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\def\sigmao{$\sigma^o$\ }
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\begin{document}
\title{The BYU QuikScat/SeaWinds Wind/Rain Product}
\author{David G. Long \\
Microwave Earth Remote Sensing Laboratory\\
	BYU Center for Remote Sensing\\
        Brigham Young University \\
        459 Clyde Building, Provo, UT  84602\\
        long@ee.byu.edu \\  
	}

\date{Version 0.0\\ \today}

\maketitle

\begin{abstract}

The SeaWinds scatterometers onboard the QuikSCAT and ADEOS II satellites
measure ocean winds on a global scale via the relationship between the
normalized radar backscattering cross-section of the ocean and the vector
wind.  Conventional wind retrieval method ignores scattering and attenuation
of ocean rain which alter backscatter measurements and corrupt retrieved
winds. Using a simple rain backscatter and attenuation model, a new
method for improving wind estimation in the presence of rain
has been developed.  Based on a wind/rain model function and maximum
likelihood estimation, the wind and rain are simultaneously estimated.
The new retrieval technique has been validated via simulation and with
NCEP and TRMM PR data.  The simultaneous wind/rain estimation
method is most accurate in the ``sweet spot'' of SeaWinds' swath and
not usable on the outer-beam edges of the swath.
This document briefly describes the new wind/rain product
developed and produced at the Brigham Young University (BYU)
Microwave Earth Remote Sensing (MERS) Laboratory from SeaWinds data.

\end{abstract}


\section{BYU Simultaneous Wind/Rain Retrieval Product User Notes}
The QuikScat/Seawinds scatterometer (Qscat) provides measurements of the
near-surface ocean wind field with
unprecedented coverage and resolution.  Qscat makes dual polarization measurements of
the normalized radar cross section ($\sigma^o$) at both vertical and horizontal
polarization using a conically scanning pencil-beam antenna.  With the
aid of a geophysical model function relating radar backscatter and
wind, these measurements are used to estimate or
retrieve the near-surface wind over the ocean.  SeaWinds 
data has been found to be highly accurate in non-raining, moderate wind
speed areas \cite{draper02,draper03}.  However, the quality of SeaWinds
retrieved winds is degraded by rain contamination, especially in
storm regions \cite{portabella01,draper02}.  Globally, rain is estimated
to affect between 4\% and 10\% of SeaWinds data.  Although some rain flagging
techniques have been developed \cite{huddleston00,mears00}, the current
wind retrieval method does not incorporate rain effects.  As a result, rain
attenuation and backscatter are interpreted as wind-induced features
\cite{weissman02}.  Rain corrupts the wind retrieval process by
altering the wind-induced radar backscatter signature. 

The Brigham Young University (BYU) Microwave Earth Remote Sensing (MERS)
Laboratory has developed a standard set of Qscat land/ice image products for
distribution.  This report describes these products.  Both conventional and
enhanced resolution products are included in the product suite. 
The following sections provide additional specific details regarding the generation and
application of the BYU L2R product.

\section{Data Set Overview}
\subsection{Data Set Identification}
Brigham Young University (BYU) SeaWinds Level 2A/2B-Derived
Simultaneous Wind/Rain Retrieval Product
\subsection{Data Introduction}
Each file contains one rev of wind and rain data grouped by rows of wind vector cells aligned 
with the along-track and cross-track axes of the spacecraft measurement swath. Level 2B 
wind vector cells are 25 km squares. Each wind vector cell row corresponds to a single cross-
track cut of the SeaWinds measurement swath. Full coverage of the earth's circumference 
requires 1624 wind vector cell rows.  
\subsection{Ojective/Purpose}
Like the SeaWinds Level 2B product, this data set provides wind and
rain estimates for use in research such as global climatic change and
air-sea interaction.
\subsection{Summary of Parameters}
The following key parameters are contained in the L2R product: 
\begin{enumerate}
  \item Wind speed and direction ambiguities from BYU simultaneous
  wind/rain retrieval
  \item Rain rate estimates associated with wind ambiguities
  \item Maximum likelihood values from BYU simultaneous
  wind/rain retrieval
  \item Various ambiguity selection flags
  \item Wind speed and direction ambiguities from conventional JPL L2B
    data file
  \item A wvc data quality flag
  \item A flag containing an estimate of which wind/rain regime each wvc falls into
  \item A rain estimate confidence flag.
\end{enumerate}

\subsection{Discussion}
 
Each SeaWinds Level 2B-Derived L2R Wind/Rain data file contains one 'rev' or  less of 
SeaWinds data. A complete rev includes all of the data that pertains
to one full orbital revolution of the spacecraft. By convention, all
SeaWinds revolutions begin and end at the southernmost orbital latitude. 

The SeaWinds Level 2B Processor generates a grid of wind vector cells (WVC)
in alignment with the along-track and cross-track axes of the spacecraft
measurement swath. Each WVC is a 25 km square. Every data element in the L2R product
referenced by the wind vector cell's cross-track and along-track
indices. The SeaWinds instrument's measurement swath extends 900 km on
either side of the satellite nadir track.  Thus, each WVC row must
contain at least 72 WVC values. To accommodate occasional measurements
that lie outside the 900 km swath, the Level 2B data design includes two 
additional WVC values at each end of each row. Each Level 2B WVC row
therefore contains a total 76 WVCs. 

HDF Scientifical Data Sets (SDS) objects store every data element in
the BYU Level 2R product.  The SDS elements are organized similarly to
the JPL Level 2B data files.

\subsubsection{Related Data Sets}
The following related data sets are available at the JPL PO.DAAC:
\begin{enumerate}
  \item SeaWinds on QuikSCAT Level 2A Ocean Wind Vectors in 25 Km Swath Grid (JPL) 
JPL PO.DAAC Product xxx 
  \item SeaWinds on QuikSCAT Level 2B Ocean Wind Vectors in 25 Km Swath Grid (JPL) 
JPL PO.DAAC Product 108 
  \item SeaWinds on ADEOS-2 Level 2A Ocean Wind Vectors in 25 Km Swath Grid (JPL) 
JPL PO.DAAC Product xxx 
  \item SeaWinds on ADEOS-2 Level 2B Ocean Wind Vectors in 25 Km Swath Grid (JPL) 
JPL PO.DAAC Product xxx
\end{enumerate}

\section{Investigator:}
%NOTE:     Please refer all questions concerning the SeaWinds Level 2B-
%Derived Wind/Rain product to the PO.DAAC 
%QuikSCAT Data Team,  qscat@podaac.jpl.nasa.gov, or the PO.DAAC User 
%Services Office, podaac@podaac.jpl.nasa.gov.  

The SeaWinds Project is a mission of the NASA Jet Propulsion Laboratory 
(JPL). Further information on the SeaWinds Project is available on-line at 
{\tt http://winds.jpl.nasa.gov}.
\ \\

\noindent
{\bf BYU SeaWinds Level 2B-Derived Simultaneous Wind/Rain Product
  Author:}\\
Dr. David G. Long\\ Brigham Young University
\ \\

\noindent
{\bf SeaWinds Science Team Lead:}\\
Dr. Michael Freilich  \\
Oregon State University 
  
%{\bf SeaWinds JPL Project Scientist:}
%Dr. W. Timothy Liu \\
%Jet Propulsion Laboratory 
  
\section{Theory of Measurements}

``Spaceborne scatterometers transmit microwave pulses to the ocean surface and measure the 
backscattered power received at the  instrument. Since the atmospheric motions themselves 
do not substantially affect the radiation emitted and received by the radar, scatterometers use 
an indirect technique to measure wind velocity over the ocean. Wind stress over the ocean 
generates ripples and small waves, which roughen the sea surface. These waves modify the 
radar cross-section (sigma-0) of the ocean surface and hence the magnitude of backscattered 
power. In order to extract wind velocity from these measurements, one must understand the 
relationship between sigma-0 and near-surface winds. This relationship is known as the 
geophysical model function." \cite{Qscatusersguide}

However, the quality of SeaWinds retrieved winds is degraded by rain
contamination, especially in storm regions.  Globally, rain is estimated
to affect between 4\% and 10\% of SeaWinds data.  Rain corrupts the
wind retrieval process by altering the wind-induced radar
backscatter signature.  Thus, rain
attenuation and backscatter may be interpreted as wind-induced features.

By synergistically combining TRMM PR and SeaWinds data, Long and Draper \cite{draper03}
demonstrated that a simple low-order model is adequate for describing the
wind/rain interaction as a function of rain rate and non-raining effective
wind $\sigma^\circ$.  Their analysis suggests that the surface
perturbation due to rain striking the water dominates the total scattering
from rain for low to moderate rain rates ($<$ 10 km mm/hr).  Since
backscatter from rain-induced waves significantly contribute to the total
backscatter, surface effects must be included in the wind/rain model.

In the wind/rain model, there are three distinct regimes.  In the first
regime, the rain dominates.  It may be possible to directly calculate the
rain rate from data in this regime.  In the second regime, the signal from
the rain and the wind are on the same order. Data from the second regime may
be used to simultaneously retrieve wind and rain.  In the third regime, the
signal from the wind dominates and current wind retrieval techniques are
adequate.  These regimes help us understand where rain has minimimal
impact on wind estimates, where rain can be retrieved and
where rain-contaminated wind can be corrected.

The degradation of SeaWinds scatterometer accuracy during rain
prompted development of a probability-based rain flag given several
rain-sensitive parameters, known as the Multi-dimensional Histogram (MUDH)
rain flag \cite{huddleston00}.  Besides the MUDH flag, a variety of other
rain flags for SeaWinds have been suggested,
e.g. \cite{connor00,jones02,mears00,portabella01}, but no formal attempt
has been made to correct rain-corrupted wind vectors.

A simultaneous wind/rain retrieval algorithm has been developed
\cite{draper032} and validated \cite{draper033} by Draper and Long.
SeaWinds simultaneous wind/rain retrieval has been shown by simulation to give the
best results in the swath sweet spot, and perform with reduced accuracy on
the swath edges (the outer 9 WVCs on either side of the swath) \cite{draper033}.
Simulation has demonstrated that wind speeds from simultaneous wind/rain
retrieval are nearly unbiased, while the wind-only retrieval produces
increasingly biased estimates as rain increases.  However, in zero-rain
conditions, the inclusion of the rain rate parameter into the retrieval
process can give rise to spurious rain rate estimates and correspondingly lower wind
retrieval accuracy at those WVCs.  It is thus most beneficial
to perform simultaneous retrieval only in raining areas.  To this end,
a combined wind-only and simultaneous wind/rain retrieval ambiguity
selection algorithm has been developed \cite{draper033}.


\subsection{Equipment:}
See JPL Level 2B documentation \cite{Qscatusersguide}.

\subsection{Data Acquisition:}
See JPL Level 2B documentation \cite{Qscatusersguide}.

\subsection{Observations:}
\subsubsection{Data Notes:}
No additional notes.
\subsubsection{Field Notes:}
No additional notes.

\section{Data Description}
\subsection{Spatial Characteristics}
Identical to JPL Level 2B.
See JPL Level 2B documentation \cite{Qscatusersguide}.
\subsection{Temporal Characteristics}
Identical to JPL Level 2B.
See JPL Level 2B documentation \cite{Qscatusersguide}.

\section{Data Characteristics:}

\subsection{Data Granularity:}
The basic granule is one data file. Each data file contains the data for one rev.  
A general description of data granularity as it applies to the Earth Observing System 
Data Gateway (EDG) appears in the EOSDIS Glossary.  

\subsection{Data Format:}
The BYU L2R files are essentially ``overlay'' files for the JPL L2B
files.  Each file consists of a single satellite rev stored in
Hierarchial Data Format (HDF).  HDF is a self-
describing data format developed by the National Center for Supercomputing Applications
(NCSA) at the University of Illinois. HDF is the standard data format for Earth Observing
System (EOS) satellites. HDF can
store large varieties of data structures, which are portable to a
large number of computing platforms and are equally accessible to routines written either in C or
in FORTRAN. All access to SeaWinds data products using either C or FORTRAN should
utilize the NCSA HDF interface library routines.

Each of the SeaWinds Level 2A, 2B, and 2R data products is divided into discrete files that are
defined by spacecraft `revs'. One rev includes all of the data acquired during a single orbital
revolution of the spacecraft. By convention, all SeaWinds revolutions begin and end at the
southernmost orbital latitude.
Each data product file includes a header. The product header contains multiple
metadata elements. The conditions specified by the metadata apply to the entire set
of data in the file. The remainder of the file contains
either those data acquired by the SeaWinds instrument over the
spacecraft measurement swath, or those data that were generated by the
processing software.  BYU L2R files use the same HDF conventions
described in the L2B software \cite{Qscatusersguide}.


Table \ref{header} shows the header of the BYU L2R product HDF file.

\begin{table}
  \caption{\label{header}Header Structure for BYU L2R Product.  Sample
  values shown.}
  \begin{center}
  \begin{tabular}{|l|l|c|}
    \hline
    {\bf Element Name} & {\bf Definition} & {\bf Max} \\
    & & {\bf Array Size}\\ \hline
    LongName &                         QuikSCAT Level 2B Ocean Wind
    Vectors & [1] \\
             & \ \ \ and Rain Rate in 25km Swath & \\
   ShortName             &            QSCATL2R  & [1] \\
   producer\_institution  &            Brigham Young University (BYU),& [1] \\
      & Microwave Earth Remote Sensing (MERS) Laboratory & \\
   InstrumentShortName   &            SeaWinds & [1] \\
   PlatformLongName      &            NASA Quick Scatterometer  & [1] \\
   PlatformShortName     &            QuikSCAT     & [1] \\             
   data\_format\_type      &            NCSA HDF       & [1] \\           
   L2Rfilename           &            QS\_S2R03221.20001592043  & [1] \\ 
   L2Afilename           &            QS\_S2A03221.20001592043  & [1] \\ 
   L2Bfilename           &            QS\_S2B03221.20001592046   & [1] \\
   WindModel             &            QS\_MODL0003         & [1] \\      
   RainModel             &            BYU V6 Quadratic     & [1] \\     
   RainThresholds        &            BYU\_rainflag\_thresholds\_V0.txt & [1] \\
   Investigator          &            David G. Long, long@ee.byu.edu (801) 422-4383 & [1] \\
   build\_id              &            1.000/2004-01-01 & [1] \\
   \hline
  \end{tabular}
  \end{center}
\end{table}

Table \ref{SDS} summarizes the SDS contents of the file.  The
individual SDS elements are described below.

\begin{table}
  \caption{\label{SDS}HDF SDS arrays within the BYU L2R Product}
  \begin{center}
  \begin{tabular}{|l|c|rrr|c|c|c|}
    \hline
 Dataset/Name     &                   Rank  & \multicolumn{3}{|c|}{Dimensions}
 & Scale   &   Offset  &  Type\\
 \hline
wvc\_row               & 1 & 1624 &    0 &    0 & 1.000 & 0.0 & int*2 \\
wind\_speed            & 3 &    4 &   76 & 1624 & 0.010 & 0.0 & int*2 \\
wind\_dir              & 3 &    4 &   76 & 1624 & 0.010 & 0.0 & uint2 \\
rain\_rate             & 3 &    4 &   76 & 1624 & 0.010 & 0.0 & int*2 \\
max\_likelihood\_est    & 3 &    4 &   76 & 1624 & 0.001 & 0.0 & int*2 \\
num\_ambigs            & 2 &   76 & 1624 &    0 & 1.000 & 0.0 & byte8 \\
wvc\_selection         & 2 &   76 & 1624 &    0 & 1.000 & 0.0 & byte8 \\
percent\_rain          & 3 &    4 &   76 & 1624 & 0.010 & 0.0 & int*2 \\
wind\_speed1           & 3 &    4 &   76 & 1624 & 0.010 & 0.0 & int*2 \\
wind\_dir1             & 3 &    4 &   76 & 1624 & 0.010 & 0.0 & uint2 \\
num\_ambigs1           & 2 &   76 & 1624 &    0 & 1.000 & 0.0 & byte8 \\
wvc\_selection1        & 2 &   76 & 1624 &    0 & 1.000 & 0.0 & byte8 \\
regime                & 3 &    4 &   76 & 1624 & 1.000 & 0.0 & byte8 \\
wvc\_selection\_opt     & 2 &   76 & 1624 &    0 & 1.000 & 0.0 & byte8 \\
set\_selection\_opt     & 2 &   76 & 1624 &    0 & 1.000 & 0.0 & byte8 \\
wvc\_quality\_flag      & 2 &   76 & 1624 &    0 & 1.000 & 0.0 & int*2 \\
rain\_confidence\_flag  & 2 &   76 & 1624 &    0 & 1.000 & 0.0 & byte8 \\
\hline
   \end{tabular}
  \end{center}
\end{table}

The wind\_speed, wind\_dir, max\_likelihood\_est, and num\_ambigs array are 
similar to the arrays of the same name from the corresponding L2B file, 
but contain the wind/rain ambiguities.  Simultaneous wind retrieval is not
done for the outer (V-pol only) swath.  In this case the rain\_rate is set
to zero and the reported wind ambiguities are from wind-only retrieval.
(Thus, in the far swath the wind\_speed and wind\_speed1 values are identical.)
Also, the wind-only retrieval is reported if the average wind speed of
the wind-only ambiguities is less than 4 m/s.  Note that wvcs with high rain rates
may result in zero wind speed ambiguities. 

The wind\_speed1, wind\_dir1, and num\_ambigs1 are the wind-only retrieval
wind ambiguities and are copies of the L2B wind ambiguities.

The various data sets contain:
\begin{enumerate}
  \item {\bf wvc\_row}: (copy of L2B wvc\_row variable) wvc row number
  \item {\bf wind\_speed:} wind speed ambiguities (m/s), in MLE value order
  \item {\bf wind\_dir:} wind direction ambiguities (deg), in MLE value order
  \item {\bf rain\_rate:} rain rate ambiguities (km mm/hr), in MLE value order
  \item {\bf max\_likelihood\_est:} MLE value from wind or wind/rain
  retrieval (units arbitrary)
  \item {\bf num\_ambigs}: number of ambiguities at each wvc (max=4)
  \item {\bf wvc\_selection:} index (1..4) of selected simultaneous wind/rain
    ambiguity based on median-filter-based ambiguity removal
  \item{\bf percent\_rain:} inferred wvc rain fraction (percent) for each ambiguity
  \item {\bf wind\_speed1:} wind-only wind speed ambiguities (m/s), in
  corresponding MLE value order (copy of L2B wind\_speed variable)
  \item {\bf wind\_dir1:} wind-only wind direction ambiguities (deg),
  in corresponding MLE value order (copy of L2B wind\_dir variable)
  \item {\bf num\_ambigs1}: number of wind-only ambiguities at each
  wvc (max=4) (copy of L2B num\_ambigs variable)
  \item {\bf wvc\_selection1}: index (1..4) of selected wind-only
    ambiguity based on median-filter-based ambiguity removal (copy of L2B wvc\_selection)
  \item {\bf regime}: estimated wind/rain retrieval regime\\
        0 - Estimated rain does not significantly affect wind (rain estimate poor)\\
        1 - Estimated rain and wind backscatter are of same order
	(both rain and wind estimate OK)\\
        2 - Estimated rain dominates wind (wind estimate quality poor)
  \item {\bf wvc\_selection\_opt:} ambiguity selection index (1..4) from
    combined simultaneous wind/rain and wind-only retreival.  Index is
    for simultaneous wind/rain ambiguities (wind\_speed) where
    {\bf set\_selection\_opt}=0 or from wind-only ambiguities (wind\_speed1)
    where {\bf set\_selection\_opt}=1
\item {\bf set\_selection\_opt}: flag to indicate which retrieval
  method is used for each wvc.  Used with {\bf wvc\_selection\_opt}.\\ 
      0 - index value {\bf wvc\_selection\_opt} for simultaneous wind/rain retrieval,\\
      1 - index value {\bf wvc\_selection\_opt} for wind-only retrieval
\item {\bf wvc\_quality\_flag:} copy of L2B wvc\_quality\_flag
\item {\bf rain\_confidence\_flag:} rain estimate quality confidence flag \\
      0 - Low confidence in estimated rain\\
      1 - High confidene in estimated rain
\end{enumerate}


\subsection{File Names}
Like JPL L2B files, the BYU L2R data file names are 23 characters in
length with the convention (for QuikSCAT and SeaWinds):
\begin{tabular}{ll}
  Level 2B: & QS\_S2Bnnnnn.yyyydddhhmm
\end{tabular}
where nnnn is the orbit rev number and yyyydddhhmm is the L2A product
generation time file extension.


\section{Errors:}
\subsection{Sources of Error:}
The normalized standard deviation of sigma-0, known as Kp, is computed to give an 
estimate of the measurement uncertainty of the backscatter. For BYU
Simultaneous wind/rain retrieval there are four major 
sources of Kp in the scatterometer system:
\begin{enumerate}
  \item the uncertainty in the receiver noise, known as communication
    Kp or Kpc
  \item the uncertainties in the geometric and gain parameters, known as retrieval Kp 
    or Kpr  
  \item the uncertainty associated with the {\em wind} geophysical
  model function, known as model Kp or Kpm
  \item the uncertainty associated with the {\em rain } geophysical
  model function, known as Kprain
\end{enumerate}
Other sources of error include attitude pointing uncertainty, instrument processing, 
and various bias errors.  
\subsection{Quality Assessment:}
See discussion in Draper and Long (2003) \cite{draper033}.

\section{Notes:}
\subsection{Limitations of the Data:}
Ambiguity selection is imperfect.  Errors in ambiguity selection may
cause errors in the wind and/or rain rate estimates.

Radar returns from land and ice correspond to different scattering processes than 
those over open ocean, and can contaminate wind vector/rain estimates. A land mask has 
been applied to the data in order to negate most contamination due to land 
and ice.  

\subsection{Known Problems with the Data:}
\subsection{Usage Guidance:}
{\bf Wind direction convention}\\ 
The oceanographic, or flow vector, convention for wind direction is adopted for 
SeaWinds on QuikSCAT. Under this convention, a wind direction of 0° implies a 
flow toward the north.  
\ \\

\noindent
{\bf Reference Height for Surface Winds}\\
The adopted reference height for all wind vectors is 10 meters.  
\ \\

\noindent
{\bf Rain Rate}\\
Units of rain rate are km-mm/hr as used by the TRMM PR.

\section{Any Other Relevant Information about the Study:}
None.
\subsection{Application of the Data Set:} 
\begin{enumerate}
  \item global and regional climate studies  
  \item atmospheric forcing, ocean response and air-sea interaction mechanism research  
  \item input to numerical weather- and wave-prediction models  
\end{enumerate}

\section{Future Modifications and Plans:}
Improvements in the ambiguity selection are in progress.

\section{Software:}
Only Matlab readers are currently available.

\section{Data Access:}
\subsection{Contact Information:}
TBD

\section{Output Proucts and Availability:}
TBD


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\end{thebibliography}



\end{document}\end