Efforts in all-sky satellite data assimilation at the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center have been focused on the development of GSI configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) and Global Change Observation Mission-Water (GCOM-W) Advanced Microwave Scanning Radiometer 2 (AMSR-2). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be relatively transparent to atmospheric gases and thin ice clouds, and highly sensitive to precipitation. Therefore, GMAOs all-sky data assimilation efforts are primarily focused on utilizing these data in precipitating regions. The all-sky framework being tested at GMAO employs the GSI in a hybrid 4D-EnVar configuration of the Goddard Earth Observing System (GEOS) data assimilation system, which will be included in the next formal update of GEOS. This article provides an overview of the development of all-sky radiance assimilation in GEOS, including some performance metrics. In addition, various projects underway at GMAO designed to enhance the all-sky implementation will be introduced

El Akkraoui, Amal

Gelaro, Ron

Gu, Wei

Jin, Jianjun

Kim, Min-Jeong

McCarty, Will

Todling, Ricardo

NASA Technical Reports Server

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Contribution to JCSDA Newsletter 2017 Summer Issue 
 
All-sky Microwave Imager Data Assimilation at NASA GMAO 
 
Min-Jeong Kim, Jianjun Jin, Amal El Akkaroui, Will McCarty, Ricardo Todling, Wei Gu, and Ron Gelaro 
(NASA GMAO) 
 
Satellite radiance observations combine global coverage with high temporal and 
spatial resolution, and bring vital information to NWP analyses especially in areas where 
conventional data are sparse. However, most satellite observations that are actively 
assimilated have been limited to clear-sky conditions due to difficulties associated with 
accounting for non-Gaussian error characteristics, nonlinearity, and the development of 
appropriate observation operators.  To expand existing capabilities in satellite radiance 
assimilation, operational centers including ECMWF, MetOffice, JMA, and NCEP have 
been pursuing efforts to assimilate radiances affected by clouds and precipitation from 
microwave sensors.  The expectation is that these data can provide critical constraints on 
meteorological parameters in dynamically sensitive regions and have positive impact on 
forecasts of precipitation. 
As described in the previous issue of the JCSDA newsletter, NCEP’s efforts to 
assimilate all-sky data in the Gridpoint Statistical Interpolation (GSI) system have been 
focused on temperature sounding data from the Advanced Microwave Sounding Unit-A 
(AMSU-A) and Advanced Technology Microwave Sounder (ATMS) in non-precipitating 
cloudy conditions. Efforts in all-sky satellite data assimilation at the Global Modeling 
and Assimilation Office (GMAO) at NASA Goddard Space Flight Center have been 
focused on the development of GSI configurations to assimilate all-sky data from 
microwave imagers such as the GPM Microwave Imager (GMI) and Global Change 
Observation Mission-Water (GCOM-W) Advanced Microwave Scanning Radiometer 2 
(AMSR-2). Electromagnetic characteristics associated with their wavelengths allow 
microwave imager data to be relatively transparent to atmospheric gases and thin ice 
clouds, and highly sensitive to precipitation.  Therefore, GMAO’s all-sky data 
assimilation efforts are primarily focused on utilizing these data in precipitating regions. 
https://ntrs.nasa.gov/search.jsp?R=20170005481 2019-08-31T08:17:17+00:00Z
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The all-sky framework being tested at GMAO employs the GSI in a hybrid 4D-EnVar 
configuration of the Goddard Earth Observing System (GEOS) data assimilation system, 
which will be included in the next formal update of GEOS.  This article provides an 
overview of the development of all-sky radiance assimilation in GEOS, including some 
performance metrics. In addition, various projects underway at GMAO designed to 
enhance the all-sky implementation will be introduced. 
 
Highlights of all-sky satellite data configuration in GEOS  
Various components of the GEOS system have been modified to assimilate cloud 
and precipitation affected microwave radiance data (Table 1).  To utilize data in cloudy 
and precipitating regions, state and analysis variables have been added for ice cloud (qi), 
liquid cloud (ql), rain (qr) and snow (qs). This required enhancing the observation 
operator to simulate radiances in heavy precipitation, including frozen precipitation.  
Background error covariances in both the central analysis and EnKF analysis in hybrid 
4D-EnVAR system have been expanded to include hydrometeors.  In addition, the bias 
correction scheme was enhanced to reduce biases associated with thick clouds and 
precipitation.  
 
a. Cloud Scattering Coefficients in CRTM  
 
The observation operator for satellite radiances in GSI consists of spatial 
interpolation and the Community Radiative Transfer Model (CRTM), version 2.2.3. 
Scattering and extinction coefficients, asymmetry factor, and phase functions associated 
with hydrometeors for microwave wavelengths are read from a lookup table built using 
the Mie calculation for various cloud types (i.e. cloud ice, cloud liquid, rain, snow, 
graupel, and hail), and for various effective radius assuming a Gamma size distribution 
(Yang et al. 2005).  Due to the known limitations of Mie scattering parameters for frozen 
hydrometeors, especially for high frequency (> 85 GHz) microwave channels (Kim 2006, 
Liu 2008, Geer and Baordo 2014), new parameters were calculated using the Discrete 
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Dipole Approximation (DDA) method for non-spherical frozen precipitation from Liu 
(2008).  Eleven different non-spherical ice crystal shapes in Liu’s database, in addition to 
scattering properties of spherical ice crystals calculated with the Mie method, are 
examined to find an optimal choice of ice crystal shape to reconstruct the cloud scattering 
coefficients for CRTM (Figure 1).  For each shape of ice crystal, a CRTM cloud 
coefficient lookup table was generated for 33 microwave frequencies between 10.65 GHz 
and 190.31 GHz, seven atmospheric temperatures between 243 K and 303 K, and 405 
effective radius sizes starting from 0.005 mm.  The maximum effective radius considered 
for rain in the new CRTM coefficients is 1.191 mm.  For snow crystals, the maximum 
effective radius considered ranges from 0.664 mm to 1.278 mm, depending on snow 
crystal shape. Field et al. (2007) particle size distribution was assumed for frozen 
hydrometeors and Marshall-Palmer size distribution (Marshall and Palmer 1948) was 
assumed for liquid hydrometeors. After replacing original cloud coefficients with new 
cloud coefficients constructed with DDA scattering parameters, Simulated GMI 
brightness temperatures based on the new cloud coefficients are found to be closer to the 
observations and exhibit less first-guess departure bias in precipitating regions than those 
based on the original coefficients (Figure 2). 
 
b. Enhanced Bias Correction 
As with clear-sky radiances in the GEOS, bias correction for all-sky microwave 
radiances is performed using a variational bias correction scheme (VarBC, Dee 2004, 
Auligne et al. 2007) which estimates bias correction coefficients as part of the variational 
assimilation. For clear sky microwave radiance data from microwave sensors such as 
AMSU-A, SSMIS, and ATMS, the bias predictors include a constant, the scan angle, a 
2nd order polynomial of the atmospheric temperature lapse rate weighted by the radiance 
weighting function, and the retrieved cloud water path. For the all-sky implementation, 
three changes were made to the original VarBC: First, the retrieved cloud liquid water 
path was removed as a predictor. Second, only near-clear sky observations with near-
clear sky background are used in updating bias correction coefficients for pre-existing 
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predictors.   Third, the mean of the observed and calculated cloud index based on 37-GHz 
brightness temperatures, (CIavg), and its square, are used as two additional bias correction 
predictors to correct the cloud amount-dependent first-guess biases. Results indicate that 
the modified VarBC scheme removes most of the bias in the first-guess departures, as 
indicated in Figure 3.  The magnitude of remaining biases associated with thick cloud and 
heavy precipitation are reduced to less than 2 K in all CI ranges. Similar results are 
obtained for all GMI channels (not shown). 
 
c. Background error covariance matrix 
The analysis control vector in the current GEOS analysis scheme includes stream 
function, unbalanced velocity potential, unbalanced virtual temperature, unbalanced 
surface pressure, relative humidity, ozone mixing ratio, and skin temperature (Rienecker, 
et al. 2008).  With the newly added control variables, the corresponding static and flow-
dependent background error covariances must be generated. Climatological statistics 
were estimated following the NMC method (Parrish and Derber 1992) using pairs of 24-
hr and 48-hr GEOS forecasts between 1 June 2016 and 16 January 2017. Ensemble 
covariances are based on the spread of the 32 ensemble forecasts from the GEOS hybrid 
scheme during each analysis cycle. 
The panels on the left side of Figure 4 show the vertical distribution of the static 
background errors for cloud liquid, cloud ice, rain, and snow water. Aside from the fact 
that the estimated errors are by construction zonally invariant, they have generally 
smooth spatial structure. Relatively large errors for liquid clouds are seen in storm tracks 
in midlatitudes. Static errors in the southern hemisphere are slightly larger than in the 
northern hemisphere.  Generally speaking, the maximum errors for cloud liquid water 
occur in the layer between 900 hPa and 850 hPa. Static background errors for cloud ice 
water show large values near the tropical tropopause, where large amounts of cloud ice 
exist in the anvils of convective clouds.  Static background errors for rain and snow are 
larger in the tropics than other latitudes. Large background errors for rain occur in the 
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tropical lower troposphere between sea level and 600 hPa, while large errors for snow 
occur in the tropical middle troposphere between 600 hPa and 450 hPa.   
The panels on the right side of Figure 4 show cross-sections of ensemble background 
errors for hydrometeors taken from GEOS on 12 December 2015 12Z.  The results 
indicate that the magnitudes of the ensemble background errors are similar to those of the 
static background errors, although ensemble-based estimates show more detailed flow-
dependent structures.  Note that there are regions with nearly zero ensemble error 
corresponding to areas where the ensemble members forecasted nearly zero clouds (clear 
sky).  In contrast, the static background errors show nonzero values over broad ranges of 
latitude.   
 
Impact Assessments Cycled	data	assimilation	experiments	were	conducted	 to	examine	 the	 impact	of	all-sky GMI radiances on GEOS analyses and forecasts.  	GEOS	was	run	in	a	hybrid	4D-EnVar	 configuration	with	 a	horizontal	 resolution	of	 0.5° for	 the	 analysis	 and	0.25°	 for	 the	 forecast.	 	The	control	 run	assimilated	all	 the	data	used	routinely	 in	GEOS	(conventional data, AMSU-A, ATMS, MHS, IASI, AIRS, GPSRO, and satellite 
wind data), while the experimental	run	assimilated	all-sky	GMI	data	additionally.		It 
was found that the all-sky GMI data generally have a significant impact on the lower 
tropospheric humidity and temperature analyses, especially in the tropics, which leads 
to improved forecasts of these quantities (Figure 5).  Similar results were obtained for 
all seasons (not shown).  In	 addition,	 a	 noticeable	 positive	 impact	 of	 all-sky	 GMI	assimilation	 on	 hurricane	 track	 forecasts	 was	 identified	 for	 Hurricane	 Melor,	which	occurred	in	the	western	Pacific	during	December	2015.	
 
 
 
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Discussion 
Currently, static and ensemble background errors have the same weight (0.5) for all 
analysis variables in GEOS. Climatological background errors for highly nonlinear and 
situation-dependent clouds and precipitation may be less meaningful compared with other 
dynamical variables. To assign much larger weight to the ensemble-based background 
errors for hydrometeors, the capability to assign different weights for hydrometeors 
versus other dynamic variables is under development. The current all-sky framework will 
be enhanced by various updates both in the forecast model and analysis scheme. For 
example, the inclusion of a two-moment microphysics scheme (Barahona et al. 2014) in 
the GEOS forecast model will provide estimates of cloud particle size distributions to the 
all-sky observation operator.  Future versions of the CRTM will account for cloud 
fraction in calculating radiances. This should improve the simulation of brightness 
temperature compared with the current version of CRTM, which considers only clear-sky 
or completely overcast conditions.  In addition, we are testing various dynamic thinning 
approaches in order to use more data in cloudy and precipitating regions.  All these 
enhancements are expected to extend the scope of all-sky radiance assimilation to include 
more microwave measurements and, in turn, lead to improved analyses and forecasts. 
 
 
 
 
 
 
 
 
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References 
Auligne T., A. P. McNally, and D. P. Dee 2007: Adaptive bias correction for satellite data in a numerical 
weather prediction system. Q. J. R. Meteorol. Soc. 133, 631-642 
Barahona1, A. Molod, J. Bacmeister, A. Nenes. Gettelman, H. Morrison, V. Phillips, and A. Eichmann1, 
2014: Development of two-moment cloud microphysics for liquid and ice within the NASA 
Goddard Earth Observing System Model (GEOS-5), GEosci. Model. Devl, 7, 1733-1766 
Dee D .P. 2004: Variational bias correction of radiance data in the ECMWF system. Preceedings of the 
ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP, Reading, 
UK, 28, June to 1 July 2004. 97-112 
Field, P. R., Heymsfield A. J., and Bansemer, A.: Snow Size Distribution Parameterization for Midlatitude 
and Tropical Ice Clouds, J. Atmos. Sci., 64, 4346-4365, 2007 
Geer A. J. and F. Baordo 2014: Improved scattering radiative transfer for frozen hydrometeors at 
microwave frequencies.  Atmos. Meas. Tech. 7, 1839-1860 
Geer A. J. and Bauer P. 2011: Observation errors in all-sky data assimilation Q. J. R. Meterol. Soc. 137, 
2024-2037. DOI:10.1002/qj.830 
Kim, M.-J. 2006. "Comparisons of single scattering approximations of randomly oriented ice crystals at 
microwave frequencies." J Geophys Research, 111 (D14201): [doi:10.1029/2005JD006892] 
Liu, G. 2008: A database of microwave single-scattering properties for nonspherical ice particles, Bulletin 
of the American Meteo. Soc., Vol 89, 1563-1570 
Marshall, J. S and Palmer, W. M. K. : The distribution of raindrops with size, J. Meteor., 5, 165-166,1948 
 
Parrish, D. F. and J. C Derber 1992: The National Meteorological Center’s spectral statistical interpolation 
analysis system. Mon. Wea. Rev., 120, 1747-1763. 
Rienecker, M.M., M. J. Suarez, R. Todling, J. Bacmeister, L. Takacs, H.-C. Liu, W. Gu, M. Sienkiewicz, 
R. D. Koster, R. Gelaro, I. Stajner, and J. E. Nielsen, 2008. The GEOS-5 Data Assimilation 
System - Documentation of Versions 5.0.1, 5.1.0, and 5.2.0. Technical Report Series on Global 
Modeling and Data Assimilation, Vol. 27, 1-118 pp. 
Yang, P., H. Wei, H. Huang, B. A. Baum, Y. X. Hu, G. W. Kattawar, M. I. Mishchenko, and Q. Fu 2005: 
Scattering and absorption property database for nonspherical ice particles in the near- through far-
infrared spectral region. Applied Optics, 44, 5512-5523. 
 
 
 
 
 
 
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Table	 1:	 Comparisons	 of	 clear-sky	 and	 all-sky	 microwave	 brightness	 temperature	
assimilation	 framework	 in	 GEOS-5	 Atmospheric	 Data	 Assimilation	 System.	 (Here	 T:	
atmospheric	 temperature,	 q:	 specific	 humidity,	 Ps:	 surface	pressure,	 oz:	 ozone	mixing	
ratio,	u:	u-wind,	v:	v-wind,	ql:	liquid	cloud	mixing	ratio,	qi:	ice	cloud	mixing	ratio,	qr:	rain	
water	mixing	 ratio,	 qs:	 snow	water	mixing	 ratio,	 and	CIavg:	mean	of	 the	observed	and	
calculated	cloud	index	based	on	37-GHz	brightness	temperatures.)	
	
 
 
 
	
	
	
	
 
 
 
 
 
 
 
 Clear-sky All-sky 
State variables T, q, Ps, oz, u, v T, q, Ps, oz, u, v, ql, qi, qr, qs 
Analysis variables T, q, Ps, oz, u, v T, q, Ps, oz, u, v, ql, qi, qr, qs 
Observation 
operator 
CRTM (v. 2.3.3) CRTM (v.2.3.3) with new cloud 
scattering coeffficents reconstructed 
with DDA scattering parameters 
Observation Error Constant for each channel of 
each sensor (& inflated during 
QC procedures) 
Symmetric error model 
(Geer and Bauer 2010) 
Quality control Screen out cloud affected 
radiance 
Keep cloud and precipitation affected 
radiance. Screen out data over ocean if 
SST < 278K. 
Bias correction 
predictors in 
VarBC  
Constant, lapse rate, lapse rate2, 
cos (zenith angle),  
and cloud liquid water path 
Constant, lapse rate, lapse rate2,  
cos (zenith angle),  
CIavg, and CIavg2  
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Figure	1:	Microwave	scattering	properties	as	a	function	of	frequency	for	a	snow	water	content	
of	0.1	g	m-3.	In	actual	CRTM	look	up	table,	extinction	coefficients	are	stored	in	[m2kg-1]	unit.	 
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Figure	2:	Comparisons	of	 simulated	GMI	166	GHz	vertically	polarized	brightness	 temperatures	
(TB)	with	 the	 observations	 near	 Hurricane	 Celia	 on	 12	 July	 2016	 00Z:	 	 (a)	 Observed	 TBs,	 	 (b)	
CRTM	simulated	TBs	with	original	 scattering	 coefficients	based	on	Mie	method,	and	 (c)	CRTM	
simulated	 TBs	with	 the	DDA	method	 calculated	 scattering	properties	 of	 3-bullet	 rosette	 snow	
crystals.		The	colorbar	shown	in	(c)	works	for	(a)	and	(b)	as	well.		
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Figure	3:	Bias	of	first-guess	departures	of	GMI	channel	13	as	a	function	of	CIavg.	Thin	solid	(Thick	
solid)	 line	 shows	 the	 biases	 before	 (after)	 using	 CIavg	 as	 additional	 predictors	 in	 VarBC.	 All	
assimilated	 data	 points	 between	 12/01-12/31/2015	 were	 used.	 Results	 only	 in	 the	 bins	 that	
have	the	number	of	data	points	greater	than	5	are	shown	in	this	figure.			
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Figure	4:	Comparisons	of	static	background	errors	(left	figures)	and	ensemble	background	errors	
(right	figures)	as	a	function	of	latitude	and	vertical	level.	(a)	and	(b):	liquid	cloud,	(c)	and	(d):	ice	
cloud,	(e)	and	(f):	rain,	and	(g)	and	(h):	snow.	
(a) (b) 
(c) (d) 
(e) (f) 
(g) (h) 
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` 
	
Figure	5:	RMS	error	differences	between	 the	GEOS	 control	 and	all-sky	GMI	experiment	 in	 the	
tropics	for	December	2015:	(a)	850hPa	specific	humidity	(b)	850hPa	temperature,	(c)	850hPa	V-
wind.	
 
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Figure	6:	GEOS	forecasts	of	the	track	of	hurricane	Melor	(December	2015):	(a)	without	and	(b)	
with	the	assimilation	of	all-sky	GMI	data. 
 
 
 
(a) 
(b) 


All-Sky Microwave Imager Data Assimilation at NASA GMAO

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