Evaluating a CONUS-Wide disALEXI Evapotranspiration Product
No abstract provided
Maiersperger, Thomas↗
Engineering topics
Hain, Christopher
Publications and source records attributed to Hain, Christopher.
Assimilation of GPM-Retrieved Marine Surface Meteorology Variables for Two Winter Storms
No abstract available
Li, Xuanli↗
Machine-Learning Image Classification and Hazard Identification of Dust and Fog in Next-Generation Geostationary Satellite Imagery
No abstract available
Berndt, Emily↗
Introducing a Satellite Agnostic Approach to Identifying and Quantifying Hail Damage Swaths over the Central United States
No abstract available
Bell, Jordan R.↗
Application of Synthetic TEMPO Products at NASA SPoRT to Accelerate Use in Air Quality and Public Health Decision Support
No abstract available
Naeger, Aaron R.↗
Improving Soil Moisture States in WRF-Hydro Using SWOT Observations
No abstract available
Elmer, Nicholas↗
Development of a Global Evaporative Stress Index Based on TIR and MW LST
No abstract available
Hain, Christopher↗
Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment
Earth science domain presents unique sets of problems that are increasingly being solved using data driven approaches. The availability of big Earth science data offers immense potential for Machine learning (ML) as evident from numerous research publications lately. However, many of these publications are not ending up as production applications mainly because the data scientists who develop the ML models are now expected to complete the ML lifecycle by deploying and scaling the models in production. We introduce ML lifecycle to the Earth science community including the opportunities and challenges that lie ahead in each phase of the lifecycle. We demonstrate the lifecycle using an Earth science problem that we used ML to address and transitioned to production.
Maskey, Manil↗
Assimilation of SWOT WSE to Improve NOAA National Water Model Initialization and Streamflow Prediction
No abstract available
WRF-Hydro↗
Assimilation of GPM Retrieved Surface Meteorology Variables with ICE-POP Case Studies
Built upon Tropical Rainfall Measuring Mission (TRMM) legacy for next-generation global observation of rain and snow. The GPM has a broad global coverage ~70°S – 70°N with a swath of 245/125-km for the Ka (35.5 GHz)/Ku (13.6 GHz) band radar, and 850-km for the 13-channel GMI. GPM also features better retrievals for heavy, moderate, and light rain and snowfall.
GPM retrieval product↗
An AI Based System for Objective Tropical Cyclone Intensity Estimation
No abstract available
Deep Learning↗
Impact of SMAP Soil Moisture Assimilation on Numerical Weather Forecasts over the Contiguous United States and East Africa
No abstract available
Numerical Weather Prediction↗
Development of a Global Evaporative Stress Index Based on TIR and MW LST
No abstract available
Hain, Christopher↗
Deepti: Deep Learning-Based Tropical Cyclone Intensity Estimation
We present the development of a deep learning model for objective estimation of tropical cyclone intensity at a higher temporal frequency, deployment of the model in production, design and implementation of the tropical cyclone monitoring and intensity estimation system and development of an interactive portal for situational awareness and evaluation of intensity estimation.
Maskey, Manil↗