Filling temporal observational gaps from the tropics to the mid-latitudes in passive microwave atmospheric sounding and clouds and precipitation remote sensing using small satellites (Conference Presentation)

Passive microwave sensors provide critical observations for initialization and validation of regional and global weather forecasting models. Although much progress has been made in global climate modeling, extreme weather events are still misrepresented in parameterizations, largely due to a lack of sufficient observations. At the same time, passive microwave sensors perform Earth observation exclusively from low Earth orbit (LEO), so their limited number leads to substantial temporal sampling gaps from the tropics and sub-tropics to the mid-latitudes, where cyclones and other precipitating storms cause the greatest damage to life and property. The accuracy, precision, and long-term stability of TEMPEST-D microwave radiometer operation on a 6U CubeSat throughout the three-year mission demonstrated the potential for substantial enhancement of temporal observations from the tropics to the mid-latitudes. Future constellations of passive microwave sensors on small satellites in LEO are expected to provide greatly enhanced temporal observations for atmospheric sounding and remote sensing of clouds and precipitation.

Authors

Institutions

Publication Details

Published
2024-10-03
DOI
https://doi.org/10.1117/12.3028895
Primary Topic
Precipitation Measurement and Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Filling temporal observational gaps from the tropics to the mid-latitudes in passive microwave atmospheric sounding and clouds and precipitation remote sensing using small satellites (Conference Presentation)

Chandrasekar Radhakrishnan, Chia‐Pang Kuo, Steven C. Reising, Shannon Brown et al.
Precipitation Measurement and Analysis
article

Filling temporal observational gaps from the tropics to the mid-latitudes in passive microwave atmospheric sounding and clouds and precipitation remote sensing using small satellites (Conference Presentation)

Chandrasekar Radhakrishnan, Chia‐Pang Kuo, Steven C. Reising, Shannon Brown, V. Chandrasekar, Christian D. Kummerow, Richard Schulte
article en

Abstract

Passive microwave sensors provide critical observations for initialization and validation of regional and global weather forecasting models. Although much progress has been made in global climate modeling, extreme weather events are still misrepresented in parameterizations, largely due to a lack of sufficient observations. At the same time, passive microwave sensors perform Earth observation exclusively from low Earth orbit (LEO), so their limited number leads to substantial temporal sampling gaps from the tropics and sub-tropics to the mid-latitudes, where cyclones and other precipitating storms cause the greatest damage to life and property. The accuracy, precision, and long-term stability of TEMPEST-D microwave radiometer operation on a 6U CubeSat throughout the three-year mission demonstrated the potential for substantial enhancement of temporal observations from the tropics to the mid-latitudes. Future constellations of passive microwave sensors on small satellites in LEO are expected to provide greatly enhanced temporal observations for atmospheric sounding and remote sensing of clouds and precipitation.

California Institute of Technology (US), Jet Propulsion Laboratory (US), Colorado State University (US)
Climate action
Openalex Percentile: Top 80%
Precipitation Measurement and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.