# Presentation: 804751

## Students as Ground Observers for Satellite Cloud Retrieval Validation

- 13th Conference on Satellite Meteorology & Oceanography
- Norfolk, VA
- Sept. 2004

- Lin H. Chambers, P. Kay Costulis, David F. Young
- NASA Langley Research Center, Hampton, VA
- Tina M. Rogerson
- Science Applications International Corporation, Hampton, VA

## OUTLINE

- Sources of student data
- What is the S’COOL Project?
- Comparisons
- Proof of Concept
- New Comparisons
- Bright Surfaces
- Conclusions

## Sources of Student Data

- The CERES S’COOL Project
- More than 35,000 complete observations
- 9,172 now have corresponding CERES data
- _[http://scool.larc.nasa.gov](http://asd-www.larc.nasa.gov/SCOOL/usedata-fr.html)_
- The GLOBE Program
- More than 2.5 M cloud data points
- http://www.globe.gov

## What is S’COOL?

-  Began Jan. 1997
-  Students’ Cloud Observations On-Line
-  K-12 Education and outreach portion of CERES:  Clouds and the Earth's Radiant Energy System
-  1700+ participants in 65 countries
-  Focused on obtaining ground-based cloud observations for validation of the CERES data

## The S’COOL Concept

- Students provide ground observations for CERES overpass
- Determine satellite overpass time
- Observe cloud properties
- Transmit results to NASA
- Compare to satellite-retrieved properties
- Data of value to CERES scientists
- Real-world learning for students

## Data

- Collected

- Cloud type
- Contrails
- Cloud cover
- Visual opacity
- Surface Cover
- Surface Measurements
- Comments

**Notes:**
This is our one page report form.  You can print this from the website and then make copies for daily use (or copy to a transparency and use washable markers).  Note that the bulk of the observation concerns the cloud identification.  We ask for 3 pieces of information about the cloud:
Cloud type (which determines cloud altitude).  Note that the satellite cannot distinguish cirrus from cirrostratus, for example.  So, from the S’COOL perspective it is much more important to pick a cloud type at the right level than it is to be 100% sure of the cloud type.  More on that later.
Cloud cover:  we need to know whether there is only one tiny piece of cloud, or whether the cloud covers most of the sky.  The best way to do this is to have students come up with their own individual estimates, then use a grade-level appropriate method to determine a consensus value for the class (for younger students, this might mean voting on clear vs PC vs MC vs overcast; for older students, this might mean estimating actual fractions, then taking an average).
Visual opacity:  this is a deliberately technical term to describe a fairly simple concept:  how much sunlight is getting through the cloud?  If you have a thick cloud cover where you cannot at all tell the location of the sun, that is opaque.  In contrast, if you have a thin cirrus cloud through which you can see blue sky, that is transparent.  In between, you have translucent cloud.  This is a judgement, and you can set your own standards to determine this, or again use a voting process.

At the bottom of the form, we also request information on Surface cover.  The most important thing is to know whether there is snow or ice on the ground, as this looks a lot like cloud when viewed from space.  We also like to know whether the trees are generally in leaf; or whether most of the leaves have fallen.
The Surface Measurements are optional.  If you have access to instruments to measure any or all of these, that data is useful to us; however it is not required.

## Comparing to Satellite

- S’COOL Site Matched to 1 degree Satellite Region
- Observation
- 	Times
- 	Within
- 	15 Minutes

## FIRST S’COOL ComparisonCloud Observations Over Gloucester, VAJanuary 13 & 17, 1997

## Initial Comparisons:Proof of Concept

-  Measurements in 1998
-  CERES on TRMM only (~50 correspondences)
-  Augmented with AVHRR and geostationary data (~50):  Analyzed by hand

## Cloud Amount Comparison

- 62% in complete agreement
- 0% in complete disagreement

- Stats:  Chi-Squared value of 82; significant to 5e-12

## Cloud Layer Comparison

## Interim Conclusions

-  Clearly some useful information
-  Insight into cloud layering
-  Insight into sparse, thin cirrus
-  Educationally a big success

## New Comparisons - 2004

-  New CERES angular models
*(see talk by Loeb this afternoon)*
-  CERES on TRMM, Terra, Aqua
-  Feb. 1998 to April 2004
-  Production data products

## Data Available

- Max:  479 (High School in Pennsylvania)
- Min:  1 (70 schools)

## Cloud Amount Comparison

- 54.5% in complete agreement
- 2% in complete disagreement

- Stats:  Chi-Squared value of 5636; significant!!!

## Students Overcast vs. Satellite Clear (48 cases)

-  **Spatial Mismatch?:**  >1/3 are schools located less than 0.1 degree from the edge of a lat/long grid box.
-  **Universal Time?:**  3 cases with incorrect UT
-  **Student/Satellite error?:**  remaining cases have no clear explanation.  Study needed.
-  **Snow:**  10 cases, yet the satellite still reports clear sky.

## Students Clear vs.Satellite Overcast (143 cases)

-  **Spatial mismatch?:**  About 22%
-  **Universal Time?:**  ~10
** ****Snow?:**  18 cases students report snow.
-  Only one satellite retrieval is suspect: low cloud temperature 2.5K below the surface temperature.
-  **Satellite/Student error?:  **stratus = clear?

## Cloud Amount Comparison

- 191 3-class errors (2%) - ~1/3 easily explainable
- 711 2-class errors (8%) - need more study
- 3271 1-class errors (36%) - may be near-matches

## First look at 1-class errors

- 24% of CERES has 5 < fc < 10

- 20% of CERES has 10 < fc < 15

- Students say
- 0-5% cloud
- Satellite says
- 5-50% cloud

## Subvisual Cirrus?

-  MODIS vs GLOBE cloud type comparison indicated some subvisual cirrus (Stephens and Rogers, 2004).
-  This CERES/S’COOL dataset:
-  19 cases where tot< 3
-  None high cloud only
-  5 cases where tot < 1
-  None high clouds
-  No evidence of subvisual cirrus in this dataset
-  May be due to location of the S’COOL student data, over land with few data points in the Tropics.

## Cloud Layer Comparison

## Effect of Bright Surfaces

-  1057 reports (~11%) with snow or ice in ground report
-  Data from 1/4 of respondents

- Max - 86
- (4th grade in NH)
- Min - 1
- (31 schools)

## Snow Effect on Cloud Amount

| All |  | Ground Observers |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  |  | Clr | PC | MC | OV |
| S
A
T | Clr | 1415 | 350 | 101 | 48 |
|  | PC | 624 | 897 | 575 | 230 |
|  | MC | 193 | 553 | 695 | 680 |
|  | OV | 143 | 187 | 489 | 1992 |

| Snow/Ice |  | Ground Observers |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  |  | Clr | PC | MC | OV |
| S
A
T | Clr | 147 | 27 | 8 | 10 |
|  | PC | 67 | 75 | 35 | 19 |
|  | MC | 35 | 59 | 68 | 95 |
|  | OV | 18 | 33 | 52 | 309 |

- Chi-squared = 671

## Snow Effect on Cloud Amount

| All - scaled |  | Ground Observers |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  |  | Clr | PC | MC | OV |
| S
A
T | Clr | 163 | 40 | 12 | 6 |
|  | PC | 72 | 103 | 66 | 27 |
|  | MC | 22 | 64 | 80 | 78 |
|  | OV | 16 | 22 | 56 | 230 |

| Snow/Ice |  | Ground Observers |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  |  | Clr | PC | MC | OV |
| S
A
T | Clr | 147 | 27 | 8 | 10 |
|  | PC | 67 | 75 | 35 | 19 |
|  | MC | 35 | 59 | 68 | 95 |
|  | OV | 18 | 33 | 52 | 309 |

## Snow Effect on Cloud Layers

| All |  | Ground Observers |  |  |
| --- | --- | --- | --- | --- |
|  |  | No Cloud | 1-layer | Multi-layer |
| S
A
T | No Cld | 950 | 615 | 100 |
|  | 1-layer | 306 | 2030 | 581 |
|  | Multi | 249 | 3306 | 1035 |

| Snow/Ice |  | Ground Observers |  |  |
| --- | --- | --- | --- | --- |
|  |  | No Cloud | 1-layer | Multi-layer |
| S
A
T | No Cld | 92 | 73 | 5 |
|  | 1-layer | 42 | 295 | 54 |
|  | Multi | 31 | 400 | 65 |

## Snow Effect on Cloud Layers

| All - scaled  |  | Ground Observers |  |  |
| --- | --- | --- | --- | --- |
|  |  | No Cloud | 1-layer | Multi-layer |
| S
A
T | No Cld | 110 | 71 | 12 |
|  | 1-layer | 35 | 235 | 67 |
|  | Multi | 29 | 383 | 120 |

| Snow/Ice |  | Ground Observers |  |  |
| --- | --- | --- | --- | --- |
|  |  | No Cloud | 1-layer | Multi-layer |
| S
A
T | No Cld | 92 | 73 | 5 |
|  | 1-layer | 42 | 295 | 54 |
|  | Multi | 31 | 400 | 65 |

## Conclusions

- First major analysis of student ground observer data to validate cloud retrievals from a satellite instrument.
- A few pitfalls are evident.
- Useful information can be derived.

## Future Plans

- Inviting S’COOL participants to do detailed analysis of their correspondences
- More analysis to be done (2-class and 1-class errors, cloud levels, opacity....)
- Data available via the Internet for analysis:
- _[http://asd-www.larc.nasa.gov/SCOOL/](http://asd-www.larc.nasa.gov/SCOOL/usedata-fr.html)__[usedata](http://asd-www.larc.nasa.gov/SCOOL/usedata-fr.html)__[.html](http://asd-www.larc.nasa.gov/SCOOL/usedata-fr.html)_

## Acknowledgments

- Science and Education support from  NASA’s Earth Science Enterprise.
- This work would not be possible without the participation of our extended network of educators and their students, and we thank them most sincerely for their efforts.