diagnosis and improvement of cloud parametrization schemes in ncep/gfs using multiple satellite...
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Diagnosis and improvement of cloud parametrization schemes in NCEP/GFS
using multiple satellite products
1Hyelim Yoo, 1Zhanqing Li2Yu-Tai Hou, 2Steve Lord
3Howard W. Barker
1University of Maryland, College Park, MD2Environmental Modeling Center, NCEP/NWS/NOAA
3Environment Canada, Toronto, Canada
Apr 24, 2013
Contents
• Introduction
• Data and Approach
• Analysis
• Application
• Summary
Image from MODIS
Background1. Introduction
AerosolTemperature
RadiationPrecipitation
• cloud-aerosol interaction
• serve as CCN
• a key regulator
• redistribute extra heat
• depending on cloud type
• cloud-aerosol-precipi. interaction
• absorption of longwave radiation
• reflection of solar radiation
Cloud
Clouds are important component in the Earth system and they interact with large scale circulation and other parts of weather and climate sys-tems.
Objectives1. Introduction Most significant sources of errors and uncertainties in weather forecast and climate models are come from the treatment of clouds due to the incomplete knowledge of the underlying physical processes (Stephens 2005) and considerable variations of cloud amounts in vertical and horizontal extent (Rossow et al. 1989; Norris 1998; Tompkins 2002; Boutle and Morcrette 2012).
GFSGFS
Diagnosis Diagnosis
Analysis Analysis
Application Application
Diagnosis of GFS model parameterization of cloud variables such as cloud fraction, cloud optical depth, liquid & ice water path
11
22
33
Assessment of atmospheric meteorologi-cal conditions leading to cloud forma-tionin the GFS model against observa-tional data
Application of Other Cloud Scheme to the GFS modelAn exponential random overlap as-sumption
▶ Cloud properties aredetermined diagnostically.
GFS
Between ▶ Diagnostic scheme
▶ Cloud water is a prognostic variable but cloud cover is a di-agnostic variable.
▶ Prognostic scheme
▶ Cloud cover, water vapour, liquid & ice condensate aredetermined prognostically.
Assign a probability density function to the total water mixing ratio equal to the sum of the water vapor, cloud water,and cloud ice mixing ratios.
Sommeria and Deardorff 1977; Mellor 1977;Bougeault 1981; Smith 1990; Cusack et al. 1999; Nishizawa 2000
Cloudiness is the result of subgrid-scale variability and is represented by relating cloud cover to relative humidity (RH).
Sellers 1976; Gates and Schlesinger 1977;Sundqvist 1978; Slingo 1980; Sundqvist et al.1989; Walcek 1994; DelGenio et al. 1996; Xu and Randall 1996
RH schemes statistical schemes
Cloud scheme1. Introduction
T
RH
CF HeightCOD
T
LW
SW
RH CF
CloudSatCALIPSO
CERES
ARMSGP site
Observation
AIRS COD
IWP
LWP
CF
MODIS
from NASA site
Ground Re-mote
Sensing
Ground Re-mote
Sensing
Active RemoteSensing
Active RemoteSensing
Passive Re-mote
Sensing
Passive Re-mote
Sensing
Cloud variables2. Data
Cloud scheme
• Application of other diagnostic cloud scheme for cloud fraction
Cloud overlap• Use of exponential random overlapping assumption with de-correlation length
Cloud properties
• Cloud fraction• Liquid water path• Ice water path • Cloud optical depth
Input as T/RH
• Comparison with satellite retrievals
• Comparison with ground-based mea-surements
Approach2. Data
Diagno-sisof GFS
Clouds
C-C satellites MODIS-CL GFS
High
Mid
Low
Cloud fraction3. Analysis
SW LW Net
Difference = CERES - GFS
CERES GFS
SW 90.91 W/m2 81.13 W/m2
LW 247.62 W/m2 252.53 W/m2
Net -2.21 W/m2 13.35 W/m2
Table 1. Global monthly mean outgoing SW, LW, and net radiation
Radiation at the TOA3. Analysis
Relative humidity (left panel) and temperature (middle panel) profiles during July 2008: from AERI, AIRS, GFS.right panel: comparison of cloud mixing ratio from VAP (dashed) and the GFS model (solid).
T
MR
RH, T, MR 3. Analysis
RH
BasedXu and Randall (1996)
Similar
GFS scheme SG scheme
An equation is from empirical formula
Slingo (1987)Gordon (1992)
Many of constants arebased on observations
Differ
Variables
Only one equation determines CFR
Several equationsdetermine CFR
T, RH, andCloud water mixing ratio
RH, convective cfr, vertical velo, lapse rate
OverlapMaximum-Random
overlapMaximum overlap
SG scheme4. Application
MODIS-CL GFS_ori GFS_SG
High
Mid
Low
Cloud fraction4. Application
A schematic illustrating the three overlap assumptions (from Hogan and Illingworth, 2000)
Cmax = Max(C1, C2) Cran = C1+C2-C1*C2
Random overlap: noncontiguous layers, Maximum overlap: contiguous layers Most widely used cloud overlap approximation in modern GCMs
Geleyn and Hollingsworth 1979
Cloud overlap4. Application
Ctrue = a*Cmax + (1-a)*Cran ,where a(Δz) = exp(-Δz/Lcf)
▶Mace and Benson-Troth
▶ For vertically continuous cloud, the degree of correlation be-tween the cloud positions de-creased with vertical separation of the layersLcf : 4 km
2002
2005
2000
EXPONENTIAL
RANDOM
▶ Pincus et al.
▶ Naud et al.
▶ Hogan and Illingworth
▶ Using CRM simula-tion, Stratiform and con-vectiveclouds have different overlap.St: Random, Con: Max
▶ Using MMCR Radar data from 4 ARM sites:SGP, TWP, Manus, Nauru Lcf : 3.9 km at SGP, 4 km at Manus, 4.6 km at Nauru
▶ Using cloud radar data from ARM with NCEP reanalysis dataLcf : 2 km at SGP, 2.3 km at Manus, 1.8 km at Nauru
2008
2010
▶ Barker
2008
▶ Using CloudSat and CALIPSO dataLcf : median value of 2 km for global scales
▶ Shonk et al.
▶ Based on two studies,they suggest a simple linear fitLcf : dependent on only latitudes
Cloud overlap4. Application
Data
CloudSat – CALIPSO data during July 2007CPR cloud maskCloudSat radar reflectivityCloud fraction at each level
Conditions
Brent’s method
Developed originally by Räisänen et al. [2004]
for use with McICA
All applications used 50,000 sub-
columns
processing
All conditions are satisfied,
Calc
ulate L
cf
CPR cloud mask > 20Radar reflectivity > -30
CPR cloud mask < 20Cloud fraction > 99 %Radar reflectivity > -30
stochastic generator
&
Lcf Calculation 4. Application
MODIS-CL GFS_ori GFS_Lcf
High
Mid
Low
Cloud fraction4. Application
Evaluation of GFS cloud properties
5. Summary
Comparison of temperature, relative humidity,
cloud water mixing ratio with satellite retrievals
And ground-based measurements
Input variables
Cloud fractions calculated from the SG cloud
scheme showed much better improvements
compared to observation.
Cloud scheme
Use of the observation-constrained Lcf leads to
an improvement for high clouds, a neutral
impact for mid and deterioration for low clouds.
Cloud overlap