Detecting and Monitoring Hazardous weather

1. Detecting and Monitoring Hazardous weather By Eza John Meteorologist National Meteorological Centre Entebbe - Uganda

2. Outline • Introduction • Severe Weather of the region, Seasons and why forecast? • Overview • Preparation of Severe Weather Forecast • Ensemble Forecasts and their use • Use of probabilities and NWP guidance

3. Outline Cont’d • Regional guidance products from RSMC - Nairobi and RSFC – Dar-Es-Salaam • Knowledge of Physical Climatology • Real Time Observations • Challenges

4. Uganda and its neighbours

5. Introduction cont’d • Uganda lies astride the equator • Its climate is considerably modified by elevation above mean sea level and relief • Mean annual min.temps. Range from 10o C to 20o C • Mean annual max. temps. Range from 25o C to 30o C

6. Introduction cont’d • Mean temps. over the whole country show no great seasonal variations apart from those of the mountainous districts of Western Uganda and around Mt. Elgon in the East • Relative Humidity is often high ranging between 70 and 100 percent • Mean monthly evaporation rates range between 125 and 200mm

7. Introduction cont’d • Much of the country receives between 1000 – 1500mm of rain per annum increasing with altitude • The Southern part of the country has two rainfall peaks, March – May and September – November • The bimodal rainfall pattern becomes less marked towards the North of the country, eventually coalescing to give one peak

8. Severe Weather Events • Severe local storms - small scale hazardous weather or hydrological events produced by thunderstorms, large hail, damaging winds or flash floods. • Other hazards - extreme heat or cold, dense fog, high winds, river flooding, and lakeshore flooding.

9. Severe Weather Events cont’d • Hazardous weather phenomena such as violent storms, floods, cyclones, tornadoes, hail and snow contribute significantly to annual property damages and loss of life • The majority of global natural disasters are related to hydro-meteorological events

10. SWFDP- EA categorises severe weather events as under: • Strong winds (≥25kts ) • Heavy Rain (≥50mm/24hrs) • Severe thunderstorm • Drought (Dry spell) • Large Waves (≥2mtrs)

11. Convective Storms • Convective Storm Detection • Is the meteorological observation of deep moist convection (DMC) • It consists of detection, monitoring and short term prediction • Convective storms can produce tornadoes as well as large hail, strong winds and heavy rain leading to flash floods

12. Convective Storms cont’d • Detection of convective storms relies on eyewitness observations and on remote sensing, especially by the use of satellites and weather radar • Some in situ measurements are used for direct detection as well, notably wind speed reports from surface observation stations

13. Convective Storms cont’d • Convective storm detection is part of the integrated warning system, consisting of prediction, detection and dissemination of information on severe weather to users such as emergency management, the media and the general public

14. Why should we invest in Severe Weather Forecasting? • Severe weather events lead to • Loss of lives • Destruction of property and infrastructure • Flash Floods and Drought • Famine and Disease • Poverty and Food Insecurity • Economic Downturn • Civil Unrest etc

15. The residents look-on helpless after the occurrence of the landslide while they resorted to using hand hoes with hope of rescuing their loved ones with the help of army soldiers (UPDF). • On March 2, 2012, a landslide in Bududa (Eastern Uganda) left 300 people dead, loss of property and thousands homeless. • However, the DoM had issued a warning which was rather ignored.

16. Floods are common in Kampala; the capital city of Uganda, western, central and Eastern regions especially during the El-Niño phenomenon. They damage structures, crops, animals and settlements. Water borne diseases like cholera, dysentery, and typhoid are also common.

17. Teso Flash Floods of Sept, 2007 Stranded villagers in Teso awaiting rescue when floods swept away their property Floods disrupted and paralysed traffic in Teso

18. Searching for Survivors after the March 1st, 2010 Landslides in Eastern Uganda

19. Landslides buried,destroyed and damaged homes

20. Landslide in upper Bulambuli in Sisiyi sub county

21. In Sebei sub region, Kween district, ngengesubcounty where floods have displaced over 5000 people due to severe rains. SWFDP workshop, 28 Nov . 03 Dec 2011, Arusha, Tanzania

22. Severe Landslides occurred in upper Bulambuli District on 28th August 2011 in 4 sub counties of Sisiyi, Buluganya, Lusha and Namisuni.

23. Some flooded homes in Bulambuli

24. Floods in lower Bulambuli district

25. Flooding in Kampala in 1stdekad of November 2011

26. Floods in Kampala

27. Flooded roads after an afternoon downpour.

28. DROUGHT IN KENYA • Source: Red-Cross-Kenya:Reach Out: January-March 2011:Issue No.42

29. The Forecast Process Weather forecasting; is the application of science and technology to predict the state of the atmosphere for a given location and at a given time Forecast products are made by collecting quantitative data about the current state of the atmosphere and using scientific understanding of atmospheric processes to project how the atmosphere will evolve.

30. Forecasting Process Preview of the past weather: • Spatial and temporal data observations, • Past Satellite images for tracking the movement of storms. • Past synoptic systems(MSLP, geopotentials, winds shear, fronts and airmasses, position of ITCZ and other controlling systems e.g cyclones) • Sources of winds, airmasses

31. Forecasting cont’d Present: • Analyze accurately morning soundings to determine the stability, moisture and winds at different levels influencing convective storm development. • Determine movement of storms using Satellites;- fronts and airmasses, position of ITCZ and other controlling systems i.e cyclones. • Determine the time of first convection. From synergie • Prognosis of synoptic systems( MSLP, geopotentials, winds shear)

32. Forecast Process cont’d • Sources of winds, airmasses • Evidence of increase in low level moisture, cape, low level convergence and divergence aloft and lifting indices within the thunderstorms threshold. • Evolution of sea breezes or urban heating which may locally enhance or suppress the cape. • Existing thunderstorms to provide new outflow. • Indications of the establishment of a low-level nocturnal jet.

33. Models outputs Model Products from HRM,WRF, NCEP,ECMWF AND UK MET. • MSLP, winds at 850 and 300hpa • Lifting mechanisms:-lifting index, shear, orography • Moisture:-Precipitable water, RH values and Sources • Link to Models products • Models outputs:-EFI and EPS Models outputs:- consensus

34. Significance for storm • Satellites, display deep convective systems. • Penetration of the storm top significantly higher than the calculated EL. • Clusters of cells that merge into one. • Cells that split and intensify. • Nature of the gust front. • Configurations of storm systems.

35. Preparation of swfdp products • RSMC-NAIROBI • SWFDP GUIDANCE PRODUCTS • SHORT-RANGE (DAY 1 AND DAY 2) • Issue Date: 10th November, 2012 • Valid for: 10th to 11th November, 2012 • Moderate heavy rains expected in several places over the region as indicated in the risk table. • Degree of confidence: Medium ( 50 – 75% ) • Day 2: Sunday 11th November, 2012

36. Moderate rains expected over several places in the region as indicated in the risk table. • Degree of confidence: Medium ( 50- 75% ) • Discussion of model products from Global and Regional centres • Models are in an agreement • Forecaster: John Eza

37. Dissemination and extraction of SWFDP Internet (Web portal) http://www.meteo.go.ke/rsmc/index.php User name:swfdp Password:swfdp1 • GUIDANCE PRODUCTS • Short Range Forecasts (1-2 days) • Day 1 • Day 2 • Risk Table • Discussion • Medium Range Forecasts (3-5 days) • Day 3 • Day 4 • Day 5 • Risk Table • Discussion • AgrometeorologyProducts • 10-days • SWFDP Evaluation • Evaluation Form • Satellite Products • RFE • Dekadals • RFE anomalies Highlighted are the links

38. Strengths and Weaknesses of NWP Modele models Generic • Inaccurate Initial Conditions • Lack of data • Imperfect data assimilation • Resolution • Horizontal resolution may cause small scale features to be missed • Vertical profile may not capture full detail e.g. inversions, localised temperature advection

39. challenges • Inadequate data • Insufficient facilities, and trained personnel • Models outputs simulations • Model data assimilations • Some models tend to underestimate precipitation amounts • Predict convective processes early and at times switch them off early

40. Challenges cont’d • Models give a time lag of about 3 hrs between what they predict and what will actually happen • At times they do not show signals for precipitation

41. Realtime Synoptic Situation

42. Wind speed at 850hpa

43. NCEP GFS Winds

44. 4 km resolution L.Vic. Pptn.map

45. Vertical Profiling

46. PREPARATION OF SEVERE WEATHER FORECAST: • Knowledge of climatology • The  importance  of  local  and  general  area climatology have a profound impact on forecasting process and decision making • Climatology  serves  as  a  guide  for  analysis and forecasting •  The  next  step  in  the procedure is to expand and refine these ideas • Example: • Movement of the ITCZ • Rain seasons of the region eg. Ug-Unimodal and bimodal regions

47. PREPARATION OF SEVERE WEATHER FORECAST: • Observational data and diagnostic tools • Key inputs to short-term prediction(‘nowcasting’) • Provide current weather • Update weather information • Monitoring development of potential severe weather • Decision making

48. PREPARATION OF SEVERE WEATHER FORECAST Cont’d • Probabilistic Forecast • ECMWF • NOAA • UKMet • EFI • Precipitation • 10M wind speed and gusts • Temperature

49. ECMWF Mslp and Pptn

50. NWS NCEP Total Precipitation