TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
TRAINEE GUIDE
FOR
ADVANCED FORECASTER COURSE – SATELLITE MODULE
S-XXX-0618
UNIT3, TOPIC 3.4 – SATELLITE OBSERVATION INGEST INTO THE NUMERICAL
WEATHER PREDICTION DATA ASSIMILATION
PREPARED BY
THE COMET PROGRAM, UNIVERSITY CORPORATION FOR ATMOSPHERIC RESEARCH,
3085 CENTER GREEN DRIVE, BOULDER, CO 80301
PREPARED FOR
COMMANDER, NAVAL METEOROLOGY AND OCEANOGRAPHY COMMAND
1100 BALCH BLVD, STENNIS SPACE CENTER, MS 39529
Satellites – Section 3.4
PAGE
1
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
TABLE OF CONTENTS
CHANGE RECORD ....................................................................................................... ii
TABLE OF CONTENTS................................................................................................ iii
TERMINAL OBJECTIVES ........................................................................................ xiv
CHANGE RECORD
Description of Change
Entered By
Date
TABLE OF CONTENTS
1.
2.
3.
4.
5.
6.
Introduction
Data Assimilation Process
Impact of Satellite in Data Assimilation and Data Limitations
Summary of Data Assimilation and Satellite
Answers to Checkpoint Questions
List of Figures
Satellites – Section 3.4
PAGE
2
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
UNIT 3: ADVANCED REMOTE SENSING CAPABILITIES
Terminal Objective 3.0: Analyze satellite imagery for atmospheric inducements.
TOPIC 3.4 – SATELLITE OBSERVATION INGEST INTO THE NUMERICAL WEATHER
PREDICTION DATA ASSIMILATION
1 - INTRODUCTION
In this lesson, we will address the following Enabling Objectives:
EO 6.1: DESCRIBE the process of assimilating satellite observation data into the NWP Data
Assimilation system without reference with 75% accuracy.
EO 6.2: RECOGNIZE the impact of satellite information data on Numerical Weather
Prediction without reference with 75% accuracy.
We will cover the following topics:
 A short introduction to environmental models
o A definition
o Navy models for the atmosphere and ocean
 The data assimilation process
 Satellite data available for assimilation
 How satellite data impact the models
WHAT IS A MODEL?
In the context of this course, we are talking about a computer-based numerical prediction
system for analyzing and forecasting weather and ocean conditions. That is, mathematical
algorithms (i.e., formulas) are assembled, based on know environmental physical
relationships, and linked into a series of modular programs.
These modules manipulate incoming information and produce four-dimensional
predictions of how the atmosphere or ocean will change in space and time.
This, of course, requires massive computer power, which the Navy has assembled at
 The Fleet Numerical Meteorology and Oceanography Center (FNMOC) at Monterey,
CA for weather forecasting, and
 The Naval Oceanographic Office (NAVOCEANO) at Stennis Space Center, MS for
ocean forecasting.
Said another way; a model serves as a vehicle for:
 Conveying information contained in observations taken from previous times to the
present time and
Satellites – Section 3.4
PAGE
3
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15

Creating a 4-D structure that is dynamically consistent with its physics and
resolution.
Conceptually, a model is to present the best possible guess of weather or ocean conditions
from the present and into the future. Note that the model may or may not represent
reality—it is just one version based on its functional requirements and internal processes.
The Naval Research Laboratory (NRL) developed the Navy’s current modeling systems.
They are:
 The Navy General Environmental Model (NAVGEM) for weather
 The Hybrid Coordinate Ocean Model (HYCOM) for oceanography
 The Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) that
couples weather, oceanography, waves, etc.
Every 6 (24) hours, FNMOC (NAVOCEANO) initiates a new forecast series or model run for
global and regional atmospheric (oceanographic) domains. Information about these
models is presented in the next unit of the Advanced Forecaster Course. Here we will
present a quick summary of the initial step in the modeling process known as data
assimilation.
THE NAVY DATA ASSIMILATION SYSTEMS
FNMOC and NAVOCEANO use:
 The Navy Atmospheric Variational Data Assimilation System (NAVDAS) that is used
in NAVGEM and COAMPS
 The Navy Coupled Ocean Data Assimilation system (NCODA) that is used HYCOM
The intent here is not to describe the inner workings of NAVDAS or NCODA but to give a
brief overview of the process and show how satellite data are used in environmental
modeling.
There is an excellent but quite long and detailed on-line COMET course “Understanding
Data Assimilation “at:
https://www.meted.ucar.edu/training_module.php?id=704#.VL_XOMZ3Q5c
CHECK QUESTION 1: Define an environmental model.
CHECK QUESTION 2: List the Navy’s three modeling systems and indicate whether they
are atmospheric, oceanographic, or both.
CHECK QUESTION 3: List the Navy’s two data assimilation systems.
Satellites – Section 3.4
PAGE
4
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
2 EO 6.1: DESCRIBE the process of assimilating satellite observation data into the NWP Data
Assimilation system without reference with 75% accuracy.
2 – DATA ASSIMILATION PROCESS
http://www.meted.ucar.edu/nwp/model_dataassimilation/index.htm
https://www.meted.ucar.edu/training_module.php?id=704#.vnpkamy80vq
https://www.meted.ucar.edu/EUMETSAT/products/
https://www.meted.ucar.edu/satmet/sat_nwp/index.htm
Http://www.ecmwf.int/en/research/data-assimilation
Data assimilation (DA) is a cyclical process that extracts content from environmental
observations that are randomly scattered in place and time. The objective is to transfer the
collected information from its original position and time into the model’s start time and
grid (position), while preserving the interrelated physical, dynamical, and numerical
consistency required for the model to make a good forecast.
DA is designed to deliver an initial atmosphere or ocean that has been properly corrected
or moved toward reality, based on observed data. This means a model will be able to start
with the best possible initial conditions, and thus has the opportunity to produce the best
possible forecast.
Satellites – Section 3.4
PAGE
5
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
Fig. 0618-3.4-01. The data assimilation process illustrated.
THE STEPS OF DATA ASSIMILATION (SIMPLIFIED), following Fig. 0618-3.4-01:
Observations are collected, collated, and formatted for model ingest (panel 1B).
They are checked for gross errors.
Data from some sources will be more highly regarded than others, and weighted
accordingly.
Some data, particularly that collected by satellites may be far too dense for the model grid
and a winnowing process called “super-obbing” is used.
In the “first guess,” the new data are merged with a previous forecast field known as the
“background.” (panel 1A).
The merger of observations and the first-guess is accomplished by calculating observation
increments (panel 2).
Satellites – Section 3.4
PAGE
6
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
This is the differences between observations and the first guess at their locations. This is
known as working in “observation space.”
Quality Control (QC) checks are performed on the increments.
If the results exceed an expected error level, the process cycles back and a new first guess
field and set of increment are created.
An Objective Analysis (OA) procedure interpolates the increments back to a model grid to
produce a grid of model corrections (panel 3) in “model space.”
The corrections are added back to the first guess to produce a new analysis (panel 4) or
set of initial conditions for the next operational forecast.
The model starts with this initial condition and runs a forecast series.
From this run, a short-term forecast (6-24 hours) delivers a new background field (a
new panel 1A), which is again blended with the next set of observations and the cycle
starts all over again.
Fig 0618-3.4-02. Illustration of the correction of a model field by data assimilation.
The result is a “routine” correction of the model output back toward the “True State,” being
some sort of a reality represented by the observations and constrained by the model, as
illustrated by a one-dimensional version of a model in Figure 0618-3.4-02.
Satellites – Section 3.4
PAGE
7
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
CHECK QUESTION 4: The objective of data assimilation systems is to: (select all that apply)
a. Exactly match observations to the model grid from a sparse observational dataset.
b. Build an initial, physically- and dynamically-consistent, model atmosphere or ocean.
c. Spread observations onto a model grid from their initial positions and time.
d. Randomly spread observations onto a model grid using consistent statistical
spreading.
CHECK QUESTION 5: When working in observation space, the DA system is attempting to
calculate the _____, or differences between the observations and the first guess at the same
location.
CHECK QUESTION 6: During the _____ phase, when the results exceed an expected error
level, the DA process cycles back and creates a new first guess data set and increments.
Satellites – Section 3.4
PAGE
8
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
SATELLITE DATA ASSIMILATION (DA) FOR ATMOSPHERIC AND OCEANOGRAPHIC
MODELS
Figure 0618-3.4-03 illustrates some of the data sets from satellites, land stations, buoys,
and other sources that are assimilated by environmental models.
Fig 0618-3.4-03. Samples of satellite data that are assimilated by atmospheric and ocean
models.
Satellites – Section 3.4
PAGE
9
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
A quick look at this figure shows us that observations are randomly distributed in space,
with many regions of very sparse coverage and others over-observed.
It is obvious that data assimilation programs need to deal with unevenly spaced data.
Light coverage over large expanses of the World’s oceans has been a major reason for
national environmental satellite programs.
Figure 0618-3.4-04 shows how the accuracy of the ECMWF forecast is impacted by satellite
data from different systems.
 Most of these systems have been discussed in Unit3, Topic 3.
 From this, we can conclude that AMSU-A is a very important component, while
Ozone from satellites is not.
 However, the Ozone measurement may be extremely useful for other forecasts such
as atmospheric pollution.
Satellites – Section 3.4
PAGE
10
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
Fig 0618-3.4-04. Illustration of the corrections to a model due to the assimilation of data
from various satellite systems.
Some data types are only available at certain times of day or for specific geographic regions
or weather conditions (Fig 0618-3.4-05).
Fig 0618-3.4-05. Examples of some data type limitations.
This table also suggests that the assimilation process must be able to work with randomly
collected observations.
Satellites – Section 3.4
PAGE
11
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
Figure 0618-3.4-06 shows how polar orbiting satellites like NOAA-19 collect data along
their paths.
Fig 0618-3.4-06. A 24-hour sequence of temperature data collected by a POES.
Satellites – Section 3.4
PAGE
12
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
Figure 0618-3.4-07 shows that obstructions like clouds can either be used to infer winds or
obscure surface observations.
Fig 0618-3.4-07. A daily collection of (top) wind observations and radiance data from
geosynchronous satellites.
The DA process must interpolate these observations onto a regular grid at an initial time
for the model run.
Satellites – Section 3.4
PAGE
13
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
In Unit 1, Topic 1, we presented some graphics from the COMET GEOS-R to demonstrate
how that system’s data are being used to observe or derive a variety of atmospheric
properties and components. Of the 16 channels that GOES-R observes, the ones used for
the products presented in Fig 0618-3.4-08 are shown in GREEN.
Satellites – Section 3.4
PAGE
14
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
Satellites – Section 3.4
PAGE
15
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
Satellites – Section 3.4
PAGE
16
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
Fig 0618-3.4-08. Applications of data from the GOES-R Advanced Baseline Imager (ABI): (a)
air quality, (b) climate, (c) cloud properties, (d) convection, (e) wildfires, (f) precipitation,
(g) surface properties, (h) storms, (i) atmospheric temperature and moisture, and (j)
winds.
https://www.meted.ucar.edu/goes_r/abi/media/graphics/application_channels_air
qual.jpg
CHECK QUESTION 7: True or False: Satellite data are evenly distributed in space and time.
CHECK QUESTION 8: True or False: Fair weather cumulus clouds are useful for tracking
troposphere winds
Satellites – Section 3.4
PAGE
17
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
3 EO 6.2: RECOGNIZE the impact of satellite information data on Numerical Weather
Prediction without reference with 75% accuracy.
3 – IMPACT OF SATELLITE IN DATA ASSIMILATION AND DATA LIMITATIONS
Satellite temperature and moisture observations (layer averages) limitations:
 Only uses mass data
o So associated winds must be inferred.
 Any radiation that is sensed comes from a deep layer of the atmosphere
o So vertical resolution is coarser than model vertical resolution.
 Microwave radiances or soundings only resolve a few layers in the entire
troposphere.
o Failure to properly vertically distribute this deep layer average information
can degrade the model forecast.
 Accurate assimilation of satellite radiances requires knowing the emissivity at the
bottom of the layer being sensed.
o This presents problems over land, so data over land are only reliable for
channels sensing the upper troposphere and stratosphere until better
surface emissivity models can be developed.
Satellite wind observation limitations:
 Only wind vector data are available
o So associated mass changes must be inferred.
 Supplies single-level data at any given location
o So vertical structure is not available.
 Assignment of height for the observation may not be highly accurate.
o In strong shear, a perfect wind speed and direction assigned to the wrong
level can produce a very large error.
Cloud and precipitation data from satellite and radar limitations:
 Many difficult inferences are needed to complete the cloud and precipitation
picture. For example:
 Model humidity fields must be adjusted, but how much and at what levels?
 How much cloud water should be added and how should it be distributed?
 If the model contains several hydrometeor types, how should the cloud water be
divided?
 Vertical motions need to be adjusted to be consistent with diabatic forcing, and thus
need to create a complete ageostrophic circulation, or possibly eliminate one if
clouds/precipitation need to be removed.
Cloud and precipitation data advantages:
 Can be used to identify where to remove model clouds and precipitation when
improperly forecast and where to add them when they are missed by the forecast.
This can be very valuable to the forecast.
 Observed precipitation can be used to improve model soil moisture.
Satellites – Section 3.4
PAGE
18
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
OPERATIONAL IMPACTS OF SATELLITE OBSERVATIONS
DA uses observations to make repeated small corrections to a series of short-range
forecasts.
The forecast models are assumed to be valid, producing a short-range forecast that needs
only minor adjustments.
If the short-range forecast or background field is incorrect, the next analysis and forecast
would be degraded.
Observations are necessary to bring the background closer to reality.
Similarly, erroneous observations can ruin a forecast.
Error checking and weighting of observations according to their know accuracies are
important components of the DA systems.
The DA is designed to make the best possible model forecast.
This means it may not fit all observations perfectly.
If you ask, why doesn’t the analyses look like my observation?, consider:
 Time marches on—your observation is always out of date
 The observation is blended into a field of others along with an earlier forecast—DA
makes smoothing and spatial agreement necessary
 Your observation may be inaccurate or wrong—weighting and error checking are
part of the process.
 Unlike a hand analysis, observations will only be used to correct scales that the
model can resolve.
The analysis process has many built-in assumptions that work well under common
conditions and fail under unusual conditions or extreme weather events.
 You should be especially alert for rapid development situations in fast flow!
 The DA analysis is most likely to have trouble in highly volatile weather situations.
CHECK QUESTION 9: True or False: Height assignment for satellite-derived winds is
relatively accurate and decreases model errors greatly.
CHECK QUESTION 10: List one advantage and one limitation of satellite imagery to the
data assimilation system.
Satellites – Section 3.4
PAGE
19
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
4 – SUMMARY OF DATA ASSIMILATION AND SATELLITE
The modeling process begins with an initial analysis. The DA system compares a previous
model run’s background fields with information from observations.
These observations are collected at different times and places. They may also involve
different variables than those being modeled. The observed data:
 Are from the many sources
 Have different coverage than the model
 Will ALL be collected at times prior to the model analysis, and
 Carry different error characteristics.
Point observations, such as radiosonde dew points at each level, are treated differently
than layer-averaged data, such as satellite precipitable water measurements.
The initial analysis must preserve dynamical, physical, and numerical consistencies in the
short-range forecast that are being corrected. It uses the observations but draws to them
only as closely as their typical errors allow.
Without enough data, badly predicted or missing features in the forecast are retained or
continue to be absent.
 With bad observations, a model will likely stray from reality.
 Bulls eyes and radical changes from the previous forecast are indications that a bad
observation has crept through the system.
DA will link corrections in one field (like temperature) with corrections in other
parameters such as winds, based on physics. In general, this is impossible for a human
analyst.
The 3D concept implies corrections at one location are assumed to require corrections at
adjacent locations, both horizontally and vertically.
Once DA prepares an acceptable initial analysis field, the model forecast begins to
“manipulate” this data.
We have covered the following topics:
 An introduction to environmental models
 A summary of the data assimilation process
 Satellite data that are assimilated by environmental models
 The impact of satellite data on the models.
Satellites – Section 3.4
PAGE
20
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
5 - CHECK QUESTIONS AND ANSWERS
CHECK QUESTION 1: Define an environmental model:
ANSWER: A computer-based numerical prediction system for analyzing and
forecasting weather and ocean conditions.
CHECK QUESTION 2: List the Navy’s three modeling systems and indicate whether they
are atmospheric, oceanographic, or both.
ANSWER: NAVGEM (atmosphere), HYCOM (oceanographic), COAMPS (coupled
atmosphere & ocean)
CHECK QUESTION 3: List the Navy’s two data assimilation systems.
ANSWER: NAVDAS and NCODA.
CHECK QUESTION 4: The objective of data assimilation systems is to: (select all that apply)
a. Exactly match observations to the model grid from a sparse observational dataset.
b. Build an initial, physically- and dynamically-consistent, model atmosphere or ocean.
c. Spread observations onto a model grid from their initial positions and time.
d. Randomly spread observations onto a model grid using consistent statistical
spreading.
ANSWER: b and c.
CHECK QUESTION 5: When working in observation space, the DA system is attempting to
calculate the _____, or differences between the observations and the first guess at the same
location.
ANSWER: increments
CHECK QUESTION 6: During the _____ phase, when the results exceed an expected error
level, the DA process cycles back and creates a new first guess data set and increments.
ANSWER: quality control
CHECK QUESTION 7: True or False: Satellite data are evenly distributed in space and time.
ANSWER: false
CHECK QUESTION 8: True or False: Fair weather cumulus clouds are useful for tracking
troposphere winds
ANSWER: true
CHECK QUESTION 9: True or False: Height assignment for satellite-derived winds is
relatively accurate and decreases model errors greatly.
ANSWER: false
CHECK QUESTION 10: List one advantage and one limitation of satellite imagery to the
data assimilation system.
Satellites – Section 3.4
PAGE
21
TRAINEE GUIDE
CIN-S-420-0618
Advanced Forecasters – Satellites, Unit 3, Topic 4 – Data Assimilation
28APR15
ANSWERS: Example advantage: Can be used to correct where there should and
shouldn’t be clouds.
Example limitation: With limited vertical understanding from satellite imagery, it
can be hard to define where moisture should be added and how much, especially
when it comes to mixed phase clouds
6 - LIST OF FIGURES
Fig. 0618-3.4-01. The data assimilation process illustrated.
Fig 0618-3.4-02. Illustration of the correction of a model field by data assimilation.
Fig 0618-3.4-03. Samples of satellite data fields that are assimilated by atmospheric and
ocean models.
Fig 0618-3.4-04. Illustration of the corrections to a model due to the assimilation of data
from various satellite systems.
Fig 0618-3.4-05. Examples of some data type limitations.
Fig 0618-3.4-06. A 24-hour sequence of temperature data collected by a POES.
Fig 0618-3.4-07. A daily collection of (top) wind observations and radiance data from
geosynchronous satellites.
Fig 0618-3.4-08. Applications of data from the GOES-R Advanced Baseline Imager (ABI): (a)
air quality, (b) climate, (c) cloud properties, (d) convection, (e) wildfires, (f) precipitation,
(g) surface properties, (h) storms, (i) atmospheric temperature and moisture, and (j)
winds.
Satellites – Section 3.4
PAGE
22