C+V Satellite Applications

Naval Research Laboratory Marine Meteorology Division

Monterey, California

Ceiling and Visibility Science Meeting19 April 2001

Rich Bankert

Satellite Meteorological Applications Section

GOES CLOUD CLASSIFICATION

Objective: Automate the cloud analysis of GOES imagery (day ornight; land or sea) in terms of traditional cloud types, clear, snow, etc.

Approach: Supervised learning methodology

* Expertly-labeled image samples (training data)

* Characteristic features computed/extracted from the five GOES channels with a subset of features chosen as a result of the application of a feature selection algorithm

* 1-Nearest Neighbor algorithm employed as the classifier using the training data represented by the selected features

* Web-based evaluation of real-time imagery by experts

GOES Cloud Classifier Development

* Supervised Learning* Expertly labeled 16km x 16km samples from GOES-WEST and GOES-EAST (2/99 – 8/00)

* Samples independently classified bythree experts

* Those samples with all three expert agreement are used in the training data

* Four classifiers Sea Day: 5937 samples, 14 classes Sea Night: 3875 samples, 12 classes Land Day: 5313 samples, 15 classes Land Night: 4109 samples, 15 classes

Red boxes indicate classified sample area

GOES Cloud Classifier Development

Additional Details

* Visible channel is 1 km resolution * Sample is 16 pixel x 16 pixel

* Shortwave and longwave Infrared channels are 4 km resolution * Sample is 4 pixel x 4 pixel

* Visible channel data is solar zenithangle corrected (albedo / cos(sza))

* Daytime classifiers are used whenmajority of pixels have sza less than75 degrees Red boxes indicate classified sample area

Feature Extraction and Selection

• Image feature characteristics extracted/computed from all five channels• Over 100 features for daytime; 45 for night• Feature vector intelligently chosen (feature selection algorithm) for each classifier• Feature vector used to represent each training sample (and real-time samples) in the classifier

Example FeaturesVisible channel textures (contrast, smoothness, disorder, etc)Simple statistics for each of the five channels: max, min, mean, std devStatistics for channel difference images Latitude, Satellite, DateClimatological SST

Sea Day Selected Features

Satellite (GOES East or GOES West)LatitudeDateChannel 1 Median and SDChannel 2 Max and MeanChannel 3 MaxChannel 4 Min, Range, and MeanChannel 5 Median(Channel 4 – Channel 5) MeanTwo Channel 1 TexturesClimatological SST

Land Day Selected Features

Satellite (GOES East or GOES West)LatitudeDateChannel 2 Min and MedianChannel 3 MedianChannel 4 Max, Range, and MedianChannel 5 Min, MedianFour Channel 1 Textures

Selected Features

GOES Cloud Classifier Algorithm

1-nearest neighbor algorithm

Training sample (class known)

Testing sample (class unknown)

Feature SpaceA Similarity Distance is computed between a testing sample and EACH training sample.

Σ (testing featurei – training featurei)2

Testing sample is classified with the class of the training sample with the minimum similarity distance.

Cu

? - Cu

GOES Cloud Classifier

Real Time Procedure

Individual pixel is classified four times (except on domain edges)

GOES Cloud ClassifierReal Time Procedure

* Individual pixel is classified four times with pixel class determined by “voting” – ties broken by random selection

* Along coast lines – classifier choice determined bypixel majority

Post processing* General class groupings (Low, Mid, High, Vertical, etc)

* Check individual pixel for visible albedo threshold If < 12% then pixel re-classified as clear

Current Real Time Classification Algorithm

GVAR stream received at NRLCalibrated/Navigated Data

Terascan Data Format (TDF) Files

C-shell scripts launched • Set classification and terascan environments• Define directory paths• Log file created• Find latest TDF file • Define image sub-area• Calculate lat, lon, sza (terascan functions)• Create land/sea table (terascan)• Call 1-nearest neighbor executable

Classification Executable• C and Fortran• Extract TDF channel data (terascan)• For each sample in an image sub-area: Determine appropriate classifier Compute features 1-nearest neighbor algorithm determines class type for each pixel in sample• Determine “final” class for each pixel in image sub-area• Pixel albedo check (clear)• General class groupings output• Create TDF output file (terascan)

C-shell scripts • Call terascan functions to create channel and classification jpgs for web display

GOES Cloud Classifier Display

Visible Channel Land/Sea Day Classifier Output

15-class output

GOES Cloud Classifier Display

8-class groupings

IR Channel General Class Output

GOES Cloud Classifier Example

Visible Channel Land/Sea Day Classifier Output

GOES Cloud Classifier Example

Visible Channel Land/Sea Day Classifier

GOES Cloud Classifier Limitations

* Mixed cloud samples • Thin cirrus over low clouds • Cloud edges (e.g, Cs shield)

* “Never enough training data” • Low clouds in arctic air over Eastern Canada

* Snow/Low cloud misclassification

* Nighttime classifications may only be useful in general classes

GOES Cloud Classifier

Subjective Evaluation

* Web display created for expert evaluation in real time * Daytime classifications are well done EXCEPT * Thin Ci over low clouds – mid cloud classification * Snow overdone (actual low clouds misclassified) * Mid/High clouds overdone in cold air

* Qualitative assessment of additional limitations, robustness, usefulness, problem areas that may be correctable, etc.

* Evaluation performed and documented throughout the year (all seasons)

Low Cloud Detection at Night using GOES Imager

Nighttime IR: Can’t see lowbecause lack of contrast with background

Nighttime Low Clouds: Bysubtracting the shortwave/longwave channels, low cloudsappear in white.

Low Cloud Product using GOES Imager

ADVANTAGES

Ability to view low clouds around the clock

Useful for unobserved areas like oceans where surface reports of low clouds, fog or poor surface visibilities are rare.

LIMITATIONS

Can not distinguish between low stratus clouds and actual fog

The nighttime images are only useful for stratus, fog, or stratocumulus They will not perform well over cumulus clouds

GOES visible is 1km (day), but IR channels are 4km (night)

Methodology Behind Blended Microwave and Geo-Infrared Data for an Operationally-Based Precipitation Analysis

• Principle: Microwave-based (SSM/I, TRMM, AMSU, etc) rainestimates are physically-based but infrequent; infrared-based (IR)estimates are indirect but updated at least hourly and provide hightemporal, multi-spectral, and spatial information

• Real-Time Adjustment: Utilize near realtime microwave-baseddata to dynamically adjust the IR over global space-time domains

•Accommodate operational applications: Nearest-time microwavedata may be several hours old, whereas clouds have been evolvingsince then. Utilize MW data for rain microphysics, IR data for space-time cloud evolution, NWP model data for dynamics

6-hour number-of-points from merged microwave sensors (F-11,13,14,15; TRMM)

6-hour number-of-points from geostationary-based technique 2000/04/27 1200 UTC

GOES-West GOES-East Meteosat Meteosat-5 GMS

GOES IR and SSMI Inputs for Rain Rate Product

Rain Rate Product

Visibility plays an important role in many Naval operations.

Strong winds blowing across the expansive deserts of Aftrica and the Middle-East lift giant dust plumes airborne.

These plumes can extend for thousands of miles beyond their places of origin.

Heavy dust storms lead to poor surface visibility and the potential for causing serious damage to jet aircraft engines.

Observing Dust Storms From Space

SeaWiFS Image: February 13, 2001 at 1255 Universal Time

Poster POC: Steven D. Miller (miller@nrlmry.navy.mil)

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument (Fig. A) has 8 channels in and around the visible part of the spectrum.

It is carried aboard the polar-orbiting SeaStar satellite (Fig. B) at an altitude of 705 km, with a spatial resolution of 1-km.

A

B

C

The SeaWiFS image in Fig. C captures a dust storm (yellow plume) from the Western Sahara desert in Africa as it streams Northward around the Canary Islands toward Portugal, where it wraps into the large-scale flow of a low pressure system (comma shape).

The strong contrast of the dust against the blue ocean provides a clear indicator for operational use.

Canary Islands.

AFRICA

SPAIN

Saharan

DustAirborne dust produces a different spectral contrast against the ocean than water or ice clouds.

Combining information from the SeaWiFS channels enables us to generate products that enhance this contrast in the satellite image.

The Science

The Satellite and Sensor

The Problems with Dust

An Example

Naval Research Laboratory Satellite Applications Section

Shown above is an color-enhanced infrared image covering part of the Tropical Western Pacific ocean. Cold cloud tops appear white, and warmer clouds are increasing shades of gray.

The ocean background is depicted here as blue (but would appear black in a normal infrared image since it is very warm compared to the high clouds in the scene).

The corresponding cloud heights (expressed in thousands of feet) are shown in the figure at right. For reference, typical standard cruising altitudes for commercial airliners are between 30,000 and 40,000 ft. The highest cloud top in the image is near 54,000 ft!

Knowing the whereabouts of these deep towers of convection is key to aviation safety, especially at night when the view ahead is limited by airborne radar.

Satellite-Derived Convective Cloud Top Heights

10,000ft 10,000ft 70,000ft 70,000ft Derived Cloud Top Heights

GOES-10/GMS-5 Infrared Image

Poster POC: Steven D. Miller (miller@nrlmry.navy.mil)

Knowledge of thunderstorm (convective) cloud top altitudes is important to commercial and DoD aircraft in order for pilots to avoid hazards such as heavy turbulence or lightning.

The NRL Convective Cloud Top Height product uses the cloud top temperature from a satellite measurement and translates this to an equivalent height in the atmosphere.

The method combines infrared (10.7 m) imagery from various Geostationary satellites and profiles of temperature with height from the Navy Operational Global Atmospheric Prediction System (NOGAPS) to estimate these heights.

Geostationary data are used to provide a time series of clouds as they deepen/dissipate.

Motivation and Method

An Example

Naval Research Laboratory Satellite Applications Section

Cloud Top Height Strengths/Weaknesses

StrengthsOver ocean (warm, stable background).Optically Thick Clouds.Tropical Profiles (steady lapse rates, few inversions, well-defined tropopause).

WeaknessesOver land (highly variable temperature background diurnally).Optically thin clouds (leading to underestimation of true cloud height).Profiles with strong mid-level inversions of temperature.Low clouds.

The figure above illustrates one of the benefits of using NOGAPS model profiles instead of the standard atmosphere when mapping satellite temperature to equivalent height. The model profile (blue) matches closely with the rawinsonde (green).

NRL Satellite Products Web Site

http://kauai.nrlmry.navy.mil/sat_products.html

* GOES cloud classification not yet available