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“How to use the products of JMA Ensemble Prediction System?”

2009.05.12-15 1st TRCG Technical Forum. “How to use the products of JMA Ensemble Prediction System?”. (B2) Applications for TC forecasts. Takuya KOMORI ( komori@met.kishou.go.jp ) Numerical Prediction Division Japan Meteorological Agency. Contents.

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“How to use the products of JMA Ensemble Prediction System?”

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  1. 2009.05.12-15 1st TRCG Technical Forum “How to use the products of JMA Ensemble Prediction System?” (B2) Applications for TC forecasts Takuya KOMORI( komori@met.kishou.go.jp )Numerical Prediction Division Japan Meteorological Agency

  2. Contents • Introduction of the One-Week EPS Products on JMA EPS-WEB • What kind of products can be seen via JMA EPS-WEB? • Exercise: a TC Case-study using JMA EPS-WEB • How should EPS products be interpreted for practical weather forecasting?

  3. JMA EPS-WEB • The products in JMA EPS-WEB are recommended • by Manual on the GDPFS (WMO No.485). • In addition to the web-site for public users, JMA provides a web-site for meteorologists and forecasters in foreign countries. • The special forecast products derived from EPS are disseminated on the website, “JMA EPS-WEB”, supporting the activity of National Meteorological and Hydrological Services (NMHSs) in Asia. • The data in this website is available for operational weather forecasting in your countries.

  4. Introduction (JMA EPS-WEB) JMA EPS-WEB provides visualized EPS products. • JMA operates an EPS web-site (EPS-WEB) for supporting the activity of National Meteorological and Hydrological Services (NMHSs). • The EPS-WEB is intended for NMHSs forecasters, not for public use. • This web site provides the JMA One-week EPS products. • Caution! The links to this website are strictlyprohibited. • Address of this web site is ….

  5. 1 2 3 JMA EPS-WEB (Visualized EPS Products)

  6. Contents: Probability Map Probability map indicate potential locations of extremely severe weather events exceeding a certain threshold. Probability of exceeding the 24mm/1day precipitation. Probability of exceeding the 48mm/1day precipitation. A A B B • Area-A: High probability in 24mm/day, while less than 5% in 48mm/day. • It will be relative small precipitation, and low probability for heavy precipitation. • Area-B: High probability in 24mm/day and middle percentage in 48mm/day. • It will be relative small precipitation. In addition, there is probability for heavy precipitation.

  7. Contents: Probability Map

  8. Contents: Probability Map “AllThres.” displays probability maps of 850hPa temperature anomalies, T850anm, exceeding four thresholds at the same valid time. The range of forecast time is from 1-day to 9-day with 1 day interval. T850anm > 2 K T850anm < -2 K Severe Weather Event T850anm > 4 K T850anm < -4 K T850anm > 8 K T850anm < -8 K

  9. Probability Map - layout and threshold - “Sequence” displays selected probability maps from 1-day up to 9-day forecast. 1-day 2-day 3-day 4-day 5-day 6-day 7-day 8-day 9-day

  10. Probability Map - area - Asia WesternPacific Northern Hemi.

  11. Probability Map - elemant - Temperature at 850hPa Daily Precipitation

  12. 1 2 3 JMA EPS-WEB (Visualized EPS Products)

  13. Contents: EPSgram (Point Forecast) EPSgrams at 70 major cities in RA-II area

  14. EPSgram (Point Forecast) • Forecast chart and probability map are used to grasp synoptic features. • In addition to synoptic features, we need the results of ensemble forecasts in a certain grid-point, which is closest to the specified forecast point. • The image plotted the forecast data as a time series is useful for users near the specified point. ex. Forecast Point: Tokyo

  15. EPSgram - Contents - Surface variables 1. Surface variable at model surface Upper air 2. Air temperature at 6 levels 3. Upper air temperature at 925hPa 4. Upper air temperature at 850hPa 5. Upper air temperature at 700hPa 6. Upper air temperature at 500hPa 7. Upper air temperature at 300hPa

  16. EPSgram – Surface Products – 1. Surface variable at model surface 6-hourly surface temperature (Box plot) 6-hourly rainfall (Box plot) Accumulated Precipitation (from initial time ) 6-hourly mean sea level pressure (Box plot)

  17. EPSgram – Upper Air Products for all Levels – 2. Air temperature at 6 levels 6-hourly surface temperature. (Box plot) • 925hPa temperature (Box plot) • Left: time series (6-hourly) • Right: Probability (%) not to exceed threshold 850hPa temperature (Box plot) 700hPa temperature(Box plot) 500hPa temperature(Box plot) 300hPa temperature(Box plot)

  18. EPSgram – Upper Air Products for Each Levels – 3. Upper air temperature at 925hPa 6-hourly 925hPa temperature (Box plot). Probability (%) not to exceed threshold 4. Upper air temperature at 850hPa 5. Upper air temperature at 700hPa 6. Upper air temperature at 500hPa 7. Upper air temperature at 300hPa

  19. Surface Temperature (degree C) Initial time Control Member Element FT+0 FT+216 6-hourly

  20. Description of Box Plot (box-and-whisker diagram) Prediction value Largest value Whisker Upper quartile (third quartile) “Box plot” represents the distribution, skewness and outlier of the forecasts in EPS. Box Median Lower quartile (first quartile) Smallest value Image of EPS distribution Interpretation of box plot Each Forecasts

  21. Box Plot (box-and-whisker diagram) Long whisker and small box Long whisker and box Biased median A few member predict extremely “high” value. When the “median” bar is located upper or lower position from the center of the box to some extent, the distribution of EPS prediction is not “Normal” type. The long box means wide distribution of EPS forecast. The uncertainty of forecast is larger. Most members predict close values. No extreme “low” values are predicted

  22. Precipitation Rate (mm / 6hours) Heavy Rain • Almost all members predict 1-6mm/6hours precipitation. • A few members predict “heavy rain”.

  23. Uncertainty of Forecast by “Box Plot” High reliability High uncertainty 5-day forecast

  24. Plume Diagram – Accumulated Precipitation (mm) – Perturbed run Control “Plume Diagram” shows possible accumulated precipitation.

  25. C B A Accumulated Precipitation (mm) Period-A: All members predict little precipitation. • Period-B: Many members including control run predict precipitation. • Some members predict heavy rain. (sharp gradient in accumulated • precipitation) Period-C: Some member predicts precipitation, which is relatively weak compared to Period-B.

  26. 1 2 3 JMA EPS-WEB (Visualized EPS Products)

  27. Let’s take a break now, and resume in 10 minutes.

  28. Exercise Time “Now, we go on to try some practical exercises using a tropical cyclone events.”

  29. Exercise Time: Introduction • The forecast products for answering the question are enclosed in CD-ROM. • You can access the forecast products with similar interface to EPS-WEB. • We focus on a forecast of Tropical Cyclone (TC) • Initial time: 30 September 2006 • The TC located in the western-North Pacific, south of Japan.

  30. Exercise Time: Introduction Tokyo • We focus on this TC (Tropical Depression: TD).

  31. Satellite Image Radar Image Surface Wind Exercise Time: Introduction Note: Tropical Cyclone Paths of Tropical Cyclone During the 45-year Period 1951-1995 Tropical Cyclone (TC): The generic terms for a non-frontal synoptic scale cyclone originating over tropic or sub-tropic oceans with organized convection and definite cyclonic surface wind circulation. TC causes strong wind and heavy rain near the core with lowest pressure. The TC is categorized by its intensity for Tropical Depression, Tropical Storm, Severe Tropical Storm and Typhoon at western-North Pacific region. The other terms are used at the other regions; ex. Hurricane.

  32. Exercise Time: Data Set Access the following contents: Exercise\Case_TC\200609301200\index.html All products are enclosed in the CD-ROM. Let’s start !!

  33. Uncertainty = large deviation (spread…) Look at “stamp map” and check the “spread”. Exercise 1 • At D+0 day (initial time), where is the largest uncertainty in mean sea-level pressure? Hint…

  34. Exercise 1 ANSWER • At D+0 day (initial time), where is the larger uncertainty in mean sea-level pressure (Psea)? • There is a larger spread in the vicinity of TC.

  35. Ensemble: 20p Control run Ensemble: 19m Exercise 1 • At D+0 day (initial time), where is the larger uncertainty in mean sea-level pressure (Psea)? • There is a larger spread in the vicinity of TC. • The large spread results from intensity of TC (See stamp map of each member).

  36. Look at “stamp map” at FT=3.0day. Exercise 2 2. At D+3 days, some ensemble members predict another TC (TC_2) east of existing TC. How many members predict TC_2 with less than 1000hPa of central pressure (Psea) at D+3 days? Hint…

  37. Ensemble: 07p Ensemble: 15p Ensemble: 16m Ensemble: 18m Exercise 2 ANSWER 2. At D+3 days, some ensemble members predict another TC (TC_2) east of existing TC. How many members predict TC_2 with less than 1000hPa of central pressure (Psea) at D+3 days? 4-Members

  38. Ensemble: 13p Ensemble: 08m Ensemble: 19m Control-run The another TC is not clear. Exercise 2 2. At D+3 days, some ensemble members predict another TC (TC_2) east of existing TC. How many members predict TC_2 with less than 1000hPa of central pressure (Psea) at D+3 days? In addition to 4-members, several members predict a weak tropical low. Indicating high probability of formation of another TC.

  39. Control-run Ensemble: 16m 3 days later T0617 T0616 Exercise 2 • There are 2-tropical cyclones at 12UTC October 3. • Although control-run could not predict T0617, several members predict the TC (T0617)-genesis. Synoptic analysis chart at 12UTC 3 October.

  40. Check the “stamp map”, ensemble mean and control-run forecast. • The ensemble mean is derived from averaging all ensemble forecasts. See the forecast of each member and compare forecast of control-run with that of ensemble members. Exercise 3 3. Describe the difference between Psea forecast of control run and that of ensemble mean at D+5.5 days (T+132h), and explain the reason for this difference. Hint…

  41. Exercise 3 ANSWER 3. Describe the difference between Psea forecast of control run and that of ensemble mean at D+5.5 days, and explain the reason for this difference. There is a strong-low (TC) near Tokyo, Japan. There is a weak low-pressure area south of JAPAN

  42. Exercise 3 ANSWER 3. Contd. Control-run Spread Member – 01m Member – 23m Ensemble - mean • The Forecast of TC position is different between ensemble members. Some member predict TC north of JAPAN, the other south of JAPAN. The spread around JAPAN is very large. • In ensemble mean forecast, the low pressure of TC is cancelled by averaging the forecasts of ensemble member.

  43. 5.5 days later Exercise 3 Synoptic analysis chart at 00UTC October 6. • Synoptic analysis chart indicates low pressure systems, south of Japan • In this case, control-run could show good performance to predict a strong low.

  44. The “EPSgram” is useful for point forecasts. Exercise 4 4. When is the TC closest to Tokyo? Answer using the time-series of control-run forecasts in Tokyo. Hint…

  45. Exercise 4 ANSWER 4. When is the TC closest to Tokyo? Answer using the time-series of control-run forecasts in Tokyo. Element Control-run (green points) T+0h T+216h • The lowest pressure in control-run is predicted on 06UTC October 6.

  46. EPSgram at Tokyo for Psea Control- run (lowest pressure) Analysis Exercise 4 4. When is the TC closest to Tokyo? Answer using the time-series of control-run forecasts in Tokyo. • The “EPSgram” is very useful for a forecast focusing on a certain point. • The control-run forecast is plotted by green marks. • The lowest pressure in control-run is predicted on 06UTC October 6.

  47. The “EPSgram” is useful for point forecasts. Exercise 5 5. When does the accumulated precipitation exceed 200mm in ensemble members (earliest time) in Tokyo? Hint…

  48. Exercise 5 ANSWER 5. When does the accumulated precipitation exceed 200mm in ensemble members (earliest time) in Tokyo? 12UTC October 4.

  49. Exercise 5 5. When does the accumulated precipitation exceed 200 mm in ensemble member (earliest time) at Tokyo. EPSgram for accumulated rainfall at Tokyo • The EPSgram for accumulated precipitation indicate that the amount of accumulated rainfall at Tokyo point will exceed on 12UTC 4 October. Probability of heavy rainfall

  50. Probability of Heavy Rain => “probability map” … Exercise 6 6. At which point does the probability of the precipitation of 48 mm/day exceeds 20 % at D+5 days? Select among following points; Fukuoka, Tokyo and Wakkanai. Wakkanai Tokyo Fukuoka Hint…

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