Using AI/ML to Explore & Develop Quickly and Efficiently

See how application of a fully trained Artificial Intelligence (AI) / Machine Learning (ML) technology applied to 3D seismic data volumes delivers an unbiased data driven assessment of entire volumes or corporate seismic data libraries quickly. Whether the analysis is undertaken using onsite hardware or a cloud based mega cluster, this automated approach provides unparalleled insights for the interpretation and prospectivity analysis of any dataset. The Artificial Intelligence (AI) / Machine Learning (ML) technology uses unsupervised genetics algorithms to create families of waveforms, called GeoPopulations, that are used to derive Amplitude, Structure (time or depth depending on the input 3D seismic volume) and the new seismic Fitness attribute. We will show how Fitness is used to interpret paleo geomorphology and facies maps for every peak, trough and zero crossing of the 3D seismic volume. Using the Structure, Amplitude and Fitness attribute maps created for every peak, trough and zero crossing the Exploration and Production (E&P) team can evaluate and mitigate Geological and Geophysical (G&G) risks and uncertainty associated with their petroleum systems quickly using the entire 3D seismic data volume.

Mass Dampers with Limited Amplitude

Mass dampers are widely used in engineering applications. We consider the effects of limitations on the damper amplitude. Using simple methods to analyze very general mass dampers, we find an upper limit to the damping. The maximum damping logarithmic decrement is δmax = 4μα, where μ is the mass ratio, and α isthe amplitude ratio of damper to structure amplitude. The result is further discussed in relation to Tuned Mass Dampers (TMDs), which can performvery well if there is enough avaliable space. In practice, amplitude limits always apply, and our result can be used to relate these to the damper performance.Our result also applies to active devices, which have to obey the limit mentioned above. Simulated tests of TMDs and other mass dampers are described. The damping is measured both by decay tests and by forced motion test. The methods agree well in the amplitude-limited regime. In other cases, decay tests are difficulet to interpret, indicating that one needs to be very careful whenmeasuring damping of 2DOF systems based solely on decay tests. We hope that our result may inform the selection and design of mass dampers in the future, where one should consider amplitude limits as the very first step.

A High-Resolution Regional Climate Model Physics Ensemble for Northern Sub-Saharan Africa

While climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterization schemes, but optimal configurations may vary e.g., from region to region. Besides land-surface processes, the most crucial processes to be parameterized in RCMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterization schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-Interim as lateral boundary conditions, we derived an ensemble of 16 physics parameterization runs for a larger domain in Northern sub-Saharan Africa (NSSA), northwards of the equator, using two different CU-, MP-, PBL-, and RA schemes, respectively, using the Weather Research and Forecasting (WRF) model for the period 2006–2010 in a horizontal resolution of approximately 9 km. Based on different evaluation strategies including traditional (Taylor diagram, probability densities) and more innovative validation metrics (ensemble structure-amplitude-location (eSAL) analysis, Copula functions) and by means of different observation data for precipitation (P) and temperature (T), the impact of different physics combinations on the representation skill of P and T has been analyzed and discussed in the context of subsequent impact modeling. With the specific experimental setup, we found that the selection of the CU scheme has resulted in the highest impact with respect to the representation of P and T, followed by the RA parameterization scheme. Both, PBL and MP schemes showed much less impact. We conclude that a multi-facet evaluation can finally lead to better choices about good physics scheme combinations.

Potential Impacts of Assimilating Every-10-Minute Himawari-8 Satellite Radiance with the POD-4DEnVar Method

The Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite provides continuous observations every 10 min. This study investigates the assimilation of every-10-min radiance from the AHI with the POD-4DEnVar method. Cloud detection is conducted in the AHI quality control procedure to remove cloudy and precipitation-affected observations. Historical samples and physical ensembles are combined to construct four-dimensional ensembles according to the observed frequency of the Himawari-8 satellite. The purpose of this study was to test the potential impacts of assimilating high temporal resolution observations with POD-4DEnVar in a numerical weather prediction (NWP) system. Two parallel experiments were performed with and without Himawari-8 radiance assimilation during the entire month of July 2020. The results of the experiment with radiance assimilation show that it improves the analysis and forecast accuracy of geopotential, horizontal wind field and relative humidity compared to the experiment without radiance assimilation. Moreover, the equitable threat score (ETS) of 24-h accumulated precipitation shows that assimilating Himawari-8 radiance improves the rainfall forecast accuracy. Improvements were found in the structure, amplitude and location of the precipitation. In addition, the ETS of hourly accumulated precipitation indicates that assimilating high temporal resolution Himawari-8 radiance can improve the prediction of rapidly developed rainfall. Overall, assimilating every-10-min AHI radiance from Himawari-8 with POD-4DEnVar has positive impacts on NWP.

Online treatment of eruption dynamics improves the volcanic ash and SO₂ dispersion forecast: case of the Raikoke 2019 eruption

In June 2019, the Raikoke volcano, Kuril Islands, emitted 0.4–1.8 × 109 kg of very fine ash and 1–2 × 109 kg of SO2 up to 14 km into the atmosphere. The eruption was characterized by several phases or puffs of different duration and eruption heights. Resolving such complex eruption dynamics is required for precise volcanic plume dispersion forecasts. To address this issue, we coupled the atmospheric model system ICON-ART (ICOsahedral Nonhydrostatic – Aerosols and Reactive Trace gases) with the 1-D plume model FPlume to calculate the eruption source parameters (ESPs) online. The main inputs are the plume heights for the different eruption phases that are geometrically derived from satellite data. An empirical relationship is used to derive the amount of very fine ash (particles < 32 µm), which is relevant for long range transport in the atmosphere. On the first day after the onset of the eruption, the modeled ash loading agrees very well with the ash loading estimated from AHI (Advanced Himawari Imager) observations due to the resolution of the eruption phases and the online treatment of the ESPs. In later hours, aerosol dynamical processes (nucleation, condensation, coagulation) explain the loss of ash in the atmosphere in agreement with the observations. However, a direct comparison is partly hampered by water and ice clouds overlapping the ash cloud in the observations. We compared 6-hourly means of model and AHI data with respect to the structure, amplitude, and location (SAL-method) to further validate the simulated dispersion of SO2 and ash. In the beginning, the structure and amplitude values differed largely because the dense ash cloud leads to an underestimation of the SO2 amount in the satellite data. On the second and third day, the SAL values are close to zero for all parameters indicating a very good agreement of model and observations. Furthermore, we found a separation of the ash and SO2 plume after one day due to particle sedimentation, chemistry, and aerosol-radiation interaction. The results confirm that coupling the atmospheric model system and plume model enables detailed treatment of the plume dynamics (phases and ESPs) and leads to significant improvement of the ash and SO2 dispersion forecast. This approach can benefit the operational forecast of ash and SO2 especially in case of complex and non-continuous volcanic eruptions like the Raikoke 2019.

Ensemble-Tailored Pattern Analysis of High-Resolution Dynamically Downscaled Precipitation Fields: Example for Climate Sensitive Regions of South America

For climate adaptation and risk mitigation, decision makers in water management or agriculture increasingly demand for regionalized weather and climate information. To provide these, regional atmospheric models, such as the Weather Research and Forecasting (WRF) model, need to be optimized in their physical setup to the region of interest. The objective of this study is to evaluate four cumulus physics (CU), two microphysics (MP), two planetary boundary layer physics (PBL), and two radiation physics (RA) schemes in WRF according to their performance in dynamically downscaling the precipitation over two typical South American regions: one orographically complex area in Ecuador/Peru (horizontal resolution up to 9 and 3 km), and one area of rolling hills in Northeast Brazil (up to 9 km). For this, an extensive ensemble of 32 simulations over two continuous years was conducted. Including the reference uncertainty of three high-resolution global datasets (CHIRPS, MSWEP, ERA5-Land), we show that different parameterization setups can produce up to four times the monthly reference precipitation. This underscores the urgent need to conduct parameterization sensitivity studies before weather forecasts or input for impact modeling can be produced. Contrarily to usual studies, we focus on distributional, temporal and spatial precipitation patterns and evaluate these in an ensemble-tailored approach. These ensemble characteristics such as ensemble Structure-, Amplitude-, and Location-error, allow us to generalize the impacts of combining one parameterization scheme with others. We find that varying the CU and RA schemes stronger affects the WRF performance than varying the MP or PBL schemes. This effect is even present in the convection-resolving 3-km-domain over Ecuador/Peru where CU schemes are only used in the parent domain of the one-way nesting approach. The G3D CU physics ensemble best represents the CHIRPS probability distribution in the 9-km-domains. However, spatial and temporal patterns of CHIRPS are best captured by Tiedtke or BMJ CU schemes. Ecuadorian station data in the 3-km-domain is best simulated by the ensemble whose parent domains use the KF CU scheme. Accounting for all evaluation metrics, no general-purpose setup could be identified, but suited parameterizations can be narrowed down according to final application needs.

Using multi-scale modeling and observations to link the eruption source parameters to the dispersion of volcanic clouds in case of the Raikoke eruption 2019

&lt;p&gt;&lt;span&gt;The Raikoke volcano emitted about &lt;/span&gt;&lt;span&gt;0.4-1.8 x 10&amp;#8313; kg &lt;/span&gt;&lt;span&gt;of ash and &lt;/span&gt;&lt;span&gt;1-2 x 10&amp;#8313; kg&lt;/span&gt;&lt;span&gt; of SO&lt;/span&gt;&lt;sub&gt;&lt;span&gt;2 &lt;/span&gt;&lt;/sub&gt;&lt;span&gt;up to 15 km into the atmosphere. However, the eruption was characterized by several puffs of different &lt;/span&gt;&lt;span&gt;time periods&lt;/span&gt;&lt;span&gt; and eruption heights. &lt;/span&gt;&lt;span&gt;Here, we&lt;/span&gt;&lt;span&gt; use the ICON-ART model in a model setup in which we resolve the phases of the Raikoke eruption. &lt;/span&gt;&lt;span&gt;W&lt;/span&gt;&lt;span&gt;e&lt;/span&gt;&lt;span&gt; calculated the eruption source parameters &lt;/span&gt;&lt;span&gt;(ESPs) online &lt;/span&gt;&lt;span&gt;by coupling &lt;/span&gt;&lt;span&gt;ICON-ART&lt;/span&gt;&lt;span&gt; to the 1-D plume model FPlume. &lt;/span&gt;&lt;span&gt;The input heights for the different eruption phases needed for FPlume are geometrically derived from GEOS-17 satellite data. An empirical relationship &lt;/span&gt;&lt;span&gt;is used to derive&lt;/span&gt;&lt;span&gt; the amount of very fine ash (particles &lt;32&amp;#181;m) which is relevant for long range transport in the atmosphere. &lt;/span&gt;In the first hours during and after the eruption, the modeled ash loading agrees very well with the observed ash loading from Himawari-8 due to the resolution of the eruption phase and the online calculation of the ESPs. In later hours, aerosol dynamical processes (nucleation, condensation, coagulation) explain the loss of ash in the atmosphere in agreement with the observations. However, a direct comparison is partly hampered by water and ice clouds overlapping the ash cloud in the observations. In case of SO&lt;sub&gt;2&lt;/sub&gt;, we compared 6-hourly means of model and Himawari data with respect to the structure, amplitude, and location (SAL-method). In the beginning, the structure and amplitude values differed largely because the dense ash cloud directly after the eruption leads to an underestimation of the SO&lt;sub&gt;2&lt;/sub&gt; amount in the satellite data. On the second and third day, the SAL values are close to zero for all parameters indicating a good agreement of model and observations. We argue that representing the plume phases and ESPs in ICON-ART by FPlume enhances ash and SO&lt;sub&gt;2&lt;/sub&gt; predictability in the first days after the eruption, especially in case of non-continuous volcanic eruptions like the Raikoke eruption 2019.&lt;/p&gt;

FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: Model validation

This paper presents model validation results for the latest version release of the FALL3D atmospheric transport model. The code has been redesigned from scratch to incorporate different categories of species and to overcome legacy issues that precluded its preparation towards extreme-scale computing. The model validation is based on the new FALL3D-8.0 test suite, which comprises a set of four real case studies that encapsulate the major features of the model; namely, the simulation of long-range fine volcanic ash dispersal, volcanic SO2 dispersal, tephra fallout deposits and the dispersal and deposition of radionuclides. The first two test suite cases (i.e. the June 2011 Puyehue-Cordón Caulle ash cloud and the June 2019 Raikoke SO2 cloud) are validated against geostationary satellite retrievals and demonstrate the new FALL3D data insertion scheme. The metrics used to validate the volcanic ash and SO2 simulations are the structure, amplitude and location (SAL) metric and the figure of merit in space (FMS). The other two test suite cases (i.e. the February 2013 Mt. Etna ash cloud and associated tephra fallout deposit, and the dispersal of radionuclides resulting from the 1986 Chernobyl nuclear accident) are validated with scattered ground-based observations of deposit load and local particle grain size distributions and with measurements from the Radioactivity Environmental Monitoring database. For validation of tephra deposit loads and radionuclides, we use two variants of the normalised root-mean-square error metric. We find that FALL3D-8.0 simulations initialised with data insertion consistently improve agreement with satellite retrievals at all lead times up to 48 h for both volcanic ash and SO2 simulations. In general, SAL scores lower than 1.5 and FMS scores greater than 0.40 indicate acceptable agreement with satellite retrievals of volcanic ash and SO2. In addition, we show very good agreement, across several orders of magnitude, between the model and observations for the 2013 Mt. Etna and 1986 Chernobyl case studies. Our results, along with the validation datasets provided in the publicly available test suite, form the basis for future improvements to FALL3D (version 8 or later) and also allow for model intercomparison studies.

Sensitivity of the Weather Research and Forecasting model (WRF) to downscaling extreme events over Northern Tunisia

Rainfall is one of the most important variables for water and flood management. We investigate the capacity of the Weather Research and Forecasting model (WRF) to dynamically downscale the ECMWF Re-Analysis data for Northern Tunisia. This study aims to examine the sensitivity of WRF rainfall estimates to different Planetary Boundary Layer (PBL) and Cumulus Physics (Cu) schemes. The verification scheme consists of three statistical criteria (Root Mean Square Error (RMSE), Pearson correlation, and the ratio bias coefficient). Moreover, the FSS coefficient (fraction skill score) and the quality coefficient SAL (structure amplitude latitude) are calculated. The database is composed of four heavy events covering an average of 318 rainfall stations. We mean by heavy event, each event occurred a rainfall of more than 50 mm per observed day at least in one rainfall station. The sensitivity study showed that there is not a best common combination scheme (PBL and Cu) for all the events. The average of the best 10 combinations for each event is adopted to get the ensemble map. We conclude that some schemes are sensitive and others less sensitive. The best three performing schemes for PBL and Cu parametrizations are selected for future rainfall estimation by WRF over Northern Tunisia.

FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: model applications

This manuscript presents different application cases and validation results of the latest version release of the FALL3D-8.0 model, an open-source atmospheric transport model. The code has been redesigned from scratch to incorporate different categories of species and to overcome legacy issues that precluded its preparation towards extreme-scale computing. Validation results are shown for long-range dispersal of fine volcanic ash and SO2 clouds, tephra fallout deposits and dispersal and ground deposition of radionuclides. The first two examples (i.e. the 2011 Puyehue-Cordón Caulle and 2019 Raikoke eruptions) make use of geostationary satellite retrievals for two purposes: first, to furnish an initial data insertion condition for the model; and second, to validate the time series of model outputs against the satellite retrievals. The metrics used to validate the model simulations of volcanic ash and SO2 are the Structure, Amplitude and Location (SAL) metric and the Figure of Merit in Space (FMS). The other two application cases are validated with scattered ground-based observations of deposit load and local particle grain size distributions from the 23 February 2013 Mt. Etna eruption and with measurements from the Radioactivity Environmental Monitoring (REM) database during the 1986 Chernobyl nuclear accident. Simulation results indicate that FALL3D-8.0 outperforms previous code versions both in terms of model accuracy and code performance. We also find that simulations initialised with the new data insertion scheme consistently improve agreement with satellite retrievals at all lead times out to 48 hours for both SO2 and long-range fine ash simulations.