Using multiple observations and retrievals to evaluate WRF-simulated MCS precipitation under different synoptic patterns and MCS stages over the CONUS
Xiguan
Dong
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson,
Oral
Mesoscale Convective Systems (MCSs) morphologically consist of a convective precipitating portion
and a non-precipitating anvil canopy. MCS precipitation is important to the atmospheric hydrologic cycle
because of heavy precipitation in convective cores (CC) and widespread precipitation in stratiform rain
(SR) regions. Both global and regional atmospheric models exhibit persistent biases in MCS initiation
location and timing, stratiform area and precipitation fraction, and anvil coverage (AC), as well as MCS
organization, in part due to a lack of comprehensive observations and retrievals. The central United States
is most prone to MCSs where MCSs contribute between 30% and 70% of warm-season rainfall. Clouds
and precipitation from MCSs are key components in the energy and hydrological cycles of the climate
system. Understanding the transition process from cloud to precipitation in MCSs is a highly desired goal,
however, there are few studies to quantitatively investigate the relationships between MCSs’ cloud
microphysical properties and precipitation.
To investigate the MCSs’ cloud and precipitation properties and their transition processes over the
CONUS, Professor Dong’s group at the University of Arizona has collected and analyzed three long-term
high-resolution observational datasets during the period 2010-2012. They are geostationary satellite
infrared brightness temperature, NEXRAD radar reflectivity from the GridRad dataset (hourly, 0.02 o x
0.02 o spatial and 1-km vertical resolutions), and hourly Stage IV multisensor precipitation dataset. Based
on this comprehensive dataset, Tian et al. (2020) tracked the MCSs, classified each MCS into three
regions (CC, SR, AC), then applied the retrieval method of Tian et al. (2016) to generate a 4D database of
the ice cloud water content and path (IWC, IWP) for MCS SR and thick AC regions. These datasets and
results have been used to evaluate the NOAA WRF simulated MCS’s cloud and precipitation properties
and their transition processes under different synoptic patterns (extratropical cycle and subtropical ridge)
and MCS stages (genesis, mature, and decay stages) in the study of Wang et al. (2019). Wang et al.
(2019) used the self-organizing map (SOM) to objectively identify synoptic patterns for the tracked
MCSs over the Southern and Northern Great Plains (SGP/NGP) during the warm season (April-
September), 2007-2014. They found two dominant synoptic patterns over these two regions: extratropical
cycle dominates during April-May but subtropical ridge is dominant pattern from June to September over
both SGP and NGP. These comprehensive datasets and results will provide an observational benchmark
for the science community to evaluate model simulations and validate satellite precipitation products.
and a non-precipitating anvil canopy. MCS precipitation is important to the atmospheric hydrologic cycle
because of heavy precipitation in convective cores (CC) and widespread precipitation in stratiform rain
(SR) regions. Both global and regional atmospheric models exhibit persistent biases in MCS initiation
location and timing, stratiform area and precipitation fraction, and anvil coverage (AC), as well as MCS
organization, in part due to a lack of comprehensive observations and retrievals. The central United States
is most prone to MCSs where MCSs contribute between 30% and 70% of warm-season rainfall. Clouds
and precipitation from MCSs are key components in the energy and hydrological cycles of the climate
system. Understanding the transition process from cloud to precipitation in MCSs is a highly desired goal,
however, there are few studies to quantitatively investigate the relationships between MCSs’ cloud
microphysical properties and precipitation.
To investigate the MCSs’ cloud and precipitation properties and their transition processes over the
CONUS, Professor Dong’s group at the University of Arizona has collected and analyzed three long-term
high-resolution observational datasets during the period 2010-2012. They are geostationary satellite
infrared brightness temperature, NEXRAD radar reflectivity from the GridRad dataset (hourly, 0.02 o x
0.02 o spatial and 1-km vertical resolutions), and hourly Stage IV multisensor precipitation dataset. Based
on this comprehensive dataset, Tian et al. (2020) tracked the MCSs, classified each MCS into three
regions (CC, SR, AC), then applied the retrieval method of Tian et al. (2016) to generate a 4D database of
the ice cloud water content and path (IWC, IWP) for MCS SR and thick AC regions. These datasets and
results have been used to evaluate the NOAA WRF simulated MCS’s cloud and precipitation properties
and their transition processes under different synoptic patterns (extratropical cycle and subtropical ridge)
and MCS stages (genesis, mature, and decay stages) in the study of Wang et al. (2019). Wang et al.
(2019) used the self-organizing map (SOM) to objectively identify synoptic patterns for the tracked
MCSs over the Southern and Northern Great Plains (SGP/NGP) during the warm season (April-
September), 2007-2014. They found two dominant synoptic patterns over these two regions: extratropical
cycle dominates during April-May but subtropical ridge is dominant pattern from June to September over
both SGP and NGP. These comprehensive datasets and results will provide an observational benchmark
for the science community to evaluate model simulations and validate satellite precipitation products.
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