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Reza Khanbilvardi (CREST Director)

Flash Flood & CREST (NOAA-Cooperative Remote Sensing Science and Technology Center) (Hydro-Climate & Land Hydrology). Reza Khanbilvardi (CREST Director) NOAA Eastern Region Flash Flood Conference, Wilkes-Barre/PA, June 3, 2010. Flash Flood Forecasting. INPUT Information.

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Reza Khanbilvardi (CREST Director)

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  1. Flash Flood & CREST (NOAA-Cooperative Remote Sensing Science and Technology Center) (Hydro-Climate & Land Hydrology) Reza Khanbilvardi (CREST Director) NOAA Eastern Region Flash Flood Conference, Wilkes-Barre/PA, June 3, 2010

  2. Flash Flood Forecasting INPUT Information Observation or Estimation ofHydrologic Variable: Precipitation (Rainfall & Snowfall) Calibration Data In Situ Data Watershed & Stream Characterization: Topography, roughness Soil Moisture Vegetation density,Infiltration,Interception Hydrograph Stream Flow Snowmelt Reservoir Release from: Rain Gauge, Radar, Satellite Products Hydrologic Models Operation Surface Runoff

  3. CREST Related Actvities/Projects: To Improve Flash Flood Forecasting: Precipitation Enhancement: • Remotely Sensed Precipitation Estimation & Improvement • Precipitation Prediction using Satellite-based Information Soil Moisture Enhancement • CREST L-band and High Frequency Microwave Radiometer • using Passive & Active Microwave for Soil Moisture Estimation • Soil Moisture & Hydrological Modeling • Application of SMAP for Flash Flood Forecasting Test-bed Snow Characteristics & Flash Flood Guidance (FFG) Syatem Sea Ice Monitoring Improve Coastal and Estuarine Flood Monitoring Development of a sub-watershed hydrologic model in Puerto Rico global retrieval of microwave land surface emissivity Improvement

  4. Precipitation Estimation & Nowcast Improvement • MW based Snowfall Detection & Estimation, • Multi Sources Rainfall Estimation to Generate Rainfall for Radar Gap • Areas, • Validation and Improvement of Satellite based Rainfall Products • Satellite-based Thunderstorm Nowcasting,

  5. Data Used: AMSU-B channels: 89-, 150-, 183±1-, 183±3-, 183±7 – GHz & Ground-based snowfall MW-based Snowfall Detection & Estimation Snowfall Estimates (mm) Data Filtering non-precipitating pixels Snowy pixels Model Estimate (Snowfall Rate) Model • Methodology: An Artificial Neural Networks System (ANN) • Data Selection • - 75% of data as Input & • 25% of data for Testing Model Performance 150-, 183±3- and 183±7 - GHz R=0.869 RMSE=0.22 Calibration - Data Filtering • Model Features: • - Channel Combinations, • - Number of Nodes • - Transfer Functions • - Number of Runs # of Nodes in Hidden Layer Channel Combination R=0.600 RMSE=0.34 Validation Snowfall Estimates (mm) # of RUNS with P-value < 0.5 Snowfall Observation (mm)

  6. Multi-Sensors Precipitation Estimation Objective: To Develop a Multi-Sensor Rainfall Retrieval Algorithm to Generate more accurate Rainfall for Radar Gap Areas. In this project, NESDIS Hydro-Estimator & NEXRAD Stage-IV at Hourly 4km x 4km Procedures (3 steps): 1) Spatial Error Correction:Apply Method of Least Squares (Brogan 1985): the method of Hills Climb to cluster Rainy pixels because the corresponding clusters are to pick up. 2) Bias Correction Bias ratio field using Inverse Distance method provides a more radar like output both spatially and intensity wise. 3) Merge Rain gauge-Radar-Satellite Rainfall to Generate Rainfall for Radar Gap areas Pixel by pixel based Successive Correction Method (SCM) has been tried for a selected gap area in the radar rainfall.

  7. Validation & Improvement of Rainfall Products • Validation of satellite-based rainfall retrieval algorithms for hurricane storms Satellite–based Hydro-estimator, GMSRA from NESDIS, PERSIANN and TRMM-3B42 RT rainfall retrieval algorithms have been evaluated at hourly and daily basis for Five very strong hurricanes: Charley, Frances, and Jeanne from 2004 and Wilma and Rita from 2005 against NEXRAD Stage-IV. Hurricane Frances (5 September 2004, 0-23 UTC) Stage IV Radar Hydro-Estimator GMSRA PERSIANN TRMM 3B42 • Validation & Improvement of NESDIS Hydro-Estimator A new algorithm is been developed to detect rainy cloud pixels using visible and infrared GOSE data. This algorithm improves the performance of the Hydro-Estimator. The plots show the preliminary comparison between the Hydro-Estimator and new algorithm. Minimizing the index number implies minimizing the average false alarm rate (FAR), maximizing the average of probability of detection (POD), and maximizing the average hit rate (HR)

  8. => Storm Detection Cloud Tracking => Extrapolation t1 t2 t3 t4 Satellite Thunderstorm Nowcasting (Transitioning GOES-based Nowcasting Capability into the GOES-R Era) Joint Project: CREST-CUNY, NWS-MDL, NESDIS, OAR-NSSL, & CIMMS RDT (Rapid Developing Thunderstorms) Model, developed by Météo-France in the framework of EUMETSAT-SAF Nowcating. - Single IR channel statistics used throughout cell lifecycle to evaluate convective activity, • Cells are detected by whether they form towers higher than a given BT threshold (6 degrees). Cells are tracked, and all properties, such as: contours,areas, growth rates, BT min, BT avg, and BT gradients around the periphery are stored. • Lookup tables of cell lifecycles are used to determine if the cell may be convective. Growthrates and roundness of the top are important parameters. Steps in Thunderstorm Nowcasting Borrow ideas from existing algorithms that do each step best.

  9. Applying Extrapolation Extrapolation is based on RDT cloud lifecycles study. Red: previous Yellow: current Green: extrapolated Investigation is needed to stabilize extrapolation

  10. The RDT model in New York Data from direct broadcast, 15 min refresh rate http://air.ccny.cuny.edu/

  11. Land Group:Soil - Snow - Vegetation • Projects: • Snow cover and snow water equivalent (SWE) retrieval from active and passive microwave sensors. • Development of merging algorithms that combines microwave and thermal infrared observations for soil moisture observations. • Improve soil moisture and snow retrieval algorithm reducing vegetation effect using NOAA-CREST Microwave Radiometers. • To improve flash flood forecasting system using satellite based gridded soil moisture data (SMAP Testbbed soil moisture data). • Sea ice monitoring using geostationary satellite data • Integration of the produced soil moisture and snow cover characteristics maps into hydrological models. • Active Microwave • Passive Microwave • Optical Sensors Vegetation Snow Cover Soil Moisture

  12. Soil Moisture Estimation & Improvement • CREST L-Band & High Frequency Microwave Radiometer • Soil Moisture Estimation using Passive and Active Microwave & Optical • Sensors, • Soil Moisture Retrieval & Hydrological Modeling, • Application of SMAP Test-bed for Flash Flood Forecasting

  13. NOAA-CREST L-band Microwave Radiometer L-Band Radiometer Frequency: 1.40 to 1.55 GHz (SMAP mission Frequency) Dual polarization (H, V) Antenna system: 1.5 x 0.7 meters Manufacturer: Radiometrics Corporation, Boulder CO. • Research Objectives: • L-band soil moisture remote sensing field experiments for calibration and validation of soil moisture retrieval algorithms. • Temporal analysis of brightness temperature variation with respect to measured soil moisture and vegetation characteristics (NDVI and NDWI) to develop (or strengthen) vegetation component of Radiative Transfer Model.  • Study the land emissivity variation under a controlled environment (roughness and vegetation). • Investigate the impact of inter rainfall time interval on the retrieval of soil moisture particularly over vegetated areas.

  14. Sensitivity Analysis of b-factor in Microwave Emission Model for Soil Moisture Retrieval Calibration and validation of radiative transfer model for soil moisture retrieval at low frequency (1.4 GHz) using better vegetation component. Neural network and fuzzy logic modeling for soil moisture retrieval. Evaluate the impact of land cover heterogeneity on soil moisture retrieval. Evaluate the vegetation impact on soil moisture retrieval for different land cover type. Soil Moisture Research Impact of land cover heterogeneity on soil moisture retrieval Lakhankar et al (2009 b) Neural network and fuzzy logic modeling for soil moisture retrieval Simulated SM Truth SM Lakhankar et al (2009 a) Seo et al (2010)

  15. Soil moisture retrieval and hydrological modeling Analysis of an Adaptive NRCS Curve Number LULC influences CN and is closely related to its changeable behavior. SM affects the CN values and also contribute to its variation due to the amount of water infiltrated. LULC and SM, therefore, are key factors for understanding CN ‘s behavior. Intraseasonal variation of the CN over a selected watershed in NJ. MOPEX data has been used. discharge and precipitation observations since 1927 Qualitative vs quantitative estimate of soil moisture Regression analysis between soil wetness indexes [TB (H) 6.9 GHz] using the end members derived at local and In-situ soil moisture observed Regression analysis between soil wetness indexes [TB (H) 10.7 GHz] using the end members derived at local and In-situ soil moisture observed Soil moisture experiment (SMEX) campaigns

  16. SMAP Test-bed data for Flash Flood Forecasting Snow Model (snow17) Snow melt Precipitation Sacramento based Soil moisture Evapo-transpiration Continuous API based Soil moisture Soil moisture accounting model Runoff SMAP testbed Soil moisture HL-RDHM Flow Flash Flood Forecasting Comparison and Evaluation • To improve flash flood forecasting system using satellite based gridded soil moisture data (SMAP Testbbed soil moisture data). • Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) developed by NOAA-NWS is currently used for Flash Flood Forecasting. River Flow or Stage, Gauge data

  17. Flash Flood Forecasting Enhancement • Snow Characteristics Flash Flood Guidance (FFG) System, • Towards a better global retrieval of microwave land surface emissivity , • Sea ice monitoring over the Caspian Sea using geostationary satellite data • Improve Coastal and Estuarine Flood Monitoring • Development of a sub-watershed hydrologic model within the western PR • study basin

  18. Snow Characteristics /Flash Flood Guidance (FFG) System

  19. CREST High Frequency Microwave Radiometers C-Band Radiometer Frequency: 37 and 89 GHz Dual polarization (H, V) Manufacturer: Radiometrics Corporation, Boulder CO. • Research Objectives: • Use high frequency microwave radiometer field experiments for calibration and validation of Snow cover and SWE retrieval algorithms. • Temporal analysis of brightness temperature variation with respect to snow depth, SWE, and Snow Grain Size.

  20. Towards a better global retrieval of microwave land surface emissivity Model Atmospheric correction according to Liebe’s model a) b) Where Upwelling (a) and downwelling (b) brightness temperature as an atmospheric contribution to the satellite observation on July 14th 2003 at 37 GHz Products a) b) Comparing 19 GHz in a) and 37 GHz in b) V and H polarization emissivities

  21. Sea ice monitoring over the Caspian Sea using geostationary satellite data The average percentage of cloud reduction because of the daily compositing ranged from 22% to 25%. Daily maps of ice distribution and concentration with minimal cloud coverage were produced. MSG SEVIRI full disk false color composited image and the portion of the image over Caspian Sea reprojected to latitude-longitude grid on 23 January 2007 at 10:15 AM UTC. SEVIRI-based sea ice map over the northern part of the Caspian Sea on 28 February 2007 at 11h15 AM UTC (right) and the MODIS true-colour image for the same day (left) The obtained correlation coefficients with IMS charts for 2007 and 2008 were 0.92 and 0.83 respectively. The technique has been proposed as one of candidate ice mapping techniques for the future GOES-R ABI instrument. Instantaneous ice maps (left column) and original MSG SEVIRI images on 23 January 2007. False color images in the right column are constructed with Ch.3 reflectance (red), HRV reflectance (green) and inverted infrared brightness temperature (blue) TEMIMI, M., ROMANOV, P., GHEDIRA, H., KHANBILVARDI, R. & SMITH, K. (2009) Sea ice monitoring over the Caspian Sea using geostationary satellite data. International Journal of Remote Sensing, Accepted.

  22. Improve Coastal and Estuarine Flood Monitoring (Establishing the Application of High Resolution Satellite Imagery) Example of inland flooded area in red Hurricane Charley 2004 track and hurricane eye location on the 08/14/2004 Use of Radarsat 1 images Additional flooded area (in red) can be seen inland and along the coast 8/15/2004 (right after hurricane Charley 2004) 11/14/2005 (low tide conditions)

  23. Development of a sub-watershed hydrologic model within the western PR study basin Remote Sensing of Evapotranspiration in the Caribbean Region (UPRM E. Harmsen) • Specific topics being investigated: • A Flood Forecast Alarm System for Western Puerto Rico • Evaluation of Upscaling Parameters and their Influence on Hydrologic Predictability in Upland Tropical Areas • Calibration and Validation of High Resolution Radar Rainfall Estimation A GOES product has been developed for PR and Hispañola to estimate ground-based solar radiation and evapotranspiration. An algorithm is being developed to perform a pixel-by-pixel daily water balance. The algorithm will provide soil moisture content which is an initial condition for the flood Nowcast model. Example of Daily Reference Evapotranspiration (mm) in Puerto Rico, May 27, 2009 * Example of Daily Reference Evapotranspiration (mm) in Haiti and the Dominican Republic, March 10, 2010 Testbed Subwatershed Basin-Scale Model Upscaling Procedure

  24. CREST Hydro-Climate Participants: NOAA-CREST Scientists: CCNY/CUNY CCNY/CUNY CCNY?CUNY CCNY/CUNY UPRM UPRM UPRM Reza Khanbilvardi Shayesteh E. Mahani Arnold Gruber Brian Vant Hull Eric Harmsen Nazario D. Ramirez Ramon Vasquez NOAA Collaborators: Ralph Ferraro Bob Kuligowski Pedro Restrepo Mamoudou Ba Robert Rabin Cezar Kongoli Stephan Smith David Kitzmiller Daniel Lindsey John R. Mecikalski NESDIS NESDIS NWS MDL OAR NESDIS MDL NWS CIMMS CIMMS

  25. CREST Land Participants: NOAA-CREST Scientists: CCNY- CUNY CCNY- CUNY CCNY-CUNY CCNY-CUNY CCNY-CUNY CCNY-CUNY CCNY-CUNY CCNY-CUNY CCNY-CUNY Reza Khanbilvardi Marouane Temimi Alvaro Gonzales Pradipat Sumakal Naira Chaouch Lenny Roytman Atiq Rahman Tarendra Lakhankar Amir Azar NESDIS NESDIS NESDIS STAR STAR NESDIS Peter Romanov Fuzhong Weng Sid Boukabara Jerry Zhan Felix Kogan Mitch Goldberg NOAA Collaborators:

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