Comparison of boundary layer cloud properties€¦ · Why do we need to study Mesoscale Convective...
Transcript of Comparison of boundary layer cloud properties€¦ · Why do we need to study Mesoscale Convective...
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Investigation of MCS Cloud-precipitation Processes
and Properties through an integrative analysis of
aircraft in-situ measurements, ground-based remote
sensing and WRF simulations
Xiquan Dong, University of Arizona
OutlineI) MCS cloud and precipitation
A: Lifecycle of classified MCS’s components
B: Evaluation of WRF simulated precipitation using
Stage IV dataset
C: Aircraft in-situ measurement and surface
retrievals during MC3E
II) MBL aerosol and cloud properties
III) CMIP5 evaluation using satellite observations
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Part IA: Lifecycle of classified
MCS’s components
Feng et al. 2011 and 2012 (JGR)
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Why do we need to study Mesoscale
Convective System (MCS) MCS has two main compoments
– Cumulus tower: important to hydrologic cycle and atmospheric circulation due to heavy rainfall
– Cirrus anvil cloud: dominate radiation budget due to large area coverage
High impacts on both weather and climate
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Cirrus anvil
(Non-precipitating)
Cumulus Tower
(Precipitating)
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Ecd
Ccu
CTEce
Ece
Ese
Esd
Csu
Ese
NEXRAD
Low-Level
Mid-Level
GOES
ConvectiveThick
Anvil
Stratiform Thick
Anvil
Deep
Cloud
Thin Anvil
Cold Cloud Shield (GOES)
Precipitation Total AnvilTotal AnvilFeng et al. (2011) JGR
KAZR/
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Studying DCS cloud and precipitation during MC3E
Con. Core Stratiform Anvil
Heavy rain
NEXRAD
KAZR
LWP/PR
Dmax
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Why we need satellite observations?
NEXRAD data
associated with
the GOES-
retrieved cold
cloud-top
temperatures
(yellow color)
However the
stratiform regions
(especially for
cirrus anvils)
(white color) were
not observed by
NEXRAD. 6
NEXRAD
GOES
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Radar Classification Example
Anvil
Anvil Echo
7Feng et al. 2011
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What are their 3D structures ?
This 3-D database will be used to investigate the
vertical and spatial structures of a MCS.
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Example of
one tracked
system using
GOES IR
temp
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Red: cold core (TIR < 215 K)
Blue: cold cloud (TIR < 235 K)
Formation
Mature
Dissipation
The formation-mature-
dissipation processes of a MCS
Feng et al. 2012
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Linked
tracking
results with
Hybrid
Classification
Red: cold core (TIR < 215 K)
Blue: cold cloud (TIR < 235 K)
Precipitation Anvil Clouds
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• Derive statistics for each system using
information from hybrid classification
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System Equivalent Radius
System Min TIR
Define Life Cycle
Stages
1 2 3 4 5
Developing Mature Dissipating
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Reason: composite systems with different lifetimes
Based on tendency of system size and TIR
Developing (1, 2)
– Before reaching min TIR
– Warm developing (TIR > 220K)
– Cold developing (TIR < 220K)
Mature (3)
– Min TIR < time < Max Radius
Dissipating (4, 5)
– Cold dissipating
– Warm dissipating
Group all systems based on defined stages
Definition from Futyan and Del Genio (2007)
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Composite by Life Cycle
Time period: May-Aug, 2010-2011 (hourly data)
Total number of tracked systems: 3995
CC expands quickly in developing stage, reach maximum
between cold developing/mature stage
SR/AC size have similar tendency: gradually grow and
reach maxima at cold dissipating stage
CC area: 9%, SR area: 18%, AC area:73% 12
Conv Core (CC) Strat Rain (SR) Anvil Cloud (AC)
Size
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Precipitation comes
almost exclusively
from convective
rain in developing
and mature stage
Stratiform rain
gradually becomes
more important as
system dissipates
CC/SR rain rate
evolution similar to
sizes
PRCC is 10 × PRSR
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CC
SR
Volume Rain Fraction (%)
Rain Rate (mm/hr)
Precipitation Evolution
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Part IA: Summary1) Developed a method to classify the MCS’s
components (CC, SR, and AC) and then
investigate their cloud and precipitation
properties.
2) Developed a tracking method to track the
MCS’s lifetime and to investigate the MCS’s
formation-dissipation processes, as well as
their precipitation properties, such as
MCS component sizes increase with lifetime
CC area: 9%, SR area: 18%, AC area: 73%
PRCC is 10× PRSR
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Part IB.
Evaluation of NSSL WRF simulated precipitation using Stage IV
dataset
Wang et al. 2018 MWR
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Objectives
To evaluate the NSSL-WRF simulated heavy
precipitation by
• Regions: (SGP vs. NGP)
• Primary precipitation type:
(convective rain CR vs. stratiform rain SR)
• Dominant atmospheric synoptic pattern:
(extratropical cyclone vs. subtropical ridge).
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• Location: SGP and NGP
• Duration: 2007-2014 warm season
(Apr. – Sep.)
• Target: Heavy precipitation events
(upper 90% of regional precipitation)
• Classification method: Self-
Organizing Map (SOM)
• Classification input: NARR data
(MSLP, wind/geopotential/RH/ at
500/900 hPa)
• Observation: NCEP Stage IV
• Simulation: Long-term WRF by NSSL
Specifications of Evaluation
NGP
SGP
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Self-Organizing Map Method (SOM) Results at SGP
TYPE I:
Extratropical
cyclone
TYPE II:
Subtropical
ridge
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• WRF: Negative bias; Type 1 better than Type 2
WRF Evaluation (SGP)
• Total precipitation: Type 1 > Type2• Spatial pattern: Type 1 zonal gradient (west-East);
• Type 2 meridional gradient (North-South)
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Diurnal cycle analysisWRF Evaluation by Class (SGP)
From left to right, diurnal variation becomes stronger
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Diurnal cycle analysis
Classes 1 and 4:
(1) Flat diurnal variation (SR); (2) Bi-modal pattern; (3) WRF well
simulates
WRF Evaluation by Class (SGP)
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Classes 3 and 5:
(1)The largest diurnal variation, (2) Follows the typical pattern,
(3) Daytime WRF well matches, (4) Nighttime WRF severely
undersimulates, and (5)Simulated convection ends too soon
WRF Evaluation by Class (SGP)
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SR vs. CR ComponentsWRF Evaluation by Class (SGP)
CR intensity/coverage is
better simulated than SR
Intensity: CR = 10 * SR
Coverage: CR = ¼ SR
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• SOM works well for the separation of
synoptic patterns (extratropical vs.
subtropical) and the dominant precipitation
types (SR vs. CR)
• WRF better matches in overall CR
intensity/coverage than SR
• Better simulation in extratropical cyclone
than subtropical ridge
Summary of Part IB
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Part IC: Aircraft in-situ
measurement and surface
retrievals during MC3E
Wang et al. (2015) and Tian et al. (2016)
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0. Introduction of field campaign and instrumentation
What: The Midlatitude Continental Convective Clouds Experiment
(MC3E)
Jensen, M. P., et al. (2010), Midlatitude Continental Convective Clouds Experiment (MC3E), DOE document, DOE/SC-ARM/10-004.
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Where: near ARM SGP central facility (36.61°N, 97.49°W)
When: From April 22 – June 6, 2011
Who: DOE ARM and NASA GPM GV
Why: Understand cloud and precipitation microphysical interactions the
and Refine GPM retrieval algorithms over land
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2/13/2019 27
KAZR and NEXRAD Reflectivity and Classification
Convective Stratiform Anvil
Core
System moved from SW to NE, passed over ARM SGP site
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How can we provide reliable ice cloud properties of
DCS from aircraft in situ data?
Approach
Step 1: Using multi-sensor to
eliminate SLWC
Step2: Constructed a full
spectrum of PSDs from
2DC+HVPS (D=120–30,000 μm)
Step3: Build a new mass-
dimension relation
IWCNEV(Dmax<4 mm)~
0.00365D2.1
Step 4: Applying this
relationship to a full
spectrum of PSDs to calculate
IWCs (best-estimated)
Cloud Top (14:30 UTC) : Dmax<4 mm, the
Nevzorov-measured IWCs are almost the same
as the best-estimated IWCs.
Near Melting band (13:45 UTC): Dmax>4 mm,
Nevzorov IWCs << best-estimated IWCs
Outstanding Issue:
Nevzorov probe measured
IWCs are lower than ground
truth because it can only
measure Dmax< 4 mm.
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Constructed a full spectrum of PSDs from 2DC+HVPS and derived Gama-
Distribution
1. Maximum Ds decrease
from 27,500 µm to
4,000 µm, whereas Nt
increase 100 times
when aircraft
ascended from 4 to 8
km
2. Gamma-type-size-
distributions have been
derived from original PSDs
as
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4 km
8 km
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ICE LIQUID APPLICATION PROPOSE
Retrieve Ice Microphysical Properties
Using empirical relationships from aircraft (Wang et al., 2015),
we can estimate the ice water content using radar reflectivity.
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Validating NEXRAD retrieved IWCs using aircraft in situ measurements
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Leg 1 Leg 2 3 4
• Statistical comparison during MC3E
Aircraft Retrievals
IWC 0.47 gm-3 0.63 (+34%)
Dm 2.02 mm 1.63 mm (-19%)
IWC
Dm
• Both IWC and Dm decrease with
height
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4D (Space+Time) NEXRAD Reflectivity and
IWC and Dm Retrievals10:15 UTC 14:15 UTC
8 km
6 km
Vertical Distributions ofRetrieved IWC atARM SGP site
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MCS Precipitation
IWPs indeed have similar spatial distributions as
precipitation:
Summer : More IWPs and Precipitation over NGP
Spring : More IWPs and precipitation over SGP
Plots courtesy of Dr. Zhe Feng from PNNL
Do IWPs have similar spatial distributions as precipitation?
Summer+ Spring
Statistical analysis of warm season MCs ice cloud
microphysical properties
Summer
Spring
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Xiquan Dong’s research group (2018-19)
My group is currently supported by
NASA CERES, NESSF and GSFC;
DOE CMDV and CESM;
NOAA R2O project; and NSF