The Detection of Mesoscale Convective Systems by the GPM Ku-Band Spaceborne Radar

Wang, Jingyu; Houze, Robert A.; Fan, Jiwen; Brodzik, S. R.; Feng, Zhe; Hardin, Joseph

doi:10.2151/jmsj.2019-058

Cited by 21 publications

(19 citation statements)

References 66 publications

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“…The identification of different cloud types and tracking their location around the MWL should reduce the spatial aggregation problems (Section 3.3.2) and the resulting uncertainties between MWL rainfall and the MSG satellite signals. In this context, the promising results demonstrated by [68,80,107,108] for raining cloud classification and [109,110] for tracking raining clouds accentuate the potential of a more accurate MWL and satellite-based raining cloud detection. Thus, future studies which consider these concepts will be undertaken.…”

Section: The Analysis Of R Mwl With Msg Seviri Datamentioning

confidence: 99%

Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation

Kumah

Hoedjes

David³

et al. 2020

Atmosphere

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Accurate rainfall detection and estimation are essential for many research and operational applications. Traditional rainfall detection and estimation techniques have achieved considerable success but with limitations. Thus, in this study, the relationships between the gauge (point measurement) and the microwave links (MWL) rainfall (line measurement), and the MWL to the satellite observations (area-wide measurement) are investigated for (area-wide) rainfall detection and rain rate retrieval. More precisely, we investigate if the combination of MWL with Meteosat Second Generation (MSG) satellite signals could improve rainfall detection and rainfall rate estimates. The investigated procedure includes an initial evaluation of the MWL rainfall estimates using gauge measurements, followed by a joint analysis of the rainfall estimates with the satellite signals by means of a conceptual model in which clouds with high cloud top optical thickness and large particle sizes have high rainfall probabilities and intensities. The analysis produced empirical thresholds that were used to test the capability of the MSG satellite data to detect rainfall on the MWL. The results from Kenya, during the “long rains” of 2013, 2014, and 2018 show convincing performance and reveal the potential of MWL and MSG data for area-wide rainfall detection.

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Section: The Analysis Of R Mwl With Msg Seviri Datamentioning

confidence: 99%

Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation

Kumah

Hoedjes

David³

et al. 2020

Atmosphere

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“…The data processing techniques and metrics we have developed in this study can be used globally for model evaluation when satellite-based radars provide global 3D radar observations. The GPM radar observations will eventually be able to provide global radar echo coverage (Houze et al, 2019), whose data have been proven consistent with NEXRAD (Wang et al, 2019b). However, as discussed in Section 2, the sampling by GPM at 1° model grid elements for only three years of GPM data is insufficient for obtaining robust grid-mean values to compare with the E3SM simulation.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Although the GPM radar frequency is higher than the NEXRAD (13.6 GHz vs. 3 GHz), our previous study quantitatively evaluated the coincident observations from NEXRAD and GPM over the CONUS, and found the 3D 130 radar reflectivity fields obtained from the two independent platform datasets are highly consistent with each other after proper smoothing of GPM data in the vertical to mimic the temporal averaging used in the GridRad processing of NEXRAD data (Wang et al, 2019b). Therefore, the 13.6 GHz in COSP is accurate for evaluation with NEXRAD.…”

Section: Nexrad Observationsmentioning

confidence: 99%

Using Radar Observations to Evaluate 3D Radar Echo Structure Simulated by a Global Model

Wang

Fan

Houze

et al. 2020

Preprint

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Abstract. The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next generation radar (NEXRAD) network over the U.S. to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central United States, which suggests that the model does not capture the mesoscale convective systems that produce much of precipitation in that region. The shape of the model estimated histogram of subgrid scale reflectivity is improved by correcting the microphysical assumptions in COSP. The model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 4 km lower than in observations – and this result is not changed by tuning any single physics parameter.

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“…Methods of CTH retrieval from both ground-and satellite-based remote sensing data have been developed for several years. As for active sensors, reflectivity echo-top height Radar or Lidar (active sensors) could act as a reference of CTH directly, including Micro-Pulse Lidar (MPL) and millimeter wavelength cloud radar (MMCR) at the South Great Plain of USA [9], Ka-band Doppler cloud radar(Beijing, China) [10], the WSR-88D(NOAA, USA) [11], the Precipitation Radar onboard the Tropical Rainfall Measuring Mission (TRMM, NASA, USA) satellite [12,13], dual-frequency radar (DPR) on board of Global Precipitation Measurement (GMP, NASA, USA) satellite [14], the Cloud Profiling Radar (CPR) of the CloudSat Mission (Het Propulsion Laboratory, USA) [6,15], and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO, NASA, USA) [16], etc. The Multiangle Imaging Spectroradiometer (MISR) onboard Terra satellite could also use the camera to retrieve stereo CTH using a stereo-matching algorithm [17].…”

Section: Introductionmentioning

confidence: 99%

Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model

Wang

Zhuang

et al. 2021

Atmosphere

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Tropical overshooting convection has a strong impact on both heat budget and moisture distribution in the upper troposphere and lower stratosphere, and it can pose a great risk to aviation safety. Cloud-top height is one of the essential concerns of overshooting convection for both the climate system and the aviation weather forecast. The main purpose of our work is to verify the application of the machine learning method, taking the random forest (RF) model as an instance, in overshooting cloud-top height retrieval from Himawari-8 data. By using collocated CloudSat observations as a reference, we utilize several infrared indicators of Himawari-8 that are commonly recognized to relate to cloud-top height, along with some temporal and geographical parameters (latitude, month, satellite zenith angle, etc.), as predictors to construct and validate the model. Analysis of variable importance shows that the brightness temperature of 6.2 um acts as the dominant predictor, followed by satellite zenith angle, brightness temperature of 13.3 um, latitude, and month. In the comparison between the RF model and the traditional single-channel interpolation method, retrievals from the RF model agree well with observation with a high correlation coefficient (0.92), small RMSE (222 m), and small MAE (164 m), while these metrics from traditional single-channel interpolation method shows lower skills (0.70, 1305 m, and 1179 m). This work presents a new sight of overshooting cloud-top height retrieval based on the machine learning method.

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The Detection of Mesoscale Convective Systems by the GPM Ku-Band Spaceborne Radar

Cited by 21 publications

References 66 publications

Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation

Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation

Using Radar Observations to Evaluate 3D Radar Echo Structure Simulated by a Global Model

Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model

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