Earth and Space Science
Latest articles in this journal
Earth and Space Science; doi:10.1029/2019ea000582
Abstract:Elves are a class of transient luminous events, with a radial extent typically greater than 250 km, that occur in the lower ionosphere above strong electrical storms. We report the observation of 1598 elves, from 2014 to 2016, recorded with unprecedented time resolution (100 ns) using the Fluorescence Detector (FD) of the Pierre Auger Cosmic‐Ray Observatory. The Auger Observatory is located in the Mendoza province of Argentina with a viewing footprint for elve observations of 3 · 106 km2, reaching areas above the Pacific and Atlantic Oceans, as well as the Córdoba region, which is known for severe convective thunderstorms. Primarily designed for ultra‐high energy cosmic‐ray observations, the Auger FD turns out to be very sensitive to the UV emission in elves. The detector features modified Schmidt optics with large apertures resulting in a field of view that spans the horizon, and year‐round operation on dark nights with low moonlight background, when the local weather is favorable. The measured light profiles of 18\% of the elve events have more than one peak, compatible with intra‐cloud activity. Within the three years sample, 72% of the elves correlate with the far‐field radiation measurements of the World Wide Lightning Location Network (WWLLN). The Auger Observatory plans to continue operations until at least 2025, including elve observations and analysis. To the best of our knowledge, this observatory is the only facility on Earth that measures elves with year‐round operation and full horizon coverage.
Earth and Space Science; doi:10.1029/2019ea001005
Abstract:Impact craters on solar system bodies are used to determine the relative ages of surfaces. The smaller the limiting primary crater size, the higher the spatial resolution in surface/resurfacing age dating. A manually‐counted database (Robbins and Hynek, 2012) of >384,000 craters on Mars >1km in diameter exists. But, because crater size scales as a power law, the number of impact craters in the size range 10m to 1km is in the tens of millions; a number making precise analysis of local variations of age, over an entire surface, impossible to perform by manual counting. To decode this crater size population at a planetary scale, we developed an automated Crater Detection Algorithm based on the YOLOv3 object detection system. The algorithm was trained by annotating images of the controlled Thermal Emission Imaging System (THEMIS) daytime infrared dataset. This training dataset contains 7,048 craters that the algorithm used as a learning benchmark. The results were validated against the manually counted database as the ground‐truth dataset. We applied our algorithm to the THEMIS global mosaic between ± 65° of latitude, returning a true positive detection rate of 91% and a diameter estimation error (~15%) consistent with typical manual count variation. Importantly, although a number of automated crater counting algorithms have been published, for the first time we demonstrate that automatic counting can be routinely used to derive robust surface ages.
Earth and Space Science; doi:10.1029/2019ea000922
Abstract:In this manuscript we present a new analysis tool, called Space‐Time‐Accuracy‐Grid (STAG) analysis, to simultaneously assess the performance of an observing system architecture across space and time. Such an analysis tool is useful to directly link science objectives (typically expressed via a targeted spatial resolution, temporal resolution, and accuracy) to the expected performance of the observing system architecture. As a proof of concept, we apply STAG analysis to analyse three potential future observing systems for mass change in the Earth system: a single pair of polar orbiting satellites (heritage GRACE and GRACE‐FO), two polar pairs of satellites, and a polar pair of satellites coupled with an inclined (70o) pair of satellites. Here, we demonstrate the use of STAG analysis to quantify the relative performance of each architecture across space [200 – 1800 km] and time [1‐30 days], offering a significantly more comprehensive assessment of performance than previous studies. Results show that the polar pair coupled with the inclined pair reduces errors (after state‐of‐the‐art postprocessing for each architecture is accounted for) relative to the single pair of satellites by 40‐60% in medium spatial scales (500‐1200 km), with the greatest benefit being for longer solution (monthly) timespans. Overall, the results from this case study highlight the importance of increasing the isotropy of the observable over simply increasing the sampling frequency. Some demonstrated benefits of STAG analysis include the ability to incorporate state‐of‐the art post‐processing methods into the analysis, and also tailor the analysis to specific geographic regions to address targeted scientific objectives.
Earth and Space Science; doi:10.1029/2019ea000893
Abstract:This study examines the impacts of humidity adjustment in a cloud analysis system on the analysis and forecast of a squall line that occurred in southeast China on 23–24 April 2007. Radial velocity data are assimilated using the ARPS three‐dimensional variational system while reflectivity data are assimilated by a cloud analysis system. Experiments with two different humidity adjustment schemes are performed, with the original and enhanced versions. Another experiment does not adjust moisture. Both schemes generally decrease the humidity in front of the convective line and increase the humidity within the convective and stratiform regions of squall line compared to no humidity adjustment, and the original scheme produces the higher humidity within precipitation regions, especially the stratiform region. Both schemes improve the forecast of squall line structure, including the leading convective line, a transition zone, and a trailing stratiform region. Among the three experiments, the enhanced scheme produces the highest precipitation forecast skill. The latent heating rates are also diagnosed to investigate the microphysical responses to the humidity adjustment. The cooling outside of the observed precipitation regions corresponding to the humidity reduction also acts to suppress spurious precipitation. Water vapor condensation into cloud water and cloud water evaporation generally dominate the latent heating/cooling below the freezing level. Compared to the enhanced scheme, the original scheme releases much more latent heat in the middle troposphere, causing more warming. This is linked to the higher cloud water condensation rate, due to the higher amount of moisture addition/adjustment by the original scheme.
Earth and Space Science; doi:10.1029/2019ea001041
Abstract:Towards the Kashi region in northwest of China, the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals from Collection 6 (C6) MYD, Collection 6.1 (C6.1) MOD and C6.1 MYD during 2016 ‐ 2017 are compared with ground‐based measurements from the Sun‐sky Radiometer Network (SONET), and the first comprehensive evaluation of the Dark Target (DT) and Deep Blue (DB) retrievals with a 10‐km spatial resolution in the latest C6.1 MYD AOD dataset during 2016‐2018 are presented. In general, C6.1 MYD AOD products (both of DT and DB algorithm) are the most effective in Kashi of the three collections, and there is an overall underestimation of DB AOD, while DT AOD that is slightly outperformed DB AOD in Kashi is overestimated on the whole. As to the factors that influence the accuracy of MODIS AOD, for DB algorithm, the overestimations of the surface reflectance and Single Scattering Albedo (SSA) that DB aerosol model assumed, can cause underestimation of DB AOD retrievals over Kashi, while the ones for DT algorithm are opposite. Besides, the coarse dust particles with lower veracity are predominant in Kashi region, which illustrated that the errors of particle size assumption in C6.1 MYD DT and DB algorithms will make large inversion error of MODIS AOD. Moreover, whatever DB or DT algorithm, the accuracy of AOD are diminished as aerosol loading increases. The more realistic aerosol models and surface characterizations are necessary during the process of generating the MODIS aerosol retrievals in Kashi region.
Earth and Space Science; doi:10.1029/2019ea000945
Abstract:The dynamical downscaling technique is used for the understanding of physical mechanisms associated with the atmospheric phenomena. We have developed high resolution analysis (6 km) for three tropical cyclones (TCs) viz. Phailin (2013), Nilofar (2014), and Chapala (2015) originated over the North Indian Ocean (NIO) using the dynamical downscaling approach. The study aimed at the identification of appropriate methodology for generating analysis so that it becomes useful for identifying the role of environmental and internal dynamics on intensification processes and structural changes of TCs. The simulations using Weather Research and Forecasting (WRF) model and four‐dimensional variation (4DVAR), hybrid three‐dimensional ensemble–variational (3DEnVAR) as well as a hybrid four‐dimensional ensemble–variational (4DEnVAR) data assimilation (DA) techniques are compared. The impact of DA is quantified by comparing errors in position, mean sea level pressure (MSLP), and maximum wind speed with the best track dataset of India Meteorological Department (IMD). The intensities of TCs simulated by three downscaling methods are validated in terms of changes in MSLP, maximum surface winds, and boundary layer and middle tropospheric relative humidity. The skills scores viz. equitable threat score (ETS), false alarm ratio (FAR), the probability of detection (POD), and biases (BIAS) are calculated to identify the best suitable DA technique. It is found that the hybrid DA techniques improve the overall quality of analysis compared to those developed using only variational DA techniques. The simulation using the hybrid 4DEnVAR DA technique is found to be better for simulation of the track, intensity changes, and structural characteristics of TCs.
Earth and Space Science; doi:10.1029/2019ea001037
Abstract:Heterogeneity is an essential characteristic of the geographic phenomenon. However, most existing researches concerning heterogeneity are based on the matrix. The bidimensional nature of the matrix cannot well support the multidimensional analysis of spatio‐temporal field data. Here, we introduce an improved tensor‐based feature analysis method for spatio‐temporal field data with heterogeneous variation, by utilizing the similarity measurement in multidimensional space and feature capture of tensor decomposition. In this method, the heterogeneous spatio‐temporal field data are reorganized firstly according to the similarity and difference within the data. The feature analysis by integrating the spatio‐temporal coupling is then obtained by tensor decomposition. Since the reorganized data have a more consistent internal structure than original data, the feature analysis bias caused by heterogeneous variation in tensor decomposition can be effectively avoided. We demonstrate our method based on the climatic reanalysis field data released by the National Oceanic and Atmospheric Administration. The comparison with conventional tensor decomposition showed that the proposed method can approximate the original data more accurately both in global and local regions. Especially in the area that influenced by the complex modal aliasing and in the period time of the climatic anomaly events, the approximation accuracy can be significantly improved. The proposed method can also reveal the zonal variation of temperature gradient and abnormal variations of air temperature ignored in the conventional tensor method.
Earth and Space Science, Volume 7; doi:10.1029/2019ea000697
Abstract:Forecast skill of three S2S models and their ensemble mean outputs are evaluated in predicting the surface minimum and maximum temperatures at subseasonal timescales over South Africa. Three skill scores (correlation of anomaly, root mean square error, and Taylor diagrams) are used to evaluate the models. It is established that the S2S models considered here have skill in predicting both minimum and maximum temperatures at subseasonal timescales. The correlation of anomaly indicate that the multi‐model ensemble outperforms the individual models in predicting both minimum and maximum temperatures for the day 1‐14, day 11‐30 and full calendar month timescales during December months. The Taylor diagrams suggest that the ECMWF model and MM performs better for the day 11‐30 timescale for both minimum and maximum temperatures. In general, the models perform better for minimum than maximum temperatures in terms of RMSE. In fact, the skill difference in terms of CORA is small.
Earth and Space Science, Volume 7; doi:10.1029/2019ea000958
Earth and Space Science, Volume 7; doi:10.1029/2019ea001058