Profiling Clouds with Satellite Imager Data and Potential ApplicationsWilliam L. Smith Jr. 1, Douglas A. Spangenberg2, Cecilia Fleeger2, Patrick Minnis1, Mandana Khaiyer2

1Science Directorate, NASA LaRC, Hampton, VA 2 SSAI, Inc., Hampton, VA

Background• Accurate cloud characterizations are needed for weather and climate applications.

• Satellite cloud retrievals (e.g.cloud top height, CTH; cloud base height, CBH; ice & liquid water path, IWP & LWP) are not being used advantageously.

Objectives• Develop a cloud water content profiling technique for application to satellite imager cloud retrievals of all cloud types

• Use method to resolve the Aircraft Icing Potential in deep cloud systems (e.g. winter storms and convection)

• Validate method (i.e. ice and liquid water content profiles, icing potential) with active sensor retrievals and in situ aircraft observations in single-layer, optically thick ice over water clouds

SummarySatellite imager cloud retrievals combined with empirical characterizations of cloud vertical structure from NWP and active sensors provide an unprecedented resolution of clouds for:

1. Weather applications: 4-D cloud properties for nowcasting and assimilation and improved diagnoses of aircraft icing potential

2. Climate applications: Improved global estimates of cloud water budgets and radiative impacts

Real-time satellite imager cloud retrievals constrain the vertical distribution of cloud water

High resolution cloud properties from satellite imagers

• The total water path (TWP) is estimated empirically from the satellite cloud retrievals for ice over water clouds.

• TWP parameterization based on correlations between GOES cloud properties and ARM MICROBASE (Radar/MWR) TWP estimates

• Note that TWP nearly twice as large as satellite IWP for densest clouds

GOES Cloud Analysis 26 Feb 2013 (1745 UTC)

• Cloud top information currently used in operations but more information is available

• Daytime optical depth retrievals provide information on cloud mass (total water path) and thickness

• Assumes single-layer but multi-layer info and retrievals are also available (future work)

GOES IWP (gm-2)

TWP

(gm

-2)

TWP Parameterization

Example for Re=40 μm

* SLIOW clouds

Relationships describing the typical vertical distribution of total cloud water content (TWC) as a function of cloud top temperature (Tt) and total water path (TWP)

Novel applications for cloud profiling method

Cloud vertical structure information from NWP, CloudSat and CALIPSO data are assessed climatologically and employed in a satellite imager profiling method

Tem

pera

ture

(K)

SLW Mass Fraction (%)

TWP <= 50 gm-2

50-200200-500

1000-2000500-1000

2000-30003000-4000

TWP > 4000 gm-2

Tem

pera

ture

(K)

SLW Probability (%)

Relationships to partition liquid from ice in mixed phase clouds (from NOAA RUC)

Example for clouds with tops < 233K245 <= Tt < 253 K

Normalized TWC

253 <= Tt < 263 K

Normalized TWC

263 <= Tt < 273 K

Normalized TWC

Tt >= 273 K

Normalized TWC

225 <= Tt < 230 K

Normalized TWC

230 <= Tt < 235 K

Normalized TWC

235 <= Tt < 240 K

Normalized TWC

240 <= Tt < 245 K

Normalized TWC

Tt < 220 K

Normalized TWC

220 <= Tt < 225 K

Normalized TWC TWP

TWP

Normalized TWC profiles derived from the NOAA Rapid Update Cycle (RUC). Similar functions are derived from CloudSat/CALIPSO data (not shown).

These information are constrained with satellite imager retrievals of TWP and cloud boundaries to derive ice and liquid water content profiles at the pixel resolution of the imager.

Validation

High Res 4-D Clouds from GEO

Pilot Reports

Improved Resolution of Aircraft Icing Conditions

GOES-13 Icing Analysis

Probability

• Profiling method provides estimates of SLW probability and LWC (icing conditions) in overlapping clouds

• Combined with Smith et al (2012) method to determine icing threat in all cloud types

• Accuracy in winter storm clouds similar to low cloud method

Compared to satellite observations, cloud water path in NWP only accurate on scales of ~ 102 km

Improved Global Estimates of Cloud IWP and LWP

The global distribution of CWP is not well known· CMIP5 models show factor of 4 differences· No consensus from observations

» CloudSat+CALIPSO good for IWC/IWP in upper troposphere

» Microwave good for LWP (oceans only)» Not much available in mixed phase regime» Remote sensing retrievals highly uncertain in

overlapping and precipitating conditions· Profiling method provides

» Consensus between active sensor and imager IWP» Consensus between imager and microwave LWP» Consistency over land and ocean» Largest uncertainty due to ML clouds?

Backups

CERES Ed4 MODIS IWP (April 2013)

CERES Ed4 MODIS LWP (April 2013)

Profile method minus CERES IWP

Profile method minus CERES LWP

Profiling method helps to overcome poorassumptions in standard methods (e.g. CERES Ed4)(i.e. accounts for mixed phase in overlapping clouds)

GOES IWC Validation with Aircraft DataNASA SEAC4RS Campaign

DC-8 2D-S probe

GOES-13

DC-8 Altitude DC-8 Altitude

DC-8 2D-S probe

GOES-13

Sept 13 2013 Sept 21 2013

GOES IWC tracks Aircraft in-situ during ascents and descents thru thick clouds

MODIS IWC and IWPComparison with CloudSat+CALIPSO

IWC (g/m3)

IWP (g/m2)

Assessed at altitudes above -20C level

Monthly Means stratified by MODIS CODIW

C (

g/m

3 )IW

P (

g/m

3 )

CODBIN

CALIPSO+CloudSat MODIS BIAS N

10-20 0.051 0.047 -8% 5083

20-40 0.087 0.083 -5% 4149

40-80 0.154 0.161 5% 2635

80-150

0.297 0.325 9% 730

150 0.568 0.480 -15% 965

ALL 0.141 0.143 1% 13562

CODBIN

CALIPSO+CloudSat

MODIS

BIAS N

10-20 191 169 -12% 5083

20-40 333 324 -3% 4149

40-80 668 767 15% 2635

80-150 1231 1507 22% 730

150 2549 2688 5% 965

ALL 551 583 6% 13562

GOES Icing (SLW) Validation

Icing Detection

Icing Intensity(Light vs MOG)

Icing PIREPS corroborate icing diagnosis capability from satellite

Jan-Mar 2013, CONUSLWP difference expressed as a fraction of the TWP

GOES Cloud Optical Depth

LWP

Diff

eren

ce (%

)

GOES Cloud Optical Depth

LWP

(gm

-2)

x – GOES embedded LWP - ARM MWR relationship

GOES LWP Validation

• GOES retrieval matches MWR observations w.r.t retrieved COD• Suggests NWP cloud phase partitioning is pretty good

Single-layer ice over water clouds

Contact: william.L.smith@nasa.gov