrieved, several classifications are performed: by gender, location, and account typology (individual users and companies). Interesting differences and commonalities are found both by geographic dimension and by gender. Similarly, some dissimilarities exist between interactions from individuals and companies. The findings of this work are significant because they can help institutions and governments to properly target public awareness efforts on climate change. [ABSTRACT FROM AUTHOR]

The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate. [ABSTRACT FROM AUTHOR]

Short-termintense precipitation (SIP; i.e., convective precipitation exceeding 20mmh21) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with a graph model and a random forest for the nowcasting of SIP. Different from the pixel-by-pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multicell convective systems. In particular, the following question could be addressed: Will a multicell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multicell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves a probability of detection (POD) of 79.2% and a critical success index (CSI) of 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms. [ABSTRACT FROM AUTHOR]

The response in [1] that the method used to determine cell thermal mass correction coefficients for SBE 41CP CTD data from Argo floats is biased as determined by [3] is valid. The alignment error manifests as a spike at 45.5 m and the cell thermal mass error manifests as lag in salinity response, such that practical salinity is reduced above base of the mixed layer at 45.8 m. Shown are (a) the salinity profiles, (b) the practical salinity error, and (c) the cumulative practical salinity bias. Nonetheless, minimizing the average practical salinity error in the binned profiles can serve as the second condition needed to determine the cell thermal mass correction coefficients to minimize bias in binned Argo data. [Extracted from the article]

During the summer of 2018, the upward-pointing Wyoming Cloud Lidar (WCL) was deployed on board the University of Wyoming King Air (UWKA) research aircraft for the Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign. This paper describes the generation of calibrated attenuated backscatter coefficients and aerosol extinction coefficients from the WCL measurements. The retrieved aerosol extinction coefficients at the flight level strongly correlate (correlation coefficient, rr > 0.8) with in situ aerosol concentration and carbon monoxide (CO) concentration, providing a first-order estimate for converting WCL extinction coefficients into vertically resolved CO and aerosol concentration within wildfire smoke plumes. The integrated CO column concentrations from the WCL data in nonextinguished profiles also correlate (rr = 0.7) with column measurements by the University of Colorado Airborne Solar Occultation Flux instrument, indicating the validity of WCL-derived extinction coefficients. During BB-FLUX, the UWKA sampled smoke plumes from more than 20 wildfires during 35 flights over the western United States. Seventy percent of flight time was spent below 3 km above ground level (AGL) altitude, although the UWKA ascended up to 6 km AGL to sample the top of some deep smoke plumes. The upward-pointing WCL observed a nearly equal amount of thin and dense smoke below 2 km and above 5 km due to the flight purpose of targeted fresh fire smoke. Between 2 and 5 km, where most of the wildfire smoke resided, the WCL observed slightly more thin smoke than dense smoke due to smoke spreading. Extinction coefficients in dense smoke were 2–10 times stronger, and dense smoke tended to have larger depolarization ratio, associated with irregular aerosol particles. [ABSTRACT FROM AUTHOR]

Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases. [ABSTRACT FROM AUTHOR]

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]

In Part I of this two-part paper a new method of predicting the total lightning flash rate in thunderstorms was introduced. In this paper, the implementation of this method into the convection-permitting Consortium for Small Scale Modeling (COSMO) model is presented. The new approach is based on a simple theoretical model that consists of a dipole charge structure, which is maintained by a generator current and discharged by lightning and, to a small extent, by a leakage current. This approach yields a set of four predictor variables, which are not amenable to direct observations and consequently need to be parameterized (Part I). Using an algorithm that identifies thunderstorm cells and their properties, this approach is applied to determine the flash frequency of every thunderstorm cell in the model domain. With this information, the number of flashes that are accumulated by each cell and during the interval between the activation of the lightning scheme can be calculated. These flashes are then randomly distributed in time and beneath each cell. The output contains the longitude, the latitude, and the time of occurrence of each simulated discharge. Simulations of real-world scenarios are presented, which are compared to measurements with the lightning detection network, LINET. These comparisons are done on the cloud scale as well as in a mesoscale region composing southern Germany (two cases each). The flash rates of individual cumulonimbus clouds at the extreme ends of the intensity spectrum are realistically simulated. The simulated overall lightning activity over southern Germany is dominated by spatiotemporal displacements of the modeled convective clouds, although the scheme generally reproduces realistic patterns such as coherent lightning swaths. [ABSTRACT FROM AUTHOR]

Uncertainties in aircraft inertial navigation system and radar-pointing angles can have a large impact on the accuracy of airborne dual-Doppler analyses. The Testud et al. (THL) method has been routinely applied to data collected by airborne tail Doppler radars over flat and nonmoving terrain. The navigation correction method proposed in Georgis et al. (GRH) extended the THL method over complex terrain and moving ocean surfaces by using a variational formulation but its capability over ocean has yet to be tested. Recognizing the limitations of the THL method, Bosart et al. (BLW) proposed to derive ground speed, tilt, and drift errors by statistically comparing aircraft in situ wind with dual-Doppler wind at the flight level. When combined with the THL method, the BLW method can retrieve all navigation errors accurately; however, it can be applied only to flat surfaces, and it is rather difficult to automate. This paper presents a generalized navigation correction method (GNCM) based on the GRH method that will serve as a single algorithm for airborne tail Doppler radar navigation correction for all possible surface conditions. The GNCM includes all possible corrections in the cost function and implements a new closure assumption by taking advantage of an accurate aircraft ground speed derived from GPS technology. The GNCM is tested extensively using synthetic airborne Doppler radar data with known navigation errors and published datasets from previous field campaigns. Both tests show the GNCM is able to correct the navigation errors associated with airborne tail Doppler radar data with adequate accuracy. [ABSTRACT FROM AUTHOR]

This paper describes the techniques used to obtain sea surface temperature (SST) retrievals from the Geostationary Operational Environmental Satellite 12 (GOES-12) at the National Oceanic and Atmospheric Administration's Office of Satellite Data Processing and Distribution. Previous SST retrieval techniques relying on channels at 11 and 12 µm are not applicable because GOES-12 lacks the latter channel. Cloud detection is performed using a Bayesian method exploiting fast-forward modeling of prior clear-sky radiances using numerical weather predictions. The basic retrieval algorithm used at nighttime is based on a linear combination of brightness temperatures at 3.9 and 11 µm. In comparison with traditional split window SSTs (using 11- and 12-µm channels), simulations show that this combination has maximum scatter when observing drier colder scenes, with a comparable overall performance. For daytime retrieval, the same algorithm is applied after estimating and removing the contribution to brightness temperature in the 3.9-µm channel from solar irradiance. The correction is based on radiative transfer simulations and comprises a parameterization for atmospheric scattering and a calculation of ocean surface reflected radiance. Potential use of the 13-µm channel for SST is shown in a simulation study: in conjunction with the 3.9-µm channel, it can reduce the retrieval error by 30%. Some validation results are shown while a companion paper by Maturi et al. shows a detailed analysis of the validation results for the operational algorithms described in this present article. [ABSTRACT FROM AUTHOR]

Fire influences global change and tropical ecosystems through its connection to land-cover dynamics, atmospheric composition, and the global carbon cycle. As such, the climate change community, the Brazilian government, and the Large-Scale Biosphere–Atmosphere (LBA) Experiment in Amazonia are interested in the use of satellites to monitor and quantify fire occurrence throughout Brazil. Because multiple satellites and algorithms are being utilized, it is important to quantify the accuracy of the derived products. In this paper the characteristics of two fire detection algorithms are evaluated, both of which are applied to Terra’s Moderate Resolution Imagine Spectroradiometer (MODIS) data and with both operationally producing publicly available fire locations. The two algorithms are NASA’s operational Earth Observing System (EOS) MODIS fire detection product and Brazil’s Instituto Nacional de Pesquisas Espaciais (INPE) algorithm. Both algorithms are compared to fire maps that are derived independently from 30-m spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A quantitative comparison is accomplished through logistic regression and error matrices. Results show that the likelihood of MODIS fire detection, for either algorithm, is a function of both the number of ASTER fire pixels within the MODIS pixel as well as the contiguity of those pixels. Both algorithms have similar omission errors and each has a fairly high likelihood of detecting relatively small fires, as observed in the ASTER data. However, INPE’s commission error is roughly 3 times more than that of the EOS algorithm. [ABSTRACT FROM AUTHOR]

This paper describes a variational data assimilation algorithm based on the Simulating WAves Nearshore (SWAN) wave spectrum model. The approach allows single-point wave spectrum observations to be used to estimate the wave field for a nearshore region under stationary conditions, assuming a spatially uniform incident wave spectrum at the offshore boundary. The assimilated data are in the form of Fourier directional coefficients, the standard output from operational wave buoys, and are used directly by incorporating the relationship between directional spectrum and the Fourier coefficients into the formulation. The algorithm was tested on data from nearshore buoys deployed off the coast of North Carolina in May 2012, and the estimated wave field is compared to both the input data and independent observation data. The results compare favorably to the independent data with overall RMS errors of 10%-20% for significant wave height, about half a second for mean wave period, and as much as three to four SWAN spectral grid cells for mean direction. Overall, the results show that the algorithm can be effectively used to estimate the offshore boundary spectrum and accurately reproduce wave conditions in the domain. [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]

Polarization diversity radar measurements such as reflectivity factor, differential reflectivity, and differential propagation phase are extensively used in rainfall estimation. Algorithms to estimate rainfall from polarimetric radar measurements are based on a model for the raindrop shape as a function of drop diameter. Most of the current algorithms use an equilibrium shape–size model for raindrops. Variation of the prevailing mean raindrop shapes from an assumed model has a direct impact on the accuracy of radar rainfall estimates. This paper develops composite algorithms to estimate rainfall from polarimetric radar data without an a priori assumption about the specific form of mean raindrop shape–size model such as equilibrium shape model. The accuracy of rainfall estimates is evaluated in the presence of random measurement errors as well as systematic bias errors. The composite algorithms, independent of a prespecified raindrop shape model, were applied to radar parameters simulated from disdrometer data collected over 3 months, and the corresponding rainfall estimates were found to be in good agreement with disdrometer estimates. The composite algorithms were also tested with Colorado State University CHILL radar observations of the 28 July 1997 Fort Collins (Colorado) flood event. The storm total precipitation estimates based on the composite algorithms developed in this paper were in much better agreement with rain gauge estimates in comparison with conventional algorithms. [ABSTRACT FROM AUTHOR]

In this paper we develop a method to quantify the accuracy of different pattern extraction techniques for the additive space–time modes often assumed to be present in climate data. It has previously been shown that the standard technique of principal component analysis (PCA; also known as empirical orthogonal functions) may extract patterns that are not physically meaningful. Here we analyze two modern pattern extraction methods, namely dynamical mode decomposition (DMD) and slow feature analysis (SFA), in comparison with PCA. We develop a Monte Carlo method to generate synthetic additive modes that mimic the properties of climate modes described in the literature. The datasets composed of these generated modes do not satisfy the assumptions of any pattern extraction method presented. We find that both alternative methods significantly outperform PCA in extracting local and global modes in the synthetic data. These techniques had a higher mean accuracy across modes in 60 out of 60 mixed synthetic climates, with SFA slightly outperforming DMD. We show that in the majority of simple cases PCA extracts modes that are not significantly better than a random guess. Finally, when applied to real climate data these alternative techniques extract a more coherent and less noisy global warming signal, as well as an El Niño signal with a clearer spectral peak in the time series, and more a physically plausible spatial pattern. [ABSTRACT FROM AUTHOR]

Hydrometeor classification methods using polarimetric radar variables rely on probability density functions (PDFs) or membership functions derived empirically or by using electromagnetic scattering calculations. This paper describes an objective approach based on cluster analysis to deriving the PDFs. An iterative procedure with K-means clustering and expectation-maximization clustering based on Gaussian mixture models is developed to generate a series of prototypes for each hydrometeor type from several radar scans. The prototypes are then grouped together to produce a PDF for each hydrometeor type, which is modeled as a Gaussian mixture. The cluster-based method is applied to polarimetric radar data collected with the CP-2 S-band radar near Brisbane, Queensland, Australia. The results are illustrated and compared with theoretical classification boundaries in the literature. Some notable differences are found. Automated hydrometeor classification algorithms can be built using the PDFs of polarimetric variables associated with each hydrometeor type presented in this paper. [ABSTRACT FROM AUTHOR]

Although existing algorithms for storm tracking have been designed to operate in real time, they are also commonly used to do postevent data analysis and research. Real-time algorithms cannot use information on the subsequent positions of a storm because it is not available at the time that associations between frames are made, but postevent analysis is not similarly constrained. Therefore, it should be possible to obtain better tracks for postevent analysis than those that a real-time algorithm is capable of producing. In this paper, a statistical procedure for determining storm tracks from a set of identified storm cells over time is described. It is found that this procedure results in fewer, longer-lived tracks at the potential cost of a small increase in positional error. [ABSTRACT FROM AUTHOR]

The local ensemble tangent linear model (LETLM) provides an alternative method for creating the tangent linear model (TLM) and adjoint of a nonlinear model that promises to be easier to maintain and more computationally scalable than earlier methods. In this paper, we compare the ability of the LETLM to predict the difference between two nonlinear trajectories of the Navy's global weather prediction model at low resolution (2.58 at the equator) with that of theTLMcurrently used in the Navy's four-dimensional variational (4DVar) data assimilation scheme. When compared to the pair of nonlinear trajectories, the traditional TLM and the LETLM have improved skill relative to persistence everywhere in the atmosphere, except for temperature in the planetary boundary layer. In addition, the LETLM was, on average, more accurate than the traditional TLM (error reductions of about 20% in the troposphere and 10% overall). Sensitivity studies showed that the LETLM was most sensitive to the number of ensemble members, with the performance gradually improving with increased ensemble size up to the maximum size attempted (400). Inclusion of physics in the LETLM ensemble leads to a significantly improved representation of the boundary layer winds (error reductions of up to 50%), in addition to improved winds and temperature in the free troposphere and in the upper stratosphere/lower mesosphere. The computational cost of the LETLM was dominated by the cost of ensemble propagation. However, the LETLM can be precomputed before the 4DVar data assimilation algorithm is executed, leading to a significant computational advantage. [ABSTRACT FROM AUTHOR]

The winding-angle (WA) method is an automatic eddy detection method based on the geometric characteristics of instantaneous streamlines. The original WA method clusters closed streamlines using a predetermined threshold. It is difficult to obtain a common threshold for accurately clustering various mesoscale eddies with variable shapes and dimensions. Moreover, the original WA method is not suitable for detecting multicore structures. In this paper, an improved WA method was proposed to more accurately identify mesoscale eddies and to detect multicore structures. It does not depend on the previously used clustering threshold; rather, it is based on the spatial relationships of streamlines to detect mesoscale eddies of various types and dimensions. Streamlines are matched with possible eddy centers (PCs), which are then grouped into different "related groups" according to the containment relationships between them and the outermost streamlines of the groups. Each group represents a vortex structure, and the number of PCs in each group represents the number of eddy cores. The eddy boundaries and eddy cores of multicore structures represented by multi-PC groups are identified by topological relationships of the streamlines. The time requirement of the improved method is higher than that of the original algorithm, although it does not demand additional memory space and utilizes fewer CPU resources. More importantly, the improved method provides more accurate identification results and greatly refines the incorrect identifications from the original method induced by the predetermined threshold. Success metrics for the improvedWAmethod are also more desirable relative to those for the original and other commonly used methods. [ABSTRACT FROM AUTHOR]

Fengyun-4A (FY-4A) is a geostationary meteorological satellite with four advanced payloads, which can be used to quantitatively detect Earth’s atmospheric system with multispectral and high spatial and temporal resolution. However, the applicable model limits the application of the FY-4A satellite data. In this paper, the empirical statistical model developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is extended for FY-4A Advanced Geosynchronous Radiation Imager (AGRI), and it is applied to observed data to evaluate the applicability of the model for AGRI measurements. To improve the accuracy of radiation estimation, the artificially intelligent particle swarm optimization (PSO) algorithm was used for model optimizing. Results show that the estimated radiation has diurnal variation that is in accord with the characteristics of radiation variation. The estimated net surface shortwave radiation (Sn) and observed values show good correlation. However, large deviations from observations are found in the estimated values when the empirical model based on MODIS is directly used to process AGRI data. Thus, the empirical statistical model based on MODIS can be applied to AGRI data, but the empirical parameters need to be revised. Optimization of the empirical statistical model by the PSO algorithm can effectively improve the accuracy of the radiation estimate. The mean absolute percentage error (MAPE) of Sn estimated by optimized models is reduced to 15%. The MAPE of the net surface longwave radiation (Ln) estimated by optimized models is reduced to 31%, and the MAPE of the net radiation (Rn) estimated by optimized models is reduced to 27%. However, for the uncertainty caused by error accumulation effect, the influence of PSO optimization on Rn is not as obvious as that of Ln. However, the analysis of error distribution shows that PSO optimization does improve the estimation results of Rn. Based on AGRI data, the surface radiation can be estimated simply, and the regional or larger-scale surface radiation retrieval can quickly be realized by this method, which has large application potential and popularization value. [ABSTRACT FROM AUTHOR]

A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The paper highlights the algorithm's ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features. [ABSTRACT FROM AUTHOR]

Identifying and separating the signal of urbanization effects in current temperature data series is essential for accurately detecting, attributing, and projecting mean and extreme temperature change on varied spatial scales. This paper proposes a new method based on machine learning to classify the observational stations into rural stations and urban stations. Based on the classification of rural and urban stations, the global and regional land annual mean and extreme temperature indices series over 1951–2018 for all stations and rural stations were calculated, and the urbanization effects and the urbanization contribution of global land annual mean and extreme temperature indices series are quantitatively evaluated using the difference series between all stations and the rural stations. The results showed that the global land annual mean time series for mean temperature and most extreme temperature indices experienced statistically significant urbanization effects. The urbanization effects in the mean and extreme temperature indices series generally occurred after the mid-1980s, and there were significant differences of the magnitudes of urbanization effects among different regions. The urbanization effect on the trends of annual mean and extreme temperature indices series in East Asia is generally the strongest, which is consistent with the rapidly urbanization process in the region over the past decades, but it is generally small in Europe during the recent decades. [ABSTRACT FROM AUTHOR]

Partial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (KDP)–reflectivity (ZH) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived KDP and ZH relationships, as well as rainfall estimates based on R(ZH) and R(KDP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of ZH measurements can effectively be enhanced; (ii) the distribution of radar-measured KDP versus ZH is more consistent with the DSD-derived KDP versus ZH; (iii) the measured ZH from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where ZH is larger than 30 dBZ; and (iv) rainfall estimates based on R(ZH) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(KDP). [ABSTRACT FROM AUTHOR]

An assumption related to clouds is one of uncertain factors in precipitation retrievals by the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory. While an attenuation due to cloud ice is negligibly small for Ku and Ka bands, attenuation by cloud liquid water is larger in the Ka band and estimating precipitation intensity with high accuracy from Ka-band observations can require developing a method to estimate the attenuation due to cloud liquid water content (CLWC). This paper describes a CLWC database used in the DPR level-2 algorithm for the GPM V06A product. In the algorithm, the CLWC value is assumed using the database with inputs of precipitation-related variables, temperature, and geolocation information. A calculation of the database was made using the 3.5-km-mesh global atmospheric simulation derived from the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) global cloud-system-resolving model. Impacts of current CLWC assumptions for surface precipitation estimates were evaluated by comparisons of precipitation retrieval results between default values and 0 mg m−3 of the CLWC. The impacts were quantified by the normalized mean absolute difference (NMAD) and the NMAD values showed 2.3% for the Ku, 9.9% for the Ka, and 6.5% for the dual-frequency algorithms in global averages, while they were larger in the tropics than in high latitudes. Effects of the precipitation estimates from the CLWC assumption were examined further in terms of retrieval processes affected by the CLWC assumption. This study emphasizes the CLWC assumption provided more effects on the precipitation estimates through estimating path-integrated attenuation due to rain. [ABSTRACT FROM AUTHOR]

Conventional analysis methods in weather and climate science (e.g., EOF analysis) exhibit a number of drawbacks including scaling and mixing. These methods focus mostly on the bulk of the probability distribution of the system in state space and overlook its tail. This paper explores a different method, the archetypal analysis (AA), which focuses precisely on the extremes. AA seeks to approximate the convex hull of the data in state space by finding 'corners' that represent 'pure' types or archetypes through computing mixture weight matrices. The method is quite new in climate science, although it has been around for about two decades in pattern recognition. It encompasses, in particular, the virtues of EOFs and clustering. The method is presented along with a new manifold-based optimization algorithm that optimizes for the weights simultaneously, unlike the conventional multistep algorithm based on the alternating constrained least squares. The paper discusses the numerical solution and then applies it to the monthly sea surface temperature (SST) from HadISST and to the Asian summer monsoon (ASM) using sea level pressure (SLP) from ERA-40 over the Asian monsoon region. The application to SST reveals, in particular, three archetypes, namely, El Niño, La Niña, and a third pattern representing the western boundary currents. The latter archetype shows a particular trend in the last few decades. The application to the ASM SLP anomalies yields archetypes that are consistent with the ASM regimes found in the literature. Merits and weaknesses of the method along with possible future development are also discussed. [ABSTRACT FROM AUTHOR]

The increased use of solar photovoltaic (PV) cells as energy sources on electric grids has created the need for more accessible solar irradiance and power production estimates for use in power modeling software. In the present paper, a novel technique for creating solar irradiance estimates is introduced.Asolar PV resource dataset created by combining numerical weather prediction assimilation model variables, satellite data, and high-resolution ground-based measurements is also presented. The dataset contains ≈152 000 geographic locations each with ≈26 000 hourly time steps. The solar irradiance outputs are global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DIF). The technique is developed over the United States by training a linear multiple multivariate regression scheme at 10 locations. The technique is then applied to independent locations over the whole geographic domain. The irradiance estimates are input into a solar PV power modeling algorithm to compute solar PV power estimates for every 13-km grid cell. The dataset is analyzed to predict the capacity factors for solar resource sites around theUnited States for 2006-08. Statistics are shown to validate the skill of the scheme at geographic sites independent of the training set. In addition, it is shown that more high-quality, geographically dispersed, observation sites increase the skill of the scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm for parallel identification of mesoscale eddies from global satellite altimetry data. By simplifying the recognition process and the sea level anomaly (SLA) contours' search range, the method improves identification efficiency compared with the previous SSH-based method even in the single-threaded process. The global SLA map is divided into several regions. These regions are identified simultaneously with a new SSH-based method. All the eddy identification results of these regions are merged seamlessly into a global eddy map. A β-plane approximation is used to calculate the geostrophic speed in the equatorial band. Compared with the computation complexity of the previous SSH-based method, which is , the computation complexity of the new method is , where K is the number of threads and L is the number of regional SLA maps. When applying the new method to the global SLA map, the computation is ~100 times faster than the previous SSH-based method on an average computer. The new method characterizes an eddy structure by radius, amplitude, eddy core, closed SLA contour, and closed SLA contour with maximum average geostrophic speed. In situ data and another global eddy dataset are applied to validate the reliability of eddies detected by the new algorithm. Global eddy mean properties, variability, and the geographical distribution of both datasets are analyzed to demonstrate the performance of this new method and to help understand eddy activities on a global scale. [ABSTRACT FROM AUTHOR]

Accumulating gridded fields over time greatly magnifies the impact of impulse noise in the individual grids. A quality control method that takes advantage of spatial and temporal coherence can reduce the impact of such noise in accumulation grids. Such a method can be implemented using the image processing techniques of hysteresis and multiple hypothesis tracking (MHT). These steps are described in this paper, and the method is applied to simulated data to quantify the improvements and to explain the effect of various parameters. Finally, the quality control technique is applied to some illustrative real-world datasets. [ABSTRACT FROM AUTHOR]

Monitoring of precipitation using higher-frequency radar systems such as X band is becoming popular. At X-band frequency, weather radar signals are attenuated along their paths due to precipitation. A network-based reflectivity retrieval technique has been developed for the Collaborative Adaptive Sensing of the Atmosphere (CASA) system, which is a radar network that can observe a weather event simultaneously by multiple radars. This paper describes the design and implementation of an architectural framework for real-time processing of the network-based attenuation correction system. The benchmarks presented here show that the system accomplishes networked attenuation correction within a few seconds, making it well suited for the CASA system. This paper presents the performance of the network-based attenuation correction system in terms of the metrics of attenuation correction as well as computational performance using CASA Integrated Project 1 (IP1) data during 2007--09 field experiments. The results show that the network-based attenuation correction algorithm works robustly in real time while retrieving attenuation-corrected reflectivity. [ABSTRACT FROM AUTHOR]

Automated quality control is a necessity for meteorological measurement networks because of an ever-increasing number of measurements. One of the basic quality control tests is to check that the observations fall into a range of acceptable values. This paper proposes an algorithm for the automatic determination of maximum and minimum thresholds for such a test. The automated algorithm results in more statistically justified thresholds compared to the operator-fed values, which some quality control systems normally use. The suggested algorithm is a tuning-free method for the determination of daily minimum and maximum limits. The basis of the algorithm is, first, to collect the essential statistics of observations during their annual cycle and, second, to use these statistics to determine the acceptable observation range. This paper introduces a semirecursive version with lower computational requirements and discusses additional algorithmic details. The algorithm is demonstrated through numerical examples, which show that limits converge to satisfactory values in a few years. [ABSTRACT FROM AUTHOR]

A technique for calibrating digital cameras for stereo photogrammetry of cumulus clouds is presented. It has been applied to characterize the formation of summer thunderstorms observed during the Cumulus Photogrammetric, In Situ, and Doppler Observations (CuPIDO) project. Starting from gross measurements of locations, orientations of cameras, and landmark surveys, accurate locations and orientations of the cameras are obtained by minimizing a geometric error (GE). Once accurate camera parameters are obtained, 3D positions of cloud-feature points are computed by triangulation. The main contributions of this paper are as follows. First, it is proven that the GE has only one minimum in the neighborhood of the real parameters of a camera. In other words, searching the minimum of the GE enables the authors to find the right camera parameters even if there are significant differences between the initial measurements and their true values. Second, a new coarse-to-fine iterative algorithm is developed that minimizes the GE and finds the camera parameters. Numerical experiments show that the coarse-to-fine algorithm is efficient and effective. Third, a new landmark survey based on a geographic information system (GIS) rather than field measurements is presented. The GIS landmark survey is an effective and efficient way to obtain landmark world coordinates for camera calibrations in these experiments. Validation of this technique is achieved by the data collected by a NASA/Earth Observing System satellite and an instrumented aircraft. This paper builds on previous research and details the calibration and 3D reconstructions. [ABSTRACT FROM AUTHOR]

This paper describes development of a method for discriminating high ice water content (HIWC) conditions that can disrupt jet-engine performance in commuter and large transport aircraft. Using input data from satellites, numerical weather prediction models, and ground-based radar, this effort employs machine learning to determine optimal combinations of available information using fuzzy logic. Airborne in situ measurements of ice water content (IWC) from a series of field experiments that sampled HIWC conditions serve as training data in the machine-learning process. The resulting method, known as the Algorithm for Prediction of HIWC Areas (ALPHA), estimates the likelihood of HIWC conditions over a three-dimensional domain. Performance statistics calculated from an independent subset of data reserved for verification indicate that the ALPHA has skill for detecting HIWC conditions, albeit with significant false alarm rates. Probability of detection (POD), probability of false detection (POFD), and false alarm ratio (FAR) are 86%, 29% (60% when IWC below 0.1 g m−3 are omitted), and 51%, respectively, for one set of detection thresholds using in situ measurements. Corresponding receiver operating characteristic (ROC) curves give an area under the curve of 0.85 when considering all data and 0.69 for only points with IWC of at least 0.1 g m−3. Monte Carlo simulations suggest that aircraft sampling biases resulted in a positive POD bias and the actual probability of detection is between 78.5% and 83.1% (95% confidence interval). Analysis of individual case studies shows that the ALPHA output product generally tracks variation in the measured IWC. [ABSTRACT FROM AUTHOR]

Atmospheric visibility is an important element of meteorological observation. With existing methods, defining image features that reflect visibility accurately and comprehensively is difficult. This paper proposes a visibility detection method based on transfer learning using deep convolutional neural networks (DCNN) that addresses issues caused by a lack of sufficient visibility labeled datasets. In the proposed method, each image was first divided into several subregions, which were encoded to extract visual features using a pretrained no-reference image quality assessment neural network. Then a support vector regression model was trained to map the extracted features to the visibility. The fusion weight of each subregion was evaluated according to the error analysis of the regression model. Finally, the neural network was fine-tuned to better fit the problem of visibility detection using the current detection results conversely. Experimental results demonstrated that the detection accuracy of the proposed method exceeds 90% and satisfies the requirements of daily observation applications. [ABSTRACT FROM AUTHOR]

Interannual and climatological variations of wind stress drag coefficient (CD) are examined over the global ocean from 1998 to 2004. Here CD is calculated using high temporal resolution (3- and 6-hourly) surface atmospheric variables from two datasets: 1) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and 2) the Navy Operational Global Atmospheric Prediction System (NOGAPS). The stability-dependent CD algorithm applied to both datasets gives almost identical values over most of the global ocean, confirming the validity of results. Overall, major findings of this paper are as follows: 1) the CD value can change significantly (e.g., >50%) on 12-hourly time scales around the Kuroshio and Gulf Stream current systems; 2) there is strong seasonal variability in CD, but there is not much interannual change in the spatial variability for a given month; 3) a global mean CD ≈ 1.25 × 10-3 is found in all months, while CD ≥ 1.5 × 10-3 is prevalent over the North Pacific and North Atlantic Oceans and in southern high-latitude regions as well, and CD ≤ 1.0 × 10-3 is typical in the eastern equatorial Pacific cold tongue; and 4) including the effects of air–sea stability on CD generally causes an increase of >20% in comparison to the one calculated based on neutral conditions in the tropical regions. Finally, spatially and temporally varying CD fields are therefore needed for a variety of climate and air–sea interaction studies. [ABSTRACT FROM AUTHOR]

Echoes in radar reflectivity data do not always correspond to precipitating particles. Echoes on radar may result from biological targets such as insects, birds, or wind-borne particles; from anomalous propagation or ground clutter; or from test and interference patterns that inadvertently seep into the final products. Although weather forecasters can usually identify and account for the presence of such contamination, automated weather-radar algorithms are drastically affected. Several horizontal and vertical features have been proposed to discriminate between precipitation echoes and echoes that do not correspond to precipitation. None of these features by themselves can discriminate between precipitating and nonprecipitating areas. In this paper, a neural network is used to combine the individual features, some of which have already been proposed in the literature and some of which are introduced in this paper, into a single discriminator that can distinguish between “good” and “bad” echoes (i.e., precipitation and nonprecipitation, respectively). The method of computing the horizontal features leads to statistical anomalies in their distributions near the edges of echoes. Also described is how to avoid presenting such range gates to the neural network. The gate-by-gate discrimination provided by the neural network is followed by more holistic postprocessing based on spatial contiguity constraints and object identification to yield quality-controlled radar reflectivity scans that have most of the bad echo removed while leaving most of the good echo untouched. A possible multisensor extension, utilizing satellite data and surface observations, to the radar-only technique is also demonstrated. It is demonstrated that the resulting technique is highly skilled and that its skill exceeds that of the currently operational algorithm. [ABSTRACT FROM AUTHOR]

Wolfson et al. introduced a storm tracking algorithm, called the Growth and Decay Storm Tracker, in which the large-scale features were extracted from radar data fields by using an elliptical filter. The elliptical filter as introduced was computationally too expensive to be performed in real time. In this paper, it is shown that although the elliptical filter is nonlinear, it can be decomposed into two parts, one of which is, under some simplifying assumptions, linear and shift-invariant. The linear component can be accelerated using fast algorithms available to compute the digital Fourier transform (DFT). Furthermore, it is shown that the nonlinear part can be written as an update equation, thus reducing the amount of computer memory required. With these improvements to the basic large-scale filtering technique, this paper reports that the large-scale filtering can be done 1-2 orders of magnitude faster. The improvement makes it possible to use the large-scale filtering technique in situations where the com-putational time and memory requirements have been prohibitive. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]

This paper extends on the first part of this series by describing four examples of 2D linear maps that can be constructed in accordance with the theory of the earlier work. The focus is again on spherical geometry, although these techniques can be readily extended to arbitrary manifolds. The four maps include conservative, consistent, and (optionally) monotone linear maps (i) between two finite-volume meshes, (ii) from finite-volume to finite-element meshes using a projection-type approach, (iii) from finite-volume to finite-element meshes using volumetric integration, and (iv) between two finite-element meshes. Arbitrary order of accuracy is supported for each of the described nonmonotone maps. [ABSTRACT FROM AUTHOR]

This paper explores the effects from averaging weather station data onto a grid on the first four statistical moments of daily minimum and maximum surface air temperature (SAT) anomalies over the entire globe. The Global Historical Climatology Network-Daily (GHCND) and the Met Office Hadley Centre GHCND (HadGHCND) datasets from 1950 to 2010 are examined. The GHCND station data exhibit large spatial patterns for each moment and statistically significant moment trends from 1950 to 2010, indicating that SAT probability density functions are non-Gaussian and have undergone characteristic changes in shape due to decadal variability and/or climate change. Comparisons with station data show that gridded averages always underestimate observed variability, particularly in the extremes, and have altered moment trends that are in some cases opposite in sign over large geographic areas. A statistical closure approach based on the quasi-normal approximation is taken to explore SAT's higher-order moments and point correlation structure. This study focuses specifically on relating variability calculated from station data to that from gridded data through the moment equations for weighted sums of random variables. The higher-order and nonlinear spatial correlations up to the fourth order demonstrate that higher-order moments at grid scale can be determined approximately by functions of station pair correlations that tend to follow the usual Kolmogorov scaling relation. These results can aid in the development of constraints to reduce uncertainties in climate models and have implications for studies of atmospheric variability, extremes, and climate change using gridded observations. [ABSTRACT FROM AUTHOR]

The predominant effect of rain on altimeter data is through the attenuation it causes of radar pulses propagating through it. Both the ERS-1 and TOPEX/Poseidon have recorded sharp decreases in the observed backscatter sigma[sup0], which have been attributed to rain events. However, the structure of rain cells and the spatial variation of rain rate can be much smaller than the altimeter footprint over which sigma[sup0] is calculated. Here an algorithm is derived to determine the rain rate and its spatial structure. This paper demonstrates that an unbiased estimator can be produced and shows the accuracy of its estimates through simulations of events of known magnitude and how the errors depend upon the accuracy of the assumptions used. Although only yielding information over a very narrow swath, rain structure and rain-rate information derived from altimetry could greatly extend records of precipitation over many areas of the oceans where little or no direct estimates exist. Routine processing of altimeter waveform data offers the prospect of a large dataset of high-resolution sections of precipitation patterns. This may be used to investigate the small-scale spatial structure of storms or in conjunction with other sensors to improve estimates of the global precipitation field. The application to real altimeter waveform data, and its validation against independent estimates of rain rate, is the subject of a forthcoming paper. [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]

This paper describes the use of convolutional neural nets (CNN), a type of deep learning, to identify fronts in gridded data, followed by a novel postprocessing method that converts probability grids to objects. Synoptic-scale fronts are often associated with extreme weather in the midlatitudes. Predictors are 1000-mb (1 mb = 1 hPa) grids of wind velocity, temperature, specific humidity, wet-bulb potential temperature, and/or geopotential height from the North American Regional Reanalysis. Labels are human-drawn fronts from Weather Prediction Center bulletins. We present two experiments to optimize parameters of the CNN and object conversion. To evaluate our system, we compare the objects (predicted warm and cold fronts) with human-analyzed warm and cold fronts, matching fronts of the same type within a 100- or 250-km neighborhood distance. At 250 km our system obtains a probability of detection of 0.73, success ratio of 0.65 (or false-alarm rate of 0.35), and critical success index of 0.52. These values drastically outperform the baseline, which is a traditional method from numerical frontal analysis. Our system is not intended to replace human meteorologists, but to provide an objective method that can be applied consistently and easily to a large number of cases. Our system could be used, for example, to create climatologies and quantify the spread in forecast frontal properties across members of a numerical weather prediction ensemble. [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]