Targeted observation is an appealing procedure for improving model predictions through the assimilation of additional collected measurements. However, studies on targeted observations in the oceanic field have been largely based on modeling efforts, and there is a need for field validating operations. Here, we report the results of a field program that is designed based on the sensitive areas identified by the Conditional Nonlinear Optimal Perturbation (CNOP) approach to improve the short-range (7 days) summer thermal structure prediction in the Yellow Sea. We found good spatial consistency in the locations of the identified sensitive areas among the hindcast and climatology runs. By introducing the technique of cycle data assimilation and the new concept of time-varying sensitive areas, we designed an observing strategy based on the identified sensitive areas, and conducted a set of Observing System Simulation Experiments prior to assessing the effectiveness of the plan on later observations. On this basis, the impact of targeted observations was investigated by a choreographed field campaign in the summer of 2019. The results of the in-field Observing System Experiments show that compared to conventional local data assimilation, conducting targeted observations in sensitive areas can double the benefits of data assimilation in thermal structure prediction. Furthermore, dynamic analysis demonstrates that the refinement of vertical thermal structures is mainly caused by the changes in the upstream horizontally advected temperature driven by the Yellow Sea Cold Water Mass circulation. This study highlights the effectiveness of targeted observations on reducing the forecast uncertainty in the ocean.

Machine learning has been widely applied in numerical weather prediction, but the incorporation of new observational sites into models trained on stations with long historical records remains a challenge. Here we propose a post-processing framework consisting of three machine learning methods: station clustering with K-means, temperature prediction based on decision trees, and transfer learning for newly-built stations. We apply this framework to post-processing forecasts of surface air temperature at 301 weather stations in China. The results show significant reductions (as much as 39.4%~20.0%) in the root-mean-square error of operational forecasts at lead times as long as 7 days. Moreover, the use of transfer learning to incorporate new stations improves forecasts at the new site by 36.4% after only one year of data collection. These results demonstrate the potential for clustering and transfer learning to boost existing applications of machine learning techniques in weather forecasting.

Next to precipitation, secondary water sources emerging from shallow groundwater and lateral redistribution of soil moisture, together with soil properties modulating their accessibility are highly important in water-limited ecosystems. However, effects of these land-associated secondary inputs are not well known over large domains given the mismatch of spatial scales of processes. Here, we quantify the role of land properties on the spatial variations of seasonal decay rate of vegetation cover over water-limited regions of Africa, using machine learning. Over the study domain, 17 % of these variations are directly attributed to land properties, and 16 % are attributed to interaction effects of land properties with climate and vegetation. Locally, total land attributed variations account for more than 60 % in hotspots with different land properties like shallow groundwater, complex topography, and favourable soil properties. Our findings lend empirical evidence for the importance of local-scale secondary water inputs over large domains.

A long-standing question in geomorphology concerns the applicability of statistical models for elevation data based on fractal or multifractal representations of terrain. One difficulty with addressing this question has been the challenge of ascribing statistical significance to metrics adopted to measure landscape properties. In this paper, we use a recently developed surrogate data algorithm to generate synthetic surfaces with identical elevation values as the source dataset, while also preserving the value of the Hölder exponent at any point (the underpinning characteristic of a multifractal surface). Our primary data are from an experimental study of landscape evolution. This allows us to examine how the statistical properties of the surfaces evolve through time and the extent to which they depart from the simple (multi)fractal formalisms. We also study elevation data from Florida and Washington State. We are able to show that the properties of the experimental and actual terrains depart from the simple statistical models. Of particular note is that the number of sub-basins of a given channel order (for orders sufficiently small relative to the basin order) exhibit a clear increase in complexity after a flux steady-state is established in the experimental study. The actual number of basins is much lower than occur in the surrogates. The imprint of diffusive processes on elevation statistics means that, at the very least, a stochastic model for terrain based on a local formalism needs to consider the joint behavior of the elevations and their scaling (as measured by the pointwise Hölder exponents).

An object-based technique was utilized to identify hydrometeor size-sorting signatures at lower levels in the convective regions of 10 mesoscale convective systems (MCSs) during the 2015 Plains Elevated Convection at Night (PECAN) field campaign. Composite statistical analysis indicates that the magnitudes of size-sorting signatures (the separation distances between rain diameter maxima and concentration maxima) are nonlinearly correlated to the echo-top height, rain mass beneath the melting level, and precipitation rates at higher percentiles. To explore this correlation, the WRF model was used to simulate one of the MCSs (the 20 June 2015 storm) during the PECAN. Statistical analysis on the model outputs indicates more active riming growth and quicker graupel fallout at warmer temperatures near areas with larger separation distances. While updraft intensity above the melting level was also positively correlated to separation distances, this correlation was only statistically significant within certain temperature ranges. Additional analyses reveal that the higher intense precipitation potential near signatures with large separation distances could be attributed to precipitation production from the melted graupel. Finally, spatial correspondence between graupel distribution at the melting level and rain distribution at the lowest model level illustrates the critical role of graupel sedimentation and sorting in creating size-sorting signatures in MCSs during the PECAN field experiment.

Anthropogenic activities are altering flood frequency-magnitude distributions along many of the world’s large rivers, yet isolating the impact of any single factor amongst the multitudes of competing anthropogenic drivers is a persistent, yet important challenge if we are to mitigate their negative consequences. The Usumacinta River in southeastern Mexico provides an ideal opportunity to study an anthropogenic driver in isolation: tropical forest conversion. This article employs a novel approach to disentangle the anthropogenic signal from climate variability, and provides valuable insights into the impact of forest conversion on flood severity. Here we analyse continuous daily time series of precipitation, temperature, and discharge to identify long-term trends, and compare ratios of catchment-wide precipitation totals to daily discharges in order to account for climatic variability. We also identify an anthropogenic signature of tropical forest conversion at the intra-annual scale, successfully reproduce this signal using a distributed hydrological model (VMOD), and demonstrate that the continued conversion of tropical forest to agricultural land use will further exacerbate large-scale flooding. We find statistically significant increasing trends in annual minimum, mean, and maximum discharges that are not evident in either precipitation or temperature records. We also find that mean monthly discharges have increased between 7% and 75% in the past decade, in contrast to mean monthly precipitation, which shows no statistically significant trend. Model results demonstrate that forest cover loss is responsible for raising the 10-year return peak discharge by 25%, while the total conversion of forest to agricultural use would result in an additional 18% rise. These findings highlight the need for a holistic approach to catchment-wide land management in tropical regions that weights the benefits of agricultural expansion against the consequences of increased flood prevalence, and the economic and social costs that they incur.

Structures in Bathys Planum, southwest of the Tharsis Rise, Mars, compare to strike-slip faults in California, western United States. Local topographic highs and lows at step-overs between fault segments are likened to pop-up structures and pull-apart basins formed due to left-lateral strike-slip displacement along the faults. The faults are interpreted to have reactivated structures that preceded the propagation of Tharsis-incipient (Stage 1 Claritas tectonic center) graben through this region.

The present study have used space observations of NO emission (OMI) and aerosol optical depth (AOD) (MODIS) from the last couple of years (2015-2019) to investigate the changes in air pollution in response to COVID-19 pandemic during the lockdown period (Mar-May, 2020) over the Indo-Pak region. Result of this study reveals a huge drop in air pollution that accounted for 40-50% reduction in NO emissions and 45% in AOD over the whole Indo-Pak region. The major metropolitan areas (cities) of the region showed a remarkable decrease in NO emissions, whereas the calculated rate of reduction was found highest for the city of Lahore (Pak) ranges between 29-52% followed by Ahmadabad (Chennai) as 27-52% (32-42%) respectively. The geospatial analysis revealed the existence of positive correlation (range from 0.23-0.50) between AOD and NO emissions, which further implies that a decrease in AOD may be attributed to reduction in NO to some extents.

We explore a metric-learning approach to create representations of sorghum images grown in a field setting. We train a convolutional neural network to embed images so that images from the same variety have similar features and images of different varieties have different features. We that these features are good at discriminating unseen cultivars, can be used to predict standard phenotypes (height and leaf length and width), and can be used to predict presence or absence of genetic mutations. We evaluate these results using TERRA-REF data from field-scale trials of hundreds of varieties of sorghum. This demonstrates an end-to-end solution for creating useful image phenotypes in

Annotation plays an essential and important role in the performance of machine learning approaches. However, manually labeling objects accurately is often time-consuming, especially when processing large image data. To improve this process, we developed a deep learning-based interactive annotation approach with application to hyperspectral imagery. Specifically, a deep network with a metric-based loss function will be pre-trained on a large training set. Encoding layers in the network are used for dimensionality reduction as well as boosting discriminability among pixels belonging to different classes in the embedding space. Then, when new images are provided, the user can provide (fast, easy, weak) annotations to indicate objects of interest and fine-tune the pre-trained network to the task at hand. In this way, the fine-tuned network can adapt for the new category and task. Annotations can be iteratively modified and corrected. Once the model is fine-tuned by quickly annotating a few images, it can then be generalized to a large image dataset of similar objects. Experiments on the MUUFL Gulfport hyperspectral imaging dataset show that our approach outperforms classical active learning methods based on support vector machine in average Intersect of Union (from around 65% to around 80%) with significant improvement on computing speeds (from 30-200 seconds to less than 10 seconds)

Scene understanding methodologies (e.g., classification, segmentation, and anomaly detection) can be costly when operating on a per-pixel basis. As remote sensing applications rely on imagery with greater resolution, pixels regularly contain similar information to their neighbors. In recent years, scene understanding approaches have leveraged superpixel algorithms to partition imagery into small homogeneous regions for a variety of tasks. Instead of performing operations on millions of pixels, thousands, or in some cases hundreds of superpixels can be used as inputs into various pipelines. Most superpixel algorithms rely solely on RGB color information to produce superpixel maps. However, when multiple co-registered modalities are available, as is the case with the National Ecological Observatory Network (NEON) tree crown dataset, it is possible to combine information from multiple sources to produce a single shared map. In this work we combine airborne hyperspectral imagery, LiDAR point clouds, and RGB imagery to obtain superpixel maps and compare the oversegmentation results to those obtained by fusing individual maps produced from each modality. Superpixels are computed using the Simple Non-Iterative Clustering (SNIC) algorithm. Oversegmentation maps are scored using standard evaluation approaches. We present results on a subset of the National Ecological Observatory Network imagery dataset.

(250 words) Micronutrients, such as iron, zinc, and sulfur, play a vital role in both plant and human development. Understanding how plants sense and allocate nutrients within their tissues may offer different venues to develop plants with high nutritional value. Despite decades of intensive research, more than 40% of genes in Arabidopsis remain uncharacterized or have no assigned function. While several resources such as mutant populations or diversity panels offer the possibility to identify genes critical for plant nutrition, the ability to consistently track and assess plant growth in an automated, unbiased way is still a major limitation. High-throughput phenotyping (HTP) is the new standard in plant biology but few HTP systems are open source and user friendly. Therefore, we developed OPEN Leaf, an open source HTP for hydroponic experiments. OPEN Leaf is capable of tracking changes in both size and color of the whole plant and specific regions of the rosette. We have also integrated communication platforms (Slack) and cloud services (CyVerse) to facilitate user communication, collaboration, data storage, and analysis in real time. As a proof-of-concept, we report the ability of OPEN Leaf to track changes in size and color when plants are growing hydroponically with different levels of nutrients. We expect that the availability of open source HTP platforms, together with standardized experimental conditions agreed by the scientific community, will advance the identification of genes and networks mediating nutrient uptake and allocation in plants.

The interannual variability in mid and lower stratospheric temperatures for the period 1984–2019 is decomposed into dynamical and radiative contributions using a radiative calculation perturbed with changes in dynamical heating, trace gases and aerosol optical depth. The temperature timeseries obtained is highly correlated with the de-seasonalised ERA5 temperature (r2>0.6 for 1995 to 2019 in the region 15 to 70 hPa). Contributions from ozone and dynamical heating are found to be of similar importance, with water vapor, stratospheric aerosols, and carbon dioxide playing smaller roles. Prominent aspects of the temperature timeseries are closely reproduced, including the 1991 Pinatubo volcanic eruption, the year-2000 water vapour drop, and the 2016 Quasi-biennial oscillation (QBO) disruption. Ozone below 20 hPa is primarily controlled by transport and is positively correlated to the upwelling. This ozone-transport feedback acts to increase the temperature response to a change in upwelling by providing an additional ozone-induced radiative temperature change. This can be quantified as an enhancement of the dynamical heating of about 20% at 70 hPa. A principle oscillation pattern (POP) analysis is used to estimate the contributionof the ozone QBO (±1 K at 70 hPa). The non-QBO ozone variability is also shown to be significant. Using the QBO leading POP timeseries as representative of the regular QBO signal, the QBO 2016 disruption is shown to have an anomalously large radiative impact on the temperature due to the ozone change (>3 K at 70 hPa).

Plant growth is the product of gene by environment (GxE) interactions during plant development. Effective characterization of plant growth under various conditions provides insight into genetic components of plant development and mechanisms of stress resilience. While the emergence of high-through phenotyping facilities provides new avenues to further understand plant development and stress responses, the large costs of such facilities are hindering the study of dynamic growth processes. To democratize high-throughput plant phenotyping, we developed three sets of image-based phenotyping devices utilizing Raspberry Pi computers and low-weight/low-cost materials to continuously monitor shoot and root growth. The process is further automated by our workflows including data collection and statistical analysis. Our devices and workflows are customizable to image a wide variety of plants and tissues. To validate our system, we measured growth of Arabidopsis rosettes, tomato roots, and characterized the relationship between cowpea growth and evapotranspiration. These results demonstrate the variety of applications for Raspberry Pi based phenotyping. Importantly, this low-cost system is ideal for studying the genetics of plant growth and identifying new components of abiotic stress tolerance in a wide variety of species.

Agronomic technological advancements provide more precise means to establish methodologies that can estimate yield response in many different ways. We are experimenting with a new image-based technique to predict the yield response of Canola using the rate of ground cover accumulation. Our trial was composed of row spacing and seeding rate as factors that influence the growth and the spatial distribution to evaluate its influence on the yield. Using the Visible Band Difference Vegetation Index (VDVI) from digital images, we estimated the ground cover and modelled the change over time. We regressed ground cover accumulation and integrated the function to calculate the area under the curve to regress against yield. Preliminary analysis indicates that the green ground cover accumulation overtime is sufficiently correlated with the yield (F=168.1, p=2.2e-16, R2=0.4694). Further, our results suggest the amount of green ground cover accumulation over time is dependent on the seeding density and row spacing. The analysis shows the higher seeding densities, 40 plants/m2 and above, acquire biomass rapidly, and the most stable yield predictions with ground cover are likely reached at similar plant densities. The most stable yield predictions in-relation to row spacing obtained from either 0.3m, 0.45m or 0.6m spacing (R-squares 0.94, 0.93, and 0.89, respectively). We are further experimenting to understand what growth period of the crop is most suitable for ground cover based yield predictions. Our primary target is to develop a high throughput image-based methodology to estimate the yield response using the ground cover accumulation rate for on-farm precision agronomy.

A large part of hydrated oceanic lithosphere consists of serpentinites exposed in ophiolites, which constitute reactive chemical and thermal systems and potentially represent an effective sink for CO2. Understanding carbonation mechanisms is almost exclusively based on studies of outcrops, which can limit the interpretation of fossil hydrothermal systems. We present stable and radiogenic carbon data that provide insights into the isotopic trends and fluid evolution of peridotite carbonation in ICDP Oman Drilling Project drill holes BA1B (400 m deep) and BA3A (300 m deep). Geochemical investigations of the carbonates in serpentinites indicate formation in the last 50 kyr, implying a distinctly different phase of alteration than the initial oceanic hydration and serpentinization of the Samail Ophiolite. The oldest carbonates (~31 to over 50 kyr) are localized calcite, dolomite, and aragonite veins, which formed between 26 to 43 degrees Celcius and are related to focused fluid flow. Subsequent pervasive small amounts of dispersed carbonate precipitated in the last 1000 yr. Macroscopic brecciation and veining of the peridotite indicate that carbonation is influenced by tectonic features allowing infiltration of fluids over extended periods of time and at different structural levels such as along fracture planes and micro-fractures and grain boundaries, causing large-scale hydration of the ophiolite. The formation of dispersed carbonate is related to percolating fluids with δ18O lower than modern ground- and meteoric water. We also show that radiocarbon investigations are an essential tool to interpret the carbonation history and that stable oxygen and carbon isotopes alone can result in ambiguous interpretations.

We present an automatic classification method of the three near-Earth regions, the magnetosphere, the magnetosheath and the solar wind from their in-situ data measurement by multiple spacecraft. Based on gradient boosting classifier, this very simple and very fast method outperforms the detection routines based on manually-set thresholds. The method is used to identify 15 062 magnetopause crossings and 17 227 bow shock crossings in the data of 11 different spacecraft of the THEMIS, ARTEMIS, Cluster, MMS and Double Star missions and for a total of 83 cumulated years. These multi-mission catalogs are easily reproducible, can be automatically enlarged with additional data and their elaboration paves the way for future massive statistical analysis of near-Earth boundaries.

Understanding catchment controls on catchment solute export is a prerequisite for water quality management. StorAge Selection (SAS) functions encapsulate essential information about catchment functioning in terms of discharge selection preference and solute export dynamics. However, they lack information on the spatial origin of solutes when applied at the catchment scale, thereby limiting our understanding of the internal (subcatchment) functioning. Here, we parameterized SAS functions in a spatially explicit way to understand the internal catchment responses and transport dynamics of reactive dissolved nitrate (N-NO3). The model was applied in a nested mesoscale catchment (457 km²), consisting of a mountainous partly forested, partly agricultural subcatchment, a middle-reach forested subcatchment, and a lowland agricultural subcatchment. The model captured flow and nitrate concentration dynamics not only at the catchment outlet but also at internal gauging stations. Results reveal disparate subsurface mixing dynamics and nitrate export among headwater and lowland subcatchments. The headwater subcatchment has high seasonal variation in subsurface mixing schemes and younger water in discharge, while the lowland subcatchment has less pronounced seasonality in subsurface mixing and much older water in discharge. Consequently, nitrate concentration in discharge from the headwater subcatchment shows strong seasonality, whereas that from the lowland subcatchment is stable in time. The temporally varying responses of headwater and lowland subcatchments alternates the dominant contribution to nitrate export in high and low-flow periods between subcatchments. Overall, our results demonstrate that the spatially explicit SAS modeling provides useful information about internal catchment functioning, helping to develop or evaluate spatial management practices.