ASAP Convective Weather Research at NCAR Matthias Steiner and Huaqing Cai Rita Roberts, John Williams, David Ahijevych, Sue Dettling and David Johnson.

Slides:



Advertisements
Similar presentations
Mission: Apply NASA measurement systems and unique Earth science research to improve the accuracy of short-term (0-24 hr) weather prediction at the regional.
Advertisements

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 National Scale Probabilistic Storm Forecasting for Aviation.
Report of the Q2 Short Range QPF Discussion Group Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux.
Calibration of GOES-R ABI cloud products and TRMM/GPM observations to ground-based radar rainfall estimates for the MRMS system – Status and future plans.
Convective and Lightning Initiation Nowcasting Research using Geostationary Satellite towards Enhancing Aviation Safety John R. Mecikalski Assistant Professor.
ENHANCEMENTS OF THE NCAR AUTO-NOWCAST SYSTEM BY USING ASAP AND NRL SATELLITE PRODUCTS Huaqing Cai, Rita Roberts, Cindy Mueller and Tom Saxen National Center.
A Spatial Climatology of Convection in the Northeast U.S. John Murray and Brian A. Colle National Weather Service, WFO New York NY Stony Brook University,
Convective Initiation Studies at UW-CIMSS K. Bedka (SSAI/NASA LaRC), W. Feltz (UW-CIMSS), J. Sieglaff (UW-CIMSS), L. Cronce (UW-CIMSS) Objectives Develop.
UW-CIMSS/UAH MSG SEVIRI Convection Diagnostic and Nowcasting Products Wayne F. Feltz 1, Kristopher M. Bedka 1, and John R. Mecikalski 2 1 Cooperative Institute.
16/06/20151 Validating the AVHRR Cloud Top Temperature and Height product using weather radar data COST 722 Expert Meeting Sauli Joro.
FAA Tactical Weather Forecasting In The
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.
Roll or Arcus Cloud Supercell Thunderstorms.
Forecasting convective initiation over Alpine terrain by means of automatic nowcasting and a high-resolution NWP model Georg Pistotnik, Thomas Haiden,
Roll or Arcus Cloud Squall Lines.
Chapter 13 – Weather Analysis and Forecasting. The National Weather Service The National Weather Service (NWS) is responsible for forecasts several times.
WWOSC 2014 Assimilation of 3D radar reflectivity with an Ensemble Kalman Filter on a convection-permitting scale WWOSC 2014 Theresa Bick 1,2,* Silke Trömel.
“1995 Sunrise Fire – Long Island” Using an Ensemble Kalman Filter to Explore Model Performance on Northeast U.S. Fire Weather Days Michael Erickson and.
Data Integration: Assessing the Value and Significance of New Observations and Products John Williams, NCAR Haig Iskenderian, MIT LL NASA Applied Sciences.
Determining Key Predictors for NCAR’s Convective Auto-Nowcast System Using Climatological Analyses Thomas Saxen, Cindy Mueller, and Nancy Rehak National.
GOES-R Risk Reduction New Initiative: Storm Severity Index Wayne M. MacKenzie John R. Mecikalski John R. Walker University of Alabama in Huntsville.
The NWS/NCAR “Forecaster Over the Loop” Fort Worth Operational Demonstration Human Enhancement of a Thunderstorm Nowcasting System Eric Nelson, Rita Roberts,
VERIFICATION OF NDFD GRIDDED FORECASTS IN THE WESTERN UNITED STATES John Horel 1, David Myrick 1, Bradley Colman 2, Mark Jackson 3 1 NOAA Cooperative Institute.
Event-based Verification and Evaluation of NWS Gridded Products: The EVENT Tool Missy Petty Forecast Impact and Quality Assessment Section NOAA/ESRL/GSD.
IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL Allen Zhao 1, John Cook 1, Qin Xu 2, and.
NASA Applied Sciences Weather Program Review Boulder, CO – November 18-19, 2008 Oceanic Convection Diagnosis and Nowcasting Cathy Kessinger, Huaqing Cai,
Update on NCAR Auto-Nowcaster Juneau, AK. The Auto-Nowcaster System An expert system which produces short-term (0-1 hr) forecasts of thunderstorm initiation,
NCAR Auto-Nowcaster Convective Weather Group NCAR/RAL.
A Thunderstorm Nowcasting System for the Beijing 2008 Olympics: A U.S./China Collaboration by James Wilson 1 and Mingxuan Chen 2 1. National Center for.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Improving Hurricane Intensity.
Experiments in 1-6 h Forecasting of Convective Storms Using Radar Extrapolation and Numerical Weather Prediction Acknowledgements Mei Xu - MM5 Morris Weisman.
The Benefit of Improved GOES Products in the NWS Forecast Offices Greg Mandt National Weather Service Director of the Office of Climate, Water, and Weather.
T he Man-In-The-Loop (MITL) Nowcast Demonstration: Forecaster Input into a Thunderstorm Nowcasting System R. Roberts, T. Saxen, C. Mueller, E. Nelson,
Nowcasting Trends Past and Future By Jim Wilson NCAR 8 Feb 2011 Geneva Switzerland.
Titelfoto auf dem Titelmaster einfügen Deutscher Wetterdienst Dr. Paul James, German Weather Service, ECAM/EMS Conference, Reading, 9. Sept NowCastMIX.
James Pinto Project Scientist II NCAR Research Applications Laboratory NCAR/RAL Perspective on Aviation-based Requirements for RUA.
Convective Storm Forecasting 1-6 Hours Prior to Initiation Dan Lindsey and Louie Grasso NOAA/NESDIS/STAR/RAMMB and CIRA, Fort Collins, CO John Mecikalski,
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.
ASAP In-Flight Icing Research at NCAR J. Haggerty, F. McDonough, J. Black, S. Landolt, C. Wolff, and S. Mueller In collaboration with: P. Minnis and W.
Recent and Planned Updates to the NCAR Auto-Nowcast (ANC) System Thomas Saxen, Rita Roberts, Huaqing Cai, Eric Nelson, Dan Breed National Center for Atmospheric.
Paper WSN05 Toulouse 5-9 September, 2005 VERIFICATION OF OPERATIONAL THUNDERSTORM NOWCASTS E. Ebert, T. Keenan, J. Bally and S. Dance Bureau of.
Federal Aviation Administration Aviation Weather Research Program (AWRP) Highlights for FPAW November 19, 2015.
Developers: John Walker, Chris Jewett, John Mecikalski, Lori Schultz Convective Initiation (CI) GOES-R Proxy Algorithm University of Alabama in Huntsville.
1 Aviation Forecasting – Works in Progress NCVF – Ceiling & Visibility CoSPA – Storm Prediction A Joint Effort Among: MIT Lincoln Laboratory NCAR – National.
COMPARISONS OF NOWCASTING TECHNIQUES FOR OCEANIC CONVECTION Huaqing Cai, Cathy Kessinger, Nancy Rehak, Daniel Megenhardt and Matthias Steiner National.
Nowcasting Convective Storms for Aviation in NCAR/RAL Convective Weather Group Cai Huaqing National Center for Atmospheric Research Boulder, CO, USA.
National Convective Weather Forecast (NCWF) Collaborators: C. Mueller, J. Pinto, D. Ahijevych, D. Megenhardt, N. Rehak Stan Trier, NCAR
Low-level Wind Analysis and Prediction During B08FDP 2006 Juanzhen Sun and Mingxuan Chen Other contributors: Jim Wilson Rita Roberts Sue Dettling Yingchun.
Nowcasting Convection Fusing 0-6 hour observation- and model-based probability forecasts WWRP Symposium on Nowcasting and Very Short Range Forecasting.
THE CHALLENGES OF NOWCASTING CONVECTION OVER THE OCEAN Huaqing Cai, Cathy Kessinger, Nancy Rehak, Daniel Megenhardt and Matthias Steiner National Center.
4 th Workshop on Hyperspectral Science of UW-Madison MURI, GIFTS, and GOES-R Hyperspectral Applications for Aviation Advanced Satellite Aviation-weather.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Combining GOES Observations with Other Data to Improve Severe Weather Forecasts.
Forecaster-Over-The-Loop Demonstration Forecaster CWSU Auto-nowcaster Rita Roberts 28 November 2007 Goals: Science and Operational Debrief NWS Program.
Case Study: March 1, 2007 The WxIDS approach to predicting areas of high probability for severe weather incorporates various meteorological variables (e.g.
Evaluation of Precipitation from Weather Prediction Models, Satellites and Radars Charles Lin Department of Atmospheric and Oceanic Sciences McGill University,
CIMSS Board of Directors Meeting 12 December 2003 Personnel: John Mecikalski (Principal Investigator) and Kristopher Bedka Objective: Develop methods to.
Investigations of Using TAMDAR Soundings in the NCAR Auto-Nowcaster H. Cai, C. Mueller, E. Nelson, and N. Rehak NCAR/RAL.
Space and Time Mesoscale Analysis System — Theory and Application 2007
60 min Nowcasts 60 min Verification Cold Front Regime
NCAR Research on Thunderstorm Analysis & Nowcasting
Center for Analysis and Prediction of Storms (CAPS) Briefing by Ming Xue, Director CAPS is one of the 1st NSF Science and Technology Centers established.
Yuanfu Xie, Steve Albers, Hongli Jiang Paul Schultz and ZoltanToth
ASAP Convective Weather Analysis & Nowcasting
A Real-Time Automated Method to Determine Forecast Confidence Associated with Tornado Warnings Using Spring 2008 NWS Tornado Warnings John Cintineo Cornell.
Winter storm forecast at 1-12 h range
Visible Satellite, Radar Precipitation, and Cloud-to-Ground Lightning
A Real-Time Learning Technique to Predict Cloud-To-Ground Lightning
WMO NWP Wokshop: Blending Breakout
Nowcast guidance of afternoon convection initiation for Taiwan
Rita Roberts and Jim Wilson National Center for Atmospheric Research
Presentation transcript:

ASAP Convective Weather Research at NCAR Matthias Steiner and Huaqing Cai Rita Roberts, John Williams, David Ahijevych, Sue Dettling and David Johnson NASA Applied Sciences Weather Program Review November 2008 in Boulder, Colorado

Height (km) Satellite Detection Time Radar Detection Different pieces of information revealed: cloud properties (satellite) precipitation (radar) electric activity (lightning) environment (surface obs, sounding) Satellite added value: earlier detection of new echoes spatial coverage for data sparse region (e.g., oceans, complex terrain) Time Satellite Radar Lightning Surface Obs Monitoring Present

Forecasting Time Monitoring Satellite Radar Lightning Surface Obs Present NWP Model Forecasting beyond 1-2 hours: data assimilation numerical weather prediction (NWP) blending of heuristic & NWP forecasts satellite CI Heuristic nowcasting: extrapolation of existing echoes growth & decay of echoes initiation of new echoes rapidly decreasing forecast skill CoSPA AutoNowcaster Oceanic Wx

DFW CoSPA ASAP ASAP Evaluations (1)Subjective evaluation of ASAP fields in real-time for Dallas/Fort Worth (DFW) (2)Case study-based evaluation of ASAP fields over parts of CoSPA domain (3)Objective statistical analysis of ASAP fields using Random Forest technique over parts of CoSPA domain approx. $40 K per year support through ASAP highly leveraged with other NCAR efforts, such as CoSPA (FAA), Dallas/Fort Worth (NWS), and Oceanic Weather (NASA ROSES)

(1) Dallas/Fort Worth Real-Time Evaluations ingested ASAP CI fields into NCAR AutoNowcaster subjective evaluation of ASAP CI nowcasts and box-averaged rate-of-change (ROC) fields Findings: data latency of approx. 25 min (15 min satellite data latency & 8-10 min processing by CIMMS) => reduction in data latency highly desirable to increase value of ASAP products daytime product only => 24 hour coverage desired (i.e., develop a nighttime product)

(2) Case Study Evaluations over CoSPA Domain Several cases selected from summer 2007: 7 & 8 June, 12 June, 18 & 19 June, 27 & 28 June, 4 & 5 July Examination of range of predictor fields from - NASA ASAP: CuMask; brightness temperature difference; box-average rate-of-change - MIT/LL: PeakyField (VIS differencing); WxClass; LowLevel Ci; ConvInit - NCAR: IR rate-of-change - Verification: WSI reflectivity and NSSL VIL Methodology: - analysis of predictor time series relative to occurrence of new convection (>35 dBZ) - assessment of relative lead time

19 June :30 UTC 17:30 UTC 21:15 UTC 21:20 UTC 17:30 UTC 19 June :30 UTC 35 dBZ-60 min 18 June dBZ-60 min 27 June 2007

Satellite predictor fields are fairly steady (reliable) with time NCAR Rate of Change and MIT/LL Peaky fields, and to a lesser extent, the NASA ASAP Box-averaged Rate of Change field, are the best satellite-based fields for predicting convection initiation The Rate of Change and Peaky fields provide the largest forecast lead times (30-60 min) These predictor fields will have great impact when used in conjunction with other predictors, e.g, - Combining best attributes of the Rate of Change Peaky, and Box Averaged fields to minimize false alarms, and - using cloud-type fields (NRL, CuMask), stability masks (STMASK), and convergence boundary interest fields (BdryGrid, WxClass, Frontal Likelihood) to mask out areas of low interest Results:

(3) Statistical Analyses Using Random Forest over CoSPA Domain Objective evaluation of ASAP product value for convective initiation based on 1 July to 27 August 2007 ASAP data Use of random forest technique as tool of choice for objective statistical analyses Random forest technique based on lots of decision trees & associated confidence votes for an “event” to happen (=> invited talk by John Williams & Haig Iskenderian) Preprocessing includes generation of additional predictors based on - maximum, minimum, average & standard deviation filters with 5, 10, 20, 40 & 80 km radius of influence - distance of ASAP CI nowcasts from pixel values exceeding 5, 6, 7 & 8 => ASAP experiments based on ~90 satellite-only predictors Random forest technique used for objective assessment of ~300 predictors for CoSPA development

4 July 2007 at 1702 UTC - ASAP CI nowcast1800 UTC - Radar verification 1745 UTC - ASAP CI nowcast1900 UTC - Radar verification

Original ASAP CI nowcastWith 5 km max spatial filtering With 10 km max spatial filteringWith 20 km max spatial filtering

Satellite-only basic predictor fields, plus many derived & enhanced fields Basic SatelliteCloud TypeOther Products VisNCAR cloud typeIR 11 Rate of Change IR 13.3NRL cloud typeMIT/LL Peaky IR 11NRL cloud type interestASAP CI Nowcast IR 6.7 IR 3.9 Relative importance ranking for top 18 predictors

Satellite-based ASAP products can help sharpen location of convective initiation when properly combined with other relevant pieces of information Spatial filtering of ASAP products may yield enhanced prediction value Data latency should be minimized Aim for 24 hour product coverage Continue objective statistical analyses of ASAP fields using random forest to optimize predictor value (e.g., for inclusion in CoSPA forecast system) Summary