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
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
500m
~1.4 km
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
September 18, 2018
3rd IPWG Workshop

3. …but can it measure precipitation?

4. CloudSat’s First Image
~25 km
~1300 km
Click: CURRENT STATUS
September 18, 2018
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
September 18, 2018
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 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
September 18, 2018
3rd IPWG Workshop

7. First Attempt at a Retrieval
South Carolina
September 18, 2018
3rd IPWG Workshop

8. NEXRAD and CPR Rainfall CPR Reflectivity (09/07/2006 – 18:43 UTC)
Sanity Check A B
CloudSat
NEXRAD KCLX
Charleston, SC
09/07/2006
18:46:46 UTC
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
September 18, 2018
3rd IPWG Workshop

9. Tropical Storm Ernesto
Click: CloudSat
September 18, 2018
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
September 18, 2018
3rd IPWG Workshop

11. The A-Train Constellation
CloudSat
1:31
Aqua
1:30
OCO
1:15
Aura
1:38
PARASOL
1:33
CALIPSO
1:31:15
Formation flying provides opportunities for product inter-comparisons and the development of multi-sensor algorithms.
September 18, 2018
3rd IPWG Workshop

12. Comparison with AMSR-E
16 days of direct pixel match-ups during August 2006
September 18, 2018
3rd IPWG Workshop

13. Global Rainfall Statistics
September 18, 2018
3rd IPWG Workshop

14. Tropics
September 18, 2018
3rd IPWG Workshop

15. Higher Latitudes
September 18, 2018
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)
CloudSat
Zsfc (Black) PIA (green)
Rainrate (mm h-1)
18.42ºS
AMSR-E 37 GHz FOV
(approximate)
September 18, 2018
3rd IPWG Workshop

17. 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!
60ºS
90ºS
September 18, 2018
3rd IPWG Workshop

18. A First Look at Snowfall from CloudSat’s Perspective
September 18, 2018
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
September 18, 2018
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.
September 18, 2018
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
September 18, 2018
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.
September 18, 2018
3rd IPWG Workshop

23. Can Rainfall be Measured With A Cloud Radar?
Simulated Near-Surface Z
Cloud
Reflectivity (dBZ)
Drizzle
LWC (gm-3) Rainrate (mm h-1)
MDS = 18 dBZ
MDS = -28 dBZ PR
CPR
September 18, 2018
3rd IPWG Workshop

24. Global Light Rainfall Statistics
September 18, 2018
3rd IPWG Workshop

25. Science Applications
September 18, 2018
3rd IPWG Workshop

26. Aerosol Impacts? GOCART Sulfate Aerosol (Feb. 01, 2000)
September 18, 2018
3rd IPWG Workshop

27. Implementation (NRL) Kirtland AFB CIRA/DPC NRL Monterey Downlink
Raw Telemetry
Level-0
Level-1B
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
Converged? Y
Compute Forward
Model Sensitivity
Compute Sx and
Increment R