Literature Review

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					                                                                         Kevin Donofrio
                                                          AT 737 – Satellite Meteorology
                                                                      Literature Review
                                                                         March 28, 2005

Atmospheric humidity and water vapor has been a major source of interest for many
satellite platforms. Because of this, there have been many types of satellite data used to
retrieve water vapor profiles. Radiosonde data is currently being used by many numerical
weather prediction models for moisture profiles. Radiosonde accuracy is equivalent to
profiles with 1-K rms accuracy in 1-km thick layers and humidity profiles with 10%
accuracy in the troposphere, and this is largely due to the fact that these are in situ
measurements. Since the radiosonde network is rather sparse and costly, we seek global
coverage of water vapor profiles, not only for forecasting, but for climate anaylsis. Since
water vapor is the best greenhouse gas, it plays a large role in the radiation budget of
Earth and the atmosphere. Satellite data is ideal for this task.

Three main types of algorithms are used to retrieve water vapor profiles. Physical
retrievals use first guess profiles, calculate weighting function and converge to a solution.
Physical algorithms do not need large databases of radiosonde data and physical process
are known during each phase of the retrieval, but they are computationally intensive.
Statistical algorithms do not directly use radiative transfer equations. Regression
methods are computationally less intensive, but you need a large set of coincident
satellite and radiosonde data. Hybrid retrievals including optimal estimation methods
which iterate a radiative transfer model subject to some constraint, and then minimize the
cost function. Apriori knowledge is needed for these retrievals, and these retrievals are
self diagnosing.

Passive microwave satellite data has been popular for retrieving water vapor, due to the
ability for global clear and cloudy sky water vapor retrieval ability. One example of
satellite data used to retrieve water vapor profiles Special Sensor
Microwave/Temperature-2(SSM/T-2) passive microwave brightness temperature
measurements. A physically based retrieval algorithm has used this data alone and in
conjunction with data from the Special Sensor Microwave/Imager (SSM/I). The SSM.T-2
is a microwave radiometer used to retrieve total integrated water vapor (TIWV) over
ocean. This algorithm does not rely on radiosonde observations, and is said to be useful
for measuring climate change on decadal scales and longer. Blakenship et al add that
physical algorithms should perform better in areas where radiosonde data is not available.
Combing SSM/T-2 data from three 183 GHz channels and SSM/I TIWV data showed an
increased yield and decreased normalized brightness temperature error. Relative
Humidity was found to be correlated more closely with ECMWF analyses and radiosonde
measurements in lower layers (surface to 850mb, 850 to 700mb, and 700 to 500 mb).
Failures of this retrieval where convergence did not occur happened in regions of
increased precipitation and cloudiness. SSM/T-2 has been found (Engelen and Stephens)
to perform well over ocean, but only works well in the mid-to upper troposphere with
lower sensitivity than infrared measurements (I.E. TOVS, discussed later in this review).
Another algorithm being used operationally is an algorithm from the National
Environmental Satellite Data and Information Service (NESDIS), which includes
adjustments for cloud liquid water and variable surface emissivity. This algorithm uses
collocated radiosonde and satellite observations as its first guess in a regression retrieval.
The NESDIS algorithm is derived from the radisonde data, which is the same data used
for validation. And, an iterative maximum likelihood approach is taken to retrieve water
vapor profiles from the Cross-track Infrared Sounder (CrIS). The CrIs sensor is flown on
the National Polar-orbiting Operational Environmental satellite System (NPOESS)
satellite. The resolution of this sensor is 15km at nadir for the moisture profile.

High Resolution Infrared Radiation Sounder (HIRS) is an operational infrared sounder
used to retrieve moisture and temperature profiles, as well as surface properties. HIRS is
on the NOAA polar orbiting satellite and has 20 spectral channels (12 longwave, 7
shortwave and 1 visible). One technique used in the past involves a neural network
technique. HIRS channels 2-8 and 11-12 are used on the HIRS sounder for the water
vapor profile. The neural network dataset is constructed using profiles from the European
Center for Medium-Range Weather Forecasts (ECMWF) system. Using a 3DVAR
system, profiles are divided into seven groups differing by the total precipitable water
vapor content of the profiles. The profiles are interpolated onto 35 presssure levels from
1000 to 0.1 hPA. A radiative transfer model simulates the HIRS channel brightness
temperatures. Specifically for the water vapor profile, mixing ratio profile are derived
from specific humidity and temperature profiles and collocated with the HIRS channel
brightness temperatures from the radiative transfer model. The water vapor mixing ration
values are from 1000 to 300hPa. Convergence tests are performed on the neural network
and a mixing ratio retrieval occurs using the input test set. Retrieved values are compared
to an output test set from the neural network to find the smallest rms. The output test set
is the “truth”. Rms errors were found to be 2.1 g/kg at 1000mb, decreasing as you go up
in the atmosphere to 1.2g/kg at 700mb, 0.7 g/kg at 600mb, and less than 0.4 g/kg above
500mb. The surface values ranged from rms error of 0.7g/kg to 3.5 g/kg. All values are
consistent with ranges in past studies. Since data is available for all seasons, long term
studies can be used on HIRS data and using neural network techniques.

There have been physical retrievals and optimal estimation retrievals done using data
from the Advanced Microwave Sounding Unit (AMSU). One algorithm used involved a
forward model for calculating radiance and its Jacobian and a minimization process from
the cost function. One of the key assumptions in these methods is that the errors in the
observations and in the apriori information are neither biased or correlated and have
Gaussian distributions, so that the best estimate of the state variable will minimize the
cost function. (Liu and Weng) Liu and Weng use AMSU data from window channels at
23.8, 31.4, 89, and 150Ghz and sounding channels near 60 GHz and 183GHz and
simulate the AMSU brightness temperatures at the top of the atmosphere. Results have
found retrieved water vapor profiles agreeing with true profiles, with peaks occurring
where clouds occur. Total precipitable water is found to be more accurate over oceans
since microwave window channels over ocean are more sensitive to the lower
tropospheric water. There are also surface emissivity problems over land, and therefore,
microwave data needs to be coupled with other observations for better results over land.
And Rosencranz uses a Bayesian approach and apriori statistics for parameters that
characterize the state of the system. Moisture is obtained from the water-vapor and
window channels. The modeling of the observation system is of critical importance,
where again, the degree of freedom on the surface emissivity poses some errors for the
remote sensing problem. Results from this approach showed fairly smooth curves,
without the fine vertical structure of radiosonde profiles. Since the highest weighting
function in this approach peaks between 300 and 500hPa, there are errors in dewpoint at
higher altitudes. Improvements are needed for the surface model, and corrections for

Another algorithm uses the method of Engelen and Stephens (1999) to retrieve profiles of
temperature and water vapor as well as cloud liquid water path and surface emissivity and
is a 1DVAR system. AMSU data is compared with radiosonde data and matched up over
the ocean. This algorithm uses also uses a physically based optimal estimation method
similar to the one above, and uses a first guess on the water vapor profiles and surface
emissivities, and minimizes the cost function to find the optimal solution. Results have
found too much moisture at low levels (<850mb) and too little at high levels. And there is
also the infrared TOVS water vapor retrieval from Engelen and Stephens, where an
optimal estimation retrieval scheme is built, with the prime advantage being that a
detailed description of the retrieval characteristics and errors can be obtained. In TOVS.
water vapor profiles were limited to the upper and mid-troposphere (they also used
SSM/T-2 for the lower troposphere), and the vertical resolution was limited. Their tests
also worked in non-cloudy conditions. A lot of apriori information is retained in the
retrievals, particularly in the lower troposphere. When they compared results to NVAP
and Pathfinder, differences were found in the subsidence regions and in dry regions of the
lower troposphere.

Future work

Any work that can be done to increase the yield is desired, particularly in cases where
past retrievals have not converged. Also, retrievals of water vapor below clouds are
desired, as well as in areas of precipitation, due to the fact that the retrieval problems are
difficult. The problem of retrieving water vapor from the measurements is highly
nonlinear even in clear atmospheres and the addition of clouds only makes it more so,
particularly due to scattering. The World Meteorological organization defined that to
significantly improve weather forecasting would require soundings of radiosonde
accuracy. In my project, using the Atmospheric Infrared Sounder (AIRS) moisture
granules and comparing with radiosonde data (which is typically supplied to the weather
models), and we would expect the structure of the humidity profile to be more detailed. If
we can improve our remote sensors to profile atmospheric variables with similar or better
accuracy then in situ radiosondes, then we would no longer depend on the sparse
radiosonde network that requires human intervention, and could have the advantage of
worldwide coverage. We could then supply our models with satellite data and improve
atmospheric humidity analysis and forecasting. The AIRS sensor has spectral coverage
from 3.7 to 15.4 µm. The requirement of the AIRS sensor is to have the atmospheric
humidity perform with accuracy of 20% in layers 2km thick in the troposphere. The goal
is to have the humidity measurements perform with accuracy of 10%, which is the
current accuracy rate of the radiosonde network. AIRS has 2378 channels, and has
spectral resolution more than 100 times greater than previous IR sounders to provide
more accurate information on the vertical profile of atmospheric moisture. Some day,
coupling this data with microwave retrievals should aid in analysis of cloudy conditions.


Atmospheric Infrared Sounder fact sheet. (n.d.). Retrieved March 10, 2005, from

Blankenship, Clay B., Abdulrahman Al-Khalaf*, and Thomas T. Wilheit: Retrieval of
water vapor Profiles Using SSM/T-2 and SSM/I Data. Journal of the Atmospheric
Sciences, 57, No. 7, 939-955.

Engelen R.J., and G.L. Stephens, 1999: Characterization of water vapour retrievals from
TOVS/HIRS and SSM/T-2 measurements. Q.J.R. Meteorol. Soc., 125, 331-351.

Forsythe, J., A. Jones and T. VonderHaar, 2004: Water Vapor Profile Retrievals from
Satellite Microwave Sounding Instruments. 13th Conf. On Sat. Meteor. And Ocean.

Liu, Q. and F. Weng. Physical Retrievals of Temperature, Water Vapor, and Cloud
Profiles from AMSU. 13th Conf. On Sat. Meteor. And Ocean.

Kidder, Stanley Q., and Thomas H. VonderHaar: Satellite Meteorology: An Introduction.

Rosencranz, P.W. Retrieval of Temperature and Moisture Profiles From AMSU-A and
AMSU-B Measurements. IEEE Trans. Geosci. Remote Sensing, 39, No.11, 2429-2435.

Shi, L. and J. Bates: Deriving Surface Skin Temperature and Atmospheric Profiles of
Temperature and Water Vapor from HIRS Measurement Using a Neural Network
Technique. 13th Conf. On Sat. Meteor. And Ocean.

Wilheit, Thomas T., 1990: An Algorithm for Retrieving Water Vapor Profiles in Clear
and Cloudy Atmospheres from 183 GHz Radiometric Measurements: Simulation
Studies.J. App. Meteor., 209, 508-515.