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The Global Precipitation Measurement (GPM) mission: Hydrological applications

Chris Kidd12

and Arthur Hou2

1Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD. USA

2

NASA/Goddard Space Flight Center, Greenbelt MD. USA

1. Background

Precipitation in the form of rainfall and snowfall is the primary input to any hydrological system. Thesourcing of accurate information on precipitation across many regions of the globe for hydrological

applications is however problematic. While some regions of the world provide good surface data sets

from rain gauge networks, many regions have inadequate coverage to reduce spatial and temporal

sampling errors; the availability of snow gauge information and measured precipitation over the oceansis extremely sparse. Precipitation estimates derived from satellite observations have the potential to

improve the quantification of precipitation at the surface, particularly over data-sparse regions.

In order for satellite precipitation products to be incorporated into hydrological models, more accurate

instantaneous estimates, especially over land and during cold seasons, are required. This requires better

spatial coverage and temporal sampling to reduce sampling errors and thus improve the estimation ofprecipitation accumulation. Alongside this requirement, some hydrological applications also require

higher spatial and temporal resolutions for local-scale applications, particularly for flash-flood analyses.

Ultimately, global precipitation data products are required that use all available satellite and ground-

based measurements.

Although there have been significant advances in the development of quantitative precipitation

estimates, particularly following the start of the Tropical Rainfall Measuring Mission (TRMM) era,further advances are needed. These require more capable space-borne sensors, more accurate retrieval

algorithms and better utilization of available data through appropriate data merging techniques made

possible through the coordinated effort to exploit the range of international precipitation-capablemissions.

2. The GPM Mission Concept

The overarching concept of the joint NASA/JAXA GPM mission (see Neeck et al. 2010) is twofold.

First, to provide, through partnerships, coordinated precipitation measurements through a constellation

of microwave radiometers necessary to achieve global coverage and sampling. Second, to improve the

accuracy and consistency of precipitation estimates from all the constellation radiometers through thecombined active and passive observations of the GPM Core satellite.The GPM constellation satellites

comprise of a number of operational and research and development satellites will help fulfill the

requirements of the GPM concept. Satellites include (or will include) the operational US NOAA andEuropean MetOp polar orbiting series, the US Defense Meteorological Program satellites, the US Joint

Polar Satellite System, together with the Japanese GCOM-W1 mission and the French-Indian Megha-Tropiques mission.


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GPM Core Observatory

The primary purpose of the GPM Core Observatory is to set a new standard for precipitation

measurements from space. It will achieve this by providing a radiometric reference to reconciledifferences among constellation radiometer using the GPM Microwave Image (GMI) as a transfer

standard. The non sun-synchronous orbital characteristic of the Core satellite is such that will cross all

of the orbital planes of the contributing satellites, enabling occasional co-temporal observations to be

made. The GPM Core will improve the sampling of the GPM constellation by filling observationalgaps between the individual partner satellites. The Core satellite, hosting both active and passive

microwave instruments will be capable of providing an a priori observational hydrometeor database

consistent with Dual-frequency Precipitation Radar (DPR) and GMI measurements to unify andimprove precipitation estimates from the Core satellite and subsequently, the constellation (Hou, 2011).

The Core satellite (see figure 1) will be placed into a non sun-synchronous orbit with an inclination of65 at an altitude of 407 km; this allows the core satellite cross the orbit planes of the other

constellation satellites, and allows sampling across the full diurnal cycle.

Figure 1: An artists impression of the GPM Core Observatory deployed in orbit. The GPM MicrowaveImager instrument is to the left, the Dual-frequency Precipitation Radar is situated at the base of the

satellite platform, while the TDRIS high-gain antenna is at the top.

Two instruments will be carried on the GPM Core satellite. The GPM Microwave Imager (GMI; see

Newell et al. 2010), a passive microwave radiometer observing frequencies ranging from 10 to 183

GHz, incorporates innovative features for improved sensor calibration (see Table 1). The Dual-

frequency Precipitation Radar (DPR) operating at Ku and Ka bands, is the first of its kind (see Table 2;see Nakamura et al. 2010)). It builds upon the TRMM-heritage, but provides retrieval accuracy and

higher sensitivity to light rainfall and snowfall detection relative to TRMM. In particular, it is capable

of providing detailed microphysical information (DSD, mean mass diameter and particle number


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density) and identification of liquid, ice, and mixed-phase regions (see Grecu et al, 2011; Seto & Iguchi,

2011; Liao & Meneghini, 2011; Minda & Chandrasekar, 2011).

Table 1: Characteristics of the GPM Microwave Imager (GMI)

Frequency Resolution

10.65 GHz (V&H) 19.4 x 32.2 km

18.7 GHz (V&H) 11.2 x 18.3 km

23.8 GHz (V) 9.2 x 15.0 km

36.5 GHz (V&H) 8.6 x 14.4 km

89.0 GHz (V&H) 4.4 x 7.3 km

165.5 GHz (V&H) 4.4 x 7.3 km

183.33 GHz (V) 4.4 x 7.3 km

183.37 GHz (V) 4.4 x 7.3 km

Table 2 Characteristics of the GPM Dual-frequency Precipitation Radar (DPR)

KuPR KaPRFrequency 13.6 GHz 35.55 GHz

Swath 245 km 120 km

Resolution 5 km 5 km

Range resolution 250 m 250/500 m

Minimum detection


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3.2 Hydrology

Hydrology remains one of the key uses of precipitation data sets, requiring information from both

surface and satellite instrumentation. The utility of improving the spatial resolution of precipitationestimates has been shown by Nikolopoulos et al (2010) in an analysis of uncertainty propagation of

satellite rainfall. The study focused on a number of drainage basins (116 km2 to 1200 km

2) over

northeast Italy, and found that the resolution of the rainfall products were critical in the determining the

hydrologic error propagation. The resolution of the satellites estimates for hydrological modeling wasalso highlighted by Gourley et al. (2011). Other hydrological-related studies have included estimating

and predicting soil moisture (e.g. Maggioni et al. 2011; Serpetzoglou et al. 2010) and the generation of

landslide maps based upon surface information and satellite-derived precipitation estimates (seeKirschbaum et al. 2012; Liao et al. 2012; Kirschbaum et al. 2009)

3.3 GPM Field CampaignsAs part of the pre-(and post)-launch activities a concerted effort has been made to study, compare and

evaluate observations and measurements made by coincident surface and satellite measurements. A

number ground-validation campaigns have been organized in preparation for the GPM mission (seeSchwaller & Morris, 2011). These include the 2010 Pre-CHUVA campaign located in Alcntara, Brazil,

to study warm rain retrieval; the Light Precipitation Validation Experiment (LPVEx) centered on

Helsinki, Finland, to study light rain, shallow liquid/solid/mixed phase precipitation (2010-2011); the

mid-Latitude Continental Convective Clouds Experiment (MC3E) in central Oklahoma (2011) and; theGPM Cold-season Precipitation Experiment (GCPEx) in Ontario, Canada (early 2012). Future GPM

ground validation will include GPM participation in the HyMeX special observation period in late

2012/3 together with campaigns planned for 2013-16.

4. Conclusions

GPM will offer more accurate instantaneous precipitation estimates through the use of newinstrumentation and exploitation of multi-satellite observations. The GPM Core satellite will host

advanced radar and radiometer sensors with higher sensitivity to light rain and solid precipitationtogether with the capable to derive estimates of particle-size distribution parameters from the radar.

Better retrieval algorithms have been developed through focused ground validation campaigns to

ensure that the new observations are fully exploited, together with the development of the next-generation multi-satellite global precipitation products. Through the inter-calibration of radiometric

data from a constellation of passive microwave sensors, unified precipitation retrievals, using a

common hydrometeor database, will be possible that is consistent with combined active/passive sensor

measurements. For hydrological applications, the generation of high-resolution downscaledregional/global precipitation products through dynamical/statistical downscaling of satellite

precipitation observations is seen as critical to improve surface rainfall estimates over regions with

ungauged catchments. In addition, the availability of near real-time global data will be very beneficialfor operational and societal applications.

5. References:Gourley, J.J., Hong, Y., Flamig, Z.L., Wang, J.H., Vergara, H., Anagnostou, E.N. 2011: Hydrologic Evaluation

of Rainfall Estimates from Radar, Satellite, Gauge, and Combinations on Ft. Cobb Basin, Oklahoma. Journal of

Hydrometeorology 12, 973-988. DOI: 10.1175/2011JHM1287.1

Grecu, M., Tian, L., Olson, W. and Tanelli, S. 2011: A Robust Dual-Frequency Radar Profiling Algorithm.

Journal of Applied Meteorology and Climatology50, 1543-1557. DOI: 10.1175/2011JAMC2655.1


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Hou, A.Y. 2011: Precipitation estimation using combined active/passive sensor information within the GPM

framework. Proceedings of the ECMWF-JCSDA Workshop on Assimilating Satellite Observations of Clouds

and Precipitation into NWP Models, 15-17 June 2010, ECMWF, Reading UK, 69-78.

Kirschbaum, D.B., Adler, R., Hong, Y., Lerner-Lam, A. 2009: Evaluation of a preliminary satellite-based

landslide hazard algorithm using global landslide inventories.Natural Hazards and Earth System Science9, 673-

686.

Kirschbaum, D.B., Adler, R., Hong, Y., Kumar, S., Peters-Lidard, C., Lerner-Lam, A. 2012: Advances inlandslide nowcasting: evaluation of a global and regional modeling approach.Environmental Earth Sciences66,

1693-1696. DOI: 10.1007/s12665-011-0990-3

Kirschbaum, D.B., Fukuoka, H. 2012: Remote sensing and modeling of landslides: detection, monitoring and

risk evaluation.Environmental Earth Sciences66, 1583-1583. DOI: 10.1007/s12665-012-1543-0

Liao, L.A. and Meneghini, R. 2011: A Study on the Feasibility of Dual-Wavelength Radar for Identification of

Hydrometeor Phases. Journal of Applied Meteorology and Climatology 50, 449-456. DOI:

10.1175/2010JAMC2499.1

Liao, Z.H., Hong, Y., Kirschbaum, D., Liu, C. 2012: Assessment of shallow landslides from Hurricane Mitch in

central America using a physically based model. Environmental Earth Sciences 66, 1697-1705. DOI:

10.1007/s12665-011-0997-9

Maggioni, V., Reichle, R.H., Anagnostou, E.N. 2011: The Effect of Satellite Rainfall Error Modeling on Soil

Moisture Prediction Uncertainty.Journal of Hydrometeorology12, 413-428. DOI: 10.1175/2011JHM1355.1

Nakamura, K., Oki, R., Iguchi, T. 2010: Dual-Frequency Radar (DPR) and the Global Precipitation

Measurement (GPM) in Japan. InRemote Sensing and Modeling of the Atmosphere, Oceans and Interactions III.

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Neeck, S.P., Kakar, R.K., Azarbarzin, A.A., Hou, A.Y. 2010: Global Precipitation Measurement (GPM)

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Newell, D.A., Rait, G., Thach, T., Berdanier, B., Draper, D., Kubitschek, M., Krimchansky, S. 2010: GPMmicrowave imager design, predicted performance and status. In IEEE International Geoscience and Remote

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Nikolopoulos, E.I., Anagnostou, E.N., Hossain, F., Gebremichael, M., Borga, M. 2010: Understanding the Scale

Relationships of Uncertainty Propagation of Satellite Rainfall through a Distributed Hydrologic Model. Journal

of Hydrometeorology11, 520-532. DOI: 10.1175/2009JHM1169.1

Schwaller, M.R. and Morris, K.R. 2011: A Ground Validation Network for the Global Precipitation

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Seto, S. and Iguchi, T. 2011: Applicability of the Iterative Backward Retrieval Method for the GPM Dual-

Frequency Precipitation Radar. IEEE Transactions on Geoscience and Remote Sensing 49, 1827-1838. DOI:

10.1109/TGRS.2010.2102766

Tian, YD Peters-Lidard, C.D. 2010: A global map of uncertainties in satellite-based precipitation measurements.

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precipitation research products into National Mosaic QPE operational system for improved precipitation

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