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

CREST CREST

ACTIVITIESACTIVITIES

CREST CREST

ACTIVITIESACTIVITIESCOASTAL & COASTAL &

TECHNOLOGY TECHNOLOGY DEVELOPMENTDEVELOPMENT

COASTAL & COASTAL & TECHNOLOGY TECHNOLOGY DEVELOPMENTDEVELOPMENT

LANDLANDLANDLAND

AIRAIRAIRAIR

HYDRO-CLIMATE HYDRO-CLIMATE

EDUCATIONEDUCATIONEDUCATIONEDUCATION

OUTREACHOUTREACHOUTREACHOUTREACH

AIR

Soil Moisture

LAND

Snow-Cover

Vegetation

HYDRO-CLIMATE

Snow-fall Studies

Validation

Precipitation

COASTAL & TECHNOLOGY DEVELOPMENT

Optical Techniques

Data Compression

Ozone/Aerosols

Aerosols

Cloud / SST Detection

Impacts

Monitoring Facilities/ Campaigns

Stratosphere

Troposphere

CREST ACTIVITIES-Research

Climate Change

Hampton UniversityValidation Efforts

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

• 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.

Area-averaged Precipitation comparison

Comparison of SAGE III and OSIRISLimb Scattering Ozone Profiles

Robert LoughmanHampton University

• 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.

SBUV 2 v8.0 Ozone Data ValidationHovakim Nazaryan

MMA: Arosa Brewer and coincident SBUV

Year

1988 1990 1992 1994 1996 1998 2000 2002 2004

[SB

UV

- B

rew

er]

/ [

(S

BU

V +

Bre

we

r)/2

],

%

-20

-10

0

10

20

NIMBUS-7NOAA-09NOAA-11NOAA-14NOAA-16

Total Ozone

Arosa: MMA=200*(Dobson-Brewer) / (Dobson+Brewer)

Year

1988 1990 1992 1994 1996 1998 2000 2002

%

-10

-8

-6

-4

-2

0

2

4

Total ozone

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.

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) andDr. I.V.Petropavlovskikh (NOAA/CIRES).

Validation of SBUV/2 and Brewer-Dobson Ozone Measurements

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 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

0 5 10 15 20 25 30 350

5

10

15

20

25

30

35

July 2003

PERSIANN Gauge

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

Daily Rain Gauge (mm/day)

Dai

ly P

ER

SIA

NN

(m

m/d

ay)

cc = 0.04 nrms = 18.89bias = 4.15

Validating Remotely Sensed Rainfall Estimates of Tropical StormsStudent: J. Fernandez, MS; Supervisors: Dr. S. Mahani & Dr. R. Khanbilvardi; Collaborator: NWS/HL (Dr. P. Restrepo)

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.

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.

12 N

00 N

80

W

68

W

Study site & Rain Gauge Map

PERSIANN Estimates vs. Rain Gauge

Longitude (Degrees, West) Dai

ly R

ainf

all E

stim

ates

(m

m/d

ay)

120

100

80

60

40

20

0

July 12, 2003

Longitude (Degrees, West)

Latitude (Degrees, North)

July 01, 2003

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.

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.

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.

A 6

ho

ur

sto

rm

in w

arm

se

as

on

(0

8,2

2,2

00

3)

Sta

ge I

VSt

age

IV

A 6

ho

ur

sto

rmin

co

ld s

ea

so

n(0

2,2

4,2

00

4)

PRILIMINARY RESULTS:

-83 -82.5 -82 -81.5 -81

-83 -82.5 -82 -81.5 -81

-83 -82.5 -82 -81.5 -81

30.0

29.5

29.0

28.5

28.0

-83 -82.5 -82 -81.5 -81

60

50

40

30

20

10

0

Latit

ude

(Deg

rees

)

Rai

nfa

ll (m

m/h

r)

Longitude (Degrees)Longitude (Degrees) Longitude (Degrees) Longitude (Degrees)

Real Time Validation of Satellite-based NESDIS Rainfall ProductsStudent: W. Harrouch & Kallol Ganguli, MS; Supervisors: Drs. S. Mahani,. R. Khanbilvardi, A. Gruber;

-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

30.0

29.5

29.0

28.5

28.0

45

40

35

30

25

20

15

10

5

HE GMSRA#2 Blended NEXRAD

Latit

ude

(Deg

rees

)

Rai

nfa

ll (m

m/h

r)

Series of two Cold and Warm StormsStorm of 22nd Aud'03

0

10

20

30

40

50

60

1700 1800 1900 2000 2100 2200 2300 2400

Hours

Ra

in R

ate

(m

m/h

r)

NexRAD

HE

GMSRA

BLENDED

Storm of 24th Feb'04

0

5

10

15

20

25

30

35

40

45

1400 1500 1600 1700 1800 1900 2000 2100

Hours

Ra

in r

ate

(m

m/h

r)

NexRAD

HE

GMSRA

BLENDED

Hydro-Estimator

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: RadarsatPassive microwave data: SSM/IOptical 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 Validation of satellite-based snow mapping algorithms

Validation of satellite-based snow mapping algorithms Validation of satellite-based snow mapping algorithms

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

Decision Tree

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

Decision Tree

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

Artificial Neural Network

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

Artificial Neural Network

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.4 104.4 102.0

48.7

46.7

44.7

42.6

40.7

Ground Data

Jan 23

Jan 24

Jan 25

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.4 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.5 104.4 102.0

48.7

46.7

44.7

42.6

40.7

110.6 108.8 106.4 104.4 102.0

48.7

46.7

44.7

42.6

40.7

Ground Data

Jan 23

Jan 24

Jan 25

No coverage

Snow

No Snow

No coverage

Snow

No Snow

No coverage

Snow

No Snow

Study Area, Covered by SSM/I34x30 pixels

Study Area, Covered by SSM/I34x30 pixels

Study Area (1)

Study Area (2)

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: RadarsatPassive microwave data: SSM/IOptical 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 Validation of satellite-based soil moisture mapping algorithms algorithms

Radarsat Image

350 km x 300 km(Res. 25 m)

Study Area (A and B)

A: 26.4 km x 96 kmB: 31.2 km x 103.2 km

A

B

9900’W 9800’W 9700’W 9600’W 9500’W 9400’W

3800’ N

Soil Moisture Data

165 km x 495 km(Res. 800 m)

3700’ N

3600’ N

3500’ N

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

SOIL MOISTURE

RADARSAT

NDVI

SM classes

Validation of satellite-based soil moisture mapping Validation of satellite-based soil moisture mapping algorithms algorithms

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 RicoResearch group:

Hamed Parsiani, Soil Moisture & vegetation with RadarNazario 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• 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 RegionNazario 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.

.

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.

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.