The Hydro-Estimator and Hydro-Nowcaster: Satellite- Based Flash Flood Forecasting Tools Robert J. Kuligowski NOAA/NESDIS Center for SaTellite Applications.

Slides:



Advertisements
Similar presentations
JMA Takayuki MATSUMURA (Forecast Department, JMA) C Asia Air Survey co., ltd New Forecast Technologies for Disaster Prevention and Mitigation 1.
Advertisements

Robert DeMaria.  Motivation  Objective  Data  Center-Fixing Method  Evaluation Method  Results  Conclusion.
Thunderstorm Nowcasting at NOAA-CREST Presented by Brian Vant-Hull, Robert Rabin CREST team: Arnold Gruber, Shayesteh Mahani, Reza Khanbilvardi CREST Students:
1 QPE / Rainfall Rate January 10, 2014 Presented By: Bob Kuligowski NOAA/NESDIS/STAR.
DoD Center for Geosciences/Atmospheric Research at Colorado State University VTC 12 September Global Precipitation Products for Data-Denied Regions.
A short term rainfall prediction algorithm Nazario D. Ramirez and Joan Manuel Castro University of Puerto Rico NOAA Collaborator: Robert J. Kuligowski.
Satellite Rainfall Estimation Robert J. Kuligowski NOAA/NESDIS/STAR 3 October 2011 Workshop on Regional Flash Flood Guidance System—South America Santiago.
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.
The Fourth Symposium on Southwest Hydrometeorology, Tucson Hilton East Hotel, Tucson, AZ September 20-21, 2007 Introduction The objective of this study.
An artificial neural networks system is used as model to estimate precipitation at 0.25° x 0.25° resolution. Two different networks are being developed,
Weather.
PERFORMANCE OF THE H-E ALGORITHM DURING THE CENTRAL AMERICAN RAINY SEASON OF 2001.
Infusing Information from SNPP and GOES-R Observations for Improved Monitoring of Weather, Water and Climate Pingping Xie, Robert Joyce, Shaorong Wu and.
GOES-R Synthetic Imagery over Alaska Dan Lindsey NOAA/NESDIS, SaTellite Applications and Research (STAR) Regional And Mesoscale Meteorology Branch (RAMMB)
March 14, 2006Intl FFF Workshop, Costa Rica Weather Decision Technologies, Inc. Hydro-Meteorological Decision Support System Bill Conway, Vice President.
Application of the Computer Vision Hough Transform for Automated Tropical Cyclone Center-Fixing from Satellite Data Mark DeMaria, NOAA/NCEP/NHC Robert.
A GOES-Eye View of Rod Scofield’s Legacy Robert J. Kuligowski NOAA/NESDIS Center for Satellite Applications and Research (STAR) National Weather Association.
Integration of Multiple Precipitation Estimates for Flash Flood Forecasting Reggina Cabrera NOAA/National Weather Service.
Satellite precipitation estimates and the CAFFG system By Rosario Alfaro.
Precipitation Types Important for Real Time Input and Forecasting
Wayne Faas Chief, NOAA National Climatic Data Center Data Operations Division December 3, 2003.
Anthony DeAngelis. Abstract Estimation of precipitation provides useful climatological data for researchers; as well as invaluable guidance for forecasters.
1 GOES-R AWG Hydrology Algorithm Team: Rainfall Potential June 14, 2011 Presented By: Bob Kuligowski NOAA/NESDIS/STAR.
GOES-R Risk Reduction New Initiative: Storm Severity Index Wayne M. MacKenzie John R. Mecikalski John R. Walker University of Alabama in Huntsville.
June Z GOES-12 Infrared 9/5/03 GOES-12 Visible 9/18/03 GOES 10 Infrared 9/1/03 TEXAS 7 DEC Z G12- IR SNOW STORM 29 JAN Z G-12 IR.
SOME EXPERIENCES ON SATELLITE RAINFALL ESTIMATION OVER SOUTH AMERICA. Daniel Vila 1, Inés Velasco 2 1 Sistema de Alerta Hidrológico - Instituto Nacional.
1 The GOES-R Rainfall Rate / QPE Algorithm Status May 1, 2011 Presented By: Bob Kuligowski NOAA/NESDIS/STAR.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Precipitation and Flash Flood.
EVALUATING THE PERFORMANCE OF SATELLITE RAINFALL ESTIMATES USING DATA FROM NAME Background and Motivation Ismail Yucel, Center for Atmospheric Sciences,
ENHANCEMENT OF SATELLITE-BASED PRECIPITATION ESTIMATES USING THE INFORMATION FROM THE PROPOSED ADVANCED BASELINE IMAGER (ABI), PART I: USE OF MODIS CHANNELS.
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Kuolin Hsu, Yang Hong, Dan Braithwaite, Xiaogang.
 Modifying existing nowcasting algorithms to nowcast rainfall for NYCMA at every 15 minutes up to 6 hours duration using satellite-based cloud information.
Weather Forecasting Chapter 9 Dr. Craig Clements SJSU Met 10.
The Hydro-Nowcaster: Recent Improvements and Future Plans Robert J. Kuligowski Roderick A. Scofield NOAA/NESDIS Office of Research and Applications Camp.
a large body of air that has the same temperature and humidity throughout classified according to where they originate during the time the air mass.
1 Flash Floods in the South-Central U.S.: What are They and How Can we Forecast Them? Matt Kelsch Thursday, 30 March 2000 UCAR Cooperative Program for.
Remote Sensing in Hydrology Robert J. Kuligowski, Ph. D. NOAA/NESDIS Office of Research and Applications Presentation to NWS/WMO.
Nowcasting Trends Past and Future By Jim Wilson NCAR 8 Feb 2011 Geneva Switzerland.
Weather Patterns Table of Contents Clouds Precipitation
The Rapid Developing Thunderstorm (RDT) product CDOP to CDOP2
VALIDATION AND IMPROVEMENT OF THE GOES-R RAINFALL RATE ALGORITHM Background Robert J. Kuligowski, Center for Satellite Applications and Research, NOAA/NESDIS,
NESDIS ORA PRESENTATION TO NWS PARTNERS WORKSHOP PRECIP, FLASH FLOODS & AVIATION DEVELOPMENTS Hank Drahos June 24, 2004
Next Week: QUIZ 1 One question from each of week: –5 lectures (Weather Observation, Data Analysis, Ideal Gas Law, Energy Transfer, Satellite and Radar)
Atlantic Simplified Track Model Verification 4-year Sample ( ) OFCL shown for comparison Forecast Skill Mean Absolute Error.
NOAA-MDL Seminar 7 May 2008 Bob Rabin NOAA/National Severe Storms Lab Norman. OK CIMSS University of Wisconsin-Madison Challenges in Remote Sensing to.
1 Nowcasting Flash Floods and Heavy Precipitation --- A Satellite Perspective Roderick A. Scofield, Ph. D.
1 Satellite QPE RFC/HPC Hydromet 02-1 COMET/Boulder, CO 28 November 2001 Bob Kuligowski NOAA/NESDIS/Office of Research and Applications Camp Springs, MD.
COMPARISONS OF NOWCASTING TECHNIQUES FOR OCEANIC CONVECTION Huaqing Cai, Cathy Kessinger, Nancy Rehak, Daniel Megenhardt and Matthias Steiner National.
An Overview of Satellite Rainfall Estimation for Flash Flood Monitoring Timothy Love NOAA Climate Prediction Center with USAID- FEWS-NET, MFEWS, AFN Presented.
a large body of air that has the same temperature and humidity throughout classified according to where they originate during the time the air mass.
JMA Japan Meteorological Agency QPE/QPF of JMA Application of Radar Data Masashi KUNITSUGU Head, National Typhoon Center Japan Meteorological Agency TYPHOON.
High impact weather nowcasting and short-range forecasting using advanced IR soundings Jun Li Cooperative Institute for Meteorological.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Combining GOES Observations with Other Data to Improve Severe Weather Forecasts.
Data Distribution/dissemination Method
SIGMA: Diagnosis and Nowcasting of In-flight Icing – Improving Aircrew Awareness Through FLYSAFE Christine Le Bot Agathe Drouin Christian Pagé.
Storms and the Movement of Air Textbook pages
CIMSS Board of Directors Meeting 12 December 2003 Personnel: John Mecikalski (Principal Investigator) and Kristopher Bedka Objective: Develop methods to.
1 Preliminary Validation of the GOES-R Rainfall Rate Algorithm(s) over Guam and Hawaii 30 June 2016 Presented By: Bob Kuligowski NOAA/NESDIS/STAR.
Encast Global forecasting.
Visible and IR Remote Sensing of Rainfall
The Convective Rainfall Rate in the NWCSAF
Comparing a multi-channel geostationary satellite precipitation estimator with the single channel Hydroestimator over South Africa Estelle de Coning South.
PGE05 CRR Convective Rainfall Rate
Daniel Vila, Luiz A. Toledo Machado
Unit 5 Section 1 Thunderstorms
GOES-R Risk Reduction Research on Satellite-Derived Overshooting Tops
Severe Storms Classified under severe storms are thunderstorms, tornadoes, and hurricanes.
PGE06 TPW Total Precipitable Water
Satellite Foundational Course for JPSS (SatFC-J)
Bob Kuligowski, Clay Davenport, Rod Scofield, Gilberto Vicente
Presentation transcript:

The Hydro-Estimator and Hydro-Nowcaster: Satellite- Based Flash Flood Forecasting Tools Robert J. Kuligowski NOAA/NESDIS Center for SaTellite Applications and Research (STAR) Camp Springs, MD USA with special appreciation to Rod Scofield ( ) who pioneered much of this work International Workshop on Flash Flood Forecasting 14 March 2006

Outline The Hydro-Estimator (HE) The Hydro-Estimator (HE) The Hydro-Nowcaster (HN) The Hydro-Nowcaster (HN) Future Work Future Work Where to Get the Data Where to Get the Data International Collaboration International Collaboration

The Hydro-Estimator (HE): Introduction The HE is NESDIS’ operational algorithm for estimating rainfall rate from infrared (IR) window (~11 µm) brightness temperatures. The HE is NESDIS’ operational algorithm for estimating rainfall rate from infrared (IR) window (~11 µm) brightness temperatures. Real-time estimates are produced globally (60°S to 60°N) at the spatial and temporal resolution of the available geostationary satellite data Real-time estimates are produced globally (60°S to 60°N) at the spatial and temporal resolution of the available geostationary satellite data –4-5 km resolution generally every minutes, depending on location.

Hourly rainfall estimates for UTC 5 January 2005 HE Example—Global Coverage

HE Example—Fine-Scale Detail 15-minute rain rate estimates estimates for UTC 10 July 2005 (Hurricane Dennis landfall)

The Hydro-Estimator: Basic Assumptions Assumes that cloud-top brightness temperature (T b )  cloud-top height  strength of convective updraft  rainfall rate Assumes that cloud-top brightness temperature (T b )  cloud-top height  strength of convective updraft  rainfall rate –Colder, higher clouds are associated with stronger updrafts and heavier rain –Warmer, lower clouds are associated with weak updrafts and light or no rain Reasonable assumption for convective (thunderstorm) clouds Reasonable assumption for convective (thunderstorm) clouds

T (K) T b =230 K T b =224 K T b =212 K T b =200 K Illustration of the IR signal from different rainfall intensities

T (K) Cumulonimbus T b =200 K Nimbostratus T b =240 K Cirrus T b =205 K Exceptions to the Rule...

The Hydro-Estimator: Rain/No Rain Differentiation The original NESDIS automated IR algorithm, the Auto-Estimator, routinely assigned rainfall to cirrus clouds and thus greatly exaggerated the spatial extent of heavy rainfall. The original NESDIS automated IR algorithm, the Auto-Estimator, routinely assigned rainfall to cirrus clouds and thus greatly exaggerated the spatial extent of heavy rainfall. The Hydro-Estimator avoids this problem by considering temperature relative to the local average: The Hydro-Estimator avoids this problem by considering temperature relative to the local average: –Colder than average=active rain area –Warmer than average=inactive cold cloud

Illustration of the HE Rain-No Rain Differentiation T (K) T b < T b Rain T b ≥ T b No Rain T b < T b Rain T b ≥ T b No Rain

H-E Rainfall Rates Initial rain rates are a function of both T b and its value relative to surrounding mean Initial rain rates are a function of both T b and its value relative to surrounding mean Adjustments using numerical weather prediction model data, including: Adjustments using numerical weather prediction model data, including: –Precipitable water to enhance rainfall in regions of high moisture availability –Relative humidity to reduce precipitation in arid regions where raindrops evaporate before reaching the ground –Wind fields interfaced with digital topography to determine orographic effects where wind blows: up slope (moistening and enhancement of rain) or up slope (moistening and enhancement of rain) or down slope (drying and reduction of rain) down slope (drying and reduction of rain)

HE PW Adjustment Sample Hydro-Estimator Rain Rate Curves as a Function of Precipitable Water PW (mm) Wetter environment

The Hydro-Nowcaster: Introduction The Hydro-Nowcaster (HN) is an algorithm for producing 0-3 h nowcasts of precipitation based on extrapolated Hydro-Estimator estimates of rainfall rate. The Hydro-Nowcaster (HN) is an algorithm for producing 0-3 h nowcasts of precipitation based on extrapolated Hydro-Estimator estimates of rainfall rate. Both advection and growth/decay are considered. Both advection and growth/decay are considered.

How the Nowcaster Works— Extrapolation Identifies clusters (regions bounded by fixed brightness temperature values) on two consecutive IR images Identifies clusters (regions bounded by fixed brightness temperature values) on two consecutive IR images Determines cluster motions based on the shift of the coldest 25% of pixels within a 100x100- pixel area that produces the best correlation between the two images Determines cluster motions based on the shift of the coldest 25% of pixels within a 100x100- pixel area that produces the best correlation between the two images Cloud motions are extrapolated out to 3 h at 15-min intervals based on the resulting motion vectors Cloud motions are extrapolated out to 3 h at 15-min intervals based on the resulting motion vectors

t=t 0 -Δt t=t 0 Vector for maximum correlation between t=t 0 -Δt and t=t 0 Extrapolated motion vector Illustration of the motion vector identification in the Hydro-Nowcaster

How the Nowcaster Works— Growth/Decay Each cluster on the current image is matched with one on the previous image according to the computed motion vector Each cluster on the current image is matched with one on the previous image according to the computed motion vector Three factors to determine growth/decay: Three factors to determine growth/decay: –Change in size of the cluster –Change in temperature of the coldest pixel –Change in mean temperature of the cluster The growth/decay factor linearly decays to zero over the 3-h forecast period to avoid unrealistic results The growth/decay factor linearly decays to zero over the 3-h forecast period to avoid unrealistic results

Illustration of time change information in the Hydro-Nowcaster t=t 0 -Δt t=t 0 Growing cluster; enhance rain rates during extrapolation Shrinking cluster; reduce rain rates during extrapolation Warming cluster; reduce rain rates during extrapolation

Example: Hurricane Katrina on 29 August h nowcast: 1200–1300 UTC 3-h nowcast: 1200–1500 UTC

Future Work The Hydro-Estimator is currently being recalibrated to address weaknesses, particularly underestimation of heavy rainfall from warm clouds The Hydro-Estimator is currently being recalibrated to address weaknesses, particularly underestimation of heavy rainfall from warm clouds Hydro-Nowcaster improvements are planned: Hydro-Nowcaster improvements are planned: –Improve the scheme for depicting cloud growth and decay—currently empirical –Develop an advection scheme for circular storms— motion vectors for multiple lags? –Account for the effects of orography on nowcasts of rainfall

Additional Plans Beginning collaboration between City College of New York, NOAA/National Weather Service, and NESDIS to evaluate and test multiple nowcasting frameworks (HN, TITAN, RDT) over the New York City metropolitan area. Beginning collaboration between City College of New York, NOAA/National Weather Service, and NESDIS to evaluate and test multiple nowcasting frameworks (HN, TITAN, RDT) over the New York City metropolitan area. Focus is on NYC, but results will be considered in operational satellite-based nowcasting development in the US. Focus is on NYC, but results will be considered in operational satellite-based nowcasting development in the US.

Where to Get Hydro- Estimator Data Graphic images of Hydro-Estimator fields worldwide are available in real time at

Where to Get Hydro-Nowcaster Data Nowcasts for 1, 2, and 3 h will be updated every 15 minutes for the entire CONUS on the NESDIS Flash Flood Web page:

International Collaboration The Instituto Meteorológical Nacional in Costa Rica is running the Hydro-Estimator in real time as input to the Central American Flash Flood Guidance (CAFFG) system, which supports 7 Central American countries. The Instituto Meteorológical Nacional in Costa Rica is running the Hydro-Estimator in real time as input to the Central American Flash Flood Guidance (CAFFG) system, which supports 7 Central American countries. The Comisión Nacional del Agua in Mexico is also running the Hydro-Estimator in real time for forecaster support. The Comisión Nacional del Agua in Mexico is also running the Hydro-Estimator in real time for forecaster support. The Hydro-Estimator and Hydro-Nowcaster source code can be made available to interested parties—contact me at The Hydro-Estimator and Hydro-Nowcaster source code can be made available to interested parties—contact me at

Questions?