IPO Progress Report for Aug-Oct 2003



GOES-R Progress Report for October-December 2005

Cooperative Institute for Research in the Atmosphere/Colorado State University

Fort Collins, CO

Project Title: Research and Development for GOES-R Risk Reduction

Principal Investigator: Thomas H. Vonder Haar, CIRA/CSU

Co-Investigators: Mark DeMaria and Debra Molenar, NESDIS/ORA

Date: January 12, 2006

FY2005 Funding: $285,000 (received Aug 2005)

Dec 2005 Balance: $225,000

October-December 2005 Accomplishments

Task 1. Mesoscale Weather Database

The Mesoscale Weather Database continues to be maintained.  A webpage which keeps track of additions to the database can be found here: .   In the coming months, any interesting new cases, as well as research results, will be added to the Mesoscale Weather Database and noted on this website. (D. Lindsey)

Task 2. Synthetic GOES-R data generation and analysis

Last quarter work continued preparing a manuscript for an AMS peer reviewed journal. Further, three extended manuscripts and posters have been finalized for the AMS 2006 annual meeting to be held in Atlanta, Georgia. (L. Grasso)

This quarter a manuscript entitled “GOES-R and NPOESS imagery of mesoscale weather events” was submitted to the Journal of Applied Meteorology. A presentation was given at the Desert Research Institute in Reno, Nevada on the creation of synthetic GOES-R imagery. In addition, numerical simulation of hurricane Lili continued as part of our data assimilation efforts. That is, a sequence of synthetic 10.35 µm GOES-R images every fifteen minute over a twelve hour period was produced. These images will be used as observations for the Maximum Likelihood Ensemble Filter (MLEF) assimilation scheme. Lastly, collaboration began with Dr. Stan Kidder using the observational operator to obtain diagnostics of an observed cloud during the CLEX-9 experiment. (L. Grasso, M. Sengupta)

Task 3.Prototype product development for fog, smoke and volcanic ash analysis

New Software for Thee-Color AREA File Generation: Software has been developed to create McIDAS-formatted AREA files from three-color imagery, such as generated by the McIDAS Combine command. The Combine command create a 24-bit three-color product that can be turned into a JPG image, but not directly into a McIDAS-formatted AREA file, nor converted into a 1-byte (8-bit) image. The new software uses an algorithm coded by Stan Kidder, which is based on Heckbert’s Median Cut Algorithm from 1982, to create a 1-byte AREA that can be displayed in a McIDAS frame and looped over multiple frames. The program creates a unique color table for each three-color image. That color table that needs to be applied to the image to have the three-color effect. This software is currently being tested on Meteosat Second Generation (MSG) data to produce three-color images and loops. An example of the new algorithm applied to MODIS data is included in Fig. 3.1

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Figure 3.1.a: An image created from an AREA file generated using the new thee-color software. This example is the “natural” color product as applied to MODIS data, consisting of the 1.6 µm, 0.8 µm, and 0.6 µm bands for the red, green, and blue bands, respectively.

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Figure 3.1.b: For reference, an image created from output of the McIDAS Combine command, for the same data as in Figure 3.1.a. The casual viewer would have a hard time noting differences between the two images.

Thee-Color AREA File Loops: The software developed to create McIDAS-formatted AREA files from three-color imagery, is now being used to create re-loadable image loops within McIDAS. Since the program optimally creates a color table unique to each three-color image, that color table needs to be appropriately named and applied to its intended image to have the proper three-color effect. This problem was worked out with appropriate software and is now being applied to Meteosat Second Generation (MSG) data to produce three-color loops. An example of a three-color MSG image is shown in Fig. 3.2 below. (D. Hillger)

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Figure 3.2: An image created from McIDAS AREA files generated using new thee-color software. This example is the MSG “natural” color product over Europe, consisting of the 1.6 µm, 0.8 µm, and 0.6 µm bands for the red, green, and blue colors, respectively. High/cirrus clouds and snow-covered ground are cyan, low clouds are white, and bare ground surfaces are natural colors (green and brown). A loop of this case is available at

Task 4: Tropical cyclone product development

Meteosat Second Generation Data: A collection of full resolution (temporal, spectral, and spatial) Meteosat Second Generation data was collected over the tropical Atlantic 1 June – 3 December for future satellite applications. (J. Knaff)

IR Imagery Collection: An automated collection of 1 km, Mercator, IR imagery over Global tropical cyclones has been started. At present NOAA Limited Area Coverage (LAC) and High Resolution Picture Transmission (HRPT), and NASA Moderate Resolution Infrared Spectroradiometer (MODIS) data are accessed and utilized. Figure 4.1 shows an example from southern hemisphere tropical cyclone number 3 from 2006. This imagery will be utilized to study the effects of increased resolution on tropical cyclone intensity and structure algorithms, and will also be provided to the GOES-R algorithm working group as proxy data for the satellite winds group. (J. Knaff, M. DeMaria)

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Figure 4.1: Example of 1-km, Mercator IR imagery that is being collected for future risk reduction activities. Tropical cyclone SH0306 on 23 November 2005 at 0405 UTC and and intensity of 115 kt is shown.

Archived Images: The AVHRR (Advanced Very High Resolution Radiometer) and the MODIS (Moderate Resolution Imaging Spectroradiometer) IR sensor provide 1-km resolution images that show details and features that are not well observed in geostionary images. We are now archiving 1-km Mercator cyclone centered remapped IR images from AVHRR and MODIS with tropical cyclones. Two examples of Hurricane Wilma are shown in Figures 4.2 and 4.3. (R. Zehr, J. Knaff)

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Figure 4.2. Hurricane Wilma MODIS 1-km IR, 0710 UTC 19 October 2005.

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Figure 4.3. Hurricane Wilma MODIS 1-km IR, 0725 UTC 24 October 2005.

Synthetic GOES-R ABI 10.35 µm Imagery: Synthetic GOES-R ABI 10.35 µm imagery has been created from a simulation of hurricane Lili. Synthetic images were created every fifteen minutes with a horizontal footprint of 2 km over a 12 hour period. These images will be treated as observations and will be used in an assimilation experiment of hurricane Lili. (L. Grasso and D. Zupanski)

Six new AIRS hurricane eye soundings were obtained in hurricanes Lili and Isabel. These are being analyzed and the results will be presented at the upcoming AMS satellite conference (M. DeMaria).

Task 5: Severe weather product development

The investigation into the utility of using multiple spectral bands from GOES and MODIS to study thunderstorm top microphysical structure continues.  Recent developments include an analysis on the effect of non-isotropic scattering of thunderstorm anvils at 3.9 µm.  In the afternoon, GOES-East typically measures larger 3.9 µm reflectivity then GOES-West, suggesting that forward scattering is preferred.  This effect will be taken into account during the development of a nowcasting product utilizing the relationship between 3.9 µm reflectivity and updraft strength. (D. Lindsey)

A manuscript entitled "GOES climatology and analysis of thunderstorms with enhanced 3.9 µm reflectivity" was submitted to Monthly Weather Review, and will likely be accepted soon.  Additionally, a poster entitled "A climatological study of ice cloud reflectivity" will be presented at the 14th Conference on Satellite Meteorology and Oceanography in Atlanta in January 2006. (D. Lindsey)

Task 6: Information content analysis using Maximum Likelihood Ensemble Kalman Filter (MLEF) data assimilation

Task 6: Information content analysis using Maximum Likelihood Ensemble Filter (MLEF) data assimilation

During this quarter I have included, in collaboration with Louie Grasso, a forward radiative transfer model into the MLEF algorithm. I have also implemented the MLEF algorithm on Columbia super-computer, located at the NASA Ames Research Center. The initial tests of the algorithm on the new computer have indicated reasonable results. With the implementation of the MLEF algorithm on this super-computer, I expect to be able to run data assimilation experiments with large ensemble size (of the order of 100). (D. Zupanski, L. Grasso)

I am currently performing data assimilation experiments on Columbia supercomputer, employing simulated GOES-R brightness temperature observations of 10.35 microns. I will present these results at the upcoming AMS meeting, held in Atlanta, GA (January 29-February 2, 2006). (D. Zupanski)

Task 7: Training Activities

GOES, MODIS, and AIRS imagery were collected for a training example for persistent fog in the Pacific Northwest during November 19-22, 2005.  Figure 7.1 shows the fog product created from MODIS Aqua imagery for a nighttime pass at 10:05 UTC on November 22, 2005.  Note the extensive areas of fog in Eastern Washington, northern Oregon, the many mountain valleys in Canada, northern Idaho, and Western Montana as well at the Peugeot Sound and out over the Pacific Ocean.   Figure 7.2 shows an AIRS spectral profile taken near the center of the fog in Washington.  Note the moisture spikes in the 10 – 12 micrometer region are warmer than the main part of the signal indicating an inversion above the fog cloud.  Also note that the temperatures in the 10-12 micrometer region are warmer than those in the 3.9 micrometer region giving the positive brightness temperature difference that is shown in the fog product. (B. Connell)

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Figure 7.1  The MODIS fog product (brightness temperature at 11 µm minus brightness temperature at 12. 0 µm) for fog in the Pacific Northwest at night on November 22, 2005 at 10:05 UTC.

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Figure 7.2   An AIRS spectral profile for a point taken near the center of the fog in Washington state.  

Presentations and Publications:

Refereed paper submission:

Grasso, L.D., M. Sengupta, M. DeMaria, 2006: GOES-R and NPOESS imagery of mesoscale weather events. Journal of Applied Meteorology (L. Grasso)

Conference paper submissions:

Connell, B.H., and F. Prata, 2006: Detecting volcanic ash and blowing dust using GOES, MODIS, and AIRS imagery. AMS 14th Conference on Satellite Meteorology and Oceanography, 29 January-3 February, Atlanta, GA.

DeMaria, M., D.W. Hillger, C. Barnet, R.T. DeMaria, 2006: Tropical Cyclone Applications of Next-Generation Operational Satellite Soundings. AMS 14th Conference on Satellite Meteorology and Oceanography. 29 January-3 February, Atlanta, GA.

Grasso, L.D., and D.T. Lindsey, 2006: Analysis of a hook echo and RFD from a simulated supercell on 8 May 2003. AMS Symposium on the Challenges of Severe Convective Storms. 29 January-3 February, Atlanta, GA.

Grasso, L.D., M. Sengupta, J.F. Dostalek, and M. DeMaria, 2005: Synthetic GOES-R and NPOESS imagery of mesoscale weather events. AMS 14th Conference on Satellite Meteorology and Oceanography. 29 January-3 February, Atlanta, GA.

Grasso, L.D., M. Sengupta, and D.T. Lindsey, 2006. A technique for computing hydrometeor effective radius in bins of a gamma distribution. AMS 14th Conference on Satellite Meteorology and Oceanography. 29 January-3 February, Atlanta, GA.

Zupanski, D., L.D. Grasso, M. DeMaria, M. Sengupta, and M. Zupanski, 2006: Evaluating the Impact of Satellite Data Density within an Ensemble Data Assimilation Approach.  AMS 14th Conference on Satellite Meteorology and Oceanography. 29 January-3 February, Atlanta, GA.

III. PLANS FOR THE NEXT THREE MONTHS:

Plans for Next Quarter:

Task 1

In the coming months, any interesting new cases, as well as research results, will be added to the Mesoscale Weather Database and noted on this website. (D. Lindsey)

Task 2

Our plans for the next three months are to (1) attend the AMS 2006 annual meeting in Atlanta, Georgia to present our GOES-R work via poster sessions, (2) continue working on hurricane Lili simulations for the MLEF assimilation tests, and (3) to begin exploratory product development in collaboration with Dr. Stan Kidder. (L. Grasso, M. Sengupta)

Task 3

Continue three-color product development as a means of combining images from different spectral regions. (D. Hillger)

Work on fog/stratus discrimination from other image features associated with clouds and land surfaces. (D. Hillger)

Plans for next quarter – present the poster “Detecting volcanic ash and blowing dust using GOES, MODIS, and AIRS imagery” by B. Connell and F. Prata at the AMS 14th Conference on Satellite Meteorology and Oceanography to be held at the end of January 2006. (B. Connell)

Task 4

Synthetic GOES-R ABI 10.35 µm Imagery: Synthetic GOES-R ABI 10.35 µm imagery has been created from a simulation of hurricane Lili. Synthetic images were created every fifteen minutes with a horizontal footprint of 2 km over a 12 hour period. These images will be treated as observations and will be used in an assimilation experiment of hurricane Lili. (L. Grasso and D. Zupanski).

Work will begin on a journal publication describing the AIRS sounding applications to tropical cyclones (M. DeMaria).

MODIS and AVHRR imagery for a variety of tropical cyclone cases will be put in standardized form, along with the aircraft ground truth data for use by the GOES-R Algorithm Working Group.

Task 5

GOES-East typically measures larger 3.9 µm reflectivity then GOES-West, suggesting that forward scattering is preferred.  This effect will be taken into account during the development of a nowcasting product utilizing the relationship between 3.9 µm reflectivity and updraft strength. (D. Lindsey)

Task 6

In the next quarter, I will evaluate the impact of varying special and/or temporal resolution of the simulated brightness temperature data on the information content measures. (D. Zupanski)

Task 7

As case data becomes available, add it to the GOES-R database and draft examples for training emphasis. (B. Connell)

IV. SPENDING PLAN:

The FY03 and FY04 project funds have been spent. The FY05 funds of $285,000 arrived at CIRA in July of 2005, with $225K remaining.

For the next three months, spending is estimated to be:

Jan 06 $20K Feb 06 $25K Mar $25K

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