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How Satellite Observations Impact NWP Analyses and Forecasts
Use of Satellite Observations in NWP
1.0.0 Introduction
1.1.0 Use of Satellite Observations in NWP
1.2.0: About the Lesson
2.0.0 Impact of Satellite Observations on NWP
2.1.0 History of Satellite Observations in NWP
2.2.0 Impact of Satellite Instrument Type on Forecast Error
2.3.0 Hurricane Sandy Experiment
2.4.0 Extent of Impact From Satellite Observations
3.0.0 Satellites, NWP, and the Forecast Process
3.1.0 General Forecast Process
3.2.0 GEO and LEO Imagers
3.3.0 Sounders
3.4.0 GPS-RO
3.5.0 Which Satellite Observations Are Assimilated
3.6.0 How NWP Models Forecast Atmospheric Processes
3.7.0 Data Assimilation System
3.8.0 Forecast Modules
4.0.0 Assimilating Satellite Observations Into DA Systems
4.1.0 Introduction
4.2.0 Accepting Data From New Instruments: Flowchart
4.3.0 Accepting Data From New Instruments: Questions
4.4.0 Assimilating Observations and Retrievals: Flowchart
4.5.0 Assimilating Satellite Retrievals
4.6.0 Assimilating Satellite Observations
4.7.0 Assimilation Questions
5.0.0 Future Advances Using Satellite Observations in NWP
5.1.0 Overview
5.2.0 DA System and NWP Model Challenges
5.2.1: Convection
5.2.2: Microphysics
5.2.3: Natural and anthropogenic aerosols
5.2.4: Surface conditions: Soil moisture
5.2.5: Surface conditions: Greenness Fraction
5.2.6: Forward Radiative Transfer Model
5.2.7: Analysis of Small-Scale Features
5.3.0 Satellite Data Challenges
5.3.1 Effect of Clouds and Precipitation on Remote Sensing
5.3.2: Satellite Wind Data Gaps
5.3.3: Microwave Sounder Deficiencies
5.3.4: Lack of MW and Hyperspectral IR Sounders on GEOs
5.3.5: Loss of Satellite Coverage
6.0.0 Summary
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6.1.0 Satellite Data, DA Systems, and NWP Model Forecasts
6.2.0 Main Points
THE LESSON
1.0.0 Introduction
1.1.0 Use of Satellite Observations in NWP
Meteorological satellite observations are integral to the forecast process. Weather forecasters rely on
them to develop situational awareness and conceptual models of the current state of the atmosphere, as
well as to verify numerical weather prediction (NWP) analyses and forecasts.
It’s commonly understood that satellite observations are used in NWP models and are important to the
quality of NWP model forecasts. However, few are familiar with the details, such as:

The types of observations used. Did you know…
[when clicked, pop up: NWP models assimilate observations from polar-orbiting and
geostationary satellites as well as Global Positioning Satellite (GPS) systems. Of these, polar
orbiter observations are used most extensively and have the greatest impact.

The role that satellite observations play. Did you know…
[pop up: Satellites provide observations in otherwise data sparse areas, such as the oceans of
the Northern Hemisphere and most of the Southern Hemisphere. Without this data, good model
forecasts would not be possible at three- to seven-day lead times in both hemispheres, and
sometimes even at two- to three-day lead times in the Southern Hemisphere.

How model limitations impact the use of satellite observations Did you know…
[pop up: NWP model and computing resource limitations prevent 95% of all satellite
observations from being assimilated.

The impact that satellite observations have. Did you know…
[pop up: Denying satellite data to NWP significantly degrades the forecast at all lead times, and
has a greater impact than denying conventional observations.
These are some of the topics addressed in this lesson.
The lesson focuses on weather prediction models, although many of the concepts and processes apply to
other environmental analyses and forecast models, such as those that monitor and forecast climate, air
quality, oceans, water resources, and ecosystem health.
The processes depicted in the lesson come from the U.S. National Center for Environmental Prediction
(NCEP) modeling center, with the Global Forecast System or GFS model serving as the main example.
These processes differ in some respects from those used in other national modeling centers, but the
ideas behind them are the same.
1.2.0: About the Lesson
This 90-minute lesson is intended to help operational meteorologists, atmospheric science students, and
other interested users understand the role and importance of satellite data in NWP analyses and
forecasts. The lesson is structured as follows.
Section 2 briefly describes the history of satellite observations in NWP and their impact on NWP model
forecast skill.
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Section 3 briefly provides background information about the types of environmental satellites that provide
input to NWP, the satellite observations that are assimilated, the major components of NWP models, and
how they forecast atmospheric behavior.
Section 4 examines how satellite observations are actually assimilated into NWP. It begins by describing
how observations from new satellite instruments are vetted for use in NWP, and then examines the
process of assimilating observations that have been deemed acceptable. You will see how a model’s
capabilities affect its use of satellite data and how satellite data helps improve model forecasts.
Section 5 describes current challenges to making optimal use of satellite observations in NWP and
explores advances in satellite and NWP systems that should address these challenges and improve
model forecasts.
Section 6 summarizes the lesson.
By the end of the lesson, users should be able to:
 Provide an example demonstrating the impact of satellite observations on NWP analyses and
forecasts
 Identify the three primary types of environmental satellites that provide observations to data
assimilation (DA) systems and NWP models
 Provide examples of the types of satellite observations that are currently assimilated into DA
systems
 Briefly describe the major components of forecast models and how they forecast atmospheric
processes
 Describe the process of vetting observations from new satellite systems for use in DA systems
 Describe the process of assimilating satellite observations and retrievals once they have been
accepted for use in DA systems
 Provide examples of how model limitations can impact the assimilation and use of satellite
observations in NWP
 Describe expected developments in satellite, DA, and NWP systems that will address current
challenges and improve NWP forecast quality
To get the most out of the lesson, users should have basic knowledge of environmental satellites and
their products as well as general knowledge of NWP models and data assimilation systems. Some of this
information can be found in the MetEd lessons NWP Training Series Course 1: NWP Basics and
Background section on Data Assimilation Systems.
2.0.0 Impact of Satellite Observations on NWP
2.1.0 History of Satellite Observations in NWP
Satellite observations were first assimilated into NWP models in the 1980s. These were vertical profiles of
atmospheric temperature and moisture (soundings), first provided by the Nimbus III research and
development polar-orbiting satellite in 1969 and later by the operational NOAA polar orbiters.
Early on, atmospheric modelers realized that the observations could be used in NWP models. But it took
over a decade to have sufficient computing power to process the huge volumes of observations,
determine the best format in which to assimilate them, and get a good estimate of the area that they
influence - in effect, to actually make the use of satellite observations in NWP a reality.
The graphic below shows the improvements in NWP model skill from 1984 through 2012 resulting from
advancements in the NWP models, the data assimilation systems that feed the initial conditions to the
models, and the observational systems (mostly satellites).
Plotted are GFS anomaly correlation scores for 500-hPa heights for the extratropical Northern and
Southern Hemisphere (NH and SH, 20 to 60N and 20 to 60S), with scores from a fixed version of the
Climate Forecast System (CFS) as a baseline for comparison. (An anomaly correlation score is basically
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a skill score for the accuracy of the planetary to synoptic-scale, i.e., the large-scale, flow. It correlates
departures from climatology in the forecast to those in the verifying analysis.) Since 1987, when Southern
Hemisphere forecasts began, there has been about a 33% improvement in anomaly correlation scores in
the Northern Hemisphere and about a 44% improvement in the Southern Hemisphere.
GFS_annualACs_1984_2013.jpg 45886
None of these improvements would have been possible without the dramatic increase in satellite
observations available to data assimilation systems. We particularly see this impact in the Southern
Hemisphere through:
 The decrease in skill gap between the Southern and Northern Hemispheres in the late 1990s and
early 2000s when the data assimilation system switched from assimilating sounding retrievals
derived from satellite observations to assimilating those observations directly
 The general increase in forecast skill from 1969 through the mid-1990s as satellite sounding
retrievals were initially added and then improved
 The large increase in forecast skill in the late 1990s and early 2000s when NOAA-15 and the first
operational microwave sounders were launched; these sounders provided atmospheric profiles
through cloud cover, which was particularly helpful across regions where persistent cloud cover
blocks visible and infrared observations
2.2.0 Impact of Satellite Instrument Type on Forecast Error
Which satellite instruments have the greatest impact on reducing forecast error? The graph below shows
observational datasets from various instruments ranked by how much they reduced forecast error from
September through December 2008 at the European Center for Medium-Range Forecasts (ECMWF).
While these results are specific to the ECMWF model, they are generally applicable to all models using
these data.
We’ll examine this graphic in more detail later in the lesson, after we’ve discussed the types of satellite
instruments that provide data to NWP. But for now, notice that sounding data from polar-orbiting satellites
and, to a lesser extent, Global Positioning Satellites-Radio Occultation or GPS-RO satellites (the red
bars) reduce forecast error by almost half.
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satobs_fcst_err_red.jpg 45893
Next, we’ll look at a case example that focuses on the impact of polar-orbiting satellite data on NWP
forecast skill.
2.3.0 Hurricane Sandy Experiment
On 29 October 2012, Hurricane Sandy made landfall along the coastal areas of New York, New Jersey,
and southern New England, causing storm surges of over ten feet. Six to seven days before the storm
tore through the area, the European Centre for Medium Range Weather Forecasts (ECMWF) NWP model
predicted that it would take a sudden left turn (turn towards the west). The U.S. National Center for
Environmental Prediction (NCEP) GFS model caught on to this unusual track about a day or two later.
Despite the differences in quality, both forecasts provided sufficient time to mitigate the loss of life and
property.
As part of an NWP forecast post-storm assessment, the ECMWF did an Observation System Experiment
(OSE) excluding polar-orbiting satellite observations from the analysis that provides the starting point for
the forecast. The OSE was performed to see the impact of losing the observations on the forecast of this
high-impact event.
The graphic below shows the 168-hour operational and OSE forecasts, and the verification for Hurricane
Sandy at the time of landfall. The control forecast included polar orbiter observations and had the
hurricane making landfall near Norfolk, VA, with strong onshore winds expected north of the storm likely
producing a storm surge up to the New York City area. Without the satellite data set (middle panel), the
forecast only called for high surf and rip tides on the northeastern U.S. coast.
sandy_without_leos.jpg 45377
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2.4.0 Extent of Impact From Satellite Observations
The Hurricane Sandy Observation System Experiment showed how essential satellite observations were
to the forecast guidance for a single event. Do satellite observations typically have such an impact on
forecast quality? And if so, how far into the forecast period is this impact felt?
To answer these questions, modelers ran two OSEs using the May 2010 version of the GFS. The GFS
control forecast included all observations. One experiment omitted all conventional observations, such as
radiosondes, while the second omitted all satellite observations. The results are shown below for 0- to
168-hour forecasts, with the Northern Hemisphere extratropics on the left and Southern Hemisphere
extratropics on the right.
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compare_nosat_noconv_data.jpg 45309
The lower panel shows that denying satellite data to NWP significantly degrades the forecast at all
forecast times in both hemispheres, and has a greater impact than denying conventional observations
(radiosondes, surface weather stations, ships, buoys, and aircraft). Notice, however, that the effects of
withholding conventional data are hardly noticed in the Southern Hemisphere.
Question: Why does withholding conventional data have less of an effect in the Southern than Northern
Hemisphere?
a.
There’s less conventional data coverage in the NH
b.
There’s less conventional data coverage in the SH **
Feedback: The correct answer is B. The graphics in the tabs show coverage for four types of observation
systems. Even with aircraft data coverage, there are significantly more conventional observations
(radiosondes and aircraft) in the Northern Hemisphere than Southern Hemisphere. In addition, a far
greater proportion of observations in the Southern Hemisphere is from satellites, which explains their
large impact on model forecast skill in that region.
4 TABS
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
radiosondes

aircraft
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data_graphs_radio.jpg 45898
data_graphs_aircraft.jpg
45897

LEO soundings

Satellite-derived winds
data_graphs_sounding.jpg
45896
data_graphs_wind.jpg
45895
3.0.0 Satellites, NWP, and the Forecast Process
3.1.0 General Forecast Process
The flowchart outlines the forecast process, from observations to the human generated forecast. Satellite
observations are integral to the entire process. They are included in the observations that enter the data
assimilation system, which creates the best possible analysis of initial conditions for starting the NWP
forecast. The downward arrow from the DA system to the model forecast refers to the use of the analysis
as the starting point for the current forecast cycle. The upward arrow from the model forecast to the DA
system reflects the DA system using the most recent NWP model forecast valid at analysis time (the “first
guess”) as the starting point for its analysis.
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gen_fcst_process.jpg 45378
The rest of this section explores the satellite and NWP boxes in more detail, focusing on the types of
satellite instruments and observations that are used in NWP, the major components of forecast models,
and how those components forecast atmospheric processes. This sets the stage for the Section 4, which
describes how observations from new satellites instruments are vetted for inclusion in DA systems and
how those observations deemed acceptable are actually assimilated.
3.2.0 GEO and LEO Imagers
Currently, three types of satellites remotely sense meteorological data:
 Geostationary earth orbiters (GEOs)
 Low earth orbiters (LEOs), a subset of which are polar-orbiting satellites
 Global positioning systems (GPS) in medium earth orbit (MEO)
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geo_leo_gpsro_satellites.jpg 45899
GEO and LEO satellites carry two types of instruments: imagers and sounders. Each provides
observations from different parts of the earth-ocean-atmosphere system.
Visible and infrared imagers mainly observe the condition of radiating surfaces, such as soil,
vegetation, water, ice, and cloud tops. A subset of infrared channels are sensitive to atmospheric water
vapor and observe atmospheric circulations from mesoscale to larger-scale hemispheric patterns.
The amount of energy that reaches an imager at a particular wavelength (commonly expressed as a
radiance) from these objects is considered a satellite observation. This energy is typically displayed as a
single channel image.
Satellite observations from multiple channels can be further processed to create products that provide
information about surface and atmospheric conditions, such as snow cover, atmospheric winds, and dust.
The graphic below shows EUMETSAT’s Dust RGB product, which detect the presence of atmospheric
dust. The product is made from the three infrared channels shown on the left. The dust stands out clearly
in magenta.
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dust_rgb_with3_RGBinputs.jpg 45381
3.3.0 Sounders
Sounding instruments sense energy in portions of the spectrum where energy is absorbed and re-emitted
by atmospheric constituents such as carbon dioxide, water vapor, and ozone. These measurements are
used to extract three-dimensional information about temperature, moisture, and other atmospheric
constituents.
A new generation of LEO sounders, known as hyperspectral sounders, sense energy at very high
spectral resolution, sampling over thousands of spectral bands. Hyperspectral sounders are capable of
providing radiosonde-like high-resolution profiles of the atmosphere.
The vertical resolution of retrieved profiles depends on the number of channels and spectral resolution
employed by the instrument. Recently launched LEOs have hyperspectral sounders with thousands of
infrared channels. The vertical resolution of this data approaches that of balloon-launched radiosondes.
Partly as a result, 60% of the satellite data assimilated comes from LEO sounders.
As with imagers, hyperspectral sounder channels can be combined to retrieve meteorological products.
Examples include total precipitable water (TPW), aerosol optical thickness, cloud top temperature and
height, cloud top water phase (water versus ice), cloud optical thickness, and particle size. These are
useful to forecasters in diagnosing such things as aviation and air quality hazards, and for NWP in
improving the analysis of temperature and moisture. High-resolution sounding data also benefits NWP
(and human) forecasts of convection and severe weather, precipitation type, and maximum and minimum
temperature.
Even though GEOs, such as the U.S. GOES-East and GOES-West, continuously observe the same
areas, the current generation sounders on board GOES-8 to 15 only have 18 infrared sounder channels,
resulting in GEO soundings with coarse vertical resolution. As a result, almost no GEO sounding data is
used in data assimilation.
High vertical resolution hyperspectral sounders are not available on the current U.S. Geostationary Orbit
Environmental Satellite (GOES) satellites, nor will they be on the next-generation GOES-R. This
degrades monitoring of the atmosphere in potentially crucial situations such as severe convection.
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The graphic below compares GEO and hyperspectral LEO soundings to radiosonde profiles from the
same location and time. In the LEO sounding, note the fine detail and excellent agreement of
temperature, dewpoint, and convective instability to the radiosonde values.
raob_vs_goes_vs_adv_sndr_profiles.jpg 45383
For more information on hyperspectral sounders, see the COMET lesson Advanced Satellite Sounding:
The Benefits of Hyperspectral Observation - 2nd Edition. For more on the use of hyperspectral sounders
in GEO vs. LEO satellites, access the COMET lesson Toward an Advanced Sounder on GOES?.
3.4.0 GPS-RO
Another type of sounding technology provides atmospheric profiles of temperature and moisture. It uses
Global Positioning Satellite (GPS) signals that are intercepted by LEO satellites, such as the COSMIC
constellation and EUMETSAT’s Metop satellites, in a process called radio occultation (RO).
GPS radio signals bend as they move through the atmosphere. The amount of bending depends on the
density of air in the signal’s path, which, in turn, depends on the temperature and moisture along the path.
The process is illustrated below, with hypothetical soundings for temperature and water vapor pressure
shown on the right. Currently, GPS satellites provide around 2,500 soundings around the world each day
for assimilation in NWP, compared to about 44,000 sounding per day from the AIRS hyperspectral
sounder on NASA’s Aqua and Terra polar-orbiting satellites.
orbital_mechanics_sounding.jpg 45901
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For more information on GPS and the extractable meteorological information, see the UCAR COSMIC
page and COMET’s COSMIC lesson.
3.5.0 Which Satellite Observations Are Assimilated
For purposes of NWP modeling, the earth-ocean-atmosphere system can be divided into three spheres:
the lithosphere (including vegetation), hydrosphere, and atmosphere. While satellites provide massive
amounts of information about all three spheres, model limitations only permit some of it to be used. Click
each sphere to see what observations and measurements are assimilated into DA systems as of 2014.
While most information comes from LEO and GPS-RO sounders, GEO sounders contribute as well.
Note that some satellite observations, such as snow cover and snow depth, are used in the analysis and
allowed to evolve throughout the forecast, while climatological values derived from multi-year satellite
observations are used for other parameters. The latter tend to evolve slowly over time, such as land use
or vegetation, which can take years to change.
earth_atmos_systems_obs_assim.jpg 45386
earth_atmos_systems_obs_assim_terr.jpg 45387
earth_atmos_systems_obs_assim_atmos.jpg 45388
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earth_atmos_systems_obs_assim_oceans.jpg 45389
In the next section, you will see where this satellite data is plugged into the DA system, how DA systems
relate to NWP models, and how NWP models generally work.
3.6.0 How NWP Models Forecast Atmospheric Processes
NWP models simulate the atmosphere and provide forecast guidance on the occurrence and values of
weather elements such as temperature, moisture, wind, and precipitation. Weather models can forecast
phenomena from tens to thousands of kilometers in size and timescales from hours to days. While this
lesson focus on NWP models, similar principles apply to climate models, which forecast at larger spatial
and longer temporal scales.
The computer programs (“modules”) in an NWP model make up its “architecture.” There are three general
categories of modules. One set, the data assimilation system, ingests observational data to prepare the
analysis or initial conditions for the forecast, while the other two, dynamics and physical
parameterizations, create the forecast. We will spend much of this lesson discussing how satellite data is
used in the DA system, starting by describing what these systems do.
3.7.0 Data Assimilation System
The DA system provides an analysis of the current state of the atmosphere and land and ocean surfaces.
To get a good NWP forecast, this analysis must be high in quality. This requires a good DA system, good
data to assimilate, and, of course, a good NWP model.
At the U.S. National Centers for Environmental Prediction (NCEP), analyses for global and large-domain
mesoscale models are done every six hours, starting at 00 UTC each day. For each analysis, the DA
system ingests observational data over a “time window” centered on the analysis time. The data is
combined with a short-range forecast or “first guess” to create a final analysis from which to start the
NWP forecast.
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The graphic below is an idealized schematic of how DA works. The blue lines represent forecasts for a
series of cycles that evolve with time. The red line and pink shading are the true atmospheric state
(“truth”) and its uncertainty. The vertical black arrows represent the DA process, where the first guess and
observations are combined to create the final analysis. Notice that while forecasts always drift away from
the true atmospheric state, the DA process pulls the first guess back toward the “truth” using the
observations. Also notice that the new analysis is never exactly the “true” atmospheric state, but lies
within the range of uncertainty in pink shading.
corcycle.jpg 45391
Since the analysis relies on the short-range forecast first guess, the quality of the NWP model is important
to the DA system. And because NWP forecasts are sensitive to their starting point or initial conditions,
they depend on a good DA. If the model first guess were not anchored to true atmospheric conditions in
the DA process each cycle, model analyses would be too far from the “truth,” which would negatively
impact NWP forecasts.
Next, we’ll look at the modules that produce the forecast, dynamics and physical parameterizations.
3.8.0 Forecast Modules
Once the DA system completes its analysis of the initial conditions, the NWP forecast begins. The
model’s dynamics and physical parameterizations each handle different aspects of atmospheric
simulation.
Dynamics
A model’s dynamics forecast the evolution of atmospheric processes that can be directly “seen” at the
model’s time and spatial scales. Depending on the model’s resolution, the dynamics may simulate
atmospheric phenomena ranging from planetary and synoptic-scale waves to narrow precipitation bands.
Some of the highest-resolution models can directly predict convective updrafts and downdrafts.
Dynamics are calculated using equations that forecast the wind and its movement of momentum, heat,
moisture, and other atmospheric constituents, such as ozone and condensed cloud water.
Physical parameterizations
Some processes occur on time and/or spatial scales too small to be directly accounted for in the model.
But since they significantly affect the atmosphere, their impacts must be estimated through physical
parameterizations. The graphic shows the processes that require physical parameterization in most
models. These include, but are not limited to, short- and longwave atmospheric radiative transfer, cloud
and precipitation microphysics, land surface and planetary boundary layer (PBL) processes, and
convection.
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paramprs.jpg 45390
Between forecast calculation times, the NWP model adds the effects of the physical parameterizations
and wind dynamics on atmospheric temperature, moisture, and momentum within a 3-D model grid box to
establish the next set of forecast values.
For more information on the basics of physical parameterization, see the physical parameterization
section of COMET’s NWP Model Fundamentals lesson.
Now that you are armed with the basics of DA systems and NWP models, we will explore how satellite
data is vetted for use in DA and the process of assimilating the data that has been deemed acceptable.
4.0.0 Assimilating Satellite Observations Into DA Systems
4.1.0 Introduction
This section focuses on the use of satellite data in NWP, examining such topics as:
 How scientists vet observations from new satellite instruments for use in DA systems
 How satellite observations are assimilated once they are accepted for use
 How NWP model capabilities impact the use of satellite observations
4.2.0 Accepting Data From New Instruments: Flowchart
When new satellite instruments are designed and orbits determined, the needs of data assimilation and
model forecasts are taken into consideration. For example, hyperspectral sounders with thousands of
infrared channels were developed in response to the need for higher vertical resolution in satellite
soundings used in DA systems. In a similar vein, new LIDAR (Light/Laser Detection and Ranging)
instruments were developed to provide more of the wind data needed to improve DA analyses.
Once an instrument is launched and begins sending observations, the data goes through a testing phase
(also known as the “mission checkout phase”) that can last several months to a year or more. The
process from pre-launch to acceptance as valid data for DA is outlined in the flowchart below. Click each
box for more information. On the next page, you’ll answer a series of questions about the process.
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new_sat_obs_flow.jpg 45302
Rollovers when each box is clicked:
Satellite successfully launched and instrument checked out
Before an instrument is even built, experiments are performed using proxy datasets [ROLLOVER: data
from other sources with similar characteristics used to simulate a new instrument], existing models, and
DA systems to predict its potential to improve DA and NWP. Once the instrument is deemed useful, it is
tested in a controlled laboratory environment to see how closely its measurements agree with
independently observed quantities like temperature and moisture. The results are combined with
information from similar instruments to assess the new instrument’s error characteristics prior to launch.
This ‘error estimate’ is used to correct the instrument’s observations and weigh their influence on the
analysis.
There are two types of observation errors:
 The average error or bias. This usually results from characteristics of the instrument. It’s
analogous to how a thermometer might always read too warm or too cold.
 The size of the residual or random error after removing the bias. For example, these errors can
come from a corrupted satellite signal (due to spacecraft anomalies or other natural or man-made
sources) that results in “bad pixels”.
When the initial error assessment is completed and an appropriate launch vehicle is available, the
instrument is put into orbit.
Monitor and assess satellite data
Once in orbit, the instrument begins to collect and transmit data to receiving stations on the ground. The
data is compared to observations from other satellite and non-satellite sources to assess their quality.
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Satellite observations are monitored and assessed continuously - from when they are first sent to when
they are incorporated into operational DA systems. That’s because:
 Instrument characteristics change over time, requiring periodic reassessment of the instrument’s
biases and errors
 A satellite instrument or part of an instrument may fail, requiring the repositioning of an existing
satellite or launch of a new instrument to take its place
 Data that are not currently used in the DA system may become useable with improvements in
NWP models, DA systems, and satellite retrieval methods that are used to produce geophysical
parameters
Is data quality good?
If the satellite observations are of good quality, satellite and NWP model experts determine if they can be
ingested by the operational NWP model. This depends on the characteristics of the model, as discussed
in decision diamond 5, ‘Is the model capable of ingesting the data?’
Satellite observations may be deemed inadequate for DA systems for a variety of reasons, such as
unexpected changes in instrument biases and errors after launch. The experts will try to improve the
observations for possible use in current or future DA systems.
Bias correct/calibrate satellite data
In some cases, an instrument’s biases can be recalculated after launch by comparing instrument
measurements to expected values for objects with known radiation characteristics on the satellite, on
earth, or in space (the moon, for example). If the recalculation sufficiently improves the data, assessment
moves to the next step. Otherwise, the data will continue to be monitored and refined for possible future
use.
Is the NWP model capable of ingesting the data?
Model dynamics and physical parameterizations determine what satellite data can be used in the DA
system. If there’s a good match between what the satellite observes and what the model predicts, the
satellite data continues moving through the process. Otherwise, it is monitored and assessed until it can
be used. The vast majority of satellite data (at least 95%) is not used because the model either doesn’t
simulate what the satellite sees, or the satellite data is redundant.
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data_used_in_nwp.jpg 45303
Here are examples of model limitations that can restrict or prohibit satellite data from being used.
 The model top is too low to include a significant portion of the stratosphere where most ozone
absorption takes place so those observations cannot be assimilated.
 The model has a high enough atmospheric top but poor vertical resolution in the stratosphere
limits the usefulness of satellite observations at that level.
 The satellite observations have much finer horizontal resolution than the model. As a result, the
satellite data in the model grid box is averaged into one superobservation (“superobbed”) or
thinned, with the observations that best represent the grid box selected.
 The satellite observations show a mesoscale convective system (MCS) but the model does not
predict convective updrafts and downdrafts (and thus the development of an MCS) so the
observations cannot be used.
 Satellite retrievals of aerosol concentration are available, but the NWP model uses a 10-year
seasonal mean climatology of aerosol concentrations. Retrieved aerosol concentrations cannot
be effectively compared to climatological aerosol concentrations to create a new aerosol
concentration analysis so they cannot be used.
If a model is capable of using the satellite observations, testing is done to see how they affect the DA
system, the analysis, and most importantly, the NWP forecasts.
Assign weight of data based on error characteristics
Laboratory results and similar satellite instruments are used to assign a preliminary analysis weight to an
observation, which determines its influence on the analysis. The larger the random error, the less weight
the observation is given. The size of the three-dimensional volume that will be influenced by the
observation is estimated.
Test in NWP DA system and then run test forecasts
Tests are performed in the DA system to assess the impact of the satellite data on the initial conditions
and forecasts. Retrospective tests are also done for the warm and cold seasons. Once modelers are
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satisfied with the results, forecasters and other end-users judge the guidance that the test forecasts
provide.
Does the forecast improve or remain the same?
If the analyses and forecasts pass judgment, the satellite data is incorporated into the next DA (or DA and
NWP) model upgrade. If the DA system and forecast degrade without finding a timely solution, the data is
set aside for later testing. In the meantime, data quality continues to be monitored and assessed.
Incorporate in Operational DA System
After the data is incorporated into the operational DA system, data quality continues to be monitored to
detect changes that might require adjustments to its bias or analysis weight. This process may even lead
to the withdrawal of the data from the DA system. This can happen if, for example:
 A new instrument gives better results
 Instrument degradation cannot be compensated for
 After the new data is implemented, the forecast is found to degrade due to an incompatibility
between the instrument data and the model
A good example of the final point occurred with total precipitable water (TPW) from Global Positioning
Systems (GPS). Environmental Modeling Center acting chief Dr. John Derber explains:
“After testing the GPS TPW and finding it acceptable, we began using the TPW in the GFS DA system.
But the GFS model climate had a dry TPW bias in the tropics in northern summer that we didn’t catch in
the testing. The new TPW observations properly increased the analyzed TPW, but the model “rained it
out” at the beginning of each forecast as the TPWs reduced to the model climate. Increased convective
heating in the tropical mid-troposphere resulted, “juicing up” the tropical circulation and affecting forecast
skill in the extratropics. We wound up having to withdraw the data from the system, even though the data
quality was good, because an adverse impact on the forecast developed.”
4.3.0 Accepting Data From New Instruments: Questions
Now that you’ve studied the flowchart, answer the following questions. Review the flowchart
[pop up graphic]
Question 1: Satellite observations continue to be monitored and assessed regardless of whether they
are actually used in DA. Why is this done? Choose all that apply.
a.
They tell us when the satellite instrument will fail
b.
They may be used in the future as NWP models improve*
c.
They are used for other purposes beyond DA*
Feedback: The correct answers are B and C. Satellite observations are monitored and assessed because
a model may evolve to the point where it’s able to use the data. High quality observations are also critical
to forecasters, who rely on them for monitoring the weather and evaluating NWP guidance, and other
users. Option A is incorrect because the data does not tell us when an instrument will fail, as has been
seen with the unexpected loss of satellite observations.
Question 2: What characteristics of an NWP model must be considered when assimilating any
observations? Choose all that apply.
a. The phenomena that the model can simulate **
b. The variables that the model directly forecasts **
c. The length of the time step between model computations
Feedback: The correct answers are A and B. For A, what the model can simulate largely determines the
satellite data used. For example, if an NWP model only indirectly accounts for thunderstorm processes
using convective parameterization, observational data reflecting convective vertical motions will not be
used in the analysis. For B, if we have satellite data for a feature that the model doesn’t predict, it will not
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be used. Option C is incorrect because the length of time between computations makes no direct
difference in what data can be assimilated.
Question 3: In what ways can model architecture limit or prevent the use of real-time satellite data?
Choose all that apply.
a. A model uses observed climatology for a quantity **
b. A model has coarse vertical resolution in the stratosphere **
c. A model predicts cloud liquid water and cloud ice and needs no satellite data for them
Feedback: The correct answers are A and B. For A, using climatology in the NWP model prevents the DA
system from using real-time satellite data for the analysis of that quantity. For option B, if a model has
coarse resolution in the stratosphere, it will limit the usefulness of ozone (and other) important satellite
observations. Option C does not prevent assimilation of real-time satellite data; in fact, it makes it critical!
Question 4: A new instrument is launched on the latest satellite. Why might the DA system not use its
observations? Choose all that apply.
a. The observations have a higher horizontal resolution than the NWP model
b. The observations decrease the skill of the NWP forecast **
c. The observations show a feature not predicted by the NWP model **
Feedback: The correct answers are B and C. For B, high quality satellite data tested in DA systems has
sometimes decreased forecast skill. This may be from interactions with other data in the model, conflict
with forecast model climatology, or other reasons. For C, if an NWP model does not predict a feature, it
will not be able to assimilate remotely sensed data of that feature. Option A is incorrect because data at
higher resolution than the NWP model can be averaged over the model grid box (superobbing) or thinned
to the resolution of the model.
Question 5: New satellite data deemed useable by a DA system is assigned a relative weight, which
determines its influence over the final DA analysis. Data with larger random error has (less**/more)
weight.
Feedback: An observation's weight in the final DA analysis is directly related to its expected random error.
Data with smaller random errors is more reliable and therefore has greater weight in the analysis.
Question 6: A new instrument was launched several months ago, and the data has been tested in a DA
system. The data will be incorporated into the operational DA system if it: (Select all that apply.)
a. Improves forecast skill**
b. Maintains forecast skill**
c. Reduces forecast skill
Feedback: The correct answers are A and B. Improved forecast skill is an obvious reason to incorporate
data from the new instrument. Maintaining forecast skill is not as obvious but the data may have a
positive impact when combined with even newer data in future testing. Data that reduces forecast skill will
not be included in the current DA system. However it may be incorporated in the next DA system upgrade
if there’s time to adjust and retest it.
4.4.0 Assimilating Satellite Observations and Retrievals: Flowchart
Once satellite observations are accepted for use in a DA system, they become part of the data compared
to the first guess (the model’s short-range forecast valid at analysis time from the previous forecast cycle).
But how is this comparison done and how is the data used?
Satellite data is used in DA systems in two forms: as satellite observations (which measure the radiance
or energy scattered, transmitted, absorbed, and reemitted from the earth-atmosphere system) and as
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satellite retrievals extracted from the observations, such as atmospheric motion vector winds,
temperature, and humidity.
The general process for assimilating satellite data, regardless of form, is shown in the flowchart below.
use_sat_obs_ret_da.jpg 45304
First, each satellite retrieval or observation is checked for gross errors to remove extreme or unphysical
values, such as an Earth surface temperature of 100oC. Gross errors can result from:
 Satellite instrument problems, such as uncorrected instrument degradation or interference from
cosmic radiation or commercial radio waves
 Errors in data processing, such as assigning the wrong height for cloud motion winds
 Cloudy fields of view for infrared channels and precipitating fields of view for microwave channels
that are not accounted for, and can interfere with obtaining soundings down to the surface
 Errors in estimating the amount of radiation emitted by the surface, which results from
uncertainties in emissivity (this is the radiative emission efficiency of an object when compared to
an ideal emitter, also known as a blackbody, which is expressed as a value between 0 and 1)
The observations or retrievals are then compared to the model first guess, with the difference calculated.
This difference is the “analysis increment,” which is given an analysis weight depending on our relative
confidence in the types of data available. For example, satellite data might get less weight in regions with
radiosondes and other highly accurate observations than over the oceans, where those types of
observations are not available. Note that the first guess is given a heavy weight since it is from a shortrange forecast of generally high quality.
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Next, the analysis increment is compared or “buddy checked” with its neighbors. If the increment is very
different from those around it, its weight will be reduced. It is not completely discarded in case the
increment has uniquely captured a significant error for which the first guess requires adjustment.
Finally, the weighted increment is combined with those from other satellite and observation platforms, and
used in the analysis to obtain the best model analysis possible. The forecast then begins, with the current
cycle’s short-range forecast valid at the next analysis time serving as the first guess for the next forecast
cycle.
4.5.0 Assimilating Satellite Retrievals
Recall that satellite data is processed in two forms, as observations and as retrievals. While both go
through the same general assimilation process, there are some important differences. Basically, the
quantities that satellites observe, such as radiance and sea surface backscatter, are not explicitly forecast
by NWP models so some processing is needed before the DA system can make the comparison. In
contrast, many satellite retrieval quantities are directly forecast by NWP models and can be matched
directly to the first guess fields.
We'll examine these differences in a bit more detail, starting with retrievals. We’ll use atmospheric motion
vector (AMV) winds as an example.
Atmospheric motion vectors are retrieved by following cloud and water vapor features in infrared and
water vapor channel imagery. The displacement over a series of images determines the direction and
speed of the wind at feature level. The height is computed from observed brightness temperatures. As
with other satellite retrievals, the AMV wind is compared directly with first guess data, in this case the first
guess wind.
Below are AMV winds from water vapor features on the left, and the (six-hour forecast) first guess 300hPa winds and heights from the NCEP NMM-B (Non-hydrostatic Mesoscale Model - Arakawa B-grid) on
the right, both valid at 12 UTC 12 April 2013. The cyan-shaded AMV wind barbs are for pressures from
250 to 350 hPa.
nam_namer_irs_satwinds_12apr2013.jpg
45902
In the satellite retrieval on the left, the wind from the AMVs is 65kt from the northwest. That’s not far from
the April climatology for this area, so this wind should pass the gross error check. In the first guess on the
right, the forecast wind in the area is 80kt from the northwest, perhaps slightly more north of west than the
AMV wind. Subtracting the observation from the first guess gives us an unweighted analysis increment of
southeast at about 15 kt for the 300-hPa vector wind.
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windincr.jpg
45903
This value is compared to the neighboring increments for wind from satellite and other sources (not
shown). If the other increments are significantly different, the typical weight used for AMVs at this height
will be reduced, with the amount of reduction depending on the size of the difference.
4.6.0 Assimilating Satellite Observations
Assimilating satellite observations requires some extra steps before the analysis increment can be
determined. We will examine these steps, using “brightness temperature” at a particular wavelength as
the satellite observation example.
First, the model first guess is converted into a simulated satellite observation (in the form of brightness
temperature) using a “forward” (radiative transfer) model. Second, the difference between the observed
and simulated brightness temperature is made as small as possible by adjusting other variables such as
temperature and moisture. And third, the adjustments to the first guess variables from this process
become the analysis increments.
The images in the tabs show the first guess, the final analysis of brightness temperature, and the satellite
image for water vapor channel 3 brightness temperature for the 12 UTC 16 January 2014 NAM analysis
cycle. Keep in mind that the final analysis includes adjustments for conventional data as well as satellite
data. As we might expect, the first guess and analysis are generally quite close. Differences are most
obvious near the U.S. East Coast, the swirl of clouds with an upper-level cyclonic circulation in the center
of the North American continent, and in the dry area in red from west of Oregon to the Gulf of Mexico and
Florida. Finally, the satellite image illustrates additional details that are not included in the DA system
because the NAM has coarser resolution than the satellite observations.
Tabs:
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First Guess
Analysis
26
1stguess_analysis_wvimage_1st.jpg
1stguess_analysis_wvimage_anal.jpg
WV Ch 3
1stguess_analysis_wvimage_wv.jpg
The rest of the process is the same as for retrievals, with the analysis increment weighted, buddy
checked, and weight adjusted if necessary before being used in the analysis with increments from all
other observation sources.
In recent years, modeling centers have distributed simulated brightness temperatures as satellite lookalike products to help forecasters and other users assess short-range NWP forecasts. At NCEP, these
simulated products are created by the same radiative transfer model used to produce the simulated
satellite observation in the DA system. For training on how to use synthetic imagery to forecast
orographic cirrus, severe weather, low clouds and fog, and cyclogenesis, see the VISIT Web site. In
addition, the MetEd website has training on using synthetic satellite data to forecast fog for aviation in a
lesson on the use of aviation products from the Weather Research and Forecast - Environmental
Modeling System (WRF-EMS) NWP model in East Africa.
4.7.0 Assimilation Questions
Question 1: Which statements capture the difference in how satellite observations and retrievals are
assimilated?
a.
Satellite retrievals are directly compared with model first guess data **
b.
Satellite retrievals are compared to model first guess data converted to simulated satellite
observations
c.
Satellite observations are directly compared with model first guess data
d.
Satellite observations are compared to model first guess data converted to simulated satellite
observations**
Feedback: The correct answers are A and D. Satellite retrievals derived from satellite observations are
compared to model data in its original form. For example, retrieved atmospheric motion vectors are
compared to first guess winds. Conversely, satellite observations are compared to a model first guess
that’s been converted to a simulated satellite observation.
Question 2: Why can satellite retrievals, but not satellite observations, be compared directly with model
first guess fields?
a) Satellite observations cannot be converted into first guess data to be able to make the
comparison
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b) The quantities that satellites observe are not explicitly forecast by NWP models so they cannot be
directly compared to first guess fields **
c) Many satellite retrieval quantities are directly forecast by NWP models so they can be matched to
first guess fields **
Feedback: The correct answers are B and C. For B, NWP models that provide the first guess in DA do not
directly predict the quantities observed by satellite instruments. The first guess data has to be converted
into a synthetic satellite observation via a radiative transfer (“forward”) model. Option C is correct because
first guess fields can be compared to satellite retrievals of that field. A good example is model winds and
satellite-derived atmospheric motion vectors.
Question 3: Which of the following satellite retrievals and observations is most likely to be considered a
gross error if used in a DA system? Choose the best answer.
a.
A sea surface temperature of -1ºC
b.
Snow cover with a surface temperature of +20ºC **
c.
A convective anvil with a brightness temperature of -50ºC
d.
A total precipitable water (TPW) of 12mm over the central U.S. in mid-March
Feedback: The correct answer is B. It is highly unlikely that a surface temperature of 20ºC could coexist
with snow cover. Option A could exist in high latitudes for seawater because of its salt content. Option C
could easily exist for any reasonably sized convective system. Option D is well within the climatological
range of TPW for the central U.S.
Question 4: A GPS-RO total precipitable water satellite retrieval is accepted after a gross error check
and has a +5 mm analysis increment when compared to the short-range forecast. The nearby analysis
increments range from -1 mm to +1 mm. What will happen to the GPS-RO analysis increment? Choose
the best answer.
a.
It will be rejected from the analysis
b.
Its analysis weight will be reduced**
c.
Its analysis weight will be increased
d.
It will be accepted with no changes
Feedback: The correct answer is B. The weight of an analysis increment that’s significantly different from
its neighbors will be reduced, not increased. This is done so it won’t adversely affect the analysis but will
still be included in case it reflects a needed correction missed by the other increments. Option A is
incorrect because data that has passed the gross error check is not rejected even if the analysis
increment is much different from its neighbors for the reason explained above. Option D is incorrect
because it could negatively impact the moisture analysis if its weight were not changed.
5.0.0 Future Advances Using Satellite Observations in NWP
5.1.0 Overview
In this section, we will identify current (2014) challenges to making optimal use of satellite observations in
NWP due to limitations in NWP models, DA systems, and satellite systems, and look at advances in all
three areas that will address them. When particular satellites are involved, we will mention them. Among
these are:

The U.S. GOES-R next generation geostationary satellite, which is scheduled to launch in 2016.
It will bring new capabilities for lightning detection, more than triple the number of infrared
channels, four times higher spatial resolution, five times faster imaging, and more accurate
measurements for observing subtle features.
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
The Joint Polar Satellite System (JPSS) polar orbiters, which are very similar to the Suomi-NPP
satellite launched in 2011. These satellites have a hyperspectral sounder (CrIS), a microwave
sounder (ATMS), and a 22 channel imager (VIIRS) that includes a day-night band for highresolution visible imaging at night. JPSS-1 is scheduled for launch in 2017.

CALIPSO and ADM-Aeolus satellites with Light Detection and Ranging (LIDAR) instruments.
CALIPSO is a research instrument for studying the role of clouds and aerosols in regulating
Earth’s weather, climate, and air quality. ADM-Aeolus will be the first satellite to directly observe
wind profiles from space and will also provide information on aerosols and clouds. ADM-Aeolus is
the European Space Agency’s Atmospheric Dynamics Mission, planned for launch in 2015.
CALIPSO is NASA’s Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation, launched
in 2006 as a research instrument. It flies in formation with NASA’s A-train constellation.

COSMIC-2, a GPS-RO follow-on mission with initial implementation scheduled in 2015.
We will begin by looking at the challenges related to DA and NWP, showing experimental results that
address them when available.
5.2.0 DA System and NWP Model Challenges
5.2.1: Convection
Challenge: If a convection-allowing model does not predict convection at a particular location in its first
guess, the model will not assimilate satellite data from active convection at that location.
Expected advance: A potential solution has been found through experimental assimilation of lightning
data in high resolution models. The experimental DA system builds thunderstorms in the analysis where
there’s lightning but no storms, and adjusts storm intensity based on lightning frequency.
The rationale for the assimilation is based on the observed correlation between increases in lightning
frequency and convective storm intensity. Also over the next decade, new GEOs with lightning detection
capabilities will provide continuous lightning coverage over much of the full Earth disk. They will be able
to observe cloud-to-ground as well as in-cloud and cloud-to-cloud lightning. The GOES-R+ satellites will
begin carrying the Geostationary Global Lightning Mapper (GLM) in 2015, and both China and
EUMETSAT have plans to launch lightning imagers later this decade.
Experimental results: We see the impact in the graphics below. The first two rows show the chance of
convective initiation within 25 miles of a point at two- and one-hour lead times for forecasts run with and
without proxies for the future GOES-R GLM lightning data. The third row shows the Storm Prediction
Center’s severe weather reports during the event and radar at the two-hour forecast lead time. The
experiment clearly provides better guidance than the control model run where no lighting was assimilated.
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lgtng_assim_exp.jpg 45912
Satellite/instrument data source: GOES-R will carry the Global Lightning Mapper (GLM), which will
provide lightning data to DA systems for convection-allowing NWP models. This should improve both
initial and forecast convection strength and location.
5.2.2: Microphysics
Challenge: Retrieved cloud microphysical properties and precipitation hydrometeors are not assimilated
in DA systems, resulting in poorer NWP forecasts of cloud and precipitation.
Expected advance: Work is being done at NCEP to assimilate microwave radiances for “all sky”
conditions (clear to cloudy) by using GFS first guess profiles of cloud water and ice in calculating the
simulated model radiance. Results have been mixed so far, with more work needed on quality control;
thinning or averaging observations and bias-correcting the microwave radiances; and determining the
weight and area of influence of the resulting analysis increments. When predicted rain, snow, and graupel
are added to the GFS microphysics, these will be included in the calculation of first guess microwave
radiance
On a second research front, work continues on assimilating retrievals of cloud and precipitation
hydrometeor data at the NOAA Joint Center for Satellite Data Assimilation. Implementation of an
operational DA system that assimilates retrievals of cloud microphysics and precipitation hydrometeors
appears to be at least several years away as of 2014.
30
Below are examples of the satellite retrievals available in 2013 from the NOAA Office of Satellite and
Product Operations (OSPO) operational MW Integrated Retrieval System (MIRS), all from NOAA-18.
Included are total integrated cloud liquid water, rain water path, and ice water path. All could be
assimilated into DA systems if the models providing the first guess directly forecasted these variables.
Operational assimilation of these quantities would improve cloud and precipitation forecasts, particularly
in the first 12 to 24 hours.
mirs_comparisons.jpg 45913
Satellite/instrument data source: The Microwave Integrated Retrieval System (MIRS), which uses
microwave observations from multiple satellite instruments, provides profiles and integrated total
quantities of moisture, cloud amount, rain, ice, snow, and graupel that are not yet assimilated into DA
systems. Once testing is complete and the data begins to be assimilated, it will directly improve NWP
cloud and precipitation forecasts, and indirectly improve other forecast variables affected by cloud and
precipitation processes.
5.2.3: Natural and Anthropogenic Aerosols
Challenge: NCEP NWP models use an aerosol seasonal climatology, rather than retrievals of actual
aerosol amount, to estimate the effects of atmospheric aerosols. While GEO and LEO satellites already
provide aerosol retrievals, there is no predicted aerosol in the first guess to compare them with.
Expected advance: NCEP plans to add an aerosol forecast model to the operational GFS, and aerosol
retrievals to the DA system in the 2015 timeframe. This will improve first guess simulated radiances,
leading to better assimilation of all sounder and imager data.
Satellite/instrument data sources: CALIPSO provides information on thin clouds, aerosol profiles, and
total column integrated quantities using three laser channels in experimental mode. ADM-Aeolus will
provide these retrievals in real time after the anticipated launch in 2015. Once DA systems and NWP
models are able to use the data operationally, we can expect improvement in the redistribution of energy
in the atmosphere and improvements in near-surface and lower troposphere forecasts.
5.2.4: Surface conditions: Soil moisture
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Challenge: Soil moisture is retrieved from space-based microwave instruments but the products are not
assimilated into DA systems. Instead, NCEP NWP models forecast soil moisture using a four-layer soil
model that is either:
 Slowly adjusted toward a seasonal climatology (GFS), or
 Initialized with values from an independent land data assimilation system (LDAS) that is updated
with analyzed precipitation and estimated evaporation values for the NMM-B (Nonhydrostatic
Multi-scale Model - Arakawa B Grid)
Assimilation of real-time soil moisture retrievals would improve the analysis and forecast of near-surface
and planetary boundary layer conditions, including the initiation of convection, especially during droughts
and floods.
Expected advances:
NESDIS (NOAA’s National Environmental Satellite, Data, and Information Service) uses surface (1- to 5cm deep) soil moisture retrievals from LEO microwave instruments to analyze surface soil moisture in the
Soil Moisture Operational Products System (SMOPS). The graphic below shows a SMOPS plot of the
fraction of the top 5-cm soil layer containing water. The patchiness results from complex land cover and
mountainous terrain, dense vegetation, and gaps in satellite coverage.
blended_soil_moisture_19aug2013.jpg 45914
Experiments assimilating these soil moisture retrievals in the GFS were run for April 2012, when
significant drought was developing in the U.S. Midwest. The resulting monthly mean soil moisture was
reduced over much of the U.S., correcting a wet bias in the GFS. Forecasts of other quantities, including
500-hPa height and rainfall, were also slightly improved (not shown).
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diff_sfc_soil_moisture.jpg 45915
Future work on satellite soil moisture retrievals will improve the weighting and area of influence of soil
moisture data and analysis increments so they can be included in the operational DA system. In addition,
microwave data from the Japanese GCOM-W (Global Change Observation Mission) polar-orbiting
satellite series will be added.
Satellite/instrument data sources:
The NESDIS operational soil moisture analysis is made by blending data from:
 U.S. Navy’s Windsat microwave radiometer on board the Coriolis polar orbiter
 EUMETSAT’s ASCAT (Advanced SCATterometer) on Metop-A, -B, and future -C polar orbiters
 MERIS (Microwave Imaging Radiometer using Aperture Synthesis) on board the European Space
Agency’s SMOS (Soil Moisture Ocean Salinity) mission launched in 2009
Plans are to incorporate soil moisture retrievals from the Japanese GCOM-W AMSR-2 microwave
instrument during 2014 to replace data from the AMSR-E instrument that failed in 2011. Among the
expected results are better near-surface temperature and moisture forecasts, improved simulation of the
planetary boundary layer, and better prediction of stability parameters in convective environments.
5.2.5: Surface conditions: Greenness Fraction
Challenge: While satellites retrieve the fraction of land with green vegetation, the information is not
assimilated in DA systems. NCEP NWP models use a monthly climatology from a five-year data set of
greenness fraction retrievals instead.
Live green vegetation transports soil moisture from below the surface into the atmosphere as water
vapor. This is an important local moisture source, and needs to be accounted for in NWP models. While
NCEP models account for vegetation greenness with a seasonally varying climatology based on satellite
observations, real-time observations can differ considerably from that climatology. We see an example
below. Click the tabs to compare the NOAA/NESDIS real-time vegetation greenness and GFS climatology
images. Both are for the same date and use approximately the same color scale. As you can see, the
observed greenness fraction is greater than climatological greenness for area A and lower for area B.
Both differences result from seasonal precipitation anomalies.
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Tab: Real-time Greenness
green_fraction_obs.jpg 45916
Tab: GFS Climatology Greenness
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green_fraction_gfs.jpg
45917
Question: Assume full sun, equal soil moisture, and otherwise identical atmospheric conditions in both
locations at the outset of an NWP forecast. How will actual 2-meter temperatures be impacted by the
vegetation greenness anomalies compared to the model’s temperature forecasts for areas A and B?
Select the best answer.
a.
Both regions will be warmer than the model forecast
b.
Area A will be cooler than the model forecast, while area B will be warmer **
c.
Area A will be warmer than the model forecast, while area B will be cooler
d.
Both areas will be cooler than the model forecast
Feedback: The correct answer is B. Area A has more green vegetation than prescribed in the GFS. The
vegetation will use more solar energy for evapotranspiration and less for sensible heating than in the
model forecast, resulting in cooler actual temperatures than in the model forecast. In area B, the opposite
is true. More solar energy is available for surface heating, resulting in warmer temperatures than in the
model forecast.
Expected advance: Using real-time greenness fraction is on the horizon in the NCEP NWP models. But
both this change and the soil moisture change discussed previously will require testing of and adjustment
to other land surface model parameters, such as soil heat and moisture conduction, vegetation
evapotranspiration, and others set to work best with the climatological greenness fraction. This takes a
great deal of time, staff, and computer resources.
Satellite/instrument data sources: Vegetation retrievals are presently available from the AVHRR
(Advanced Very High Resolution Radiometer) imagers on several polar-orbiting satellites. Higher
resolution greenness fraction is also available from MODIS (Moderate Resolution Imaging
Spectroradiometer) and from the VIIRS (Visible Infrared Imaging Radiometer Suite) imager on the current
Suomi-NPP and upcoming JPSS satellites. These retrievals will improve near-surface model forecasts of
temperature and dewpoint, planetary boundary layer height and stability, and convective indices like
Lifted Index and CAPE (convective available potential energy).
5.2.6: Forward Radiative Transfer Model
Challenge: Recall that when satellite observations are assimilated into DA systems, the model fields
must first be converted into simulated satellite data using a forward radiative transfer model. The forward
model would provide more accurate simulated data if it incorporated more radiatively active components
(trace gases and aerosols) that are already available from satellite retrievals but currently excluded from
DA systems.
Expected advance: As computing resources increase, more atmospheric constituents will be included in
the NWP and forward models. As a result, less bias correction will be needed to account for their
absence. This will, in turn, lead to better assimilation of satellite data in DA systems and a better DA
system analysis.
Satellite/instrument data sources: For aerosols, LIDAR instruments on CALIPSO and ADM-Aeolus will
provide aerosol measurements that can be used in the forward model. For trace gases, data will come
from the hyperspectral infrared channels on AIRS (Atmospheric Infrared Spectrometer) carried by NASA’s
Aqua satellite, IASI (Infrared Atmospheric Sounding Interferometer) on MetOp-A, -B, and future -C polar
orbiters, and CrIS (Cross-track Infrared Sounder) on the Suomi-NPP satellite and future JPSS polar
orbiter series.
5.2.7: Analysis of Small-Scale Features
Challenge: High-resolution NWP models have difficulty both assimilating small-scale features in their
analyses and correctly predicting their evolution. This results from inconsistencies between such features
and the initial analysis fields, which in turn can be caused by problems properly assimilating high
resolution data. For example, analyzed small-scale clouds, such as cumulus, require a consistent
35
analysis of water vapor, upward motion, and cloud water at these same scales, and in turn the NWP
model needs to be able to properly maintain these scales in its forecasts. An additional complication is
that poor forecasts of small-scale features adversely affect the analysis first guess.
Expected advance: Addressing this challenge will require improvements to both DA systems and NWP
models. High-resolution data from satellites (and other surface-based sources such as dual-polarization
radar) will need to be properly assimilated into the analysis. With better understanding of the small-scale
features in the data, scientists will create DA analyses and NWP models that better assimilate the smallscale motions that support small-scale features, such as gravity waves and convective updrafts and
downdrafts.
Satellite/instrument data sources: The following instruments will provide the small-scale observations
needed to support the forecast of small-scale features:
 Infrared imagers (for cloud tops)
 GOES-R’s Global Lightning Mapper
 High-resolution infrared and microwave sounders
 Other non-satellite sources
5.3.0 Satellite Data Challenges
5.3.1 Effect of Clouds and Precipitation on Remote Sensing
Challenge: NCEP cannot assimilate infrared soundings in cloudy regions where some of the most highimpact weather occurs because the soundings cannot be placed accurately in the vertical. That’s due to
our inability to accurately determine cloud top height, which is where the bottom of a retrieved sounding
would be.
Expected advance: Microwave data over the ocean is used to correct satellite pixels that are partially
covered by clouds, in a process known as “cloud clearing.” This enables the assimilation of IR
observations from pixels with cloud contamination. Ongoing experiments and better instrument data
continue to improve MW sounding radiances over the oceans, which increases the number of useful IR
and MW soundings.
Satellite/instrument data sources: The joint U.S. Japanese Global Precipitation Mission (GPM),
expected to launch in 2014, will carry both a dual-frequency precipitation radar and microwave imager for
measuring precipitation, storm system structure, and cloud and precipitation hydrometeors. Together with
the GCOM-W, Metop, and JPSS satellites, microwave instruments will improve the characterization of
clouds and their content. This will further extend satellite sounding coverage into areas affected by clouds
and precipitation.
5.3.2: Satellite Wind Data Gaps
Challenge: There is insufficient wind data from the current satellite suite to improve wind analyses in DA
systems.
Expected advance: Atmospheric motion vectors are currently retrieved by two methods. The first method
tracks cloud and water vapor features using visible and infrared imagery from LEO and GEO satellites.
While useful, these motion vectors are susceptible to significant vertical placement errors. The problem
can be mitigated with LIDAR, which is similar to radar but uses pulsing laser light rather than radio
frequencies. Data from the joint U.S./French CALIPSO research satellite LIDAR has been used for proof
of concept experiments.
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By 2015, the European Space Agency will launch ADM-Aeolus and its ALADIN Doppler wind LIDAR.
ADM-Aeolus is a research mission that will provide near-real-time retrievals of wind direction and speed in
the lowest 20 km of the atmosphere with global coverage. Assimilation of the data should be easier
because of experience with CALIPSO. Unfortunately, no follow-on missions are currently planned beyond
CALIPSO and ADM-Aeolus.
The other wind retrieval method relies on the detection of microwave radar signals reflected off a
roughened ocean surface. This allows for the indirect measurement of near-surface ocean winds (about
10m above the surface) under most conditions. The exceptions include moderate to heavy rainfall and
actual winds over 50 knots. India’s OceanSat-2 satellite and the European Metop satellites currently carry
instruments called scatterometers that measure near-surface ocean winds across the globe at least twice
daily. However, their limited swath width results in large gaps between orbits. Additional scatterometer
instruments are expected to be launched over the coming decade to help provide more frequent coverage
and reduce coverage gaps.
Satellite/instrument data sources: The same LIDAR instruments used to retrieve aerosol quantities on
CALIPSO and ADM-Aeolus will provide high-resolution wind measurements by retrieving the direction
and velocity of aerosol particle movement. For near-surface ocean winds, current OceanSat-2 and Metop
ASCAT scatterometer data will be augmented in the future with additional scatterometers that have wider
viewing swaths to reduce data gaps between orbits and provide enhanced coverage.
5.3.3: Microwave Sounder Deficiencies
Challenge: The vertical resolution of microwave sounders is coarser than that of infrared and
hyperspectral infrared sounders. This leads to the loss of potentially important details in temperature and
moisture structure.
Expected advance: To improve the vertical resolution of microwave sounders, satellites need additional
microwave channels. This will provide better sounding in and below clouds, and produce soundings that
provide improved information for NWP.
Satellite/instrument data sources: The NOAA/NASA MicroMAS (Microsized Microwave Atmospheric
Satellite) is a 4Kg 3U CubeSat hosting a passive microwave spectrometer that could provide initial highresolution microwave sounding data for testing in assimilation systems. The first launch is planned for
2014. MicroMAS will ultimately make up a constellation of CubeSats called the Distributed Observatory
for Monitoring of Earth (DOME). DOME would achieve superior spatial, spectral, and radiometric
resolution compared to current systems, and would provide the needed high-resolution microwave
sounding data for DA systems at a relatively low cost when compared to current conventional satellite
platforms.
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5.3.4: Lack of MW and Hyperspectral IR Sounders on GEOs
Challenge: GEOs do not currently have microwave or hyperspectral infrared sounders to continuously
monitor the atmosphere. A microwave capability in GEO orbit would offer significant benefits for observing
precipitation while hyperspectral sounders would provide critical wind and high resolution sounding
information in the lower atmosphere for forecasting convection.
Expected advance: Unfortunately, no sounders will be on the next generation GOES-R, a significant gap
for forecasters and DA systems. Data from the GOES-R Advanced Baseline Imager (ABI) will replace
current infrared sounder data to produce legacy products only. These products will have higher horizontal
and temporal resolution, and expanded geographical coverage when compared to the current GOES
sounders. This is expected to partially compensate for the reduction in sounding channels and coarse
vertical resolution.
A new generation of GEO hyperspectral infrared sounders will come online during the next decade as
EUMETSAT and the China Meteorological Administration move forward with plans for their next
generation GEO satellites.
For more information on GOES-R ABI, see the COMET lesson “GOES-R ABI: Next Generation Satellite
Imaging.” For more information on forecast problems that could be addressed with hyperspectral
sounders on GOES satellites, see the COMET lesson “Toward an Advanced Sounder on GOES?”
5.3.5: Loss of Satellite Coverage
Challenge: The loss of satellite coverage, particularly from LEOs, degrades DA analyses and NWP
forecasts. This is especially challenging in a period of constrained budgets
Expected advance: Instrument failure is always a risk with satellite platforms, and the risk increases as
an instrument ages. To mitigate the possibility of data gaps, we usually have additional satellites in orbit,
with instruments that can replace failing ones. Plus, overlap between current satellites and the launch of
new series is typically planned to minimize the risk of complete data loss.
While the current fleet of GEOs will be replaced starting in 2015, the next series of operational LEOs will
not start launching until 2017. This creates the potential for data loss from operational LEOs as current
satellites exceed their useful life around that time. There are no current plans to accelerate the
deployment of the next generation of LEOs.
6.0.0 Summary
6.1.0 Satellite Data, DA Systems, and NWP Model Forecasts
Data assimilation systems produce the starting point or analysis for an NWP forecast.To do this, they take
a short-range NWP forecast or first guess valid at the analysis time, and use observations within a time
window centered on the analysis time to adjust the first guess to the best analysis possible. This becomes
the starting place for the current forecast cycle.
The forecast part of the NWP model consists of two modules. One deals with dynamical equations, the
other with physical parameterizations. Dynamical equations forecast processes that are large enough to
be analyzed and forecast at the model's resolution, such as short wave troughs. Physical
parameterizations estimate the effect of processes that are smaller than the model can forecast directly,
such as convection and atmospheric transmission of long- and shortwave radiation.
The needs of DA systems and NWP help drive new instrument design. Observations from new
instruments are quality tested before and after launch. They are then tested in the DA system, and NWP
forecasts are run from these test analyses. If the quality of the forecasts improves or is maintained, the
new data are accepted for operational use. Otherwise, they will not be used.
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All satellite data are continuously monitored, even after they are accepted into operations. Any
degradation in data quality is corrected if possible. Otherwise, the data are removed from the DA
observational stream. Other satellites with the same or similar instruments may then be considered in
order to replace the lost data.
Satellite data are assimilated operationally in two forms, as observations and as retrievals. When retrieval
products are used, they are compared directly to the matching first guess data to determine an analysis
increment. When observations are used, the first guess data is converted into simulated satellite
observations and adjusted to bring it and the actual observations as close to each other as possible.
These adjustments are then used as analysis increments. From this point forward, observations and
retrievals are handled in the same manner: weighted, buddy checked, and weight adjusted if they're
significantly different from neighboring increments. In the last step, the increments from all observational
platforms are combined to make the final analysis.
Satellite data are essential for making good forecasts in high-impact situations, and for improving overall
model forecast skill. This is especially true in and downstream from data-sparse areas.
To improve satellite data and NWP and DA systems in the future, challenges need to be met in three
areas. NWP models and their DA systems need to make better use of existing satellite data, particularly
in the areas of clouds and precipitation, explicitly predicted convection, natural and human-made
aerosols, and real-time surface conditions such as soil moisture and vegetation greenness. Preparatory
work needs to continue so the satellite observations from GOES-R and the next generation of NOAA LEO
satellites will be used effectively.
In the area of satellite remote sensing, we need to increase the resolution of microwave sounders,
improve the usefulness of sounder data in areas of clouds and precipitation, increase the amount of
satellite wind data, and add hyperspectral infrared instruments to geostationary satellites. In addition, we
need to address the potential loss of U.S. LEO data if there's a gap between the decommissioning of any
current satellites and the launch of next generation satellites.
6.2.0 Main Points
These are the main points to take away regarding satellite data, DA systems and NWP:
 Satellite data is vital for DA systems to produce good analyses for NWP forecasts.
 Satellite data is tested extensively before being used in operational DA systems.
 Satellite data is monitored continuously to assure good quality, regardless of whether it’s used in
operational DA systems or not.
 Before satellite data is useable in DA, it has to match data in the NWP model, which depends on
the model’s dynamics and physics.
 In operational DA systems, satellite data may be either rejected or have a reduced influence in
the analysis.
 Satellite data is vital to the quality of NWP forecasts for individual events and over the long term.
 Satellite data provides important guidance to forecasters on using NWP in the forecast process.
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