Overview
This document provides an overview of the precipitation products available from the CloudSat 94 GHz nadir-looking cloud profiling radar (CPR). This precipitation dataset is particularly valuable for high-latitude and cold-region observations of precipitation, which have previously been limited by infrequent sampling and sparse observational networks. Rain data is not available over land (snowfall is), but precipitation occurrence data is available everywhere.
Provider’s contact information
The CloudSat CPR was developed jointly by NASA/JPL and the Canadian Space Agency (CSA). The Standard Data Products are distributed by the CloudSat Data Processing Center, located at the Cooperative Institute for Research in the Atmosphere at Colorado State University in Fort Collins.
Dataset Point of Contact:
NOAA CDR Program
DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce
+1 (828) 271-4800
gpcp_contacts@noaa.gov
Licensing and citation
Cite as:
Haynes, J. M.,T. S. L'Ecuyer, G. L . Stephens, S. D. Miller, C. Mitrescu, N. B. Wood, and S. Tanelli, 2009: Rainfall retrieval over the ocean with spaceborne W-band radar. J. Geophys. Res., 114, D00A22, https://doi.org/10.1029/2008JD009973.
Variable name and units
| Name | Description | Units | Frequency | Collection of data |
|---|---|---|---|---|
| 2C-PRECIP-COLUMN, precip_flag | Precipitation occurrence | Flag for phase (rain, snow, mixed) and likelihood of precipitation (certain, probable possible) | Available everywhere | |
| 2C-RAIN-PROFILE, rain_rate | Rain rate for “certain” rain or mixed phase precipitation | mm/hr | Only values available are over ocean | |
| 2C-SNOW-PROFILE, snowfall_rate_sfc | Surface snowfall rate for mixed (melted fraction < 0.1) or snowy phase. | mm (liquid water)/hr |
Spatial coverage and resolution
Satellite observations lie between 82°N-S. The satellite has a 1.7 km × 1.3 km footprint. Global coverage is achieved in 16 days for cells of 100 km side.
Temporal coverage and resolution
Local overpass times are nominally at 1:31 A.M. and 1:31 P.M. This may have changed in recent years as the satellite orbit changed in 2018, when CloudSat exited the A-train.
Information about observations (number, homogeneity)
More observations available at higher latitudes. For example, see figure 1(a) in Palerme et al. (2014). Monthly mean data are created only based on a limited number of samples since the ground track repeats every 16 days.
Methodology
The Cloud Profiling Radar (CPR) is a 94 GHz nadir-looking radar which measures the backscatter from clouds and hydrometeors as a function of distance from the radar. The main objective of this instrument is to provide vertical cross sections of non-precipitating cloud liquid and ice water content and particle size, but it also resolves precipitation systems. This relatively low frequency radar achieves excellent cloud detection sensitivity.
The algorithm determines the presence of surface precipitation, and quantifies the intensity, based on the CPR observations. The algorithm makes use of the radar reflectivity near the surface of the earth and an estimate of path integrated attenuation (PIA) determined from the surface reflection characteristics to determine precipitation occurrence (over all surface types) and intensity (over water surfaces).
Auxiliary data: The current state of the atmosphere, including the atmospheric temperature, pressure, specific humidity, surface wind speed, and sea surface temperature are assessed from the ECMWF forecast model matched to the CPR track. The presence of sea ice (and inland lake ice) is determined from the daily sea-ice product from SSM/I (Special Sensor Microwave Imager/Sounder) produced by the National Snow and Ice Data Center.
Information about the technical and scientific quality
Note that the radar signal used in the CloudSat algorithm can be saturated in intense rain, so a tendency to underestimate precipitation has been noted by users of the product. It is the most sensitive sensor to low intensity rain and snow events.
Normal satellite performance only between 2006-2011, after which a malfunction has caused offline periods and reduced observations. It should be noted that daytime only refers to times when the satellite has line-of-sight to the sun, so surface nighttime measurements may still be available.
Limitations and strengths for application in North Canada
Good sampling over the high latitudes, unlike many other sensors. This makes it suited to North Canada applications.
Only describes precipitation occurrence (including snowfall rate) over land.
References to documents describing the methodology and/or the dataset
Haynes, J. M.,T. S. L'Ecuyer, G. L . Stephens, S. D. Miller, C. Mitrescu, N. B. Wood, and S. Tanelli, 2009: Rainfall retrieval over the ocean with spaceborne W-band radar. J. Geophys. Res., 114, D00A22, https://doi.org/10.1029/2008JD009973.
Overview of the 2C-PRECIP-COLUMN precipitation algorithms for CloudSat (https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2c-precip-column/2C-PRECIP-COLUMN_PDICD.P1_R05.rev1_.pdf)
Overview of the 2C-RAIN-PROFILE algorithms for CloudSat (https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2c-rain-profile/2C-RAIN-PROFILE_PDICD.P1_R05.rev0_.pdf)
Overview of the 2C-SNOW-PROFILE algorithms for CloudSat (https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2c-snow-profile/2C-SNOW-PROFILE_PDICD.P1_R05.rev0_.pdf)
Link to download the data and format of data
Download here after logging in (http://www.cloudsat.cira.colostate.edu/data-products/level-2c)
File naming conventions (http://www.cloudsat.cira.colostate.edu/data-products)
Basics on granules, etc. (https://ccplot.org/pub/resources/CloudSat/CloudSat%20Data%20Users%20Handbook.pdf)
Examples of reading HDF4 file with Python:
- https://moonbooks.org/Articles/How-to-read-CloudSat-2B-GEOPROF-GRANULE-HDF4-file-using-python-and-pyhdf-/
- https://hdfeos.org/zoo/index_openCDPC_Examples.php
Publications including dataset evaluation or comparison with other data in Canada
Behrangi, A., Y. Tian, B. H. Lambrigtsen, and G. L. Stephens, 2014: What does CloudSat reveal about global land precipitation detection by other spaceborne sensors? Water Resources Research, 50(6), 4893–4905. https://doi.org/10.1002/2013wr014566.
Kay, J. E., C. Genthon, T. L’Ecuyer, N. B. Wood, and C. Claud, 2014: How much snow falls on the Antarctic ice sheet? The Cryosphere, 8(4), 1577–1587, https://doi.org/10.5194/tc-8-1577-2014.
Kodamana, R., and C. G. Fletcher, 2021: Validation of CloudSat-CPR Derived Precipitation Occurrence and Phase Estimates across Canada. Atmosphere, 12(3), 295. https://doi.org/10.3390/atmos1203.
L’Ecuyer, T. S., and G. L. Stephens, 2002: An estimation-based precipitation retrieval algorithm for attenuating radars., J. Appl. Meteor., 41, 272-285. https://doi.org/10.1175/1520-0450(2002)041<0272:AEBPRA>2.0.CO;2.
Wood, N. B., T. S. L'Ecuyer, F. L. Bliven, and G. L. Stephens, 2013: Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity, Atmos. Meas. Tech., 6, 3635-3648, doi:10.5194/amt-6-3635-2013.
Wood, N. B., T. S. L'Ecuyer, A. J. Heymsfield, G. L. Stephens, D. R. Hudak, and P. Rodrigues, 2014: Estimating snow microphysical properties using collocated multisensor observations. J. Geophys. Res. Atmos., 119, 8941-8961, doi:10.1002/2013JD021303.