Data assimilation and reanalysis for monsoon...
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Transcript of Data assimilation and reanalysis for monsoon...
Introduction
• In the recent past, India making sustained efforts to build a seamless weather forecasting system starting from nowcasting to seasonal forecasting by
- strengthening its observational network & Data Reception system
- updating its Global Data Assimilation & Forecasting (GDAF) system - carrying out research and development activities.
• National Center for Medium Range Weather Forecasting (NCMRWF) responsible for developing data processing and GDAF systems.
• In the present talk a brief account of GDAF systems will be presented with an emphasis on Data assimilation and utilisation of new data sets in it.
Mihir (Cray-XC40) 2.8 Peta Flops HPC
• NGFS ( 1534 L64)
• GFS 13.0.0
• 4D- EnsVar (GSI)
• 1994 -2007 - SSI
• 2007 onwards GSI
• 2016 3DHybrid
• 2017 4D-EnsVar
• Assimilates 3D,3DR, scatsat & MT-Saphir &ROSA
Models & Assimilation systems at NCMRWF
• NCUM (N512L70)
• UM 10.2 /10.8(12 km)
• 4D –VarEnsemble
• Since 2012
• Seamless prediction system .
• Coarse resolution couple model (NEMO+UM)
Meghatropiques SAPHIR & ROSA
Oceansat -2 SCAT & ROSA
INSAT- 3D NCMRWF Data Handling system
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Asia –Pacific RARS
Special efforts are being mad to improve availability and timeliness of time critical polar orbiting satellite data over our region by becoming member in Asia-Pacific RARS group.
Data from 3 IMD HRPT stations data are being operationally used now. IMD Chennai HRPT station data is critical to RARS group as it covers data gap area
Contributions regional international partners
MT-ROSA and INSAT AMV’s are made available GTSScatsat data shared with South Korea etc.
Number of Indian RS/RW stations increased to 39 and employing gpssonde
GTS/ftp DATA
BUFR TANK
NCEP DUMP
Obstore Converter
Obstore O/Ps Surface Obstore
Upper Air Obstore
Aircraft Obstore
Satwind Obstore
Scatwind Obstore
Satrad Obstore
GPSRO obstore
NCUM Observation Processing
System (OPS)
Data Pre-processing for NCUM
Prasad 2012
4D-Ens-VAR Analysis system
Presently tests are going on for implementing V14 GSI by changing ensemble resolution from T-574 to 1534 ,using NSST and including Indian satellite s 3D, 3DR , scasat and MT
Prasad & Johny 2016 & 2017 JEES
WMO CBS Scores Jan 1999-Dec2017
The skill of NWP forecasts from NCMRWF, improved around 2011 and it is attributed to the increase in the resolution of the model, increase in the amount of data being assimilated, improvements in data assimilation techniques. It prompted to carry out retropspective analysis for the period 2000-2011. By including the operational analysis that is generated using same GDAF system this data availble for the period 2000-2016 at T574 resolution. The reanalysis data is being used in extended & Long range Forecasts. Verification of Day 03 Forecasts against Indian Radiosondes Root Mean Square Error (RMSE) of 850 hPa winds in m/s (2005-2015)
The anomaly correlation values are comparatively higher in the T574 GFS with a gain of 1 day in the skill of the forecasts.
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•Prasad et al JESS ,Vol 123, No2, Mar 2014, 347-258.
showing trends in analysed daily rainfall over Indian land mass and Wang index for Indian Summer monsoon season computed from re-analysis (2000-2011) data sets.
showing Trend of 2m surface temperature during reanalysis period
Prasad et al 2017 current science vol 112
Identification of heat wave episodes
• The average duration of heat waves is 10-days, with longest (shortest) episode lasting for 20 (7) days in the year 2003 (2007, 2008, and 2013).
• An average dura�on of severe heat wave is 3.5−days longer than that of a normal heat wave. • The onset of heat wave can be seen at the earliest (latest) on 09 May (15 June) in 2002 (2005)
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Study region (16o−22o N; 76o−82o E)
Heat wave
Total no.of heat waves 10 (100 hot days)
Intra-annual variability of heat wave episodes over east coast of India
• The dashed black (red) line in Figure 8b represent the area-averaged climatological Tmax plus 4 (5) oC.
• A total of 10 heat wave episodes were obtained from 2000−2015. Out of these four (six) severe (normal) heat waves are obtained.
• Intensity of heat waves averaged over east coast of India has shown an increase of 0.06 oC per heat wave and maximum intensity is seen in the year 2015.
• It exhibits the presence of intra-annual variability over the study region.
Sandeep and Prasad , Pure Apply geoph 2017
Indian Monsoon Data Assimilation and Analysis (IMDAA) Reanalysis
Domain: 30 ° E - 120 ° E, 15 ° S - 45 ° N
LBCs : ECMWF Reanalysis
Model & Resolution: UM10.2, ~ 12 km (800 x 576 grid points), 63 vertical levels
Data Assimilation Method: 4D-VAR
Objective: Satellite-era (1979 - Present) high resolution reanalysis over India and surrounding regions
Observations from ECMWF Archival for the entire period
Additional Conventional observations –
Surface & Sonde from IMD
Present Status: More than 15 years of reanalysis is completed. Reanalysis data set will be available to users early next year (grib format)
IMDAA Domain
Time-series of averaged daily precipitation over India (mm/day) for (a) JJAS 2008 and (b) JJAS 2009
Additional Observations for IMDAA from IMD/NCMRWF
SYNOP observations stations over IMDAA region (12 UTC of 25 April 2014)
Number of SYNOP observations during April 2014 (00 UTC)
Conclusion
• 2000-2016 Retrospective analysis data is available on monsoon portal.
• NCUM regional analysis(IMDDA) project will be completed soon for the period 1979 to till date.
• Tests runs are being carried out using T1534L64 GFS with 4D-EnsVar ENKF at same horizontal resolution . It will support GFS based forecasts systems in India,
