**Observation of Clouds and Precipitation\
through spaceborne Doppler radar:\
designing a system to improve current atmospheric dynamics models**

TABLE OF CONTENTS

[PROJECT DESCRIPTION 1](#project-description)

[1 Objectives and expected significance
1](#objectives-and-expected-significance)

[1.1 Scientific goals 1](#scientific-goals)

[1.2 Specific contributions 4](#specific-contributions)

[2 Relation to the present state of knowledge
6](#relation-to-the-present-state-of-knowledge)

[3 Relation to previous work done on the project and to related work in
progress elsewhere
7](#relation-to-previous-work-done-on-the-project-and-to-related-work-in-progress-elsewhere)

[4 Research Plan 9](#research-plan)

[4.1 Summary 9](#summary)

[4.2 Detailed plan for the first year
10](#detailed-plan-for-the-first-year)

[4.3 List of collaborations 16](#list-of-collaborations)

[4.4 Export control 16](#export-control)

[4.5 References 16](#references)

[4.6 Definition of the main relevant parameters and symbols
19](#definition-of-the-relevant-parameters-and-symbols)

[5 Education Plan 20](#education-plan)

[6 Facilities 20](#facilities)

[APPENDIX A -- Curricula vitarum i](#appendix-a-curricula-vitarum)

[Curriculum Vitae: Dr. Simone Tanelli, Principal Investigator
ii](#curriculum-vitae-dr.-simone-tanelli-principal-investigator)

[Curriculum Vitae: Dr. Eastwood Im
iv](#curriculum-vitae-dr.-eastwood-im)

[Curriculum Vitae: Dr. Ziad S. Haddad
vi](#curriculum-vitae-dr.-ziad-s.-haddad)

[Curriculum Vitae: Prof. Luca Facheris
vii](#curriculum-vitae-prof.-luca-facheris)

[APPENDIX B --Budgetary Information
b](#appendix-b-budgetary-information)

[APPENDIX C --Letters c](#appendix-c-letters)

# PROJECT DESCRIPTION {#project-description .unnumbered}

## Objectives and expected significance

This project aims at assessing the potential contribution of a
spaceborne Doppler Atmospheric Radar (DAR) to the current efforts to
improve our knowledge of the atmospheric dynamics and mycrophisics.

A Doppler Atmospheric Radar (DAR) is a spaceborne coherent radar
designed to detect hydrometeors and their motion. Two main
sub-categories of atmospheric radars can be roughly discriminated by
their operating frequency, the Precipitation Radars (PR, at 35 GHz or
lower) and Cloud Profiling Radars (CPR, at 35 GHz or higher).

A DAR provides measurements of radar reflectivity factor (*Z~m~*) and
average vertical velocity (*v~p~*) of hydrometeors over the globe with
high spatial resolution (The quantities used throughout this proposal
are described in Section 4.6).

### Scientific goals

The following ***scientific goals*** will be addressed in this proposed
study on DAR:

-   Estimation of vertical profiles of the Vertical component of Wind
    and wind divergence (***VW)***;

-   Characterization of Convection (***CC***);

-   Estimation of vertical profiles of Latent Heating release/absorption
    (***LH***);

-   Classification of precipitating systems (*e.g.*, Convective vs.
    Stratiform) (***CS***);

-   Classification of Hydrometeors (*e.g.*, snow, graupel, rain and
    cloud ice) (***HC***);

-   Estimation of the Parameters of the hydrometors Size Distribution
    and of the rain rate (***PSD***);

Goals ***HC*** and ***PSD*** pertain to the study of clouds microphysics
and have a direct influence on the performances of rain retrieval
algorithms (especially those based on passive sensors, see e.g., Adler
et al. 1991). Goals ***VW***, ***CC,*** ***CS*** and ***LH*** are
directly connected to the study of the atmosphere on a global scale and
constitute the main drive for proposing this research activity. The
relevance of each scientific goal towards the general framework of
atmospheric monitoring from space is sketched in Table 1 and is
discussed in the remainder of this section together with the expected
contribution of Doppler measurements.

#### Estimation of the Vertical component of Wind (**VW**)

Vertical air motion is a state variable in models of atmospheric
circulation, from global planetary scales down to cloud-resolving
microscales. Also, the directly derived quantities of wind divergence,
mass flux and latent heating release are the key unknowns that drive the
dynamics of atmospheric systems (Yanai et al. 1973). However, aside from
isolated wind profilers or occasional airborne missions, there are
currently no systems capable of sampling the vertical component of the
wind over any significant spatial extent. In general, the mean Doppler
velocity *v~p~* observable by a DAR at angles close to nadir can be
expressed as *v~p~ = w + v~t~* where *v~t~* is the average terminal
velocity of the hydrometeors in still air and *w* is the vertical wind
velocity. The problem of estimating *w* from *v~p~* is therefore
strictly related to the estimation of *v~t~*. In rain, such estimate can
be obtained by first estimating the first-order statistics of the
drop-size distribution (DSD) and then forward calculating the average
terminal velocity through the available relations between terminal
velocity and drop size. This research will focus on two algorithms to
obtain first-order statistics of the DSD: the first is an available
dual-frequency radar algorithm, and the second is a new combined
reflectivity-Doppler algorithm. The former will be applied to retrieve
*w* in convective systems while the latter will be developed and then
applied in stratiform systems. The corresponding error models in DSD
parameter estimates will be combined with the error model for the *v~p~*
measurements in order to determine the final uncertainty on estimates of
the vertical wind *w*. The details of the research plan defined to
achieve this goal are provided in the description of task I in this
proposal.

#### Characterization of Convection (**CC**)

The availability of vertical profiles of the vertical component of wind
represents a unique opportunity for the atmospheric science community to
achieve a better understanding of the dynamics of precipitating systems.
The characterization of convection can be pursued with two approaches:
the first relies on the direct retrieval of parameters relevant to the
modeling of convection in cloud ensemble models and in coarse-scale
models (*e.g.*, heating profile or divergence profile), the second aims
at defining significant statistics of the observable radar/radiometric
quantities (see *e.g.*, Boccippio 2003). In both cases, DAR can
guarantee the high horizontal (\<4 km) and vertical resolutions (\<0.5
km) required to resolve the most relevant atmospheric processes and it
can allow the direct calculation of the statistics relative to vertical
air motion (*e.g.*, occurrence and intensity of up- and down-drafts for
any given location, season, and climatological regime). The
high-resolution characterization of convection is necessary to improve
the performances of GCM's since it has been found that the
parametrization of convection through its first-order moment (i.e., the
ensemble mean), is not adequate to represent correctly the impact of
convection (Lin and Neelin, 2002). Also, it has been demonstrated that,
in order to estimate the higher-order moments of convection, the
temporal evolution of convection has to be accounted for. However, due
to technological constraints which will almost certainly remain in the
next decade, the size and weight of high-resolution radars precludes
their deployment on geostationary orbits. Confined to low earth orbits,
the spatial and temporal sampling of such instruments are quite
drastically limited, to the point where they can only capture occasional
\"snapshots\" of any weather system, separated in time by several hours.
It is thus necessary to develop a method to fill the gap between the
instants at which highly detailed vertical profiles can be obtained at a
given location. It is here proposed to develop technique capable of
integrating DAR measurements with VIS/IR data obtained from
geostationary platforms such as GOES, Meteosat and GMS (half-hourly) and
radiometric measurements (3-hourly as planned for the Global
Precipitation Measurement mission -- GPM, Flaming et al. 2001) to derive
the characteristic temporal scale of evolution of convection together
with its vertical structure.

#### Hydrometeor Classification (**HC**)

***HC*** -- Discrimination of frozen, liquid and mixed phase
hydrometeors is of paramount importance for any retrieval algorithm.
Vertical velocity in still air is mainly determined by hydrometeor
density, and therefore mean Doppler velocities provide a very reliable
tool to discriminate, for example, snow from rain. In particular, since
the typical vertical velocities of snow and rain differ by several m/s,
even the first level accuracy requirement of 1 m/s on spaceborne Doppler
measurements appears to be sufficient to provide a significant
contribution to this goal. Doppler information, together with
reflectivity measures has been used recently also to detect and analyze
the melting layer in stratiform precipitation from airborne radar
measurements (Tanelli et al. 2003c). The excellent results allow to
foresee analogous performances in HC from spaceborne measurememnts.

#### Estimation of first order parameters of the Particle Size Distribution (**PSD**)

The need of multiparametric measurements for estimating the main
parameters of drop size distribution (generically *M~w~* representing
the mass content, *D~m~* the average diameter, and *s~m~* the drop
diameter spread) is well understood. Due to the non-linearities between
the desired physical parameters and the observed radiometric quantities
the estimation of these three parameters is critical even if one is
interested only in the rainfall rate. Most of the efforts in the
development of multiparametric rain retrieval algorithms stem from the
current availability of two kinds of spaceborne sensors: passive
(multi-frequency radiometers) and/or incoherent radars (single-frequency
as in TRMM or multi-frequency such as the one planned for GPM). However
it has been demonstrated through ground-based radars that information on
*D~m~* and *s~m~* is provided by Doppler measurements from vertically
pointing radars. Although the accuracy on vertical velocity estimates
expected from spaceborne systems is worse than that achieved by
ground-based radars (*e.g.*, 0.1 m/s), it appears to be sufficient to
provide a significant contribution to the estimation of *D~m~* (see
detailed description of the Research Plan) provided that the background
air vertical motion can be neglected (*e.g.*, in stratiform systems) or
estimated through ancillary information.

#### Convective/Stratiform classification (**CS**)

Classification of rain systems affects several aspects of global
atmospheric monitoring, from radiation budget studies to latent heat
release mapping and from rain retrieval algorithms to climatological
studies. Spaceborne precipitation radars are already recognized as the
optimal tool to discriminate stratiform systems from convection and
other precipitation types (Awaka et al. 1997). This is mainly due to
their higher spatial (horizontal and vertical) resolution as compared to
passive sensors. While the performances of the classification are
satisfactory (Durden et al. 2003), some inconsistencies are observed.
Furthermore, the extension of the observed latitudinal region from TRMM
to GPM will preclude the usage of some a priori conditions on the
altitude of the brightband used in the current TRMM algorithm for the
detection of the brightband. The inclusion of Doppler capabilities will
further improve the accuracy of such classification since the vertical
velocity is the parameter that defines whether convection is present or
not. Further discrimination between stratiform rain and warm rain
processes is possible through detection of the melting layer (discussed
before).

#### Vertical profiling of Latent Heating (**LH**)

The importance of latent heating estimates is widely recognized by the
scientific community and actually the need for the four-dimensional
distribution of latent heating is a fundamental goal of the Tropical
Rainfall Measuring Mission (TRMM). Admittedly, "*it is important for
modelers of large-scale circulations to know the location and amount of
heat release in the atmosphere. They need to know its vertical profile
as well as its horizontal distribution*" (Simpson et al. 1988, see also
Puri 1987 and Hack and Schubert 1990). As recently demonstrated by Tao
et al. (2001), three independently developed algorithms aiming at
estimating the latent heat profiles from TRMM sensors provide estimates
of magnitude of maximum heating rates consistent among each other (4-6 K
day^-1^) and comparable to those provided by budget studies (4-10 K
day^-1^). On the other hand, several differences are evident in the
retrieved vertical profiles and no evidence is yet available to
determine which algorithm (if any) is performing best. Among the
possible sources of error three factors seem to dominate: estimation of
surface rainfall rate, classification of convective vs stratiform rain
system type, and characterization of the vertical motion of the air mass
in convection, in different portions of the globe and different seasons.
In general, "*the only direct means to obtain the latent heating profile
is through knowledge of the vertical velocity field and the ability to
classify the precipitation into convective, stratiform and transitioning
modes"* (Houze 1997). Therefore the expected significance of a DAR is
related to the aforementioned significance in ***PSD*** , ***CS*** and
***VW*** science goals. Furthermore, one of the three algorithms (Yang
and Smith 2000 and refs. within) considered in Tao et al. (2001), is a
profile-type physical inversion scheme that assumes zero vertical wind.
This algorithm will be the testbed for the performances of DAR in
retrieving latent heating profiles (in fact it is straightforward to
calculate the latent heating profile with and without the information on
vertical velocity).

### Specific contributions

> The main ***specific contributions of the proposed research
> activity*** are represented in the diagram of Figure 1 and are
> described in this section.

#### Definition of optimal DAR configurations

Optimal DAR configurations for observing the dynamics of precipitating
and non-precipitating systems will be defined at three different
operating frequencies (i.e., Ku band, Ka band and W band)***.*** The
direct observables of a DAR are the mean vertical velocity of
hydrometeors (*v~p~*) and attenuated radar reflectivity factor (*Z~m~*)
at one or more operating frequencies. This research activity will focus
on the accuracy on *v~p~* estimates, however, all aspects of system
design and performances in measuring atmospheric parameters (e.g.,
maximum swath width vs number of samples per acquisition, use of pulse
compression vs. short pulse, use of single- vs. multiple-frequency,
sensitivity vs. spatial resolution etc.) will be considered. This first
goal will be referred to hereinafter as ***DAR-SA*** (DAR-System
Analysis) and will allow reliable cost-benefits tradeoff studies for
future missions considering the use of Doppler technology in atmospheric
monitoring.

#### Analysis of DAR performances in estimating vertical wind velocity

A detailed analysis of the performances of optimal DAR configurations in
estimating vertical wind velocity (*w*) from *v~p~* and *Z~m~*
measurements will be carried out to support the scientific goal
***VW***. To this purpose, DAR configurations including use of
multi-frequency measurements are expected to provide significantly
better performances than single-frequency configurations and will be
therefore analyzed carefully. Some attention will be dedicated also to
off-nadir measurements to retrieve horizontal components of wind.

#### Development of multiparametric (Doppler) algorithms

Development of algorithms relying on dual-frequency reflectivity
profiles and mean Doppler velocity profiles for: 3a)
convective/stratiform classification of precipitating systems
(***CS***), 3b) direct estimation of the vertical profiles of latent
heat release/absorption (***LH***), 3c) hydrometeor classification
(***HC***), 3d) estimation of parameters of drop size distribution
(***PSD***). The performances of these algorithms will be compared to
the analogous existing, non-Doppler, algorithms for spaceborne
observations.

#### Development of a technique to estimate vertical structure and temporal evolution of convection from DAR measurements and images form geostationary platforms

In the last year of research an approach to characterize the vertical
structure and temporal evolution of convection will be developed. It is
proposed to do this with the help of the half-hourly IR data obtained
from geostationary platforms such as GOES, Meteosat and GMS and of
3-hourly data obtained by the constellation of satellites planned for
the Global Precipitation Measurement (GPM) mission. Specifically,
recognizing that the IR channels are directly sensitive only to the
cloud tops, we propose to use them to track the evolution of the
underlying weather systems during the period between consecutive DAR
passage times. By condensing this tracking information into one
\"advection vector field\" for each IR image during the DAR gap, whose
direction is that of the local translation which maximizes the
correlation between the previous and current IR images, one obtains a
time series of vector fields indicating the most likely direction of
advection of any weather system from one DAR sample to the next. The
detailed DAR-estimated arrays of rainrate, wind divergence and latent
heating can then be projected to any point in time between two
consecutive DAR snapshots by simply advecting the arrays at the two
instants according to the IR-derived advection fields, then averaging
the two advected quantities using weights that are commensurate with
their distance in time between the instants of the two snapshots and
with the relative magnitude of the correlation along the forward and
backward advection directions. The radiometric measurements, obtained at
the intermediate time scale of 3 hours, will be used to discriminate
short-lived phenomena (less than 6 hrs) from more organized systems
(e.g., mesoscale convective systems) that affect most the atmospheric
circulation on the large-scale. In fact, the radiometric measurements,
initialized by the high-resolution radar profiles, can provide accurate
estimates on the intensity of convection and on the stage of its
evolution.

***Overall, this proposal is relevant to the following NASA
objectives:***

-   Weather research to improve forecast duration and reliability
    (highest relevance)

-   Climate variability, change and prediction

-   Water and energy cycle research for climate and water resources
    management

## Relation to the present state of knowledge

It is generally accepted that a DAR is potentially the optimal
instrument to monitor precipitating and non-precipitating systems and
their dynamics (Meneghini and Kozu 1990). However, the technological
challenges presented by a DAR have been, in the past, the main reason of
concern for the design of a spaceborne mission. Because of such
concerns, no spaceborne Doppler radar for atmospheric observation is yet
operating. However, the technology to develop such instrument has
developed significantly in the last ten years and spaceborne DAR
technology is now available. In fact, a Doppler Cloud Profiling Radar is
currently under study in Japan for an ESA program (European Space Agency
2001) and a spaceborne Doppler precipitation radar concept is currently
being studied at NASA (Im et al., 2002b).

As far as the accuracy in wind estimates is concerned, it is evident
(see e.g. Tao at al. 1987) that a 1 m/s resolution is sufficient to
capture almost all upwards mass flux in convective regions and \>50% of
downward mass flux in the stronger downdrafts. The systematic study of
DAR configurations has completed a preliminary phase (Amayenc 1992,
Tanelli et al. 2003a). Our studies show that a notional DAR
configuration can guarantee accuracies of 0.7 m/s for Signal to Noise
Ratios (SNR) \> 10dB in *v~p~* estimates (Tanelli et al. 2003) and the
expected accuracies of dual-frequency algorithms seems to guarantee an
additional error on the estimate of average terminal velocity in the
order of 0.6 m/s or less. It is therefore expected that a systematic
performance analysis of different DAR configurations can indeed
guarantee the desired accuracy of 1 m/s (rmse). However, more work is
needed to confirm this result, and to determine how the 0.5 m/s
accuracy, needed to satisfy a wider range of science needs, could be
achieved.

The impact of vertical velocity measurements on atmospheric models is
well represented by the fact that, at present, only sporadic (in time
and/or space) profiles of the vertical component of the wind are
available and for this reason their asimilation is limited to a few
cases. On the other hand, the fields of low-level horizontal winds are
monitored with more systematicity (*e.g.*, single-.and dual-Doppler
measurements from networks of ground based radars or scatterometric
measurements above the oceans) and their assimilation has already proven
to be very beneficial to the performances of atmospheric models.
Notably, the fields of horizontal winds are currently used to obtain
model-based estimates of the vertical motions (see e.g. the vorticity
method used in the GBVTD technique proposed in Lee et al. 1999 for
meso-scale models) which are ultimately a necessary input for the
atmospheric model. The availablity of direct measurements of vertical
velocity from a spaceborne system would allow the systematic validation
of this approach or, directly, the assimilation of vertical velocity
fields in the models.

On the related issue of the characterization of convection, recent
studies aimed at identifying several different regimes of precipitation
directly from the characteristics of TRMM/PR reflectivity profiles (see
*e.g.*, Boccippio et al. 2003). The promising results show that
convective regimes common to different regions of the world exist.
However, it was also shown that some ambiguity in the discrimination
process is due to the uncertainties in the convective-stratiform
classification. While the use of lightning occurrence in the same region
was used to resolve this ambiguity, the availability of co-located and
simultaneous vertical velocity profiles would allow a more reliable
classification with little or no further assumptions. Furthermore, field
campaigns have demonstrated that the convective regimes (and the
associate latent heating profiles) not only change from place to place
(Houze 1989), but they also change significantly at the same location
and within the same season (see e.g., Cifelli et al. 2003), hence adding
further uncertainty to the characterization of convection if no
co-located and simultaneous profiles of vertical velocity are available.

In general, there is no systematic study available to determine the how
the performances in estimating *v~p~* translate in attaining the related
scientific goals. That is, there is no quantification, through an error
model and realistic simulations, of the accuracy that we could achieve
in estimating the vertical wind velocity *w*, or of the improvements
that we would obtain in the vertical profiling of latent heating or in
the classification of precipitating systems by incorporating vertical
velocity measurements. Such performance analysis should be compared to
what is currently achievable through non-Doppler systems (both active
and passive). This performance analysis would constitute a reference
point for cost-benefits evaluations when designing a mission.

## Relation to previous work done on the project and to related work in progress elsewhere

This research will benefit from the following achievements that we
obtained in the last four years in the study of Doppler and
dual-frequency Precipitation radars under IIP and GPM programs.

i)  ***Three dimensional simulator of spaceborne Doppler radar (DS3)***.
    This simulation tool described in Tanelli et al. (2002a) allows to
    simulate the full Doppler spectrum observed by a spaceborne radar.
    The simulator is applied to 3-D high-resolution datasets of cloud
    systems. Such datasets have been obtained from JPL airborne Doppler
    radars ARMAR (Durden et al. 1994) and APR-2 (Sadowy et al. 2003) and
    from a Cloud Resolving Model. DS3 has been successfully used to
    simulate realistic Doppler spectra from rainfall accounting for: a)
    Mie scattering from hydrometeors in all phases, b) Nonuniform Beam
    Filling (NUBF), c) sea surface scattering under arbitrary wind
    conditions, d) pointing errors and oscillations, e) multi-frequency
    operating mode, and f) short-pulse or pulse compression operating
    mode.\
    The proposed research will be initially devoted to the refinement of
    DS3 by: a) inclusion of multiple-scattering effects (most relevant
    to Ka and W band configurations), b) refined e.m. modeling of
    mixed-phase and frozen spheroidal hydrometeors.

ii) ***Study on the impact of Nonuniform Beam Filling on Doppler
    measurements***. The effect of NUBF on Doppler measurements is
    significant (Tanelli et al. 2002a), however the new Combined
    Frequency-Time technique (CFT) signal processing technique was
    developed and the results prove that CFT is capable to remove
    NUBF-induced errors (Tanelli et al. 2001 and Tanelli et al. 2003d).
    This study motivated a new AIST study on an on-board processor for a
    spaceborne Doppler precipitation radar at NASA/JPL.\
    The proposed research will consider the use of the CFT technique as
    alternative or complement to standard spectral moment estimators
    such as the Pulse Pair (PP) technique or the Spectral Analysis (SA,
    based on Discrete Fourier Transform) techique.

iii) ***Extension of the theoretical framework to assess the
     performances of spectral moment estimators for relatively broad
     spectra typical of a spaceborne application***. Performances of
     spectral moments estimators in the field of atmospheric radars had
     focused, in the past, mainly to narrowband spectra (typical for
     ground-based and airborne radars). A generalized theoretical
     approach has been developed to estimate bias and standard deviation
     of estimates of the first spectral moment (*i.e.*, the average
     Doppler velocity) for broad-band spectra typical of spaceborne
     applications (Tanelli et al. 2002b). Excellent agreement was found
     with simulated radar signals, and a new iterative implementation of
     the SA based estimators was developed to guarantee optimal
     performances for spaceborne applications.\
     The proposed research activity will benefit of these results. No
     further research is planned towards this task other than minor
     refinements of the SA estimators (related to the choice of the
     optimal data windowing).

iv) ***Doppler velocity errors induced by pointing uncertainties and
    instabilities and new techniques to correct for it***.
    Pointing-induced errors were evaluated through DS3. Pointing error
    budgets were defined accordingly (Tanelli et. al 2003a). Also, it
    was proven that application of spectral moment estimators to the sea
    surface return can correct for pointing-induced errors when
    pointing-error requirements are not met (Im et al. 2002). The
    results were obtained for one specific DAR configuration, they will
    be extended to all selected DAR optimal configurations.

v)  ***Level 1a processing of the dual-frequency airborne Doppler radar
    APR-2.*** As described in the research plan, the data gathered by
    the JPL APR-2 during several experiments constitute the core of the
    dataset used in this proposed research. The proponent has developed
    a software package for the level 1a processing (including external
    calibration and correction for navigation errors), visualization and
    editing of all APR-2 datasets. The software is being used to deliver
    the APR-2 products for the experiments from 2000 through 2003.

vi) ***TREMC algorithm for pattern tracking and feature morphing*** The
    TREMC (Tracking Radar Echoes through Multiscale Correlation)
    algorithm (Tanelli et al. 1999, Facheris et al. 1999) was developed
    for calculating the advection fields from sequences of constant
    elevation maps from multiparametric ground based radars. The TREMC
    algorithm will be modified in order to calculate the advection
    fields from sequences of images from geostationary VIS/IR sensors.
    The modified algorithm will be used in the third year of proposed
    activity to estimate the temporal evolution of convection.

vii) ***Dual Frequency algorithms for retrieval of parameters of
     hydrometeor size distribution***. Several dual-frequency algorithms
     have been developed or are currently under development mainly as
     part of the effort tied to the GPM dual frequency radar. Two
     algorithms under development within the Atmospheric Radar Science
     and Engineering Group at JPL are a particle-filter based algorithm
     (Dr. S.L. Durden) and a bayesian algorithm (Dr. Z.S.Haddad and
     Dr. J. Meagher). These algorithm are providing promising results in
     retrieving vertical profiles of parameters of the DSD (J.Meagher
     and Z.S.Haddad, 2001b) and of the size distribution of frozen
     hydrometeors (J.Meagher and Z.S.Haddad, 2001a). They will be
     applied to APR-2 data to provide the reference 3D dataset of
     rainfall rate and mean drop diameter.

Related ongoing efforts carried out elsewhere that will directly
contribute to this project are:

viii) ***The studies on Doppler cloud radar performed at CRL in Japan***
      for the phase A study for the EarthCare mission (ESA 2001) are
      related to this proposal. Their independent research on the
      characterization of the Doppler radar signal observed by a DCR
      (Kobayashi 2002) was used to validate the simulator DS3 and their
      results on the performances of Pulse Pair operation for velocity
      estimation (Kobayashi et al. 2002b) were compared to the results
      described in iii). They also investigated the general effect of
      navigation errors and the effectiveness of sea surface Doppler
      processing to correct for it (Kobayashi and Kumagai 2003) and
      those results were compared to those described in iv). Starting
      from the fall of 2003, Dr. Satoru Kobayashi will visit our group
      at NASA/JPL as a visiting research scientist for one year to
      further expand our collaboration in this area.

ix) ***The study on the contribution of multiple scattering*** by Prof.
    F.S.Marzano in Italy (Marzano et al. 2003) is of interest to further
    improve the DS3 simulator for its operation at high operating
    frequencies. An agreement was recently reached to collaborate on
    these matters starting in the winter 2003. Dr. Marzano will provide
    the 1-D multiple scattering code that will be incorporated in the
    DS3 simulator by enabling 3-D simulation of multiple scattering
    effects.

x)  ***The studies on the characterization of convection*** by Prof.
    Neelin at UCLA contributed to define the ***CC*** goal in this
    proposal. Further discussions with Prof. Neelin will help in
    focusing the proposed research to the needs of the atmospheric
    modeling community. For the same purpose, experts in GCM's data
    assimilation have been contacted (*e.g.*, Dr. Mahfouf, Canada).

## Research Plan

### Summary

**Year 1 (**goals ***DAR-SA*, *HC*** and ***PSD*):**

I.  Preparation of the reference library of multiparametric profiling
    algorithms. Preparation of 3-D datasets & acquisition of ancillary
    datasets (geostationary VIS/IR and radiometric soundings).

II. Improvements of the existing 3-D Doppler radar simulator DS3.

III. Systematic testing of DAR performances in estimating *v~p~* for
     several different configurations through DS3 and the retrieval
     algorithms described in (Tanelli et al. 2003). Selection of 1
     optimal configuration for each operating frequency and of optimal
     multi-frequency configurations.

**Year 2 (**goals ***CS*, *CC*, *PSD,* *VW*** and ***LH*):**

IV. Retrieval of vertical wind velocity and DSD parameters through
    multiparamentric (Doppler) algorithms from DAR measurements.

V.  Convective Stratiform classification with DAR: comparison of Doppler
    Radar vs. non-Doppler radar (such as GPM). Evaluation of the impact
    on rainfall retrievals and Latent Heat estimates through the
    Convective Stratiform Heating (CSH) algorithm.

VI. Latent heating profiling: comparison of DAR algorithm vs CSH and HH.

**Year 3 (**goal ***CC*):**

VII. Adaptation of the TREMC tracking algorithm to the geostationary
     VIS/IR images. Development of the intensity scaling algorithm for
     combining VIS/IR advection with radiometric soundings.

VIII. Characterization of convection through DPR vertical profiles and
      products of step VII.

### Detailed plan for the first year

#### Preparation of Datasets and Basic Profiling Algorithms (Task I)

The datasets provided by the airborne Doppler radar APR-2 (Sadowy et al.
2003) are: high-resolution (200-800 m horizontal resolution, 37 m
vertical resolution), dual-frequency (Ku and Ka band), dual polarization
(Linear Depolarization Ratio, LDR), and Doppler (at both frequencies).
Case studies representing various precipitating regimes in different
climatological areas are already available from several experiments
(*e.g.*, CAMEX-4, Wakasa Bay Experiment, LRR Validation) and will
constitute the core datasets to simulate DAR operations and assessing
its performances. These datasets will be processed by a reference
library of algorithms (described below) to generate the \'truth\'
high-resolution 3-D fields of hydrometeor parameters *M,R* (only for
raindrops)*,D~m~* and *v~p~*. These truth fields will be used in DAR
simulations as done in Tanelli et al. (2002a). Note that several
truth-fields will be obtained from each APR-2 dataset whenever ambiguity
in the retrieval arises (*i.e.*, if different results are obtained by
adopting different assumptions on the DSD shape, or if the modeled error
on one or more of the retrieved DSD parameters is large). The rationale
behind this choice is that realistic datasets are needed, and the
availability of several \'alternative\' truth fields from the same
observation is beneficial to explore DAR sensitivity to such
ambiguities. In a further phase of research, the retrieved fields will
be also validated by comparing them to the observations of other
instruments during the same experiment (*e.g.*, TRMM or AQUA overpasses,
airborne Cloud profiling radar ACR, airborne radiometers, ground based
radars, etc.). Case studies of convection will be selected and the
related VIS/IR images and radiometric soundings will be collected for
the third year activities.

Also, 3D tropical systems simulated through the Cloud Resolving Model
developed by Dr. S. Hristova-Veleva will be used to futher investigate
DAR performances. The advantages of CRM-generated fields are threefold:
they provide realistic fields of all kinds of scatterers (as opposed to
the simple categories of frozen, liquid and mixed-phase provided by the
APR-2 datasets), they include a 3-D field of wind vectors, and they are
generated over a large area that allows to test scanning-related issues.
Finally, 3-D fields obtained through multipolarimetric Doppler ground
based radar might be added to the datset for further testing of the DAR
performances.

*Task I.a - Melting Layer: detection through multiparametric algorithm,
and correction of excess attenuation.*

As outlined in Figure 1, the general retrieval approach considered
throughout this research activity follows a two step approach: first the
multiparametric profile is used to classify the portions of frozen,
mixed phase, and liquid hydrometeors; then, the retrieval of each single
portion is carried out accordingly to the inversion model specific for
that portion. It is widely recognized that it is crucial to develop a
reliable inversion model for the melting layer of precipitation. In
fact, while the profiling of mixed-phase hydrometeors inside the melting
layer is probably beyond the reach of a spaceborne system (because of
the limited vertical resolution, typically \>200 m), two key pieces of
information can be obtained to improve estimates of the underlying layer
of precipitation: an estimate of the \'excess attenuation\' (*i.e.*, the
additional attenuation caused by the melting layer as compared to the
attenuation that is estimated if that portion of the profile is
considered liquid water), and an estimate of the evolution of the size
distribution of particles between the beginning and the end of the
melting process.

It is proposed here to use the APR-2 data to validate the melting layer
models compared in Olson et al. (2001) and the one described in Klaassen
(1990). In fact, the APR-2 multiparametric, high vertical resolution,
dataset allows to analyze in detail the observed profile of the
precipitating system. The validation procedure will be that proposed by
Olson et al. (2001). In that paper the overall effect of the melting
layer is demonstrated (*i.e.*, presence of excess attenuation and bright
band in radar profiles), but no evidence is available to prefer one
model vs the others. In fact, the authors point out that the height
variation of melting particle radiative properties is significant on
scales of about 50m. However, their validation is carried out with
TRMM/PR data that have a 250 m vertical resolution. It is expected that
the higher resolution APR-2 data will be sufficient to compare the
models of the melting layer and select one (or, possibly, lead to the
development of a new model stemming from the acquired experience). To
this regard, a multiparametric algorithm for detection of the upper and
lower boundaries of the melting layer has been recently developed and is
providing extremely accurate statistics that characterize the melting
layer of precipitation (Tanelli et al. 2003c). These statistics confirm
and extend the findings described in Durden et al. (1997) and identify
very significant correlations between parameters observable by a DAR and
the finer-scale characteristics of the melting layer. Furthermore APR-2
multipolarimetric measurements will be useful to validate more complex
e.m. models such as that described in Zhang et al. (1996) that accounts
for non-spherical hydrometeors (see also Task II.b).

*Task I.b - Retrieval of DSD parameters and vertical wind velocity
through multiparametric (Doppler) algorithms*

Several groups of algorithms are used to estimate the first-order
parameters of the hydrometeor size distribution from non-Doppler
spaceborne systems (single and dual frequency radar, multifrequency
radiometric or radar/radiometer combined algorithms). In principle, one
can modify any of them to exploit the information provided by Doppler
measurements of average vertical velocity. In this proposal we focus
first on single-frequency radar algorithms, and then dual-frequency
(single-Doppler) radar algorithms. The study of further multiparametric
algorithms is postponed to further research activities to avoid issues
related to the use of different instruments.

Figure 2 shows the isopleths of three radar observables: radar
reflectivity (*Z*), specific attenuation (*k*) and mean Doppler velocity
(*v~p~*) as a function of liquid water content (*M*), mean (mass
wighted) drop diameter (*D~m~*) and normalized drop standard deviation
(*s~m~*). These values were calculated for a modified Gamma distribution
for the DSD at 14 GHz. The mean Doppler velocity is weighted by the
backscattering cross-section and is here calculated assuming zero
vertical wind velocity and the terminal velocity vs. drop diameter law
used in Haddad et al. (1997).

While each of the three observable parameters is only moderately
affected by the variability of *s~m~* (small spread of dashed and dotted
curves around the solid curves), it is clear that the measurements of
*v~p~* are more sensitive to *D~m~* than *Z* or *k*. Furthermore, for
radar operating at attenuating frequencies such as DAR, *Z* and *k* are
both derived from measurements of the apparent reflectivity factor
*Z~m~* and are therefore not truly independent (errors in estimating one
reflect to the other). On the other hand *v~p~* measurements are not
proportional to the returned power and are affected by different error
sources, they can therefore be considered independent from the pair
(*Z,k*). One should also note that even if *v~p~* measurements are
obtained with a 1 m/s accuracy, they provide valuable information on
*D~m~* and they do reduce the uncertainty on the estimation of DSD
parameters.

*R*) and the mean drop diameter. The contribution of *v~p~* measurements
to this goal is best seen by adopting the DSD parametrization proposed
in Haddad et al. (1997) for the gamma distribution: *R* is the rain rate
estimated in still air, *D\"* is a parameter proportional to *D~m~* and
*s\"* is proportional to *s*. The advantage of these three parameters is
that they are less statistically dependent among each other. Figure 3a
is analogous to Figure 2, and figures 3b and c represent the same
parameters calculated for the higher operating frequencies.

Two well known facts are represented in Figure 3: the intrinsic
ambiguity of any *Z-R* relation and the relative robustness of *k-R*
relations (especially at Ka band). Also, one can note that even if the
pairs (*Z,k*) are derived with small errors, large portions of the
(*R,D\"*) space show little sensitivity to the (*Z,k*) pair. One example
is illustrated in panels d, e, and f for a light rain situation:
assuming *R*=1 mm/hr and *D~m~* = 1, the black isopleths represent the
retrieval ambiguity (represented by the area within the pair of curves)
in the very optimistic assumption that *Z* is known within ±1 dB. The
blue and red isopleths are similarly calculated for uncertainties of *k*
within ±0.1 dB and *v~p~* ±0.5 m/s (realistic for DAR). For this
specific example the most evident contribution of *v~p~* in reducing the
ambiguity of the retrieval is at 14 GHz.

This rationale can be extended directly to dual-frequency algorithms. In
fact, the use of algorithms exploiting the information present in
differential measurements (*e.g.*, using the pairs (*Z~14~-Z~35~,
Z~14~*), (*k~35~-k~14~, Z~14~*) or even triplets obtained by similar
combiations) allows in principle to reduce significantly the range of
ambiguity. However, the aforementioned problems arising from the fact
that these parameters are obtained from the same measurements (of
returned power) are only partially mitigated by the fact that two power
measurements are now available (since the returned power at the two
frequencies is not completely independent in a statistical sense, one
reason being that all scatterers that fall in the Rayleigh regime at
both frequencies contribute equally to the observed reflectivity factor
at the two frequencies). Since all these algorithms aim at retrieving
*R* and *D~m~* , the availability of *v~p~* would help in increasing the
constraining of such retrieval (*i.e.*, three observables for two
unknowns) and therefore they should increase our confidence in the
retrieved parameters.

The bayesian rain algorithm currently under development by Dr. Haddad
and Dr. Meagher is structured in such a way that the inclusion of *v~p~*
measurements together with their uncertainty in the measurement appears
to be readily feasible. This will be the first action taken within this
task.

Further algorithms will be considered in a second phase. Priority will
be given to algorithms using only Doppler retrievals at one frequency
for retrieval of raindrop size distribution parameters. Algorithms using
dual-frequency Doppler measurements will be considered only if such DAR
configurations are considered feasible (note that if beam matching is
required, Doppler measurements at higher frequencies are in general
significantly less reliable than those at the lowest frequency, see
Tanelli et al. 2003). The rationale behind the use of dual-Doppler
measurements is also represented in Figure 3. In fact, since *v~p~* is
weighted by the backscattering cross section, it follows that *v~p~* at
higher frequencies is mainly determined my the motion of the smaller
particles (compare isodoppler curves in panels a, b and c). Finally,
algorithms that aim at the retrieval of particle size distributions of
frozen and mixed phase hydrometeors might be considered as a
continuation of this activity.

The algorithms described so far will be applied initially only when no
significant vertical air motion is detected (*e.g.*, the profile is
classified as stratiform). Profiles classified as convective will be
used to test the wind retrieval algorithm: in this case, only the *Z~m~*
measurements are used to retrieve the parameters of DSD (through
dual-frequency non-Doppler algorithms). Then a drop-size vs. terminal
velocity law is used to calculate *v~t~.* Finally *w* is obtained as
*v~p~-v~t~*.

It is worth to remember that *v~t~* is the backscattering weighted
average terminal velocity and not the mass weighted average terminal
terminal velocity *v~tm~*. When *v~tm~* is desired (*e.g.*, when
calculating mass flux quantities as rain-rate or latent heating through
a hydrometeor profiling algorithm) the offset *v~t~* - *v~tm~* must be
accounted for. This offset is shown in Figure 4a for *s\"*=0.38 and the
standard deviation of such offset as *s\"* varies is shown in Figure 4b.
This means that if R and D\" are known, and assuming that no information
is available on *s\"*, the additional error committed in estimating
*v~tm~* from *v~t~* is always \<0.3 m/s.

This discussion was carried out assuming only a Gamma DSD for sake of
simplicity. However other DSD forms (including non-analytical,
empirically obtained DSD\'s) are presently accounted for in the
development of the dual-frequency algorithms that constitute the core
for the multiparametric algorithms discussed here.

#### Improvements to the Doppler radar simulator DS3 (Task II)

*Task II.a - Multiple scattering*

The typical weather radar equation is obtained assuming a first-order
approximation for the scattering from hydrometeors. Such approximation
is valid as long as the number density and size of the hydrometeors,
relatively to the wavelength, is such that attenuation dominates over
scattering. While this is true for a wide range of rainfall rates at Ku
band, several studies have pointed out that the first-order
approximation does not hold even for moderate rainfall rates at Ka band
or higher frequencies. Multiple-scattering causes the measured
reflectivity to be larger than that predicted by the first-order
approximation. Recent APR-2 observations of rainfall systems in the
Wakasa Bay experiment confirmed the Ka band LDR signatures predicted by
by a second-order approximation model (no LDR signal is predicted by a
single-scattering model when observing raindrops from an angle close to
vertical). The contribution of multiple scattering is relevant to the
Doppler measurements since such contribution to the overall signal is
markedly incoherent and will therefore correspond to a decrease in the
effective SNR for Doppler measurement
purposes.![](media/image3.png){width="3.15in"
height="2.7604166666666665in"}![](media/image4.png){width="3.15in"
height="2.7604166666666665in"}

Two approaches will be considered: the one proposed in Ito et al. (1995)
and the one proposed in Marzano et al. (2003). The latter approach will
be considered first since a cooperation agreement is already defined
with Prof. Marzano (the current algorithm is monodimensional, but it can
be extended to 3D by incorporating the core equations in the DS3). The
two approaches will be compared with existing measurements (mainly
multipolarimetric from APR-2 and ground based radars but further
datasets will be sought).

*Task II.b - Refinement of e.m. modeling of frozen and mixed phase
hydrometeors.*

Frozen hydrometeors are currently modeled as spheres in DS3, snow
density is fixed at 0.1 g/cm^3^ and graupel density is fixed at 0.6
g/cm^3^. Also, A very simplified model is currently employed in DS3 for
mixed phase hydrometeors. A refined model is required to properly
account for the effects on propagation by a layer of melting particles
(see task I.a), to estimate the latent heat absorption of melting
particles, and to validate algorithms for retrieval of snowfall and
cloud properties.

An e.m. model for oblate (for precipitating snow) and prolate (for ice
cristals) spheroids will be incorporated. Both the e.m. model proposed
in Zhang (in collaboration with Prof. L. Facheris) and the T-matrix (in
collaboration with Prof. F.S.Marzano) approach will be considered and
compared. The choice of the model for mixed-phase hydrometeors will be
driven by the results of task I.a. The selected models will be then
incorporated in DS3 which is already designed to readily accomodate any
improved e.m. models.

#### Performance analysis and optimization of DAR configurations (Task III)

Once tasks I and II are completed, the analysis of DAR performances
consists simply of a systematic use of DS3 on the reference datasets.
The experience gained so far during the preliminary DAR system analyses
(Tanelli et al. 2003a) allows us to consider the ranges shown in Table 2
for several configuration parameters. Furthermore the following specific
issues will be addressed: a) 2 scanning strategies: narrow swath for CFT
processing, wide swath for DFT/PP processing; b) Antenna sidelobe level;
c) Range sidelobe level (if pulse compression is used); d) Use of
frequency diversity (two frequencies close to the central operating
frequency); e) Use of polarization diversity; f) Use of staggered
pulses; g) Matching beams vs Maximization of illuminated area (when
multiple frequencies are used)

The criteria used to evaluate the performances of each configuration are
the following: a) Biases and standard deviation of *v~p~* estimates in
rain; b) Biases and standard deviation of *v~p~* estimates in clouds; c)
Minimum detectable reflectivity; d) Performances in classifying the
precipitating system; e) Performances in estimating DSD parameters; f)
Performances in estimating latent heat vertical profile through the
hydrometeor profiling algorithm (this is considered a good overall
diagnostic since such estimate involves all the previous).

A total of seven optimal configurations will be selected: one for each
of the 3 main operating frequencies, and four more configurations will
use two or three operating frequencies. The use of technologies that
increase the radar system complexity (*e.g.*, polarization diversity,
large antenna sizes) will be considered only if they bring a comparably
significant improvement in the performances.

A secondary output of this phase will be generated as we simulate with
DSR the satellite measurements of the TRMM radar coinciding with APR-2
measurements (from the WAKASA BAY experiment).

### List of collaborations

Dr. Eastwood Im (Atmospheric Radar Science and Engineering Group, JPL)
-- Collaboration on Doppler radar performance modeling, analysis and
optimization.

Dr. Ziad Haddad (Atmospheric Radar Science and Engineering Group, JPL)
-- Collaboration on the development of Dual-Frequency radar algorithms.

Prof. Luca Facheris (Univ. of Florence, Italy) -- Collaboration to
upgrade the Spaceborne Doppler Radar simulator to account for the
effects of spheroidal frozen and mixed-phase hydrometeors, and
collaboration to adaptat the existing algorithm for estimation of
advection fields through temporal analysis of high-resolution images to
the proposed application in the combined use of Dar data with
geostationary VIS/IR data.

Prof. Frank S. Marzano (Center of Excellence CETEMPS, University of
L\'Aquila, Italy, recent verbal agreement) -- Collaboration to upgrade
the Spaceborne Doppler Radar simulator to account for the effects of
multiple scattering from hydrometeros.

Other scientists and researchers that will have advisory role or will be
otherwise involved in the proposed activity -- Dr. S.L. Durden (JPL) on
DAR system analysis, Dr. J.-F. Mahfouf (RPN, Canada) on data
assimilation in GCM's, Prof. J.D. Neelin (UCLA) on parametrization of
convection, Dr. S.Kobayashi (Japan) on Doppler signal characterization.

### Export control 

This project includes a collaboration between Simone Tanelli (Jet
Propulsion Laboratory), Professor Luca Facheris (University of
Florence), and Professor Frank S. Marzano (Center of Excellence CETEMPS,
University of L'Aquila, Italy) to upgrade the Spaceborne Doppler Radar
simulator, so that it accounts for the effects of spheroidal frozen and
mixed-phase hydrometeors and multiple scattering. The simulator is a
software program that simulates the signal received by the radar. It
does not, however, simulate details of the electronics or composition of
the radar system. This collaboration will also focus on adapting an
existing algorithm that estimates advection fields through temporal
analysis of high-resolution images, to the proposed application in the
combined use of Doppler Atmospheric Radar data with geostationary
visible and infrared (VIS/IR) weather data. At this time, all of the
work is at the level of fundamental research, and no state department
licenses are required.

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### Definition of the relevant parameters and symbols

  -------------- --------------------------------------------------------------------------------------------------------------------------------------- -----------
  **Quantity**   **Definition**                                                                                                                          **Units**

  *D*            Particle Diameter                                                                                                                       cm

  *N(D,r)*       Particle Size Distribution (at range *r* from radar)                                                                                    m^-3^
                                                                                                                                                         cm^-1^

  *v~T~(D)*      Particle Terminal Velocity                                                                                                              m s^-1^

  *v~P~(D)*      Particle velocity relative to the radar (*v~P~(D)≅v~T~(D)+w* for near nadir looking radar where *w* is the vertical component of wind)  m s^-1^

  *M(r)*         Equivalent Liquid Water Content $M = \frac{\text{πρ}}{6}\int_{}^{}{D^{3}N(D,r)\text{dD}}$                                               g m^-3^

  *R(r)*         Rain Rate $R = 0\text{.}6\pi\int_{}^{}{v_{P}(D)D^{3}N(D,r)\text{dD}}$                                                                   mm hr^-1^

  *D~m~(r)*      Mean Drop Diameter $D_{m} = \frac{\text{πρ}}{6M}\int_{}^{}{D^{4}N(D,r)\text{dD}}$                                                       cm

  *s~m~(r)*      Normalized Drop Std. Dev.                                                                                                               \-
                 $s_{m} = \left\lbrack \frac{\text{πρ}}{6\text{MD}_{m}^{2}}\int_{}^{}{D^{3}(D - D_{m})^{2}N(D,r)\text{dD}} \right\rbrack^{0\text{.}5}$   

  *v~tm~(r)*     Mean (mass-weighted) terminal velocity $v_{\text{tm}} = \frac{\text{πρ}}{6M}\int_{}^{}{v_{T}(D)D^{3}N(D,r)\text{dD}}$                   m s^-1^

  *K~W~*         Water dielectric factor                                                                                                                 \-

  *λ*            Operating wavelength                                                                                                                    cm

  *σ~b~(D)*      Backscattering cross-section                                                                                                            cm^-2^

  *σ~t~(D)*      Total extinction cross-section                                                                                                          cm^-2^

  *η(r)*         Radar reflectivity $\eta(r) = \int_{}^{}{\sigma_{b}(D)N(D,r)\text{dD}}$                                                                 cm^2^ m^-3^

  *Z(r)*         Equivalent Radar reflectivity factor $Z(r) = \frac{\lambda^{4}}{\pi^{5}\left| K_{W} \right|^{2}}\eta(r)$                                mm^6^ m^-3^

  *k(r)*         Specific Attenuation $k(r) = \text{log}_{\text{10}}(e)\int_{}^{}{\sigma_{t}(D)N(D,r)\text{dD}}$                                         dB km^-1^

  *Z~m~(r)*      Measured reflectivity factor $Z_{m}(r) = Z(r) \cdot \text{10}^{- \int_{0}^{r}{k(r)\text{dr}}}$                                          mm^6^ m^-3^

  *v~p~(r)*      Mean Doppler velocity $v_{p} = \frac{1}{\eta}\int_{}^{}{v_{P}(D)\sigma_{b}(D)N(D,r)\text{dD}}$                                          m s^-1^
  -------------- --------------------------------------------------------------------------------------------------------------------------------------- -----------

Table 3: Quantities and symbols relevant to Doppler Atmospheric Radar

## Education Plan

Despite the recognized contribution of spaceborne remote sensing to our
understanding of the atmospheric processes, the community of the
atmospheric sciences is still struggling to cope with the new
techniques, and approaches that are required when making use of such
data. There is the need to create a stronger connection between the new
generation of atmospheric scientists and the experts of remote sensing
so that the former can make a better use of the available information
and the latter can focus their efforts towards the more important
scientific needs. This need was evident during the proponent's past
teaching activity at the University of Florence and at local
administrations in Tuscany (Italy), and it has been confirmed recently
by the independent opinion of estimated educators such as Prof. Neelin
at UCLA Dept. of Atmospheric Sciences. It is the proponent task to
contribute to strengthen this link.

The following educational activities have already been planned within
the context of this proposal:\
1) Series of lectures at UCLA within the framework of Prof. David J.
Neelin series of seminars. (see endorsement letter by Prof. Neelin)

2\) Series of lectures at the Univ. of Florence (Italy), within Prof.
Luca Facheris course (see letter of collaboration by Prof. Facheris).

This regularly scheduled lectures are meant to be a moment of contact
with the students, and the proponent is committed to provide information
on the general subject of accessing and making use of remotely sensed
data as well as the more specific subjects related to the field of
spaceborne atmospheric radars and radiometers. While the latter will be
addressed mainly on the proponent's personal experience and knowledge,
the former, more general subjects will be faced by relying on the
proponent personal contacts with experts in the fields of image
processing, data fusion, e.m. propagation and scattering, information
dissemination and GIS.

The details and mechanisms to evaluate the throughput of the information
delivered to the students will be defined before the activity starts,
together with the Professors responsible for the courses.

## Facilities

The Radar Science and Engineering section at JPL has a long history of
designing and building spaceborne radar hardware for planetary
exploration and earth remote sensing. This section facility includes
several thousand square feet of lab space and extensive microwave
components and test equipment. Dedicated laboratory facilities will be
provided to support this research. The section has a wide range of
networked computers (PC's, MacIntoshes, and Unix workstations). JPL also
extensive facilities for design, fabrication and testing of RF hardware.
The antenna RF tests will be done in JPL's one-kilometer outdoor
far-field range.

#  {#section .unnumbered}

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# APPENDIX A -- Curricula vitarum  {#appendix-a-curricula-vitarum .unnumbered}

## Curriculum Vitae: Dr. Simone Tanelli, Principal Investigator {#curriculum-vitae-dr.-simone-tanelli-principal-investigator .unnumbered}

Atmospheric Radar Science and Engineering Group

Jet Propulsion Laboratory, Pasadena, CA 91109

Tel: 818-354-0195; Email: \<simone.tanelli@jpl.nasa.gov\>

> Simone Tanelli received the \"Laurea\" degree (*summa cum laude*) in
> Electronic Engineering and the Ph.D. in Methods and Technologies for
> Environmental Monitoring from the University of Florence (UF, Italy)
> in 1995 and 1999, respectively. From 1995 to 2000 he was with the
> Radar and Radiocommunications Lab. at UF. Within that period he
> developed retrieval algorithms for the measurement of several
> atmospheric constituents through tomographic monitoring systems
> operating in the microwave and infrared regions. He also partecipated
> to rainfall measurement campaigns with the multiparametric (dual-pol
> and Doppler) weather radar P55C and developed algorithms for the
> analysis and interpretation of the radar data. Since 1999 he has been
> with the Jet Propulsion Laboratory, and has been focusing on the study
> of spaceborne Doppler precipitation radar systems and on the
> developement of algorithms for the retrieval of rainfall parameters.
> His current research interests focus on the retrieval of precipitation
> parameters through multiparametric (Doppler, dual-frequency and
> dual-polarization) measurements from airborne and spaceborne radars.
>
> Dr. Tanelli was Lecturer of the course of Theory of Signals at the
> Univ. of Florence, Italy in 1998 and 1999, in 1998 he also taught the
> "Hydrological Monitoring" course to prepare technical staff of local
> administrations in Tuscany.
>
> Dr. Tanelli has contributed to the NASA TRMM and GPM programs, to the
> Mesoscale Alpine Programme (international research initiative devoted
> to the study of atmospheric and hydrological processes over
> mountainous terrain), to the ENVISAT data exploitation program (ESA)
> and to 5 more programs in the field of atmospheric monitoring (under
> the Italian Space Agency ASI, the Italian National Research Council
> CNR, and the Italian Ministry for University and Scientific Research).
> He has published over 40 articles in refereed journals and conference
> proceedings, and 2 technical reports.
>
> **Recent publications relevant to the proposed research:**

Tanelli S., E.Im, S.L.Durden, L.Facheris, D. Giuli and E.A. Smith, 2003:
Rainfall Doppler velocity measurements from spaceborne radar: overcoming
nonuniform beam filling effects *J. Atmos. And Oce. Tech.,* American
Meteorological Society, in press.

Tanelli S., E. Im, S. L. Durden, L. Facheris, D. Giuli, 2002: The
Effects of Nonuniform Beam Filling on Vertical Rainfall Velocity
Measurements with a Spaceborne Doppler Radar, *J. of Atmos. and Oce.
Tech.*, American Meteorological Society, **19**, 1019-1034

Tanelli S., J. Meagher, S.L. Durden and E.Im, 2003c: Multiparametric
airborne radar observations of the melting layer during the Wakasa Bay
Experiment, *Proc. of the 31^st^ Conf. on Radar Met.,* Seattle Aug. 6-12
2003, 33-34.

Tanelli S., E.Im, S.L.Durden and L.Facheris, 2003a: System and
Performance Analyses of Spaceborne Doppler Radars for Measuring Vertical
Rainfall Velocity, *Proc. of the 83rd AMS Annual Meeting, Feb 9-13 2003,
Long Beach CA, USA*. (CD-ROM, Paper \# 1.2 in 7^th^ Symp. on Integrated
Observing Systems: The water cycle)

Im E., E.A.Smith, S.L.Durden, S.Tanelli, J.Huang, Y. Rahmat-Samii,
M.Lou, 2003: Conceptual design of a geostationary radar for hurricane
studies, *Proc. Of IGARSS'03, Toulouse (F), July 22-25, 2003,* 3 pp

Im E., S. Tanelli, R. Mascelloni, L. Facheris, 2002: Spaceborne rainfall
Doppler radar measurements: correction of errors induced by pointing
uncertainties, *Third Int'l Asia-Pacific Symposium on Remote Sensing,
Hangzhou (RPC), Oct. 23-27 2002. Proceedings of SPIE*, **4894**,
230-239.

Tanelli S., E. Im, L. Facheris, E. A. Smith, 2002: DFT-based spectral
moment estimators for spaceborne Doppler precipitation radar. *Third
Int'l Asia-Pacific Symposium on Remote Sensing, Hangzhou (RPC), Oct.
23-27 2002. Proceedings of SPIE*, **4894**, 218-229.

Cuccoli F., L.Facheris, S.Tanelli, D.Giuli, 2001: Microwave attenuation
measurements in satellite-ground links: spectral analysis for water
vapor profiles retrieval. *IEEE Trans. on Geosc. and Rem. Sens.*,
**39**, 645-654.

Facheris L.,S. Tanelli, F. Argenti, D.Giuli, 1999: Wavelet Applications
to Multiparameter Weather Radar Analysis. In \"*Information Processing
for Remote Sensing*\" Prof. C.H. Chen Ed. for World Scientific
Publishing Co., p. 187-207.

Giuli D., L.Facheris and S. Tanelli, 1999: Microwave Tomographic
Inversion Technique based on a Stochastic Approach for Rainfall Fields
Monitoring. *IEEE Trans. on Geosc. and Rem. Sens.*, **37**, 2556 - 2568.

Tanelli S., L. Facheris, F. Cuccoli and D.Giuli, 1999: Tracking Radar
Echoes by Multiscale Correlation: a nowcasting weather radar
application, *SPIE Proc. of Remote Sensing / Image and Signal Processing
for Remote Sensing V EOS/SPIE Symposium*, *Firenze (I), Sept. 21 -- 24
1999*, p. 228-238.

Giuli D., L.Facheris and S. Tanelli, 1997: A new microwave tomography
approach for rainfall monitoring over limited areas. *Physics and
Chemistry of the Earth (Elsevier Science Pub.)*, **22**, 265-273

## Curriculum Vitae: Dr. Eastwood Im {#curriculum-vitae-dr.-eastwood-im .unnumbered}

Atmospheric Radar Science and Engineering Group

Jet Propulsion Laboratory, Pasadena, CA 91109

Tel: 818-354-0492; Email: \<eastwood.im@jpl.nasa.gov\>

> Dr. Eastwood Im received his B.S., M.S. and Ph.D. degrees in
> Electrical Engineering from the University of Illinois, Urbana, in
> 1981, 1982, and 1985, respectively. He has been with the Jet
> Propulsion Laboratory, California Institute of Technology since 1986.
> His research interests are in the areas of spaceborne meteorological
> radar science and remote sensing, and advanced radar system and
> technology studies. In particular, he has developed radar algorithms
> for the retrievals of precipitation intensity and surface topography,
> and the two-color laser altimetry algorithms for measuring surface
> pressure and tectonic motions. He has been involved in several design
> studies on spaceborne earth remote sensing radars, including
> precipitation radars, altimeters, and millimeter-wave cloud radars.
> Currently, Dr. Im is the Radar Instrument Manager of the NASA ESSP
> CloudSat Mission, and the Supervisor of the Atmospheric Radar Science
> and Engineering program at JPL.
>
> Dr. Im has been a member of the Tropical Rainfall Measuring Mission
> (TRMM) Science Team and a Principal Investigator in TRMM radar rain
> profiling algorithms and TRMM radar calibration, and the Principal
> Investigator of the IIP Second-Generation Precipitation Radar (PR-2)
> technology study. He has also been a Co-Investigator of several other
> NASA research studies. Currently, he is the Principal Investigator in
> four NASA earth science research studies: the IIP dual-frequency
> wide-swath scanning rain radar antenna study; the IIP NEXRAD-in-Space
> radar study; the CAMEX-4 airborne radar's rainfall observation
> experiment, and the validation of the EOS AQUA AMSR-E's rainfall
> measurements. He is also the Co-Investigator on NASA ESE AIST's
> (Advanced Information System Technology program) advanced spaceborne
> rain radar processor; and the Guest-Investigator on the radar study of
> Titan's atmospheric methane precipitation in NASA's Cassini Mission.
> He has been a member of science steering group on the NASA Global
> Precipitation Mission since its inception, focusing particularly in
> the areas of radar performance and technology development and
> infusion.
>
> Dr. Im is a Senior Member of the Institute of Electrical and
> Electronics Engineers, and a member of American Meteorological
> Society, Eta Kappa Nu, and Tau Beta Pi. He received the University of
> Illinois Graduate College Dissertation Research Grant in 1984; the
> GT&E Fellowship in 1984; Kemper Fellowship and Exxon Fellowship in
> 1985, NASA Group Achievement Awards in 1998 and 2002, JPL Award of
> Excellence in 2002, and eight NASA New Technology Awards. Dr. Im
> served as the Associate Editor of the AMS Journal of Atmospheric and
> Oceanic Technology in 1999-2000, and as a member of the Technical
> Program Committee and a Technical Session Chair/Co-Chair of the IGARSS
> symposia between 1999 and 2003. He has published over 130 articles in
> refereed journals and conference proceedings, and 11 technical
> reports.
>
> **Recent publications relevant to the proposed work:**

Durden, S.L., L. Li, E. Im, and S.H. Yueh, 2003: A surface reference
technique using airborne Doppler radar measurements in hurricanes.
Journal of Atmospheric and Oceanic Technology, 20, 269-275.

Sadowy, G., A.C. Berkun, W. Chun, E. Im, and S.L. Durden, 2003:
Development of an advanced airborne precipitation radar. Microwave
Journal, 46, 84-98.

Durden, S.L., E. Im, Z.S. Haddad, and L. Li, 2003: Comparison of TRMM
Precipitation Radar and airborne radar data. Journal of Applied
Meteorology. 42, 769-774.

Li, L., E. Njoku, E. Im, P. Chang, K. St.Germain: A Preliminary Survey
of Radio-Frequency Interference over the U. S. in Aqua AMSR-E Data. IEEE
Trans. Geosci. Remote Sensing. Accepted.

Tanelli, S., E. Im, S.L. Durden, L. Facheris, D. Giuli, and E.A. Smith,
2002: Rainfall Doppler velocity measurements from spaceborne radar:
overcoming NUBF effects. Journal of Atmospheric and Oceanic Technology.
Accepted.

Im, E., S.L. Durden, R.K. Kakar, C.D. Kummerow, and E.A. Smith, 2002:
The next generation of spaceborne rain radars: science rationales and
technology status. SPIE Proceedings on Microwave Remote Sensing of the
Atmosphere and Environment III, 4894, 178-189.

Im, E., S. Tanelli, R. Mascelloni, and L. Facheris, 2002: Spaceborne
Rainfall Doppler radar measurements: Correction of errors induced by
pointing uncertainties. SPIE Proceedings on Microwave Remote Sensing of
the Atmosphere and Environment III, 4894, 230-239.

Li, L., E. Im, S.L. Durden, and Z.S. Haddad, 2002: A surface wind
model-based method to estimate rain-induced radar path attenuation over
ocean. J. Atmos. Oceanic Technol.. 19, 658-672.

Tanelli, S., E. Im, S.L. Durden, L. Facheris, and D. Giuli, 2002: The
effects of nonuniform beam filling on vertical rainfall velocity
measurements with a spaceborne doppler radar. J. Atmos. Oceanic
Technol.. 19, 1019-1034.

Durden, S.L., E. Im, F.K. Li, R. Girard, and K. Pak, 2001: Surface
clutter due to antenna sidelobes for spaceborne atmospheric radar. IEEE
Trans. Geosci. Remote Sensing, 39, 1916-1921.

Coppens, D., Z.S. Haddad, and E. Im, 2000: Estimating the uncertainty in
passive microwave rain retrievals. J. Atmos. Oceanic Technol., 17,
1618-1629.

## Curriculum Vitae: Dr. Ziad S. Haddad {#curriculum-vitae-dr.-ziad-s.-haddad .unnumbered}

Atmospheric Radar Science and Engineering Group

Jet Propulsion Laboratory, Pasadena, CA 91109

Tel: 818-354-1218; Email: \<zsh@albertovo.jpl.nasa.gov\>

> Ziad S. Haddad received the B.A. degree in Mathematics and Computer
> Science from UCLA in 1980, and the Ph.D. degree in Mathematics from
> the Massachusetts Institute of Technology in 1983. He has taught
> mathematics at M.I.T., analyzed radar detection and tracking problems
> at the M.I.T. Lincoln Laboratory, modeled wave propagation in complex
> media at A.T.T. Bell Laboratories, taught mathematics at the
> University of California at San Diego, and pursued research in
> nonlinear filtering and estimation. He has been with the Jet
> Propulsion Laboratory since 1991. His current research activities
> include the development of stochastic algorithms for the retrieval of
> precipitation and the estimation of soil parameters from remote
> sensing data.
>
> Dr. Haddad is a member of the American Mathematical Society, Sigma Xi
> and Phi Beta Kappa.
>
> **Recent publications relevant to the proposed research:**

Li, L., E. Im, S.L. Durden and Z.S. Haddad, 2002: Influence of sea
surface backscattering on the spaceborne radar measurements of rainfall,
J. Atmos. Ocean. Tech., vol. 19, 658 672.

Meagher, J.P., Z.S. Haddad and E. Im, 2001: Principal component analysis
for rain drops and its application to the remote sensing of rain, Q. J.
Royal Met. Soc., vol. 128, 559-571.

Coppens, D. Z.S. Haddad and E. Im, 2000: Estimating the uncertainty in
passive microwave rain retrievals, J. Atmos. Ocean. Tech., vol. 17,
1618-1629.

Coppens, D. and Z.S. Haddad, 2000: Effects of raindrop size distribution
variations on microwave brightness temperature calculation, J. Geophys.
Res., vol. 105, pp 24483 24489.

Haddad, Z.S., E.A. Smith, C.D. Kummerow, T. Iguchi, M.R. Farrar, S.L.
Durden, M. Alves and W.S. Olson, 1997: The TRMM \'Day-1\'
radar/radiometer combined rain-profiling algorithm, J. Meteor. Soc.
Japan, vol. 75, 799-809.

Haddad, Z.S., D.A. Short, S.L. Durden, E. Im, S. Hensley, M.B. Grable
and R.A. Black, 1997: A new parametrization of the rain drop size
distribution, I.E.E.E. Trans. Geosci. Remote Sensing, vol. 35, 532-539.

Haddad, Z.S., E. Im, S.L. Durden and S. Hensley, 1996: Stochastic
filtering of rain profiles using radar, surface-referenced radar, or
combined radar-radiometer measurements, J. Appl. Meteor., vol. 35,
229-242.

## Curriculum Vitae: Prof. Luca Facheris {#curriculum-vitae-prof.-luca-facheris .unnumbered}

Dept. of Electronics and Telecommunications,

University of Florence, Florence, Italy

Tel: +39 055 4796 274; Email: \<facheris@ingfi1.ing.unifi.it\>

> Luca Facheris received the "Laurea" (M.S.) degree (cum laude) in
> Electronic Engineering from the University of Florence, Italy, in 1989
> and the Ph.D. degree in Electronic and Information Engineering in 1993
> from the University of Padua, Italy. Since 1993 he served as an
> Assistant Professor in the Telecommunications area at the Department
> of Electronic Engineering of the University of Florence, where
> currently he is Associate Professor in the same area, in charge of
> teaching and assisting several courses at the Department of
> Electronics and Telecommunications (Signal Theory, Radar Systems and
> Techniques, Telecommunications). Since 2000 he is in the main
> commission of the Ph.D. course in "Methods and Technologies for
> environmental monitoring" and in that of the International Ph.D course
> on "Risks management for the built environment" between the University
> of Florence and the TU of Braunschweig (Germany). His main research
> activity is in the area of data and signal processing for atmosphere
> and Earth surface monitoring by means of active systems (radar
> systems, point-to-point transmitter-receiver links also inserted in
> tomographic networks). In particular his expertise is in polarimetric
> and Doppler radars, synthetic aperture radars, ground and spaceborne
> weather radars, methods for monitoring rainfall and the atmospheric
> components by means of attenuation measurements (single and
> multifrequency) made both at microwaves and infrared.
>
> He has been and is involved, also with the role of responsible for his
> Department, in a number of national and international projects
> (European Space Agency, Italian Space Agency, European Union, Italian
> Ministry for University and Research, National Research Council)
> concerning the above research issues.
>
> **Recent publications relevant to the proposed research:**
>
> Giuli D., L. Facheris: \"SAR application of a signal coding technique
> for single-hit measurement of the target scattering matrix\" European
> Transactions on Telecommunications and Related Technologies, Vol. 2,
> n. 6, Nov.-Dec. 1991, pagg. 675-688
>
> Giuli D., L. Facheris: \"Homopolar and heteropolar generalized
> ambiguity functions in polarimetric radars\" in: \"Direct and Inverse
> Methods in Radar Polarimetry\", Part 2, W. M. Boerner et al. editors,
> 1992 Kluwer Academic Publishers, The Netherlands, pagg. 1367-1388
>
> Giuli D., L. Facheris, M. Fossi: \"Radar target scattering matrix
> measurement through orthogonal signals\" IEE Proceedings, Part F, No.
> 4, Vol. 140, agosto 1993, pagg. 233-242
>
> Facheris L., D. Giuli, A. Freni: \"An integrated model for simulation
> of dual linear polarization radar measurement fields\" IEE
> Proceedings, Part F, No. 4, Vol. 140, agosto 1993, pagg. 223-232
>
> Giuli D., L. Facheris: \"Performance of interpulse signal coding in
> interleaved-pulse polarimetric SAR\" European Transactions on
> Telecommunications, Vol. 4, No. 5, pagg. 555-567, Sept.-Oct. 1993
>
> Giuli D., L. Baldini, L. Facheris: \"Simulation and modeling of
> rainfall radar measurements for hydrological applications\" Natural
> Hazards, 9, 1994 Kluwer Academic Publishers, The Netherlands, pagg.
> 109-122
>
> Baldini L., L. Facheris, D. Giuli, E. Caporali, E. Palmisano:
> \"Analysis of radar and raingauge measurements for a critical
> meteorological event in Tuscany\" Surveys in Geophysics, 16, 1995
> Kluwer Academic Publishers, The Netherlands, pagg. 253-264
>
> Scarchilli G., E. Gorgucci, D. Giuli, L. Baldini, L. Facheris, E.
> Palmisano: \"Weather radar calibration by means of the metallic sphere
> and multiparameter radar measurements\" Il Nuovo Cimento, Vol. 18C, 1,
> n. 1, gennaio-febbraio 1995, pagg. 57-70
>
> Giuli D., M. Gherardelli, L. Facheris, L. Mori e P. Mazzetti:
> \"Improving rainfall estimates derived from dual polarization C-band
> radar reflectivity data\" Physics and Chemistry of the Earth, Vol. 22,
> No. 3-4, pagg. 253-257, Elsevier Science Ltd., 1997
>
> Capolino F., L. Facheris, D. Giuli, F. Sottili: \"E.m. models for
> evaluating rain perturbation on the NRCS of the sea surface observed
> near nadir\" IEE Proc. Radar, Sonar and Navigation, vol. 145, n. 4,
> luglio 1998, pagg. 226-232
>
> Capolino F., L. Facheris, D. Giuli, F. Sottili: \"Rainfall profile
> retrieval through spaceborne rain radars utilizing a sea surface NRCS
> model\" IEE Proc. Radar, Sonar and Navigation, vol. 145, No. 4, luglio
> 1998, pagg. 233-239
>
> Facheris L., S. Tanelli, D. Giuli, F. Argenti \"Wavelet applications
> to multiparameter weather radar data analysis\" in \"Information
> Processing for Remote Sensing\" a cura di W.H. Chen, edito da World
> Scientific Publishing, pagg. 187-207, 1999
>
> Giuli D., L.Facheris, S. Tanelli \"Microwave tomographic inversion
> technique based on a stochastic approach for rainfall fields
> monitoring\" IEEE Transactions on Geoscience and Remote Sensing, Vol.
> 37, No. 5, settembre 1999, pagg. 2536-2555.
>
> Cuccoli F., L. Facheris, S.Tanelli, D.Giuli \"Number range
> bibMicrowave attenuation measurements in satellite-ground links: the
> potential of spectral analysis for water vapor profiles retrieval\"
> IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No 3,
> marzo 2001, pagg. 645-654
>
> Cuccoli F., L. Facheris \'\'Estimate of the tropospherical water vapor
> through microwave attenuation measurements in atmosphere\'\' IEEE
> Transactions on Geoscience and Remote Sensing, Vol. 40, 4, aprile
> 2002, pagg. 735 -- 741
>
> Tanelli S., E. Im, S. L. Durden, L. Facheris, D. Giuli \"The effects
> of nonuniform beam filling on vertical rainfall velocity measurements
> with a spaceborne Doppler radar\" Journal of Oceanic and Atmospheric
> Technology, Vol. 19, luglio 2002, pagg. 1019-1034

#  {#section-9 .unnumbered}

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# APPENDIX B --Budgetary Information  {#appendix-b-budgetary-information .unnumbered}

#  {#section-18 .unnumbered}

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# APPENDIX C --Letters {#appendix-c-letters .unnumbered}