1. DATA USED ABSTRACT OBJECTIVES  Vigorous testing of HN and RDT will be carried out for NYCMA  Improvement to the models will be carried out to suite the NYCMA. This might include various parameters as realized from the results analysis.  The use of 700mb wind fields as a steering current for convective storms will be studied.  Automation of nowcasting will be developed with products made available to the public domain through internet STUDY AREA The study area is located in Eastern United States, between latitudes 36º N to 47º N, and longitudes 67º W to 82º W. Two of the selected nowcasting algorithms: Hydro-Nowcaster (HN) and Rapid Developing Thunderstorm (RDT) have already been installed to be tested for nowcasting over the NYCMA. The HN Model: The HN Model: The HN algorithm focuses on two primary components of nowcasting. These are: 1.Motions of rain cells by identifying cloud clusters bounded by isotherms of brightness temperature (BT) and dignosing their motions in a 100 x 100-pixel regions 2.Growth or Decay of the rain cells is based changes of characteristics of cloud cluster of one image to the next. The chnges considered are cloud cluster size, temperature of coldest pixel and average temperature of the cluster. The RDT Model: The RDT Model: The RDT model can be split into 3 stages; 1.Detecton of Cloud System using an adaptative temperature threshold of infrared image (GOES’ IR 10.8) 2.Tracking of cloud system using an algorithm based on the geographical overlaping of the cells. 3.Discrimination of the convective systems among the tracked clouds by identifying trajectories of the cloud systems using either lighting data or the IR characteristics of the cloud systems. The output is the RDT product coded in BUFR format and stored in binary files and can be visualized as in the figure below. NOWCASTING ALGORITHMS New York City Metropolitan Area (NYCMA) HN was implemented for the storm developed on June 12 th 2006 at 17:15 UTC. The GOES IR cloud information for this and previous time at 17:02 UTC in addition to relative humidity (RH), precipitable water (PW) and temperature (T), were used as input of the HN model to nowcast the progression, growth/decay, and intensity of the storm for the next 3 hours. The GOES IR images used and the HN output are presented below. National Oceanic and Atmospheric Administration (NOAA/ NESDIS) Cooperative Research Program (CoRP) 3 rd Annual Science Symposium, Fort Colins, Colorado, 15 – 16 August 2006 Left: METEOSAT infrared image of the 15/08/97 at 1800 UTC; Right: Detected “cells” with colors indicating the temperature threshold used to detect the corresponding cluster.Source: SAF-NWC- IOP-MFT-SCI-SUM-11_v1.0.doc PRELIMINARY RESULTS: METHODOLOGY 1.Infrared (IR) cloud-top brightness temperature (BT) from GOES 10 satellite (Channel 4 [10.8  m] wavelength), was used as input for the both models. Information about other cloud properties such as relative humidity, temperature, and precipitable water were also used for rainfall estimation in conjunction with IR cloud information. 2.NEXRAD stage III and IV rainfall product and rain gauge observations are used for evaluating the nowcasts. Accurate forecasting of convective precipitation for time periods of less than a few hours (Nowcasting) has always been a big challenge to scientists and engineers. Nowcasting, up to 6 hours, in New York City Metropolitan area (NYCMA) is a new challenge because the NYCMA is the largest in the Nation and accurate reliable nowcasts of heavy precipitation would have enormous economic and social value. Over the years the performance of the quantitative precipitation forecasts (QPF) using numerical weather prediction (NWP) models, weather radar data, lightning detectors and satellite imagery as their primary tool for detection of convective storms, have significantly improved, despite some shortcomings on each system. NWP model QPF continues to lag in skill during the first several hours after model initialization, due largely to the “spin-up” problem of having to produce dynamically consistent vertical motion fields. Satellites data have the ability to overcome many of the limitations of NWP model and radar data. Geostationary satellites are capable of providing information about cloud properties at very high temporal resolution (15 minutes) on continuous basis and therefore addressing many of the previous systems’ shortcomings. Efforts in this study will be directed in trying several existing nowcasting algorithms from NOAA-NESDIS, EUMETSAT, NSSL, and NCAR for the NYCMA and modifying the suitable one for using satellite- based cloud information. Modifying an existing nowcasting algorithm for NYCMA to nowcast rainfall at every 15 minutes up to 6 hours duration using satellite-based cloud information. Selected existing nowcasting models are:  Hydro-Nowcaster (HN) from NOAA-NESDIS,  the Rapidly Developing Thunderstorm (RDT) models from France,  the NSSL WDSSII Multiscale Storm Identification and Forecast algorithm,  the Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) module of the NCAR Autonowcast (ANC) system  Yellow  Yellow contour = newly detected system  Yellow  Yellow line = the trajectory of the convective system.  Red  Red contour = the system is growing  Violet  Violet contour = the system is mature.  Blue  Blue contour = the system is decreasing.  Green  Green contour = the convective system in the previous satellite image.  Black  Black arrow = the expected move of the convective system  Visualization of the RDT Product superimposed on its IR.10.8 image GOES IR, at 17:02 UTCGOES IR, at 17:15 UTC Rainfall Nowcastes, at 18:15 UTC (after 1-hour)Rainfall Nowcastes, at 19:15 UTC (after 2-hours) 47 46 45 44 43 42 41 40 39 38 37 36 47 46 45 44 43 42 41 40 39 38 37 36 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 Latitude (Degrees, North) 47 46 45 44 43 42 41 40 39 38 37 36 47 46 45 44 43 42 41 40 39 38 37 36 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 Latitude (Degrees, North) 20 15 10 5 0 20 15 10 5 0 Rainfall INCH Rainfall INCH 47 46 45 44 43 42 41 40 39 38 37 36 47 46 45 44 43 42 41 40 39 38 37 36 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 Latitude (Degrees, North) Longitude (Degrees, West) 20 15 10 5 0 20 15 10 5 0 Rainfall INCH Rainfall INCH 47 46 45 44 43 42 41 40 39 38 37 36 47 46 45 44 43 42 41 40 39 38 37 36 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 Latitude (Degrees, North) Longitude (Degrees, West) Satellite-Based Nowcasting over the New York City Metropolitan Area Bernard.Mhando & Nasim Nourozi, of Graduate Center of City University of New York Dr. Shayesteh Mahani and Dr. Reza Khanbilvardi, Department of Civil Engineering, City College of New York at CUNY The GOES image have been reproduced into rainfall images showing increasing intensity (color changes) and spatial scatering of the storm. This is compared to Stage III radar data for validation.  Visualization of HN Results. The GOES image have been reproduced into rainfall images showing increasing intensity (color changes) and spatial scatering of the storm. This is compared to Stage III radar data for validation. FUTURE WORK