NOAA- CREST Institutional Members
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CUNY City College
University of Puerto Rico, Mayaguez
CUNY Lehman College
CUNY Bronx Community College
Columbia University
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University of Maryland - Baltimore
County
Bowie State University-Maryland
Hampton University-Virginia
Raytheon, and other Industrial
Partners
EDUCATION
AIR
LAND
CREST
ACTIVITIES
OUTREACH
HYDRO-CLIMATE
COASTAL &
TECHNOLOGY
DEVELOPMENT
CREST ACTIVITIESResearch
AIR
Stratosphere
Ozone/Aerosols
COASTAL &
TECHNOLOGY
DEVELOPMENT
Data
Compression
Troposphere
Aerosols
Cloud / SST
Detection
Monitoring
Facilities/
Campaigns
Impacts
Optical
Techniques
LAND
HYDROCLIMATE
Soil Moisture
Precipitation
Snow-Cover
Validation
Vegetation
Snow-fall
Studies
Climate
Change
Hampton University
Validation Efforts
Validation of NESDIS Hydro-Estimator (HE) over North American Monsoon
Experiment (NAME) Region
Ismail Yucel (HU), Bob Kuligowski (NOAA-NESDIS)
Senior HU student
Area-averaged Precipitation comparison
• Day-to-day fluctuations and the overall trend along
the comparison period are captured well by the
HE precipitation estimates.
NAME Region
• Rain gauge locations
• Each colored layer is
assigned to a specific elevation
group.
Comparison of SAGE III and OSIRIS
Limb Scattering Ozone Profiles
Robert Loughman
Hampton University
SBUV 2 v8.0 Ozone Data Validation
Hovakim Nazaryan
• SBUV 2 v8.0 Ozone Data Validation using satellite data from
SAGE II, III and HALOE. Comparisons at near coincident points
using monthly weighted means.
• Study of the Time Dependence of the Differences between the
measurements from the SBUV/2 and other instruments.
• Trend analysis using statistical models applied to ozone time
series, including weighted least squares fits to the models with
mean, linear, annual, semi-annual QBO, Solar, and
autoregressive noise terms. PCA analysis of the QBO term.
Validation of SBUV/2 and Brewer-Dobson Ozone Measurements
[SBUV - Brewer] / [ (SBUV + Brewer)/2 ], %
MMA: Arosa Brewer and coincident SBUV
Dr. Stanislav Kireev
20
NIMBUS-7
NOAA-09
NOAA-11
NOAA-14
NOAA-16
10
0
-10
Total Ozone
-20
1988
1990
1992
1994
1996
1998
2000
2002
2004
Year
CREST-HU related activity:
•Development of algorithms to retrieve total and
profile
ozone
data
from
ground-based
measurements made with Dobson and Brewer
spectrometers;
•Intercomparison and validation of ozone data
between ground-based and space borne (SBUV)
observations;
Arosa: MMA=200*(Dobson-Brewer) / (Dobson+Brewer)
Research is in close collaboration with
Dr. L.E.Flynn (NOAA/NESDIS) and
Dr. I.V.Petropavlovskikh (NOAA/CIRES).
4
2
0
%
-2
-4
-6
-8
Total ozone
-10
1988
1990
1992
1994
1996
1998
2000
2002
Year
Comparison of monthly mean anomalies (MMA) of
total ozone measurements for Brewer vs. SBUV
(upper panel) and Brewer vs. Dobson (lower panel)
during 1988-2004.
CUNY-Research Activities
Atmospheric
(Drs. S. Ahmed, B. Gross, and F. Moshary)
• Validation and refinement of Aerosol Optical
Depth products in urban environments using
Aeronent Sky Radiometers
• Development of Lidar -Profiling capabilities to
Validate and Calibrate up-coming Calipso
aerosol profiles
• Sensitivity analysis on the role that imprecise
calibration of HIRS-2 sensors have on cloud
heights through CO2 slicing
• Validate correlations between near surface
backscatter measurements and surface level
PM2.5 measurments from particle samplers
CUNY Cal-Val Research Activities
Coastal Waters
(Drs. S. Ahmed, A. Gilerson, F.Moshary, B. Gross)
• Validation and refinement of Bio-Optical
Models for Chlorophyll and Suspended
solids through Chesepeake and Long
Island Field Campaigns
• Radiometric Validation and Calibration of
Hyperspectral AISA Instrument on
Chesepeake
• Validation and theoretical analysis for the
improvement of Landsat Bathymetry
Validating Remotely Sensed Rainfall Estimates of Tropical Storms
Student: J. Fernandez, MS;
Supervisors: Dr. S. Mahani & Dr. R. Khanbilvardi;
Collaborator: NWS/HL (Dr. P. Restrepo)
Study site & Rain Gauge Map
OBJECTIVE:
July 12, 2003
120
Latitude (Degrees, North)
100
80
60
40
20
0
Longitude (Degrees, West)
Longitude (Degrees, West)
Time Series of Rainfall Estimates
& Rain Gauge, July 2003
Daily Rainfall Estimates (mm/day)
July 01, 2003
PERSIANN Estimates
vs. Rain Gauge
00 N
Preliminary conclusion is:
satellite-based rainfall
estimates seem to be over
estimated with compare to the
rain gauge observations, at
daily, 0.25 x 0.25 resolutions.
PERSIANN Estimates vs.
Rain Gauge, July 2003
100
35
PERSIANN
Gauge
cc = 0.04
nrms = 18.89
bias = 4.15
90
Comparing the remotely sensed rainfall estimates with
rain gauge observations for whole month, demonstrates
displacement between satellite and gauge as well as
overestimated estimates. Sometimes, satellite shows rainy
clouds over the gauges with zero rainfall and also vise
versa. The reason is under investigation.
30
80
70
Daily PERSIANN (mm/day)
25
20
15
60
50
40
30
10
20
5
10
0
68 W
Evaluating satellite-based tropical rainfall estimates, such as: PERSIANN, GPCP,
and TRMM, with compare to the rain gauge observations.
Colombia in South America, with about 8000 to 13000 (mm/yr) average annual
precipitation, is selected for study area.
80 W
12 N
0
5
10
15
July 2003
20
25
30
35
0
0
10
20
30
40
50
60
Daily Rain Gauge (mm/day)
70
80
90
100
Real Time Validation of Satellite-based NESDIS Rainfall Products
Student: W. Harrouch & Kallol Ganguli, MS;
Supervisors: Drs. S. Mahani,. R. Khanbilvardi, A. Gruber;
OBJECTIVE:
Validating high resolution satellite-based NESDIS rainfall products versus NEXRAD (Stage IV) and gauge
rainfall, useful for improving their relevant algorithms, in both cold and warm seasons.
Blended
NEXRAD
45
40
29.5
35
30
29.0
25
20
28.5
15
Hydro-Estimator
10
5
28.0
-83
-82.5
-82
-81.5
-81
-83
-82.5
-82
-81.5
-81
-83
-82.5
-82
-81.5
-81
-83
-82.5
-82
-81.5
-81
60
50
29.5
40
29.0
30
20
28.5
10
28.0 -83
81
-82.5
-82
-81.5
-
-83
81
Longitude (Degrees)
-82.5
-82
-81.5
-
-83
81
Longitude (Degrees)
-82.5
-82
-81.5
-
-83
81
Longitude (Degrees)
-82.5
-82
-81.5
-
0
Stage IV
Latitude (Degrees)
A 6 hour storm
in warm season
(08,22,2003)
30.0
Rainfall (mm/hr)
Latitude (Degrees)
A 6 hour storm
in cold season
(02,24,2004)
GMSRA#2
Rainfall (mm/hr)
HE
30.0
Stage IV
PRILIMINARY RESULTS:
Longitude (Degrees)
Series of two Cold and Warm Storms
Storm of 24th Feb'04
NexRAD
Storm of 22nd Aud'03
NexRAD
HE
HE
45
GMSRA
60
BLENDED
GMSRA
40
BLENDED
50
30
Rain Rate (mm/hr)
Rain rate (mm/hr)
35
25
20
15
40
30
20
10
10
5
0
1400
1500
1600
1700
1800
Hours
1900
2000
2100
0
1700
1800
1900
2000
2100
Hour s
2200
2300
2400
Comparing NESDIS hourly Hydro-Estimator
(HE), GMSRA#2 & Blended rainfall estimates
with NEXRAD Stage-IV rainfall images and
hourly time series with the rain gauge
observations.
Validation of satellite-based snow mapping algorithms
Research Group:
Juan Carlos Arevalo, Amir Azar, Adenrele Ibagbeola (Graduate students, CCNY-CUNY)
Gillian Cain, (Undergraduate student , CCNY-CUNY)
Dr. Hosni Ghedira (Assistant Professor , CCNY-CUNY)
Dr. Reza Khanbilvardi (Professor , CCNY-CUNY)
Collaborators:
Dr. Norman Grody (NOAA-NESDIS)
Dr. Peter Romanov (NOAA-NESDIS)
Satellite Data
Active microwave data: Radarsat
Passive microwave data: SSM/I
Optical Data: AVHRR
Tested algorithm
SSM/I-based snow cover filtering algorithm developed by Norman Grody (NOAA-NESDIS).
Algorithms to be tested
• Energy-and-mass-balance model actually used by the National Weather Service (NOHRSC,
NOAA-NWS)
• Automated GOES-based snow cover and snow fraction mapping algorithm developed by Peter
Romanov (NOAA-NESDIS)
Validation of satellite-based snow mapping algorithms
Study Area (1)
Decision Tree
Ground Data
Study Area, Covered by SSM/I
34x30 pixels
Jan 23
48.7
48.7
46.7
46.7
46.7
44.7
44.7
44.7
42.6
42.6
42.6
40.7
110.6
Jan 24
Jan 25
40.7
108.8
106.5
104.4
102.0
110.6
40.7
108.8
106.5
104.4
102.0
110.6
48.7
48.7
48.7
46.7
46.7
46.7
44.7
44.7
44.7
42.6
42.6
42.6
40.7
110.6
Study Area (2)
Artificial Neural Network
48.7
40.7
108.8
106.4
104.4
102.0
110.6
106.5
104.4
102.0
110.6
48.7
48.7
46.7
46.7
46.7
44.7
44.7
44.7
42.6
42.6
42.6
106.5
104.4
102.0
110.6
104.4
102.0
108.8
106.5
104.4
102.0
108.8
106.5
104.4
102.0
40.7
40.7
108.8
106.5
40.7
108.8
48.7
40.7
110.6
108.8
108.8
106.5
104.4
No coverage
Snow
No Snow
102.0
110.6
Validation of satellite-based soil moisture mapping
algorithms
Research Group:
Tarendra Lakhankar, Nasim Jahan, (Graduate students , CCNY-CUNY)
Parmis Arfania (Undergraduate student , CCNY-CUNY)
Dr. Hosni Ghedira (Assistant Professor , CCNY-CUNY)
Dr. Reza Khanbilvardi (Professor , CCNY-CUNY)
Collaborator:
Dr. Norman Grody (NOAA-NESDIS)
Satellite Data:
Active microwave data: Radarsat
Passive microwave data: SSM/I
Optical Data: AVHRR, LANDSAT
Study Area:
Oklahoma (97d35'W, 36d15'N)
Experiment Validation:
SGP97: Southern Great Plains 1997 campaign operated by NASA. Validation of the data
measured by ESTAR Instrument (Electronically Scanned Thinned Array Radiometer)
Validation of satellite-based soil moisture mapping
algorithms
3800’ N
Soil Moisture Data
165 km x 495 km
(Res. 800 m)
A
3700’ N
Study Area (A and B)
A: 26.4 km x 96 km
B: 31.2 km x 103.2 km
3600’ N
B
Radarsat Image
350 km x 300 km
(Res. 25 m)
3500’ N
9900’W
9800’W
9700’W
9600’W
9500’W
RADARSAT
NDVI
9400’W
Oklahoma (97d35'W, 36d15'N)
SM classes
SOIL MOISTURE
UMBC CREST Cal/Val Activities
•Regional East Atmospheric Lidar Mesonet (REALM)
•UMBC lidar station (elastic, Raman, DABUL lidars)
•REALM data center
•Parameters (extinction, backscatter, AOD, PBL structure)
•US Air Quality Weblog
•GOES Aerosol/Smoke Product (GASP) validation (w/ NESDIS)
Cal/Val effort at
NOAA-CREST-UPRM,
\Puerto Rico
Research group:
Hamed Parsiani, Soil Moisture & vegetation with Radar
Nazario Ramirez & Ramon Vasquez: Hydro-Estimator
Ramon Vasquez, Cloud Height
Fernando Gilbes, Ocean
Calibration of Radar Remote Sensing as Applied
to Soil Moisture and Vegetation Health Determination
Hamed Parsiani
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The Material Characteristics in Frequency Domain (MCFD) algorithm calculates the
MCFD for each GPR image which is used as a signature to determine soil moisture,
soil type, and vegetation index. The usage of properly trained Neural Network acts as
a calibrator for the GPR in soil moisture, or soil type determination.
Vegetation Health is obtained by calibrating the power of MCFD, using the linear
relationship between the NDVI obtained by spectroradiometer and the MCFD power.
The range for calibration and its accuracy for the vegetation health have been
determined.
The basic accuracy in both soil characteristics and vegetation information depend on
the reception of images with quality wavelets. An algorithm is developed which permit
Automatic Quality Wavelet Extraction (AQWE). Currently a 1.5 GHz antenna has
been used for this research.
Validation of Hydro-Estimator Algorithm for Puerto Rico Region
Nazario Ramirez & Ramon Vasquez
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This is the first time that the Hydro-Estimator (HE) algorithm is validated over a
tropical region.
Puerto Rico has a density rain-gauge network that provides the unique data set to
conduct an accurate validation.
The USGS monitors, in Puerto Rico, 120 rain-gauges & records rainfall every 15
minutes. Estimation of precipitation was generated by the same spatial and
temporal distribution using the HE algorithm.
SEAWIFS VALIDATION IN COASTAL WATERS
OF WESTERN PUERTO RICO
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Fernando Gilbes
Mayagüez Bay is a semi-enclosed bay in the west coast of Puerto Rico that suffers
spatial and temporal variations in phytoplankton pigments and suspended sediments
due to seasonal discharge of local rivers.
New methods and instruments have been used as part of NOAA CREST project,
allowing a good understanding of the processes affecting the signal detected by
remote sensors.
A large bio-optical data set has been collected during several cruises in Mayagüez
Bay. Remote Sensing Reflectance, Chlorophyll-a, Suspended Sediments, and
absorption of Colored Dissolved Organic Matter (CDOM) were measured spatially
and temporally. These values were used to evaluate SeaWiFS OC-2 and OC-4 biooptical algorithms in the region.
Remote sensed Chlorophyll-a concentrations were compared against in situ
Chlorophyll-a concentrations. The results show that these algorithms overestimate
the actual Chlorophyll-a.
It is clearly demonstrated that the major sources of this error is the variability of
CDOM and total suspended sediments. The main working hypothesis establishes a
possible relationship between CDOM and the clays in those sediments.
The analyses of SeaWiFS images also verify that its spatial resolution is not
appropriate for these coastal waters. The available data demonstrate that improved
algorithms and different remote sensing techniques are necessary for this coastal
region.
We plan to continue these efforts to validate and calibrate ocean color sensors in
Mayagüez Bay, like MODIS and AVIRIS. We aim to improve the remote sensing
techniques for a better estimation of water quality parameters in coastal waters,
specifically Chlorophyll-a, CDOM absorption, and suspended sediments.
Validation of cloud top height retrieval by
MODIS and MISR instruments
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Cloud top heights can be good indicators of the presence of different types of
clouds over a region.
This information about clouds may provide an input to some climate models
that will predict future total water content between other related climate
phenomena.
The Caribbean data of the Moderate Resolution Imaging Spectroradiometer
(MODIS) and the Multi-Angle Imaging Spectroradiometer (MISR) were obtained
from the EOS Data Gateway (EDG).
Available lidar instrumentation does not provide sufficient information about
cloud profiles. Cross-comparisons of MODIS and MISR instruments can
retrieve cloud top heights.
In this work, cloud top pressures and cloud top heights measured by MODIS
and MISR are compared.
variations between MODIS and MISR cloud top heights may indicate the
retrieval of two different cloud heights over the same area.
Highest difference between MISR and MODIS high clouds vary between 15 and
19 kilometers.
MISR retrieval performance for high clouds is twice the MODIS retrieval
performance. MISR and MODIS cloud values coincide in less than 1% of the
total observed area and the cloud height value is 14km.
A temporal analysis that shows the variation of MODIS cloud top heights over
San Juan, Puerto Rico is also presented.
Results show the ability of MODIS to detect low clouds at tropical regions.
MISR is a better instrument to measure high clouds. MODIS retrieval methods
can identify thicker clouds which are low clouds and MISR retrieval methods
can identify thinner clouds which are high clouds.