International Workshop on Terrestrial Water Cycle Observation and Modeling from Space: Innovation and Reliability of Data Products Satellite Precipitation Estimation over the Tibetan Plateau and Perspectives for
new Satellite Missions
Federico Porcù1*, Uarda Gjoka1,2, Stefano Dietrich3, Paolo Sanò3, Daniele Casella3, Alberto Mugnai3
1. Department of Physics and Earth Sciences – University of Ferrara, via Saragt 1, I-44122 Ferrara, Italy
([email protected])
2. Department of Information Technology – Aleksander Moisiu University, Durres, Albania
3. ISAC-CNR, via Fosso del Cavaliere 100, 00185 Rome, Italy
ƒ ABSTRACT
Precipitation estimation is a challenge for atmospheric remote sensing: a number of satellite sensors,
with different sensitivity to precipitation, are commonly used to feed estimation techniques.
Precipitation signature in the radiation measured from an orbiting sensor varies across the wavelength:
is generally low in the visible-infrared and higher in the microwave. On the other hand, due to
diffraction reasons, microwave sensors are only operated on low orbit satellites, resulting in high
revisiting time and large footprint at the ground. To overcome these limitations, multisensor
approaches are pursued, combining microwave and VIS-IR measures in order to mutually mitigate
disadvantages and enhance capabilities.
In the frame of CEOP-AEGIS an effort was undertaken to perform precipitation estimation on the
Tibetan Plateau, where the knowledge of precipitation systems is very low and the ground-based
observation system is poor. A summary of the results achieved in the Project is presented, with
emphasis on satellite precipitation estimation, showing advantages and drawbacks of the considered
techniques. A new Artificial Neural Network multisensor technique has been implemented on the
Plateau, by using infrared METEOSAT-7 channels, ground radar rainrate measurements and
microwave satellite estimates. Comparison with ground data and global scale precipitation products
are considered and the role of orography and diurnal cycle on the precipitation intensity and spatial
distribution is evaluated. The results are also considered with a look in the next future, when other
sensors, dedicated to precipitation measurement, such as the Dual-frequency Precipitation Radar, on
board the GPM Core Observatory, will be available.
ƒ KEYWORD: PRECIPITATION, REMOTE SENSING, RADAR.
ƒ INTRODUCTION
The quantitative estimation of spatial distribution of precipitation in the Tibetan Plateau (TP) is a key
aspects for the understanding of water cycle processes and the estimation of water resources. Over
TP observation relies on a sparse raingauge network and few ground based weather radar with
coverage strongly limited by beam blocking due to orography. The satellite point of view is thus a
valuable option for precipitation monitoring over TP, and several studies have been carried out to
highlight precipitation characteristics [1]. The need of satellite observations was originally addressed
by Ueno [2], who implemented different visible-infrared (VIS-IR) and passive microwave (PMW)
International Workshop on Terrestrial Water Cycle Observation and Modeling from Space: Innovation and Reliability of Data Products algorithms to estimate precipitation for the monsoon season, adapting different algorithm to the TP
climatology, highlighting the need estimation algorithms improvement and adaption. To assess the
relationship between satellite data an precipitation at the ground in experimental way, two important
issues must be addressed: the understanding of the precipitation structure over TP to emphasize the
algorithm concept, and the measurement of accurate aerial precipitation amounts to calibrate and
validate the algorithms. Both these issues have been addressed in 1998, during the GAME/Tibet
project, when the study of precipitation by a Doppler Radar and a dense raingauge network has been
carried out, taking also advantage of the observation of the Tropical Rainfall Measuring Mission
(TRMM) and Chinese GEO satellite FY/2 [3].
A new algorithm for precipitation over land by deriving the optical thickness from the Brightness
Temperature (BT) of the TRMM Microwave Imager (TMI) was proposed [4]. Based on a microwave
radiative transfer equation, two indices, Index of Soil Wetness and Polarization Index, are used.
Daily rain amounts have also been validated over the Dasa Basin, about 300 km2 wide, with 11
raingauges. Three major shortcomings for quantitative precipitation estimation were outlined: 1) the
assumption of no emission from atmosphere and rain at 85 GHz was too simplistic; 2) the temporal
sampling of TMI is inadequate to resolve the rapidly varying precipitation patterns; 3) the
relationship between precipitation layer optical thickness and actual rainrate depends upon the
unknown hydrometeor type.
Similar approach was pursued in [5] using BT to estimate precipitation rate over TP. They used 4
raingauges data to assess and calibrate the rainrate at the ground with two parameters computed as
linear combinations of BT: Scattering Index and Polarization Corrected Temperature. An
intercomparison study shows that [5], tuned exclusively for TP, is advantageous over a global
techniques.
More recently, Yin et al. [6] used different MW and MW-IR based algorithm to infer monthly
precipitation over TP. A total of 50 parameters describing orography (height, slope, aspect,
orientation, among others) computed over a 1x1 km grid are used to “correct” the outputs of the
different techniques, through a principal component analysis. This analysis is strongly dependent on
the cumulation time used for precipitation evaluation and can be only qualitatively referred to shorter
time intervals, as requested for hydrological purposes.
ƒ SATELLTE PRECIPITATION ESTIMATION TECHNIQUES
Clouds and precipitation structures interact with electromagnetic radiation selectively: different
hydrometeors scatter and/or absorb radiation over different frequency ranges, in relation to their phase,
shape, size, density and habit. At VIS-IR wavelength clouds are mostly opaque bodies: the radiation
interacting with precipitation layers usually do not reach the satellite sensor. VIS-IR radiation carries
information on the cloud top structure, and an estimate of the rainrate below can only be indirectly
inferred, by means of statistical pattern recognition techniques. In the MW spectrum, instead, cloud
droplets and crystals are transparent to low frequencies (around 10 GHz), so that the emission of
raindrops can be measured from satellite. Al higher frequencies (e.g. 87 GHz), scattering from cloud
top ice particles becomes relevant: multispectral techniques are able to infer the vertical distribution of
hydrometeors and compute precipitation at the ground, based on physical properties of the
particle-radiation interactions.
Since PMW sensors, due to diffraction problems, have useful sized footprint only if on board Low
Earth Orbit (LEO) satellites, more advanced techniques make use of both VIS-IR (ensuring continuous
International Workshop on Terrestrial Water Cycle Observation and Modeling from Space: Innovation and Reliability of Data Products coverage) and PMW (providing direct information of cloud/precipitation structure) data, to provide
blended precipitation products [1,7].
A two step strategy has been designed to retrieve precipitation over the TP: 1) two microwave-based,
physically-founded algorithms provide high quality precipitation fields; 2) different techniques are
used to merge the MW high quality maps and high resolution VIS-IR GEO observations.
ƒ Physically based, microwave algorithms
The climatology of the precipitation over TP is clearly divided in a monsoon season, occurring in
warm months (JJAS), where diurnal convection takes place and night time stratified precipitation
occur [2,3,8], and a dry season for the rest of the year characterized by sparse snowfalls.
The data from the 94-GHz nadir-looking Cloud Profiling Radar (CPR) on board the LEO CloudSat
spacecraft [9] are used to retrieve snowfall rates over TP during dry months. The algorithm
developed and applied to TP hydrological basin is derived by Kulie and Bennartz [10]. The data
considered is the vertical reflectivity profiles (2B-GEOPROF) and makes use also of ancillary data
from the ECMWF model. The algorithm selects CPR vertical profiles with a vertically continuous
cloud path (i.e. reflectivity exceeding the -15 dBz threshold) for at least 1 km. A lower limit of 1.3
km above the ground is set to avoid clutter and a temperature threshold of 273 K is used to screen out
wet snow cases. Finally, a reflectivity snow rate-reflectivity (S-R) relationship of the type Z=aSb, is
applied to the lowest cloudy bin of the considered profile.
The a and b parameters in the Z-S relation should be experimentally computed, by on site
observation of ice particle shape, not available in this project, then we used values obtained by
scattering simulations on bullet rosettes, as a typical, averaged, hydrometeor shape.
For the monsoon season we considered an algorithm based on PWM, which is expected to work
reasonably well over land: the Cloud Radiation Database (CRD) [11]. This algorithm is a rainfall
retrieval scheme that works on conical scanner data: for this project data from SSMIS are used. The
algorithm is based on a cloud radiation database constructed as follows [12]. A cloud vertical profile
data set is assembled by means of cloud resolving model outputs (the Non-hydrostatic Modeling
System of the University of Wisconsin is used to this end), then a radiative transfer algorithm is
applied to simulate the radiances upwelling from the modeled cloud profiles. For the algorithm
set-up over TP and used in this work, the global database was completed by three model runs over
cases studies of convective precipitation over TP. When a set of satellite radiances ad different
frequency is measured from the satellite sensor, the database is searched for the cloud profile whose
simulated radiances better match the observed ones, and the precipitation rate related to the selected
profile is assigned at the ground. This selection of profiles is carried out using the Bayesian distance
among the measured BT vectors, and the corresponding simulated vectors of the previously selected
set of profiles of the CDR database. This algorithm is currently applied in different regions (Europe
and U.S.) with encouraging results.
ƒ PMW-VIS-IR blended techniques
Several blending techniques are proposed in the literature, based on different principles, algorithms
and data [13]. In this work the blended product are obtained at spatial and temporal resolution of
METEOSAT-7 IR data: 5x5 km (at nadir) and 30 minutes, respectively. METEOSAT-7 is on a GEO
orbit at 57 degrees of latitude.
We started attempting three different approaches: Passive Microwave combined Global Convective
Diagnostic (PM-GCD) [14], Calibrated Negri Adler Wetzel (C-NAW) [15], and Artificial Neural
International Workshop on Terrestrial Water Cycle Observation and Modeling from Space: Innovation and Reliability of Data Products Network (TANN) [16]. The first approach, based on the dependence of convective rainrate from the
temperature difference between the infrared window (λ = 11.0 μm) and the water vapor (WV, λ = 6.5
μm) channels. The correlation between rainrate and temperature difference is usually good if applied
over Europe and at global scale [14], but over TP was not satisfactory, probably due to variable water
vapor tropospheric content and to the presence of nighttime stratiform rain, not sensed at IR
wavelength. For this reason, the PM-GCD was abandoned, after first sensitivity studies.
The C-NAW [15] first uses IR GEO observations to delineate high precipitation areas by means of the
coldest 10% fraction of the cloud-top temperature and then MW observations from LEO satellites to
estimate mean rainfall values within such areas. This technique mostly applies to convective clouds,
but is also successfully used for stratified of mixed cloud types, if the precipitation coefficient are
carefully calibrated. Over the TP the calibration was performed by using the CPR snowfall technique,
for the cold months, and the output of the CRD algorithm for the monsoon seasons.
The use of ANN to provide a blended product is open to a number of options, as seen in the literature
[16]. During training phase, the ANN learn to reproduce, given a set of input (i.e. satellite data), a
known output (i.e. precipitation at the ground), setting up a transfer function that weights input to
obtain corresponding output. We used as input data the BT from METEOSAT-7 channels in the IR and
WV, and their local variability features (i.e. local average and local standard deviation over a 3x3 pixel
neighborhood) then used two different approaches for the reference precipitation data for ANN
training. Details on the ANN structure and training-testing-validation procedures can be found in [16].
For the first release of the product (hereafter referred as TP ANN calibrated with radar, TANN-R) the
training set used weather radar as reference ground data. Five weather radar (two over Qinghai and
three around Lhasa), operated by China Meteorological Administration (CMA), provided calibrated
precipitation data for selected case studies. The ANN training was performed over these case studies
for the monsoon season, and over the CPR snowfall data for the rest of the year.
For the second release of the product (TANN-S) the training is performed on a supervised dataset
where reference rainfall values are obtained by CDR estimates, for the monsoon season. For the dry
season, the same calibration as TANN-R is used. The advantages of this approach is that the training
set spans all over the season, and not only on selected case studies: a wider variety of precipitation
systems is then included in the dataset. A possible drawback is that the CRD estimates is expected to
perform poorer than the radar estimate.
ƒ Global reference products
Two well assessed global product are also considered as reference in this work: the TRMM
Multi-satellite Precipitation Analysis (TMPA) 3B42 product [17], and the Climate Prediction Center
MORPHed precipitation (C-MORPH) [18]. Both products, freely available, have a ground resolution
of 0.25x0.25 degrees and the rainrate is averaged over three hours.
The TMPA is a calibration-based sequential scheme for combining precipitation estimates from
multiple sensors, as well as gauge analyses [17]. The 3B-42 estimates are produced in four stages; (1)
the microwave precipitation estimates are calibrated and combined, (2) infrared precipitation
estimates are created using the calibrated microwave precipitation, (3) the microwave and IR
estimates are combined, and (4) rescaling to monthly data is applied, with the use of available
raingauge data.
The C-MORPH uses a different approach: half hourly IR data from GEO satellites are used as a
mean to transport the microwave-derived precipitation features during the intervals between
International Workshop on Terrestrial Water Cycle Observation and Modeling from Space: Innovation and Reliability of Data Products subsequent PMW observations. Propagation vector-matrices are produced by computing spatial lag
correlations on successive IR images of GEO satellite, which is used to propagate the microwave
derived precipitation estimates. The shape and the intensity of the precipitation features are modified
(morphed) during the time between microwave sensor scans by performing a time-weighted linear
interpolation. The method is flexible to permit precipitation morphing from any satellite PMW
sensor (AMSU, TMI, AMSR-E, SSM/I).
Both products are validated with raingauges observation over different regions of the word, showing
a general underestimation of moderate rainrates, with better performances of 3B42, which, however
makes direct use of raingauges data. No validation is available over TP yet.
ƒ RESULTS AND DISCUSSIONS
Three years of satellite data are processed, and 30 minutes of instantaneous rainrate/snowrate over the
METEOSAT-7 spatial grid are retrieved. As mentioned, the lack of a complete ground reference
dataset prevent a systematic validation campaign, especially for the dry season. Even for the monsoon
season the ground radar have a limited spatial coverage that prevents a statistically significant
validation. Therefore, the analysis presented here is mainly an intercomparison among the different
estimates.
As an example, in Figure 1 three hourly rainfall maps for the 29/08/2009 at 18:00 UTC are shown.
Radar network shows a precipitation structure with low precipitation in the 3 hours, and one small
peak with cumulated rain around 10 mm (a). CMORPH (b) completely misses the precipitation
structures, detecting only low rain accumulation but slightly misplaced with respect to radar
observations. CNAW (c) detects several precipitating areas in the region, with rain amount comparable
with radar observations, but also a misplacement is noted. The 3B42 (d) estimates is rather good in
detecting rain areas, but the amount of rain is underestimated. TANN-R (e) and TANN-S (f) show
similar shapes in the precipitation patterns, and the wet areas is the largest among the estimates. The
higher resolution shows the presence of a structure of precipitation field not seen at lower resolution.
TANN-S estimates higher rain amount than any other technique.
A further approach in the intercomparison among precipitation product is presented in Figure 2a,
where the TP basin averaged daily rainrate is plotted for year 2010. In Figure 2b the black line is the
rain amount computed as the mean value of the five techniques used (C-NAW, TANN-R, TANN-S,
3B42 and CMORPH), while the red line is the corresponding standard deviation. All the techniques
are sensitive to the onset and end of the monsoon season, when also show higher agreement (low
standard deviation). For the first two months the precipitation is very low: well below 0.5 mm for both
the global products, but 3B42 shows few peaks above 1 mm, while blended techniques have values
between 1 and 2 mm on the average. A very large peak, probably due to snow at the ground is
estimated by TANN-R, and other by other techniques but at markedly lower values, except CMORPH.
Around day 80 CMORPH has a 10-day wide peak not sensed by other techniques, while between day
100 and 150 the techniques agree with rather constant averaged value around 1 mm, with some
isolated peaks observed by 3B42. Around day 150 there is the onset of monsoon season TANN-R and
TANN-S and the precipitation amount increases for all the techniques: average values are now
between 3 and 5 mm, except for CMORPH, that keeps below the other curves. Sharp peaks, during 1 o
2 days, are seen during the monsoon season: sometimes they are observed by more than one technique,
but in general not with the same values. Even TANN-R and TANN-S are far to be similar, with the
latter generally overestimating.
International Workshop on Terrestrial Water Cycle Observation and Modeling from Space: Innovation and Reliability of Data Products Figure 1. Three hourly cumulated rain as seen from different techniques: (a) QPE from Tibet ground radar
network; (b) CMORPH; (c) 3B42; (d) CNAW; (e) TANN-R; (f) TANN-S.
Figure 2. TP-averaged daily rain amount for the year 2010 as retrieved from the five techniques (a), and
mean value (black line, left scale) and standard deviation (red line, right scale) of the daily amount (b).
Around day 300, after the monsoon season has finished, the amount of rain decreases with the
agreement of all the techniques, except CMORPH, that in this case tends to overestimate precipitation
amount. TANN and CNAW are highly correlated.
International Workshop on Terrestrial Water Cycle Observation and Modeling from Space: Innovation and Reliability of Data Products ƒ CONCLUSIONS AND FURTHER PERSPECTIVES
The aim of this work is the implementation and applications over three years of data of an array of
satellite precipitation techniques. Two physically based techniques have been implemented for snow
rate, based on CPR data (for cold months) and rainrate derived from SSMI/S data (for monsoon
season). These techniques, given the high revisiting time, are not suitable for direct use as precipitation
monitoring tool, but are used as calibrator of multisensor techniques based on GEO IR data.
Two approaches are tested to merge MW retrieval to IR data: entity based and fully statistical. The first
approach (C-NAW) requires the definition of precipitating area as separated entities, and the
assignment of rainrates as determined by coincident MW estimates. The full statistical approach has
been pursued by implementing an ANN technique, trained on ground radar data (TANN-R) or PMW
retrieval (TANN-S) for the wet season, and on Cloudsat-CPR snowfall retrieval during dry months.
Finally, two global precipitation products have been considered for reference and intercomparison: the
CMORPH and the TMPA product 3B42. All the techniques have been implemented for the 3 years and
the results compared at different spatial and temporal scales.
The analysis of daily rain amount has shown that in general CMORPH and TMPA are able to estimate
rain amount larger than the ones estimated by other techniques during the monsoon season. In cold
months global techniques underestimate precipitation amount and areas, resulting in a dry bias with
respect to IR calibrated techniques. Case studies compared with ground radar data on convective
episodes shown that global products tend to underestimate precipitation areas, while IR calibrated
techniques provides reliable rainrate patterns. Unfortunately, the number of radar case studies was not
large enough to allow significant validation studies, and also non data were available for cold months.
Annual precipitation cumulated maps show marked differences among the techniques: IR calibrated
techniques generally overestimate precipitation amount by a factor of 2 with respect of global products.
Reasons for discrepancies are probably in the role the IR data have on the techniques: when the rain
area delineation is based on IR, overestimation is likely to occur, while when the MW estimate is used,
underestimation of low precipitation areas is expected.
Further studies are needed to resolve these ambiguities, and the forthcoming full exploitation of the
Global Precipitation Measurement mission will surely help on this task, and probably give a clearer
picture of precipitation properties over TP. In particular, the GPM Core Observatory (GPM-CO), to be
launched next year on a 65 degree sun-synchronous orbit, will host the Dual-frequency Precipitation
Radar (DPR), a Ka-Ku band radar, and a high resolution, multichannel PMW rain radiometer called
the GPM Microwave Imager. The GPM-CO will serve as the calibration reference system and the
fundamental microphysics sensor package, especially on region with scarce ground observations,
supporting an integrated satellite measuring system made up of six to ten constellation satellites,
equipped with PMW sensors.
ƒ ACKNOWLEDGEMENT
The work described here was supported by the European Commission (Call FP7-ENV-2007-1 Grant
nr. 212921) as part of the CEOP-AEGIS project (www.ceop-aegis.org) coordinated by the Université
de Strasbourg. The TMPA 3B42 data used in this study were acquired as part of the NASA's
Earth-Sun System Division and archived and distributed by the Goddard Earth Sciences (GES) Data
and Information Services Center (DISC) Distributed Active Archive Center (DAAC).
International Workshop on Terrestrial Water Cycle Observation and Modeling from Space: Innovation and Reliability of Data Products CMORPH products have been downloaded from the NOAA-CPC ftp site: ftp.cpc.ncep.noaa.gov/
precip/global_CMORPH/. The METEOSAT-7 MVIRI data were downloaded from the EUMETSAT
Earth Observation Portal: http://www.eumetsat.int/Home/Main/DataProducts/ProductNavigator/
index.htm?l=en). The availability of radar and raingauge data was granted by prof. Liping Liu of the
CMA, and the help of Wei Zhuang in data managing is acknowledged.
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