Overview of The Weather Companys Principal Forecasting Methodologies
Peter Neilley, Bruce Rose, Joseph Koval, Todd Hutchinson, Paul Bayer, Jeff McDonald, John Mathews, William Cassanova, Dale Eck, Neil McGillis, Shaun Tanner The Weather Company Eric Floehr Intellovations, LLC or How Atmospheric and Computer Sciences have created Forecasts on Demand NWA 2015, Plenary III Big Data and the Traditional Forecasting Paradigm
Forecast foundational datasets are exploding Weather Obs Models Final Forecast Users Post Procs Forecasters which are overwhelming the system NWA Annual 2015, Neilley et al. TWCs New Forecasting Paradigm
Therefore, we set out to create a new forecasting paradigm. Some key tenants of our approach were to: Take fully-appropriate advantage of the richness of modern NWP output Allow forecasts to update continuously at the pace of the input data Preserve human forecaster influence Be easily adaptable to new input data and new scientific methods Have globally ubiquitous content Optimize forecast accuracy NWA Annual 2015, Neilley et al. Some Key Characteristics
The Weather Companys Forecasts on Demand Paradigm Some Key Characteristics On Demand: Forecastsare created and deliveredat the time of request Human Input: Forecasterinfluence retained Fresh: Forecasts alwaysbased partly on the latestfrom all input sources. Precise: Forecasts builtfrom full resolution inputdata, not interpolated pointforecasts. Optimized:Variousstatistical and scientificmethods govern optimalforecast assembly Forecasters Forecasts On Demand Users NWP Post Processors A true On Demand forecast creation system NWA Annual 2015, Neilley et al. Real-time Publication and Distribution Proprietary Weather Obs
The TWC Forecasts on Demand EcoSystem Obs on Demand Engine Real-time Publication and Distribution On Demand Users Govment Weather Obs Proprietary Weather Obs Govment NWP Models Proprietary NWP Global Weather Data Forecasts on NWA Annual 2015, Neilley et al. Forecasts on Demand Engine Human Forecasters Over the Loop
The Forecasts on Demand Engine Forecasts on Demand Engine For details see: AMS WAF2015: 12B.8and AMS16 EIPS: 13.B1 AMS WAF2015:Hutchinson et al., 12B.7 AMS WAF2015: Rose et al., 7B.5 Input Weather Data 1-15 Day Forecast Engine Forecasts on Demand Core Users 0-6 hr Forecast Engine Human Forecasters Over the Loop NWA Annual 2015, Neilley et al. TWCs Forecasts-on-Demand 1-15 Day Forecast Engine:
A multi-model, multi-method blending approach 162 inputs, including: Global and regional models Individual ensemble members(where available) Various MOS forecast products Reforecast-based post- processor outputs. A statistically-optimized blendis based on recent verificationand continuously updated The blend is unique at eachlocation, time, parameter System updates with eachnew input arrival For details see:AMS WAF2015: 12B.8Koval et al., NWA Annual 2015, Neilley et al. TWCs Forecasts-on-Demand 0-hr Forecast Engine:
A blended nowcasting approach Key Characteristics Multi-method, nowcasting system using 6 inputs Principal inputs to the precipitation forecastsare: Radar advection forecasts (where available) Our internal, HRRR-like global NWP Human forecasters. Forecasts update as new input data arrives, no less than once every 15 minutes (in No. Amer and Europe) See AMS WAF201512B.7Hutchinson et al. NWA Annual 2015, Neilley et al. Percent Correct Forecasts
Comparative Forecast Skill: YTD - US Day 1-3 Source: ForecastWatch.com From ForecastWatch 2015 YTD Overviewstatistic, defined as: Overview = .25 * MaxT %Cor + .25 * MinT %Cor+ .50 * Precip %Cor Computed using about 340K forecasts from each providor from 770 1st order US sites Not shown here: Redundant TWC brands Redundant NDFD Climo & Persistence Percent Correct Forecasts NWA Annual 2015, Neilley et al. US, Precip ETS (>0) Days 1-3
Broader Accuracy Metrics Source: ForecastWatch.com Max Temp Min Temp Precipitation US, MaxT MAE Days 1-3 US, MinT MAE Days 1-3 US, Precip ETS (>0) Days 1-3 MAE (F) MAE (F) ETS US, MaxT %Busts Days 1-3 US, MinT %Busts Days 1-3 US, Precip %Cor Days 1-3 Percent Percent Percent NWA Annual 2015, Neilley et al. Overview statistic: Combined Mx/Mn/Pcp %Correct
Global Metrics: Overview statistic:Combined Mx/Mn/Pcp %Correct EUROPE US ASIA Day 1-3 Days 6-9 Days 3-5 Source: ForecastWatch.com Forecasts on Demand Operations
Deployed in the Amazon cloud in 4 global regions Typically delivers 11B forecasts/day Peak load of 26B/day or 250K/sec. Mean forecast creation time ~ 11 ms. Mean total request-to-delivery time < 300 ms. Used by all TWC/WSI consumer forecast systems (The Weather Channel, weather.com, WeatherUnderground, Intellicast, etc.) Also drives our partners weather including Apple, Google, Yahoo!, IBM and a majority of the domestic TV stations. NWA Annual 2015, Neilley et al. Moving Forward Expansion of input data, particularly internationally
Models, (e.g. UKMO, GEM, JMA, FIM global models), Additional observations and remote sensing for nowcasting Non-linear multi-model integration techniques Forecast PDFs and event/threshold forecasts (e.g. prob,.of 6 of snow) Weather object- based blending techniques NWA Annual 2015, Neilley et al. From the AMS Weather Anal. and Forecasting Conf, 2015:
For More Details...... From the AMS Weather Anal. and Forecasting Conf, 2015: 7B.5 A Human Over the Loop Forecast Paradigm at The Weather Company (TWC), Rose et al., 12B Day Weather Forecast Guidance at The Weather Company, Koval et al. 12B hour Weather Forecast Guidance at The Weather Company, Hutchinson et al Upcoming at the AMS Annual Meeting: EIPS 13B.1 Recent Advances in The Weather Companys 1-15 Day Forecasting Guidance Infrastructure, Koval et al. ProbStats 6.5 Consensus forecasting using constrained, regularized regression. Williams et al. NWA Annual 2015, Neilley et al.