Dataset Information

Development of Algorithm for Discriminating Hydrometeor Particle Types with a Synergistic Use of CloudSat and CALIPSO.

ABSTRACT: We developed a method for classifying hydrometeor particle types, including cloud and precipitation phase and ice crystal habit, by a synergistic use of CloudSat/Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). We investigated how the cloud phase and ice crystal habit characterized by CALIOP globally relate with radar reflectivity and temperature. The global relationship thus identified was employed to develop an algorithm for hydrometeor type classification with CPR alone. The CPR-based type classification was then combined with CALIPSO-based type characterization to give CPR-CALIOP synergy classification. A unique aspect of this algorithm is to exploit and combine the lidar's sensitivity to thin ice clouds and the radar's ability to penetrate light precipitation to offer more complete picture of vertically resolved hydrometeor type classification than has been provided by previous studies. Given the complementary nature of radar and lidar detections of hydrometeors, our algorithm delivers thirteen hydrometeor types: warm water, supercooled water, randomly-oriented ice crystal (3D-ice), horizontally-oriented plate (2D-plate), 3D-ice+2D-plate, liquid drizzle, mixed-phase drizzle, rain, snow, mixed-phase cloud, water+liquid drizzle, water+rain and unknown. The global statistics of three-dimensional occurrence frequency of each hydrometeor type revealed that 3D-ice contributes the most to the total cloud occurrence frequency (53.8%), followed by supercooled water (14.3%), 2D-plate (9.2%), rain (5.9%), warm water (5.7%), snow (4.8%), mixed-phase drizzle (2.3%), and the remaining types (4.0%). This hydrometeor type classification provides useful observation-based information for climate model diagnostics in representation of cloud phase and their microphysical characteristics.

SUBMITTER: Kikuchi M

PROVIDER: S-EPMC7430508 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of Algorithm for Discriminating Hydrometeor Particle Types with a Synergistic Use of CloudSat and CALIPSO.

Kikuchi M M Okamoto H H Sato K K Suzuki K K Cesana G G Hagihara Y Y Takahashi N N Hayasaka T T Oki R R

Journal of geophysical research. Atmospheres : JGR 20170910 20

We developed a method for classifying hydrometeor particle types, including cloud and precipitation phase and ice crystal habit, by a synergistic use of CloudSat/Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). We investigated how the cloud phase and ice crystal habit characterized by CALIOP globally relate with radar reflectivity and temperature. The global relationship thus id ...[more]

PMID: 32818127

Similar Datasets

Project description:BACKGROUND:Outdoor mobility is an important aspect of older adults' functional status. GPS has been used to create indicators reflecting the spatiotemporal dimensions of outdoor mobility for applications in health and aging. However, outdoor mobility is a multidimensional construct. There is, as of yet, no classification algorithm that groups and characterizes older adults' outdoor mobility based on its semantic aspects (ie, mobility intentions and motivations) by integrating geographic and domain knowledge. OBJECTIVE:This study assesses the feasibility of using GPS to determine semantic dimensions of older adults' outdoor mobility, including destinations and activity types. METHODS:A total of 5 healthy individuals, aged 65 years or older, carried a GPS device when traveling outside their homes for 4 weeks. The participants were also given a travel diary to record details of all excursions from their homes, including date, time, and destination information. We first designed and implemented an algorithm to extract destinations and infer activity types (eg, food, shopping, and sport) from the GPS data. We then evaluated the performance of the GPS-derived destination and activity information against the traditional diary method. RESULTS:Our results detected the stop locations of older adults from their GPS data with an F1 score of 87%. On average, the extracted home locations were within a 40.18-meter (SD 1.18) distance of the actual home locations. For the activity-inference algorithm, our results reached an F1 score of 86% for all participants, suggesting a reasonable accuracy against the travel diary recordings. Our results also suggest that the activity inference's accuracy measure differed by neighborhood characteristics (ie, Walk Score). CONCLUSIONS:We conclude that GPS technology is accurate for determining semantic dimensions of outdoor mobility. However, further improvements may be needed to develop a robust application of this system that can be adopted in clinical practice.

Dataset Information

Development of Algorithm for Discriminating Hydrometeor Particle Types with a Synergistic Use of CloudSat and CALIPSO.

Publications

Development of Algorithm for Discriminating Hydrometeor Particle Types with a Synergistic Use of CloudSat and CALIPSO.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets