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Encast Global forecasting.

Published byTrevor Arnold Modified over 6 years ago

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Presentation on theme: "Encast Global forecasting."— Presentation transcript:

1 Encast Global forecasting

2 ENcast What it is: Extremely accurate weather forecasting solution available globally How is works: ENcast infuses localized, real-time weather information into numerical weather predictions to improve the accuracy of short-term forecasts. It uses the highest-resolution models and updates the forecast frequently with the latest observations. ENcast addresses the biggest numerical weather prediction challenges that inhibit increasing forecast accuracy and sets a new standard for accurate hourly weather forecasts from 0 to 15 days by combining: Earth Networks Weather & Lightning Observations Government and Proprietary Weather Observations High-Resolution Models including ECMWF Hourly Updates High-Speed Computing Products Usage Formats: Hosted web tool, data feeds EN.Mktg.Int_MM20_

3 ENcast Solution Advantages
Global: Coverage for entire World Hyper-Local: Existing Network and Flexible Siting for new stations Fastest Updates: Hourly; uses proprietary EN real-time data Lowest Forecast Error: Accuracy and Nowcast Advantage Turnkey Solution: All forecasting needs in one solution NCAR Solution: Proprietary forecast benefits from NCAR research and development EN.Mktg.Int_MM20_

4 ENcast Global Forecasts
EN.Mktg.Int_MM20_

5 Global Weather Forecast Consumer Applications
ENcast Engine Forecast Inputs ENcast Engine Hi-Res Weather Network ECMWF and Ensembles Earth Networks Lightning and DTA Polygons Hourly Updates Real-time Validation for Model Improvements Global Forecast Deterministic Global Forecast Ensemble Avg. Canada Global Multiscale Canada Met Center Ensemble ECMWF Deterministic ECMWF Ensemble Prediction Global Weather Forecast Consumer Applications Enterprise Applications Result: Forecasts that is 25% more accurate Predicted vs. Actual RMSE

6 1. 2. 3. ENcast Forecasts Consumer Forecast Enterprise Forecast
City Level: Forecast based on City Location Forecast Period: 10-Day Day/Night Updating: 2x per Day or Premium Hourly out 6 Days Spatial Resolution: 4km CONUS/Europe 12.5km Global Consumer Forecast 1. Sensor Level: A Forecast for any Sensor Location Earth Networks, METAR, SYNOP Forecast Period: 15-Day Hourly Updating: 2x per Day or Premium Hourly out 6 Days Spatial Resolution: Data directly from a Point! Enterprise Forecast 3.5% RMSE Improvement over Location! 2. Same as Sensor, only from a Lat/Lon location and not utilizing sensor data from the site Spatial Resolution: 4km CONUS/Europe 12.5km Global Enterprise Forecast 3.

7 15-Day Hourly Forecast and Hourly Update up to 6 Days
ENcast Sensor Latitude/Longitude Hourly Forecast Updates Day 1 Day 6 Day 15 Premium Hourly Updating Service Updates twice/day 15-Day Hourly Forecast and Hourly Update up to 6 Days EN.Mktg.Int_MM20_

8 Parameters - - Sensor Lat/Lon City Temperature 24hr High Temperature
24hr High Temperature   - 24hr Low Temperature    - Wind Direction Wind Speed/Gust Dew Point Cloud Cover Thunderstorm Probability 1hr Precipitation Probability - 1hr Accumulated Precipitation  - Precip Probability/Type Visibility    Mean Sea Level Pressure Forecasted Sfc Insolation Fog Probability Surface Pressure Rain Probability Ice Probability Snow Probability

9 ENcast: Hourly Point Forecasts
Sensor Forecast Accuracy Point Forecast Visualización de ENcast, basado en web, para cada opción y los siguientes parámetros: City Forecast Temperature 24hr High Temperature 24hr Low Temperature Wind Direction Wind Speed Dew Point Cloud Cover Thunderstorm Probability 1hr Precipitation Probability 1hr Accumulated Precipitation Fog Probability Visibility Rain Probability Surface Pressure Surface Insolation

10 ENcast ENcast provides the best in forecast verification by ingesting the top models and using our proprietary Total Lightning data to reach the most accurate forecast outcomes. EN.Mktg.Int_MM20_

11 Model Inputs Used in ENcast Global Forecast Solution
EN.Mktg.Int_MM20_

12 ENcast Improvements from Observation and Lightning
Direction and Speed Vector of Cell Determined by Running Mean of Movement of Cell Centroids Lightning Cell Cell Intensity Determined by Density of Lightning in Cell Clustering Algorithm Tracks Cell Centroid Each Minute EN.Mktg.Int_MM20_

13 ENcast Improvements from Observation and Lightning
Lightning Cell Polygon Length = 45 min - Updates every 15 min EN.Mktg.Int_MM20_

14 ENcast Improvements from Observation and Lightning
EN.Mktg.Int_MM20_

15 ENcast Improvements from Observation and Lightning
Adjusted POP is used if it is higher than original ENcast T’storm POP EN.Mktg.Int_MM20_

16 ENcast Improvements from Observation and Lightning
Brier score comparisons of POTS before and after post-processing. EN.Mktg.Int_MM20_

17 ENcast Improvements from Observation and Lightning
Sparse population of high quality, high frequency METAR/SYNOP observations allows for additional mesonet observations with the same quality requirements to be added to post-processing. EN.Mktg.Int_MM20_

18 ENcast Improvements from Observation and Lightning
EN.Mktg.Int_MM20_

19 ENcast Improvements from Observation and Lightning
West Coast                  Intermountain West 230         Northern Plains         78           Southern Plains                Great Lakes        349         Southeast                   Mid-Atlantic                  Northeast        Total 1996 Groupings by Region and Elevations (m) 0 – 49.5      50 – 99.5         100 –      200 –      300 –      500 –       1000 –    2000 – 4000      Total EN.Mktg.Int_MM20_

20 ENcast Improvements from Observation and Lightning
EN.Mktg.Int_MM20_

21 ENcast Improvements from Observation and Lightning
EN.Mktg.Int_MM20_

22 ENcast Improvements from Observation and Lightning
Elevation RMSE Analysis for Temperature (C) EN.Mktg.Int_MM20_

23 ENcast Improvements from Observation and Lightning
Region RMSE Analysis for Temperature (C) Note: Dew point analysis is not included as results were similar to temperature. EN.Mktg.Int_MM20_

24 ENcast Improvements from Observation and Lightning
EN.Mktg.Int_MM20_

25 Lowest Forecast Error Performance: Uses Best-Performing Global Models
Multiple Ensembles: Factors Multiple Ensemble Groups Self Improvement: Learns from Observation Data Accuracy: Forecast Error Beats Other Models EN.Mktg.Int_MM20_

26 Global Weighted Average RMSE
Sept.-Dec. 2014 EN.Mktg.Int_MM20_

27 Global Weighted Average RMSE
Sept.-Dec. 2014 EN.Mktg.Int_MM20_

28 RMSE Analysis per Model (0-15Hr)
Sept.-Dec. 2014 EN.Mktg.Int_MM20_

29 RMSE Analysis per Model (5-Day)
Sept.-Dec. 2014 EN.Mktg.Int_MM20_

30 RMSE Analysis per Model
Sept.-Dec. 2014 EN.Mktg.Int_MM20_

31 RMSE Percent Improvement
Sept.-Dec. 2014 EN.Mktg.Int_MM20_

32 RMSE Percent Improvement (0-15Hr)
Sept.-Dec. 2014 EN.Mktg.Int_MM20_

33 RMSE Percent Improvement
Sept.-Dec. 2014 EN.Mktg.Int_MM20_

34

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