JPL Publication 04-008

![](media/image1.png){width="1.0in" height="0.875in"}

Scientific Impacts of Wind Direction Errors

W. Timothy Liu, Seung-Bum Kim, Tong Lee, Y. Tony Song, and Wenqing Tang

Jet Propulsion Laboratory, Pasadena, California

Robert Atlas

Goddard Space Flight Center, Greenbelt, Maryland

**National Aeronautics and**

**Space Administration**

**Jet Propulsion Laboratory**

**California Institute of Technology**

**Pasadena, California**

***May 2004***

The research described in this publication was carried out at the Jet
Propulsion Laboratory, California Institute of Technology, under
contract with the National Aeronautics and Space Administration (NASA).
It was supported by NASA's Ocean Vector Wind Program. A portion of the
work was performed at the NASA Goddard Space Flight Center, Greenbelt,
Maryland.

**ABSTRACT**

An assessment on the scientific impact of random errors in wind
direction (\< 45°) retrieved from space-based observations under weak
wind (\< 7 m/s) conditions was made. Half of the winds over global
oceans are below 7 m/s, in their long-term averages, and these weak
winds cover most of the tropical, sub-tropical, and coastal oceans.
Introduction of these errors in the semi-daily winds causes, on average,
5% changes of the yearly mean Ekman and Sverdrup volume transports
computed directly from the winds, respectively. These poleward movements
of water are the main mechanisms to redistribute heat from the warmer
tropical region to the colder high-latitude regions, and they are the
major manifestations of the ocean's function in modifying Earth's
climate. Simulation by an ocean general circulation model shows that the
wind errors introduce a 5% error in the meridional heat transport at
tropical latitudes. The simulation also shows that the erroneous winds
cause a pile-up of warm surface water in the eastern tropical Pacific,
similar to the conditions during El Niño episode. Similar wind
directional errors cause significant change in sea-surface temperature
and sea-level patterns in coastal oceans in a coastal model simulation.
Previous studies have shown that assimilation of scatterometer winds
improves 3--5 day weather forecasts in the Southern Hemisphere. When
directional information below 7 m/s was withheld, approximately 40% of
the improvement was lost.

**ACKNOWLEDGMENTS**

Yunjin Kim and Victor Zlotnicki of Jet Propulsion Laboratory first
articulated the need of this study.

**TABLE OF CONTENTS**

Section 1. Introduction [1](#introduction)

Section 2. Distribution of Winds Under Threshold
[2](#distribution-of-winds-under-threshold)

Section 3. Global Ocean Response [5](#global-ocean-response)

Section 4. Coastal Ocean Response [13](#coastal-ocean-response)

Section 5. Numerical Weather Prediction
[16](#numerical-weather-prediction)

Section 6. Discussion [17](#discussion)

# Introduction

Even for the proven technique of scatterometry, the accuracy of
retrieving vector winds is degraded below a wind-speed threshold of
about 2--3 m/s (e.g., Freilich, 1999; Plant, 2000). The threshold
appears to be much higher for the experimental technique of polarimetric
radiometry. The United States Windsat/Coriolis mission, launched in
January 2003, was the first instrument designed to demonstrate the
polarimetric radiometer approach for acquiring accurate, all-weather
measurements of surface vector winds from space. It will serve as a risk
reduction program for the Conical Microwave Imager Sounder (CMIS) to
operate on the National Polar-orbiting Operational Environmental
Satellite System (NPOESS). The prelaunch aircraft flight experiments
demonstrated appreciable wind direction signals for moderate and high
winds (\> 7 m/s), but also indicated insufficient sensitivity to wind
direction for lighter winds (below 7 m/s) at Windsat incident angles
(e.g., Yueh et al., 1999).

This is a report on the potential scientific impacts caused by
anticipated errors in wind direction. To understand the amount and
location of winds under the threshold, the distribution of the mean and
variability of winds over global ocean computed by Wenqing Tang is
described in Section 2. The wind data used in the ocean studies are
described in Section 3.1.

Ocean affects climate mainly by storing solar heating during the warmer
seasons and releasing it in colder seasons and by moving the heat from
the warm ocean in low latitudes to the cooler oceans in high latitudes.
Zonal redistribution of heat in the ocean mixed layer, caused by the
relaxation of the trade winds in the equatorial Pacific, also causes the
major climate episodes of El Niño and La Niña. The meridional
redistribution of heat is accomplished through wind-driven Ekman and
Sverdrup transport, the impact of which, as computed by Seung-Bum Kim,
is described in Section 3.2. Through an ocean general circulation model
(OGCM), Tong Lee simulated the sea surface temperature and surface
dynamic topography and the impact on meridional heat transport and zonal
heat distribution in the tropical ocean; his results are presented in
Section 3.3. Large portions of the population lives near the coast, and
coastal oceans are the most productive parts of the global oceans.
Coastal oceans are known to be more sensitive to surface forcing than
open oceans because the momentum and energy from the atmosphere are
absorbed by relatively small volumes of shallow water. The impact of a
coastal ocean simulated by a coastal model by Tony Song is described in
Section 4. Weather affects our daily lives and the impact in global
numerical weather prediction by Robert Atlas is explained in Section 5.

# Distribution of Winds Under Threshold

Figure 1, based on 4 years of daily-gridded ocean wind vector
measurements from September 1999 to August 2003, is derived from the
NASA's QuikSCAT scatterometer and shows that half of winds blowing over
the global ocean are under 7 m/s, and a fourth are under 5 m/s. In
general, the wind speeds derived from Special Sensor Microwave Imager
may have slightly higher values and those from the scatterometers on
European remote sensing satellites may have slightly lower values than
QuikSCAT measurements, while the numerical weather prediction (NWP)
products of the National Center for Environmental Prediction (NCEP) have
good agreement with QuikSCAT measurements. It is clear from Figure 2
that the average wind speeds are below 5 m/s for most of the tropical
and coastal oceans. The subtropical oceans will also be included if the
threshold is moved up to 7 m/s.

![](media/image2.png){width="5.80748031496063in"
height="4.291732283464567in"}

![](media/image3.png){width="6.0in" height="4.2362204724409445in"}

The variability is also low over tropical and subtropical oceans, as
shown in Figure 3. The variability is higher in the storm tracks of
North Pacific, North Atlantic, and the Roaring Fifties (over the
circumpolar currents around Antarctica). The winds in these regions are
strongest in their respective winter season. Much of the increase in
root-mean-square deviations is due to these higher seasonal changes in
high latitudes. High winds are usually transient and require high
resolution (temporal and spatial).

![](media/image4.png){width="5.83740157480315in"
height="4.13740157480315in"}

# Global Ocean Response

**3.1 Wind Forcing**

The wind data used in the oceanographic studies (described in Sections
3.2, 3.3, and 4) are obtained from the monthly averages of NCEP
reanalysis fields for 2000. The direction error is modeled to be random
and to have a white spectrum. The size of the error is at the maximum of
± 45° of the NCEP wind direction. Only if the wind speed is less than 7
m/s is the random error introduced. The introduction of directional wind
error was performed to the 12-hourly wind field instead of time mean.

**3.2 Ekman and Svedrup Transports**

Ekman transport, which is the poleward transport of water near the ocean
surface driven by winds, and Sverdrup transport, which is the poleward
transport integrated over the ocean depth caused by wind-driven ocean
circulation, are functions of a surface wind vector. They are computed
from the wind fields described in Section 3.1. The Ekman transport is
computed at 5° intervals from 10° to 45° in both hemispheres. Between
10°S and 10° N, the Coriolis force approaches zero, resulting in the
singularity. The random direction error perturbs the Ekman transport by
about 5% (Figure 4) at 10° N and 10° S. The perturbation is largest at
10° in both hemispheres and becomes smaller towards the higher
latitudes. This latitudinal dependence arises because the wind stress
tends to be greater in the subtropics than in the tropics. The size of
the perturbation in the Ekman transport is very close to that in the
zonal wind stress, since the relationship between the Ekman transport
and the wind stress is linear.

![](media/image5.png){width="5.666535433070866in"
height="2.972047244094488in"}![](media/image6.png){width="5.541732283464567in"
height="3.0in"}

It is interesting to observe that the absolute magnitude of the Ekman
transport always decreases due to the random direction wind error. The
reason is that the wind direction is predominantly zonal in the tropics
where most of the perturbation occurs; therefore, the error acts to
reduce the zonal wind speed even if the direction error is random.

The Sverdrup transport is computed by integrating the transport at one
point, between the eastern and western boundaries of the oceans. In the
subtropical gyre (between about 15° and 45° in latitude in both
hemispheres), the directional error reduces the strength of the
wind-driven gyre because of the weakening westerlies at around 45° and
easterlies at around 15°. This reduction appears as positive and
negative anomalies in the Northern and Southern Hemispheres respectively
(Figure 5). The error in the zonal wind stress is more positive near 5°
N than at 15° N due to more frequent perturbation at lower latitudes
(Figure 7). This results in the positive perturbation in Sverdrup
transport between 5° N and 15° N.

![](media/image7.png){width="5.996527777777778in"
height="4.6402777777777775in"}

Around 30°N, where the wind stress curl is at maximum and therefore the
wind-driven circulation is best represented (Sekine and Kutsuwada,
1994), the perturbation in the Sverdrup transport to the direction error
is 3% (Figure 6). In the tropical Pacific, the Sverdrup transport varies
by about 7% responding to the random error in the wind direction. The
greater sensitivity in the tropics can be explained by the lower wind
speed in the tropics than at 30°N, resulting in more frequent
perturbation.

![](media/image8.png){width="5.530555555555556in"
height="6.009722222222222in"}

**3.3 Simulation of Ocean's Response by OGCM**

A global OCGM is used to evaluate the impact of random errors in the
direction of weak wind (\< 7 m/s). The model is the same as that used by
Lee et al. (2002) to study the impact of the Indonesian throughflow on
ocean circulation. Briefly, it is a version of the MIT OGCM (Marshall et
al. 1997a) with a horizontal resolution of 1º × 0.3º in the tropics and
1º × 1º in the extratropics. There are 46 vertical levels with a depth
increment ranging from 10 m above 150 m to intervals of 400 m at depth.
Two advanced mixing schemes are employed: the so-called K-Profile
Parameterization (KPP) vertical mixing (Large et al., 1994) and the GM
isopycnal mixing (Gent and McWilliams, 1990). The model is forced at the
surface by 12-hourly wind stress and by daily heat and freshwater fluxes
obtained from the NCEP reanalysis (Kalnay et al., 1996).

The control and pertubation experiment are both one-year integrations
(year 2000) forced by winds described in Section 3.1, with and without
random directional errors.

Figure 7 shows the difference in zonal wind stress with and without
introducing the random directional error (upper panel). The random
directional noises result in a westerly bias in the time-mean zonal wind
in the tropical Pacific and Atlantic Ocean. This is because the wind
over these regions is predominantly weak easterly wind. Therefore, any
error in direction would cause a reduction of the easterly (i.e.,
introducing a westerly bias). The fact that a mid-latitude westerly has
little bias is because wind is generally strong there and so little
directional error perturbation is applied.

The lower panel of Figure 7 shows the impact of the directional wind
error at sea level. The biased westerly in the perturbed wind causes the
sea level to pile up in the eastern equatorial Pacific and Atlantic. The
opposite is true for the equatorial Indian Ocean because the perturbed
wind is more easterly (the mean wind is westerly there). Figure 8 shows
the impact of the directional wind error on simulated temperature at sea
surface and the near tropical thermocline (150 m). A bias of about 1º C
is seen in the eastern equatorial Pacific cold tongue region because the
biased wind (more westerly) suppresses upwelling there by depressing the
thermocline. The opposite is true off the western equatorial Pacific
because thermocline is being lifted up. The 1º C bias in the cold tongue
region is quite substantial because it is as large as the threshold of
anomalous temperature for that region that is used to define an El Niño.

![](media/image9.jpeg){width="5.817361111111111in"
height="5.342361111111111in"}

![](media/image10.jpeg){width="5.8in" height="5.154861111111111in"}

Figure 9 shows the impact on the meridional transport stream function
(upper panel) and on meridional heat transport (lower panel), both
integrated zonally around the globe. The meridional overturning
circulation and poleward heat transport in the equatorial region are
reduced by about 5% due to the random directional wind error.

![](media/image11.jpeg){width="5.924305555555556in"
height="4.782638888888889in"}

# Coastal Ocean Response

The coastal modeling experiments are based on the Regional Ocean Model
System (ROMS), an evolution of the S-Coordinate Rutgers University Model
(SCRUM), originally developed by Song & Haidvogel (1994). Similar to
SCRUM, ROMS is a conservative, finite-volume discretization of the
hydrostatic primitive equations in boundary-following coordinates with a
variable free surface, Jacobian schemes for the pressure terms (Song,
1998), and includes new features of high-order upstream-biased
advection, KPP mixing, and parallel computing capabilities (Shchepetkin
& McWilliams, 2001). This model has been shown particularly suitable for
coastal studies (e.g., Marchesiello et al., 2001).

Several other community ocean models are also available, including
Modular Ocean Model (MOM) (Bryan, 1969), Miami Isopycnic-Coordinate
Ocean Model (MICOM) (Bleck & Chassignet, 1994), Princeton Ocean Model
(POM) (Blumberg & Mellor, 1987), and MIT model (Marshall et al., 1997b).
Each of these models has its distinct advantages depending on specific
applications. However, we have demonstrated expertise and experience
with ROMS. In particular, several coastal and basin-scale models based
on ROMS have been established on JPL's supercomputers. Based on these
coastal and basin-scale models, we have developed a set of nested
high-resolution coastal models for the United States West Coast in
particular for this study.

Our basin-scale model is the Pacific Ocean with a horizontal resolution
of a 0.5º × 0.5º covering the area from 45° S to 65° N and 100° E to 70°
W. The water depth is divided into 20 topography-following levels, with
a minimum depth of 50 m near the coastal wall and a maximum depth of
5500 m in the deep ocean. The model starts with the initial conditions
of annual mean temperature and salinity \[Levitus et al., 1994\]. The
surface forcing are monthly mean air-sea fluxes of momentum, heat, and
freshwater from NCEP/NCAR reanalysis. For the heat and salt flux, a
thermal feedback term is applied. The model is first spun-up for 30
years with the annual-mean forcing, and then integrated from year 1948
to year 2003 by monthly-mean forcing. This basin-scale model provides
open boundary conditions for our high-resolution United States West
Coast model.

Our high-resolution United States West Coast model extends in latitude
from 30º N to 48º N following the United States West Coastline. Using
orthogonal curvilinear coordinates, the coastal region is divided into
480 × 520 grid cells, with an average grid size of 5 × 5 km. Such a
fine-grid is necessary for resolving coastal features such as upwelling
fronts, filaments, and eddies, as will be shown later. Although some of
the coastal processes we will examine are internally driven and can be
modeled regionally, most are driven by some combination of local and
remote forcing. Therefore, the coastal model is nested into the Pacific
Ocean model to offer a compromise for obtaining high-resolution in the
coastal regions of interest and minimizing the overall computational
coast, without losing the influence of the large-scale dynamics. The
coastal model is run for 10 years (1991--2000) to reach a
quasi-equilibrium state.

Our study focuses on the most representative coastal
phenomena---wind-driven upwelling in the United States West Coast, which
has characteristics of being remotely forced and locally intense. To
assess the impact of the spatial and temporal variability of low winds
on coastal circulations and upwelling fronts, we carried out two
experiments. From the quasi-equilibrium solution (the 10^th^ year), the
coastal model continues for another run with the winds described in
Section 3.1, and with the control runs and perturbation runs using
winds, with and without adding directional errors.

Figure 10 shows the results from these two runs and their differences.
The upper panel is the sea-surface height and the lower panel is the
sea-surface temperature. Both results are 5-day averaged in July 10,
2001. Obvious differences can be seen between these two results,
particularly in the SSH results that are directly related to wind stress
curl. Although the perturbation run does not alter the large-scale
pattern of the coastal water off the U. S. west coast, the difference is
significant. The physical reason for this difference is that coastal
upwelling is an unstable system and controlled by the balance between
wind stress curl and cross-shelf pressure gradient force. Once the wind
error is introduced, the balance has to be re-adjusted. Such an
adjustment process can lead to baroclinic instability and result in
different upwelling frontal patterns, eddy fields, and filaments. These
are the results seen in the differences between these two runs.

![](media/image12.pct){width="5.995833333333334in"
height="4.801388888888889in"}

![](media/image13.png){width="5.996527777777778in"
height="4.733333333333333in"}

# Numerical Weather Prediction

The Geosynchronous Earth Orbit Satellite (GEOS-4) data assimilation
system, which is operational at NASA/GSFC was used to estimate the
impact of directional errors on numerical weather prediction. GEOS-4
consists of the fvGCM (Finite Volume General Circulation Model) and PSAS
(Physical Space statistical Analysis System). For the experiments
described below, data assimilation was performed for the period of April
10, 2003 -- May 15, 2003. The data assimilation experiments were each
followed by a series of 26 five-day forecasts, starting on April 15 and
running every day. Three experiments were conducted:

-   "Control" run (includes no satellite surface winds used, but the
    full complement of operationally-used observational data);

-   "SeaWinds" experiment (includes adds surface vector winds from
    SeaWinds on QuikSCAT to the "control");

-   "Perturbation" run (includes "control" and SeaWinds data but with
    direction information removed from winds at or under 7 m/s).

In the PSAS analysis wind vectors above the surface layer of the
atmosphere are used

in a three-dimensional multivariate (wind/mass) analysis scheme. These
wind

vectors are computed from the scatterometer surface wind observations
according

to Monin-Obukhov similarity theory. Other (ship and buoy) surface wind

observations are used the same way.

The results of the three experiments are shown in Figure 11. Anomaly
correlation results represent the average of the 26 forecasts for each
of the experiments. Anomaly correlation is an important measure of a
forecast performance. It represents the correlation between forecast and
observed departures from a mean climate state, and is a sensitive
measure of differences in the pattern between forecast and observed
fields. The green curve ("SeaWinds") shows that adding scatterometer
winds improves both the skill and the prediction period over the black
curve ("control"), while the red curve ("perturbation") shows that part
of this improvement is lost when the directional information for wind
speed under threshold is removed. The impact loss varies from 41% -- 46%
for 3--4 day forecasts.

# Discussion

We used constructed winds to illuminate the significance of accurate
wind direction measurements under weak wind conditions. The percentage
change in the anomaly correction in NWP, the Ekman and Sverdrup
transports are, as expected, small because they are integrated over
global oceans or across ocean basins. Yet, their impact on our
characterizing and understanding of weather and climate changes may
still be very significant. The strong impact in tropical oceans where
the trade wind dominates is clearly evident. The impact on NWP is
significant in the 3--4 day forecast range.

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