HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus GmbHGöttingen, Germany10.5194/hess-19-4275-2015Correction of real-time satellite precipitation with satellite soil moisture observationsZhanW.wzhan@princeton.eduPanM.https://orcid.org/0000-0003-3350-8719WandersN.https://orcid.org/0000-0002-7102-5454WoodE. F.https://orcid.org/0000-0001-7037-9675Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USADepartment of Physical Geography, Utrecht University, Utrecht, the NetherlandsW. Zhan (wzhan@princeton.edu)22October201519104275429128April201516June20153October20157October2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/19/4275/2015/hess-19-4275-2015.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/19/4275/2015/hess-19-4275-2015.pdf
Rainfall and soil moisture are two key elements in modeling the interactions
between the land surface and the atmosphere. Accurate and high-resolution
real-time precipitation is crucial for monitoring and predicting the onset of
floods, and allows for alert and warning before the impact becomes a
disaster. Assimilation of remote sensing data into a flood-forecasting model
has the potential to improve monitoring accuracy. Space-borne microwave
observations are especially interesting because of their sensitivity to
surface soil moisture and its change. In this study, we assimilate satellite
soil moisture retrievals using the Variable Infiltration Capacity (VIC) land
surface model, and a dynamic assimilation technique, a particle filter, to
adjust the Tropical Rainfall Measuring Mission Multi-satellite Precipitation
Analysis (TMPA) real-time precipitation estimates. We compare updated
precipitation with real-time precipitation before and after adjustment and
with NLDAS gauge-radar observations. Results show that satellite soil
moisture retrievals provide additional information by correcting errors in
rainfall bias. The assimilation is most effective in the correction of medium
rainfall under dry to normal surface conditions, while limited/negative
improvement is seen over wet/saturated surfaces. On the other hand,
high-frequency noises in satellite soil moisture impact the assimilation by
increasing rainfall frequency. The noise causes larger uncertainty in the
false-alarmed rainfall over wet regions. A threshold of 2 mm day-1
soil moisture change is identified and applied to the assimilation, which
masked out most of the noise.
Introduction
Precipitation is perhaps the most important variable in controlling energy
and mass fluxes that dominate climate and particularly the terrestrial
hydrological and ecological systems. Precipitation estimates, together with
hydrologic models, provide the foundation for understanding the global energy
and water cycles (Sorooshian, 2004; Ebert et al., 2007). However, obtaining
accurate measurements of precipitation at regional to global scales has
always been challenging due to its small-scale, space–time variability, and
the sparse networks in many regions. Such limitations impede precise modeling
of the hydrologic responses to precipitation. There is a clear need for
improved, spatially distributed precipitation estimates to support
hydrological modeling applications.
In recent years, remotely sensed satellite precipitation has become a
critical data source for a variety of hydrological applications, especially
in poorly monitored regions such as sub-Saharan Africa due to its large
spatial coverage. To date, a number of fine-scale, satellite-based
precipitation estimates are now in operational production. One of the most
frequently used is the Tropical Rainfall Measuring Mission Multi-satellite
Precipitation Analysis (TMPA) product (Huffman et al., 2007). Over the
17-year lifetime since the launch of the Tropical Rainfall Measuring
Mission (TRMM) in 1997, a series of high-resolution (0.25∘ and
3-hourly), quasi-global (50∘ S–50∘ N), near-real-time,
TRMM-based precipitation estimates have been developed and made available to
the research and applications communities (Huffman et al., 2007, 2010). Flood
forecasting and monitoring is one major application for real-time satellite
rainfall products (Wu et al., 2014). However, the applicability of satellite
precipitation products for near-real-time hydrological applications that
include drought and flood monitoring has been hampered by their need for
gauge-based adjustment.
While it is possible to create such estimates solely from one type of sensor,
researchers have increasingly moved to using combinations of sensors in an
attempt to improve accuracy, coverage and resolution. A promising avenue for
rainfall correction is through the assimilation of satellite-based surface
soil moisture into a water balance model (Pan and Wood, 2006). Over land, the
physical relationship between variations in soil water storage and rainfall
accumulation contain complementary information that can be exploited for the
mutual benefit of both types of products (Massari et al., 2014; Crow et al.,
2009). Unlike instantaneous rain rate, satellite surface soil moisture
retrievals utilize low-frequency microwave signals and possess some memory
reflecting antecedent rainfall amounts.
Studies have demonstrated that in situ (Brocca et al., 2009, 2013; Matgen et
al., 2012) and satellite (Francois et al., 2003; Pellarin et al., 2008, 2013;
Brocca et al., 2014) estimates of surface soil moisture could contribute to
precipitation estimates by providing useful information concerning the sign
and magnitude of antecedent rainfall accumulation errors. In particular,
Brocca et al. (2014) estimated daily rainfall on a global scale based on
satellite SM products by inverting the soil water balance equation. Crow et
al. (2003, 2009, 2011) corrected space-borne rainfall retrievals by
assimilating remotely sensed surface soil moisture retrievals into an
Antecedent Precipitation Index (API) based soil water balance model using a
Kalman filter (Kalman, 1960). However, these studies focused on multi-day
aggregation periods and a space aggregated correction at 1∘ resolution
for the corrected precipitation totals. This limits their applicability in
applications such as near real-time flood forecasting. Wanders et al. (2015)
tried to overcome this limitation by the correction of 3-hourly satellite
precipitation totals with a set of satellite soil moisture and land surface
temperature observations. One important conclusion by Wanders et al. (2015)
is that their results showed the limited potential for satellite soil
moisture observations for correcting precipitation at high resolution if
“all-weather” – i.e., microwave-based – land surface temperatures are not
available coincidently, as was the case with AMSR-E.
But this is not always the case, and it is also noted that current
low-frequency microwave soil moisture missions (specifically SMAP and SMOS)
do not have radiometers at frequencies useful for estimating land surface
temperatures, even though a 37 GHz sensor is part of the AMSR2 system. In
fact, SMAP and SMOS use LST from weather model analysis fields in their
algorithms. Unfortunately, the lowest microwave frequency of AMSR2 precludes
retrieving soil moisture from many areas with heavy vegetation, and AMSR2 has
a significant dry bias with less availability than AMSR-E, but is no longer
operable. So, improvements to satellite precipitation from the Global
Precipitation Mission products must rely solely on satellite soil moisture
products, and the improvements to the assimilation algorithms are the goal of
this study.
Thus, we focus exclusively on the usefulness of assimilating soil moisture
products to improve satellite rainfall. We propose as part of the work how to
improve the generation of rain particles and the bias correction of the
satellite soil moisture observations, as well as to enhance the assimilation
algorithm to maximize the information that can be gained from using soil
moisture alone to adjust precipitation. Due to the very strong and
complicated spatial structure of precipitation, that is non-Gaussian and
non-stationary in both time and space (Wanders et al., 2015), a more advanced
method is applied to generate possible precipitation fields than were used in
earlier studies or in Wanders et al. (2015) (see Sect. 2.2.2). Furthermore, a
more advanced bias-correction method is also applied to account for the
reported problems in the second-order statistics of the soil moisture
retrievals. We used a soil moisture remote sensing product to improve the
real-time remote sensing precipitation product, TMPA 3B42RT, through a
particle filter (PF), and therefore offer an improved basis for
quantitatively monitoring and predicting flood events, especially in those
parts of the world where in situ networks are too sparse to support more
traditional methods of hydrologic monitoring and prediction. The
precipitation enhancement experiments are carried out over the
continental US (CONUS) and the precipitation skill is validated against the
NLDAS gauge-radar precipitation product. Section 5 presents a comparison of
the results from this study to the earlier studies related to improving
satellite precipitation.
Error statistics of recovered precipitation and effect of surface
saturation in the idealized experiment (mm day-1).
Random replicates of satellite precipitation are generated based on real-time
TMPA (3B42RT) retrievals and its uncertainty (Pan et al., 2010), which are
then used to force the VIC land surface model (LSM) where one output of
interest is surface soil moisture. Satellite soil moisture data products are
compared and merged with the 3B42RT product to improve the accuracy of the
satellite precipitation estimates. A schematic for the study approach is
provided in Fig. 1. Based on real-time 3B42RT retrievals, a set of possible
precipitation estimates (a.k.a. replicates or particles)
{pit}i=1,2,…,N is generated with assigned initial prior
probability weights {wi}i=1,2,…,N. These rainfall rates are
then used to force the VIC land surface model to produce soil moisture
predictions {θi}i=1,2,…,N. Retrievals of AMSR-E
satellite surface soil moisture using the Land Surface Microwave
Model (LSMEM) (Pan et al., 2014) are then merged with the LSM-based soil
moisture within the particle filter (PF) that compares AMSR-E/LSMEM changes
in soil moisture, ΔSM, to the LSM predicted soil moisture changes.
From these, posterior weights {wi+}i=1,2,…,N are calculated
for each precipitation member (particle) that takes into account the
uncertainties of AMSR-E/LSMEM ΔSM retrievals. From these updated
weights, an updated precipitation probability distribution is constructed,
where the precipitation particle with the highest probability is taken as the
“best” adjusted precipitation estimate (3B42RTADJ). The
procedure is carried out over the continental US (CONUS) region on a
grid-by-grid basis (0.25∘) and a daily time step. Allowing for a
6-month model spin-up period, the adjustment is done from January 2003 to
July 2007.
Schematic for the dynamic assimilation of AMSR-E/LSMEM ΔSM
into TMPA (3B42RT) with the particle filter (PF).
Modeling, statistical tools and data sourcesThe particle filter
Data assimilation methods are capable of dynamically merging predictions from
a state equation (i.e., the land surface model) with measurements (i.e.,
AMSR-E retrievals) to minimize uncertainties from both the predictions and
measurements. It is assumed that the source of uncertainty in the land
surface model predictions comes solely from the real-time satellite
precipitation, so that the particle filter (PF) provides an algorithm to
update the precipitation based on the AMSR-E retrievals. The state evolution
of a particle filter from discrete time t- 1 to t can be represented as
θt=ftθt-1,pt,κt,αt,
where θt is the first-layer soil moisture at time t, whose value
is predicted by the state equation (Eq. 1) as ft(•), and in the
study is the hydrological model VIC, which takes in forcing data, including
precipitation (pt) and other forcings (κt); and simulates land
surface states (soil moisture and soil temperatures at various levels, snow,
etc.) and fluxes (evapotranspiration, runoff) at time t. Herein we are
basically interested only in the first-layer (top 10 cm) soil moisture state
and precipitation forcing, so other states and fluxes are not explicitly
shown. αt is the random error in the prediction of θt,
whose statistics are known but not its value at any specific time.
At time t, the satellite surface soil moisture retrieval, θt*,
can be related to the VIC modeled first-layer soil moisture θt as
θt*=htθt,βt,
where ht is taken as a regression that transforms the VIC simulated
first-layer soil moisture to satellite surface soil moisture. βt is
the noise in this regression relationship. The two noises αt and
βt are assumed to be independent of each other at all times t.
Schematic for the strategy for processing prior and posterior probability
densities in the particle filter. The missing rainfall event in TMPA (circled
in the right panel of a, correspond to red triangle in b)
against satellite signals as detected by AMSR-E/LSMEM ΔSM (circled in
the left panel of a, correspond to red triangle in c),
and recovered by assimilating AMSR-E/LSMEM ΔSM into TMPA (marked by
red triangle in d).
At time t, given a 3B42RT precipitation estimate,
ptsat, a set of N precipitation
replicates {pti}i=1,2,…,N and their associated initial
prior probability weight {wti}i=1,2,…,N are generated.
gptsat∼pti,wtii=1,2,…,N∑i=1Nwti=1g( ) is a probability density function. For N precipitation replicates,
{pti}i=1,2,…,N, the propagation of the states from time
step (t- 1) to t is by the VIC land surface model represented in
Eq. (1). The VIC land surface model simulates the 10 cm first-layer soil
moisture, {θti}i=1,2,…,N, for each precipitation
replicate:
θti=ftθt-1,pti,κt,αti=1,2,…,N,
with the associated weights assigned to the precipitation member:
θti,wtii=1,2,…,N=ftθt-1,pti,κt,αt,wtii=1,2,…,N.
If the satellite soil moisture retrieval at time t is θt*, the update of precipitation forcing is
accomplished by updating the importance weight of each replicate given the
“measurement” θt*:
wti+∼gθti|θt*i=1,2,…,N∑i=1Nwti+=1.
The likelihood function g(θti|θt*) can be derived from
ht and g(βt). The schematic of the utilized strategy is shown in
Fig. 2 with a synthetic example of a missing
rainfall pattern in the TMPA compared with satellite ΔSM. The
primary disadvantage of the particle filter is the large number of
replicates required to accurately represent the conditional probability
densities of pt and θt.
When the measurements exceed a few hundred, the particle filter is not
computationally practical for land surface problems. Considering computation
efficiency, we set the number of independent particles, N, from the prior
distribution to be 200.
Statistics of NLDAS precipitation given 3B42RT precipitation
measurement. Boxplot shows the minimum, 15 % quantile, 30 % quantile, median, 70 % quantile, 85 % quantile and
maximum value of NLDAS precipitation given 3B42RT precipitation in a certain bin.
Precipitation replicates generation
We generate precipitation replicates, {pti}i=1,2,…,N,
based on statistics comparing NLDAS and 3B42RT precipitation, as
shown in Fig. 3. Given a 3B42RT precipitation
measurement (binned by magnitude), with bin minimum and maximum indicated in
Fig. 3, precipitation replicates are generated
based on the corresponding 15th, 30th, 70th, 85th percentiles
and the maximum NLDAS precipitation of the particular quantile
bin as follows: 15 % of the replicates are generated with values uniformly
distributed from 0 and 15th percentile; 15 % of replicates with
values from 15th to 30th percentile; 20 % of replicates with
values from 30th percentile to the median; 20 % of the replicates
generated from the median to 70th; 15 % with values from 70th to
85th percentile; and 15 % from the 85th percentile to the
maximum precipitation value. Note that although the generation of particles
is based on statistics calculated from NLDAS, results show little difference
generating precipitation ensembles uniformly distributed between 0 and 200 mm day-1.
AMSR-E/LSMEM soil moisture retrievals
The soil moisture product is derived from multiple microwave channels of the
Advanced Microwave Scanning Radiometer for EOS (AMSR-E) instrument. The
retrieval algorithm by Pan et al. (2014) is an enhanced version of the Land
Surface Microwave Emission Model (LSMEM). The near surface soil moisture and
vegetation optical depth (VOD) are estimated simultaneously from a dual
polarization approach that utilizes both horizontal (H) and vertical (V)
polarizations measurement by the space-borne sensor. The input AMSR-E
brightness temperature comes from the NSIDC AMSR-E/Aqua Daily Global
Quarter-Degree Gridded Brightness Temperatures product (overlapping swaths in
the same day are truncated so that only the latest one is present).
Consequently, the soil moisture retrievals are also gridded at 0.25∘
with one ascending map and one descending map at the daily time step. A
maximum threshold value of 0.6 m3 m-3 has been applied manually
to reduce error from open water bodies. According to Pan et al. (2014), the
soil moisture data set based on observations from AMSR-E are shown to be
consistent at large scales in terms of reproducing the spatial pattern of
soil moisture from VIC land surface model simulation. Ascending soil moisture
retrievals (equatorial crossing time 01:30 LT – local time) is assimilated
in this study.
Similarly, while the spatial patterns of the basic statistics of
AMSR-E/LSMEM SM retrievals compare well to VIC simulations (Pan et al.,
2014), VIC has its top layer (10 cm), which is deeper than the detection
depth of AMSR-E, so that the mean and temporal variability of the retrievals
are higher than the VIC simulated soil moisture (Fig. 4 in Pan et al.,
2014). Considering this difference between detection depths, we pre-process
soil moisture retrievals for each pixel as follows:
Rescale soil moisture retrievals (AMSR-E/LSMEM SM) to have the same
minimum and maximum range as VIC-simulated first-layer soil moisture.
Calculate a daily soil moisture change. As satellite retrievals are
manually truncated to be no more than 0.6 m3 m-3 (equivalent to
60 mm of water in the top soil layer in VIC), retrievals larger than
0.6 m3 m-3 are excluded.
Fit a second-order polynomial regression model with ΔSM (all
units in mm of water in the top layer) from satellite and VIC simulation on
a monthly basis and 3 × 3 grid scale (window).
After pre-processing, the distribution of soil moisture change matches fairly
well with ΔSMVIC (Fig. 4). The mean absolute difference
reduces from a spatial average of 5.25 to 0.71 mm day-1, with
relatively larger value over the eastern CONUS. According to Pan et
al. (2014), the no-skill or negative-skill areas occur mostly over eastern
dense forests due to vegetation blockage of the soil moisture signal (Pan et
al., 2014). The accuracy of soil moisture retrievals is also limited by
mountainous topography and the occurrence of snow and frozen ground during
winter whose identification from satellite observations is often difficult.
For the purpose of this study, we assign zero weight to the
3B42RTADJ and rely exclusively on the initial 3B42RT
precipitation for time steps when the VIC model predicts snow cover or frozen
surfaces.
Empirical cumulative distribution function
of changes in soil moisture from top layer soil moisture from NLDAS
precipitation forced VIC simulation (black), and AMSR-E/LSMEM soil moisture
retrieval before (red) and after (blue) pre-processing.
VIC land surface model
The Variable Infiltration Capacity (VIC) model (Liang et al., 1994)
is used to dynamically simulate the hydrological responses of soil
moisture to precipitation, surface radiation and surface meteorology. The VIC
model solves the full energy and water balance over each 0.25 ∘ grid
cell independently, thus ensuring its computational efficiency. The
assumption of independency poses limitation on the application of LSM at very
high spatial resolution (e.g., 1 km × 1 km) over large areas.
Three-layer soil moisture is simulated through a soil–vegetation–atmosphere
transfer (SVAT) scheme, which also accounts for sub-grid-scale heterogeneity
of vegetation, soil and topography. A detailed soil moisture algorithm
description can be found in Liang et al. (1996). The VIC model has been
validated extensively over CONUS by evaluating soil moisture and simulations
to observations (Robock et al., 2003; Schaake et al., 2004).
Two cases with recovered spatial rainfall pattern in the idealized
experiment after merging satellite soil moisture retrieval on
(a)–(e) 27 October 2003 and
(f)–(j) 22 March 2006.
Idealized experiment
Before applying the particle filter assimilation algorithm to 3B42RT
precipitation estimates, we conducted an idealized experiment where we treat
the NLDAS precipitation as the “truth” and the NLDAS precipitation forced
VIC simulations as “satellite observed” soil moisture. As an idealized
experiment, we adjust TMPA real-time precipitation estimates based on these
“satellite observations”. Phase 2 of the North American Land Data
Assimilation System (NLDAS-2) rainfall forcing combines hourly WSR-88D radar
analyses from the National Weather Service (NWS) and daily gauge reports
(∼ 13 000/day) from the Climate Prediction Center (CPC) (Ek et al.,
2011). The data set, with a spatial resolution of 0.125∘ and hourly
observations, was pre-processed into 0.25∘ daily precipitation to be
consistent with that of 3B42RT and AMSR-E/LSMEM SM data sets. Hourly NLDAS
and 3-hourly 3B42RT precipitation is aggregated into daily precipitation
defined by a period shifted ∼ 7.5 h into the future
(21:00–21:00 LT), allowing for a necessary
delay for soil moisture to respond to incoming rainfall. The idealized
experiment is designed to test whether the algorithm is able to retrieve
rainfall forcing with soil moisture change, assuming that the soil moisture
observations are 100 % accurate.
Results show that, with the knowledge of first-layer soil moisture change
(via the “satellite observations”), the adjustment is able to recover
intensity and spatial pattern of forcing precipitation (Fig. 5g). Average
mean absolute error (MAE) of daily rainfall amount is reduced by 52.9 %
(2.91 to 1.37 mm day-1) over the region. Figure 5a to e shows an
example of the recovered rainfall field from the idealized experiment for
27 October 2003. The spatial pattern matches the original NLDAS precipitation
well.
Effect of surface soil saturation
While successfully recovering the general pattern of NLDAS precipitation
based on first-layer soil moisture, the idealized experiment is not always
able to recover the precipitation volume due to the fact that the top-layer
soil moisture alone does not contain the complete memory of the previous
day's rainfall. Deeper soil moisture, evapotranspiration and runoff also
carry part of this information. As the surface gets wetter, the VIC
first-layer soil moisture has smaller variation. If the incoming
precipitation brings the surface to saturation, the VIC model redistributes
the soil moisture vertically though vertical moisture flow and generates
runoff. Hence, soil moisture increments, ΔSM, near saturation are less
correlated with incoming precipitation as they change minimally to additional
incoming rainfall. An example demonstrating this saturation effect is shown
in Fig. 5f to j. When incoming precipitation brings the surface SM to (near)
saturation, there is very limited improvement after the adjustment. Because
of the low sensitivity of the soil surface to precipitation, there is little
change in ΔSM in response to precipitation variations among the
replicates. It is almost always the case that the algorithm is not able to
find a “matching” ΔSM.
We separately evaluate the skill improvement in the recovered NLDAS
precipitation with and without surface saturation. Figure 6 (statistics
provided in Table 1) confirms the effect of surface saturation on adjusted precipitation, which is well
described in previous studies (e.g., Brocca et al., 2013, 2014). The
recovered precipitation, when the surface soil is saturated, only contributes
more noise rather than an improvement to the rainfall estimates. The VIC
model computes the moisture flow between soil layers using an hourly time
step. If the first-layer soil moisture exceeds its maximum capacity, it is
considered to be a surface saturation case. As seen in Fig. 5, there is very
limited or negative skill in the recovered precipitation under saturated
surface soil moisture conditions. Such circumstances are identified and the
AMSR-E/LSMEM ΔSM observation disregarded by assigning zero weight to
the 3B42RTADJ values. Thus, for wetter areas with heavy
precipitation that potentially would bring the surface soil moisture to
saturation, the 3B42RT product is less likely to be adjusted according to
satellite ΔSM, and the best precipitation estimate is 3B42RT.
Accuracy of recovered precipitation in an idealized experiment:
(a) overall performance and separately comparing the improvement
performance of recovered NLDAS precipitation (b) with and
(c) without surface saturation condition. Statistics provided in
Table 1.
Effect of SM uncertainty
In the idealized experiment, NLDAS-VIC soil moisture is taken as truth with
zero uncertainty associated with (θt*). However, this assumption
is not valid for real satellite SM retrievals, mean absolute error of which
is approximately 3 % vol. vol.-1
(McCabe et al., 2005). To consider this, we added error to the “truth” SM
(normally distributed with zero mean and standard deviations of 1, 2, 3,
4 and 5 mm), and simulated the effect of SM uncertainty to evaluate the
associated adjustment errors. Figure 7 shows that larger soil moisture
observation errors lead to larger error variation after adjustment. This also
suggests that soil moisture responds to precipitation non-linearly based on
different initial conditions. An estimated wetter surface has lower
sensitivity to an incoming rainfall amount, resulting in larger error in the
recovered NLDAS precipitation. As shown in Fig. 7, the error standard
deviation of the recovered NLDAS precipitation increases with surface water
content (statistics shown in Table 2). As we add noise larger than
N(0.1 mm) into “true” SM observation, there is a wet bias of
approximately 1 mm day-1 regardless of first-layer soil moisture
level. This suggests that when the difference between first-layer SM and
saturation is less than 8 mm, the median of the errors in the recovered
NLDAS precipitation grows from 0.16 to 1.89 mm day-1 when we add
N(0.5 mm) noise, while the inter-quantile range (IQR) increases from
1.71 to 7.04 mm day-1. Acknowledging such a wet bias, to avoid
introducing any more unintentional bias in the 3B42RTADJ
estimates, we take as zero the uncertainty of AMSR-E/LSMEM SM retrievals,
i.e., we take ht(θt) as our single observation θt*
and adjust the 3B42RT estimates accordingly.
Error statistics of recovered NLDAS based on ΔSM (with added
errors) conditioned on first-layer soil wetness for the idealized experiment
(mm day-1).
[Recovered[VIC first-layer SM]<-30-30 to -25-25 to -20-20 to -15-15 to -12-12 to -10-10 to -9-9 to -8>-8NLDAS]--[maximum]*[NLDAS]mmmm day-1No errorMedian0.040.030.020.020.020.030.030.040.16IQR0.140.080.070.070.080.120.210.291.711.0Median0.861.071.081.030.990.970.970.940.66IQR1.521.721.771.831.962.082.142.192.592.0Median0.681.071.401.561.521.441.511.641.54IQR1.762.092.883.453.633.733.733.733.913.0Median0.150.801.201.411.471.511.651.841.88IQR1.362.163.043.733.743.794.345.245.474.0Median0.220.560.831.151.301.401.631.881.97IQR0.992.362.483.994.054.705.535.525.635.0Median0.000.150.520.901.101.271.541.811.89IQR1.622.542.914.434.515.955.905.797.04
* First-layer soil depth is 100 mm with a SM capacity of
∼ 45 mm, depending on porosity.
Error in recovered NLDAS precipitation
given surface moisture condition. Recovered NLDAS is based on using
“truth” soil moisture and soil moisture with normal error: N(0.1 mm),
N(0.2 mm), N(0.3 mm), N(0.4 mm) and N(0.5 mm). Statistics provided in Table 2.
26 May 2006 rainfall pattern in 3B42RT (b) against
NLDAS (d) as detected by AMSR-E/LSMEM ΔSM (a), and
recovered rainfall field (3B42RTADJ) by assimilating AMSR-E/LSMEM
ΔSM (c). Gray shading shows area without soil moisture
retrievals.
Pearson correlation coefficient between
AMSR-E/LSMEM ΔSM and precipitation (from 1 January 2003 to 31 July 2007):
(a) NLDAS, (b) 3B42RT and (c) 3B42RTADJ; annual mean precipitation
in (d) NLDAS, (e) 3B42RT and (f) 3B42RTADJ
of time steps with AMSR-E/LSMEM ΔSM retrievals.
Frequency of rainy days in 3B42RT, 3B42RTADJ and NLDAS with
(a) 0.1 mm day-1 and (b) 2 mm day-1 rainfall
threshold to define a rain day.
It is noteworthy that the soil moisture change is calculated based on
previous days' soil water contents. Therefore errors tend to accumulate over
time until they are “re-set” when a significant precipitation event takes
place. This type of uncertainty accounts for a small portion of the total
error in the adjusted precipitation (black being the no-error case in Fig. 7
with the “true” change in soil moisture from every time step). As complete
global coverage is not provided with each orbit of the AMSR-E sensor, on
average 44.01 % of the time steps (< 0.6 m3 m-3) during the
study period have observations, with more frequent overpasses at higher
latitudes (Fig. 4e in Pan et al., 2014). This observation gap unavoidably
introduces extra uncertainty into the retrieval of the precipitation signal.
To further avoid possible additional errors, we update the forcing rainfall
when a ΔSM temporal match (±0.4 mm) is available, and keep the
original precipitation if a match is not available.
Improvement on real-time precipitation estimates and their validation
The adjustment of real TMPA 3B42RT retrievals based on AMSR-E/LSMEM
ΔSM is carried out using the methods described in Sect. 2.2.3, and results
from the idealized experiment (Sect. 3) with regard to the circumstances
where an adjustment is applied.
An example of TMPA 3B42RT adjustment is provided in Fig. 8, where a snapshot
of the rainfall field is shown (Fig. 8b) and compared with NLDAS on
26 May 2006 and the adjusted rainfall pattern based on AMSR-E/LSMEM
ΔSM. The 3B42RTADJ rainfall field (Fig. 8c) is similar in
terms of its spatial distribution compared to NLDAS precipitation estimates
(Fig. 8d).
On average, TMPA 3B42RT and AMSR-E/LSMEM ΔSM have a spatial Pearson
correlation coefficient of 0.37 (shown in Fig. 9, left panels), compared
to 0.52 for the correlation between NLDAS and ΔSM. After the
adjustment procedure, the Pearson correlation coefficient between
3B42RTADJ and AMSR-E/LSMEM ΔSM increases to 0.53 (shown in
Fig. 9), indicating that the correction method is successful. A below-average
increase in correlation is found over the western mountainous region, the
Great Lakes region and the eastern high vegetated and populated region.
Additionally, the satellite soil moisture suffers from snow/ice/standing
water contamination, which affects the potential for improved results after
correction. The 3B42RTADJ has significant improvement over 3B42RT
in terms of long-term precipitation bias. The bias in 3B42RT annual mean
precipitation is reduced by 20.6 %, from -9.32 mm month-1 spatial
average in 3B42RT to -7.40 mm month-1 in 3B42RTADJ
(shown in Fig. 9, right panels). Frequency of rain days generally increases
significantly everywhere (Fig. 10). The NLDAS data (Fig. 10, right panels)
suggest an almost constant drizzling rainfall over parts of the western mountainous area (Montana, Idaho, Wyoming
and Colorado), while assimilating AMSR-E/LSMEM ΔSM data sets
do not have a signal of higher rainfall frequency
(Fig. 10, middle panels). This is possibly due to deficiencies in satellite
retrievals over the mountainous areas and frequent presence of snow and ice
(3B42RT is not updated under such circumstances).
Distribution of 3B42RT and 3B42RTADJ precipitation error
compared to NLDAS. Statistics are provided in Table 3.
FAR and POD of 3B42RT (top panels) and 3B42RTADJ (bottom
panels) with (a) 0.1 mm day-1 and
(b) 2 mm day-1 rainfall thresholds to define a rain event.
Figure 11 shows the assimilation results for the
grids and days with soil moisture observations, using the NLDAS
precipitation as a reference. Overall, the method is successful in
correcting daily rainfall amount when 3B42RT overestimates precipitation
(3B42RT - NLDAS > 0). Mean standard deviation (SD) of
3B42RTADJ- NLDAS is between 1 and 3 mm day-1 (statistics provided in
Table 3). When 3B42RT underestimates rainfall
(3B42RT - NLDAS < 0), the assimilation has limited improvement on
3B42RT. This is due to the effect of surface saturation. In terms of adding
rainfall, effectiveness of the assimilation is limited under the following
two circumstances.
The presence of wet conditions or (near) saturation. There is higher
probability bringing the surface to saturation (wetter condition) when the
assimilation adds rainfall into 3B42RT. However, soil moisture increments are
less sensitive to incoming precipitation on wetter soil. Therefore, an error
in ΔSM often translates into 3B42RTADJ in a magnified
manner.
The presence of very heavy precipitation, which typically brings the
surface to saturation, hence not results in an update of 3B42RT, is not updated.
If, by a small probability, the surface is wet (nearly saturated) but not
completely saturated after a heavy rainfall, the updated 3B42RT also suffers
from large uncertainty (explained in 1) above).
The effect of the assimilation conditioned on 3B42RT rainfall amount is
further evaluated by skill scores. Figure 12 presents probability of
detection (POD) and false alarm rate (FAR) in 3B42RT and
3B42RTADJ, using NLDAS as the reference data set. The rain event
threshold is set to be 0.1 and 2 mm day-1. This is possibly due to
lower soil moisture variability in satellite retrievals over the dry,
mountainous areas and frequent presence of snow and ice (3B42RT is not
updated under such circumstances). For a 0.1 mm day-1 threshold, both
FAR and POD increase in 3B42RTADJ except for the mountainous
region, whereas, for a 2 mm day-1 threshold, there is only a slight
increase in FAR in most of the eastern US region. The overestimation of rain
days is also absent when the 2 mm day-1 event threshold is applied,
which suggests that most of the excessive rainy days have a less than
2 mm/day rain amount. Consistent with Wanders et al. (2015), spatially,
larger improvements are found in the central US. The area coincides where
higher AMSR-E/LSMEM ΔSM accuracy is found (non-mountainous regions
with little urbanization and light vegetation). Despite the regional
variability, these excessive rainy days are a result of the high-frequency
noise in AMSR-E/LSMEM soil moisture retrievals identified by Pan et
al. (2004) and Wanders et al. (2015).
Mean and standard deviation (SD) of 3B42RT and 3B42RTADJ compared to NLDAS
precipitation (mm day-1).
[3B42RT]-[NLDAS]<-25-25 to -20-20 to -15-15 to -10-10 to -5-5 to -2-2 to -0.5-0.5 to 0.50.5 to 22 to 55 to 1010 to 1515 to 2020 to 25> 25(mm day-1)[3B42RT]-Mean-32.32-22.19-17.13-12.09-6.98-3.22-1.09-0.021.113.206.8711.9616.9721.9527.35[NLDAS]SD8.521.421.421.421.390.850.430.120.430.841.371.391.371.382.08[3B42RTADJ]-Mean-31.24-20.31-14.79-9.69-4.81-1.600.161.080.440.210.02-0.060.00-0.03-0.12[NLDAS]SD11.036.406.125.344.082.731.881.181.862.292.602.913.012.742.41
The applied method is ineffective for light rainfall < mm, where the
adjustment tends to over-correct precipitation by adding excessive rainfall
– mostly the result of the high-frequency AMSR-E noise. The MAE of light
rainfall (< 2 mm day-1) increased from 0.65 mm day-1 in
3B42RT to 0.99 mm day-1 in 3B42RTADJ. On the other hand,
satellite soil moisture assimilation is very effective in correcting
satellite precipitation higher than
2 mm day-1: the MAE of medium to large rainfall
(≥ 2 mm day-1) decreased from 7.07 mm day-1 in 3B42RT to
6.55 mm day-1 in 3B42RTADJ. The effect of the assimilation
is different over the western mountainous region, the north-to-south
central US band and the eastern US.
Probability that the added rainy days (3B42RT = 0 mm day-1,
3B42RTADJ> 0 mm day-1) are true rain
events (NLDAS > 0 mm day-1) given corresponding AMSR-E/LSMEM ΔSM.
The western mountainous region has a dry climatology with more frequent
rainfall in small amounts. The white noise in ΔSM, negatively
impacting 3B42RTADJ, is comparable to the positive improvement brought
by actual light rainfall signals in ΔSM. Therefore, the assimilation
of ΔSM has no significant impact in these regions.
The north-to-south band over the central US experiences more medium to
high (≥ 2 mm day-1) rainfall. In
addition, the region is lightly vegetated (annual mean LAI < 1) with low
elevation (< 1500 m), where soil moisture retrievals are of higher
accuracy. Soil moisture climatology is wetter in the west, causing larger
variations in 3B42RTADJ error from the white noise ΔSM (as
discussed in Sect. 3.2). Despite that, satellite soil moisture is most
effective correcting medium to large rainfall under normal surface
conditions.
The decreased skill in 3B42RTADJ over the eastern US is primarily
attributed to both precipitation and soil moisture climatology, a wet climate
with more medium to high rainfall, neither of which
is suitable for soil moisture assimilation.
In summary, the high-frequency noise in soil moisture product causes a major
limitation. The noise impacts adjusted precipitation by introducing false
alarm rain days. It is difficult to distinguish the noise and retrieve the
true rainfall signals. A remedy to prevent the excessive rain days is
applying a cutoff ΔSM threshold when rain days are added, at the
expense of neglecting a part of the true rainfall signals. Figure 13 shows
the probability of added rainy days being consistent with NLDAS
(NLDAS > 0 mm day-1) with respect to ΔSM. When a new rainy
day is added (3B42RT = 0 mm day-1,
3B42RTADJ> 0 mm day-1) based on AMSR-E/LSMEM
ΔSM of 2 mm day-1, there's approximately 78 % chance that the
added rain day is a true event (NLDAS > 0 mm day-1); That is,
∼ 22 % chance that it is a false alarm
(NLDAS = 0 mm day-1). When AMSR-E/LSMEM ΔSM is larger than
2 mm day-1, the probability of added rainy days being true event is
even higher, up to 90 % chance. Here we applied a threshold of
2 mm day-1 to AMSR-E/LSMEM ΔSM. That is, when new rainy days
are introduced (3B42RT > 0, 3B42RTADJ > 0), we discard the update and
keep the no-rain day if AMSR-E/LSMEM soil moisture increment is below 2 mm.
Note that, the probability of the false alarms depends on soil moisture
climatology: the wetter soil moisture climatology, the larger uncertainty in
the signal. Therefore, this threshold should vary in accordance with local
soil moisture climatology, i.e., a larger threshold over the wetter
eastern US and a smaller threshold over the drier western US. Nevertheless,
after the 2 mm day-1ΔSM threshold is applied, expectedly, the
statistics are largely improved: FAR is decreased significantly from 0.519
(wo. ΔSM threshold) to 0.066 (w. ΔSM threshold). MAE of light
rainfall (< 2 mm day-1) in 3B42RTADJ decreased from
0.99 to 0.64 mm day-1, compared to 0.65 mm day-1 in 3B42RT. For
medium to large 3B42RT rainfall (≥ 2 mm day-1), it effectively
increased POD (0.362 in 3B42RT vs. 0.386 in 3B42RTADJ
w. ΔSM threshold) and decreased FAR (0.037 in 3B42RT vs. 0.030 in
3B42RTADJ w. ΔSM threshold). Further work is needed to
characterize, distinguish and decrease the high-frequency noise in SM
retrievals. Figure 13 gives an example of evaluating the impact of SM
uncertainties in assimilation as curves derived over different topography can
be quantitatively compared.
Comparison to other studies
Many other studies have utilized satellite microwave brightness temperatures
or soil moisture retrievals to constrain satellite precipitation estimates
(Pellarin et al., 2008), estimate precipitation (e.g., Brocca et al., 2013)
or improve precipitation estimates through assimilation (Crow et al., 2009,
2011). Here, we review their approaches and findings in light of the results
of this study, and compare our results with some of these studies to gain
insight into their robustness and consistency.
Pellarin et al. (2008) used the temporal variations of the AMSR-E 6.7 GHz
brightness temperature (TB) normalized polarization difference,
PR = (TBV- TBH)/(TBV+ TBH),
to screen out anomalous precipitation events from a 4-day cumulative
satellite-estimated precipitation (EPSAT-SG: Chopin et al., 2005) from 22 to
26 June 2004 over a 100 × 125 km box centered over Niger in West
Africa. This was extended in Pellarin et al. (2013), where an API-based water
balance model was used to correct three different satellite precipitation
products (CMORPH, TRMM-3B42 and PERSIANN) over a 4-year period in West Africa
at three 0.25∘ grids in Niger, Benin and Mali). The new algorithm was
evaluated by comparing the corrected precipitation to estimates over the
0.25∘ grids from ground-based precipitation measurements. A sequential
assimilation approach was applied where AMSR-E C-band TB measurements were
used to estimate a simple multiplicative factor to the precipitation
estimates in order to minimize the difference between observed (AMSR-E) and
simulated TBs in terms of root mean square error (RMSE). The results show
improvements over those found in Pellarin et al. (2009). Specifically, the
Pellarin et al. (2013) study shows that the proposed methodology produces an
improvement of the RMSE at daily, decadal and monthly timescales and at the
three locations. For instance, the RMS mean error decreases from 7.7 to
3.5 mm day-1 at the daily timescale in Niger and from 18.3 to
7.7 mm day-1 at the decadal timescale in Mali.
Crow et al. (2003, 2009, 2011) demonstrated the effectiveness of the
assimilation of remotely sensed microwave brightness temperatures or
retrieved soil moisture in estimating precipitation based on airborne
measurements over the Southern Great Plains (USA) region (Crow et al., 2003);
2- to 10-day accumulated precipitation within a simple API water budget model
and assimilation scheme over CONUS (Crow et al., 2009); and 3-day, 1∘
precipitation accumulation over three African Monsoon Multidisciplinary
Analysis (AMMA) sites in West Africa with an enhanced assimilation scheme and
an API moisture model (Crow et al., 2011). Crow et al. (2009) recommend
against estimating precipitation at a larger scale than 3 days based on
assimilating AMSR-E/LSMEM soil moisture.
Brocca et al. (2013) estimated precipitation by inverting the water budget
equation such that precipitation could be estimated from changes in soil
moisture. The inverted equation was calibrated using in
situ, 4-day averaged observations at two sites in Spain and Italy. In
Brocca et al. (2014), the same approach was used globally to estimate daily
precipitation at 1∘ spatially. Five-day cumulated rainfall estimates
are derived from three satellite-derived soil moisture data sets (AMSR-E
LPRM, ASCAT and SMOS), and linearly interpolated to daily values, for their
precipitation estimation algorithm. No formal data assimilation was carried
out. The newly created precipitation data set was compared to two satellite
precipitation products (TRMM-3B42RT, GPCC) and two gauge-based precipitation
products (GPCP, ERA-Interim). Five-day accumulated rainfall data, aggregated
to a 1∘ spatial resolution, are considered in their assessment
analyses with promising results. But, they do note that their approach has
“poor scores in reproducing daily rainfall data”. Ciabatta et al. (2015)
derived daily rainfall product using ASCAT over Italy and integrated it with
TMPA 3B42RT precipitation. The merged product also shows promising results.
In the study reported here, four advances have been made over these earlier
studies: (i) we adopted a state-of-the-art dynamic land surface model that
has demonstrated high skill in simulating soil moisture when driven by
high-quality precipitation data (Schaake et al., 2004); (ii) we applied a
state-of-the-art data assimilation procedure based on particle filtering so
as to extract (and hopefully maximize) the information content from the
satellite most effectively; (iii) we increased the resolution of the
precipitation estimation window down to 1 day, exceeding the conclusions in
these earlier studies that the finest temporal resolution is 3 to 5 days.
Additionally we increased (or matched) the spatial resolution to
0.25∘, limited primarily by the satellite soil moisture product
resolution; and (iv) previous studies are based on the assumption that the SM
retrievals are 100 % accurate and contain no errors. We evaluated this
assumption by analyzing the impact of uncertainties associated with the soil
moisture retrievals. These advances offer important benefits when satellite
precipitation products are used for applications such as flood forecasting.
Admittedly, by aggregating in space and time, the improvement is more robust
since some errors are averaged out.
Wanders et al. (2015) performed a comprehensive inter-comparison study using
multiple satellite soil moisture and land surface temperature (LST) data at
fine temporal scale (3-hourly). Compared to their study, ours focuses on
using soil moisture exclusively from one satellite and retrieval algorithm,
and in improvements to the assimilation algorithm, specifically, (i) the
longer temporal period (2010–2011 in Wanders et al. (2015) vs. 2002–2007 in this study), (ii) the temporal
resolution (3-hourly vs. daily), and (iii) the particle generation and
bias-correction method. We present in the paper improvements in the
generation of rain particles and the bias-correction of the satellite soil
moisture observations, as well as enhancements to the assimilation algorithm
to maximize the information that can be gained from using soil moisture alone
in adjusting precipitation. Due to the very strong and complicated spatial
structure of precipitation, that is non-Gaussian and non-stationary in both
time and space, a more advanced method is applied to generate possible
precipitation fields than used or presented in earlier studies or in Wanders
et al. (2015). Furthermore, a more advanced bias-correction method is also
applied to account for the reported problems (Wanders et al., 2015) in the
second-order statistics of the soil moisture retrievals, and (iv) SM
retrieval products (and overpasses) used in assimilation. Our improved
results are based on soil moisture retrievals from ascending overpasses only
(vs. both descending and ascending overpasses from multiple data sets, i.e.,
AMSR-E/LSMEM, ASCAT and SMOS). Our exclusive focus on the usefulness of soil
moisture product promises more applicability especially for improving
satellite precipitation from the Global Precipitation Mission products. The
descending overpasses have generally better performance than the ascending,
suggesting the potentials of further improvements.
A quantitative comparison of Wanders et al. (2015) and our results is
provided below. Despite the different time periods between Wanders et
al. (2015, 2010–2011) and in our study (2002–2007), Wanders et al. (2015)
show decreasing POD (-15.0 to -46.4 %, depending on the different
products used) and FAR (-47.2 to -89.1 %, depending on the different
products used) for all rainfall after assimilation using either (single or
multiple) SM products alone or SM + LST data combined (see Table 4 of
Wanders et al., 2015). While in our study, after applying ΔSM
threshold, medium to large 3B42RTADJ rainfall
(≥ 2 mm day-1) has an increase in POD (+6.6 %) and decrease
in FAR (-18.9 %). Furthermore, the significant dry bias in adjusted
precipitation (see Fig. 6 of Wanders et al., 2015) is not present in our
results (Fig. 9). This is due to improvements in our precipitation ensemble
generation and bias correction scheme. Wanders et al. (2015) applied an
additional step generating precipitation particles sampling from a
3 × 3 window that over-eliminates most of the excessive rainfall
along with some real signal. We suggest loosening this constraint to a larger
window size or to sample from adjusted precipitation instead of original
3B42RT precipitation. However, sampling from adjusted precipitation at each
time step would significantly increase the computational demand, limiting the
potential for a global application at high temporal/spatial resolution.
Furthermore, the outcome is quite different for the distribution of soil
moisture retrievals after pre-processing (Fig. 9 of Wanders et al. (2015)
vs. Fig. 4 in our study) due to the different methods used. After
pre-processing, distributions of soil moisture retrievals are more similar to
that of NLDAS precipitation forced, VIC modeled first-layer soil moisture.
CDF-matching used by Wanders et al. (2015) is based on the assumption that
satellite soil moisture and modeled soil moisture respond to heavy rainfall
in the same way – essentially having a rank correlation of 1. However, that
is not observed because of the shallower detection depth of the satellite
soil moisture. On the other hand, using the pre-processing method presented
in this study, the signal of near-saturation in AMSR-E/LSMEM ΔSM tends
to be overestimated after pre-processing, which indicates a heavy rain event
that is often accompanied with surface saturation and thus does not provide
effective information for the assimilation. The other benefit of the
second-order polynomial regression lies in its non-linearity. An error in the
soil moisture product impacts the precipitation adjustment in a predictable
way, allowing for a more systematic post-processing treatment. Based on the
known error characteristics, we demonstrate a potential remedy to deal with
the error by applying a 2 mm day-1 cutoff ΔSM threshold.
Meanwhile, it is also highlighted that the cutoff threshold should be
variable and positively correlated with local soil moisture climatology. We
acknowledge that the soil moisture product used in Wanders et al. (2015) is a
blended product of multiple satellite soil moisture data sets. It is not
clear how its error characteristics impact the adjusted precipitation.
Conclusion and discussion
Based on the retrieved soil moisture from AMSR-E using the LSMEM retrieval
algorithm, we propose an assimilation procedure to integrate soil moisture
information into the VIC land surface model so as to improve real-time,
satellite precipitation estimates. The ability to estimate rainfall amount
is now enhanced with the above improvements, especially for correcting
medium rainfall amounts. However, constrained by the noise in AMSR-E TBs and
thus soil moisture retrievals, the assimilation is not effective in
detecting missed rainfall events. The improved precipitation estimates,
referred to as 3B42RTADJ estimates, are overall consistent in
reproducing the spatial pattern and time series of daily rainfall from NLDAS
precipitation. The results illustrate the potential benefits of using data
assimilation to merge satellite retrievals of surface soil moisture into a
land surface model forced with real-time precipitation. Potentially the
method can be applied globally for areas meeting vegetation cover and
surface condition constraints that allows for soil moisture retrievals.
Under these conditions, the approach can provide a supplementary source of
information for enhancing the quality of satellite rainfall estimation,
especially over poorly gauged areas like Africa.
Nonetheless, some caution is required. The results of this study show that
the adjusted real-time precipitation tends to add additional rain (frequency)
resulting in more time steps with rain but lower regional average in the
western US and slightly higher regional average in the eastern US. It is also
noticed that the precipitation adjustments are insensitive under saturated
soil moisture conditions. A wetter surface magnifies any error associated
with satellite observation by incorrectly adjusting precipitation. These
errors, mixed with the “real” signal, generally add approximately
∼ 2 mm of precipitation (or higher), depending on the soil moisture
climatology. It is important to consider these circumstances when
observations are used so as to avoid introducing additional error. With these
identified limitations, continued research is needed to assess the biases in
the real-time precipitation retrievals on a local to regional basis so the
assimilation system can be modified accordingly.
The assimilation scheme used here assumed that the errors were attributed to
the real-time precipitation retrievals, but the precipitation estimates
after adjustment includes errors from additional sources. The two primary
sources are errors in soil moisture retrievals and errors in the land
surface model that include model parameterizations (poorly or insufficiently
represented processes as well as scale issues) and parameter errors
(insufficient calibration). There are also errors in other model forcing
fields besides precipitation. Further studies are needed to assess the
attribution of these error sources to the total error. Such research will
further improve the use of real-time satellite-based precipitation for
global flood monitoring.
Besides the clear, heavy dependency of the assimilation effectiveness on the
accuracy of satellite soil moisture product, it is also important to acquire
adequate knowledge on the error characteristics of satellite soil moisture
retrievals. Knowledge of the soil moisture errors could be important and the
assimilation methods (including precipitation ensemble generation and
pre-/post-processing method) should be chosen accordingly. On the other hand,
the presence of data gaps between overpasses could be a large source of
uncertainty with data assimilation. Further effort towards reliable
spatial-temporal continuous (gap-filled) satellite soil moisture data sets is
needed.
While it has been illustrated in this study that the enhancement of real-time
satellite precipitation estimates can be realized through an assimilation
approach using satellite soil moisture data products and a particle filter,
additional satellite-based observations (e.g., multi-sensor soil moisture
products) or variables (e.g., land surface temperatures as shown in Wanders
et al. (2015), inundated areas) could be added/replaced in the assimilation
process with different levels of complexity, e.g., by applying constraints on
the particle generation. This opens up a great number of opportunities in
using space-borne observations for supplementing direct retrievals of
precipitation.
Acknowledgements
This research was supported through NASA grant NNX13AG97G
(Multi-sensor enhancement of real-time satellite precipitation
retrievals for improved drought monitoring) under the Precipitation
Measurement Mission. Part of this research was financially supported by NWO
Rubicon 825.15.003. This support is gratefully acknowledged.
Edited by: W. Wagner
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