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A First Look at the CloudSat Precipitation Dataset

A First Look at the CloudSat Precipitation Dataset. Tristan L’Ecuyer S. Miller, C. Mitrescu, J. Haynes, C. Kummerow, and J. Turk. The Cloud Sat Mission. 500m. ~1.4 km.

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A First Look at the CloudSat Precipitation Dataset

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  1. A First Look at the CloudSat Precipitation Dataset Tristan L’Ecuyer S. Miller, C. Mitrescu, J. Haynes, C. Kummerow, and J. Turk

  2. The CloudSat Mission 500m ~1.4 km 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. • Nadir pointing, 94 GHz radar • 3.3s 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 The Cloud Profiling Radar 3rd IPWG Workshop

  3. …but can it measure precipitation?

  4. CloudSat’s First Image ~25 km ~1300 km http://cloudsat.cira.colostate.edu/index.php Click: CURRENT STATUS 3rd IPWG Workshop

  5. 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 3rd IPWG Workshop

  6. 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 ofinformation 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 3rd IPWG Workshop

  7. First Attempt at a Retrieval South Carolina 3rd IPWG Workshop

  8. Sanity Check CloudSat NEXRAD KCLX Charleston, SC 09/07/2006 18:46:46 UTC B A NEXRAD and CPR Rainfall Default M-P Tropical CloudSat Rainrate (mm h-1) CPR Reflectivity (09/07/2006 – 18:43 UTC) A B Distance (km) A B 3rd IPWG Workshop

  9. Tropical Storm Ernesto http://www.nrlmry.navy.mil/nexsat_pages/nexsat_home.html Click: CloudSat 3rd IPWG Workshop

  10. 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 3rd IPWG Workshop

  11. The A-Train Constellation CALIPSO CloudSat The A-Train 1:31:15 1:31 Aqua 1:30 PARASOL 1:33 OCO 1:15 Aura 1:38 Formation flying provides opportunities for product inter-comparisons and the development of multi-sensor algorithms. 3rd IPWG Workshop

  12. Comparison with AMSR-E 16 days of direct pixel match-ups during August 2006 3rd IPWG Workshop

  13. Global Rainfall Statistics 3rd IPWG Workshop

  14. Tropics 3rd IPWG Workshop

  15. Higher Latitudes 3rd IPWG Workshop

  16. Pixel-Level Comparisons 157.7ºW 157.8ºW 17.95ºS 8.0 6.0 4.0 2.0 0.0 Rainrate (mm h-1) Z (dBZ) Zsfc (Black) PIA (green) CloudSat Rainrate (mm h-1) 18.42ºS AMSR-E 37 GHz FOV (approximate) 3rd IPWG Workshop

  17. Frozen Precipitation 15ºN • 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! 15ºS 60ºS 90ºS 3rd IPWG Workshop

  18. A First Look at Snowfall fromCloudSat’s Perspective 3rd IPWG Workshop

  19. 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 3rd IPWG Workshop

  20. 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. 3rd IPWG Workshop

  21. 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 3rd IPWG Workshop

  22. 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. 3rd IPWG Workshop

  23. Can Rainfall be MeasuredWith A Cloud Radar? Simulated Near-Surface Z Cloud Drizzle Cloud MDS = 18 dBZ Reflectivity (dBZ) PR CPR MDS = -28 dBZ 1 6 10 15 0.1 1 10 100 LWC (gm-3) Rainrate (mm h-1) 3rd IPWG Workshop

  24. Global Light Rainfall Statistics 3rd IPWG Workshop

  25. Science Applications 3rd IPWG Workshop

  26. Aerosol Impacts? GOCART Sulfate Aerosol (Feb. 01, 2000) 3rd IPWG Workshop

  27. Implementation (NRL) Level-0 Level-1B Downlink Raw Telemetry Kirtland AFB CIRA/DPC NRL Monterey New CPR L1B File MODIS, AMSR-E CRON NOGAPS T/Q Sigma_0 Database UPDATE σ0 Database Gas Extinction IGBP Database Particle Scattering Linux Processing Cluster LOOP OVER ALL SHOTS N Z>Zthresh? Driver Defines Input Metadata Read Ancillary Databases Read CPR Data Cloud Mask/Class Y DO RETRIEVAL! Calculate Error Stats & Store All Data Interpolate T/Q Profile Determine Gas Extinction Define Constraints (e.g., PIA, LWP) First Guess (Z/R) RETRIEVAL LOOP N Optimal Estimation Y Converged? Compute Forward Model Sensitivity Compute Sx and Increment R

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