evaluating the impact of cloud-aerosol- precipitation ... 2017/17 z li... · evaluating the impact...
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Introduction
Evaluating the Impact of Cloud-Aerosol-
Precipitation Interaction (CAPI) Schemes
on Rainfall Forecast in the NGGPS
Zhanqing Li
S.S. Lee, M. Jiang, L. Zhang, J. Liu
University of Maryland
NOAA/NCEP/EMC Collaborators
Sarah Lu, Yu-Tai Hou, Jun Wang, Shrinivas Moorthi
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1. Use a variety of observation datasets to identify and
quantify the impact of aerosol on cloud and precipitation.
Investigate any dependence of model biases on aerosol
properties under different meteorological conditions.
2. Evaluate the performance of the GFS baseline model in
simulating clouds and precipitation in order to
identify/attribute any forecast errors to aerosol-cloud-
precipitation interactions.
3. Select certain cloud systems, run the CRM with NGGPS-
selected parameterization schemes, and compare the CRM
against GFS/NGGPS results .
Major Tasks
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The paradigm of our approach
CRM Evaluation
of impact of CAPI in rain
forecast
Model Resolution
GFS
Multi-platform
Observations
Model Parameterization
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Mechanisms of CAPI
Aerosol-Cloud Interaction (ACI)
2017/8/18
Increase AOT
Decrease the solar radiation
that reaches the surface
Less evaporating
water vapor Less convective
energy
Create more but smaller droplets
Slow down the coalesce process
and rime onto ice hydrometeors stabilization of the atmosphere
Suppress precipitation
Suppress the rain
at low level
The formed ice particle
release large amount
of latent heat
Invigorate
stronger rainfall
Aerosol-Radiation Interaction (ARI)
For Light rain:
AOT +, precipitation -
For stronger rain:
AOT +, precipitation ? Li et al. (2017)
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Diurnal Cycle of Rainfall from Deep Clouds
Contrast between Clean & Dirty Air
Guo et al. (2016, JGR) Lee et al. (2016, JGR)
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-3
-2
-1
0
1
2
Tue Thu Sat Mon Wed Fri Sun
Mt. Hua
Vis
ibili
ty A
no
ma
ly (
%)
p = 0.0461
-3
-2
-1
0
1
2
Tue Thu Sat Mon Wed Fri Sun
SEChina
p = 0.036
Vis
ibilit
y A
no
ma
ly (
%)
-20
-15
-10
-5
0
5
10
15
Tue Thu Sat Mon Wed Fri Sun
SEChina_Plain_Thunder
Th
un
de
rsto
rm D
ay
s A
no
ma
ly (
%)
ap = 0.0035
Visibility Thunderstorms
Central China
SE China
-20
-15
-10
-5
0
5
10
15
20
Tue Thu Sat Mon Wed Fri Sun
Mt. Hua
Th
un
de
rsto
rm D
ay
s A
no
ma
ly (
%)
ap = 0.0138
Strong absorption
Weak absorption
Yang et al. (2016, GRL)
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2017/8/18
Jiang et al. (2017, ACPD)
GFS Precipitation Model Error and Aerosol Loading
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2017/8/18 Jiang et al. (2017, ACPD)
Standard Deviation of Rainfall Forecast Error and Aerosol Loading
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2017/8/18
GFS overestimates light rain and
underestimates heavy rain
Jiang et al. (2017, ACPD)
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A Close-up look at the problem for Marine St Clouds
• NCEP GFS forecast output at 1 degree resolution. Output is 3-h
intervals and start from 00Z.
Global Forecast System
Satellite observations
• MODIS Terra MOD08 level 3 product for cloud properties and AOD.
• AIRS Level 3 product for atmosphere temperature profiles.
• CERES EBAF-surface product for surface fluxes.
• CALIPSO aerosol vertical profiles
Focus on the southeast Atlantic ocean and west coast of
southern Africa in 2016.
Study area & time
Surface ARM observations • Azores ARM field experiment
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Comparison of Low Clouds from GFS & MODIS
GFS MODIS
Jan 2010
July 2010
Jan 2016
July 2016
Older GFS
Newer GFS
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The new SC scheme does not destroy stratocumulus
clouds off the west coasts of America and Africa as the
old scheme does. (Han &Pan 2011)
Is it possible the negative
bias might related to the
biomass burning aerosols ?
Many observations and
modeling studies show that
absorbing aerosols above the
marine stratocumulus clouds
tends to increase the cloud
cover.
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worldview.earthdata.nasa.gov/
Jan Apr
Aug Oct
Fire and Thermal anomalies
MODIS monthly AOD
APR JAN AUG
Most biomass burning
events occur during Aug
(Jan) in the south(central)
Africa but fewer during
April.
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Low-level cloud fraction
APR AUG JAN
GFS GFS GFS
MODIS MODIS MODIS
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GFS GFS GFS
CERES CERES CERES
APR AUG JAN
Downward shortwave radiation
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APR AUG JAN
GFS GFS GFS
AIRS AIRS AIRS
Lower-troposphere stability (LTS) LTS =θ700- θ1000
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Liu and Li (2017, in preparation)
Mechanisms of Aerosol & Marine PBL Clouds Interactions
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CF (a and b) and CTH (c and d) as a function of AODcolumnar for (a and c) ACA and N-ACA cases (red and blue dots and lines, respectively) and (b and d) with ACAL and ACAH (blue and red dots and lines, respectively).
• Significant increase in CF for both cases;
• Stronger increase in
CF for ACA (ACAH) than N-ACA (ACAL) cases;
• For N-ACA cases, CTH increases slightly, while for ACA cases, CTH significantly decreases;
• Stronger increase in CF for ACAH than ACAL cases; Liu and Li (2017, in preparation)
Change of Cloud Fraction with Aerosol Loading
Aerosol above clouds
No Aerosol above clouds
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Seoul case (D03) the Houston case
•A mesoscale system of convective clouds 29.42o N, 94.45o W •07:00 LST June 18th – 03:00 LST June 19th in 2013
• A mesoscale system of convective
clouds, 37.57o N, 126.57o E •09:00 LST (local solar time) July 26th – 09:00 LST July 27th 2011
WRF simulations to test the effects of model resolution and ACI
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Liquid-water path
Houston case Seoul case
Lee et al. (2017, ACPD)
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Precipitation frequency
Seoul case Houston case
Lee et al. (2017, ACPD)
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Improve Cloud & Aerosol Parameterizations in the NGGPS by Identifying Model Deficiencies and Finding Solutions
using Observations and Model Simulations
1. Effects of model resolutions and
microphysics parameterizations on the
simulations of clouds, precipitation, and their
interactions with aerosols in the GFS.
Figure 1. Sensitivity tests of modeled cloud water content
to varying resolution and aerosol loading.
Findings
a) The GFS severely underestimates LWP due
largely to the coarse resolution.
b) The coarse resolution causes an
underestimation of aerosol effects on clouds.
c) Despite sub-grid parameterizations, the
coarse resolution is still a major problem in
accounting for aerosol-cloud interactions.
Publications
Lee, S.-S. et al., 2017: Effects of resolutions and microphysics parameterizations on the simulations of clouds, precipitation, and their interactions with aerosol, ACP (under review).
2. GFS systematically overestimates
rainfall for light rain but under-predicts
it for heavier rain.
Finding: At least a part of the error may be
accounted for by a lack of aerosol effects
that suppress light rain and enhance heavy
rain.
Publications: Jiang, M., Z. Li, et al., 2016: Impact of aerosols on precipitation from deep convective clouds, J. Geophys. Res. – Atmos., 121, doi:10.1002/2015JD024246.
Jiang, M., Z. Li, et al., 2017, Potential influences of neglecting aerosol effects on the NCEP GFS precipitation forecast, ACP under revision.
Rain forecast error
Rain forecast standard error
3. Evaluation of the GFS model in
cloud properties and meteorological
variables using observations from mu
Finding: The underestimation of low
clouds has been improved in general
but still persists over regions of strong
absorbing aerosol.
Publications:
Liu, J., Z. Li, and M. Cribb, 2016: Response of marine boundary layer cloud properties to aerosol perturbations associated with meteorological conditions from the 19-month AMF-Azores campaign, J. Atmos. Sci., 73, doi:10.1175/JAS-D-15-0364.
Liu, J., and Z. Li, 2017, Aerosol Indirect Effect on Marine
Boundary Layer Clouds in Cases with and without an Above-
Cloud Aerosol Layer, in preparation.
WRF Model Simulations
Observations from China
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Future studies needed
Better understanding how small-scale updrafts control the
sensitivity, frequency distributions of updrafts.
Understanding the development of turbulence and cumulus
schemes on cloud simulations in the GFS/NGGPS
Continue to examine GFS performance in simulating clouds,
and other meteorological variables under the different aerosol
conditions using satellite and ground measurements
Close-up investigation on aerosol effects on rainfall (physical
analysis), with particular attention towards interactions
between dynamic and thermal variables susceptible to aerosol.
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Thank you !