Suman Goyal Sc E /In-charge SYNOPTIC Application Unit Satellite Meteorology Davison

Similar documents
Forecast and Tracking the Evolution of Cloud Clusters (ForTraCC) Using Satellite Infrared Imagery: Methodology and Validation

Thomas Fiolleau Rémy Roca Frederico Carlos Angelis Nicolas Viltard.

Studying cloud properties from space using sounder data: A preparatory study for INSAT-3D

ANALYSIS OF THUNDERSTORM CLIMATOLOGY AND CONVECTIVE SYSTEMS, PERIODS WITH LARGE PRECIPITATION IN HUNGARY. Theses of the PhD dissertation

A comparison of NOAA/AVHRR derived cloud amount with MODIS and surface observation

Volcanic Ash Monitoring: Product Guide

Overview of the IR channels and their applications

The impact of window size on AMV

EUMETSAT Satellite Programmes

Nowcasting: analysis and up to 6 hours forecast

GOES-R AWG Cloud Team: ABI Cloud Height

152 OBJECTIVE, AUTOMATIC TRACKING OF PRE-GENESIS TROPICAL DISTURBANCES WITHIN THE DEVIATION ANGLE VARIANCE METHOD

Cloud Grid Information Objective Dvorak Analysis (CLOUD) at the RSMC Tokyo - Typhoon Center

FLOODALERT: A SIMPLIFIED RADAR-BASED EWS FOR URBAN FLOOD WARNING

Clear Sky Radiance (CSR) Product from MTSAT-1R. UESAWA Daisaku* Abstract

VIIRS-CrIS mapping. NWP SAF AAPP VIIRS-CrIS Mapping

Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction

CALCULATION OF CLOUD MOTION WIND WITH GMS-5 IMAGES IN CHINA. Satellite Meteorological Center Beijing , China ABSTRACT

Evaluations of the CALIPSO Cloud Optical Depth Algorithm Through Comparisons with a GOES Derived Cloud Analysis

Validation of SEVIRI cloud-top height retrievals from A-Train data

WMO 7 International Workshop on Volcanic Ash, Anchorage, AK

Active Fire Monitoring: Product Guide

Nowcasting of significant convection by application of cloud tracking algorithm to satellite and radar images

SOLAR IRRADIANCE FORECASTING, BENCHMARKING of DIFFERENT TECHNIQUES and APPLICATIONS of ENERGY METEOROLOGY

Nowcasting applications. Hungarian Meteorological Service

Best practices for RGB compositing of multi-spectral imagery

8B.6 A DETAILED ANALYSIS OF SPC HIGH RISK OUTLOOKS,

Project Title: Quantifying Uncertainties of High-Resolution WRF Modeling on Downslope Wind Forecasts in the Las Vegas Valley

Measurement of the effect of biomass burning aerosol on inhibition of cloud formation over the Amazon

Estimating Firn Emissivity, from 1994 to1998, at the Ski Hi Automatic Weather Station on the West Antarctic Ice Sheet Using Passive Microwave Data

Regional Forecast Center Timişoara 15. Gh. Adam St., Timişoara, Romania,

RAPIDS Operational Blending of Nowcasting and NWP QPF

Partnership to Improve Solar Power Forecasting

"Attività di modellistica numerica previsionale meteorologica al Servizio IdroMeteoClima di ARPA Emilia-Romagna"

Improved diagnosis of low-level cloud from MSG SEVIRI data for assimilation into Met Office limited area models

NOWCASTING OF PRECIPITATION Isztar Zawadzki* McGill University, Montreal, Canada

SATELLITE IMAGES IN ENVIRONMENTAL DATA PROCESSING

Environmental Remote Sensing GEOG 2021

IMPACT OF SAINT LOUIS UNIVERSITY-AMERENUE QUANTUM WEATHER PROJECT MESONET DATA ON WRF-ARW FORECASTS

An Introduction to the MTG-IRS Mission

163 ANALYSIS OF THE URBAN HEAT ISLAND EFFECT COMPARISON OF GROUND-BASED AND REMOTELY SENSED TEMPERATURE OBSERVATIONS

USING THE GOES 3.9 µm SHORTWAVE INFRARED CHANNEL TO TRACK LOW-LEVEL CLOUD-DRIFT WINDS ABSTRACT

Development of a. Solar Generation Forecast System

NCDC s SATELLITE DATA, PRODUCTS, and SERVICES

EVALUATING SOLAR ENERGY PLANTS TO SUPPORT INVESTMENT DECISIONS

Weather Radar Basics

A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning.

Comparative Evaluation of High Resolution Numerical Weather Prediction Models COSMO-WRF

The STC for Event Analysis: Scalability Issues

Developing Continuous SCM/CRM Forcing Using NWP Products Constrained by ARM Observations

SYNERGISTIC USE OF IMAGER WINDOW OBSERVATIONS FOR CLOUD- CLEARING OF SOUNDER OBSERVATION FOR INSAT-3D

SIXTH GRADE WEATHER 1 WEEK LESSON PLANS AND ACTIVITIES

Satellite Remote Sensing of Volcanic Ash

Mid latitude Cyclonic Storm System. 08 _15 ab. jpg

ECMWF Aerosol and Cloud Detection Software. User Guide. version /01/2015. Reima Eresmaa ECMWF

LANDSAT 8 Level 1 Product Performance

Big Data Assimilation Revolutionizing Weather Prediction

Solarstromprognosen für Übertragungsnetzbetreiber

Traffic Monitoring Systems. Technology and sensors

SEA START Climate Change Analysis Tool v1.1

Preliminary summary of the 2015 NEWS- e realtime forecast experiment

Options for filling the LEO-GEO AMV Coverage Gap Francis Warrick Met Office, UK

McIDAS-V Tutorial Displaying Polar Satellite Imagery updated September 2015 (software version 1.5)

MiSeq: Imaging and Base Calling

1 In this report, "tropical cyclone (TC)" is used as a generic term that includes "low pressure area (LPA)", "tropical depression

MSG MPEF Products focus on GII Simon Elliott Meteorological Operations Division

Wind resources map of Spain at mesoscale. Methodology and validation

Synoptic assessment of AMV errors

A Real Case Study of Using Cloud Analysis in Grid-point Statistical Interpolation Analysis and Advanced Research WRF Forecast System

Development of an Integrated Data Product for Hawaii Climate

Interactive simulation of an ash cloud of the volcano Grímsvötn

Assimilation of cloudy infrared satellite observations: The Met Office perspective

Ensuring the Preparedness of Users: NOAA Satellites GOES R, JPSS Laura K. Furgione

WxFUSION. A. Tafferner. Folie 1. iport Meeting DLR OP

Providing drivers with actionable intelligence can minimize accidents, reduce driver claims and increase your bottom line. Equip motorists with the

Precipitation nowcasting at Finnish Meteorological Institute

Remote sensing of precipitable water vapour and cloud cover for site selection of the European Extremely Large Telescope (E-ELT) using MERIS

Satellite monitoring of oil spills in the Mediterranean Sea for

Obtaining and Processing MODIS Data

HAZARD RISK ASSESSMENT, MONITORING, MAINTENANCE AND MANAGEMENT SYSTEM (HAMMS) FOR LANDSLIDE AND FLOOD. Mohd. Nor Desa, Rohayu and Lariyah, UNITEN

METEOSAT 8 SEVIRI and NOAA AVHRR Cloud Products. A Climate Monitoring SAF Comparison Study. Meteorologi. Sheldon Johnston and Karl-Göran Karlsson

Analysis of Cloud Variability and Sampling Errors in Surface and Satellite Measurements

Transcription:

Suman Goyal Sc E /In-charge SYNOPTIC Application Unit Satellite Meteorology Davison

Introduction What is ForTraCC? It is an algorithm for tracking and forecasting radiative and morphological characteristics of mesoscale convective systems (MCSs) through their entire life cycles using geostationary satellite thermal channel information (10.8 µm).

ForTraCC is a nowcasting software developed by Brazilian Meteorologist for the purpose of convective cell identification tracking and nowcasting by using developed convective cells from the Satellite Data. The Software is originally designed for GOES data Satellite Application unit with the help of SAC, Ahmedabad implemented this software partially on INSAT-3D data The verification of software is in process for Indian region Some verification Results for a particular event are shown.

Algorithm of ForTraCC The main steps of the algorithm are the following: A cloud cluster detection method based on a size and temperature threshold A statistical module to identify morphological and radiative parameters of each MCS A tracking technique based on MCS overlapping areas between successive images A forecast module based on MCS evolution in previous time steps In ForTraCC Vila (2005) method are used and In this case a minimum of 150 pixels considering the image resolution of 4X4 km is taken i.e. minimum size should be 2400 2 km

Tracking technique of ForTraCC The tracking methodology is based on tracking algorithm presented in Mathon & Laurent (2001) The tracking of convective clouds is based on area overlapping method and assumes that there are common pixels in two consecutive images A brightness temperature threshold of 235 K has been chosen for MCS detection because this threshold seems to be suitable for detecting clouds associated with convection. In this technique simply assumes that a cloud at a later time corresponds to that at an earlier time when, considering the previous constraints of size and temperature, there are common pixels in consecutive images.

Tracking methodology Continuity Spliting Merger Situation of Convective clouds obtain during algorithm

MCS center of mass displacement estimation V(t-1) = estimated displacement vector in the previous time VP(t) = predicted displacement vector for next 30 min (considering no change in displacement vector) ΔV(t)= V(t)-VP(t) V(t) is the real MCS displacement The estimation/prediction of vector displacement of MCS for next 30 min is Limitation: Applicable for continuity condition VE(t+1)=V(t) + ΔV(t) Not applicable for merging and splitting Conserve MCS s shape and size

Mesoscale convective system displacement estimation

MCS life cycle Phase (Growth and Decay)

Verification of Satellite based nowcasting technique The thunderstorm event reported by Met Office Event Date : 03-MAY-2014 Event: Thunder Squall reported at Safdarjung (New Delhi) at 03/1354-03/1355 UTC & Alipore (Kolkata) from 03/1336-1337 UTC

Procedure for generating forecast files At least 4 consecutive HDF files are taken for generation of forecast up to 6hrs. Hdf5 Data taken for the generation of forecast file for this case. 1000 1030 1100 1130 For this case the data of 1130 UTC is taken as last for which the forecast up to 1730 UTC are generate for every 30 min. The verification of forecast with actual observation by min CTT of the convective cell are shown

2 hour Fortracc forecast verification 2 Hr. forecast Actual 1330 UTC 213.9 28.8,76.4 206.6 22.1,87.5 218.8 29.0,75.6 209.0 22.3,88.0 2.5 Hr. forecast Actual 1400 UTC 214.1 28.8,76.5 205 21.8,87.5 218.8 29.0,75.6 204.9 22.0,88.2

3 hour Fortracc forecast verification 3 Hr. forecast Actual 1330 UTC 215.5 28.7,77.4 206.3 22.2,87.2 218.8 29.0,75.6 204.9 22.0,88.2 3.5 Hr. forecast Actual 1400 UTC 215.4 28.7,77.6 220 29.3,75.5 204.9 22.0,88.2 207.2 22.2,87.4

4 hour Fortracc forecast verification 4 Hr. forecast Actual 1330 UTC 217.5 28.4,77.1 203.1 21.7,87.6 218.8 29.0,75.6 209.0 22.3,88.0 4.5 Hr. forecast 203.3 21.7,87.7 218.2 28.4,77.2 220 29.3,75.5 204.9 22.0,88.2

CTT comparison over Delhi area Time/fcst hr. fcst actual Min. CTT Position Min CTT Position 1330/ 2 hr. 213.9 28.8,76.4 218.8 29.0,75.6 1400/ 2.5 hr. 214.1 28.8,76.5 220 29.3,75.5 1330/ 3 hr. 215.5 28.7,77.4 218.8 29.0,75.6 1400/ 3.5 hr 215.4 28.7,77.6 220 29.3,75.5 1330/ 4 hr. 217.5 28.4,77.1 218.8 29.0,75.6 1400/ 4.5 hr. 218.2 28.4,77.2 220 29.3,75.5

Min CTT comparison over Alipore area Time/fcst hr. fcst actual Min. CTT Position Min CTT Position 1330/ 2 hr. 206.6 22.1,87.5 209.0 22.3,88.0 1400/ 2.5 hr. 205 21.8,87.5 204.9 22.0,88.2 1330/ 3 hr. 206.3 22.2,87.2 209.0 22.3,88.0 1400/ 3.5 hr 207.2 22.2,87.4 204.9 22.0,88.2 1330/ 4 hr. 203.1 21.7,87.6 209.0 22.3,88.0 1400/ 4.5 hr. 203.3 21.7,87.7 204.9 22.0,88.2

Forecast using animation

J & K Disaster

Procedure for implementation Possible ways to do this: To put the information of area, min CTT, centroid and extent by using locally developed algorithm named FLOODFILL. METHODOLOGY of Floodfill Extracting pixels with given temperature value (here it is 243 K(-30 C)) Run flood fill algorithm for 243 K (-30 C) 223 K (-50 C) 203 K (-70 C) to separate cells Output cell characteristics: Latitude range Longitude range Centre Area Min. temperature

Validation of Nowcasting Technique The correlation graph of Min cloud top brightness temperature for 120 (2hrs) and 150 (2.5 hrs) forecast with actual BT.

Consequences of Convective activity Uttarakhand Disaster J & K Disaster

धन यव द