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