NASA-CR-20_072

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iXINVESTIGATION OF CLOUD PROPERTIES AND ATMOSPHERIC PROFILES

WITH MODIS

SEMI-ANNUAL REPORT FOR JAN-JUN 1997

Paul Menzel, Steve Ackerman, Chris Moeller, Liam Gumley, Kathy Strabala,

Richard Frey, Elaine Prins, Dan LaPorte and Walter Wolf

CIMSS at the University of Wisconsin

Contract NASS-31367

ABSTRACT

The WINter Cloud Experiment (WINCE) was directed and supported by personnel from

the University of Wisconsin in January and February. Data sets of good quality were

collected by the MODIS Airborne Simulator (MAS) and other instruments on the NASA

ER2; they will be used to develop and validate cloud detection and cloud property

retrievals over winter scenes (especially over snow). Software development focused on

utilities needed for all of the LVq¢ product executables; preparations for Version 2 software

deliveries were almost completed. A significant effort was made, in cooperation with

SBRS and MCST, in characterizing and understanding MODIS PFM thermal infrared

performance; crosstalk in the longwave infrared channels continues to get considerable

attention.

TASK OBJECTIVES

Software Development

The five UW science production software packages (cloud mask, cloud top properties,

cloud phase, atmospheric profiles, and ancillary data) continue to evolve. Science

algorithms are being tested with MAS data from both the SUCCESS and WINCE

experiments. Visualization tools to view data and products efficiently continue to be

developed. Common utilities needed for processing in the DAAC environment are being

finalized for use with all science production software packages.

MODIS Infrared Calibration

UW is working closely with SBRS and MCST in evaluating the MODIS thermal infrared

calibration data from the thermal vacuum test. Paul Menzel, Dan LaPorte and Chris

Moeller participated in the MODIS Thermal Infrared Calibration Team Meeting in January

at the University of Miami. UW has been analyzing SBRS characterization of the infrared

spectral response functions and longwave infrared crosstalk. Dan LaPorte worked with

the MODIS engineering team during the thermal vacuum calibration of the PFM and is

coordinating data set transfers to MCST and UW.
WORK ACCOMPLISHED

MODIS Infrared Calibration

The MOD1S Thermal Infrared Calibration Team met at the University of Miami on 9-10

January 1997. MODIS instrument performance, delivery and testing schedules were

discussed. Inspection of characterization data of the MODIS system relative spectral

response (RSR) functions for the longwave infrared (LWIR) revealed a 5 nm shift to

longer wavelengths, based on spectral position comparisons between the RSR and forward

model data. Chris Moeller presented simulations with MAS data showing that the

expected crosstalk has significant effect on brightness temperatures that must be

corrected. It appears likely that ground corrections to radiances will be necessary for the

MODIS on the am platform, but it is hoped that an instrument fix can be arranged for the

MODIS on the PM platform. It was stressed that corrections not possible for the PFM

should be considered for the FM 1, schedule and cost permitting. Paul Menzel presented

results showing successful application of the MODIS calibration algorithm to GOES data;

the MODIS correctly characterizes the detector voltage as a quadratic function of the

incoming radiance (GOES has radiance as a quadratic of detector voltage). The team

concurred that considerable progress had been made in the last three months toward

characterizing MODIS PFM infrared performance, understanding the best approaches

toward ground processing of radiances, and suggesting instrument changes to ameliorate

problems on FM1. However some infrared bands remain problematic and more needs to

be done. Several action items were assigned to UW: (1) UW will work with SBRS and

MCST to determine the best RSR for the PC infrared spectral bands (31 through 36 are

likely to have a 5 nm shift). Band 34 RSR seems out of family and is still puzzling. (2)

UW will generate tables using FASCODE to correct for water vapor absorption in the

LWlR and MWIR for various configurations of the vacuum test. These have since been

delivered to SBRS for on site assistance in interpreting PV RSR test results. Possible

spectral shifts in bands with sufficient absorption features (24, 25, 26, 27, 28, and 30) will

be estimated by comparing calculated and measured spectra. (3) UW will study the

current gain settings for the infrared bands to assure that truncation error is less than

NEDR. (4) UW will suggest to NOAA that in order to minimize complications caused by

the scan mirror emissivity, a subset of the data near the onboard blackbody angle should

be considered. UW has suggested that NOAA consider data at -45 to - 5 degrees from

nadir.

As guidance for assessing SpMA spectral position accuracy of all PC band RSR ambient

data (collected in Dec. 1996), Chris Moeller assessed the spectral position accuracy for all

channels of band 35. Because of the shape of the SpMA image on the MODIS focal

plane, it is expected that spectral position accuracy of the RSR data is a function of

channel number (i.e. position on the focal plane). The analysis matches the spectral

position and shape of the 13.88 micron Q branch CO2 absorption feature in the measured

RSR data with that from LBLRTM (Line-By-Line-Radiative-Transfer-Model) forward

model transmittances. Ambient laboratory conditions (temperature, humidity, CO2

concentration, etc.) were used as input to LBLRTM. The LBLRTM transmittances were
convoluted to the SpMA bandpass characteristics at a series of spectral positions given by

SpMA reported spectral position plus a small spectral correction (on the order of

nanometers). An example of this analysis is given in Figure 1. The assessment for all

channels shows that spectral correction is a systematic function of channel number, with

the exception of channel 10 which appears "out of family" (Figure 2). A separate analysis

of central wavelength as a function of channel number (performed at GSFC) indicates a

similar dependence although actual values do not agree. Further data analysis and

discussion are required to resolve discrepancies.

Data for MODIS PV band RSR have not yielded definitive spectral correction results for

those bands/channels. A special fine-spectral increment data set was collected after

allowing ambient air to leak back into the beam path within the purged tent environment

fronting the MODIS Calibration Chamber. The approach was to measure RSR across

several spectral features in the presence of sufficient atmospheric attenuation to define

those spectral features. By comparing to forward model spectral features (calculated

using LBLRTM w/HITRAN96 database), a spectral correction for the PV band MODIS

RSR data set can be estimated. RSR data was collected from 6750 to 6901 nm at 1 nm

spacing (13 nm FWHM), covering water vapor absorption features at about 6780 and

6860 nm. Unfortunately, the special data set did not show sufficient features to make a

useful spectral correction assessment. Apparently, additional atmospheric water vapor

attenuation in the beam path was needed to manifest spectral features in the special data

set. Another source of information for assessing spectral correction of MODIS PV band

RSR is the coarse-spectral increment RSR data collected in ambient conditions. A

forward model spectral correction will be attempted using that data; however, the coarse

increment data set wants for better spectral resolution to define the small spectral

adjustments (nanometers) expected. The coarse-spectral increment data can also be used

in a central wavelength assessment of spectral correction. This approach requires an

assumption of spectral uniformity from channel to channel within band (a reasonable

assumption for channels 2-9 but not likely for channels 1 and 10). In all likelihood, some

combination of these two approaches will be used to specify final MODIS PV band RSR

spectral correction. To the extent useful, component data will also be inspected to aid the

spectral correction analysis.

Dan LaPorte has been working with the engineering team to understand the nature of the

PC band crosstalk (band 31 into bands 32 through 36) for PFM. Initial estimates from

measurements in ambient conditions (UAID ! 194x) indicate that the peak values of

crosstalk (primarily east west) when viewing clear sky typical conditions are roughly 10%

of band 31 leaks into bands 35 and 36, about 4% into band 34, and about 1% into bands

32 and 33. In partly cloudy conditions when band 31 views the surface and band 36 views

a cloud, the crosstalk can increase the band 36 radiance output by as much as 50%. Single

field of view applications of these channels will be challenged, but it's very likely that 5 x 5

km applications will be useful after correction for crosstalk. However, much remains to

be done. Efforts by SBRS, MCST, and UW continue to assess crosstalk issues; a thermal

calibration (RC02) data set collected in thermal vacuum is under evaluation for additional

understanding and quantification of PC band crosstalk.
Thebenefitof the spaceroll to MODIS productswasinvestigatedby UW. Thebenefitof

thecorrectionfor scanmirror effectson GOES-8/9weredocumented;calibrationefforts

by NOAA/NESDIS(Mike Weinreb)succeededinreducingtheeastwestbiasin radiances

measuredwhenviewingspaceandtheimprovementin GOEStotal watervaporretrieval

wasnoticeable(thefirst guessrmserrorwasreducedby 0.2mmor 5%without correction

and0.8mmwith correctionor 20%). ChrisMoellerattendedaNASA discussionof the

spaceroll for thePM platform;heshowedtheimpactof a scanmirror uncertaintyof .02

onMODIS infraredwindowbrightnesstemperaturesto begreaterthan0.5 C. The

MODIS cloudmaskalsoproducedsignificantlylessconfidentresults. With a spaceroll

that characterizestheMODIS IR calibrationto within 1%,theseuncertaintiesinMODIS

derivedproductsarelargelymitigated.It wasdecidedto roll the PM platformto space

duringcheckout.

MODIS Validation Activities

UW submitted a flight request to the NASA Airborne Science and Applications Program

for FY98. Seven ER-2 missions (30 hours) were requested. Mission objectives include

(1) evaluation of the new scanning HIS and NPOES Atmospheric Sounder Testbed

(NAST) instruments which will fly with MAS for validating MODIS products, (2) NOAAK underflights, and (3) overflights of the DOE ARM site in Oklahoma in conjunction with

MODIS overpasses late in FY98 for first assessment of MODIS performance. These

flights will support MODIS cloud properties and atmospheric profiles and Level 1B

radiance validation. The MODIS validation plan has been updated to reflect changes in

near and long term validation activities, primarily related to ER-2 deployment and

underflight of MODIS.

MODIS Software Development

Software development efforts lead by Kathy Strabala have focused on common utilities

accessed by all UW MODIS science production software packages. Common routines to

automatically create the UW MODIS output products during production, read and extract

common metadata, open and extract L1B radiances and geolocation information, and

write error and status messages to the correct log files have all been written and tested

using SDST approved toolkits and libraries. Care has also been taken in adding diagnostic

statements to all product subroutines which can be output to a runtime ascii file by setting

a flag in the main program. As this common utility effort nears completion, we await final

input versions to finish code development and testing.

Version 2 MODIS code has been modified so that the make files are relatively consistent

for the different products. A Revision Control System (RCS) has been set up for all of the

MODIS product software. The RCS directories contain an archive of all of the changes

that will be made for the V2 MODIS product code. In the process, RCS ID's have been

included in the V2 MODIS product code such that each executable contains information

of the versions of all the subroutines used to create that executable.
A developmentenvironmentfor theUW Version2 deliverieswassetup ontheUW SGI.

V1 algorithmcodeswereretrievedfrom CM at GSFCandrecompiled.All sourcetext

fileswerecheckedinto RCS.ThelatestsimulatedMODIS datawasobtainedfrom GSFC

andinstalledonmodsgi(Figure3).

Codeto createtheoutputHDFfiles for theMODIS productshasbeenwritten. Thisisa

multi-stepprocess.In thefirst step,a C programreadsintheMODIS file specsin CDL

format. TheC codetakesthefile specsandcreatestwo FORTRANsubroutineswhich

arecalledbythemainprogramto assistin creatingtheoutputHDF files. Thisensures

that theoutputfile structurewill identicallymatchthefile specifications.Genericreadand

write routineshavealsobeencreatedwhichmovedatain andout of theHDF outputfiles.

Testingof theeffectsof theHDF compressionroutinesonthesizeof theoutput cloud

maskfile is underway

Progresshasalsobeenrealizedin aMODIS dataset. UsingIDL routines,WalterWolf

hasselectedMAS datathat matchestheMODISchannels,resampledtheMAS datato the

MODIS resolution(usingLiamGumley'sIDL routine),andthencreatedfiat files

containingMODIS simulateddata. For watervaporchannels(missingonMAS), a second

IDL routinereadsHIS water vaporchanneldata,matchesthelatitudeandlongitudeto the

MAS latitudeandlongitude,andcreatessimulatedMODIS datafor theassociated

channels.Thisroutinealsocreatesfiat filesof latitude,longitude,solarazimuth,solar

zenith,sensorazimuth,andsensorzenithdata. An asciitimerecordfile is alsocreated.

Considerableeffort wasexpendedin providingMAS quicklookcapabilityfor theWINCE

experiment.Softwareoriginallydevelopedfor SUCCESSwasmodified,updated,and

documentedto runontheNASA AmesSunworkstation. Thissoftwarewassuccessfully

installedandtestedonthe Sun,andwasusedduringWINCE by UW andNASA

personnelto generateMAS quicklookimagerywithin 24hoursof ER-2landing. NASA

Ameswill now usethissoftwareroutinelyto produceMAS quicklookimageryin thefield.

Visualization Software

Two MAS visualization tools were released via the Web to GSFC and ARC.

(a) MASVIEW: http://cimss.ssec.wisc.edu/-gumley/masview/masview.html

MASVIEW displays single bands of full resolution MAS imagery read from any of the

following formats (which are identified automatically):

- Exabyte raw format as recorded onboard the aircraft (57344 bytes per block),

- University of Wisconsin Intermediate format created by readtape5.f(74200 bytes per

block),

- NASA/GSFC I-tDF Level IB format.

Some of the options available in MASVIEW include:

- Display data as counts, radiance (HDF only for bands 1-25), or brightness temperature,

- B&W or Color Postscript output (user can customize size and orientation),
- Caneasilyflip betweenanynumberof images(movies!),

- IR bandscanhaveareversedgreyscalecolortablesocloudsarebrightandlandis dark,

- If HIS datais available,plot HIS FOV locationsonMAS image,

- SaveimageasGIF,

- Displaysubsets(e.g.scan1-1500)of largeHDF scenes,

- Skipeveryother scanlineto compressimagesvertically.

(b) DISPLAYQUICK: http://cimss.ssec.wisc.edu/-gumley/displayquick/displayquick.html

DISPLAYQUICK displaysMAS subsampledquicklookdatain a scrollingimagewindow,

allowingauserto view anentireflight of data.HIS spectrumlocationsmaybeplottedon

top of theimagery,andby clickingontheimagewith themouse,thenearestHIS

spectrumwill beplotted.Thistool wasusedfor interactivedemonstrations(ona laptop)

at the MRS Science Team meeting and the MRS ATBD reviews.

A new visualization tool for MODIS Airborne Simulator data was demonstrated at the

MODIS Science Team meeting in May. This tool incorporated many of the features of

SIVIS (http://mistral.larc.nasa.gov/-vasanth), but was written in IDL to provide

portability. This program was designed from the start to be easy-to-use; to provide a set of

features appropriate to cloud mask algorithm development, and to provide publication

quality graphics output. This program was demonstrated on an IBM Unix laptop to the

MODIS Atmosphere Group.

A newer version of this program has recently been developed and released within CIMSS

for testing. It reads GSFC produced MAS HDF files directly, and runs on any PC or

workstation (with IDL installed). It has a simple interface (Figure 4) which offers a set of

features appropriate to the MODIS Atmosphere Group. GIF and Postscript file output is

as simple as clicking on the 'GIF' and 'PS' buttons at top right. This program has already

run on SGI, Sun, IBM, Linux, and Windows NT platforms (it should also work on Power

Macs). New features to be added are RGB image combinations, identification of band

wavelengths, and fusion of HIS data. CIMSS intends to develop a similar visualization

program for MODIS.

HIRS Cloud Climatology

The HIRS CO2 cloud climatology now contains eight boreal winter (December -

February) and eight boreal summer (June - August) seasons from June 1989 to May 1996.

Cloud occurrence, height, and effective emissivity are determined with the CO 2 slicing

technique that accounts for clouds partially filling the sensor field of view and semitransparency of some clouds. More than 40% of the HIRS observations find cirrus

clouds; their presence appears to be gradually increasing about one percent per year. This

unprecedented high detection of cirrus clouds is supported by recent aircraft experiments

during SUCCESS that found nearly continual presence of nearly invisible layers of small

ice crystals mostly transparent near 10 microns but highly absorbing toward 12 microns.

Comparisons with cloud studies conducted by ISCCP reveal similar trends from year to

year, but the ISCCP cirrus detection is less than half that of HIRS. Comparisons with
SAGEhighcloudfrequencydistributionsarequitegood;globalpatternsarewell

correlated.A paperonthiswork waspresentedattheninthInternationalTOVS Study

Conferenceheldin Igls,Austria20- 26February1997andwill bepartof thetechnical

proceedingsof that conference(Menzelet al., 1997).

Adjustmentsto the global statistics have been necessary. Results from before May 1991

were processed with clear sky radiances inferred from non nadir (out to 25 degrees from

nadir) observations of HIRS. Results after May 1991 used only near nadir radiances (out

to 10 degrees from nadir). Cloud properties were calculated only for near nadir

observations throughout this study (before and after May 1991), but the difference in clear

sky radiance fields has required an adjustment to the cloud statistics. This has eliminated

the noticeable increase in cirrus cloud cover that occurred at May 1991; prior to May

1991 cirrus cloud detection has been reduced by 5_6%, opaque clouds increased by 6.4%,

resulting in clear sky reduction by 0.8%.

Cloud Mask

The AVHRR and MAS cloud mask algorithm have evolved in several areas:

(a) Testing of prototype clear-sky brightness temperature maps was completed using

AVHRR Global Area Coverage data from the Atlantic Ocean. Single-pixel, high quality

(confidence of unobstructed surface > 95%) clear-sky values of I 1 _m brightness

temperature were obtained by the AVHRR cloud masking algorithm in ocean regions

during daylight hours. These values were incorporated into an eight-day composite equalarea grid at 25 km resolution. The composite file was updated after each day was

processed, that is; the clear-sky observations from day one of the previous eight days was

eliminated and those from the day just processed were added. Mean, minimum and

maximum brightness temperatures were made available to the nighttime algorithm to be

used as additional input to the cloud mask. The additional information was found to aid in

detection of low-level clouds at night, particularly in the eastern South Atlantic where

stratus clouds are very common. A total of 16 days were processed.

(b) A new land/sea tag file was obtained and implemented in the AVHRR and MAS cloud

mask algorithms. The file contains seven categories of surfaces: land, coast, intermittent

water, shallow inland water, deep inland water, shallow ocean, and deep ocean. The

additional information is useful for identifying discontinuities on the surface which may

have an adverse effect on the cloud mask output, such as ocean and lake shorelines and

river deltas.

(c) A surface snow-detection algorithm provided by Brian Baum of Langley Research

Center has been implemented in the AVHRR cloud mask. Even though the AVHRR has a

limited number of channels for this task, the method has made great improvements in

polar, daytime cloud discrimination.
(d) In responseto favorableresultsfrom testsintheAVHRR cloudmaskalgorithm,

severalchangeshavebeenmadeto theMAS cloud mask code. A new land/sea tag file

has been implemented which contains more information than the previous version. New

routines for use in coastal regions have been added. These employ the concept of a

coastal "zone", rather than a strict land/water separation, and use threshold tests

appropriate for land surfaces. Although some errors still occur (sometimes due to

imperfect navigation), results are improved over the earlier method A number of

ecosystem types have been identified where the visible ratio test (ratio of channel 7 and

channel 2 reflectances) is not appropriate to use. These are mostly desert and semi-arid

regions. The "wetland" flag has been replaced with a "desert" flag as it reflects a different

path through the cloud mask algorithm. The "polar daytime" processing path has been

eliminated from the code. Instead, the program will automatically determine whether or

not snow or ice is present and apply the appropriate threshold tests. A land shadow

detection algorithm has been added along with a routine to identify thick smoke.

(e) Routine cloud masking of AVHRR Global Area Coverage (GAC) data has begun.

Three orbits of data covering roughly the same areas of the earth are processed each day.

The areas include major deserts (Saudi Arabian Peninsula), tropical rain forests (Amazon),

and mid-latitudes (central North America). The intent is to monitor seasonal changes in

radiance data as applied to cloud mask threshold tests and to identify those regions where

cloud detection thresholds vary appreciably with season. Variations in surface and air

temperatures, crop production and harvesting, and biomass burning are only a few items

which could change expected radiance values and have adverse effects on the output cloud

mask.

In April, Steve Ackerman visited Goddard and discussed the cloud masking approach with

MODIS scientists. There are several groups making use of the UW cloud mask algorithm

that operates on the MAS HDF files. Several scientists have used this algorithm on their

experimental data sets. This approach has lead to improvements of the cloud mask and

represents a frame work for post launch validation activities.

Improvement on detecting heavy-aerosol loading continued during this quarter using

SCAR-B data. This remains a difficult problem and one that we will continue to work on

during the next quarter.

The latest version of the cloud mask algorithm that operates on the MAS HDF files is now

available. This includes some cloud detection improvements based on work conducted

here at UW and by investigators at GSFC.

Steve Ackerman attended the GLI meeting in Japan as a representative of the MODIS

atmosphere group. He presented an approach of adapting the MODIS cloud mask to the

GLI approach. The GLI team accepted this approach. The GLI mask will be a 16 bit

mask, similar in character to the first 16 bits of the MODIS mask. Final details about the

GLI cloud mask structure will be worked out this quarter.
AVHRR LAC datais usedinthedevelopmentof theMODIS andMISR cloudmask

algorithms.Throughdiscussionswith Drs.ClothiauxandDi Girolamoof theMISR team,

wehaveidentifieda datasetandanapproachof how to comparetheMODIS andM1SR

cloudmaskingproceduresprior to thelaunchof MODIS. Thiscomparisonbeganthis

quarterandwill continue,andhopefullybecompleted,inthenextquarter.

GOES Biomass Burning Program

The following work is being funded under separate NASA (NAGW-3804) and NOAA

contracts, but is included in this report as it is relevant to MODIS fire detection activities.

(a) Daily multispectral GOES-8 imagery (at 1145 UTC) collected during the SCAR-B

field program in South America (15 Aug.-15 Sept., 1995) were processed with an updated

version of the GOES-8 Automated Smoke/Aerosol Detection Algorithm (ASADA,

version 2.0). The algorithm uses single and multiband difference thresholds (including

visible, 4, 11, and 12 micron data), solar zenith angle corrected aibedo calculations, and

solar and satellite viewing geometry in the process of distinguishing smoke/haze from

multi-level clouds, low-level moisture, and sun glint. The revised algorithm shows

significant improvements in identifying sun glint contamination and in locating smoke in

northwestern Brazil where it is often difficult to separate smoke from low-level moisture

in the Amazon. Version 2.0 also includes a test to check for clouds in nearest neighbor

pixels. This test has significantly reduced false positive identification of smoke along

gradients between clear and cloud contaminated pixels. An example of GOES-8 visible

imagery and the GOES-8 ASADA (version 2.0) derived albedo product for 28 and 29

August are shown in Figure 5. A series ofGIFs (GSA*.GIF) showing the GOES-8

ASADA derived albedo product for each day during SCAR-B is available via anonymous

tip at winds.ssec.wisc.edu. Efforts are under way in Version 2.5 of the ASADA to

improve identification of smoke in areas where the background albedo is low (over water)

and in areas where the smoke albedo signature is very high and often incorrectly

characterized as cloudy.

The SCAR-B GOES-8 diurnal data set was reprocessed with version 2.0 of the GOES-8

ABBA. Version 2.0 contains a number of changes resulting in a more robust algorithm

including a new approach to cloud clear and characterize the background brightness

temperature field. The technique uses a combination of visible albedo, single band

thresholds in the 4 and 11 micron infrared windows, and a 4 minus 11 micron difference

histogram approach with variable thresholds based on the local solar zenith angle. This

approach appears to do a better job of cloud clearing and provides an improved

representation of the 4 micron background brightness temperature field. This technique

has significantly reduced the occurrence of false positive fires especially within several

hours of local noon in areas of enhanced solar reflectance in the 4 micron band and is

better equipped to distinguish sub-pixel cloud contamination. Version 2.0 is less

dependent on predetermined thresholds to account for viewing conditions and ecosystem

characteristics. The GOES-8 ABBA (Version 2.0) results indicate significant

improvements in identifying and processing fires under a variety of viewing conditions and
in differentecosystems.Figure6 showsanexampleof the diurnalGOES-8ABBA fire

productfor 24August1995.A seriesofGIFs (G8F*GIF) showingthediurnalGOES-8

ABBA fire productfor each day during SCAR-B is available via anonymous ftp at

winds.ssec.wisc.edu.

(b) Capabilities for early warning fire detection in North America were investigated as part

of a collaborative multi-sensor (GOES, POES, DMSP) assessment study coordinated by

Chris Elvidge at the National Geophysical Data Center (NGDC). Initial versions of the

North American GOES ABBA have been implemented for applications in New Mexico

(GOES-8) and Alaska (GOES-9). The revised GOES-8/-9 ABBA (version 2.0) was used

to process half-hourly multi-spectral GOES-8 imagery collected on 9-13 June 1995 in

New Mexico and half-hourly GOES-9 imagery on 24 June 1996 in Alaska. The New

Mexico data set provides a good example of the capabilities and limitations of GOES fire

detection in the southwestern US. The GOES-9 data collected over Alaska documented a

number of fires between 60 and 70°N indicating the potential for GOES-9 fire detection

and diurnal monitoring at northerly latitudes. A summary of CIMSS GOES ABBA results

for New Mexico and intercomparisons with NOAA AVHRR and DMSP-OLS fire

observations are presented in the NGDC draft report "Wildfire Detection with

Meteorological Satellite Data: Results From New Mexico During June of 1996 Using

GOES, AVHRR, and DMSP-OLS" (C. Elvidge, et al, 1997).

DATA ANALYSIS

WINCE Field Experiment

The WINter Cloud Experiment (WINCE) data collection phase was completed in

February 1997. WINCE is an early effort in a series of planned ER-2 deployments in

support of MODIS product development and validation in both the pre-launch and postlaunch phases. WINCE was designed to study clouds, snow, and ice in the complex

mixed scene (clouds over snow/ice background) conditions of winter environments in

support of the algorithm development for MODIS cloud and snow products. A NASA

ER-2 aircraft, the primary data collection platform, was deployed from Madison WI from

23 Jan - 13 Feb, 1997. The ER-2, carrying an instrument complement including the

MODIS Airborne Simulator (MAS), High-spectral resolution Interferometer Sounder

(HIS), Cloud Lidar System (CLS), Microwave Imaging Radiometer (MIR), Tilt Scan

CCD Camera (TSCC), and RC-10 camera, flew 10 missions (52 hours) in the 21 day

period. UW ground based measurements included the UW uplooking HSRL lidar, AERI

for atmospheric profiles and constituents, downlooking radiometry of snow surfaces and

snow crystal sampling, and classonde balloon launches. As much as possible, these

ground based measurements were collected during every ER-2 flight. GOES, TOVS and

standard NWS measurements were captured and archived for each day as well as

cooperative observer networks for snowfall and depth. The WINCE data set covers many

different combinations of cloud and surface conditions and captures simultaneous

observations from several other observing systems; it includes thin/thick low/high cloud

over snow, ice, water and bare land backgrounds, clear scenes of snow, ice and water

10
surfaces,daytimeandnighttimeconditions,overpassesof ground-basedmeasurements

(snowdepth,emissivity,uplookinginterferometersandlidar,atmosphericchemistry,

classondes),andunderpassesof thepolarorbitingADEOSsatellite(OCTS,IMG, and

POLDER). Many data sequences include transitions from cloudy to clear scenes over

uniform snow background (day and night), bare ground to snowcover, and snow to

icecover to open water.

Work has begun on prioritizing science data sets for WINCE. A meeting of WINCE

instrument PIs was held at GSFC in May. During the meeting discussion of data flights

and instrument data quality were held The objectives of each PI are met by a variety of

ER-2 flight days during WINCE. Some level of data exchange has begun. A plan was

also set forth to hold a WINCE science meeting in the Fall of FY98. Highlights of the

data collection include:

Date Flight Highlights

1/28 97041

1/29 97042

1/30 97043

2/2 97044

2/6 97045

2/8 97046

2/9 97047

2/10 97048

2/12 97049

2/13 97050

clear snow scenes, cirrus over snow, ADEOS underflight

thin to thick cirrus transition, bare ground to snow transition

repeat overpasses of water and ice cloud with UW based HSRL

thin cloud over L. Superior ice, ADEOS underflight

thin cloud over Hudson Bay ice and ice leads

clear water over L. Huron, water to lake ice to snow transition

snow scenes w/Hall ground based measurements

night flight, thin to thick cirrus transition over snow background

clear snow scenes over Hall site in WI, ADEOS w/TSCC

thin cirrus scenes over HSRL, ADEOS underpass.

For example, data scenes over Hudson Bay show thin low cloud over the ice and snow

covered bay (Figure 7). This transmissive cloud is difficult to discern in MAS visible

imagery (because of bright snow/ice background) and is essentially invisible in the 11

micron window data (the imagery shows dramatic ice leads over the entire region).

Strong evidence that the clouds exist is given by cloud shadows on the ice background and

by CLS downlooking lidar data from the ER-2 platform. This is an example of W1NCE

data that will be used to severely challenge and refine the skill of the MODIS cloud mask

algorithm. Cloud detection spectral tests using MAS 4 micron data will be tested on this

scene. A second example (Figure 8) shows thin cirrus scenes over snow background in

North Dakota. The MAS 1.88 micron channel (similar to MODIS 1.38 micron) does a

good job of depicting the thin cirrus coverage. Unfortunately, the 1.38 micron data will

not be useful at night. Nighttime cirrus scenes over snow and ice are however included in

the WINCE data collection and will be investigated for cirrus detection.

A WINCE homepage was created at: http://oldthunder, ssec.wisc, edu/wince/wince, html.

The homepage includes a daily quicklook archive with flight summaries, flight tracks,

quicklook MAS imagery, and interesting data segments. CLS instrument quickview

11
imageryandgroundbasedmeasurementsof snowdepthsat Madison,WI havebeenadded

to thehomepageaswell astabulatedsummariesof datacollectionandobjectives.

A modifiedMAS processingprocedurehasbeeninstitutedfor MAS datasetdissemination

from theGSFCDAAC; it hasbeenstreamlinedwith theintentof speedingupthe

distributionof thesedata TheMAS WINCE datasetrepresentsthefirsttestof this

procedure.ThreeMAS dataflightshavebeenselectedas"Golden"daysfor highpriority

processingwith apreliminaryradiometriccalibration.Thosedaysare6 Feb(fit 97-045),9

Feb(fit 97-047)and12Feb(fit 97-049). Thesedatasetsbecameavailableto WINCE

participantsandinstrumentPIsby 1May, lessthan3 monthsafterthecompletionof the

WINCE datacollection.MAS final calibrationassessment for theWINCE datasetis

underway.A MASfinal calibrationWINCE datasetis plannedfor releasein FY98.

MAS SUCCESS Data Processing

Final calibration for the MAS SUCCESS data set has been completed. Laboratory

spectral measurements collected in March and June 1996 have been reviewed and

corrected for the ambient conditions in the Ames calibration laboratory. Ambient

laboratory atmospheric absorption was estimated using the Line-By-Line-RadiativeTransfer-Model (LBLRTM) with the HITRAN96 database. The high spectral resolution

forward model output is convoluted with the monochromator characteristics to remove

spectral dependencies. Using several well-defined spectral features, the monochromator

spectral position reporting accuracy was assessed. This assessment was based on a

comparison of absorption feature shape as given in the MAS output and the convoluted

forward model data. It was observed that small spectral corrections to the

monochromator reported spectral position resulted in much improved absorption feature

shape matching, and removal of spectral features from the atmospherically corrected SRF.

The spectral correction is a function of the diffraction grating used for the spectral

measurements; spectral corrections were on the order of 1% of total spectral bandpass for

MAS channels. The spectral corrections were incorporated into the final MAS calibration

for the SUCCESS data sets.

The March and June 1996 atmospherically corrected SRF showed spectral position

discrepancies for Ports 3 and 4 of about 30 and 70 nm respectively. In order to answer

the question of how to apply this discrepancy to the MAS SUCCESS data set, the MAS

and well-calibrated HIS data sets were compared for various flights during the experiment.

The comparison for data early in the SUCCESS deployment on 13 April 1996 revealed

that MAS-HIS biases were sharply reduced, especially for the spectrally sensitive MAS

channels 34-36, 43, and 48-50 when using the June '96 SRF instead of the March '96

SRF. This is a strong indication that the June '96 SRF should be applied and the March

'96 SRF ignored. Similar bias reduction was realized in comparisons of the MAS-HIS

biases for other data flights during SUCCESS (April 08, April 15, May 03, May 08). The

MAS SUCCESS final calibration data set is now being processed at the GSFC DAAC

using the June '96 atmospherically corrected SRF.

12
A newMAS fasttransmittancemodelhasbeengeneratedusingthefinal SRFfor the

SUCCESSdataset. Themodelisbeingusedin forwardcalculationsfor MODIS cloud

top propertiesalgorithmsandin surfaceandatmospherictemperature/moistureretrievals

usingMAS data. Theimpactof spectraluncertaintyonthecloudtop propertieshasbeen

investigatedusingthemodel. UsingMAS-HIS SUCCESSdatasetbiasesasguidance,

equivalentspectralpositionerror(about5-10%ofMAS FWHM bandpass)wastestedto

investigatethecloudtop pressuresensitivityto spectralerror. Earlyindicationsarethat

this spectralerrorimpactis lessthantheimpactby othererror sources(surface

temperatureestimate,atmosphericcharacterization,iceparticleemissivity,etc.).

VisitingscientistDr. Youri PlokhenkoisprocessingtheApril 13datasetoverOklahoma

to investigateMAS temperature/moistureretrievalsensitivityto surfaceemissivity.Using

aphysicalretrievalalgorithmto simultaneouslydetermineatmospheric

temperature/moistureandsurfaceemissivity,hehasfoundsurfaceemissivitysolutions

rangingfrom .95to .98in thelongwaveinfrared(8.6to 14.2micron)MAS channelsand

.91to .98in theshortwaveinfrared(3.0to 5.3micron)MAS channels.Theeffectof nonblackbodysurfaceemissivityis demonstratedin Figure9; theupperpanelshowsthe920

mbmixingratioderivedwith anon-blackbodysurfaceemissivityfor partof theflight track

onApril 13andthelowerpanelshowsthesamederivedwith ablackbodysurface.

Patternsof moistureandthestrengthof gradientsareclearlyaffected.Thesepreliminary

resultswill bestudiedfurther.

PAPERS

Ackerman,S.A., C. C. Moeller,K. I. Strabala,L. E. Gumley,W. P.Menzel,1997:Cloud

PropertiesFromMAS ObservationsDuringSUCCESS:Implicationsfor MODIS, Spring

Meetingof AGU, May 27-30,Conferenceon SubsonicAircraft: ContrailandCloud

EffectsSpecialStudy(SUCCESS),Baltimore,Maryland.

Ackerman,S.A., 1997:Aircraftremotesensingof cloudmicrophysics.OSAWinter

TopicalMeetings.OpticalRemoteSensingof the Atmosphere,February10-14,SantaFe

New Mexico.

Elvidge,C.D., D.Pack,E. Prins,E. Kihn,K. Baugh,J.Kendall,1997: Wildfire Detection

with MeteorologicalSatelliteData: ResultsFromNew MexicoDuringJuneof 1996

UsingGOES,AVHRR, andDMSP-OLS. Submittedto Remote Sensing Change

Detection: Environmental Monitoring Methods and Applications, Ann Arbor Press.

King, M., S-C Tsay and S. A Ackerman, 1997: MODIS Airborne Simulator: Radiative

properties of smoke and clouds during ARMCAS and SCAR-B, Third International

Airborne Remote Sensing Conference and Exhibition, 7-10 July, Copenhagen, Denmark.

Menzel, W. P., D. P. Wylie, K. I. Strabala, and R. Frey, 1997: Eight years of global cirrus

cloud statistics using HIRS. Technical Proceedings of the ninth International TOVS

Study Conference.

13
Menzel,WP., andE.M. Prins,1996: Monitoringbiomassburningwith thenew

generationofgeostationarysatellites.In Biomass Burning and Global Change, Volume 1,

edited by J.S. Levine, pp. 56-64, The MIT Press, Cambridge, MA.

Moeller, C. C., D. D. LaPorte, K. I. Strabala, P. Hajek, and W. P. Menzel, Spectral

characterization of MODIS Airborne Simulator (MAS) LWIR bands and application to

MODIS science data cloud products. Paper accepted for presentation at the Earth

Observing Systems II Conference, SPIE, July 28-29, 1997.

Prins, E.M., and W.P. Menzel, 1996: Investigation ofbiomass burning and aerosol

loading and transport utilizing geostationary satellite data. In Biomass Burning and Global

Change, Volume 1, edited by J.S. Levine, pp. 65-72, The MIT Press, Cambridge, MA.

Prins, E.M., W.P. Menzel, D.E. Ward, 1997a: GOES-8 ABBA Diurnal Fire Monitoring

during SCAR-B. In SCAR-B Proceedings, edited by V.W.J.H. Kirchhoff, pp 153-157,

Transtec Editorial, Sao Paulo, Brazil.

Prins, E.M., W.P. Menzel, J.M. Feltz, and DE. Ward, 1997b: An Overview of GOES-8

ABBA Results for SCAR-B: Diurnal Fire Activity and Regional Smoke Coverage.

Presented at the SCAR-B Special Session of the Spring AGU Meeting, Baltimore, MD,

27-30 May 1997.

Strabala, K. I., S. A. Ackerman, C. C. Moeller, L. E. Gumley, R. A. Frey, J. Y. Li and W.

P. Menzel, 1996: Cloud Properties Determined from MODIS Airborne Simulator (MAS)

SUCCESS Observations. Poster to be presented at the ERIM Third International

Airborne Remote Sensing Conference and Exhibition to be held in Copenhagen, Denmark

July 7-10, 1997.

MEETINGS

Dan LaPorte attended the Pre-environmental Review at Santa Barbara Remote Systems in

California on 6-7 January.

Paul Menzel, Dan LaPorte and Chris Moeller attended the MODIS Thermal Infrared

Calibration Team Meeting at the University of Miami on 9-10 January.

Elaine Prins attended the Conference on GIS and Remote Sensing Applications to Disaster

Management in Greenbelt, MD on 14-15 January 1997 and presented a paper on the

GOES-8 ABBA and future applications for early warning wildfire detection and

monitoring in North America.

Steve Ackerman presented a paper at the Optical Remote Sensing of the Atmosphere

Conference, February 10-14, Santa Fe New Mexico, titled "Aircraft remote sensing of

cloud microphysics."

14
PaulMenzelchairedtheEOSPM platformATBD reviewin Columbia,MD on 11- 13

March1997.

ElainePrinsattendedtheLangleyDAAC UWG (Distributed Active Archive Center Users

Working Group) Meeting in Hampton, VA on 13-14 March 1997.

Elaine Prins attended a meeting on 17 March at NASA GSFC to discuss the direction of

the NASA MTPE sponsored interdisciplinary science study to characterize aerosol forcing

over the Atlantic Basin associated with urban/sulfate, Saharan dust, and biomass burning

aerosols and CIMSS participation in this effort.

Steve Ackerman attended a cloud mask discussion at GSFC with other MODIS team

members in April.

Chris Moeller attended the PM-1 Spacecraft Maneuver meeting at GSFC on 6 May.

Chris Moeller, Kathy Strabala, Liam Gumley, Steve Ackerman, Dan LaPorte and Walter

Wolf attended the MODIS Science Team meeting held 14-16 May in Greenbelt MD.

Steve Ackerman presented a poster at the AGU meeting in Baltimore, Maryland from 27-

30 May titled "Cloud Properties From MAS Observations During SUCCESS:

Implications for MODIS."

Elaine Prins attended the SCAR-B science workshop and the AGU meeting in Baltimore,

Maryland from 27-30 May and presented a paper at the AGU SCAR-B special session

titled "An Overview of the GOES-8 ABBA Results for SCAR-B: Diurnal Fire Activity

and Regional Smoke Coverage."

Chris Moeller, Liam Gumley, and Dan LaPorte attended the MAS Instrument Summit

meeting, 10-11 June at Ames Research Center, CA.

15
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Figure 1. Blowup of MODIS atmospheric corrected RSR for band 35, channel 5. Applying a

correction to the SpMA spectral reporting position creates a much better match of forward model

generated transmittances with the observed spectral feature shape at 13.88 microns in the RSR

measurements. The spectral feature in the resulting atmospherically corrected RSR is eliminated

by the spectral correction.

80

MODIS RSR

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Figure 2. MODIS band 35 spectral correction for each channel. The estimated spectral correction

is arrived at by matching spectral features of observed RSR data with that of forward model

transmittances. "?" indicates that channel 10 yielded ambiguous results for spectral correction

estimate and at this time is considered to be "out of family". Uncertainty of spectral correction

considered to be about +/- 2nm.

16
Figure3:TheimagebelowshowssimulatedMODISBand31radiancedataextractedfromthe

mostrecent(March1997)MODISLevelIB file format.Theimagehasbeenresampledto an

isotropicMercatorprojection.Latitudeandlongitudedataforevery5thlineandpixelis now

storedintheMODISlkm resolutionHDFfiles,andthisdatawasinterpolatedtothesame

resolutionastheimagedata.GraphicsarefromIDL usingCIMSSsoftware

MODIS Bond .3_ Sirculoted Deta for August 19£6 (Version 2 t18, March 1997)

17
Figure 4: Shown below is a screen capture from a new MAS visualization program developed at CIMSS.

The image shows MAS band 2 (0.65 microns), while the scatter plot shows a daytime cloud phase test. On

the X axis is band 45 (11 micron) brightness temperature, and on the Y axis is band 10 (1.64 micron)

reflection function divided by band 4 (0.74 micron) reflection function. The colored rectangles on the image

correspond to the same colored points on the scatter plot. Data was read directly from a MAS HDF file.

Similar functionality will be developed by CIMSS for visualizing MODIS data. This program was

developed in IDL and has been run on SGI, Sun, IBM and Linux Unix platforms, and on Windows NT

PC's (screen capture below is from an SGI).

18
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Figure 7. MAS imagery over frozen Hudson Bay on 6 February 1997. Low transmissive clouds

in the visible data (note cloud shadows) are almost invisible in the infrared (note prevalence of ice

leads in the thermal data). MAS data from WINCE are being used to assess and improve MODIS

cloud masking capability in difficult winter scene conditions such as is illustrated above.

21
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Spatial distribution of humidity variations (mixing ratio [g/Kg], 920mB)

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70

Spatial distribution of humidity variations (mixing ratio [g/Kg], 920mB)

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Figure 9. The effect of non-blackbody surface emissivity. Upper panel shows the 920 mb mixing

ratio derived with a non-blackbody surface emissivity for part of the flight track on April 13; lower

panel shows the same derived with a blackbody surface.

23