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Daniel J Cecil

Publications and source records attributed to Daniel J Cecil.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

NASA’s Atmosphere Observing System (AOS), From A Precipitation Perspective

NASA is developing the Atmosphere Observing System (AOS) mission as part of its Earth System Observatory (ESO) in response to priorities identified in the 2017 Earth Science Decadal Survey. AOS addresses the Decadal Survey’s call for missions measuring the targeted observables “clouds, convection, and precipitation”; “aerosol and cloud radiative properties”, and “aerosol vertical profiles”. AOS is currently in Phase A, the concept and technology development phase, with plans for satellite launches in the late 2020’s and early 2030’s, and suborbital measurements to include field campaigns after those satellite launches. Key precipitation-related instrumentation includes Doppler radars capable of measuring clouds and precipitation, and passive microwave radiometers with channels between 89-700 GHz. Other instrumentation includes dual-wavelength backscatter lidars, a multi-wavelength and multi-angle polarimeter, a far infrared imaging radiometer, and aerosol and moisture limb sounders that will contribute to studies of coupled aerosol-cloud-precipitation processes. From the perspective of precipitation science, a Ku-band Doppler radar in a 55° inclined orbit provided by JAXA will continue the heritage of precipitation radar measurements made by the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM), while adding information about Doppler-derived particle vertical motions. With assumptions about particle terminal velocities, the Doppler measurements will enable estimates of the vertical air motion in storms. A higher frequency (W and/or Ka band) radar on a satellite in a polar sun-synchronous orbit will add similar information with greater sensitivity to clouds and to light precipitation. Passive microwave radiometers for AOS will be less capable than those from TRMM and GPM from a precipitation-measurement perspective, but more capable of adding information about cloud processes. Synergies among these and other instruments are expected to advance process-level understanding of aerosols, clouds, and precipitation.

Daniel J Cecil↗
NASA NTRS2022

Satellite-Based Characterization of Convection and Impacts from the Catastrophic 10 August 2020 Midwest U.S. Derecho

The catastrophic derecho that occurred on 10 August 2020 across the Midwest United States caused billions of dollars of damage to both urban and rural infrastructure as well as agricultural crops, most notably across the state of Iowa. This paper documents the complex evolution of the derecho through the use of low-Earth orbit passive-microwave imager and GOES-16satellite-derived products complemented by products derived from NEXRAD weather radar observations. Additional satellite sensors including optical imagers and synthetic aperture radar (SAR) were used to observe impacts to the power grid and agriculture in Iowa. SAR improved the identification and quantification of damaged corn and soybeans, as compared to true-color composites and Normalized Difference Vegetation Index (NDVI). A statistical approach to identify damaged corn and soybean crops from SAR was created with estimates of 1.97 million acres of damaged corn and 1.40 million acres of damaged soybeans in the state of Iowa. The damage estimates generated by this study were comparable to estimates produced by others after the derecho, including two commercial agricultural companies.

Derecho↗
NASA NTRS2016

Automated Storm Tracking and the Lightning Jump Algorithm Using GOES-R Geostationary Lightning Mapper (GLM) Proxy Data

This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA), which are important elements in the transition of the LJA concept from a research to an operational based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid, VIL), and lightning information (flash rate density). Evaluations showed that the spatial scale of tracked features or storm clusters had a large impact on the lightning jump system performance, where increasing spatial scale size resulted in decreased dynamic range of the system's performance. This framework will also serve as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the system's performance is evaluated with adjustments to parameter sensitivity. The system's performance is evaluated using the probability of detection (POD) and false alarm ratio (FAR) statistics. Of the algorithm parameters tested, sigma-level (metric of lightning jump strength) and flash rate threshold influenced the system's performance the most. Finally, verification methodologies are investigated. It is discovered that minor changes in verification methodology can dramatically impact the evaluation of the lightning jump system.

lightning jump↗
NASA NTRSDate not supplied

Detecting Hail from Space: Using a Multi-Frequency Passive-Microwave Retrieval to Analyze the Global Climatology and Diurnal Cycle of Severe Hail

Severe hail poses myriad threats to society, causing extensive damage to infrastructure and agriculture. As hail is severe, relatively infrequent, and highly localized, it is difficult to measure in-situ and if left unresolved in models and precipitation retrievals, hail can cause large errors and uncertainties. The difficulty in measuring hail in situ and the inconsistency of surface-based hail reporting drive the motivation to use spaceborne remote-sensing platforms to retrieve hail and construct climatologies in a globally uniform way. We leverage the scattering signatures of severe hail in spaceborne passive-microwave datasets paired with surface hail reports to construct a multi-frequency hail retrieval using Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) data. Using coincident Global Precipitation Measurement (GPM) Ku-band precipitation radar, we assessed this retrieval and several others in the literature for their effectiveness and regional variability. We use this retrieval to construct global passive-microwave climatologies of severe hail using the TRMM, GPM, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and extend into the pre-TRMM era to the Special Sensor Microwave Imager/Sounder (SSMI/(S)) data. We also leverage the sensors in inclined orbits to assess the diurnal variability of severe hail globally and the effect the diurnal cycle has on the detection of hail by sensors in sun-synchronous orbit. The goal is to construct a robust, multi-decade multi-satellite climatology of hail. As part of the NASA Disasters Applied Sciences program, we assess these climatologies against other satellite severe weather datasets and use these climatologies in collaboration with stakeholders and end-users to help them assess risk, and improve the prediction, preparation, and response to severe storms around the world.

Sarah D Bang↗
NASA NTRSDate not supplied

Detecting Hail from Space: Algorithms, Climatologies, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and subject to geographical or societal biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in a globally uniform way. We have developed a hail detection algorithm that leverages the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. We use this retrieval to construct global climatologies of severe hail using several different spaceborne sensors: the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and are working to extend into the late 1980’s using the Special Sensor Microwave Imager/Sounder (SSMI/(S)) data. Using coincident Global Precipitation Measurement (GPM) Ku-band precipitation radar, we assessed this retrieval and several others in the literature for their effectiveness and regional variability. We developed a passive-microwave algorithm that corresponds tightly to radar reflectivity and gives the least appearance of regional biases compared to other passive-microwave approaches in the literature. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. We will discuss ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D Bang↗
NASA NTRSDate not supplied

Precipitation Science at NASA MSFC

The Precipitation Research Group in NASA MSFC’s Earth Science Branch (ST-11) focuses on observations of precipitation (rain, snow, and hail) from a variety of perspectives: ground-based radars, surface gauge networks, airborne instruments, and spaceborne measurements from onboard satellites. Current work includes identifying signatures of hail and strong thunderstorms from spaceborne measurements and assessing those signatures against multiple satellite datasets and ground-based radar observations. The Precipitation Team is also involved in the development and maintenance of NASA’s global-gridded multi-satellite precipitation product (IMERG) and operating and maintaining the GPM Validation Network (VN): a software package that geometrically matches the reference ground-based weather radar observations with GPM satellite observations in 3D. The team is also responsible for the Advanced Microwave Precipitation Radiometer (AMPR) used in airborne field campaign research, which recently was used to collect data on thunderstorms that produce terrestrial gamma-ray flashes (TGFs) in the Airborne Lightning Observatory for FEGS and TGFs (ALOFT) field campaign. While the Precipitation Group largely supports NASA’s Global Precipitation Measurement (GPM) mission and Precipitation Science Team, the team also looks to the future Precipitation Measurement Mission (PMM) and Investigation of Convective Updrafts (INCUS) missions.

Sarah D Bang↗