02/21/2013
University of Maryland 2nd Year Annual Report
NASA Grant NNX11AF54G to the University of Washington
Titled: “Using MODIS and CERES Data to Improve Energy Balance Snowmelt Modeling"
Principal Investigator: Laura Hinkelman, University of Washington
1. At Issue
Modeling and prediction of snowmelt-driven hydrologic methods in the Western US suffers
from lack of energy balance information. The objective of this portion of the project is to
develop appropriate energy balance data sets using MODIS observations at 5-km spatial
resolution with a focus on shortwave (SW) fluxes. It is expected that such information will
improve the quality of snowpack modeling by accounting
for the spatial variability of the energy that drives the
melting. Since this is a novel approach to integration of
high spatial resolution satellite data into snow-melt
modeling, many new issues need to be dealt with. These
include validation of the fluxes derived from a 5-km pixels;
the ground radiometers used for evaluation integrate the
entire sky dome above and as such, the two types of
observations are not compatible. At the same time, it is
known that the spatial variability in the surface energy
budget will impact the snow-melt and should be accounted
for. Scale issues need to be addressed for developing a
realistic approach to the evaluation process, to be described
in what follows.
Figure 1.
Study domain.
2. Proposed Work
We propose to examine the benefits of using satellite SW and longwave (LW) surface
flux data instead of parameterizations used in current snow-melt models. This will be achieved
by driving a range of snowmelt models with surface radiation data derived from MODIS and
CERES measurements. Comparisons will first be made at individual instrumented sites
1
throughout the mountainous western US to be extended to entire river basins in the Sierra
Nevada, as illustrated in Figure 1. Model improvement will be assessed based on agreement
between the simulated and measured temporal progression of snow water equivalent (SWE). The
differences between the CERES (3-hourly with 1 grid spacing) and MODIS (twice daily with 5km grid spacing) data will enable us to evaluate the relative importance of spatial and temporal
sampling of radiation to snowmelt model performance and to investigate optimum methods of
combining temporal and spatial flux information.
3. Original Work Plan
Used will be observations from Terra and Aqua to produce surface radiative fluxes in support
of snow-melt modeling. The inference scheme to be implemented for the retrieval of shortwave
(SW) fluxes is described in Wang and Pinker (2009) where it was also evaluated at 10 spatial
resolution. Additional evaluation of this product is presented in Pinker et al. (2009; Niu et al.
(2010; Niu and Pinker (2011); feasibility of implementation of the methodology at 5-km and its
application has been illustrated in Wood et al. (2007) and Su et. al. (2008). Information on LW
fluxes will be produced from MODIS at 10 resolution at least. Original plan for data to be
processed was:
For shortwave: Oct - Dec 2003 and Jan – July 2005. Data for 2004 b produced previously have
been provided to the PI and collaborators for testing.
For longwave: October 2003 to July 2004 and October 2004 to July 2005.
4. Work done
4.1
Original version of model for shortwave (SW) fluxes (v1.0)
Original model designated for use in this work is described in (Wang and Pinker, 2009).
Shortwave (SW) radiative fluxes are computed in seven spectral intervals (0.2-0.4, 0.4-0.5, 0.50.6, 0.6-0.7, 0.7-1.19, 1.19-2.38, 2.38-4.0 μm) assuming a plane-parallel, vertically
inhomogeneous, scattering and absorbing atmosphere. Water vapor absorption is parameterized
following Ramaswamy and Freidenreich (1992), and Chou et al. (1999). Ozone absorption in the
ultraviolet and in the visible is computed following Lacis and Hansen (1974). The single
scattering properties and vertical profiles of aerosols were derived from the Optical Properties of
Aerosols and clouds (OPAC) software package (Hess et al., 1998). Five atmospheric aerosol
2
vertical profiles (Continental, Desert, Maritime, Arctic and Antarctic) are used with the inference
scheme. Cloud extinction coefficients, cloud single scattering albedo and asymmetry factor are
computed from the parameterization of Slingo (1989) and Edwards et al. (1996) for water clouds
and Chou et al.(2002) for ice clouds. Multiple scattering is dealt with using a delta-Eddington
approximation following Wiscombe (1977). Vertical profiles are from the standard atmospheres
of Kneizys et al. (1980). TOA solar spectral irradiance data are from MODTRAN 3.
4.2
Modified version of model for shortwave (SW) fluxes (v2.0)
The two main features of v1.0 that have been modified are related to the way snow
information is use (needed for computing the surface albedo) and the way cloud optical depth
and drop size are computed from the MODIS data. Snow information at daily time scale is used
as available from both Terra and Aqua and if bot available, the average is used. If no information
is available at daily time scale, the 8 day composite is used. Description of the first issue:
For the MODIS Level-2 swath data, the cloud phase information is hidden in a parameter
named “Quality_Assurance_1km”; details are provided in Appendix A.
When calculating the shortwave fluxes over Western US at 5-km scale, the cloud fraction,
cloud optical depth and cloud drop size are gridded from the 1-km scale
“Quality_Assurance_1km” data. In the previous version, information used for gridding is Byte 2,
named “Primary retrieval processing path”. In the current version, we make use of the “Multi
Layer Cloud Flag” (Byte 4) values to get the cloud phase information. Firstly, the 1-km
resolution swath image is grouped into 5-km resolution image with each grid of size of 5 x 5
pixels. Within these 25 pixels, “Cloud Mask Undet” (000) is treated as missing values and does
not count in calculation. The single layer and multi-layer unknown type clouds (110 and 111) are
classified as water type, namely:
5 km water cloud fraction is calculated as #(010+011+110+111) / [25-#(000+001)];
5 km ice cloud fraction is calculated as #(100+101)/[25-#(000+001)];
5km cloud optical depth is calculated at log scale, namely:
5 km cloud optical depth = exp (mean (log (cloud optical depth from 1-km pixels)));
5 km cloud drop size is calculated as the mean of pixel value within the 5 x 5 block.
4.3
LW model used
The downwelling surface longwave (LW) model (hereafter, /UMD_v2) is driven with a
combination of Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 cloud
3
parameters and information from the European Centre for Medium-Range Weather Forecasts
(ECMWF) ERA-Interim model. To compute the clear sky component of LW a two layer feedforward artificial neural network is implemented; it is trained with simulations derived from runs
of the Rapid Radiative Transfer Model (RRTM). When computing the cloud contribution to LW,
the cloud base temperature is estimated by using an independent artificial neural network
approach of similar architecture as previously mentioned, and parameterizations. The cloud base
temperature neural network is trained using spatially and temporally co-located MODIS and
CloudSat Cloud Profiling Radar (CPR) and the Cloud-Aerosol Lidar and Infrared Pathfinder
Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal
Polarization (CALIOP) observations. Daily average estimates of LW were compared against
ground measurements from BSRN giving an overall correlation coefficient of 0.98, root mean
square error (rmse) of 15.84 W m_2, and a bias of _0.39 W m_2.
4.4
Model inputs
Table 1.
SW Model Input
Time and Geolocation
Day of Year, Hour of Day
Longitude, Latitude
Sun Position
Cosine of Solar Zenith Angle
Aerosol
Aerosol Optical Depth
Cloud
Water Cloud Fraction
Water Cloud Optical Depth
Water Cloud Droplet Effective Radius
Ice Cloud Fraction
Ice Cloud Optical Depth
Ice Cloud Droplet Effective Radius
Cloud Top Pressure
Surface
Surface Elevation
Surface Pressure
surface Albedo
Profile
Column Amount of Ozone
Column Amount of Precipitable Water
4
Table 2.
LW Model Input
Time and Geolocation
Day of Year, Hour of Day
Longitude, Latitude
Sun Position
Cosine of Solar Zenith Angle
Cloud
Mean Cloud Fraction
Ice Cloud Fraction
Infrared Cirrus Fraction
Cloud optical thickness
Cloud Water Path
Cloud Effective Emissivity
Liquid Cloud Effective Radius
Ice Cloud Effective Radius
Cloud Top Pressure
Surface
Surface Elevation
Surface Pressure
2 Meter Temperature
2 Meter Dewpoint Temperature
Surface Albedo
Total Column Water Vapor
Profile
Geopotential Height
Temperature
Relative Humidity
Column Amount of Ozone
Column Amount of Precipitable Water
4.5
Model Input Data Sources
4.5.1 For SW
o 5-km resolution upward and downward fluxes are processed with MODIS level-2 swath
data for year 2005.
o Aerosol data are from collection 5.1 MOD04_L2 (Terra) and MYD04_L2 (Aqua). MISR
aerosol data are used for the regions where aerosol data from MODIS are missing.
5
o Cloud data are from collection 5.1 MOD06_L2 (Terra) and MYD06_L2 (Aqua).
o Profiles are from collection 5 MOD07_L2 (Terra) and MYD07_L2 (Aqua). NCEP
reanalysis precipitable water data are used to fill the MODIS missing value.
o Surface albedo data are the Filled Land Surface Albedo Product (http://modisatmos.gsfc.nasa.gov/ALBEDO/index.html)
o Snow information at daily time scale is used as available from both Terra and Aqua and if
bot available, the average is used. If no information is available at daily time scale, the 8
day composite is used.
4.5.2 For LW↓
MODIS data at 10 resolution and ERA Interim (the higher resolution as available from NCAR).
4.6
Processing issues with 5-km data
Figures 2 and 3 show two examples of the downward SW fluxes from the 5-km observations.
For each day, there are 4, 5 or 6 swaths passing over the target region. Number of swath depends
on the day of the year and satellite orbit. The example is for the first day of 2005. There are 4
swaths from each satellite (Terra and Aqua) that cover the Western US. The passing time are
shown in the figures. The number of observations available for every station depends on the
station location and time. Investigations were conducted on how to optimally combine all the
swaths to get a uniform daily coverage of the region
17:35
19:15
19:20
17:40
Figure 2.
SW↓ flux from MODIS (Terra) swath data for
2005.001 over western USA. Swaths are observed at following
times: 17:35, 17:40, 19:15 and 19:20 GMT.
.
6
21:00
19:20
20:55
19:15
Figure 3.
SW↓ flux from MODIS (Aqua) swath data for
2005.001 over western USA. Swaths are observed at following
times: 19:15, 19:20, 20:55 and 21:00 GMT.
4.7
Preparation of data for evaluation of SW↓ fluxes
The flux data processed from the MODIS swath product are pixel by pixel with footprint of
about 5-km. The ground observation instrument usually has a field of view around 1700 looking
upward. The actual sky area that contributes to the downward flux may be much large then 5-km.
To facilitate the choosing of a good matching scale between the satellite retrieval and ground
observations, all satellite retrievals that are within 50 km radius around the station are recorded
and used in analysis.
4.8
Data Processed with SW V2.0
Initially, data that were provided to the University of Washington have been processed
with v1.0 of UMD_SRB_MODIS algorithm. Once v2.0 was in place the data were reprocessed
with the updated version for following periods: Jan-July 2003; Jan-July 2004; Jan-July 2005;
Jan-July 2009. The data were processed for all the relevant swaths so a complete coverage of the
required region can be obtained. The region for which this information was derived is: Western
US (from the eastern foothills of the Rockies to the Pacific, specifically: 300-500 N, 1250-1050
W).
Upon request from the University of Washington, a data sub-set was prepared to create
match-up files over stations where snow water equivalent (SWE) information was available as
7
well as selected information on radiative fluxes. The data were extracted over each station for a
50 km radius. Similar data were extracted for all the SURFRAD/BSRN stations for evaluation of
model output at stations known to be of highest quality. This was subsequently extended to
stations in Oregon, where additional ground truth is available. The LW data were provided at
regional scale since they were produced at lower resolution due to the limitations imposed by the
resolution of the ERA Interim profiles that are needed for computing the LW fluxes.
4.9
Data Processed with LW V2.0
Daily values of LW↓ fluxes at 10 spatial resolution covering the entire region of interest
were submitted for October 2003-December 2005.
5.
Validation Results
One of the critical issues in the validation process of satellite retrievals against ground
measurements is the issue of scale, namely, do the ground measurements and the satellite
retrieval measure the same thing? Since the satellite estimates made here are of the highest
spatial resolution available so far, and the ground measurements are at high temporal resolution
(original averaging starts at 1 min) (at least, for the SURFRAD/BSRN stations), it is possible to
investigate the matching problem and its impact on the results. Comparison against ground
observations was done at different spatial and temporal scales and for all surface conditions and
by separating snow and no snow conditions, mountain site vs no mountain sites, and winter vs
summer.
5.1
Validation of MODIS 5-km instantaneous surface SW↓ fluxes
We compare instantaneous MODIS based SW↓ fluxes with different averaging times of
ground observations (10, 20, 30, 60 min) at SURFRAD/BSRN sites (TBL, FPK and BON). The
results show that the 60 min averaging yields the best results in terms of BIAS, STD and
Correlation Coefficient. Therefore, the 60 min averaging time period for ground observations
was selected. Evaluated were also done with different satellite space scales around match-up
ground sites (5, 10, 25, 50 km radius). A 50 km radius is nearly 10; averaging at such scale
yielded best results. Therefore, most validation results were performed at such a scale. Results
are shown in Figure 4. As seen, the scatter over the mountain site is larger than over the flat sites
while the bias at the mountain site is comparable to the ones of flat terrain (the number of data
points over the flat sites is larger than at the mountain site). The data cover the entire year,
including snow situations.
8
Figure 4.
Evaluation of MODIS 5-km instantaneous SW fluxes for 2005 at a mountain
location of a) TBL (CO) and flat terrain locations b) FPK (MO) and BON (Ill). The MODIS data
were averaged over 50 km radius and ground observations were averaged over 60 min.
Version2.0 data were used here.
Figure 5.
Evaluation of MODIS 5-km instantaneous SW fluxes for 2005 for a) no snow
conditions and b) snow conditions at FPK, TBL and BON sites. The MODIS data were averaged
over 50 km radius and ground observations were averaged over 60 min. Version1.0 data were
used here.
9
Evaluation of MODIS 5-km instantaneous SW fluxes for 01/01/2009 to 06/30/2009 was
performed at the at mountain sites of USFmf, USFuf, USFwf, USMe2, USMe3, and USGLE
where SWE measurements are made. The MODIS data were averaged over 50 km radius and
ground observations were averaged over 60 min. Version2.0 data were used here.
The results show only a relatively small bias (1.2 %) while the scatter is higher than at the other
sites used.
Figure 6.
Evaluation of MODIS
5-km instantaneous SW fluxes for
01/01/2009 to 06/30/2009 at mountain
sites of USFmf, USFuf, USFwf,
USMe2, USMe3, and USGLE. The
MODIS data were averaged over 50
km radius and ground observations
were averaged over 60 min. Version2.0
data were used here.
10
Figure 7.
Evaluation of MODIS 5-km instantaneous SW fluxes for 03/01/2005 to
07/01/2005 Oregon sites of a) Burns b) Eugene c) Hermiston d) combined at all 3 sites. The
MODIS data were averaged over 50 km radius and ground observations were averaged over 60
min. Version1.0 data were used here (http://solardat.uoregon.edu/SolarData.html).
5.2
Validation of hourly SW↓ at
Because MODIS swath SW↓ are initially calculated as instantaneous fluxes at the overpass
time, the fluxes at a missing time (Fm) are obtained by multiplying the instantaneous fluxes (Fi)
by the ratio of the solar zenith angle (Z) cosine at the missing time to that at instantaneous time.
Fm= Fi ( CosZm / CosZi )
Averaging the instantaneous SW↓ and all missing SW↓ within 30 min before and after the
observational time generates the hourly SW↓. Averaging all the daytime instantaneous SW↓
generates the daily SW↓.
Figure 8.
Evaluation of MODIS 5-km hourly SW fluxes for 01/01/2009 to 07/31/2009 at
FPK and BON. The MODIS data were averaged over 50 km radius and ground observations
were averaged over 60 min. Version2.0 data were used here.
11
Figure 9.
Evaluation of MODIS
5-km daily SW fluxes for 01/01/2009
to 07/31/2009 at FPK and BON. The
MODIS data were averaged over 50
km radius and ground observations
were averaged over 60 min. Version2.0
data were used here. Points outside 3
std were removed (5 points, 1%).
5.3.
Validation of MODIS surface LW↓ fluxes
Monthly Downward LW fluxes from UMD_SRB_MODIS were evaluated at four SURFRAD
sites over the US and at independent sites where high quality observations are available. Time
series for the SURFRAD sites are shown in Figure 10. Statistical results are shown in Figure 11,
illustrated good agreement between satellite estimates and ground measurements.
12
Figure 10.
Figure 11.
Time series of LW fluxes at selected SURFRAD sites for years
Validation of monthly mean LW from UMD_SRB_MODIS at 4 SURFRAD sites
(bon, fpk,gwn, psu) for 01/2003 to 06/2007.
Additional stations selected for validation are located at:
BER (32.27N 64.67W, Bermuda), CAR (44.08N 5.06E, France) , PAY (46.82N 6.94E,
Switzerland).In Figure 12 (left) shown are results for daily LW from the UMD_SRB_MODIS
model the BER site for 2009; (middle) CAR (right) all 3 stations (BER, CAR, and PYR).
13
Figure 12.
(left) shown are results for daily LW from the UMD_SRB_MODIS model the
BER site for 2009; (middle) CAR (right) all 3 stations (BER, CAR, and PYR).
6.
Spatial and temporal variability
Figure 13 is an example of monthly mean SW↓ surface flux for January 2005 at 0.05
degree grid resolution. The MODIS instantaneous swath data were first normalize by daily mean
solar zenith angle, and then re-gridded to 0.05 degree regular grid. Re-gridding uses a simple
averaging method, namely, all pixels that fall in a 0.05 box are averaged. If no pixels fall into a
grid box, the box value is set to missing. Normalized fluxes from Terra and Aqua are combined
to get the daily fluxes. The monthly mean values are averaged from the daily data. Snow
information is dealt with as follows: information on snow at daily time scale is used as available
from both Terra and Aqua; if both are available, the average is used. If no information available
for a particular day, the 8 day composite is used. All figures were plotted (or re-plotted) in
January 2013.
14
Figure 13.
Monthly mean SW↓ for Jan 2005 using MODIS 5-km observations from terra and
Aqua. The swath pixels were re-gridded to a 0.05 deg grid. No filling was used for missing data.
Daily mean is calculated as: (daily mean solar zenith angle/instantaneous sza)x(instantaneous
SW). Snow information from daily MODIS information is used.
Figure 14 shows the mean day-to-day variability over the wester US domain. The day-to-day
variability is calculated as the standard devation (STD) of daily values for a month (January
2005). Each pixel in the figure gives the STD for each location. The calculation is based on the
grided data, so each pixel is of 0.05 degree resolution. The absolute value of STD is dominated
by latitud. In the southern part, correspoding to higher sun elevation, SW↓ flux variability is
larger.
15
Figure-14.
Mean day-to-day variability of SW↓ flux over one month (Jan 2005) using
MODIS 5-km observations from Terra and Aqua. The swath pixels were re-gridded to a 0.05 deg
grid. No filling was used for missing data. Daily mean is calculated as: (daily mean solar zenith
angle/instantaneous sza)x(instantaneous SW↓).
Figure 15 is similar to Figure 14, but shows the relative variability. In Figure 15 the STD is
scaled by the monthly mean value of SW fluxes. Although from Figure 14, the absolute value is
higher in the southern part, the relative values show opposite observations, the low sun elevation
region has higher relative STD.
16
Figure 15.
Relative day-to-day variability, the day to day variations are scaled by monthly
mean SW for each grid. The other procedures are the same as described in Figures 13 and 14.
Figure 16 shows the spatial variability (standard deviation) around every pixel for the 1st of
January 2005. In this figure, fluxes were first normalized to daily mean value as described above,
and then the STD is calculated over a 10 x 10 block around each pixel. Since the pixel resolution
is 0.05 degree, the size of a 10 x 10 block is about 0.5 degree. Large variability is mainly seen
around cloud edges or in the region of scattered clouds.
17
Figure 16.
Spatial variability represented by the std for 10x10 grid boxes of 0.05 deg. The
other procedures are as described for Figures 13 and 14.
Figure 17 shows the frequency distributions of the variability values shown in Figure 16. The
mode value is around 8 W/m2.
Figure 17.
Histogram of the spatial variability shown in Figure 16.
18
Figure 18.
Same as Figure 16 but for monthly mean fluxes. SW↓ fluxes were first averaged
to monthly means. Followed the same procedure as for Figure 16, namely, the spatial STD over
a 10 x 10 block is calculated for each pixel.
Figure 19 is the frequency distribution of the spatial variability of the monthly mean flux. This
distribution is closer to Gaussian than that for the daily mean flux. The mode value is around 10
W/m2.
Figure 19.
Histogram of spatial variability of the monthly mean SW fluxes for January 2005,
namely, the std over a 10x10 boxes of 0.5 deg using monthly mean SW.
Figure 20 shows the domain averaged spatial variability as a function of the block size. Figure
16 and Figure 18 show the images of the spatial variability. In Figure 20, every data point on the
19
line represents the average value over a whole image. The curves show the domain average
spatial variability change as function of block size. In the figure, the monthly curve is flatter than
the daily curve, which indicates that the mean spatial variability of a monthly flux filed is more
uniform that that for a daily flux field. Given a daily mean field and a monthly mean flux field,
in a block region of the same size, one has more chance to find large jumps in a daily filed.
Monthly mean process filters the flux values and gives a more smooth flux field than the daily
filed. However, this is not always the case, as shown in the figure, in scale less than about 0.8
degree, a monthly mean filed is actually more variable than a daily field. The reason may be that
the monthly mean process reduces the spatial correlation between the neighbor points. Monthly
mean process is a temporal average process, when doing so the spatial correlation among the
neighbor points in a daily filed is reduced.
Figure 20.
Spatial variability as function of block size, N of 0.05 degree grids. Values are
averaged over entire domain. SW daily values are for January 1, 2005; monthly values are for
January 2005.
Terra and Aqua satellites fly over the region at different times. One is around 19 UTC and one is
around 21 UTC. There are about 2 hours lag between the two satellites. During the 2-hour period,
cloud distributions may change and solar illumination conditions may change as well. Figure 21
20
shows the monthly mean differences between the daily fluxes from Terra and Aqua. Here daily
mean is computed for each satellite by solar zenith angle normalization method.
Figure 21.
Monthly mean difference in SW between Terra and Aqua for January 2005. Terra
and Aqua instantaneous values were first normalized to daily means and then the difference was
taken between the two images.
Figure 22 shows the frequency distribution of day to day variation. The day to day variation is
computed as the differences between two successive days in January 2005 (day 2 minus day 1).
Daily values are combined from Terra and Aqua. Since this is for January, so the positive mean
difference value is mainly due to the solar declination angle change.
21
Figure 22.
Frequency distribution of monthly mean day to day variation. Day to day
difference is computed as: flux between each consecutive day from the beginning of the month
to the end for each grid point in the entire domain. Time period is January 2005; domain is
(long:-125-100; lat: 30 55).
Summary
 Inference schemes were modified to allow implementation with MODIS 5-km
observations and input data.
 Procedures were developed for evaluating the inferred fluxes against ground observations.
They were implemented against numerous well maintained and calibrated ground
observations as well as against observations in the region of interest.
 Procedures were developed to obtain spatial and temporal variability metrics that can be
used in snow-melt modeling.
Following needs to be addressed:
 How well is the diurnal cycle represented?
 Variability statistics needs to be redone for region of interest only.
22
References
Chou et al., 1999. A solar radiation parameterization (CLIRAD-SW) for atmospheric studies.
NASA Tech. Memo, NASA Goddard Space Flight Center, Greenbelt, MD, 38 pp.
Ma, Y. and R. T. Pinker, 2012. Shortwave Radiative Fluxes from Satellites: An Update. JGRAtmospheres, 117, Issue D23, DOI: 10.1029/2012JD018332
Niu Xiaolei, Pinker Rachel T.; Cronin Meghan F., 2010. Radiative fluxes at high latitudes.
Geophys. Res. Lett 37 Article Number: L20811 DOI: 10.1029/2010GL044606
Niu, X. and R. T. Pinker, 2011. Radiative Fluxes at Barrow,Alaska: A Satellite View. J. Climate,
JOURNAL OF CLIMATE, 24 (21), 5494-5505, DOI: 10.1175/JCLI-D-11-00062.1
Nussbaumer, E. A. and R. T. Pinker, 2012a. Estimating Surface Longwave Radiative Fluxes
from Satellites utilizing Artificial Neural Networks. JGR-A, 2011JD017141.
Ramaswamy, and S. M. Freidenreich, 1992. A study of broadband parameterizations of the solar
radiative interactions with water vapor and water drops, J. Geophys. Res., D11, 11,48711,512)
23