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NASA'S Earth System Observatory-Atmosphere Observing System
Scott A. Braun;John Yorks;Tyler Thorsen;Dan Cecil;Dalia Kirschbaum
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Year: 2022 | Conference Paper |
Cited by: Papers (8)
NASA has begun pre-formulation studies for the Earth System Observatory, a constellation of observatories designed to implement the recommendations of the 2017 NASA Earth Science Decadal Survey. For the atmosphere, the Atmosphere Observing System (AOS) will focus on aerosols, clouds, convection, and precipitation, their mutual interactions, and interactions with atmospheric radiation. AOS consists...Show More
Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System
Manil Maskey;Rahul Ramachandran;Muthukumaran Ramasubramanian;Iksha Gurung;Brian Freitag;Aaron Kaulfus;Drew Bollinger;Daniel J. Cecil;Jeffrey Miller
Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclone wind speed using satellite data at a given time with varying success. This article presents a deep...Show More
Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment
Manil Maskey;Rahul Ramachandran;Iksha Gurung;Brian Freitag;J. J. Miller;Muthukumaran Ramasubramanian;Drew Bollinger;Ricardo Mestre;Daniel Cecil;Andrew Molthan;Chris Hain
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Year: 2019 | Conference Paper |
Cited by: Papers (6)
Enterprises are making machine learning for production as an integral part of their future roadmaps and Earth science domain is no exception. However, there is common problem in transitioning machine learning from science to production due to a major difference in constructing a model versus deploying it for people to use to make decisions. Phases of machine learning lifecycle that includes model ...Show More
Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment
Manil Maskey;Rahul Ramachandran;Iksha Gurung;Brian Freitag;J. J. Miller;Muthukumaran Ramasubramanian;Drew Bollinger;Ricardo Mestre;Daniel Cecil;Andrew Molthan;Chris Hain
Year: 2019 | Conference Paper |
Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network
Ritesh Pradhan;Ramazan S. Aygun;Manil Maskey;Rahul Ramachandran;Daniel J. Cecil
Tropical cyclone intensity estimation is a challenging task as it required domain knowledge while extracting features, significant pre-processing, various sets of parameters obtained from satellites, and human intervention for analysis. The inconsistency of results, significant pre-processing of data, complexity of the problem domain, and problems on generalizability are some of the issues related...Show More
Calibration of hurricane imaging radiometer C-band receivers
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Year: 2017 | Conference Paper |
The laboratory calibration of airborne Hurricane Imaging Radiometer's C-Band multi-frequency receivers is described here. The method used to obtain the values of receiver front-end loss and injected noise diode temperature is presented along with the expected RMS uncertainty in the final calibration. Internal Warm load was excluded from the calibration due to an apparent anomaly.Show More
Characteristic of a digital correlation radiometer back end with finite wordlength
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Year: 2017 | Conference Paper |
The performance characteristic of a digital correlation radiometer signal processing back end (DBE) is analyzed using a computer simulator. The particular design studied here corresponds to the airborne Hurricane Imaging radiometer which was jointly developed by the NASA Marshall Space Flight Center, University of Michigan, University of Central Florida and NOAA. Laboratory and flight test data is...Show More
HIRAD Brightness Temperature Image Geolocation Validation
Saleem Sahawneh;W. Linwood Jones;Sayak K. Biswas;Daniel Cecil
The Hurricane Imaging Radiometer (HIRAD) is an airborne microwave radiometer developed to provide wide-swath hurricane surface wind speed and rain rate imagery for scientific research. This letter presents a geometric evaluation of the brightness temperature (Tb) images produced by HIRAD for high-contrast land/water targets. Methodologies used to validate geolocation accuracy and spatial resolutio...Show More
Validation of rain rate retrievals for the airborne Hurricane Imaging Radiometer (HIRAD)
Maria Jacob;Matin Salemirad;W. Linwood Jones;Sayak Biswas;Daniel Cecil
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Year: 2015 | Conference Paper |
On board of the NASA's Global Hawk (AV1) aircraft there are two microwave, namely: the passive microwave Hurricane Imaging Radiometer (HIRAD), and the active microwave High-altitude Imaging Wind and Rain Airborne Profiler (HIWRAP). This paper presents results from an unplanned rain rate measurement validation opportunity that occurred in 2013, when the Global Hawk aircraft flew over an intense tro...Show More
Hurricane Imaging Radiometer wind speed and rain rate retrievals during the 2010 GRIP flight experiment
Saleem Sahawneh;Spencer Farrar;James Johnson;W. Linwood Jones;Jason Roberts;Sayak Biswas;Daniel Cecil
2014 13th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad)
Year: 2014 | Conference Paper |
Microwave remote sensing observations of hurricanes, from NOAA and USAF hurricane surveillance aircraft, provide vital data for hurricane research and operations, for forecasting the intensity and track of tropical storms. The current operational standard for hurricane wind speed and rain rate measurements is the Stepped Frequency Microwave Radiometer (SFMR), which is a nadir viewing passive micro...Show More
The Hurricane Imaging Radiometer: Present and future
Timothy L. Miller;M. W. James;J. B. Roberts;S. K. Biswas;D. Cecil;W. L. Jones;J. Johnson;S. Farrar;S. Sahawneh;C. S. Ruf;M. Morris;E. W. Uhlhorn;P. G. Black
2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS
Year: 2013 | Conference Paper |
Cited by: Papers (2)
The Hurricane Imaging Radiometer (HIRAD) is an airborne passive microwave radiometer designed to provide high resolution, wide swath imagery of surface wind speed in tropical cyclones from a low profile planar antenna with no mechanical scanning. Wind speed and rain rate images from HIRAD's first field campaign (GRIP, 2010) are presented here followed, by a discussion on the performance of the new...Show More
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