Development is well-advanced for the next version of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG), labeled Version 07. IMERG is a key output of the U.S. GPM Science Team, and V07 will be the second generation in which data from both the Tropical Rainfall Measuring Mission (TRMM) and GPM projects are combined into a single, uniformly processed record, currently starting in June 2000. This presentation will show several examples of successes and challenges in V06, and use these to illuminate the upgrades that have been pursued for V07. For example, the V06 IMERG near-real-time products (Early and Late Runs) show regional biases because they do not have climatological calibration (despite the documentation), and this will be done in V07. As well, the time series of precipitation-rate histograms shows a seam in the transition from TRMM calibration to GPM Core Observatory calibration at the start of June 2014. V07 will benefit from better continuity in the input calibration datasets across that boundary. A third issue is that the Kalman filter used in IMERG a) introduces a variable amount of smoothing, and b) depends on relatively simple measures of input data quality. Both of these are revisited in V07. We will report the status of IMERG Version 07 processing as of the conference time, and introduce some topics that are being considered for the future, including improved uncertainty estimates, addition of sub-monthly gauge information, and strategies for incorporating precipitation estimates from multiple, relatively short-lived small satellites.

Flood depth grids from U.S. Federal Emergency Management Agency (FEMA) provide model-output estimates of the depth of water that can, on average, be expected to occur at various return periods for localized areas. However, use of these depth grids can be limited by spurious data and an insufficient number of return periods for certain planning applications. This research proposes a new method for estimating flood depth grids to address these shortcomings. The Gumbel distribution is used to characterize the flood depth-return period relationship for grid cells for which the data are plausible. Then the Gumbel parameters of slope (α) and intercept (u) are used to project flood elevations for extreme return periods for which an entire area can be assumed to be submerged. Spatial interpolation methods are then used to impute the flood elevations for spurious or missing grid cells. Then, the flood depth is recomputed from the flood elevations, once they are re-calculated at the shorter return periods. Validation of this technique for a Metairie, Louisiana, U.S.A. study area suggests that the cokriging spatial interpolation technique provides the most suitable estimates of flood depth, provided that the FEMA-generated model output is assumed to provide the “correct” results. These methods may assist engineers, developers, planners, and others in mitigating the world’s most widespread and expensive natural hazard.

The Interstellar Boundary Explorer (IBEX) mission has shown that variations in the ENA flux from the outer heliosphere are associated with the solar cycle and longer-term variations in the solar wind. In particular, there is a good correlation between the dynamic pressure of the outbound solar wind and variations in the later-observed IBEX ENA flux. The time difference between observations of the outbound solar wind and the heliospheric ENAs with which they correlate ranges from approximately two to six years or more, depending on ENA energy and look direction. This time difference can be used as a means of “sounding” the heliosheath, that is, finding the average distance to the ENA source region in a particular direction. We apply this method to build a three-dimensional map of the heliosphere. We use IBEX ENA data collected over a complete solar cycle, from 2009 through 2019, corrected for survival probability to the inner heliosphere. We divide the data into 56 “macro-pixels” covering the entire sky, and as each point in the sky is sampled once every six months, this gives us a time series of 22 points per macro-pixel on which to time-correlate. Consistent with prior studies and heliospheric models, we find that the shortest distance to the heliopause dHP is slightly south of the nose direction (dHP ~ 110 – 120 au), with a flaring toward the flanks and poles (dHP ~ 160 – 180 au). The heliosphere extends at least ~350 au tailward, which is the distance limit of the technique.

Based on the observational dataset SA-OBS and model outputs from the Community Earth System Model Large Ensemble project, this study investigates heatwaves in Southeast Asia in the current and future warmer climate. A heatwave is detected when the daily maximum temperature exceeds the 90th percentile threshold at each grid for at least three consecutive days. Three characteristics describing the frequency, duration, and amplitude of heatwaves are examined, including the sum of heatwave days per year (HWF) satisfying the heatwave definition, the length of the longest yearly heatwave event (HWD), and the hottest amplitude of the hottest yearly heatwave event (HWA). Results indicate that increased global warming is associated with substantial changes in heatwave characteristics over Southeast Asia, with more frequent heatwaves, longer heatwave duration, and higher extreme temperatures. The increase in HWA has a linear growth against global warming levels with distinct regional differences between the Maritime Continent and the Indochina Peninsula due to their different heat content of lower atmospheric boundaries. In contrast, those in HWF and HWD have nonlinear growth characteristics. The projected warmer future tends to be associated with a higher risk ratio value with the occurrence of rarer extreme heatwaves relative to the current climate. These results reiterate the potential risks of extreme regional heatwaves if global warming is unrestricted.

A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current continental cropland extent maps of Africa are either inaccurate, have too coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland extent map for the African continent is therefore recognized as a gap in the current crop monitoring services. Using Digital Earth Africa’s Open Data Cube platform, and working in conjunction with multiple regional African geospatial institutions, we co-develop a 10 metre resolution cropland extent map over the African continent using a Random Forest machine learning classifier and an annual time-series of Sentinel-2 satellite images. Members of the regional African geospatial institutions (RCMRD, OSS, Afrigist, AGRHYMET, and NADMO) were instrumental in defining the specifications of the product, in developing and implementing a continental scale reference data collection strategy, and assisted with iterative model building. The cropland extent map comes packaged with three layers: a pixel-based classification, a pixel-based cropland probability layer, and an object-based segmentation filtered classification. All the components of Digital Earth Africa’s cropland extent map: models, reference data, code, and results are open source and freely available online through Digital Earth Africa’s mapping and analysis platforms. A fuller description of the dataset, including methods, the validation results, and how to access the different datasets can be seen on the DE Africa user guide: https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.html

Accurate loss assessment plays a vital role in understanding the economic risk of natural hazards, for planning, mitigation, and actuarial purposes. Because of its juggernaut status as the most widespread and costly hazard, both nationally and around the world, loss assessment due to flood is particularly important. One of the shortcomings in existing flood loss models is to partition the structure (or building) economic value of loss into that borne by the homeowner and that covered by flood insurance. The goal of this research is to model the loss incurred by the homeowner and that incurred by the National Flood Insurance Program, considering flood damage, building replacement value, flood insurance coverage amount, deductible, and flood characteristics (slope and y-intercept of the loss vs. return period curve). A Monte Carlo approach is used to calculate the annual average loss due to flood at the individual homeowner scale. Multiple linear regression (MLR) and Classification and Regression Tree (CART) models are trained to provide the output of the owner’s share of the loss. The CART model outperformed the MLR model with lower RMSE and MSE values and a higher R² value (0.95) on the test data set. Because out-of-pocket expenses due to flood can be devastating to financial security, the results of this study support and inform the proactive decision-making process that homeowners can use to self-assess their degree of preparation and vulnerability to the flood hazard.

​​We believe that the future of Earth Observation (EO) is in fusion, harmonization, and interoperability of satellite imagery. Intensified monitoring leads to better understanding of land use and reduction of maintenance costs for all Land Cover products. RapidAI4EO is an initiative that aims to establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. The goal is to provide intensified monitoring of Land Use (LU), Land Cover (LC) changes at a much higher spatial resolution and temporal cadence than is possible today. Key objectives are to explore, evaluate, and quantify state of the art deep learning algorithms and methodologies that leverage three meter, daily time series, in conjunction with higher spectral resolution Sentinel-2 imagery.  Consortium Partners: The RapidAI4EO projects brings together Planet Labs PBC, the operator of the world’s largest fleet of Earth-imaging satellites and the recognized leader of the CubeSat revolution, VITO, the main production center of the Copernicus Global Land Service, Vision Impulse, a recent spin-off of German Research Center for Artificial Intelligence (DFKI, the largest research center for Artificial Intelligence in the world and one of the two European NVIDIA AI Labs), the International Institute for Applied Systems Analysis (IIASA) whose Center for Earth Observation and Citizen Science (EOCS) devises new approaches and technologies to collect data on land cover and land use, and Serco Italia, a worldwide service provider to governments, international agencies and industries, and operator of the ONDA DIAS platform. The objectives of RapidAI4EO are: 1) the creation and release of the most comprehensive spatiotemporal EO training sets ever produced for machine learning;  2) the development and implementation of novel AI solutions for continuous change detection that leverage these data sets; 3)the ability to drive frequent temporal updates of the Corine Land Cover (CLC) product; and 4)to demonstrate improved LULC mapping using harmonized Sentinel-2 and very high resolution, high cadence data streams.

Groundwater is critical in sustaining streamflow, especially in mountain catchments, because of its ability to supply baseflow in the absence of precipitation. In water-limited arid and semi-arid mountain environments, the need to characterize groundwater recharge and discharge has grown in tandem with demands to effectively manage current and future water resources. However, studying groundwater is challenging in complex terrain due to limited field measurements. Nearly a decade of monitoring data collection at Gordon Gulch in the Colorado Front Range provides a unique opportunity to study such an environment. The field data is used to parameterize and calibrate a groundwater flow model (MODFLOW-NWT). Model results reveal spatial and temporal patterns in groundwater recharge and discharge to the stream. Groundwater is recharged primarily by one to two recharge events each year, driven by spring snowmelt and rain. The majority of groundwater recharge occurs in upper Gordon Gulch and is stored in saprolite and weathered bedrock. Groundwater is discharged to the stream via long, deep flowpaths sourced from upper Gordon Gulch and short, shallow flowpaths from soil and saprolite in lower Gordon Gulch. Using Gordon Gulch as a case study, this model and data analysis contribute to a larger effort to understand and constrain the mechanisms driving groundwater recharge and groundwater-stream exchanges in semi-arid, montane environments.

Subaqueous dunes are fascinating morphological features that exist in diverse environments such as the deep sea, continental shelves and inland streams or rivers. Due to their rhythmic and oftentimes very frequent occurrence along the predominant flow direction, the analysis of these bedforms is usually assigned to bedform identification tools. Such algorithms automatically determine crest and trough locations and calculate dune dimensions accordingly. Over the last years, the number of these tools has notably increased with specialized methodologies for every environment and bedform scale. Although many of them are readily available to interested researchers, there may be uncertainty as to which method should be applied in view of a specific research question. As authors of some of the most recent bedform identification tools, we have started to systematically compare our approaches by analyzing an agreed set of diverse bathymetric data. The bed features assessed in this context range from river dunes formed under unidirectional flow over tidally constrained compound dunes to bed elevation profiles gained from flume experiments. The resulting dune characteristics, which each scientist obtained by applying his/her particular algorithm, are thereupon contrasted in a qualitative and quantitative manner uncovering the similarities and differences between individual methodologies. Our preliminary results suggest a strong influence of the original focus of each algorithm and therefore corroborate the need for systematic comparison. In the next step, the gained insights will be used to find and explain the optimum fields of application and, in the end, provide user-oriented guidelines that may support the bedform community in deciding which identification tool should be used for what purpose.

The increasing amount of Synthetic Aperture Radar (SAR) satellites orbiting the Earth, their increasing time and space resolution, the variety of wavelengths, polarizations and looking geometries, the shortening of data availability latency and the lengthening of archive databases offer unprecedented opportunities for Earth observation and hazard monitoring. The downside is that it brings new challenges for processing that huge amount of data and for making the results quickly analyzable. To fully benefit from these advances in SAR, it requires efficient data processing infrastructure (optimized for processing speed, storage usage and security), efficient data visualization, and standardization of the final products for easy ingestion by conventional analysis tools. We present here the InSAR Mass processing Toolbox for Multidimensional time series (MasTer), which can combine any type of SAR data to produce unsupervised vertical and horizontal ground deformation time series. MasTer is optimized to automatically download SAR data, select the appropriate interferometric pairs, perform the interferometric mass processing, compute the geocoded deformation maps, invert and display the velocity maps and the 2D time series on a web page updated incrementally as soon as a new image is made available. The incremental architecture allows updating the time series within the shortest time possible (typically a few hours) as soon as a new SAR image is provided. Several steps are self-evaluating to ensure robust and reliable processing. Moreover, recent methodological improvement consists in the computation of a coherence proxy to guide the pair selection optimization balancing the use of each image as primary and secondary image during the differential interferometric (DInSAR) processing. Such a pair selection increases both the processing efficiency and the signal-to-noise ratio of the time series. MasTer also allows the production of time series of coherences or SAR amplitude images, which can be used e.g. for land use monitoring or geomorphological changes detection. The capabilities and performances of MasTer will be illustrated with several examples. Software and manual are available upon request from the authors.

The volcanic eruption that began on 19 March 2021 at Fagradalsfjall is the first one to occur on the Reykjanes Peninsular for nearly 800 years and in Fagradalsfjall for about 6000 years. The feeder-dike was injected from a magma reservoir whose top is at about 10 km depth below the surface (but the reservoir itself reaches much greater depths). The dike formation involved at least two roof ruptures and resulting dike segments. The first occurred on 24 February and the second on 14 March 2021. The first rupture, marked by earthquakes of M23 close to the contact between the roof and the magma, occurred in the eastern half of the toppart of the reservoir. An injected dike segment propagated towards the surface but became arrested at the depths of 0.5-2 km. As its vertical propagation became arrested, while continuing to receive magma, the segment spread laterally, reaching an overall maximum dike strike-dimension (length) of about 10 km. The second rupture, also marked by earthquakes of M2-3, occurred about 1 km to the west of the first rupture. The injected dike segment following the second rupture eventually resulted in the dike propagating to the surface to feed the eruption which started on 19 March. We estimate the average vertical rate of the feeder-dike propagation at about 0.02 m s-1. This is an order of magnitude lower than common rates of lateral dike propagation in rift zones, yet similar to the average rate during the Bardarbunga (Iceland) 2014 dike propagation (around 0.04 m s-1). The initial volcanic fissure fed by the second dike segment had a length of less than 200 m and an opening of a fraction of a metre. Subsequently, several more ‘dike-fingers’ reached the surface and generated volcanic fissure segments. The total length of the discontinuous, segmented fissure is many hundred metres – but only one crater is presently active. Using the aspect ratio of the fissure and basic fracture mechanics, we estimate the magmatic overpressure (driving pressure) at the beginning of the eruption as about 3 MPa. The low driving pressure and small fissure opening displacement and length are in harmony with the very low volumetric flow (effusion) rate of about 10 m3 s-1.

Radiation hazards at commercial aviation altitudes up to suborbital space have been known for decades including those from galactic cosmic rays (GCRs), solar energetic particles (SEPs), and more recently radiation belt particle precipitation (RBPP). The complex radiation field that derives from these primary particle sources creates safety concerns for aerospace crew and passengers. Because of this safety hazard, the Automated Radiation Measurements for Aerospace Safety (ARMAS) program was developed to provide global aerospace radiation environment monitoring. The ARMAS TRL 9 operational system has now achieved monitoring from the surface of the Earth into Low Earth Orbit (LEO) with aircraft, high altitude balloon, suborbital vehicle, satellite and ISS flights over the past year. We present the latest results from i) the various flight domains; ii) the calibrations of the ARMAS system with the Tissue Equivalent Proportional Counter (TEPC); and iii) the ongoing real-time data assimilation of ARMAS data into the RADIAN system using NAIRAS v2 baseline global fields and CARI-7 verifications. We also describe progress towards 24/7 atmospheric monitoring from both the perspective of new sensor development as well as new stratospheric monitoring platforms.

The NASA Mars 2020 Perseverance rover mission will collect a suite of scientifically compelling samples for return to Earth. On the basis of orbital data, the Mars 2020 science team* identified two notional sample caches to study (1) the geology of Jezero crater, collected during the prime mission and (2) the ancient crust outside of Jezero crater, collected during a possible extended mission. Jezero crater geology consists of well-preserved, Early Hesperian to Late Noachian deltaic and lacustrine deposits sourced from a river system that drained Noachian terrain. The crater floor comprises at least two distinct units of sedimentary or volcanic origin whose relationship to the deltaic deposits is presently unclear. Remotely-sensed data reveal signatures of carbonate+olivine and clay minerals within crater floor and crater margin units. Samples from within Jezero that comprise the prime mission notional sample collection thus include: crater floor units; fine- and coarse-grained delta facies, the former with potential to preserve organic matter and/or biosignatures, the latter to possibly constrain the type and timing of sediment deposition; chemical sediments with the potential to preserve biosignatures; a sample of crater rim bedrock; and at least one sample of regolith. The region of southern Nili Planum, directly outside the western rim of Jezero crater, is geologically distinct from Jezero crater and contains diverse Early or even Pre-Noachian lithologies, that may contain records of early planetary differentiation, magnetism, paleoclimate and habitability. The notional sample collection from this region will include: layered and other basement rocks; megabreccias, which may represent blocks of (pre-)Noachian crust; basement-hosted hydrothermal fracture fill; olivine+carbonate rocks that are regionally significant and may be related to units within Jezero crater; and mafic cap unit rocks. The samples described are notional and may change with ongoing surface investigations. However, the samples we anticipate collecting align well with community priorities for Mars exploration, addressing geologic diversity, potential ancient biologic activity on Mars, planetary evolution, volatiles, and human health hazards. *Many other Mars 2020 team members were involved in this planning

The IGS contribution to ITRF2020, based on the third IGS reprocessing campaign (repro3), comprises daily position estimates for 1905 globally distributed GNSS stations. As an essential step of the ITRF2020 preparation, a detailed analysis of the IGS repro3 station position time series has been carried out, including identification of offsets, modeling of post-seismic displacements, confrontation with deformation predicted by geophysical loading models, and characterization of systematic and random errors. This presentation covers the different aspects of this analysis, the methods used and the lessons learned.

Substantial progress on machine learning (ML) models and graphical processing units (GPUs) has stimulated emerging research in applications of ML to earth science. As snow is a vital component of the global hydroclimate system, precise snowpack prediction is of considerable value for science and society. In this work, we have trained three different ML models (LSTM, CNN and Attention) to predict daily snow water equivalent (SWE) with both dynamic and static features in the Western Contiguous United States from Snow Telemetry (SNOTEL) observations. Dynamic features include precipitation, minimum and maximum temperature, minimum and maximum relative humidity, specific humidity, solar radiation and wind velocity. Static features are latitude, longitude, elevation, diurnal anisotropic heating (DAH) index and topographic radiative aspect (TRASP) index. This choice of features allows us to produce high-resolution maps of regional SWE for a given set of input meteorological conditions. The importance and the sensitivity of input variables will be tested by several explainable AI methods including feature permutation and integrated gradient. The ML-based dataset is further up-sampled and compared with the 4km gridded SWE dataset from the National Snow & Ice Data Center (NSIDC), which is from a physical-based model. Future SWE estimates are also produced under climate conditions projected by CMIP class models, along with associated uncertainty estimates based on our sensitivity analysis. The ML models are demonstrated to be a fast and accurate method of producing high-resolution SWE estimates with minimal computing power.

Eddy Covariance method has been actively used by expert micrometeorologists for over 30 years, covering 2155 stationary locations globally, and numerous mobile campaigns over land and water surfaces. Latest measurement technologies and automated processing software are rapidly expanding the use of the method to non-micrometeorological research. Regulatory and commercial uses of the method also increase year-by-year. Despite widening adoption of the method, academic investigators outside the area of micrometeorology and the majority of non-academic investigators are still not familiar enough with the proper implementation of the method required for conducting high-quality, reliable, traceable, and defensible measurements in their respective areas of interest. Although data collection and processing are now automated, the method still requires significant care to correctly design the experiment, set up the site, organize and analyze the large amount of data. Efforts of the flux networks (e.g., FluxNet, Ameriflux, Asiaflux, ICOS, NEON, OzFlux, etc.) have led to major progress in the standardization of the method. The project-specific workflow, however, is difficult to unify because various experimental sites and purposes of studies demand different treatments, and site-, measurement- and purpose-specific approaches. To address this situation, step-by-step instructions were created to introduce a novice to general principles, requirements, applications, processing and analysis steps of the conventional Eddy Covariance technique in the form of the free electronic resource, a 660-page textbook titled “Eddy Covariance Method for Scientific, Regulatory, and Commercial Applications”. The explanations are provided using easy-to-understand illustrations and real-life examples, and text is written in a non-technical language to be practically useful to those new to this field. Information is provided on theory of the method (including the state of methodology, basic derivations, practical formulations, major assumptions, sources of errors, error treatments, etc.), practical workflow (e.g., experiment design, implementation, data processing, quality control and analysis), data sharing and flux stations networking, key alternative methods, and the most frequently overlooked details.

Worldwide coastal land-margins are prone to many flood hazards such as astronomical tides, tropical cyclones, sea-level rise, and extreme precipitation events. Compound flood events, in which two or more flooding mechanisms occur simultaneously or in close succession (Santiago-Collazo et al., 2019, https://doi.org/10.1016/j.envsoft. 2019.06.002), can exacerbate the inundation impacts due to the highly non-linear interaction of coastal and hydrologic processes. Furthermore, sea-level rise will increase the hazard at low-gradient coastal land-margins when assessing future projections due to its non-linear nuance on the compound flood (Santiago-Collazo et al., 2021, https://doi.org/10.3389/fclim.2021.684035). Therefore, there is an urgent need to develop new technologies capable of comprehensively studying compound flood events and identifying hotspots prone to these inundations. This research aims to develop a technique capable of defining and classifying coastal land- margins based on physically-based criteria due to surface flow hydrodynamics. A one-dimensional (1-D) hydrodynamic model was used to quantify the hydrodynamic response of thousands of different combinations of input parameters (e.g., astronomical tides, storm surge, precipitation, and landscape) that define a coastal land-margin. This 1-D fully-coupled model, based on the shallow water equations, was applied at a national spatial scale, considering several coastal watersheds within the Gulf of Mexico and the US East coast. One of the main goals of this tool is to identify coastal land-margins vulnerable to compound flood hazards over broad spatial scales (e.g., national or global scale). Findings suggest that low-gradient (e.g., slopes less than 0.01 m km-1) coastal land-margins are more susceptible to compound flood impacts than ones with a steeper gradient under most flooding scenarios. Future research will focus on applying this tool on a worldwide basis to test its capabilities at low-resolution, scarce data regions. A worldwide classification of coastal land-margins may help authorities, policy-makers, and professionals converge on better coastal resilience measures, such as comprehensive compound flood analysis to delineate accurate compound flood hazard maps.Full online poster version at agu2021fallmeeting-agu.ipostersessions.com/Default.aspx?s=FA-1F-20-67-21-4E-E7-69-9F-89-1E-33-BB-3D-2D-40

Accurate sub-seasonal to seasonal (S2S) weather forecast between 2 weeks and several months is crucial to making informed decisions regarding changes in the risk of extreme weather events, resource management, agriculture, forestry, public health, energy, etc. However, significant gaps exist between the needs of society and what forecasters can produce, especially over longer lead times. Using three goodness-of-fit metrics, this study examined the ability of the SOMs-generated CFSv2 to forecast the correct (observed) circulation pattern, as opposed to the actual observed gridded field over a 90-day forecast period. Mean sea-level pressure (MSLP), near-surface wind (wnd10m), 850-mb temperature (t850), and 700-mb geopotential heights (z700) from the North American Regional Reanalysis were used to categorize the synoptic-scale circulation for three regions (East, West, and Gulf) across North America from January 1979 – December 2016. Expectedly, forecast skill generally decreased from the first day down to the skill of climatology (after 10 -15 days) and also varied regionally, seasonally, and between variables. The forecasts for the winter and summer seasons outperformed others, while t850 and z700 forecasts outperformed wnd10m, except in the west region. More importantly, this study found that the SOMs-generated CFSv2 forecasts improve upon the skill of the raw CFSv2 forecast near the one 1 – 2 weeks lead time. This study thus demonstrates the potential utility of a SOMs-based forecasting method in medium-range weather forecasts.