The Science Behind Trust

In a world where climate risk is one of the greatest challenges of our time, trust in data-driven insights is paramount

Leaders in Climate Science and Innovation

At Jupiter, that trust is built on an exceptional foundation: a world-class team of scientists whose expertise spans meteorology, climatology, hydrology, statistics, machine learning, and beyond.
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Jupiter’s science team is not just composed of experts; they are pioneers—leaders in their respective fields, with deep experience in research, modeling, and real-world applications. Their collective knowledge ensures that Jupiter’s climate analytics remain at the forefront of accuracy, reliability, and innovation.

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Science First: The Foundation of Jupiter Intelligence

Unlike other climate risk analytics companies that prioritize speed to market, Jupiter was founded on the best available climate science above all else. From day one, the company took the time to build its foundation on rigorous research, ensuring that every insight, every model, and every forecast is rooted in scientific integrity.
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This unwavering commitment to scientific rigor is what makes Jupiter unique—it is not just about providing data, but about delivering the most reliable and actionable climate intelligence available.

“We built the company from science outward with the express intent of building trust in our methodology. And so, we're able to explain what we do, we're able to explain strengths and weaknesses, we're able to explain uncertainty, we're able to explain what these models are good at, what they're not good at, and how to interpret the data in ways that a lot of other companies just can't do, because they haven't invested in the science."
– Josh Hacker, Chief Science Officer and Co-founder, Jupiter Intelligence

Bridging Science and Real-World Impact

Jupiter’s mission extends beyond research—it’s about delivering actionable intelligence that empowers organizations to make informed decisions in the face of climate change. Experts in climate science and business applications ensure that Jupiter’s analytics are not only scientifically robust but also accessible and actionable for industries ranging from banking and finance to critical infrastructure.

Why Trust Jupiter?

The answer is simple: Jupiter Intelligence is built on science. Its team of leading experts ensures that every model, every dataset, and every projection is backed by years of rigorous research and real-world validation.

Disciplined V&V

Continuously validating the use of sound approaches with state-of-the-art verification requiring the use of emerging observation technologies and analytical techniques ensures the same high-quality data you put in, comes out as high-quality resilience planning.

Dozens of Models

Jupiter minimizes the downstream impact of a single GCMs weaknesses on your resilience plans.

Debiasing

To improve data reliability, Jupiter adjusts the model outputs to correct for biases ensuring your plans are as precise as they can be.

Downscaling

Jupiter takes the 100km resolution to 30km and to 90m for both portfolio and asset-level analysis.

Demonstrating Leadership in Science

The depth and breadth of Jupiter’s commitment to science is reflected in how its scientists engage with the broader, worldwide scientific community. The following listings fall into three key categories:

  • Peer-Reviewed Papers – Rigorous, validated research that advances global climate science.
  • Scientific Presentations – Conference talks and sessions that share insights and foster collaboration.
  • Panels and Leadership Roles – Active participation in shaping the scientific agenda and future of the field.
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Leading the Conversation on Climate Risk

Discover how Jupiter’s world-class scientists are shaping the future of climate risk analytics through groundbreaking research and expert presentations

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YearMeeting/ ConferenceCitationSource
2025AMSGhazni, W. & Kapoor A. (2025). Climate Price Bubble: Economic Impact in US Housing Market. AMS 2025 Meeting, New Orleans, LA. [Link]
2025AMSHoffman, A., Sain, S., Oyler, J.W., McNicholas, C.J., Scannell, H., Pucciarelli, J., & Hacker, J. (2025). Use of machine learning and AI to improve the utility of climate risk data. AMS 2025 Meeting, New Orleans, LA.[Link]
2025AMSPerry, E., Hampson, H.M., Oyler, J.W., & Rogers, J. (2025). An Automated Cloud-Based Hydrodynamic Flood Modeling Workflow. AMS 2025 Meeting, New Orleans, LA. [Link]
2025AMSWeatherhead, E.C., Arnold, M., Hoffman, A., Hampson, H.M., & Ghazni, W. (2025). Climate Risk Information to Build Resilience Among the World's Most Vulnerable. AMS 2025 Meeting, New Orleans, LA. [Link]
2025AMSWeatherhead, E.C. (2025). Advances in Accuracy and Continuity of Climate Observations: International Coordination and Prioritization. AMS 2025 Meeting, New Orleans, LA. [Link]
2025AMSWeatherhead, E.C. (2025). Planning our Evolving Climate Observing System: Identifying Priorities and Evaluation Techniques. AMS 2025 Meeting, New Orleans, LA. [Link]
2025AMSMcNicholas, C.J., Hoffman, A., & Sain, S. (2025). Enhancing Climate Projections: A Multivariate Probabilistic Weather Generator Using a Denoising Diffusion Model. AMS 2025 Meeting, New Orleans, LA. [Link]
2024AGURogers, J., Sain, S., & Hacker, J. (2024). How climate and population change affect global flood exposure and vulnerability. AGU 2024 Meeting, Washington DC. [Link]
2024AGULiu, Z., Scannell, H., Hampson, H., Rogers, J. (2024). A Hybrid Physics-ML Approach for Coastal Storm Surge Hazard Mapping. AGU 2024 Meeting, Washington DC. [Link]
2024AGUPerry, E. & Hacker, J. (2024). Evaluating Flood Risk Trajectories from Compound Hazards in Greater Boston: The Impact of Future Climate Scenarios. AGU 2024 Meeting, Washington DC. [Link]
2024AMSHacker, J. (2024). Private Sector Contributions to CIROH: The Role of Jupiter Intelligence. AMS 2024 Meeting, Baltimore, MD.[Link]
2024AMSWeatherhead, E.C., Hampson, H.M., Hoffman, A., & Arnold, M. (2024). Understanding and Addressing Climate Risks in the Caquetá River Valley. AMS 2024 Meeting, Baltimore, MD. [Link]
2024AMSWeatherhead, E.C., Arnold, M., Hoffman, A., & Evans, M. (2024). Addressing Environmental Risks in the Changing Climate of Nepal. AMS 2024 Meeting, Baltimore, MD. [Link]
2024AMSWeatherhead, E.C., Arnold, M., Hoffman, A., Yonan, C., Hampson, H.M., Mansfield, J., & Evans, M. (2024). Principles for Using Climate Science to Address Climate Justice. AMS 2024 Meeting, Baltimore, MD. [Link]
2024AMSWeatherhead, E.C., & Lee, S. (2024). Metrology for Climate Action. AMS 2024 Meeting, Baltimore, MD. [Link]
2024AMSPerry, E. & McNicholas, C.J. (2024). A High Resolution Global Pluvial Flood Model for Predicting Physical Risk in a Changing Climate. AMS 2024 Meeting, Baltimore, MD. [Link]
2024AMSHacker, J. & Harr, P.J. (2024). Setting the Bar for Climate Risk Scenario Analysis. AMS 2024 Meeting, Baltimore, MD. [Link]
2024AMSHarr, P.J. (2024). Sensitivity Analysis for Assessing Climate Change Impacts on the Economy in an Integrated Assessment Model. AMS 2024 Meeting, Baltimore, MD. [Link]
2024AMSWeatherhead, E.C. (2024). Towards a U.S. Framework for Continuity of Satellite Observations of Earth’s Climate and for Supporting Social Resilience. AMS 2024 Meeting, Baltimore, MD. [Link]
2024AGU Ocean Sciences Kalra, J., Rogers, H., Hampson, H., Scannell, H. (2024). Modeling Coastal Flood Risk in a Changing Climate: Improvements to a Global Model. AGU Ocean Sciences Meeting, New Orleans, LA. [Link]
2024Climate Security Fellows, NPS Rogers, J.S. (2024). Physical hazards affected by climate change. Climate Security Fellows, US Navy, Naval Postgraduate School, Monterey, CA. [Link]
2023EGUEmmanouil, S., Langousis, A., Perry, E., Madaus, L., Hacker, J., & Anagnostou, E.N. (2023). The effects of extreme rainfall trends on compound flood risk: A case study over Greater Boston. EGU 2023 General Assembly Conference Abstracts (p. 18244). [Link]
2023AGU Perry, E., Madaus, L., & Hacker, J. (2023). Assessing the effects of climate change on flood risk estimates for urban coastal areas: A case study over Greater Boston. AGU 2023 Meeting, San Francisco, CA. [Link]
2023AGUWeatherhead, E.C., Arnold, M., Hoffman, A., Yonan, C., Hampson, H.M., Purdy, M., Mansfield, J., & Evans, M. (2023). Principles and Examples of Climate Justice within the United Nations Development Goals Framework. AGU 2023 Meeting, San Francisco, CA. [Link]
2023AGUSain, S. R. (2023). Non-Stationarity and Uncertainty for Physical Climate Risk (invited). AGU 2023 Meeting, San Francisco, CA.[Link]
2023AGURogers, J., Menela, M., Sain, S.R., Madaus, L., & Hacker, J. (2023). Both climate change and population change affect global exposure to flooding. AGU 2023 Meeting, San Francisco, CA.[Link]
2023AGUMenela, M., Bigelow, D.G., Madaus, L., & Sain, S.R. (2023). Frequency of Low Streamflow Events Below Environmental Limits Increases Globally. AGU 2023 Meeting, San Francisco, CA. [Link]
2023Interdisciplinary Workshop on Weather and Climate Extremes Hoffman, A., & Sain, L.S. (2023). Climate risk analytics and extremes: Applications in the private sector and areas of open research. Interdisciplinary Workshop on Weather and Climate Extremes, Clemson, SC, May 16–18. [Link]
2023AGUWeatherhead, E.C. (2023). Towards a U.S. Framework for Continuity of Satellite Observations of Earth’s Climate and for Supporting Social Resilience. AGU 2023 Meeting, San Francisco, CA. [Link]
2023ASP Distinguished Lecture SeriesHoffman, A.L. (2023). Data science and applied statistics in climate risk analysis. NCAR ASP Distinguished Lecture Series, Boulder, CO. [Link]
2023AMSWeatherhead, E.C. & Madaus, L.E. (2023). Pairing Climate Expertise with Open Data and Software to Maximize Climate Insights at Jupiter Intelligence. AMS 2023 Meeting, Denver, Colorado. [Link]
2023AMSHacker, J.P. (2023). Toward Combined Physical and Transition Risk Assessment. AMS 2023 Meeting, Denver, Colorado.[Link]
2023AMSHacker, J.P. (2023). Applying Socioeconomic Context to Weather, Water and Climate Data to Better Understand Our World Around Us. AMS 2023 Meeting, Denver, Colorado. [Link]
2023AMSDhakal, R., Arnold, M., Evans, M., & Weatherhead, E.C. (2023). Climate Services for Sustainable Disaster Risk Management in Nepal. AMS 2023 Meeting, Denver, Colorado.[Link]
2023AMSHarr, P.A. & Turney, A. (2023). Projecting North Atlantic Tropical Cyclone Activity Using a Statistical Synthetic Storm Model. AMS 2023 Meeting, Denver, Colorado. [Link]
2023AMSHarr, P.A., Hacker, J.P., & Nawiux, J. (2023). Integration of Physical and Transition Risk for Complete Climate Risk Assessments. AMS 2023 Meeting, Denver, Colorado. [Link]
2023AMSHampson, H.M., Perry, E., & Rogers, J. (2023). Automated Extraction of Hydrologically Consistent Sub-domains for Flood Model Simulations. AMS 2023 Meeting, Denver, Colorado. [Link]
2023AMSWeatherhead, E.C., Evans, M., Georgas, N., Hampson, H.M., & Hoffman, A. (2023). Climate Services to Help Conservation of Critical Ecosystems and Climate Change Adaptation in Colombia. AMS 2023 Meeting, Denver, Colorado. [Link]
2023AMSHarr, P.A. & Sain, S. (2023). A Fully Bayesian Spatial Copula Model for Joint Frequency Analysis of Extreme Events. AMS 2023 Meeting, Denver, Colorado. [Link]
2023AMSPerry, E. (2023). A Highly Parallelizable Flooding Emulator. AMS 2023 Meeting, Denver, Colorado. [Link]
2023AMSMcNicholas, C.J. (2023). A Machine Learning Approach to Create Global Sub-Kilometer Climatologies for Downscaling Projections of Climate Extremes. AMS 2023 Meeting, Denver, Colorado. [Link]
2023UC Irvine Climate Tech Seminar Rogers, J.S. (2023). Modeling climate impacts at scale: How climate change-influenced floods will affect global populations. UC Irvine Climate Tech Seminar, Irvine, CA. [Link]
2023Advancing Sustainable Urban Infrastructure Workshop, Stanford GSB Rogers, J.S. (2023). How climate-change-influenced floods will affect global populations. Advancing Sustainable Urban Infrastructure Workshop, Stanford Graduate School of Business, Stanford, CA. [Link]
2023EGUKalra, T., Rogers, J., Dhakal, R., Hampson, H., Scannell, H., & Eilerman, S. (2023). Advancements in a Global Statistical Coastal Flood Modeling Framework. EGU General Assembly. , E.C. (2023). Towards a U.S. Framework for Continuity of Satellite Observations of Earth’s Climate and for Supporting Social Resilience. AGU 2023 Meeting, San Francisco, CA. [Link]
2023SIAMSain, S.R. (2023). Climate risk analytics and data science: Assessing risk in a changing climate (keynote). SIAM Front Range Student Conference, Denver, CO.
2023ISISain, S.R. (2023). Spatial statistics, extremes, and climate risk analytics (invited). ISI World Congress, Ottawa.
2023TIESSain, S.R. (2023). Data science and the assessment of climate risk. TIES Webinar Series.
2022AGUScannell, H., Sain, S.R., & Rogers, J. (2022). Joint projections for coastal sea level and its uncertainty from storm surge, tides, and sea level rise. AGU 2022 Meeting, Chicago, IL. [Link]
2022AGUManeta, M., Daneshvar, F., Hampson, H., Perry, E., Madaus, L., Sain, S.R., Dhakal, R., Oyler, J., Kalra, T., Scannell, H., & Rogers, J. (2022). A scalable, cloud-based modeling framework for global-scale flood risk assessment. AGU 2022 Meeting, Chicago, IL. [Link]
2022AGUDaneshvar, F., Maneta, M., Hampson, H., Dhakal, R., Oyler, J., & Rogers, J. (2022). Computationally-efficient, High-Resolution Inland Flood Inundation Depth Prediction at the National Scale. AGU 2022 Meeting, Chicago, IL. [Link]
2022AGU Rogers, J., Maneta, M., Sain, S.R., & Madaus, L. (2022). Effects of climate change on inland flood impacts: Insights from a coupled high-resolution coastal and fluvial flood model. AGU 2022 Meeting, Chicago, IL. [Link]
2022AGUPerry, E., Rogers, J., & Hampson, H. (2022). An Automated Cloud-Based Flood Model Preprocessing Workflow. AGU 2022 Meeting, Chicago, IL. [Link]
2022AGUWeatherhead, E.C., & Blumberg, A.F. (2022). Assessing Reliability of Actionable Climate Risk Information: New Techniques and Approaches. AGU 2022 Meeting, Chicago, IL. [Link]
2022AGUWeatherhead, E.C. (2022). Towards a U.S. Framework for Continuity of Satellite Observations of Earth’s Climate and to Support Societal Resilience. AGU 2022 Meeting, Chicago, IL. [Link]
2022JSMHoffman, A., L.S., Sain, J., Jacobson (2022). Data Science and Applied Statistics in Climate Risk Analysis: Industry Applications in Environmental Statistics. Topic Contributed Papers presented at Joint Statistical Meetings (JSM) 2022, Washington DC, August 6–11. [Link]
2022AMSHacker, J.P. (2022). A Framework for Assessing Climate Risk Analytics. AMS 2022 Meeting, Houston, TX. [Link]
2022AMSCipullo, M.L., Hacker, J.P., & Madaus, L.E. (2022). Estimating Future Projections of a Simplified Wet-Bulb Globe Temperature Metric in CMIP6 Models. AMS 2022 Meeting, Houston, TX. [Link]
2022AMSWeatherhead, E.C. (2022). Update on Changes in Weather Persistence: Bridging Input Observations and Process Understanding. AMS 2022 Meeting, Houston, TX. [Link]
2022AMSHarr, P.A. & Sain, S. (2022). Comparison of Univariate and Bivariate Return Periods Using Stationary and Nonstationary Analyses. AMS 2022 Meeting, Houston TX. [Link]
2022AMSHoffman, A. & Madaus, L.E. (2022). Understanding Global Wildfire Risk across Historical and Future Scenarios in CMIP6. AMS 2022 Meeting, Houston TX. [Link]
2022AMSMiddlemas, E.A., Madaus, L.E., Hoffman, A.L., Sain, S.R., & Yacalis, G. (2022). Downscaling Global Heat Exposure Metrics to Subkilometer Resolutions. AMS 2022 Meeting, Houston TX. [Link]
2022AMSWeatherhead, E.C., Blumberg, A., & Georgas, N. (2022). Verification and Validation of Climate Risk Products. AMS 2022 Meeting, Houston TX. [Link]
2022AMSWeatherhead, E.C. (2022). Planning Climate Observations to Address Critical Challenges (Invited Presentation). AMS 2022 Meeting, Houston TX. [Link]
2022AMSHarr, P.A., Sain, S.R., Madaus, L.E., & Jordi, A. (2022). Future Changes in the Localized Risk of Extreme Wind Events Due to Modified Environmental Sources. AMS 2022 Meeting, Houston TX. [Link]
2022AMSMiddlemas, E.A. (2022). Artificial Intelligence for Subseasonal-to-Seasonal (S2S) Prediction. AMS 2022 Meeting, Houston TX. [Link]
2022AMSPullen, J. (2022). Forum on Climate-Linked Economics Part I. AMS 2022 Meeting, Houston TX. [Link]
2022ASA JSM Sain, S.R. (2022). Improving pipelines for assessing flood hazard under climate change (invited). ASA JSM, Washington, D.C.
2022IMSISain, S.R. (2022). Downscaling for climate risk analytics. Climate Model Evaluation and Uncertainty, IMSI, Chicago, IL.
2022ENVRSain, S.R. (2022). Data science and climate risk (invited). ENVR Workshop, Provo, UT.
2022AGU Ocean Sciences Yin, J., Rogers, J.S. (2022). Correcting channel depth in coarse resolution geospatial datasets. AGU Ocean Sciences Meeting (remote).[Link]
2021AGUZarekarizi, M., Yu, M., Davies, I.P., Yacalis, G., & Sain, S.R. (2021). Toward Improved Prediction of Flood Inundation Maps: Application of Long Short-Term Memory Networks. AGU 2021 Meeting, New Orleans, LA. [Link]
2021AGUYu, M., Zarekarizi, M., & Yacalis, G. (2021). Analyzing the Performance of Long Short-Term Memory Networks for Capturing Extreme Streamflow. AGU 2021 Meeting, New Orleans, LA.[Link]
2021AGUKalra, T. (2021). Modeling the Dynamics of Salt Marsh Formation. AGU 2021 Meeting, New Orleans, LA. [Link]
2021AGUWeatherhead, E.C. (2021). Homogenization of Critical Surface, In Situ and Satellite Observations for Long-Term Trend Evaluation. AGU 2021 Meeting, New Orleans, LA. [Link]
2021AGUWeatherhead, E.C. (2021). SORCE/TIM's Overlap Analysis: Absolute Scale Comparison and Stability Estimate. AGU 2021 Meeting, New Orleans, LA. [Link]
2021AMSHoffman, A.L., Sain, S., & Jacobson, J. (2021). Flexible Python-Based Statistical Workflow for Flood Risk Estimates Applied across CMIP6 Models. AMS 2021 Virtual. [Link]
2021AMSHacker, J.P., Smith, C., Hoffman, A.L., & Jordi, A. (2021). From Operational to Planning: Fire and Fire Risk Predictions across Scales. AMS 2021 Virtual. [Link]
2021AMSWeatherhead, E.C. (2021). A Community Effort to Unify Verification and Validation Efforts—Upcoming Metrics Workshop. AMS 2021 Virtual. [Link]
2021DOE A1AESPSain, S.R. (2021). Machine Learning and Climate Risk Assessments (invited). DOE A1AESP Workshop.
2021Monash Univ Dept of Economics and Business Statistics Sain, S.R. (2021). Climate risk analytics and data science @ Jupiter Intelligence. Department of Econometrics and Business Statistics, Monash University, Melbourne, AU (remote).
2020AGUBlumberg, A.F. & Sorkin, R. (2020). There is no vaccine for sea level rise. AGU 2020 Meeting, Virtual. [Link]
2020AGUHoffman, A.L., Sain, S.R., & Jacobson, J. (2020). Flexible methodology for hyperlocal flooding risk due to sea level rise. AGU 2020 Meeting, Virtual. [Link]
2020AGUZarekarizi, M., Madaus, L., & Sain, S.R. (2020). Global projections of extreme flood inundation depth and extent under climate change. AGU 2020 Meeting, Virtual. [Link]
2020AGUZarekarizi, M. (2020). Navigating a Nonacademic Research Career: Gaining Tips and Insights on How to Stand Out from the Crowd. AGU 2020 Meeting, Virtual. [Link]
2020AGUYacalis, G. (2020). Assessing the potential of deep neural networks for emulating coupled superparameterization in climate models under real geography boundary conditions. AGU 2020 Meeting, Virtual. [Link]
2020AMSHarr, P.A., Sain, S.R., & Madaus, L. (2020). Identifying Nonstationary Risk in an Era of Changing Environmental Forcings. AMS 2020 Meeting, Boston, MA. [Link]
2020AMSHoffman, A., Madaus, L., Pullen, J., & Hacker, J. (2020). Machine Learning Downscaling of Extreme Heat Events in New York City. AMS 2020 Meeting, Boston, MA. [Link]
2020AMSLuna-Cruz, Y.Y. (2020). Joint Session 6.2 – Women in the Tropics, Part I. AMS 2020 Meeting, Boston, MA. [Link]
2020AMSLovin, E.L., Blumberg, A.F., Weatherhead, E.C., Rodriguez, V., Ramaswamy, V., & Saleh, F. (2020). Validating Flood Model Simulations Using Camera Information and Crowd-Sourced Data. AMS 2020 Meeting, Boston, MA. [Link]
2020AGU Ocean Sciences Rogers, J.S., E. Yang, S., Sain, A., & Karsepack, J. (2020). Correcting model bias in multiscale coastal storm surge models with climate change. Poster presented at AGU Ocean Sciences Meeting, San Diego, CA. [Link]
2020International Conference on Climate Informatics Groenke, B., Madaus, L., & Monteleoni, C. (2020, September). ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows. In Proceedings of the 10th International Conference on Climate Informatics (pp. 60–66). [Link]
2019AGUHarr, P., Sain, S.R., & Madaus, L. (2019). Identifying Non-Stationary Risk in an Era of Changing Environmental Forcings. AGU 2019 Meeting, San Francisco, CA. [Link]
2019AGURamaswamy, V. & Saleh, F. (2019). An Efficient Approach for Generating Large Scale Unstructured Computational Mesh for Flood Models. AGU 2019 Meeting, San Francisco, CA. [Link]
2019AGUWeatherhead, E.C. (2019). Satellite requirements for overlap techniques and time needed to create a continuous data record. AGU 2019 Meeting, San Francisco, CA. [Link]
2019AMSHarr, P.A. (2019). Reducing Risk and Increasing Resilience to High-Impact Events: Roles of the Weather, Water, and Climate Communities. AMS 2019 Meeting, Phoenix, AZ. [Link]
2019AMSHarr, P., & Sain, S.R. (2019). Analysis of Risk due to Compound Extreme Events in the Coastal Urban Environment. AMS 2019 Meeting, Phoenix, AZ. [Link]
2019AMSHacker, J. (2019). Numerical Forecasts of Deep Convection in a Tropical Mountainous Environment in Support of the Colombian Civil Aviation Authority (Aerocivil). AMS 2019 Meeting, Phoenix, AZ. [Link]
2019AMSWeatherhead, E.C. (2019). Catalyzing Innovations With Observations: Building What We Need, Not What We Have. AMS 2019 Meeting, Phoenix, AZ. [Link]
2019Climate and Weather Seminar, Lawrence Livermore NL Rogers, J.S. (2019). Improving ocean model predictions for climate risk assessment. Climate and Weather Seminar, Lawrence Livermore National Lab, CA.
2019Dept of Civil & Env Engineering, UC Berkeley Rogers, J.S. (2019). The urban ocean: coastal flooding in an uncertain climate. UC Berkeley, Department of Civil and Environmental Engineering, Berkeley, CA.
2019AGA Operations Conference Sain, S.R. (2019). Machine learning for assessing non-stationary hazards and mitigating risk from extreme weather events. AGA Operations Conference, Nashville, TN.
2019Harvard Data Science Initiative Sain, S.R. (2019). Data science @ Jupiter. Harvard Data Science Initiative, Harvard University, Cambridge, MA.
2019UCSB Dept of Applied Mathematics Sain, S.R. (2019). Data science @ Jupiter. Department of Applied Mathematics, University of Colorado, Boulder, CO.
2019CSM Dept of Applied Mathematics & Statistics Sain, S.R. (2019). Data science @ Jupiter. Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, CO.
2018AGUSain, S.R., Harr, P., Madaus, L., & Eilerman, S. (2018). High-resolution estimation of coastal flood risk. AGU 2018 Meeting, Washington DC. [Link]
2018AGUKalra, T. (2018). Redistribution of particulate organic carbon following marsh lateral erosion in a back-barrier estuary. AGU 2018 Meeting, Washington DC. [Link]
2018AGUMadaus, L., Hacker, J., & Exby, J. (2018). Scalable, cloud-based WRF simulations and analysis with community climate and reanalysis datasets. AGU 2018 Meeting, Washington DC. [Link]
2018AGUHacker, J. (2018). Enabling Transparency and Reproducibility in Geoscience Through Practical Provenance and Cloud-Based Workflows Posters. AGU 2018 Meeting, Washington DC. [Link]
2018AGUHacker, J. (2018). Enabling Transparency and Reproducibility in Geoscience Through Practical Provenance and Cloud-Based Workflows. AGU 2018 Meeting, Washington DC. [Link]
2018AGUSaleh, F., Blumberg, A.F., Abdeddaim, A., Tufféry, J., Exby, J., Eilerman, S., Harr, P., Jordi, A., Etherton, B., Rogers, J., Hacker, J., Karsepack, A.R., Madaus, L., & Sain, S.R. (2018). Compound Coastal Flood Hazards in a Changing Climate. AGU 2018 Meeting, Washington DC. [Link]
2018AGUHacker, J., Madaus, L., Pullen, J.D., Sain, S.R., Pucciarelli, J., & McDermott, P. (2018). Urban Heat in the Future: A Scalable and Flexible System for Evaluating Changing Risks. AGU 2018 Meeting, Washington DC. [Link]
2018AGUHacker, J. (2018). Emerging Data Science and Machine Learning Opportunities in the Weather and Climate Sciences. AGU 2018 Meeting, Washington DC. [Link]
2018AGUWeatherhead, E.C. & Karsepack, A. (2018). How Long Will It Take To Identify Trends in Tropical Cyclones? AGU 2018 Meeting, Washington DC. [Link]
2018AGUSaleh, F. & Weatherhead, E.C. (2018). Weather/Climate Ensembles and Downscaling Methodologies for Hydrologic Prediction Systems: Methods, Process Uncertainties, Applications, and Verification. AGU 2018 Meeting, Washington DC. [Link]
2018AMSSaleh, F. & Pullen, J. (2018). Working with Decision-Makers to Improve Energy-Water System Resiliency in the Lower Hudson River Basin. AMS 2018 Meeting, Austin, TX. [Link]
2018AMSJordan, A., Saleh, F., Gregorson, N., Blumberg, A., Ramaswamy, V. (2018). Next Generation of Coastal Ocean Operational Systems: Site-Specific Forecasts for the Urban Ocean in the New York–New Jersey Metro Region. AMS 2018 Meeting, Austin, TX. [Link]
2018AMSWeatherhead, E.C. (2018). Community Global Modeling: Next Generation Global Prediction System (NGGPS) and beyond: Improvements in Global Models, Key Components of Global Models and Statistical Techniques to Evaluate Those Improvements—Part I. AMS 2018 Meeting, Austin, TX. [Link]
2018AMSWeatherhead, E.C. (2018). The New Weather Enterprise: Toward Better Public, Private, and Academic Collaborations. AMS 2018 Meeting, Austin, TX. [Link]
2018AMSWeatherhead, E.C. (2018). Fostering Open Communication about Forecast Improvement (Invited Presentation). AMS 2018 Meeting, Austin, TX. [Link]
2018International Conference on Urban Climate Blumberg, A.F. (2018). The Urban Ocean—A New Frontier (Invited Presentation). In 10th International Conference on Urban Climate/14th Symposium on the Urban Environment. AMS. City College New York, NY. [Link]
2018AMS Conference on Weather Analysis and Forecasting Madaus, L., Hacker, J., & Exby, J. (2018). Cloud-Based WRF Downscaling Simulations at Scale Using Community Reanalysis and Climate Datasets. In 29th Conference on Weather Analysis and Forecasting/25th Conference on Numerical Weather Prediction. AMS.
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