Extending CloudSat Observations to Regional Domains Via Type- Dependent Statistics

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1 CIRA Extedig CloudSat Observatios to Regioal Domais Via Type- Depedet Statistics Steve D. Miller Cooperative Istitute for Research i the Atmosphere (CIRA) Colorado State Uiversity Fort Collis, CO With Rich Bakert ad Cristia Mitrescu Naval Research Laboratory Moterey, CA 5 th Aual CORP Sciece Symposium Orego State Uiversity August 13, 2008

2 Outlie CloudSat missio ad sesor overview Motivatio for study, ad hypothesis Cocept of type-depedet statistics Methodology Prelimiary results Applicatio to swath data Summary

3 CloudSat Missio Overview Provide, from space, the first global survey of cloud profiles (top/base/multi-layer) layer) ad cloud physical properties (water, ice, precipitatio) eeded to evaluate ad improve the way clouds, precipitatio ad eergy are represeted i global models used for weather forecasts ad climate predictio. Lauched 28 April 2006, CloudSat is a NASA missio i formatio flight withi the A-Trai satellite costellatio (1330 LTAN). Carries a W-bad (94 GHz; 3 mm wavelegth) Cloud Profilig Radar (CPR) dbz sesitivity, detects most cloud & light precipitatio structures. 240 m vertical, 1.7x1.3 km horizotal resolutio No-scaig; provides 2-D curtai slices through the cloudy atmosphere. MODIS AMSR-E Covective Rai Rates Heights (mm/hr) b a 5mm/hr 8km kft 24 Tropical Storm Alberto: 0735 UTC Height (km) 0 a 0 Distace (km) 600 b

4 Why Exted the Data? There are several ways the operatioal commuity ca potetially use CloudSat iformatio. Examples iclude: - Geeral Aviatio: : cloud ceiligs, multi-layered layered cloud systems, cloud cotet for potetial icig assessmets - DoD: : aircraft lauch ad recovery operatios, surveillace, etc. - U.S. Coast Guard: : clear lie-of of-sight to surface below upper cloud deck. CloudSat curretly provides a first-look ear real-time dataset (at ~3-6 6 hr latecy) tailored to these commuities, but curtai slices remai of limited use. Fidig a way to exted the vertical detail from the CPR to clouds i the regio would icrease relevace to operatioal users, ad possibly also to NWP model aalysis (data assimilatio) if ucertaities ca be quatified. q

5 Hypothesis A local slice provides potetially useful iformatio about the surroudig cloud field. The dyamics associated with cloud formatio led themselves to characteristic vertical distributios of cloud liquid water mass which may be captured c statistically ad applied regioally to cloud of similar type. Clouds that form as a result of covective mechaisms ted to have base heights (ceiligs) that are characteristic of the regioal-scale eviromet (i.e., liftig/covective codesatio levels). Withi the cofies of quatifiable ucertaities, the curtai observatios o of CloudSat may be combied with temporally-matched matched covetioal passive sesor swath data (2-D D imager data; e.g., MODIS, also o the A-Trai, A GOES, MTSAT, MeteoSat). The result is a 3-D 3 D structure for the topmost cloud layer (ad i certai situatios, two-layer cloud profiles detectable by passive techiques). This research seeks to determie to what extet these assumptios s are valid for various cloud classificatios.

6 Water Cotet Vertical Structure 2.3 e+06 samples Top 1 Height (Normalized) Image Courtesy of UCAR 0 Water Cotet (Normalized) 1 Base 0 Usig CloudSat Level 2 Cloud Water Cotet (2B-CWC; R03) ad Cloud Sceario Classificatio (2B-CLDCLASS) products, we computed liquid/ice water cotet (g/m 3 ) profiles for each cloud type. Results were ormalized to maximum water cotet foud i profile ad betwee cloud base (0) ad top (1). Iitial statistics were compiled over Nov 2006 through Ja 2007 (125 orbits). A full missio reaalysis is uderway.

7 Results: Vertical Structure Vertical structure cosistet with expected LWC profiles of covective/stratiform types - Cumulus: growth of droplets i ascedig air - Cirrus: growth of IWC i fall streaks prior to sublimatio Potetial cotamiatio i lower extremities of imbostratus, deep covectio, ad some cumulus clouds due to precipitatio (although L2 product attempts to scree out).

8 Cloud Base/Top Heights Cirrus Top Cumulus Top Orbital sub-segmet example Base Base Used Cloud Sceario Classificatio to compute departures i base/top height for cotiguous cloud layers of a give cloud type, traced from a referece poit. Potetial cotamiatio i lower extremities of imbostratus, deep covectio, ad some cumulus clouds due to precipitatio. Surface clutter problem may peg base heights of the stratus/stratocumulus layers.

9 2 Year Composite of Traces (> 3 E+08 cloud layer samples).

10 Global Statistics Cloud Base Top Distace (km)

11 Zoal Statistics Cumulus Stratocumulus Cirrus Deep Nimbostratus Altocumulus Altostratus Covectio Tops NHEM3 NHEM2 NHEM1 TROP SHEM1 SHEM2 SHEM3 Distace (km) Bases Distace (km)

12 Applicatio to Swath Data CloudSat Groud Track Cloud Type Top Height Water Path Give a CloudSat pass over a 2-D swath of cloud data (e.g., GOES i the example above), how ca we use the type-depedet statistics o base/top height correlatio ad vertical structure of water cotet to create a pseudo 3-D approximatio?

13 Coceptual Approach Type: Cumulus Stratocumulus Cirrus OVERLAP (thi over thick) Top Height: Retrieved Top Liquid Height Water (Upper): Path (LWP): Retrieved Retrieved CIRRUS STRATOCUMULUS CUMULUS Note: ay differeces betwee CloudSat ad passive imager cloud classificatio algorithms must be recociled prior to applyig this method. Base Height (Upper): As demostrated previously Base Height: B b N 2 = 1 δ ( ) = d N 1 δ ( d N Top Height t = 1 2 (Lower): = 1 δ ( ) = d T N 1 2 Distribute LWP = 1 δ ( d ) I Betwee Top Base Ad Base Height (Lower): As demostrated previously Accordig to: LWP: Partitio total colum based o earby sigle-layer data, if available. 2 )

14 Summary ad Next Steps A techique for providig vertically-resolved cloud iformatio for the top-most layer(s) of passive imager swath data, based o cloud type depedet statistics from CloudSat, is ow i developmet. Early results show type-depedet structures i stadard deviatio that are cosistet with our basic physical uderstadig of cloud dyamics/morphology. Issues with precipitatio sesitivity ad surface clutter stad as caveats. Some cloud types (esp. altostratus, cirrus, ad deep covectio cloud tops) show strog zoal depedecies i stadard deviatio behavior. Need to re-do the aalysis for o-cotiguous cloud layers, cosider regioal ad seasoal depedecies, possible breakdow of assumptios i frotal zoes, etc. Performace of techique ca be assessed usig alog-groudtrack CloudSat data. As the CloudSat dataset cotiues to grow, statistics for the stratified datasets will become icreasigly robust.

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