Graeme Stephens • Colorado State University Satellite observations and the MJO Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Relevant Comments Ka band (8mm) ARM MMCR W-band (3 mm) Cloudsat CPR 1. Millimeter-wave radars (MWR), traditionally used to study ‘large scale non-precipitating clouds’, in fact have much to offer in study of moist, precipitating convection 2. Observations of MWRs are a step beyond the artificial and all too common state where we deal with clouds and precipitation (both in models and obs) as separate chain of events of the water cycle. 3. In embracing this broader framework, we are perhaps beginning to uncover new ideas about cloud and preciptation structures of moist convection with implications to the diabatic heating. Graeme Stephens • Colorado State University

Surface MWR Observations Manus Island Ka-band radar (ARM MMCR) - Clothiaux et al (2000) combined product - 10 s / 45 m resolution Surface radiation / meteorology Six year (1999-2005) record of daily mean OLR, interpolated from AVHRR, 2.5 x 2.5 degree grid are used to identify the MJO events (34 events) > 2.1 million radar profiles were composited Graeme Stephens • Colorado State University

MWR Classification of convection Two-parameter classification system used to identify precipitating convective regimes of ‘self-similar’ vertical cloud structures Stephens and Wood (2007). ETH ≡ height of cloud-top echo PTH ≡ depth of penetration of the 10 dBZ echo ETH PTH 10 dBZ echo P(Column max Ze < x) No precip Precip x Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Classification Deep convection Shallow convection but from Ground also deep cnvection High cloud over low precipitating convection, ….- or - deep cloud with shallow precip (stratiform) MMCR CloudSat X1 - Clouds that did not meet precip criteria Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University TRMM equivalent Shipborne MMCR Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University MJO active MJO transition The cloud structures of each respective storm regime associated with different synoptic forcings are ‘identical’ - what differs is the relative frequency of occurrence of each class that defines the convective envelope JASMINE Monsoon Crystal FACE Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Class A and B are very common at Manus and contribute ~ 1/3 of rainfall. Two examples of storm class A/B Overall, ~ 40% of the observed precip during the MJO cycle derives from multi-layered systems.… implications for depth of atmosphere heating in this mode? Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University CloudSat and A-train results Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Some relevant details Nadir pointing, 94 GHz radar 3.3s pulse  480m vertical res, over- sampled at ~240m 1.4 km horizontal res. Calibration better than 2 dBZ Sensitivity ~ -28 dBZ (-31 dBZ) Dynamic Range: 80 dB 1. Formation with the A-Train Two main components of design - CPR and formation flying 500m ~1.4 km demonstrated post launch Hardware continues to operate with nominal performance and the mission is funded through 2010 Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University 2006 Dec/Jan MJO 17 16 15 1400km 30 km Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Level 2 products: 2B geoprof 2B geoprof-lidar 2B-cloudclass 2B-tau 2B-CWC 2B-flxhr Ancillary products - MODIS and ECMWF met data Auxiliary products Aux-Mod06 Aux-ECMWF Aux-SSF Aux-TRMM Aux-AMSRE Cloud mask by bin, reflectivity, MODIS cloud mask Cloud lidar fraction matched to CloudSat Cloud type classification radar+ Optical depth using MODIS +geoprof Cloud water, ice -radar+ only, radar+2B-tau Fluxes & heating rate, products above + ECMWF+ Properties matched to CloudSat- partially done liquid + solid precipitation, incidence and amount is to be added Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Joint Lidar-Radar product 532 Total Attenuated Backscatter CPR Reflectivity Jay Mace Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University CloudSat is the most sensitive detector of precipitation currently in orbit today (sensitive down to O~1mm/day) Using the surface reflectivity it is possible to determine the path attenuation and with information of reflectivity near the surface, we can identify the likelihood of precipitation at the surface thus identify where rain is occurring and its intensity - ‘easy over ocean’. We can also determine precipitation intensity - this is more a more ‘controversial’ endeavor Precipitation (enhanced products) - incidence and amount Haynes et al. 2007 Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Frequency of deep clouds> 6km Frequency of multi-layering This is consistent with the ARM TWP obs - that the vertical structure of tropical cloud systems (including precipitating systems) are frequently (~ 40%) multi-layered Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University JJA incidence latitude rate mm/hr AMSR-E CloudSat accumulation Incidence & accumulation similar in tropics but vastly different in mid-higher latitudes (accumulation results & subsequent val are works in progress) Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University CP-ETH histograms This mode is only partially the deep stratiform mode of convective precipitation - it also represents the multi-layered modes of precipitation MMF Missing deep ice/ precipitation mode NICAM global CSRM JJA, 30N/S CloudSat Cloud echo top height (ETH) against the precipitation ETH => ETH of -30 dBZ versus ETH of 10 dBZ Luo et al., 2007 Graeme Stephens • Colorado State University

Tropical west Pacific histograms: 2006/12 – 2007/02 CloudSat MetUM N320L50 Reasonable ice microphysics? Evaporating ice – or T dependence in convective cloud ice fraction? Lack of mid-level cloud Lack of non-drizzling low cloud Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Accumulated oceanic precipitation 20N-S 40N-S CloudSat CloudSat Low mid high Low mid high Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Summary/Comments MWRs, and CloudSat specifically, are powerful new tools for studying the properties of tropical convection. Diagnostic tools for analyzing these new observations are alos progressing. These observations and related tools are steps in building some understanding of clouds and their relationship to tropical precipitation. These observations, related analysis tools and the understanding produced will prove invaluable as modeling of the MJO inevitably moves the cloud-scale modeling through CSRM/CRMs Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Classification Deep convection Shallow convection High cloud over low precipitating convection, ….- or - deep cloud with shallow precip CTH MMCR – CTH GMS + Deep convection Graeme Stephens • Colorado State University

latitude The dreary extra-tropics cloud fraction (lidar/radar) JJA precip/cloud fraction Grey range ~ uncertainty range Precip to surface probable Precip to surface certain Results over oceans Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University CloudSat 30S-30N Stephens and Wood, 2007 Graeme Stephens • Colorado State University

Conclusions Future Work and Work in Progress Quantification of the latent heating due to these precipitating systems, better understanding of microphysics and mesoscale dynamics of multi-layer systems Representation of these cloud layers in data sets from other sensors CloudSat will essentially eliminate the ambiguity associated with attenuation and allow development of near-global climatologies of multi-layer cloud Evaluation of cloud resolving model ability to simulate these observed vertical cloud structures Graeme Stephens • Colorado State University

Graeme Stephens • Colorado State University Identify MJO ‘events’ at Manus using a minimum OLR-based criteria Analyze all cloud radar profiles, surface meteorology, and radiosonde data for 14 days on either side of this event Group the profiles by their time relative to the event, d ≡ 0 Lag -2 Lag -1 Lag 0 Lag +1 Lag +2 -14 d -7 d -3 d +3 d +7 d +14 d d = 0 Group observed cloud profiles according to similar characteristics (cloud height, thickness, layer boundaries, surface precipitation, …) Use this technique to identify the dominant types of cloud systems present in various phases of the MJO cycle Graeme Stephens • Colorado State University