Uncertainty in rainfall prediction of landfalling tropical cyclones over India: Impact of data assimilation. U. C. Mohanty

Transcription

1 Uncertainty in rainfall prediction of landfalling tropical cyclones over India: Impact of data assimilation U. C. Mohanty Krishna K. Osuri, Raghu Nadimpalli School of Earth Ocean and Climate Sciences Indian Institute of Technology, Bhubaneswar India S.G. Gopalakrishnan, HRD, AOML

2 Introduction Objective Results OUTLINE Realtime prediction of TC and associated inland rainfall Mesoscale data assimilation: Satellite derived wind data assimilation DWR reflectivity and radial wind observations Conclusions

3 INTRODUCTION Coastal regions of India (particularly East coast) frequently receive heavy to very heavy rainfall due to landfalling Tropical Cyclones (TCs). Precipitation is one of the difficult parameters to forecast/ simulate with NWP models. In recent years, there have been significant improvement in prediction of track over NIO basin, but skill of numerical models need to be improved in o Intensity prediction o Structure prediction o Inland rainfall prediction This can be achieved some extent by o increased model resolution (at cloud resolving) o Improved model parameterization schemes o High resolution data assimilation

4 Studies showed that the meso-scale/convective scale features are well resolved by improving the model initial condition with high spatial and temporal observations like DWR, satellite products, etc. The ability to anticipate the formation, intensity and rainfall structure of landfalling TCs remains a major challenge for researchers as well as forecasters.

5 Objective To improve the forecast skill of high resolution model for heavy rainfall associated with Landfalling tropical cyclones

6 Real time prediction of Tropical cyclones over North Indian Ocean

7 TCs during (Total 172 cases) Basin Name (Intensity) Simulations period in 12-hr interval Observed Landfall No. of forecasts Gonu (SuCS) 00 UTC 2 12 UTC 5 June UTC 6 June (over Oman) 8 Arabian Sea Cyclones (5 TCs) Yemyin (CS) 00 and 12 UTC 25 June UTC 26 June 2 Phyan (CS) 12 UTC 9 00 UTC 11 Nov 2009 Between UTC 11 Nov 4 Phet (VSCS) 12 UTC 31 May 00 UTC 6 June UTC 6 June (LF-2) 12 Murjan (CS) 00 UTC October UTC 25 October Akash (CS) 12 UTC UTC of 14 May UTC 15 May 3 Sidr (VSCS) 12 UTC UTC 15 Nov UTC 15 Nov 8 Nargis (VSCS) 12 UTC 27 April 00 UTC 2 May UTC 2 May 10 Rashmi (CS) 00 UTC UTC 26 Oct UTC 27 Oct 4 KhaiMuk (CS) 12 UTC UTC 15 Nov UTC 16 Nov 5 Nisha (CS) 12 UTC Nov UTC 27 Nov 3 Bijli (CS) 12 UTC UTC 17 Apr UTC 17 April 6 Aila (SCS) 12 UTC UTC 25 May UTC 25 May 4 Bay of Bengal cyclones (20 TCs) Ward (CS) 12 UTC UTC 13 Dec UTC 14 Dec 7 Laila (VSCS) 12 UTC May UTC 20 May 5 Giri (VSCS) 12 UTC UTC of 22 Oct UTC 22 Oct 4 Jal (VSCS) 00 UTC of 4 7 Nov UTC 7 Nov 7 Thane (VSCS) 00 UTC UTC 29 Dec UTC 30 Dec 8 Nilam (CS) 00 UTC UTC 31 Oct UTC 31 Oct Mahasen (CS) 00 UTC UTC 16 May UTC 16 May PHAILIN (VSCS) 7-12 Oct UTC 12 Oct (SCS) Helen Nov UTC 22 Nov Lehar (VSCS) Nov UTC 28 Nov HudHud (VSCS) 7 12 Oct UTC 12 Oct Total Number of cyclones during

8 WRF model configuration for cyclone prediction Model Dynamics Horizontal Resolution Domain Time step Map projection Horizontal Grid System Vertical co-ordinates Radiation TCs over Arabian Sea Longitude : 48 E 78 E Latitude : 5 N 28 N Resolution : 9 km Surface Layer IC & BC : GFS model (0.5 x0.5 ) Cumulus scheme PBL scheme Micro physics Initial and boundary conditions Non-hydrostatic 27 km / 18 km / 9 km WRF model configuration 3 N 28 N and 78 E 103 E (BOB) 5 N 30 N and 48 E 78 E (AS) 30s Mercator Arakawa C- Grid Terrain following hydrostatic Longitude: pressure 77 E 102 co-ordinates E (51 levels) Dudhia scheme for long and short wave radiation Thermal Diffusion scheme Kain Fristch Yonsui University scheme WSM-3 TCs over Bay of Bengal Latitude : 3 N 28 N Resolution : 9 km GFS model analysis and forecast fields ( resolution)

9 TC Phailin (08 12 October 2013) White line is IMD OBS track RED track is Model Predicted TC Phailin (96 hour) Forecast based on 12 UTC of 8 October 2013 TC Phailin (72 hour) Forecast based on 12 UTC of 9 October 2013 Landfall point error is 29 km Time error 5 hrs ahead Landfall point error is 16 km Time error 2 hrs ahead

10 TC PHAILIN with different initial conditions Mean Track errors (km) Comparison of Track Prediction of TC PHAILIN With Different operational Models Forecast length (hour) 10

11 Time series of pressure drop at Gopalpur (landfall point) Observed Model averaged value

12 24-hr accumulated rainfall (cm) during landfall day for TC PHAILIN (Number represents IMD OBSERVED RAINFALL at 108 stations) Observed peak rain (~40 cm) is over Northern parts of Odisha Modelled rainfall over South Odisha Modelled rainfall over South Odisha 72 hour forecast 24 hour forecast Though model-predicted track was good, rainfall distribution and intensity is not realistic. Model showed peak rainfall over South Odisha and Andhra Pradesh

13 Airakhol Anandpur Athmalik Balikuda Banarpal Banpur Barmul Bijepur Chaibasa Chandikhol Daitari Daspalla G Udayagiri Hindol Jaipur Jhumpura K Nuagaon Keonjhag Korei Madanpur Mohana Naraj Nayagarh Nimpara Odagaon Papunki Phiringia Puri Raghunat Rajghat Rampur Salepur Sukinda Telkoi Tigiria Tiring 24-hr accumulated rainfall (cm) during landfall day for TC PHAILIN (Verification at 108 stations) OBS IMD Model RMSE=7 cm At most of the stations, model underestimated the rainfall. When compared with TRMM rainfall, Model overestimates the rainfall

14 IIT-BBS Storm Surge Prediction for TC PHAILIN (84 Hours in Advance of Landfall)

15 Chandipur Dhamara Paradip Puri Gopalpur Landfall Srikakulum Vishakhapatnam Time series of Surge at different location along the coast (TC PHAILIN) PEAK SURGE ELEVATIONS AT THE BOUNDARY (IC: ) Sea surface elevation (m) Left_LF Landfall Gopalpur Puri Time (hrs) 15

16 Disaster of TC Hudhud in Visakhapatnam, India Visakhapatnam Airport

17 Mean track errors (km) Mean Intensity Errors (Knots) HUDHUD Forecast (12 UTC 8 12 UTC 11 Oct 2014) Landfall between 6-9 UTC 12 Oct 2014 with intensity of very severe cyclonic storm (100 knots) source: IMD Mean Track (km) and Intensity (Knots) Errors

18 Based on IC: 12 UTC 09 Oct 2014 (96 hr forecast) Landfall between 6-9 UTC 12 Oct 2014 with intensity of very severe cyclonic storm (100 knots) source: IMD Blue line represent time of landfall

19 based on IC: 00 UTC 11 Oct 2014 (36 hr forecast)

20 24-hr accumulated rainfall prediction in real time TRMM rainfall Model predictions 96h fcst 72h fcst 48h fcst 24h fcst

21 24-hr accumulated rainfall (cm) during landfall day for HudHud (Verification at 103 stations) RMSE: 8 cm CC : 0.25

22 TC Lehar (60 hour) Forecast based on 00 UTC of 26 Nov 2013 Track prediction from different initial conditions for TC LEHAR TC Lehar (60 hour) Forecast based on 00 UTC of 26 November 2013 Model predicted weakening of Lehar into depression when approaching land as observed. Model simulated showers/low rainfall as observed. Possible reasons are 1. Dry air incursion along the southwestern periphery of the low level circulation (SSMIS 91 GHZ MICROWAVE IMAGE) 2. Cooler sea surface (passage of prior cyclone, Helen) 3. Increased vertical wind shear

23 VSCS Madi over Bay of Bengal (8 12 Dec 2013) OBSERVATIONAL facts Madi's pole ward track was explained by the strong subtropical ridge located to the east of the system. Another subtropical ridge located over India had steered the system southwestward. On December 11, Madi's LLCC became clearly exposed after dry air wrapped around the southern part of the system. This weakened Madi into a Cyclonic Storm TC Madi (96 hour) Forecast based on 12 UTC of 08 December 2013 Model predicted sudden return and weakening of Madi. It intensified to very severe cyclonic storm stage ( knots) while moving northward till 12UTC 10 Dec.2013 After that, it suddenly returned back and started moving in southwest ward. It started loosing its intensity and reached to depression stage (<33knots) within one day.

24 Mean track errors for NIO cyclones during (under operational setup) These error statistics are based on 100 TC cases 500 Mean Errors for NIO TCs with different resolutions Mean Intensity Errors (10m winds m/s) Mean DPE (km) km 18 km 9 km Forecast length (hour) Mean DPE (km) Mean Errors for NIO TCs with different resolutions km 9 km % of improvement Forecast length (hour) Recent cyclone Giri (20-22 Oct 2010) Observed TC Location Initial cyclone vortex position error is about 60 km Model TC Location Osuri et al. 2013, JAMC

25 Mean DPE (km) Errors for recurving TCs 27 km 18 km 9 km Forecast length (hour) Mean errors for recurving TCs Improvement is significant with high resolution for recurving TCs. Mean track errors w.r.t intensity at initialization Stronger cyclones can be tracked with minimum errors compared to marginal cyclones or depressions. Mean DPEs (km) Mean errors w.r.t Intensity at initialization at 27 km resolution DD CS SCS Forecast length (hour)

26 Bias ETS Mean ETS and Bias of 24-hr accumulated rainfall during landfall day (in real time) ETS Bias Thresholds (mm) 0 Model is overestimating rainfall (comparing with TRMM observations). Skill (ETS) is good up to 5 cm rainfall, thereafter decreasing sharply.

27 Impact of Satellite derived winds Assimilation on Track and Intensity

28 Data and Experiments Derived winds of QSCAT (wind speed and direction) SSM/I (wind speed) and Kalpana water vapor wind and CMVs Cyclone GONU (2 7 June 2007) SIDR (12 16 Nov 2007) NARGIS (27 Apr 3 May 2008) KhaiMuk (11 15 Nov 2008) Aila (22-25 May 2009) Laila (17 21 May 2010) Location Arabian Sea Bay of Bengal Bay of Bengal Bay of Bengal Bay of Bengal Bay of Bengal

29 Satellite derived wind ingested into the model initial condition of TC NARGIS SSMI, QSCAT and Kalpana winds for 12 UTC of 28 April 2008 (b) o 2851 SSMI QSCAT o 377 Kalpana WV and CMVs

30 Satellite derived wind ingested into the model initial condition of TC GONU SSMI, QSCAT and Kalpana winds for 00UTC of 2 June 2007 o SSMI QSCAT o Kalpana 33

31 24 cases Mean VDEs (km) CNTL NARGIS 3DVAR IC: 00UTC of 2 June 2007 CNTL GONU 3DVAR 10 m wind (m s -1 ) Impact of Satellite winds on track and intensity of cyclones Wind field at 925 hpa 10 m wind (m s -1 ) IC: 00UTC of 2 June IC: UTC of 28 April 2008 GONU case MSLP (hpa) IMD CNTL 3DVAR _12 Time (datehour) (ddhh) 29_12 30_12 01_12 02_12 Mean VDEs (km) CNTL 3DVAR Forecast Cases length (hour) An average 8 m/s error is improved in 3DVAR run compared to CNTL run. Osuri et al., 2012, IJRS 224

32 24-hr accumulated rainfall valid for landfall day (2-3 May 2008) for TC NARGIS IC: 12UTC of 28 April (a) TRMM (b) CNTL (c) 3DVAR cm Improved track leads to improvement in rainfall structure with satellite data

33 Mean (of 5 cases) ETS and Bias of 24-hr accumulated rainfall (mm) valid for landfall day ETS - Lines Mean at landfall Bias - histograms Bias CNTL(Bias) 3DVAR(Bias) CNTL(ETS) 3DVAR(ETS) Thresholds (mm) ETS

34 Landfall over Iran 7 8 June 2007 Landfall over Oman 6 7 June hr accumulated rainfall valid for landfall day for SuCS GONU GONU has two landfalls 1) over Oman and 2) over Iran cm

35 Mean (of 5 cases) ETS and Bias of 24-hr accumulated rainfall (mm) valid for landfall ETS - Lines Bias - histograms Bias CNTL (Bias) 3DVAR (Bias) CNTL (ETS) 3DVAR (ETS) Thresholds (mm) ETS

36 24-hr accumulated rainfall valid for landfall day for SIDR (15 16 Nov 2007) TRMM CNTL 3DVAR In Sidr case, the initial vortex is completely covered by QSCAT path.

37 Impact of DWR reflectivity and radial wind on the rainfall prediction of landfalling Tropical Cyclones over Bay of Bengal

38 Experiments and Data used Three numerical experiments are carried out CNTL - With out Data Assimilation GTS - With Assimilation of GTS data DWR - Assimilation of GTS + DWR data GTS includes : DWR includes : SYNOP, AWS, SHIP, TEMP, PILOT, BUOYS, SATOB, SATEM, AIREP etc. Reflectivity and Radial velocity of Kolkata DWR

39 Cyclone Name SIDR (Very severe cyclone) Aila (Severe cyclone) Laila (Severe cyclone) Jal (Severe cyclone) Cases Initial conditions Data density Case 1 00 UTC of 13 Nov Case 2 12 UTC of 13 Nov Case 3 00 UTC of 14 Nov Case 4 12 UTC of 14 Nov Case 5 00 UTC of 23 May Case 6 12 UTC of 23 May Case 7 00 UTC of 24 May Case 8 12 UTC of 24 May Case 9 12 UTC of 17 April Case UTC of 18 April Case UTC of 18 April Case UTC of 19 April Case UTC of 19 April Case UTC of 6 Nov Case UTC of 6 Nov Case UTC of 7 Nov Radar Coverage Kolkata DWR Kolkata DWR Chennai DWR Chennai DWR

40 Data distribution (of SYNOP, AIREP, SOUND, METAR, QSCAT, SSMI and Kolkata DWR respectively) for Case-1 (TC Sidr) at 00 UTC 13 Nov N 20 N 10 N 178 SYNOP 45 AIREP 54 SOUND 38 METAR EQ 80 E 90 E 100 E 80 E 90 E 100 E 80 E 90 E 100 E 80 E 90 E 100 E 30 N 25 N 20 N 2187 QSCAT 3239 SSMI 24 N 23 N 22 N 10 N 21 N EQ 80 E 90 E 100 E 80 E 90 E 100 E 20 N Kolkata DWR 19 N 85E 86E 87E 88E 89E 90E 91E 92E

41 Data distribution (of SYNOP, AIREP, SOUND, METAR, QSCAT, SSMI and Kolkata DWR respectively) for Case-14 (TC Jal) at 00 UTC 6 Nov N 20 N 10 N EQ 204 SYNOP 33 AIREP 80 E 90 E 100 E 80 E 90 E 100 E 54 SOUND 80 E 90 E 100 E 41 METAR 80 E 90 E 100 E 30 N 20 N 1923 SSMI 736 SATOB 16 N 15 N 14 N 13 N Chennai DWR: 12 N 10 N 11 N 10 N EQ 80 E 90 E 100 E 80 E 90 E 100 E 9 N 77E 78E 79E 80E 81E 82E 83E 84E

42 As the rainfall structure and intensity prediction of Sidr is poorly predicted by the ARW model, this case is taken up to study the impact of DWR observations. The cloud bands are observed by DWR Kolkata though it is far away from the DWR station and of course, when it approaches, the inner-core details are observed by the DWR. Therefore, the experiments are conducted in two scenarios: 1. Impact of TC environment observed by the DWR on TC prediction 2. TC Inner-core observations on TC prediction

43 DWR+GTS Assimilation CNTL (No Assimilation GTS data Assimilation IMD Best track CASE-1

44 Assimilation of DWR TC environment observations 10-m wind (m/s) OBS GTS CNTL DWR TC-SIDR 10m wind speed (m/s) Time (date hour) Forecast hour (hrs)

45 24-hr accumulated rainfall valid for landfall day (a) TRMM CNTL GTS DWR SIDR (15 16 Nov 2007) (b) cm AILA (25 26 May 2009) (c) Laila (20 21 May 2010) (d) Jal (7 8 Nov 2010

46 Model simulated Reflectivity along with Observed reflectivity CASE 1 (SIDR) CASE 5 (Aila)

47 Assimilation of Inner-core observations from DWRs

48 Laila cyclone: Initial vortex position and structure 10-m winds (m/s) CNTL DWR TC symbol: Observed location of TC Laila at 12UTC 19 May 2010 Plus symbol: 6-hr cycled model generated initial vortex CNTL simulated cyclone is continuously intensifying without landfall While, DWR system showed improvement in track and landfall

49 Laila cyclone: Inner-core improvements with DWR obs CNTL GTS DWR winds W vel & Temp Anamoly Titled vortex is improved with DWR observations, while both CNTL and GTS runs could not. Warm core structure is also improved with DWR observations

50 Laila cyclone: 3hrly rain rate and track (Initial condition 12UTC 19 May 2010 (60 hr forecast) TRMM CNTL GTS DWR CNTL GTS DWR RMSE (cm) Correlation Coeff Based on 76 IMD station rainfall

51 Impact of Microphysics: Inner-core Reflectivity assimilation on Hydrometeors structure CNTL (no-data assimilation) WarmRain_Microphysics IcePhase_Microphysics contours (cloud water Mixing ratio); shaded (Rain water mixing ratio) Inner-core Reflectivity assimilation

52 Impact of Microphysics: Inner-core Reflectivity assimilation on Rainfall structure WarmRain_Microphysics IcePhase_Microphysics CNTL CNTL TRMM Rainfall WarmRain_Microphysics Reflectivity Assimilation IcePhase_Microphysics Reflectivity Assimilation

53 Station Name Lat Long OBS Rain Warm-CNTL Ice-CNTL Warm-DWR Ice-DWR Addanki Bollapalli Chimakurty Darsi Jarugaumalli Kondepi Korisapadu Kothapatnam Kurichedu Maadipadu Machavaram Mangalagiri N. G. Padu Nakirekkallu S.N. Padu Savalyapuram Tadepalle Tallur Vinukonda Statistics based on 76 rainfall stations Warm-CNTL Ice-CNTL Warm-DWR Ice-DWR Mean RMSE Correlation

54 Phialin cyclone: Real-time HWRF model rainfall guidance (3 km nest) HWRF modeling system: 6-hr cyclic runs with vortex relocation and initialization No-data assimilation Numbers represents IMD station rainfall (showed >15 cm rainfall) Predicted rainfall peaks over Northern parts of Odisha (close to observed)

55 CONCLUSIONS The NWP model performance for the prediction of heavy rainfall due to landfalling Cyclones can be improved with mesoscale data assimilation Assimilation of DWR reflectivity and radial wind showed maximum impact on heavy rainfall simulation. Improved track prediction correct the rainfall structure up to some extent, Needs further investigation. Cloud resolving model is certainly a solution. Multi DWR data assimilation and Improved land surface conditions can further improve rainfall prediction

56 Thank You