Eric M. Kemp

Science Systems and Applications (United States)

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C. D. Peters‐Lidard

Goddard Space Flight Center

Sujay V. Kumar

Goddard Space Flight Center

Jerry Wegiel

Goddard Space Flight Center

Mian Chin

Goddard Space Flight Center

David M. Mocko

Science Applications International Corporation (United States)

James V. Geiger

Goddard Space Flight Center

Jossy P. Jacob

Goddard Space Flight Center

Zhining Tao

Hunan University

R. Ferraro

Jet Propulsion Laboratory

T. Iguchi

Earth System Science Interdisciplinary Center

Cooperative Institutions

Fermi National Accelerator Laboratory

275

European Organization for Nuclear Research

144

Centre National de la Recherche Scientifique

109

Colorado State University

University of Chicago

University of California, Irvine

Brookhaven National Laboratory

Boston University

Institut National de Physique Nucléaire et de Physique des Particules

California Institute of Technology

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Data for figures in Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_

Zenodo (CERN European Organization for Nuclear Research) (2021)

Eric M. Kemp Jerry Wegiel Sujay V. Kumar David M. Mocko Jossy P. Jacob

Tar files containing gridded metrics, domain-wide metric means and confidence intervals, and rain-gauge reports used to generate figures in Kemp et al (2021).

Citation:
Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_.

Geiger counter

10.5281/zenodo.5714443

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ECH Volume 7 issue 2 Front matter

The Journal of Ecclesiastical History (1956)

William H. Schmidt J. M. C. Toynbee John Perkins T. W. Manson David Daube

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Content (measure theory)

10.1017/s0022046900072511

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NASA’s Global Precipitation Measurement Mission: Leveraging Stakeholder Engagement & Applications Activities to Inform Decision-making

Remote Sensing Applications Society and Environment (2022)

Andrea Portier Dalia Kirschbaum Mekonnen Gebremichael Eric M. Kemp Sujay V. Kumar

The application of satellite precipitation estimates from NASA's Global Precipitation Measurement (GPM) Mission for decision-making has been a focus for the mission since launch. As a result, GPM data have enabled a range of applications that address societal needs, including water resource management, crop forecasting, ecological monitoring, disaster response, public health, aviation, weather forecasting, and climate modeling, among others. GPM applications activities have continued to focus on user engagement through in person trainings and interviews, workshops, webinars, and educational outreach activities. The goals of these efforts are to synthesize community data needs in order to effectively support and enable decision-making across agencies, academia and the global community. While these efforts have helped the GPM mission establish a large stakeholder community that encompasses federal and state partners, academic institutions, and private and nonprofit companies, there remains difficulties associated with accessing, processing, and applying the data to support or enable applications. In this article, we present GPM applications strategies and approaches used to enhance the applications value of GPM data, and most importantly, demonstrate how these efforts have and can inform different decision-making contexts. This work also provides a discussion on key lessons learned from the user community and how this information can be utilized to help better support and shape applications approaches for future NASA Earth Science missions.

Outreach

Stakeholder Engagement

10.1016/j.rsase.2022.100853

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The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems

Geoscientific model development (2018)

Kristi R. Arsenault Sujay V. Kumar James V. Geiger Shugong Wang Eric M. Kemp

Abstract. The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.

Data Processing

Data pre-processing

Sensor Fusion

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10.5194/gmd-11-3605-2018

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Development of Global Operational Snow Analysis at the US Air Force 557th Weather Wing

Yeosang Yoon Eric M. Kemp Sujay V. Kumar Jerry Wegiel C. D. Peters‐Lidard

The outdated SNODEP snow depth retrieval algorithm is replaced by the Foster et al. (1997; 2005) approach, which considers the effects of variations in forest cover. The simple blending algorithm (IDW) is replaced by the Bratseth scheme, a successive correction algorithm that converges to the solution provided by Optimal Interpolation (OI). Outdated quality control datasets are updated and quality control algorithms are reorganized to ensure the performance of the snow analysis. The spatial resolution of snow and ice estimates are increased from 25-km to 10-km.USAF-SI are fully integrated into the global operational land analysis configuration at the USAF 557th WW.

Interpolation

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Winter precipitation characteristics in western US related to atmospheric river landfalls: observations and model evaluations

Climate Dynamics (2017)

Jinwon Kim Bin Guan Duane E. Waliser R. Ferraro Jonathan L. Case

Atmospheric research

Landfall

Sichuan basin

10.1007/s00382-017-3601-5

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Upgrading the United States Air Force Land Information System: Improvements to Global Precipitation Analysis

Eric M. Kemp

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Enhancing Winter Climate Simulations of the Great Lakes: Insights from a New Coupled Lake-Ice-Atmosphere (CLIAv1) Model on the Importance of Integrating 3D Hydrodynamics with a Regional Climate Model

Pengfei Xue Chenfu Huang Yafang Zhong Michael Notaro Miraj B. Kayastha

Abstract. The Laurentian Great Lakes significantly influence the climate of the Midwest and Northeast United States, due to their vast thermal inertia, moisture source potential, and unique heat and moisture flux dynamics. This study presents a newly developed coupled lake-ice-atmosphere (CLIAv1) modeling system for the Great Lakes by coupling the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) regional climate model (RCM) with the three-dimensional (3D) Finite Volume Community Ocean Model (FVCOM) and investigates the impact of coupled dynamics on simulating the Great Lakes' winter climate. By integrating 3D lake hydrodynamics, CLIAv1 addresses the limitations of traditional one-dimensional (1D) lake and demonstrates superior performance in reproducing observed LSTs, ice cover distribution, and the vertical thermal structure of the Great Lakes compared to the NU-WRF model coupled with the default 1D Lake Ice Snow and Sediment Simulator (LISSS). CLIAv1 also enhances simulation of over-lake atmospheric conditions, including air temperature, wind speed, and sensible and latent heat fluxes, underscoring the importance of resolving complex lake dynamics for reliable climate projections. More importantly, this study addresses the crucial question about what are the key processes influencing lake thermal structure and ice cover that are missed by 1D lake models but effectively captured by 3D lake models. Through process-oriented numerical experiments, we identify key 3D hydrodynamic processes – ice transport, heat advection, and shear production in turbulence – that explain the superiority of 3D lake models over 1D lake models, particularly in cold season performance and lake-atmosphere interactions. Properly resolving these processes using 3D hydrodynamic model is crucial for successfully simulating the lake-ice-atmosphere coupled Great Lakes winter system. This research underscores the necessity of incorporating 3D hydrodynamic models in RCMs to improve our predictive understanding of the Great Lakes' response to climate change. The findings advocate for a shift towards high-resolution, physics-based modeling approaches to ensure accurate future climate and limnological projections for large freshwater systems.

Climate simulation

Transient climate simulation

10.5194/gmd-2024-146

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Development of high-resolution dynamic dust source function - A case study with a strong dust storm in a regional model

Atmospheric Environment (2017)

Dongchul Kim Mian Chin Eric M. Kemp Zhining Tao C. D. Peters‐Lidard

Dust storm

Moderate-resolution imaging spectroradiometer

10.1016/j.atmosenv.2017.03.045

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Citations (63)

Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region

Journal of Hydrometeorology (2021)

Michael Notaro Yafang Zhong Pengfei Xue C. D. Peters‐Lidard Carlos Alberto dos Santos Cruz

Abstract As Earth’s largest collection of fresh water, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

10.1175/jhm-d-21-0025.1

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Citations (17)