dod center for geosciences/atmospheric research at colorado state university corp symposium august...

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, 2010 1

Statistical Extrapolation of Vertically Resolved Cloud Statistical Extrapolation of Vertically Resolved Cloud Information from CloudSat/CALIPSO Observations to Information from CloudSat/CALIPSO Observations to

Regional SwathsRegional Swaths

John Forsythe, Steven D. Miller, Phil Partain and Tom Vonder HaarCooperative Institute for Research in the Atmosphere (CIRA)

Colorado State UniversityFort Collins, CO

with

Rich Bankert and Jeff HawkinsNaval Research Laboratory

Monterey, CA

CIRA


Research QuestionsResearch Questions• To what extent do limited (but detailed) observations of To what extent do limited (but detailed) observations of

cloud vertical structure (water content) and geometric cloud vertical structure (water content) and geometric boundaries (top/base) relate to surrounding clouds?boundaries (top/base) relate to surrounding clouds?

• Can this concept be applied to ‘vertical-slice’ Can this concept be applied to ‘vertical-slice’ observations from active sensors (radar/lidar) to observations from active sensors (radar/lidar) to augment the information provided from passive sensor 2-augment the information provided from passive sensor 2-D imagery? Can we use sparse cloud observations to D imagery? Can we use sparse cloud observations to create local 3-D cloud scenes?create local 3-D cloud scenes?

CIRAHypothesisHypothesis

• Certain kinds of clouds form under characteristic Certain kinds of clouds form under characteristic environmental conditions of temperature, moisture, environmental conditions of temperature, moisture, stability, etc., that occur over regional/synoptic scales.stability, etc., that occur over regional/synoptic scales.

• Local observations of clouds occurring within that Local observations of clouds occurring within that regional/synoptic-scale environment may provide useful regional/synoptic-scale environment may provide useful information about the surrounding cloud field.information about the surrounding cloud field.

CloudSat CloudSat (cloud (cloud radar) and radar) and CALIPSOCALIPSO (cloud (cloud lidar) have been lidar) have been providing vertical providing vertical cloud profiles at ~ cloud profiles at ~ 1.1 km horizontal 1.1 km horizontal and 240 m vertical and 240 m vertical resolution since resolution since 2006. 2006.


RelevanceRelevance• Cloud base estimation in data void regions• UAV operations (visibility / icing)• Model evaluation (e.g. scene contributions to the

NCAR Model Evaluation Tool)• Representativeness of data near taken

CloudSat/CALIPSO track (like in a field experiment)

CIRA


Opaque Ice Opaque Ice cloudcloud

Water cloudWater cloud

Target is under a Target is under a Cirrus cloud, use Cirrus cloud, use these observationsthese observations

30000’

3000’

10000’

Current Dilemma: Given sparse ceiling observations, how to determine the ceiling over a data-void region?

xx

xx

xx

1.1. Average the observations?Average the observations?

2.2. Be cautious and use the lowest value?Be cautious and use the lowest value?

3.3. Use the nearest observation?Use the nearest observation?

30000’

xx

100 km (notional)100 km (notional)


• GSIP (GOES Surface and Insolation Product) Cloud Classification (applied to MODIS channels used here):

A 5-channel (0.65, 3.9, 6.7, 11, 12-13 µm) physically-based pixel-level cloud classification with heritage from AVHRR and GOES (Pavolonis and Heidinger, 2005). These are the baseline channels for most contemporary geostationary meteorological satellites.

Additional published spectral tests using MODIS 1.38 and1.61 µm channels added.

Visible / Infrared Cloud Typing to Visible / Infrared Cloud Typing to Provide Spatial ContextProvide Spatial Context CIRA


CIRACloudSat Track CloudSat Track

5 channel MODIS GSIP (“GOES 5 channel MODIS GSIP (“GOES Imager-like”) Imager-like”)

5 channel MODIS GSIP + 1.38 µm 5 channel MODIS GSIP + 1.38 µm and synthesized 1.61 and synthesized 1.61 µm µm

MODIS-derived cloud type provides spatial context. The MODIS-derived cloud type provides spatial context. The GSIP classifier (Pavolonis and Heidinger, 2005)) shown GSIP classifier (Pavolonis and Heidinger, 2005)) shown

for Typhoon Choi-wan case, September 15, 2009.for Typhoon Choi-wan case, September 15, 2009.


Typhoon Choi-wan MODIS Visible (view from east) with CloudSat / CALIPSO curtain overlaid in colors of MODIS cloud type.

10 km

10 km

0 km

0 km

September 15, 2009September 15, 2009

Question: What is the cloud vertical structure away from the curtain?


Brief Review of Method to Brief Review of Method to Gather Cloud Type Spatial Gather Cloud Type Spatial

StatisticsStatistics


1) Form Geometric “Traces”1) Form Geometric “Traces”Cirrus

Top

Base

Cumulus

Top

Base

• Used Cloud Scenario Classification to compute departures in base/top height for contiguous cloud layers of a given cloud type, traced from a reference point.

Orbital sub-segment example

CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, 201010

2) Composite these Traces2) Composite these Traces

> 3 E+08 cloud layer samples

CIRA


3) Compute Statistics on the 3) Compute Statistics on the Composites of TracesComposites of Traces

Cloud TopCloud Base

Distance (km)

CIRA

Statistics also collected by Statistics also collected by region, season and surface typeregion, season and surface type


The Application ConceptThe Application Concept

CIRRUSCIRRUS

STRATOCUMULUSSTRATOCUMULUS

CUMULUS

Cloud Type & Top Height: Retrieved

Liquid Water Path (LWP): Retrieved

1) Estimate the cloud base height:

2) Distribute LWP between cloud Top and Base according to cloud type:

N

n nn

N

n nn

n

d

d

z

Z

12

12

)(1

CIRA


Taking the Next Steps Towards 3-D Taking the Next Steps Towards 3-D Cloud Fields – Behavior of the GSIP Cloud Fields – Behavior of the GSIP

Classifier Classifier


Good news, supposed to be multilayer

Deep clouds

From MODIS cloud mask

Where Modis Cloud Mask says cloudy, a cloud layer is almost always present (i.e. Number of Layers > 0)

# of cloud layers (GEOPROF_LIDAR) by GSIP MODIS class. January 20, 2009. Daylight granules equatorward of 50°


5 channel classification 5 channel classification

# of cloud layers (GEOPROF_LIDAR) by GSIP MODIS class. January 20, 2009. Daylight granules equatorward of 50°

5 channel + 1.38 and 1.61 5 channel + 1.38 and 1.61 µm testsµm tests

More Overlap GeneratedMore Overlap Generated


Aggregated cloud thickness (m) by GSIP MODIS class. January 20, 2009. Daylight granules equatorward of 50°

Missed clouds are thin

Almost always > 5 km thick

“true” cirrus

Lower cloud masked

Very similar to Glaciated class

Perhaps the most variable class

clearclear Partly CloudyPartly Cloudy LiquidLiquid SupercooledSupercooled

Opaque IceOpaque Ice CirrusCirrus OverlapOverlap

5-channel classification 5-channel classification


SummarySummary

• A technique for providing vertically-resolved cloud information for the top-most layer(s) of passive imager swath data, based on cloud type dependent statistics from CloudSat/CALIPSO, has shown some skill.

• Early results show skill in prediction of cloud ceilings when applying the correlative approach and constraining cloud type.

• As the active datasets continue to grow, statistics for the stratified datasets will become increasingly robust.

CIRA

dod center for geosciences/atmospheric research at colorado state university corp symposium august...

Documents

cloud information

sparse cloud observations

cirrus cloud

vertical cloud profiles

surrounding cloud field

d cloud scenes

local observations of

cloudsatcalipso observations