Transcript Document
A First Look at the CloudSat Precipitation Dataset Tristan L’Ecuyer S. Miller, C. Mitrescu, J. Haynes, C. Kummerow, and J. Turk The CloudSat Mission Primary Objective: To provide, from space, the first global survey of cloud profiles and cloud physical properties, with seasonal and geographical variations needed to evaluate the way clouds are parameterized in global models, thereby contributing to weather predictions, climate and the cloud-climate feedback problem. The Cloud Profiling Radar Nadir pointing, 94 GHz radar 3.3s pulse 500m vertical res. 1.4 km horizontal res. Sensitivity ~ -28 dBZ Dynamic Range: 80 dB Antenna Diameter: 1.85 m Mass: 250 kg Power: 322 W 500m ~1.4 km July 21, 2015 3rd IPWG Workshop 2 …but can it measure precipitation? CloudSat’s First Image ~25 km ~1300 km http://cloudsat.cira.colostate.edu/index.php Click: CURRENT STATUS July 21, 2015 3rd IPWG Workshop 4 Applications A few examples from other talks: Testing rainfall detection capabilities of PMW sensors Calibrating high temporal resolution global rainfall datasets Evaluating PMW rainfall estimates over land surface Comparisons with global rainfall statistics from other sensors (particularly at higher latitudes) Global statistics of frozen precipitation Other science applications: Evaluating physical assumptions in PMW algorithms (eg. beamfilling/vertical structure/freezing level) Assessing the significance of light rainfall and snowfall in the global energy and water cycles Aerosol indirect effects on precipitation July 21, 2015 3rd IPWG Workshop 5 Probabilistic Philosophy Algorithm: ∙ infer vertical profile of precipitating LWC/IWC and surface rainrate from the observed reflectivity profile and an integral constraint (eg. PIA or precipitation water path) Strengths: ∙ Probabilistic retrieval framework adopted – Allows formal inclusion of multiple forms of information including a priori knowledge and additional measurements and/or constraints – Explicitly accounts for uncertainties in all unknown parameters – Provides quantitative measures of uncertainties including relative contributions of all forms of assumed knowledge and measurement error ∙ CPR offers higher spatial resolution than other sensors that directly measure ∙ precipitation Sensitivity to continuum of clouds, drizzle, rainfall, and snowfall facilitates studying transition regions Weaknesses: ∙ Strong attenuation at 94 GHz can lead to retrieval instability ∙ Single-frequency method limits information regarding ∙ the dielectric properties of the melting layer and restricts DSD assumptions CPR is nadir-pointing providing only a 2D slice of the real world July 21, 2015 3rd IPWG Workshop 6 First Attempt at a Retrieval South Carolina July 21, 2015 3rd IPWG Workshop 7 Sanity Check CloudSat NEXRAD KCLX Charleston, SC 09/07/2006 18:46:46 UTC NEXRAD and CPR Rainfall Rainrate (mm h-1) Default M-P Tropical CloudSat CPR Reflectivity (09/07/2006 – 18:43 UTC) Distance (km) July 21, 2015 3rd IPWG Workshop 8 Tropical Storm Ernesto http://www.nrlmry.navy.mil/nexsat_pages/nexsat_home.html Click: CloudSat July 21, 2015 3rd IPWG Workshop 9 Applications A few examples from other talks: Testing rainfall detection capabilities of PMW sensors Calibrating high temporal resolution global rainfall datasets Evaluating PMW rainfall estimates over land surface Comparisons with global rainfall statistics from other sensors (particularly at higher latitudes) Global statistics of frozen precipitation Other science applications: Evaluating physical assumptions in PMW algorithms (eg. beamfilling/vertical structure/freezing level) Assessing the significance of light rainfall and snowfall in the global energy and water cycles Aerosol indirect effects on precipitation July 21, 2015 3rd IPWG Workshop 10 The A-Train Constellation CALIPSO 1:31:15 CloudSat 1:31 PARASOL 1:33 Aqua 1:30 OCO 1:15 Aura 1:38 Formation flying provides opportunities for product inter-comparisons and the development of multisensor algorithms. July 21, 2015 3rd IPWG Workshop 11 Comparison with AMSR-E 16 days of direct pixel match-ups during August 2006 July 21, 2015 3rd IPWG Workshop 12 Global Rainfall Statistics July 21, 2015 3rd IPWG Workshop 13 Tropics July 21, 2015 3rd IPWG Workshop 14 Higher Latitudes July 21, 2015 3rd IPWG Workshop 15 Pixel-Level Comparisons 157.7ºW 17.95ºS 157.8ºW 8.0 4.0 Rainrate (mm h-1) 6.0 Z (dBZ) 2.0 CloudSat Zsfc (Black) PIA (green) Rainrate (mm h-1) 0.0 18.42ºS July 21, 2015 AMSR-E 37 GHz FOV 3rd IPWG Workshop (approximate) 16 Frozen Precipitation 15ºN 15ºS CloudSat’s sensitivity makes it ideal for detecting snowfall. The region poleward of 60º is sampled ~4 times more frequently than an equal area region at the equator! July 21, 2015 60ºS 3rd IPWG Workshop 90ºS 17 A First Look at Snowfall from CloudSat’s Perspective July 21, 2015 3rd IPWG Workshop 18 Radar-Only Retrieval Very preliminary inversion of CPR reflectivities to infer snowfall rate Assumes exponential distribution of snow particles Similar probabilistic retrieval framework as rainfall retrieval First goal is detection and discrimination from light rainfall July 21, 2015 3rd IPWG Workshop 19 Final Thoughts Early results from CloudSat confirm its potential for detecting and quantifying light rainfall and snow toward answering the question: “How important are light rainfall and snow in the global hydrologic cycle and energy budget?” Two development streams: (a) PIA-based detection and column-mean rainrate, (b) full probabilistic vertical structure retrieval. Ultimately merged into a single product. First products from both algorithms for the first 6 months of operation may be available as early as years’ end. More comprehensive validation of products is underway. July 21, 2015 3rd IPWG Workshop 20 Outline Applications: highlight the need for these observations with particular emphasis on PMW and science apps. – use the global distribution as a specific example Briefly re-iterate CloudSat’s purpose and lead into “What about rain?” First image confirms what sensitivity studies showed years’ earlier – CloudSat CAN see rainfall Algorithm: VERY briefly point out measurements, retrieved parameters, and philosophy for getting from one to the other Example: point out that results are reasonable (only) not validation Ernesto: CloudSat even capable of seeing heavier precipitation Point out advantage of A-Train: co-located obs. from AMSR-E/CloudSat like a TRMM PR/TMI Show two types of comparisons: (a) Initial statistical comparisons over a 16 day period using a stripped-down PIA-based version of the algorithm, (b) more detailed pixel-level comparison (shows relative footprint sizes and demonstrates testing of detection, freezing level, vertical distribution, beamfilling, etc.). Conclude with snowfall – not many talks on this so far but CloudSat sampling + sensitivity makes it a very useful snowfall sensor. Results are VERY preliminary but demonstrate detection capability July 21, 2015 3rd IPWG Workshop 21 Significance CloudSat’s contribution to global precipitation observations may be to assess the importance of light rainfall and snow in the global energy budget and hydrologic cycle. July 21, 2015 3rd IPWG Workshop 22 Can Rainfall be Measured With A Cloud Radar? Simulated Near-Surface Z Drizzle Cloud Reflectivity (dBZ) Cloud MDS = 18 dBZ PR MDS = -28 dBZ 1 6 10 15 0.1 LWC (gm-3) July 21, 2015 1 CPR 10 100 Rainrate (mm h-1) 3rd IPWG Workshop 23 Global Light Rainfall Statistics July 21, 2015 3rd IPWG Workshop 24 Science Applications July 21, 2015 3rd IPWG Workshop 25 Aerosol Impacts? GOCART Sulfate Aerosol (Feb. 01, 2000) July 21, 2015 3rd IPWG Workshop 26 Implementation (NRL) Downlink Raw Telemetry Level-0 Kirtland AFB Level-1B CIRA/DPC NRL Monterey New CPR L1B File Linux Processing Cluster MODIS, AMSR-E NOGAPS T/Q Gas Extinction Particle Scattering CRON Sigma_0 Database IGBP Database UPDATE σ0 Database LOOP OVER ALL SHOTS Driver Defines Input Metadata Read Ancillary Databases Read CPR Data Cloud Mask/Class Z>Zthresh? Y DO RETRIEVAL! Interpolate T/Q Profile Determine Gas Extinction Define Constraints (e.g., PIA, LWP) N Calculate Error Stats & Store All Data First Guess (Z/R) RETRIEVAL LOOP Optimal Estimation Compute Forward Model Sensitivity Compute Sx and Increment R N Converged? Y