near-surface air temperature retrieval derived from amsu-a and sea surface temperature

Near-Surface Air Temperature Retrieval Derived from AMSU-A andSea Surface Temperature Observations

DARREN L. JACKSON

Cooperative Institute for Research in Environmental Sciences, and NOAA/Earth System

Research Laboratory, Boulder, Colorado

GARY A. WICK

NOAA/Earth System Research Laboratory, Boulder, Colorado

(Manuscript received 29 October 2009, in final form 3 March 2010)

ABSTRACT

A 10-m air temperature (Ta) retrieval using Advanced Microwave Sounding Unit A (AMSU-A) and

satellite-derived sea surface temperature (Ts) observations is presented. The multivariable linear regression

retrieval uses AMSU-A brightness temperatures from the 52.8- and 53.6-GHz channels and satellite-derived

daily sea surface temperatures to determine Ta. A regression error of 0.838C using 841 matched satellite and

ship observations demonstrates a high-quality fit of the satellite observations with in situ Ta. Validation of the

retrieval using independent International Comprehensive Ocean–Atmosphere Dataset (ICOADS) ship and

buoy observations results in a bias of 20.218C and root-mean-square (RMS) differences of 1.558C. A com-

parison with previous satellite-based Ta retrievals indicates less bias and significantly smaller RMS differ-

ences for the new retrieval. Regional biases inherent to previous retrievals are reduced in several oceanic

regions using the new Ta retrieval. Satellite-derived Ts–Ta data were found to agree well with ICOADS buoy

data and were significantly improved from previous retrievals.

1. Introduction

High-resolution and accurate estimates of turbulent

heat fluxes are considered essential for improving fore-

casts of numerical weather prediction models and global

climate models. Retrieving turbulent heat fluxes is chal-

lenging because of inaccuracies from parameterization

using bulk formulas and difficulties in acquiring accurate

input parameters for the bulk formulation. Accurate

meteorological inputs to the bulk formulas have been

difficult to acquire from satellite remote sensing methods

because of limitations in direct retrieval of near-surface

parameters, so these inputs have typically been acquired

from ship and buoy observations. However, unlike ship

and buoy observations, satellite observations can provide

global coverage on a daily time scale, thus providing the

potential for daily global heat flux products. Accurate

global observations of air temperature (Ta) are consid-

ered essential for more accurate retrieval of sensible heat

flux at the ocean surface (Curry et al. 2004). Therefore,

a new satellite method to retrieve Ta is presented in this

paper.

High-quality Ta observations remain difficult to re-

trieve using satellites because of difficulties in resolving

the temperature layer near the ocean surface using exist-

ing satellite instrumentation. Therefore, indirect satellite

methods have generally been utilized to derive Ta ob-

servations. Liu (1988) used near-surface humidity re-

trievals to estimate Ta by assuming 80% relative humidity

over the ocean surface. Jourdan and Gautier (1995) used

satellite-derived precipitable water (PW) to estimate Ta

by deriving a relationship between monthly mean PW and

Comprehensive Ocean–Atmosphere Dataset (COADS)

Ta data. Konda et al. (1996) retrieved Ta by using satellite-

derived estimates of sea surface temperature (Ts), wind

speed, and specific humidity. Clayson and Curry (1996)

used Ts observations and a parameterization of Ta–Ts

based on a satellite-derived cloud classification tech-

nique. Jones et al. (1999) used a neural network approach

for retrieving Ta using monthly mean satellite-derived

Corresponding author address: Darren L. Jackson, Cooperative

Institute for Research in Environmental Sciences, University of

Colorado/NOAA/Earth System Research Laboratory, 325

Broadway R/PSD2, Boulder, CO 80305–3337.

E-mail: [email protected]

OCTOBER 2010 N O T E S A N D C O R R E S P O N D E N C E 1769

DOI: 10.1175/2010JTECHA1414.1

� 2010 American Meteorological Society

PW and Ts data to train the retrieval. Jackson et al.

(2006) introduced a multi-instrument approach that used

Advanced Microwave Sounding Unit A (AMSU-A)

and Special Sensor Microwave Imager (SSM/I) micro-

wave brightness temperatures directly to determine Ta.

Roberts et al. (2010) recently developed a neural net-

work approach that uses SSM/I radiances and Ts ob-

servations to retrieve several surface parameters including

Ta. Only Jackson et al. (2006) and Roberts et al. (2010)

use satellite brightness temperatures directly to retrieve

Ta, and these studies provide methods that can increase

the spatial and temporal resolution of the satellite Ta

products and reduce the retrieval error that has plagued

earlier studies.

Jackson et al. (2006) determined that a multisensor sat-

ellite approach could achieve more accurate satellite-

derived Ta observations by combining AMSU-A and SSM/I

satellite observations. This resulted in root-mean-square

(RMS) differences with independent validation data of

1.968C, which was a 1.08C less than previously published

algorithms. The AMSU-A 52.8-GHz channel was found

to significantly improve the Ta retrieval because this

temperature-sensing channel has a weighting function

that peaks in the lower troposphere near the 900-mb

level. However, a significant cold bias for Ta , 158C and

an RMS difference near 28C still did not meet the re-

quirement of a Ta accuracy of 0.968C (Jackson et al.

2006) needed to achieve a sensible heat flux accuracy of

10 W m22. The goal of this study is to develop a satellite

Ta retrieval that further reduces these Ta retrieval

errors.

2. Retrieval and validation

Recognizing the generally close relationship between

Ts and Ta resulted in consideration of using Ts as a

predictor for the Ta regression retrieval. Therefore, the

satellite-derived Ts data described in Reynolds et al.

(2007) were used as input to the regression model to de-

termine the regression error impact on the Ta retrieval.

The version of Reynolds Ts data used in this study only

used satellite data from the Advanced Very High Reso-

lution Radiometer (AVHRR) satellite. AMSU-A data

were taken from the National Oceanic and Atmospheric

Administration’s (NOAA) NOAA-15 satellite in this

study to develop the retrieval; however, other satellites

flying AMSU-A instruments could be used to retrieve

Ta. The input training data were taken from Jackson

et al. (2009), which used high-quality research vessel

observations primarily from NOAA’s Earth System Re-

search Laboratory (ESRL). Collocation requirements

between the ship and satellite observations were 3 h and

50 km. Ship Ta observations were adjusted to 10-m

height using the Coupled Ocean–Atmospheric Response

Experiment (COARE) version 3 bulk flux model (Fairall

et al. 2003). The final regression equation used to predict

Ta at 10 m is

Ta 5�67.8532 1 0.801 546Ts 1 0.443 879Tb52.8

�0.170 060Tb53.6

, (1)

where Ts is the sea surface temperature in degrees Cel-

sius, and Tb52.8 and Tb53.6 are AMSU-A brightness

temperatures in kelvins at 52.8 and 53.6 GHz, re-

spectively. The 53.6-GHz channel was also included

because it has some sensitivity to lower tropospheric

temperature even though its weighting function peaks in

the middle troposphere, and it potentially removes tem-

perature variability detected above the lower tropo-

sphere. The regression RMS difference was 0.838C

using 841 satellite matches to ship observations. Jackson

et al. (2006) included SSM/I observations in their Ta

algorithm (hereafter called AMMI), but these data did

not improve the regression when using both AMSU-A

and Ts data as input to the regression. A benefit of not

including SSM/I is the potential for increased temporal

and spatial Ta coverage because some of the AMSU-A

instruments on other satellites do not match well with

SSM/I observations since they do not have overpasses

with SSM/I that meet the requirement for 3-h time co-

incidence. Not including Ts with Tb52.8 and Tb53.6 re-

sulted in a regression RMS difference of 1.938C, thus

demonstrating a greater than 18C improvement in re-

gression error from including Ts.

Validation of the Ts and AMSU-A retrieval for Ta

(hereafter referred to as AMSST) was carried out using

independent Ta observations from the International

COADS (ICOADS) International Maritime Meteoro-

logical Archive (IMMA) version 2.4 dataset (Worley

et al. 2005). The ICOADS data are primarily a combi-

nation of observations from voluntary observing ships

(VOS) and buoys. These data have been used in previous

studies for satellite validation of 10-m specific humidity

(Qa) (i.e., Bentamy et al. 2003; Jackson et al. 2009) and

heat flux (DaSilva et al. 1994; Zeng et al. 2009) data.

ICOADS air temperature observations are taken at

various heights from the ocean surface with ICOADS

ship observations generally measured at levels exceed-

ing 10 m and buoy observations typically measured at

3 m. This height difference could result in a bias be-

tween buoy and ship observations and a bias between

ICOADS and satellite 10-m Ta observations. However,

difficulties in adjusting the ICOADS observations to

10 m are due to a lack of sensor height information for

many of the observations. This issue is discussed in greater

1770 J O U R N A L O F A T M O S P H E R I C A N D O C E A N I C T E C H N O L O G Y VOLUME 27

detail in Jackson et al. (2009). For the current study, 645

coincident nighttime ICOADS buoy and ship air tem-

perature observations for 1999 were found to have an

absolute difference of 0.298C. This difference can be

considered the uncertainty in the Ta uncertainty due to

measurement height differences of the ICOADS data.

The retrieval validation and product comparison were

conducted with datasets using SSM/I observations, so the

sampling of the AMSST Ta data for this validation was

restricted to locations where AMSU and SSM/I obser-

vations were coincident within these grid cells. A land

mask was applied to all satellite observations since the

highly variable and large land surface emissivity is not

accounted for in this retrieval. Furthermore, rain-rate

observations exceeding 0 mm h21 and cloud liquid wa-

ter (CLW) observations exceeding 0.1 mm retrieved from

the SSM/I observations were excluded in the comparison

since rainfall and CLW can contaminate SSM/I-derived

products used to derive Ta retrieval in methods used to

compare with the AMSST Ta retrieval.

Validation of the Ta retrieval used the same ICOADS

observations and technique described in Jackson et al.

(2009). Both satellite and ICOADS observations were

averaged to 0.58 spatial and 3-h temporal resolution

maps. Matches between satellite and in situ observations

were determined from these grid maps. Figure 1 gives

the validation results for both the AMSST and the

AMMI Ta retrieval using matched satellite/ICOADS

data for 49 167 observations in 1999. The AMSST Ta

shows a bias of 20.218C and RMS difference of 1.558C,

which are both significantly smaller than the AMMI Ta

retrieval. AMSST Ta is slightly lower than ICOADS for

most ICOADS Ta bins but generally warmer than the

AMMI Ta retrieval. AMSST Ta is warmer than ICOADS

for Ta , 08C, whereas the AMMI Ta is less biased but has

large RMS differences. At this low Ta range, greater

scatter for both satellite brightness temperatures and Ts

observations occur in the training dataset, and satellite

brightness temperatures show generally less sensitivity

to Ta fluctuations. This problem has also been shown

with Ta values derived from Roberts et al. (2010). AMSST

and AMMI retrievals are both colder than ICOADS for

Ta exceeding 308C. A small number of observations in the

training dataset for this Ta range may contribute to this

validation bias. Bias and RMS differences for other re-

trievals such as Jackson et al. (2006), Liu (1988), Jourdan

and Gautier (1995), and Konda et al. (1996) are provided

in Table 1. These retrievals have biases exceeding 18C

and RMS differences exceeding 48C when using all of the

matched data from 1999.

Local solar heating of daytime ICOADS Ta sensors

(Kent et al. 1993) may explain part of the bias between

ICOADS and AMSST Ta. Table 1 shows that when the

validation results are separated by day and night matches

for AMSST, the bias for day-only observations is larger

than the night-only observations and is consistent with

FIG. 1. Scatter diagram comparing AMSST and AMMI Ta re-

trievals with ICOADS Ta for 1999. Error bars represent one stan-

dard deviation. Bins are offset between AMSST and AMMI Ta data.

TABLE 1. Ta validation statistics using ICOADS for 1999. Bias is defined as satellite Ta 2 ICOADS Ta.

AMSST Jackson et al. (2006) (AMMI) Liu (1988) Jourdan and Gautier (1995) Konda et al. (1996)

Obs. No. Bias RMS Bias RMS Bias RMS Bias RMS Bias RMS

Day 1

night

All 49167 20.21 1.55 21.33 2.64 21.60 4.46 21.28 4.12 22.52 6.40

Ship 34677 20.25 1.67 21.48 2.81 21.86 4.82 21.52 4.44 22.92 6.87

Buoy 14400 20.08 1.24 20.96 2.15 20.95 3.44 20.69 3.18 21.53 5.06

Day

only

All 31620 20.40 1.57 21.21 2.58 21.24 4.36 20.98 4.07 21.95 6.03

Ship 21029 20.49 1.75 21.36 2.80 21.51 4.80 21.22 4.48 22.36 6.18

Buoy 10496 20.21 1.11 20.88 2.06 20.68 3.25 20.47 3.06 21.09 4.59

Night

only

All 22885 0.07 1.42 21.37 2.55 21.82 4.29 21.46 3.91 22.90 6.44

Ship 13910 0.12 1.57 21.65 2.83 22.39 4.83 21.97 4.38 23.77 7.25

Buoy 9124 0.01 1.15 20.91 2.03 20.92 3.25 20.66 3.02 21.52 4.92


possible solar heating of the sensors. Interestingly, the

other retrievals show less bias for day-only than the night-

only matches with ICOADS. Even though the average

ICOADS Ta does increase 0.888C from night-only to day-

only observations, all satellite retrievals except for the

AMSST retrieval respond with a greater increase in Ta

from night to day. Diurnal changes in SST are not con-

sidered a factor since the average daytime increase in

ICOADS Ts was only 0.098C. The bias for buoy-only

observations for all of the retrievals gives less bias than

ship-only observations. Nighttime observations from the

buoys are likely to be the most accurate observations, and

the AMSST buoy night-only data give the lowest bias

of 0.018C. Generally, the AMSST Ta compares most

favorably with ICOADS by having the lowest bias

and RMS differences for all matched conditions with

ICOADS.

Jackson et al. (2009) demonstrated that Qa bias can be

affected by large-scale subsidence-induced temperature

inversions in the North Pacific during the summer months;

therefore, regional bias of the AMSST Ta was examined

and compared to previous retrievals. The geographical

distribution of Ta bias is given in Fig. 2 for AMSST and

AMMI Ta. Bias is computed using ICOADS Ta data

over a 1-yr period during 1999 and binned onto a 0.58

spatial resolution grid map. Bias with the AMMI Ta

retrieval in Fig. 2a shows satellite retrieval under-

estimation of Ta in several regions such as the Medi-

terranean Sea, the east coast of Australia, the Caribbean

Sea, and the northeastern Atlantic Ocean. This pattern

of bias is also true for Ta retrievals of Liu (1988), Jourdan

and Gautier (1995), and Konda et al. (1996) (not shown).

Figure 2b shows that the AMSST Ta retrieval gives

generally less bias in these regions. Other regions where

AMMI overestimates Ta such as the western coastlines of

North and South America also show less bias for AMSST.

The mean bias for AMSST Ta averaged for all map lo-

cations is 20.218C and for AMMI Ta is 21.338C. This

shows significant reduction in bias using the AMSST Ta

retrieval. Despite these improvements, there are some

regions where AMSST has significant positive bias such

as regions in the Atlantic along the east coast of North

FIG. 2. Ta bias maps for 1999 of (a) AMMI 2 ICOADS Ta and (b) AMSST 2 ICOADS Ta.


America and in the northern Pacific near Japan. The

warm Gulf Stream and the Kuroshio likely cause over-

estimation of Ta, particularly when cold air outbreaks

occur over these currents. Overall, Figs. 2a and 2b show

that the regional bias relative to ICOADS has been sig-

nificantly reduced by using Ts in the AMSST Ta retrieval.

3. Discussion

Differences between Ts and Ta drive the sensible heat

flux exchange between the ocean and atmosphere, so the

relationship of Ta with Ts is examined for the satellite

and ICOADS data. One important question is whether

the use of Ts in the AMSST Ta retrieval diminishes the

utility for using these Ta observations for sensible heat

flux retrievals. A Ta retrieval was developed only using

a linear fit to the Ts data, hereafter referred to as SST Ta,

to help examine the spatial and temporal affects of

combining AMSU-A and Ts observations. The question

is whether the temporal and spatial structure of the

AMSST Ta field is dominated by Ts. Figure 3 shows the

Ta difference between AMSST and SST Ta retrievals for

January and July of 1999. The largest differences occur

over the North Pacific and Atlantic Oceans. Cold air

outbreaks from Asia result in colder air overlying a rel-

atively warm ocean. Negative Ta differences around

Japan, over the Mediterranean Sea, and over the Gulf

Stream east of North America indicate that retrievals

using the AMSU-A satellite observations result in colder

Ta observations. Conversely, July Ta differences show

values to be warmer than what would be predicted using

a retrieval based solely on Ts. The structure of this Ta

difference is also evident over Southern Hemisphere and

shows seasonal variability. Therefore, the AMSST Ta

retrieval does show structure that differs from Ts.

The relationship of the Ts–Ta difference as a function

of Ts is compared between satellite and ICOADS ob-

servations. Figure 4 gives a binned scatter diagram show-

ing the Ts–Ta relationship for both satellite (Reynolds

Ts 2 AMSST Ta) and ICOADS Ts–Ta as a function of Ts

FIG. 3. Maps of AMSST Ta 2 SST Ta for 1999 for (a) January and (b) July.


for night-only matched observations. The matched satel-

lite and ICOADS observations are filtered to only include

data where jReynolds Ts 2 ICOADS Tsj , 0.258C so to

isolate the differences between the satellite and ICOADS

data caused by differences in the Ta data. The Ts–Ta

relationship is shown to have the same mean difference

for most values of Ts, and Ts is approximately 1.308C

warmer than Ta on average for both products. While the

mean Ts–Ta relationship for satellite and ICOADS is

very close, the variability in the Ts–Ta differences is

smaller for the satellite data. Standard deviations for the

ICOADS data are 1.768C whereas the satellite data have

standard deviations of less than half that value. The

satellite retrieval is less likely to capture large Ts–Ta

deviations for several reasons. Since the AMSST Ta

retrieval uses Ts as a predictor for the retrieval, the re-

trieval will have a tendency to converge toward Ts. The

satellite observations also measure radiance for broad

layers of the atmosphere, so shallow layers of cold or

warm air are difficult to detect. Also the footprint of the

satellite data is much larger than the point measurement

of the ship, so the satellite measurements smooth out

temperature gradients that could cause significant de-

partures in Ts–Ta.

Figure 5 gives histograms of the Ts–Ta relationship

for the ICOADS data and for the satellite products

(Reynolds Ts and AMSST Ta) for all available ship and

buoy matches during 1999. The matches were filtered

the same as data in Fig. 4 so that only Ts differences

between ICOADS and satellite that are less than 0.258C

are included. This restriction more significantly reduced

the number of ship observations by 79% compared to

the 51% reduction in buoy observations. The satellite

histograms are shifted right of the ICOADS histograms

and show a more narrow distribution. The best agree-

ment between satellite and ICOADS occurs for the

buoy histograms where the peak values occur at the

same Ts–Ta bin. Most of the large positive and negative

ICOADS Ts–Ta values that contribute to the broader

ICOADS distribution originate from the ship observa-

tions. Potentially larger errors in the ship-only Ts and Ta

data contribute to part of this wider distribution since

VOS observations are generally considered to have larger

errors than buoy observations. Differences in the geo-

graphical distribution in sampling of the ship-only and

buoy-only observations may also contribute these dif-

ferences. Buoy observations are concentrated in the

tropical Pacific and coastal regions of North America

and Europe, whereas the ship data are concentrated

along shipping routes in the North Pacific and Atlantic

Oceans.

A histogram comparison of Ts–Ta for buoy-only ob-

servations for the current and previously published re-

trievals is shown in Fig. 6. The previous retrievals all

show a broad distribution with significantly more large

negative and positive values than are shown from the

FIG. 4. A comparison of 1999 night-only data between Reynolds

Ts–AMSST Ta and ICOADS Ts 2 ICOADS Ta data plotted as

function of Ts using 28C bins. Squares and dotted curve represent

mean and one standard deviation for satellite Ts–Ta, and triangle

and dotted curves represent mean and one standard deviation for

ICOADS Ts–Ta. Satellite and ship matches were filtered such that

jReynolds Ts 2 ICOADS Tsj , 0.258C.

FIG. 5. Ts–Ta histogram data for 1999 using satellite data

(Reynolds Ts 2 AMSST Ta) and ICOADS data for all matched

observations, ship-only observations, and buoy-only observations.

Histogram bins have a width of 0.58C. Matches were filtered as in

Fig. 4.


ICOADS buoy data. The AMMI distribution does have

a more narrow distribution relative to the other satellite

products and a reduced number of large negative values,

but the AMMI distribution peaks at much warmer Ts–Ta

value than the buoy distribution. The AMSST distri-

bution has a similar width to the buoys and the peak

agrees with the buoys, but this distribution does show

fewer negative Ts–Ta observations and warmer Ts–Ta

values than the buoys and is slightly shifted to right of

the ICOADS buoy distribution. It should be noted that

Roberts et al. (2010) (not shown here) has also shown

a Ts–Ta histogram that compares well with in situ data.

They also used SST as a predictor; however, they uti-

lized a neural network approach that uses SSM/I rather

than AMSU-A satellite observations. Therefore, even

though Ts–Ta using AMSST has a more narrow distri-

bution relative to other satellite products, it does agree

better with Ts–Ta buoy observations.

4. Conclusions

These results indicate that combining AMSU-A ob-

servations with Ts daily mean observations provides a

useful method for the retrieval of 10-m air temperature

observations. Regression RMS differences of 0.838C are

significantly less than in previously published methods.

Validation with ICOADS data indicates an overall bias

of 20.218C and RMS difference of 1.558C, which was

shown to be significantly better than previous satellite

Ta retrievals. Regional biases were found to be smaller

for the AMSST retrieval than previous methods for

several oceanic regions. Examination of the Ts–Ta re-

lationship, which is important for computation of sen-

sible heat flux using the bulk formula, indicates that the

mean satellite Ts–Ta observations agree well with

ICOADS Ts–Ta observations for most observations.

Reduced variability for the AMSST Ta retrieval does

hinder detection of the largest Ts–Ta differences, likely

because the Ta algorithm uses Ts as a predictor in its

retrieval. The Ts–Ta histogram distributions for satellite

and ICOADS are similar with satellite Ts–Ta slightly

warmer than ICOADS, and the ICOADS distributions

were generally broader particularly for the ship obser-

vations. However, the Ts–Ta relationship using the

AMSST Ta retrieval, when compared with previous

retrievals, did show a substantial improvement when

compared with the Ts–Ta distribution from buoy ob-

servations in ICOADS. The AMSST Ta retrieval pro-

vides a relatively simple global method for observing

near-surface temperature and provides a potentially use-

ful dataset for computing global fields of sensible heat flux

over the oceans of the earth.

Acknowledgments. This project supported in part by

funding from NASA Grant NNM04AA67I in collabo-

ration with Dr. Franklin R. Robertson at the Marshall

Space Flight Center. The ICOADS data are from the

Research Data Archive (RDA), which is maintained by

the Computational and Information Systems Labora-

tory (CISL) at the National Center for Atmospheric

Research (NCAR). NCAR is sponsored by the National

Science Foundation (NSF). The original data are available

from the RDA (http://dss.ucar.edu) in dataset ds540.1.

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near-surface air temperature retrieval derived from amsu-a and sea surface temperature

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