Retrievals of falling snow from space represent an important data set for understanding the Earth's atmospheric, hydrological, and energy cycles, especially during climate change. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new and retrievals are still undergoing development with challenges remaining. This work reports on the development and testing of retrieval algorithms for the Global Precipitation Measurement (GPM) mission Core Satellite, launched February 2014, with a specific focus on meeting GPM Mission requirements for falling snow

Lott, Benjamin

Munchak, Stephen J.

Petersen, Walter A.

Ringerud, Sarah

Skofronick-Jackson, Gail

NASA Technical Reports Server

FALLING SNOW ESTIMATES FROM THE GLOBAL PRECIPITATION MEASUREMENT 
(GPM) MISSION 
 
Gail Skofronick-Jackson1, Stephen J. Munchak1, Sarah Ringerud2, Walter Petersen3, Benjamin Lott4 
 
1NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA, 301-614-5720 
Gail.S.Jackson@nasa.gov, 2NASA Postdoctoral Program (NPP) NASA Goddard Space Flight Center, 
3Marshall Space Flight Center, 4University of North Dakota 
 
ABSTRACT 
 
Retrievals of falling snow from space represent an important 
data set for understanding the Earth’s atmospheric, 
hydrological, and energy cycles, especially during climate 
change. Estimates of falling snow must be captured to 
obtain the true global precipitation water cycle, snowfall 
accumulations are required for hydrological studies, and 
without knowledge of the frozen particles in clouds one 
cannot adequately understand the energy and radiation 
budgets. While satellite-based remote sensing provides 
global coverage of falling snow events, the science is 
relatively new and retrievals are still undergoing 
development with challenges remaining (e.g., [1], [2], [3]). 
This work reports on the development and testing of 
retrieval algorithms for the Global Precipitation 
Measurement (GPM) mission Core Satellite [4-5], launched 
February 2014, with a specific focus on meeting GPM 
Mission requirements for falling snow. 
 
Index Terms— Precipitation, snow, microwave, 
satellite, validation 
 
1. INTRODUCTION 
 
The GPM Core Observatory was launched 27 February 
2014 from Tanagashima Island, Japan. The prime mission 
lifetime (instrument design life) is 3 years (to May 2017) but 
fuel is projected to last well beyond that, with the GPM 
Core Observatory lasting potentially 20+ years if the 
instruments do not fail. One of GPM’s requirements is to 
measure rain rates from 0.2 to 110 mm/hr and to detect the 
presence of falling snow.  
The cornerstone, or anchor, of the GPM mission is the 
GPM Core Observatory in a unique 65o non-Sun-
synchronous orbit at an altitude of 407 km serving as a 
physics observatory and a calibration reference to improve 
precipitation measurements by a constellation of 8 or more 
dedicated and operational, U.S. and international passive 
microwave sensors. This orbit allows for highly 
sophisticated observations of precipitation in the mid-
latitudes where a majority of the population lives.  GPM’s 
constellation concept sets the GPM Core Observatory 
spacecraft’s orbit to allow for coincident measurements with 
partner precipitation satellite sensors (as listed in [5]). These 
coincident measurements help to remove biases in the 
passive microwave brightness temperatures (and hence the 
resultant precipitation retrievals) among the various sensors 
using GMI as the calibrator. This allows for next-generation 
unified precipitation estimates globally but with fine 
temporal and spatial scales.  
GPM has several retrieval product levels ranging from 
raw instrument data to instantaneous swath precipitation 
estimates to gridded and accumulated products and finally to 
multi-satellite merged products. The latter merged product, 
called IMERG, is available with a 5-hour latency with 
temporal resolution of 30 minutes and spatial resolution of 
0.1o x 0.1o (~10km x 10km) grid box. Some products have a 
1-hour latency for societal applications such as floods, 
landslides, hurricanes, blizzards, and typhoons and all have 
late-latency high-quality science products. 
 
2. FALLING SNOW ESTIMATES FROM GPM 
 
Estimates of falling snow from ground and space based 
sensors have been difficult due to the physical 
characteristics of snowflakes including their complex 
shapes, sizes, fall patterns, melting fractions, and densities; 
and their radiative characteristics including weak falling 
snow signatures with respect to background (surface, water 
vapor) signatures for passive sensors over land surfaces [6], 
differences in near surface snowfall and total column snow 
amounts, and any polarization effects due to oriented ice 
particles in clouds [7]. While these challenges are slowly 
being resolved, knowledge of their impact on expected 
retrieval results is an important key for understanding falling 
snow retrieval estimations. 
Because of these challenges and because of the early 
timeline of the development of mission requirements, 
GPM’s documents only require the “detection” of falling 
snow. Nevertheless, falling snow rates are routinely 
produced for GPM data. Several processes for the detection 
of falling snow will be discussed in the IGARSS 
presentation (herein only one process will be described). 
https://ntrs.nasa.gov/search.jsp?R=20180004348 2019-08-31T15:15:16+00:00Z
The error bars associated with the snowfall rates have many 
uncertainties from both the satellite retrievals and from the 
ground based observations. These will also be described 
during the presentation and herein this manuscript. 
We focus on the GPM Level 2 instantaneous swath 
retrievals of precipitation from data from both the GPM 
Microwave Imager (GMI), from the Dual-frequency 
Precipitation Radar (DPR), and the combined DPR-GMI 
algorithms. The 166V, 166H, 183±3, and 183±7 GHz 
channels on the GMI were added to TRMM’s 9 channels 
from 10-89 GHz and designed to observe the smaller 
precipitation particles associated with light rain and falling 
snow found in the mid-latitudes. The Ka-band (36 GHz) 
channel on the DPR is also useful for light rain and falling 
snow and is sensitive to different particle size distributions. 
GMI retrievals are based on a Bayesian framework [8]. 
The at-launch a priori Bayesian database is generated using 
proxy satellite data merged with surface measurements 
(instead of models). In March 2016, the Bayesian database 
was replaced with the more realistic observational data from 
the GPM spacecraft radar retrievals and GMI data. It is 
expected that the observational database will be much more 
accurate for falling snow detection [9] and retrievals 
because that database will take full advantage of the 166 and 
183 GHz snow-sensitive channels. In March 2017, the 
algorithms will be further improved when Version 05 is 
implemented.  
Our work has shown that for GMI retrievals knowing if 
the land surface is snow-covered, or not, can improve the 
performance of the algorithm. Improvements were made to 
the algorithm that allow for daily inputs of ancillary snow 
cover values and also updated Bayesian channel weights for 
various surface types. 
GMI retrievals rely on the 2 meter air temperature 
(from model data) to determine if frozen snow or liquid rain 
[15]. DPR retrievals rely on the temperature at the lowest 
range gate detectable to distinguish between rain versus 
snow. Note that because of surface clutter issues and the 
cross track scanning nature of DPR, it cannot sense all the 
way to the Earth’s surface and typically sees only 500 to 
2000 m above the surface. 
 
3. DETECTION EFFORTS AND ACHIEVEMENTS 
 
Within 3 weeks after launch, GPM’s GMI was able to detect 
and measure falling snow (Fig. 1 left). When compared to 
ground data (Fig. 1 right) clearly there are inconsistencies in 
the patterns and amounts/intensity of the estimated falling 
snow. Now both the satellite estimates and the ground data 
will have errors and uncertainties, and so it is a matter of 
reducing these errors and uncertainties in both the satellite 
products and the ground radar datasets. 
Some of these errors and uncertainties are due to 
unknowns and variability in the Z-S relationships for the 
ground based radars, some are due to complications with 
near surface falling snow (blowing snow, melting, density 
estimates), some are due to surface temperature estimates or 
models not able to match the actual surface (or near surface 
air) temperatures, and some are due to the non-linear and 
under-constrained relationships between the observations 
(whether they be satellite or ground-based) and the physical 
properties of the falling snow. 
 
 
Figure 1: This snow event occurred March 17, 2014 and deposited 
more than 7” of snow in the Washington, DC metro area. Left: 
GMI retrievals of liquid rain and falling snow. Right: Ground 
measurements from NOAA’s National Mosaic & Multi-Sensor 
QPE (CONUS 3D radar mosaic at 1km resolution) [10]. 
 
One major area of validation is proving that we can 
detect falling snow. Prior publications show that, 
theoretically, GPM should be able to detect falling snow at 
rates of > 0.5-1.0 mm/hr (melted rate) or about 1 cm/hr or 
higher (fluffy rate) [9], [12]. Thus GPM is only expected to 
be able to estimate moderate and high snow rates due to the 
instrument capabilities on the GPM Core Observatory. For 
lighter snow rates, one must turn to CloudSat [13].  
One study was to use data from surface observations to 
validate falling snow events as detected by GPM’s Dual-
Frequency Precipitation Radar (DPR). Specifically, ground 
observations (ASOS/AWOS) from Iowa Environment 
Mesonet database were compared to DPR estimates (Fig. 2). 
Cases where DPR reported 0 mm h-1 whiel the 
ASOS/AWOS reported non-zero snow rate can be explained 
by a few factors. The first reason is that there could be 
missing data at a specific point. Another possible reason is 
that DPR cannot pick up shallow events as observed in lake 
effect snow events. It is also possible that there is error in 
the ground observation. For example, what a human 
observer sees as light snow could actually just be blowing 
snow. 
The main goal of ASOS/AWOS investigation was 
achieved: GPM can correctly classify precipitation as snow 
as shown in Figure 2. It should be noted that GPM does well 
classifying precipitation when the satellite actually detects 
it: as a majority of light snow cases and all of moderate 
snow cases correctly classified. However, this was too small 
of sample size to make conclusions about heavy snow. This 
work showed GPM can detect liquid equivalent snowfall 
rates 0.2 mm h-1. Other detection process studies will be 
reported during the IGARSS presentation. 
  
 
Figure 2: Light and moderate snowfall observations depend on 
observed visibility. Snowfall intensity increases with increasing 
precipitation rates. Majority of light snow events around 0.5 mm h-
1 whereas majority of moderate snow events closer to 0.8 mm h-1. 
Note that an observation with a precipitation rate greater than 8 
mm h-1 is not shown on the moderate snow plot. 
 
 
4. FALLING SNOW RATE RETRIEVALS 
 
Falling snow rate retrievals over North America can be 
compared as shown in Figure 3 for the GMI and DPR 
estimates (the Combined DPR+GMI estimates are similar to 
the DPR estimates but with slightly lower snow rate 
magnitudes. One can easily see that the GMI estimates 
much less snow over the Bay of Alaska (and ocean overall) 
than the DPR or combined. This is likely due to the 
differences in the retrieval processes. DPR is likely 
classifying the precipitation as snow higher in the 
atmosphere (500-2000m) where DPR detects the presence 
of precipitation whereas GMI uses the 2 m air temperature 
(which as one can imagine, may be warmer than freezing 
above the warmer ocean surface.  
To test the theory that the problem lies in the 
differences in the algorithm processes between DPR and 
GMI, the DPR retrievals were re-run using the 2 m air 
temperature algorithm [15] to indicate liquid rain versus 
falling snow. The results of that test are shown in Figure 4. 
Clearly, these DPR falling snow retrievals are much closer 
to the GMI results. This does not necessarily mean that the 
GMI approach is better than the DPR approach since there 
are instrumentation and algorithm limitations and 
advantages for both DPR and GMI. The truth lies between 
the two approaches. 
 
 
 
Figure 3: Average snow rates for two years of GMI (top) and DPR 
(bottom) data. March 2014-February 2016. 
 
 
Figure 4: Average snow rates for two years of DPR data using the 
GMI approach to indicate frozen precipitation versus liquid 
precipitation. March 2014-February 2016. 
 
 
5. RESULTS 
 
Falling snow validation efforts are underway as 
reprocessed and improved algorithms were released in 
early 2016 and Version 05 will be released in sprig 
2017. Prior to this new retrieval algorithm, the falling 
snow estimates were unreliable. The validation focus 
was on snow in the US where relatively robust ground 
data exists. In addition, data from pre-launch GPM 
falling snow field campaigns [14] will be used if 
possible. Validation comparisons will be made for 
falling snow patterns, extent, intensity (or more likely 
the amount in the near surface levels since intensity is 
very much dependent on snowflake density and fall 
rate which are not easily available to the satellite (and 
for limited for ground based sites). 
Accumulated snow amounts will also be used as 
part of the validation. It is likely that, for now, 
mountainous terrain or areas where melting snow 
might occur, that validation will be rather uncertain. 
Indeed, falling snow estimates and validation are about 
50 years behind (and 10 times more complex) than 
falling liquid rain estimates. 
 
5. CONCLUSIONS 
 
The GPM mission is well on its way to providing 
essential data on precipitation (rain and snow) from 
micro to local to global scales via providing 
precipitation particle size distributions internal to the 
cloud, 5-15 km estimates of regional precipitation and 
merged global precipitation. Once TRMM data is 
recalibrated to the high quality standards of GPM (and 
as GPM continues to operate), TRMM and GPM 
together, with partner data) can provide a 25-30+ year 
record of global precipitation. Scientists and hazard 
decision makers all over the world value GPM’s data. 
 
6. REFERENCES 
 
[1] Skofronick-Jackson, G. M.-J. Kim, J. A. Weinman, and D. E. 
Chang, “A Physical Model to Determine Snowfall over Land by 
Microwave Radiometry,” IEEE Trans. Geosci. Remote Sens., vol. 
42, pp. 1047–1058, May 2004. 
[2] Noh, Y.-J., G. Liu, E.-K. Seo, and J. R. Wang 2006: 
Development of a snowfall retrieval algorithm at high microwave 
frequencies, J. Geophy. Res., 111, D22216, 
doi:10.1029/2005JD006826. 
[3] Chen F. W. and D. H. Staelin, “AIRS/AMSU/HSB 
precipitation estimates,” IEEE Trans. Geosci. Remote Sens., 41, 
pp. 410-417, 2003. 
[4] A. Y. Hou, G. Skofronick-Jackson, C. D. Kummerow and J. M. 
Shepherd, “Global precipitation measurement,” Precipitation: 
Advances in Measurement, Estimation, and Prediction (Ed. Silas 
Michaelides), Springer-Verlag, pp. 131-164, 2008. 
[5] Arthur Y. Hou, Ramesh K. Kakar, Steven Neeck, Ardeshir A. 
Azarbarzin, Christian D. Kummerow, Masahiro Kojima, Riko Oki, 
Kenji Nakamura, Toshio Iguchi, The Global Precipitation 
Measurement (GPM) Mission, Bulletin of the American 
Meteorological Society, May 2014. 
[6] G. Skofronick-Jackson and B. Johnson, Surface and 
Atmospheric Contributions to Passive Microwave Brightness 
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[7] Wang, J. R.; Skofronick-Jackson, G. M.; Schwaller, M. R.; 
Johnson, C. M.; Monosmith, W. B.; Zhang, Z. “Observations of 
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Millimeter-Wave Imaging Radiometer”  IEEE Trans. Geosci. 
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[8] Kummerow, C. D., S. Ringerud, J. Crook, D. Randel and W. 
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[9] Skofronick-Jackson; Johnson, B.T.; Munchak, S.J., "Detection 
Thresholds of Falling Snow From Satellite-Borne Active and 
Passive Sensors," IEEE Transactions on Geoscience and Remote 
Sensing, July 2013, doi: 10.1109/TGRS.2012.2227763  
[10] NOAA Multi-Radar/Multi-Sensor dataset for validation: 
http://www.nssl.noaa.gov/projects/mrms/ accessed 8 January 2016 
[11] GPM Validation: http://pmm.nasa.gov/science/ground-
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[12] Munchak, S.J., Skofronick-Jackson, G., Evaluation of 
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[13] Graeme L. Stephens, et al., and The CloudSat Science Team, 
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[14] Gail Skofronick-Jackson, et al., “Global Precipitation 
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Falling Snow Estimates from the Global Precipitation Measurement (GPM) Mission

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