scholarly journals Verification of Hydrometeor Properties Simulated by a Cloud-Resolving Model Using a Passive Microwave Satellite and Ground-Based Radar Observations for a Rainfall System Associated with the Baiu Front

2009 ◽  
Vol 87A ◽  
pp. 425-446 ◽  
Author(s):  
Hisaki EITO ◽  
Kazumasa AONASHI
Keyword(s):  
Passive Microwave ◽  
Radar Observations ◽  
Cloud Resolving Model ◽  
Baiu Front

Comparing the Convective Structure and Microphysics in Two Sahelian Mesoscale Convective Systems: Radar Observations and CRM Simulations

2013 ◽  
Vol 141 (2) ◽  
pp. 582-601 ◽  
Author(s):  
Nick Guy ◽  
Xiping Zeng ◽  
Steven A. Rutledge ◽  
Wei-Kuo Tao
Keyword(s):  
Three Dimensional ◽  
Mesoscale Convective Systems ◽  
Modeling Framework ◽  
Microphysics Scheme ◽  
Radar Observations ◽  
Convective Systems ◽  
Convective Structure ◽  
High Reflectivity ◽  
Mesoscale Convective ◽  
Cloud Resolving Model

Abstract Two mesoscale convective systems (MCSs) observed during the African Monsoon Multidisciplinary Analyses (AMMA) experiment are simulated using the three-dimensional (3D) Goddard Cumulus Ensemble model. This study was undertaken to determine the performance of the cloud-resolving model in representing distinct convective and microphysical differences between the two MCSs over a tropical continental location. Simulations are performed using 1-km horizontal grid spacing, a lower limit on current embedded cloud-resolving models within a global multiscale modeling framework. Simulated system convective structure and microphysics are compared to radar observations using contoured frequency-by-altitude diagrams (CFADs), calculated ice and water mass, and identified hydrometeor variables. Vertical distributions of ice hydrometeors indicate underestimation at the mid- and upper levels, partially due to the inability of the model to produce adequate system heights. The abundance of high-reflectivity values below and near the melting level in the simulation led to a broadening of the CFAD distributions. Observed vertical reflectivity profiles show that high reflectivity is present at greater heights than the simulations produced, thought to be a result of using a single-moment microphysics scheme. Relative trends in the population of simulated hydrometeors are in agreement with observations, though a secondary convective burst is not well represented. Despite these biases, the radar-observed differences between the two cases are noticeable in the simulations as well, suggesting that the model has some skill in capturing observed differences between the two MCSs.

Passive Microwave Rainfall Error Analysis Using High-Resolution X-Band Dual-Polarization Radar Observations in Complex Terrain

2018 ◽  
Vol 56 (5) ◽  
pp. 2565-2586 ◽  
Author(s):  
Yagmur Derin ◽  
Emmanouil Anagnostou ◽  
Marios N. Anagnostou ◽  
John Kalogiros ◽  
Daniele Casella ◽  
...  
Keyword(s):  
High Resolution ◽  
Error Analysis ◽  
Complex Terrain ◽  
Passive Microwave ◽  
Dual Polarization ◽  
Radar Observations ◽  
X Band

scholarly journals Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part II: Evaluation of Estimates Using Independent Data

2006 ◽  
Vol 45 (5) ◽  
pp. 721-739 ◽  
Author(s):  
Song Yang ◽  
William S. Olson ◽  
Jian-Jian Wang ◽  
Thomas L. Bell ◽  
Eric A. Smith ◽  
...  
Keyword(s):  
Rain Rate ◽  
Sampling Error ◽  
Contextual Information ◽  
Total Error ◽  
Bias Reduction ◽  
Passive Microwave ◽  
Latent Heating ◽  
Cloud Resolving Model ◽  
Surface Rain Rate ◽  
Heating Profiles

Abstract Rainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5°-resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r ∼0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5°-resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5°-resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated radar. Error model modifications for nonraining situations will be required, however. Sampling error represents only a portion of the total error in monthly 2.5°-resolution TMI estimates; the remaining error is attributed to random and systematic algorithm errors arising from the physical inconsistency and/or nonrepresentativeness of cloud-resolving-model-simulated profiles that support the algorithm.

The Features of Radar Observations of Snow Cover from Space-Borne Carriers

2007 ◽  
Vol 66 (12) ◽  
pp. 1133-1141
Author(s):  
S. Ye. Yatsevich ◽  
V. B. Yefimov ◽  
I. A. Kalmykov
Keyword(s):  
Snow Cover ◽  
Radar Observations
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