1 / 37

Probabilistic Forecast - Uncertainty

Probabilistic Forecast - Uncertainty. Yuejian Zhu Ensemble Team Leader EMC/NCEP/NWS/NOAA Presents for NWP Forecast Training Class March 31, 2015, Fuzhou, Fujian, China. Probabilistic Forecast. Background review Statistical forecast is a probabilistic forecast - example

varen
Télécharger la présentation

Probabilistic Forecast - Uncertainty

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Probabilistic Forecast- Uncertainty Yuejian Zhu Ensemble Team Leader EMC/NCEP/NWS/NOAA Presents for NWP Forecast Training Class March 31, 2015, Fuzhou, Fujian, China

  2. Probabilistic Forecast • Background review • Statistical forecast is a probabilistic forecast - example • Early application: precipitation forecast • Early application: plume – time evolution with uncertainty • Approaches • Ensemble Forecast System • Ensemble Users Workshop • Common products • Tropical storm forecast • Precipitation forecast • Convective system forecast • Anomaly forecast • Ocean wind (or wave high) forecast • The values of probabilistic forecast • The information content • Economic values • Applications • Daily weather – temperature and precipitation • Severe weather and extreme weather • Flooding (and/or reservoir) control • And et al…

  3. Example of statistic forecast (2007) • 对1975年至2007年历史资料分析结果，这33年间，北京8月8日出现降雨的概率是41%。降雨和不降雨的可能大概各占一半。 • 降雨概率还可以细分，奥运开幕式是8月8日晚上举行，而晚上8点到12点的降雨概率是25%。 • 41%的概率仅是从有无降雨的角度分析的，真正会造成较大影响的是暴雨。 • 从1975年以后的资料看，8月8日出现过两次暴雨，分别为1994年和2000年。 • 2008年的情况究竟如何，目前还不太好说。

  4. 我们可以提供确定性预报吗? 五十年前 (1954) 经验预报 统计预报 今天: 提出不确定性预报 基于现代的数值预报 数值集合预报 1954 数值预报业务化 开始有了确定性预报

  5. 降水的不确定性预报 20mm/24hrs (0%) Precipitation 2mm/24hrs (30%)

  6. Probabilistic Forecast • Background review • Statistical forecast is a probabilistic forecast - example • Early application: precipitation forecast • Early application: plume – time evolution with uncertainty • Approaches • Ensemble Forecast System • Ensemble Users Workshop • Common products • Tropical storm forecast • Precipitation forecast • Convective system forecast • Anomaly forecast • Ocean wind (or wave high) forecast • The values of probabilistic forecast • The information content • Economic values • Applications • Daily weather – temperature and precipitation • Severe weather and extreme weather • Flooding (and/or reservoir) control • And et al…

  7. 5th NCEP Ensemble User Workshop • Logistics • Workshop organized by EMC/NCEP and DTC/NCAR (co-organizer) • May 10-12 2011, Laurel, MD, 90+ participants • NWS Regions (6), Headquarters (17), NCEP (44) • OAR (5), other government agencies (4), private (2), academic (5) & international (11) • For further info, see: http://www.dtcenter.org/events/workshops11/det_11/ • Main Theme • How to support NWS in its transition from single value to probabilistic forecasting • Goal is to convey forecast uncertainty in user relevant form • 46 presentations • Covering all ensemble forecast systems • SREF, GEFS/NAEFS, Wave ensemble, CFS and NMME • Reports from NCEP Service Centres and Regions (WFOs) • E.g., first numerical ensemble-based 2-day tornado, week 3-4, monthly MJO outlook • Working groups • Ensemble configurations - Ensemble forecasting • Statistic post processing - Reforecast/hindcast generation • Probabilistic product generation - Forecaster’s role and training • Ensemble data depository / access - Database interrogation / forecaster tools • Outcome / Recommendations • Prepared report for NWS roadmap reference • Plan for immediate steps (interim solution to be implemented in 2-3 years) • Outline for long term solution and resource requirements (5-10 years) • All activities to be coordinated under NWS Forecast Uncertainty Program (NFUSE)

  8. Main Theme • Continue to support NWS in its transition from single value to probabilistic forecasting • Continue to convey forecast uncertainty in user relevant form • Review NCEP ensemble forecast systems • Review probabilistic forecast products

  9. Plan • Goal • Capability to answer any question related to future weather, climate, & water conditions, including forecast uncertainty • Example • What is joint probability of heavy precipitation, strong wind or severe weather? • Resource limitations • Computing power & storage, telecommunication, workforce training, etc. • Two-stage approach • Interim stage • Increase probabilistic forecast information across all time scales related to weather, water and climate products and services. • Improve the communication of uncertainty prediction products. • Limited capability with approximations (short term) • Major enhancement of forecast system, leading toward long term solution • Final stage • Full capability • Built on Interim stage achievements • Requires long term budget planning and commitments

  10. 6th NCEP Ensemble User Workshop25-27 March 2014, College Park, MD The workshop brought together developers and users of ensemble forecast systems and products, as well as the research and the applications communities interested in the use of ensembles. Following topics have been addressed to the workshop during three days course • Review progress on the generation and use of operational products since the 5th workshop that took place in 2011. • Discuss plans for future efforts and collaborations • Define actions to continue support the NWS in its transition from single value to probabilistic forecasting

  11. Probabilistic Forecast • Background review • Statistical forecast is a probabilistic forecast - example • Early application: precipitation forecast • Early application: plume – time evolution with uncertainty • Approaches • Ensemble Forecast System • Ensemble Users Workshop • Common products • Tropical storm forecast • Precipitation forecast • Convective system forecast • Anomaly forecast • Ocean wind (or wave high) forecast • The values of probabilistic forecast • The information content • Economic values • Applications • Daily weather – temperature and precipitation • Severe weather and extreme weather • Flooding (and/or reservoir) control • And et al…

  12. Current Hurricane Forecast Cone of death

  13. Strike Probability (NHC) NHC started to calculate strike probability forecast products since 2004. It has been phased out since they introduced wind speed probability. NHC’s method of calculation is based on single deterministic and uncertainties (cone) from historic analysis and forecast The map above is a hurricane strike probability map for Hurricane Charley from August, 2004. It maps the probability, in percent, that the center of the storm will pass within 75 statute miles of a location during a 72 hour time interval. Contour levels shown are 10% (yellow), 20% (green), 50% (orange) and 100% (red).

  14. Example of Hurricane Katrina 0-120 hours 120km radius Early prediction: Friday – August 26 NHC’s prediction ECMWF ensemble forecast: Strike probability from Friday – August 26

  15. Ensemble based strike probability: Accumulated probability of 0-120 hours at 65 nautical miles radius of tropical storm forecast tracks. Courtesy of Jiayi Peng

  16. An experimental multi-model product Dot area is proportional to the weighting applied to that member •= ens. mean position* = observed position Courtesy of Tom Hamill

  17. Example: The tracker puts out 4 time a day for all cyclones (Northern Hemisphere)

  18. Map of PQPF and Precip Types: every 6 hours, 4 different thresholds Total precipitation Rain Freezing Ice pellets Snow

  19. Surface Wind Probability from 90 members (ocean)

  20. NAEFS products – Metagram (examples)

  21. 3-Category forecasts (CPC web products) Week-2 Precipitation Above normal Below normal Week-2 Temperature

  22. Probabilistic Forecast • Background review • Statistical forecast is a probabilistic forecast - example • Early application: precipitation forecast • Early application: plume – time evolution with uncertainty • Approaches • Ensemble Forecast System • Ensemble Users Workshop • Common products • Tropical storm forecast • Precipitation forecast • Convective system forecast • Anomaly forecast • Ocean wind (or wave high) forecast • The values of probabilistic forecast • The information content • Economic values • Applications • Daily weather – temperature and precipitation • Severe weather and extreme weather • Flooding (and/or reservoir) control • And et al…

  23. Ensemble Forecast – Information Content ensemble mode considers as most frequent forecasts Toth and Zhu (2000) Statistics show a 7.5-day fully probabilistic forecast or 6-day categorical forecast has as much information content as 5-day control forecast. Or fully probabilistic forecast has more than twice as much information content at day-5.

  24. ... Small and large uncertainty. 1 day (large uncertainty) = 4 days (control) = 10-13 days (small uncertainty) Probabilistic Evaluation(useful tools) Reference: Toth, Zhu and Marchok, 2001: WAF

  25. EMC Web Products http://www.emc.ncep.noaa.gov/gmb/yluo/html_pqpf/rmop.html Reference: Toth, Zhu and Marchok, 2001: WAF

  26. The Relative Measure of Predictability (RMOP) Application of theory To illustrate the use of RMOP, consider the graphic of RMOP from the ensemble run of 00 UTC 17 December 2003 valid 00 UTC 22 December 2003 (a 120-hr forecast), found right: The shading indicates the RMOP of the ensemble mean 500-hPa height at each grid point, compared to ensemble forecasts of 500-hPa height over the previous 30 days. These are in 10% increments as indicated by the color bar at the bottom of the graphic. Shading at 90% indicates that at least 9 of 10 ensemble forecasts in the past 30 days had fewer ensemble members in the same "bin" as the ensemble mean than the present forecast. In this case, the trough in the eastern US is 90% predictable relative to ensemble forecasts in the past 30 days. The blue numbers over each box represents the percentage of time that a forecast with the given degree of predictability has verified over the past 30 days. Here, over the 90% predictability box we see that only 72% of the forecasts with 90% relative predictability at 120 hours have verified in the same climatological bin as the observed 500-hPa height at 120 hours over the past 30 days. Note that in general, the values are generally lower than the RMOP numbers below the bar. This is because: The underlying forecast model is imperfect, The initial conditions are imprecise, and The atmosphere behaves chaotically. We can expect verification percentages to decrease with Increasing forecast lead time, During the warm season, and During relatively unpredictable regimes in all seasons. In this example, we see that only 72% of the forecasts with 90% relative predictability at 120 hours have verified in the same climatological bin as the observed 500-hPa height, over the past 30 days of ensemble forecasts.

  27. Decision Theory Example 停工一天， 损失一万元 返工一次， 花费二十万元 建筑施工 预报最低气温为3度，低於零度的可能性为20%, 怎么办 ? 气温低於零度， 绝对停工 题外话：企业如何收益?

  28. Optimal Threshold = 15% Decision Theory Example Forecast? YES NO Critical Event: sfc winds > 50kt Cost (of protecting): $150K Loss (if damage ): $1M Hit False Alarm Miss Correct Rejection YES NO $150K $1000K Observed? $150K $0K Courtesy of Tony Eckel

  29. Economic Value of Forecast TABLE. Contingency table indicating the costs and losses accrued by the use of weather forecasts, depending on forecast and observed events. Zhu and etc.. 2002: BAMS 1. Expected Expense: 2. Economic Value: or Where o is the climatological frequency of the event (or o=h+m), r=C/Lp which is the ratio of the cost of protection to the amount of potential loss that can be protected

  30. Probabilistic Forecast • Background review • Statistical forecast is a probabilistic forecast - example • Early application: precipitation forecast • Early application: plume – time evolution with uncertainty • Approaches • Ensemble Forecast System • Ensemble Users Workshop • Common products • Tropical storm forecast • Precipitation forecast • Convective system forecast • Anomaly forecast • Ocean wind (or wave high) forecast • The values of probabilistic forecast • The information content • Economic values • Applications • Daily weather – temperature and precipitation • Severe weather and extreme weather • Flooding (and/or reservoir) control • And et al…

  31. Hanson Dam flood threat – special guidance http://www.emc.ncep.noaa.gov/gmb/wx20cb/PQPF_24hr/PQPF_24h.html 4 times per days (initial forecast) 24 hours PQPF for every 6-hours out to 16 days Covers most of CONUS, and east pacific Special thresholds for 1, 2, 4 and 6 inches

  32. Example of Experimental Ensembles from approach 1 Yampa River Below Craig, CO GFS and CFS based ensembles: experimental products updated daily at Colorado RFC (CBRFC) & California-Nevada RFC (CNRFC) flood GFS CFS www.cbrfc.noaa.gov/devel/hefs/ 34

  33. Background !!!

  34. 1. By using equal climatological bins (e.g. 10 bins, each grid points)2. Counts of ensemble members agree with ensemble mean, (same bin)3. Construct n+1 probabilities for n ensemble members from (2).4. Regional (NH, weighted) Normalized Accumulated Probabilities (n+1)5. Calculate RMOP based on (3), but 30-d decaying average.6. Verification information (blue numbers): historical average (reliability)

  35. Ensemble mean 10 Climatological equally likely bins Example of 1 grid point 10 ensemble forecasts The value of ensemble members agree to ensemble mean is 4/10 or 40% (probability) There are 10512 points ( values ) for global at 2.5 * 2.5 degree resolution 10 ensemble members could construct 11 probabilities categories, such as 0/10 (0%), 1/10(10%), 2/10(20%), 3/10(30%), 4/10(40%), 5/10(50%), 6/10(60%), 7/10(70%), 8/10(80%), 9/10(90%), 10/10(100%) Sum of each grid point for above 11 probabilistic categories by area weighted and normalized for global or specified region Get: 0/10 1/10 2/10 3/10 4/10 5/10 6/10 7/10 8/10 9/10 10/10 .029 .047 .077 .085 .100 .135 .116 .089 .081 .070 .177 sum of these = 1.0 (1.007 here) 2.9% 7.6% 15.3% 23.8% 33.8% 47.3% 58.9% 67.8% 75.9% 82.9% 100% accumulated values There is 30-day decaying average of above values ( last line ) in the data-base and updated everyday. Assume these are 30-day decaying average values In this case, point value is 4/10, RMOP value of this point is 33.8%

More Related