Precipitation Data from Field Projects

Brenda Dolan

ECSPrecip Seminar Series #2

20 September 2022

Why are field projects important for observations of precipitation?

• Intensely observe interesting / critical / missing / remote locations
• Ocean!
• Deploy instruments to meet specific scientific goals
• Challenges with field projects
• Short time periods
• Discontinuous in space, time
• Instrument issues / missing data
• Different operational characteristics
• Variety of data formats, PIs, data homes

• Deployable precipitation radars (X, C, S-band)
• Cloud and mm-wavelength radars (W, Ka-, Ku-)
• Shipborne radars
• Airborne radars
• Vertically pointing
• Continuous wave (MRR)

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Precipitation Measurements:

Radar

https://metek.de/product/mrr-2/

S-POL

NPOL

NPOL

SEA-POL

XSCAR

SEA-POL

APR-3

D3R

SMART-R

DOW

NOXP

Measurements:

• Reflectivity, differential reflectivity (Z_dr), specific differential phase (K_dp), Doppler velocity

MRR2

• Pros:
• Rainfall (or snowfall) over large area
• Large spatial coverage area (1000 km²)
• High spatial resolution (~ 1 km)
• Relatively high temporal resolution
• Microphysics in the cloud
• DSD or HID

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• Cons:
• Products are a derived quantities
• Different rainfall retrievals between projects
• Data quality / calibration
• Blockage, attenuation, precipitation phase
• Different scanning strategies
• “Heartbeat”
• 6, 10, 12, 15, 20 minutes
• PPI
• Number of elevation angles, timing
• Good for rainmapping, widespread coverage
• RHI
• High-resolution vertical slices
• Best for process and microphysics

PRECIP 2022 Yonaguni Japan SEA-POL

Precipitation Measurements: Radar

NPOL OLYMPEX 2015

• Calculation of R from measurements
• Z-R, R-K_dp, R-Z_dr, A_h-R
• Blended
• Some relationships depend on location
• Derived from other instruments (disdrometers)
• Uncertainty, Errors
• Done at lowest level or fixed (gridded) height?

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Precipitation Measurements: Radar

Z = AR^b

Pluvios, Parsivals (APUs), 2D videos, gauges, tipping buckets

Precipitation Measurements: Disdrometers and In Situ

APU

2DVD

HyVIS

Particle Probes

UND Citation

Measurements:

• Particle size distributions, fall speeds, number concentrations, axis ratios

Balloon and aircraft probes

• Pros:
• Size distributions
• Integrated product to get precipitation amounts
• High temporal resolution (30 sec – 3 mins)
• Cons:
• Quality control
• Splashing, phase
• Wind influences
• Measurement parameters of instrument
• Large/ small particles
• Point measurements
• Variability of precipitation

Precipitation Measurements: Disdrometers and In Situ

• Optical disdrometers on ships sailing around the world
• Mounted to freely turn with the winds
• Snow and rain (precipitation phase)
• Include a bunch of environmental characteristics as well (SST, T, wind velocity)

Precipitation Measurements: OCEANRAIN

Klepp et al. 2017

Data Access

• DOE
• https://adc.arm.gov/discovery/
• Very nice visualization, organized by data type, field project, location, measurement….
• Can pick up data from globus, wget file list, thredds
• Different versions of the data (Raw, QC, moments, products, PI products)
• Downloads a README, dataquality file, version controlled, DOI provided
• NASA
• https://ghrc.nsstc.nasa.gov/home/field-campaigns
• Organized by field project
• Qced data available
• Readme files
• DOIs provided
• https data access
• NSF / NCAR
• https://www.eol.ucar.edu/all-field-programs
• OpenDAP or FTP
• OCEANRAIN
• https://oceanrain.cen.uni-hamburg.de/index.php?id=2752
• Qced data from 2011-2020
• Matched with some environmental information like SST, T,

Data Specifics

• Radar
• Typically 2 levels of data
• “Level 2”: Corrected radial moment data (radar gates, azimuth, elevation, range)
• Cf Radial files (netCDF compliant)
• Tools like LROSE (http://lrose.net) or PyART (https://arm-doe.github.io/pyart/) to read and process the data
• Z, Zdr, PhiDP / Kdp, rhoHV, VR, SW, NCP or SQI
• Sometimes this includes rain rate or versions of hydrometeor identification
• “Level 3”: Gridded data products
• netCDF gridded files
• Includes “best estimate” products of Rain rate, hydrometeor identification, sometimes calculated DSD
• Disdrometer
• Text or netCDF files
• Bin data – number of drops per bin size, fall speed
• Integrated data – rain rate, liquid water content, total number of drops, reflectivity

Summary

• Field campaigns are a critical source of precipitation observations
• Detailed and targeted measurements in challenging and data-sparse locations
• Provide high resolution spatial and temporal data related to amounts, types, particles, size distributions
• Field campaign data can be challenging due to complexities of the instruments, locations, and discontinuities
• Data often require specialized and careful QC
• Rainfall from radar is a retrieved quantity – how was it calculated? What are the uncertainties?
• Different scanning strategies provide different opportunities – what level are the data reported at?

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Thank you!