An official website of the United States government
Abstract. Winter precipitation forecasts of phase and amount are challenging, especially in Northeast United States where mixed precipitation events from various synoptic systems frequently occur. Yet, there are not enough quality observations of winter precipitation, particularly microphysical properties from falling snow or mixed phase precipitation. During the winters of 2021–2022, 2022–2023, and 2023–2024, the NASA Global Precipitation Measurement (GPM) Ground Validation (GV) program conducted a field campaign at the University of Connecticut (UConn). The goal of this campaign was to observe various phases of winter precipitation and winter storm types to validate the GPM satellite precipitation products. Over the three winters at UConn, a total of 40 instruments were deployed across two observing sites that captured 117 precipitation events, including 19 phase transition events as indicated by the PARSIVEL2. These instruments included scanning and vertically pointing radars, along with suites of in-situ sensors. In addition, an unmanned aircraft system has been deployed in 2023–2024. Here, an overview of the different field deployments, instrumentation, and the datasets collected are presented. To showcase the observations, this article features a wide-ranging set of measurements collected from the instrument suite for the 28 February 2023 storm, during which six to eight inches of snow accumulated at the two different observing sites. Also included is a discussion on how these observations can be combined with other datasets to validate ground-based and remote sensing measurements and highlight important atmospheric processes that impact winter precipitation phase and amount. The datasets collected from this GPM GV field campaign are available at https://doi.org/10.5067/GPMGVUCONN/DATA101 (Cerrai et al., 2025).
more »
« less
Cross-Validation of CSU-Chivo Radar and GPM During Relampago
This paper describes the deployment and features of CSUCHIVO radar during the RELAMPAGO campaign in Argentina. Intercomparison with GPM-DPR is done using Volume Matching. Vertical profile analysis of storms is also shown and a list of GPM overpasses and summary of the tallest storms during the campaign are also included. The results show that CHIVO agree well with GPM in terms of reflectivity and microphysical structure of clouds and show the value of CHIVO for ground validation.
more »
« less
- Award ID(s):
- 1661863
- PAR ID:
- 10162824
- Date Published:
- Journal Name:
- IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
- Page Range / eLocation ID:
- 7586 to 7589
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract The electrical charge carried by raindrops provides significant information about thunderstorm electrification mechanisms, since the charge acquired by hydrometeors is closely related to the microphysical processes that they undergo within clouds. Investigation of charges on raindrops was conducted during the Remote sensing of Electrification, Lightning, And Meso‐scale/micro‐scale Processes with Adaptive Ground Observations field campaign. A newly designed instrument was used to determine simultaneously the fall velocity and charge for precipitating particles. Hydrometeor size and charge were measured in Córdoba city, Argentina, during electrified storms. Temporal series of size‐charge of single raindrops were recorded for two storms, which were also monitored with a Parsivel disdrometer and Lightning Mapping Array. The results show that the magnitude of the electric charges range between 1 and 50 pC and more than 90% of the charges are mainly carried by raindrops >1 mm, even though most of the raindrops are smaller than 1 mm. Furthermore, the measurement series show charged hydrometeors of both signs all the time. A correlation between the sizes and the charges carried by the raindrops was found in both storms.more » « less
-
Abstract The Echo Classification from COnvectivity (ECCO) algorithm identifies convective and stratiform types of radar echo in three dimensions. It is based on the calculation of reflectivity texture—a combination of the intensity and the heterogeneity of the radar echoes on each horizontal plane in a 3D Cartesian volume. Reflectivity texture is translated into convectivity, which is designed to be a quantitative measure of the convective nature of each 3D radar grid point. It ranges from 0 (100% stratiform) to 1 (100% convective). By thresholding convectivity, a more traditional qualitative categorization is obtained, which classifies radar echoes as convective, mixed, or stratiform. In contrast to previous algorithms, these echo-type classifications are provided on the full 3D grid of the reflectivity field. The vertically resolved classifications, in combination with temperature data, allow for subclassifications into shallow, mid-, deep, and elevated convective features, and low, mid-, and high stratiform regions—again in three dimensions. The algorithm was validated using datasets collected over the U.S. Great Plains during the PECAN field campaign. An analysis of lightning counts shows ∼90% of lightning occurring in regions classified as convective by ECCO. A statistical comparison of ECCO echo types with the well-established GPM radar precipitation-type categories show 84% (88%) of GPM stratiform (convective) echo being classified as stratiform (convective) or mixed by ECCO. ECCO was applied to radar grids for the continental United States, the United Arab Emirates, Australia, and Europe, illustrating its robustness and adaptability to different radar grid characteristics and climatic regions.more » « less
-
null (Ed.)Abstract The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional “grid-by-grid analysis,” the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for “hybrid” data-driven and physics-driven estimates in order to make optimal usage of satellite observations.more » « less
-
Abstract A particular strength of lightning remote sensing is the variety of lightning types observed, each with a unique occurrence context and characteristically different emission. Distinct energetic intracloud (EIC) lightning discharges—compact intracloud lightning discharges (CIDs) and energetic intracloud pulses (EIPs)—produce intense RF radiation, suggesting large currents inside the cloud, and they also have different production mechanisms and occurrence contexts. A Low‐Frequency (LF) lightning remote sensing instrument array was deployed during the RELAMPAGO field campaign in west central Argentina, designed to investigate convective storms that produce high‐impact weather. LF data from the campaign can provide a valuable data set for researching the lightning context of EICs in a variety of subtropical convective storms. This paper describes the production of an LF‐CID data set in RELAMPAGO and includes a preliminary analysis of CID prevalence. Geolocated lightning events and their corresponding observed waveforms from the RELAMPAGO LF data set are used in the classification of EICs. Height estimates based on skywave reflections are computed, where prefit residual data editing is used to improve robustness against outliers. Even if EIPs occurred within the network, given the low number of very high‐peak current events and receiver saturation, automatic classification of EIPs may not be feasible using this data. The classification of CIDs, on the other hand, is straightforward and their properties, for both positive and negative polarity, are investigated. A few RELAMPAGO case studies are also presented, where high variability of CID prevalence in ordinary storms and high‐altitude positive CIDs, possibly in overshooting tops, are observed.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IGARSS.2019.8898835
