Noaa crest institutional members
This presentation is the property of its rightful owner.
Sponsored Links
1 / 21

NOAA- CREST Institutional Members PowerPoint PPT Presentation

  • Uploaded on
  • Presentation posted in: General

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.

Download Presentation

NOAA- CREST Institutional Members

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

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

NOAA- CREST Institutional Members

Noaa crest institutional members










Hampton university validation efforts

Hampton UniversityValidation Efforts

Noaa crest institutional members

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.

Comparison of sage iii and osiris limb scattering ozone profiles

Comparison of SAGE III and OSIRISLimb Scattering Ozone Profiles

Robert Loughman

Hampton University

Noaa crest institutional members

SBUV 2 v8.0 Ozone Data Validation

Hovakim Nazaryan

  • SBUV 2 v8.0 Ozone Data Validationusing 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.

Noaa crest institutional members

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.

Cuny research activities atmospheric drs s ahmed b gross and f moshary

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

Cuny cal val research activities coastal waters drs s ahmed a gilerson f moshary b gross

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

Noaa crest institutional members

Latitude (Degrees, North)

Longitude (Degrees, West)

Longitude (Degrees, West)








July 01, 2003

July 12, 2003

Daily Rainfall Estimates (mm/day)

12 N

80 W

68 W

00 N

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


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.

PERSIANN Estimates vs.

Rain Gauge, July 2003

Time Series of Rainfall Estimates

& 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.

Noaa crest institutional members

Stage IV
















Latitude (Degrees)

Rainfall (mm/hr)

-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

Stage IV













Latitude (Degrees)

Rainfall (mm/hr)

-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

Longitude (Degrees)

Longitude (Degrees)

Longitude (Degrees)

Longitude (Degrees)

Real Time Validation of Satellite-based NESDIS Rainfall Products

Student: W. Harrouch & Kallol Ganguli, MS; Supervisors: Drs. S. Mahani,. R. Khanbilvardi, A. Gruber;


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.


A 6 hour storm

in cold season



A 6 hour storm

in warm season


Series of two Cold and Warm Storms

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.

Noaa crest institutional members

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)

Noaa crest institutional members

Validation of satellite-based snow mapping algorithms

Study Area (1)

Study Area (2)

Noaa crest institutional members

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)

Noaa crest institutional members

3800’ N


Soil Moisture Data

165 km x 495 km(Res. 800 m)

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

Radarsat Image

350 km x 300 km(Res. 25 m)

Oklahoma (97d35'W, 36d15'N)

3500’ N

9900’W 9800’W 9700’W 9600’W 9500’W 9400’W

Validation of satellite-based soil moisture mapping algorithms




SM classes

Umbc crest cal val activities

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

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

Noaa crest institutional members

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.


Noaa crest institutional members



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.

Noaa crest institutional members

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.

  • Login