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 ACTIVITIES CREST ACTIVITIES COASTAL & TECHNOLOGYDEVELOPMENT TECHNOLOGYDEVELOPMENT LANDLAND AIRAIR HYDRO-CLIMATE EDUCATIONEDUCATION OUTREACHOUTREACH

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 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 OSIRIS Limb Scattering Ozone Profiles Robert Loughman Hampton 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 Validation Hovakim Nazaryan

Comparison of monthly mean anomalies (MMA) of total ozone measurements for Brewer vs. SBUV (upper panel) and Brewer vs. Dobson (lower panel) during 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). Validation of SBUV/2 and Brewer-Dobson Ozone Measurements

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) 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 (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  W68  W Study site & Rain Gauge Map PERSIANN Estimates vs. Rain Gauge Longitude (Degrees, West) Daily Rainfall Estimates (mm/day) 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 hour storm in warm season (08,22,2003) Stage IV A 6 hour storm in cold season (02,24,2004) PRILIMINARY RESULTS: Latitude (Degrees) Rainfall (mm/hr) 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; HE GMSRA#2 Blended NEXRAD Latitude (Degrees) Rainfall (mm/hr) Series of two Cold and Warm Storms 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: 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) 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: 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

Radarsat Image 350 km x 300 km (Res. 25 m) Study Area (A and B) A: 26.4 km x 96 km B: 31.2 km x km A B 99  00’W 98  00’W 97  00’W 96  00’W 95  00’W 94  00’W 38  00’ N Soil Moisture Data 165 km x 495 km (Res. 800 m) 37  00’ N 36  00’ N 35  00’ N Oklahoma (97d35'W, 36d15'N) SOIL MOISTURE RADARSATNDVI SM classes Validation of satellite-based soil moisture mapping 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 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 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..

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.