NOAA CREST Institutional Members

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NOAA- CREST Institutional Members

• CUNY City College
• University of Puerto Rico, Mayaguez
• CUNY Lehman College
• CUNY Bronx Community College
• Columbia University

• University of Maryland - Baltimore County
• Bowie State University-Maryland
• Hampton University-Virginia
• Raytheon, and other Industrial Partners

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LAND
CREST
ACTIVITIES
EDUCATION
COASTAL TECHNOLOGY DEVELOPMENT
AIR
HYDRO-CLIMATE
OUTREACH
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Hampton UniversityValidation Efforts
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Validation of NESDIS Hydro-Estimator (HE) over
North American Monsoon Experiment
(NAME) Region
Ismail Yucel (HU), Bob Kuligowski (NOAA-NESDIS)
Senior HU student
NAME Region
Area-averaged Precipitation comparison

• Rain gauge locations
• Each colored layer is assigned to a specific
  elevation group.

• Day-to-day fluctuations and the overall trend
  along
• the comparison period are captured well by
  the
• HE precipitation estimates.

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Comparison of SAGE III and OSIRISLimb Scattering
Ozone Profiles
Robert Loughman Hampton University
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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.

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Validation of SBUV/2 and Brewer-Dobson Ozone
Measurements

• Dr. Stanislav Kireev
• 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
• Research is in close collaboration with
• Dr. L.E.Flynn (NOAA/NESDIS) and
• Dr. I.V.Petropavlovskikh (NOAA/CIRES).

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.
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CUNY-Research ActivitiesAtmospheric(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

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CUNY Cal-Val Research ActivitiesCoastal
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

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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 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.
PERSIANN Estimates vs. Rain Gauge
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.
Time Series of Rainfall Estimates Rain Gauge,
July 2003
PERSIANN Estimates vs. Rain Gauge, July 2003
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.
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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.
PRILIMINARY RESULTS
A 6 hour storm in cold season (02,24,2004)
Hydro-Estimator
A 6 hour storm in warm season (08,22,2003)
Series of two Cold and Warm Storms
Comparing NESDIS hourly Hydro-Estimator (HE),
GMSRA2 Blended rainfall estimates with NEXRAD
Stage-IV rainfall images and hourly time series
with the rain gauge observations.
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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

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Validation of satellite-based snow mapping
algorithms
Study Area (1)
Study Area (2)
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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

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Validation of satellite-based soil moisture
mapping algorithms
RADARSAT
NDVI
SOIL MOISTURE
SM classes
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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)

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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

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• Calibration of Radar Remote Sensing as Applied
• to Soil Moisture and Vegetation Health
  Determination
• Hamed Parsiani
• 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
• 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.
• .

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• SEAWIFS VALIDATION IN COASTAL WATERS
• OF WESTERN PUERTO RICO
• 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 bio-optical 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.

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• Validation of cloud top height retrieval by
• MODIS and MISR instruments
• 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.