NASA's Global Precipitation Measurement Mission

Ground Validation Segment

Mathew R. Schwaller

NASA / Goddard Space Flight Center

Greenbelt, MD, USA 20771

mathew.r.schwaller@nasa.gov

***Abstract---*NASA is designing a Ground Validation Segment (GVS) as
one of its contributions to the Global Precipitation Measurement (GPM)
mission. The GPM GVS provides an independent means for evaluation,
diagnosis, and ultimately improvement of the GPM spaceborne measurements
and precipitation products. NASA's GPM GVS concept calls for a
combination of direct observations executed within a Multidimensional
Observing Volume (MOV) and model-based analyses executed by a Satellite
Simulator Model (SSM). The MOV consists of ground-based instruments that
measure local surface and atmospheric properties required for GPM
validation. The SSM utilizes MOV measurements in a forward numerical
model. The goal of the SSM forward modeling is calculation of the
following properties: top-of-atmosphere microwave radiative quantities
to within sensor noise of those measured by the GPM Core Satellite,
precipitation quantities identical to those generated by the standard
GPM precipitation retrieval algorithms, and quantitative/objective error
estimates of both sets of quantities. At present, the GVS is in the
early design stage and various scenarios have been generated to assess
how it will be used in the GPM era. The GPM GVS will be operational in
the year prior to the launch of the GPM core satellite, which has a
launch date scheduled for December 2010.**

I. INTRODUCTION: GPM AND GROUND VALIDATION

GPM Ground Validation (GV) supports the overall GPM mission objectives
including improvement in predicting terrestrial weather, climate, and
hydrometeorology through a better observational understanding of the
global water cycle. To help meet these objectives, the GPM GV has the
following subsidiary goals:

> 1\. Quantify the uncertainties in GPM standard precipitation retrieval
> algorithms
>
> 2\. Contribute to the improvement of GPM precipitation retrieval
> algorithms throughout the mission by
>
> \- validating the radiative transfer models that serve as the basis
> for the GPM precipitation retrieval algorithms
>
> \- validating the assumptions needed to parameterize these models, and

3.  Understand the time and space error characteristics of GPM
    precipitation products generated by these algorithms.

Achieving these goals is seen as a necessary step for improved GPM data
products and for increased utilization of these products in Global
Climate Models (GCMs), Numerical Weather Prediction (NWP) models, and
hydrometeorological models for climate and weather forecasting. An
overview of the GPM mission, including the GPM Microwave Imager (GMI)
and Dual-frequency Precipitation Radar (DPR) instruments can be found in
Flaming (2004).

The conceptual framework for GPM GV is based on the notion of a
"multidimensional observing volume" and "satellite simulator model" as
briefly described below.

II\. [Ground Validation Components]{.smallcaps}

*A. Multidimensional Observing Volume (MOV)*

The MOV consists of ground-based instruments that measure local surface
and atmospheric properties required for GPM validation. The
specification of MOV instrumentation is still to be determined, but
example sensors may include surface precipitation gauges and
disdrometers, ground-based radars and radiometers, Doppler profilers,
and other meteorological sensors. These instruments will be used in the
direct observation or retrieval of parameters that drive the forward
model within the SSM (as described below). Specifying the parameters
required by the SSM is still to be determined, but the MOV instruments
may measure, for example, active and passive microwave (MW) radiation
intensities (e.g., radar reflectivity, Z, and radiometer brightness
temperature, Tb), meteorological quantities (e.g., temperature,
pressure, winds), hydrological constituent concentrations (e.g., water
vapor, liquid/ice hydrometeor mixing ratios), hydrometeor size spectra
(including, for example, liquid droplet and ice particle size
distributions), and precipitation rates.

*B. Satellite Simulation Model (SSM)*

The SSM utilizes MOV measurements in a forward numerical model, the goal
of which is the calculation of: microwave radiative quantities identical
to within sensor noise of those measured by the GPM Core Satellite (or
other targeted satellites), precipitation quantities identical to those
generated by the standard algorithms, and quantitative/objective error
estimates of both sets of quantities. The SSM thus supports the two main
objectives of the GPM-GV: error characterization of the standard
retrieval products for data assimilation in real-time weather and
hydrometeorological forecasting, and continuous improvement of the GPM
standard algorithms via reporting of product bias and uncertainty.

There is a third concept associated with GPM ground validation---the
notion of a "Super Site." The working definition for a GV Super Site is
simply this: it is an instrumented ground location that executes all of
the functions associated with a GV MOV and SSM. Taken together, the MOV,
SSM, GV super sites, and supporting systems and services make up the GPM
*Ground Validation Segment* (GVS).

![](media/image1.jpeg){width="3.49375in" height="2.990972222222222in"}

Figure 1. Conceptual framework for the GPM Ground Validation Segment
(GVS).

It is understood that the GPM precipitation algorithms will be built on
the highly successful TRMM algorithms and thus are expected to produce
precipitation estimates at least comparable in accuracy to TRMM
products. These forward models, though not perfect, do yield credible
and useful precipitation estimates. A key role for the GV is therefore
to provide additional atmospheric and microphysical measurements to
improve retrieval algorithms by reducing uncertainties and assumptions
in these forward models. The conceptual framework for the GPM GV is
illustrated in Figure 1.

At present, the GPM GVS is in the early design stage, with preliminary
and "critical" (final) design reviews scheduled for February 2006 and
June 2007, respectively. The development of the GVS is scheduled to
begin after successful completion of these reviews. GVS operations will
begin in the year prior to the launch of the GPM core satellite, which
has a launch date scheduled for December 2010. The sections below
describe three scenarios that are intended to explain how the GVS will
be used during operations, and how its use will meet the goals described
in Section I of this paper.

III\. [Pre-Launch Ground Validation Scenario]{.smallcaps}

The time frame for this scenario is set during GVS pre-launch
operations. At this time the GVS routinely collects measurements of
atmospheric variables and ground-level precipitation. The atmospheric
measurements are used to generate standard products of
top-of-the-atmosphere (TOA) T[b']{.smallcaps}s and Z's, as well as
precipitation estimates based on running the standard retrieval
algorithm both constrained with ground measurements and in its normal,
unconstrained mode. After several months of observations, but just prior
to the launch of the GPM Core Satellite, an algorithm developer on the
GPM Precipitation Measuring Mission (PMM) Science Team notes that there
appears to be a striking bias in the results of the algorithm run in the
constrained and unconstrained mode when estimating of low rainfall rates
in the case of convective storms over NASA's oceanic Super Site. He
reports this observation during a PMM Science Team meeting. But before
taking any action to revise the standard algorithm, the PMM Science Team
decides to wait to see if TOA and retrieval results are confirmed in
actual measurements of the GPM Core Satellite.

GVS system elements exercised in this scenario:

> • Pre-launch measurements by the MOV; estimates of TOA TB and Z;
> constrained and unconstrained retrievals
>
> • Reporting from the GVS to the PMM Science Team.

Other features:

> • The GPM core satellite is not yet in orbit, but the GVS can simulate
> satellite observations and algorithm retrieval.

IV\. [Post-Launch Algorithm Validation Scenario]{.smallcaps}

The time frame for this scenario is set during the second year of GPM
core satellite operations. An algorithm developer from the PMM Science
Team believes that she has an improvement in passive microwave model
parameterization that permits better characterization of convective rain
over the ocean. She searches the GVS for a representative sample of rain
events over the GPM oceanic "super site" and orders 100 precipitation
events of interest: 30 had corresponding satellite overpasses, 70 did
not. She runs the GPM standard retrieval and her new retrieval algorithm
using the GVS-SSM-estimated TOA Tb's for the full 100 events in the
unconstrained mode, and she also runs the retrieval constrained with
GVS-MOV measurements of atmospheric variables. The unconstrained
standard algorithm over-estimates precipitation in low rain rates (see
the previous scenario), but her unconstrained "new" algorithm, her
constrained new algorithm, and the constrained standard algorithm all
converge on an estimate that compares favorably with GVS-MOV ground
measurements of precipitation. The algorithm developer publishes her
results. The results are reviewed at a meeting of the PMM Science Team,
and the team subsequently recommends that her algorithm improvement
should be adopted by GPM. She delivers her code and documentation to the
GPM Precipitation Processing System (PPS) which integrates the
improvement into the standard algorithm. The GVS also integrates the
improvements into the SSM forward model. The new forward model is used
to generate subsequent GVS products, and to re-process appropriate
products in the GVS archive.

GVS system elements exercised in this scenario:

> • GVS search and order
>
> • GVS MOV measures and records precipitation and the atmospheric
> properties needed by the SSM
>
> • GVS SSM generates products of TOA Tb (and Z)
>
> • GVS interface to the PPS for receipt of algorithm updates.

Other features:

> • GPM algorithm improvement is *supported* by the GVS, but there is no
> direct requirement on the GVS for conducting algorithm improvement.
>
> • Improved algorithms are integrated into the PPS and GVS following
> approval by the PMM Science Team.

V. [Global Error and Bias Determination Scenario]{.smallcaps}

The time-frame for this scenario covers a number of years in the pre-
and post-launch GPM era. For the purpose of this scenario, let's assume
that a NASA Research Announcement (NRA) is released in 2009 that
includes a solicitation for proposals to develop algorithms that
estimate global precipitation retrieval errors, bias, and spatial error
correlation, with the goal of including these algorithms in the
operational PPS.

An atmospheric scientist with a background in modeling, error analysis
and assimilation submits a successful proposal to estimate microwave
radiometer precipitation errors using GMI, DPR and GVS data. In 2010,
the Principal Investigator (PI) places a standing order in the newly
operational GVS for all rainfall events over the two NASA GVS sites.
Over a period of six months he completes an analysis of the temporal
autocorrelation in the GVS measurements of precipitation events, and he
begins to collect a statistically independent sample of data.

The PI begins his analysis using GPM constellation data and GVS data to
estimate model and retrieval errors for the PPS constellation product.
To estimate model errors, he computes the variance in satellite
observations of Tb compared to GVS model-based estimates. To estimate
retrieval errors, he computes the variance in PPS-computed instantaneous
precipitation rate compared to GVS estimates of precipitation rate in
the same field of view. This approach only provides error statistics for
a small part of the full database of precipitation retrievals used by
PPS algorithm.

In 2011, the PI begins work on a global instantaneous precipitation
error and bias estimates following the successful launch and checkout of
GPM. For this he plans to use the DPR-derived estimates of atmospheric
variables that can be used to parameterize the standard algorithm in
much the same way that the algorithm is constrained with ground
measurements by the GVS. Indeed, he uses GVS data to validate the
fidelity of the DPR-based parameterization, to assess the retrieval
results for both DPR-parameterized algorithm compared to the
GVS-parameterized algorithm, and to compare both of these results to
precipitation actually measured at the GVS super sites.

Satisfied with the results so far, he enhances his global error
algorithm by accumulating statistics for the difference between the
\[Standard-Algorithm(constrained by DPR) --
Standard-Algorithm(unconstrained)\] for each bin of Tb ensembles in the
standard algorithm database. He publishes a paper on algorithm bias, and
as 2012 draws to a close he plans to use the database as the foundation
for a grant renewal focused on estimating global error covariance in
rainfall retrievals.

GVS system elements exercised in this scenario:

> • GVS search, order, standing order
>
> • GVS MOV measures and records precipitation and the atmospheric
> properties needed by the SSM
>
> • GVS SSM generates products of TOA Tb, Z, and constrained
> precipitation retrievals
>
> • GVS interface to PPS for receiving PPS product subsets.

Other features:

> • Global error estimation is *supported* by the GVS, but there is no
> direct requirement on the GVS for making global error estimates (it is
> assumed that global error products will be generated by the PPS based
> on algorithms provided by the PMM Science Team).

VI\. [Conclusions and Next Steps]{.smallcaps}

At present, the GPM GVS is in the early stage of design. The set of
objectives described in Section I of this paper are intended to provide
the guiding direction as the GVS progresses through design and into
development. Additionally, the MOV and SSM described in Section II
provide a conceptual framework for achieving GPM GV objectives using
measurements made with ground-level instrumentation (via the MOV), and
model-based analysis (via the SSM).

The set of scenarios presented earlier illustrate how the GVS MOV and
SSM will be used during the GPM era. These scenarios are useful for two
purposes. First, they provide a check, verifying that the objectives of
the GPM ground validation can be met with the "end state" version of
GVS. Secondly, the scenarios are a point of departure for system
engineering, particularly in the development of a GVS functional
architecture, requirements, and operations concept.

A comparison of the scenarios to the GPM ground validation objectives
listed in Section I shows a relatively good match-up between the two.

Scenarios 1 and 2 address the quantitative uncertainties in the standard
precipitation retrieval algorithms (Goal 1) when comparing ground-based
estimates of precipitation to precipitation estimates generated by
standard GPM algorithms. This is essentially the standard approach to
validation: comparing satellite retrievals to "ground truth" and
evaluating the differences. Although this is valuable approach, it has
two major limitations for the GPM mission. The first limitation is
related to the problem of sample size. The joint probability of a
precipitation event at a GVS Super Site coinciding the field of view of
a GPM satellite overpass is relatively low, and this is especially true
for the DPR (swath width of about 245 km for the Ku band and about 100
km for the Ka band in the across-track direction; Satoh et al., 2003).
The ground truth approach to validation may result in unacceptably small
sample sizes of coincident precipitation-overpass events even after a
period of 3 years, the nominal lifetime of the GPM core satellite. The
second problem with this approach is one of algorithm degeneracy. Many
combinations of TOA Tb's or Z's can map to a single precipitation rate
in the GPM standard algorithms. Consequently, a comparison of the
algorithm retrieval to ground truth may reveal that they are the same
(or different), but simple comparisons will not help explain how or why
those similarities (or differences) exist.

Scenarios 1 and 2 demonstrate how the GVS can contribute to the
improvement of GPM precipitation retrieval algorithms (Goal 2). These
scenarios show how the MOV-SSM combination utilizes a forward model to
simulate TOA observations collected by the DPR and GMI instruments on
the GPM satellite. These simulated observations will be compared to
actual satellite measurements. This approach avoids the degeneracy
problem noted above because any source of error in the comparison can be
traced to errors in the ground-based instrumentation, to the forward
model, or (however unlikely) to error or bias in the GPM instruments.
The model-based approach to validation also ameliorates the sample size
problem because the forward model can be run for any precipitation
event, whether the satellite is in view or not. The criterion for
validating the radiative transfer models that serve as the basis for the
GPM precipitation retrieval algorithms thus becomes a comparison of GVS
model-based estimates with satellite TOA observations. If they are
identical to within some noise level then we will have confidence that
they are accurate for the range of atmospheric conditions observed by
the MOV. If they are different, then either the MOV measurements or the
SSM model is in error. The errors identified by the GVS will be used
over the mission lifetime in a series of successive improvements to the
forward model, to the parameterization of the forward model, to the GPM
standard retrieval algorithms, and to the ground instrumentation.

Finally, as illustrated by scenario 3, the GVS can contribute to
understanding the space and time error characteristics of GPM
precipitation products generated by the GPM standard algorithms (Goal
3). Clearly, the GVS observations will be limited to the relatively
small geographic area of NASA's continental and oceanic Super Sites.
Consequently, the ability of the GVS to provide error and bias estimates
will also be limited to its small geographic regime. Generalizing from
local to global error estimates will require that the key GVS parameters
have some counterpart (proxy) that can be observed globally, so that the
true variability can be estimated with some confidence. In the GPM era,
it is expected that such global proxies may be retrieved from
measurements made by the Dual-frequency Precipitation Radar (DPR), and
perhaps other instruments. However, no methods currently exist for
generalizing local GVS error estimates to a large regional or even
global scale. Thus, it is expected that global error characterization
will be conducted as a research activity that is beyond the scope of the
GVS. Once such research is mature, it is expected that global error
products will be generated by the Precipitation Processing System (PPS)
based on algorithms provided by the science community. The GVS will
provide error reports to the PPS as input for the generation of GPM
global error products.

Because NASA's GVS is in an early design stage, the feasibility of
actually implementing the GPM GVS according to the conceptual framework
presented here has not yet been demonstrated. To address this issue, a
team was selected by peer-review to study the feasibility of the MOV and
SSM concepts identified above, and to contribute to the final design of
NASA's GVS. The members selected for this team include:

V. Chandrasekar, Colorado State University

Robert Meneghini, NASA/GSFC

William Olson, NASA/GSFC

Steven Rutledge, Colorado State University (team leader for a consortium
that includes additional members from other institutions)

Sandra Yuter, University of Washington

Reports from the GPM Ground Validation feasibility studies are expected
in late summer or early fall of 2005. These results, along with a
planned prototype activity, will contribute to the design of NASA's GPM
Ground Validation Segment. A preliminary GVS design is expected to be
complete in February 2006. The final GVS design is expected to be
complete by July 2007. Following this, instrumentation will be fielded,
analysis capabilities established, and the GVS will be operational in
the year prior to the launch of the GPM core satellite, which has a
launch date scheduled for December 2010.

REFERENCES

Flaming, G.M., "Measurement of Global Precipitation," Proceedings of the
International Geoscience and Remote Sensing Symposium, Anchorage,
Alaska, September 20-25, 2004.

Satoh, S., R. Oki, N. Takahashi, T. Iguchi, "Development of spaceborne
dual-frequency precipitation radar and its role in the global
precipitation measurement," EGS-AGU-EGU Joint Assembly, Nice, France,
6-11 April, 2003 (abstract).