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South Asian Regional Reanalysis (SARR) Ashish Routray National Centre for Medium Range Weather Forecasting (NCMRWF) Ministry of Earth Sciences Government of India Motivation for South Asian Regional Reanalysis Due to the direct societal impacts, interest in Regional Hydroclimate (precipitation, surface temperature, soil moisture, stream flow, drought indices, etc.) is intense and growing. National Action Plan on Climate Change Government of India Prime Minister’s Council on Climate Change 3.8.2 ……. Regional data reanalysis projects should be encouraged. …….. South Asian Regional Reanalysis (SARR) A Collaborative Project between Ministry of Earth Sciences, Government of India and National Oceanic and Atmospheric Administration, Department of Commerce, United States of America Specific SARR Goals Refinement in methods of precipitation and radiances assimilation. Conduct a 5-year pilot-phase reanalysis (to test and optimize data stream organization and the geographic domain and assimilating model choices) Develop high-resolution SST analysis for the Indian ocean from satellite and in-situ observations, including moorings, drifters and Argo floats Design techniques for assimilation of aerosols Generate a high spatio-temporal resolution (≤25 Km, ≤3 hours) climate data set for the 1979-2009 period over the South Asian land-ocean region. Responsibilities of the Parties NOAA agrees to: Provide MoES full access to the archived observations used in the global reanalysis projects. Provide technical help, training, and guidance in organization of data streams and in the implementation of the regional reanalysis model. Provide training to MoES scientists in regional reanalysis techniques and procedures during 6-8 week annual visits to US institutions and NOAA laboratories. Share the NCEP data processing and quality control procedures during reanalysis project with MoES scientists. Support travel of NOAA and US university scientists to India in connection with SARR project activities. MoES agrees to: • Provide NOAA full access to all historical and current meteorological observations as per requirement of the project over the Indian subcontinent and Indian Ocean, including those from Indian satellites. •Execute the South Asian Regional Reanalysis project through NCMRWF. •Provide full-time modeling scientists to develop, implement, and test numerical codes. •Provide 4-6 full time Ph.D. scientists to design, test, and implement various assimilation schemes in the numerical model. •Provide high-speed mainframe computer resources for execution of this computationally intensive project. •Provide storage devices and skilled manpower (data management specialists) to organize data streams, data archival, data dissemination, and webpage design and maintenance. •Provide continuous high-speed internet access to project scientists, including visiting ones. •Provide lodging and boarding for visiting US project scientists. Exchange visits • NOAA will provide training to 2-3 MoES scientists in regional reanalysis techniques and procedures during 6-8 week annual visits to the University of Maryland and NOAA's National Centers for Environmental Prediction (NCEP). • NCEP will seek resources and assistance from NOAA's International Activities office in meeting its responsibilities. • NOAA and MoES scientists will meet yearly to discuss the project's progress, and to strategize on how to best accomplish the project goals. • NOAA and MoES will separately cover travel costs associated with exchange visits for their respective technical and scientific personnel. Milestones SARR IA signed in September 2008 in New Delhi 1st Annual Review by JEM held in October 2009 in New Delhi Functional Group created at NCMRWF for SARR in November 2009 SARR Scoping Workshop held in New Delhi in February 2010 2nd Annual Review by JEM held in October 2010 in Washington DC The SARR Project is being carried out with an objective that the SARR Products shall be useful for Climate Diagnostics, Climate Variability, Climate Change, Model Verification/Tuning It is expected that The SARR project will provide an Atmosphere-Land- Ocean surface state description where consistency between circulation and hydroclimate components is assured. To achieve the goal, assimilation of rainfall, radiance, and aerosol observations in numerical weather prediction models shall be carried out SARR Project Team at NCMRWF Sarat C. Kar Project Management Ashish Routray Assimilation- Lead Prashant Mali Modeling- Lead Jaganabdhu Panda Modeling (worked for about 3 months and left in September 2010) K. Sowjanya Assimilation (worked for about 1 year and left in September 2011) Sapna Rana Diagnostics (worked for about 1 year and left in November 2011) Domain chosen for SARR Lat: 150S-450N (286 pts) Lon: 400E-1200E (332 pts) Res.: 25 km (pilot phase) 18 km (final SARR) Cen-lat: 17.50N Cen-lon: 80.00E SARR NCEP OBSERVATION IMD DATA BANK NCMRWF Countries in SARR domain INCOIS Field ISRO Experiments DATA from FIELD EXPERIMENTS 25 Paradip 20 DS4 TS2 (SK) latitude (N) 15 Chennai DS3, TS1 (SD) Land Surface Processes 10 C Experiment (LASPEX) 5 BOBMEX T 0 C 70 75 80 85 90 95 longitude (E) Figure 1. Cruise track and time series (TS) observation positions. Period: 16 July - 30 August 1999. TS1 - 13N,87E; TS2 - 17.5N, 89E. SK - ORV Sagar Kanya, SD - INS Sagardhwani, DS3 & DS4 - met ocean b uoys Z ARMEX C PROWNM B STORM Programme SARR Scoping Workshop held in New Delhi, India (February 10-11, 2010) 9 scientists from USA and about 20 scientists from India participated. Analysis method and the model as well as domain of analysis finalized. WRF model (3.1 version) and WRF-3DVar shall be used to carry out SARR Pilot phase. The Workshop recommended an implementation strategy for success of the SARR project. Work plan at NCEP • Training on methodology for assimilation of the radiance data (mainly the older period radiance data) using the GSI system so that a similar technique can be developed later for the WRF- 3DVAR analysis system. • As part of the training, experiments using radiance data assimilation for Indian summer monsoon seasons (mainly for older period) using the NCEP GSI system and document impact assessment. • Familiarization with the available diagnosis package for monitoring and for calculation of statistics of the radiance data utilized in the assimilation cycle. SARR Pilot Phase Experiments (1999-2003) Analysis Scheme & Model for SARR Pilot Phase WRF 3.1 and WRF-VAR (3.1) has been chosen for SARR Pilot phase experiments Several modeling and assimilation experiments have been carried out using past data. Most of the experiments are for July 1999 using NCEP & NCMRWF observation datasets Challenging regions for obs. data Sound Average Number of Observations per Av. Number of TEMP observation per day day in July 1999 reaching particular height in July 1999 300 300 00Z 12Z 250 250 200 200 00Z 06Z 150 150 12Z 18Z 100 100 50 50 0 0 Total TEMP WIND PILOT >800hPa 800-450hPa 450-100hPa <100hPa Blocks- 42 and 43 Mean RMSE of wind components from different observations at model initial time Mean RMSE of O-B and O-A for U (m/s) 4 O-B O-A 3.5 U (m/s) 3 2.5 sound pilot geoamv airep synop ship Types of Obs. Mean RMSE of O-B and O-A for V (m/s) 5 O-B O-A 4 V (m/s) 3 2 1 0 sound pilot geoamv airep synop ship Types of Obs. SARR Test runs with NCEP & NCMRWF data Mean of RMSE of O-B for U (m/s) Mean of RMSE of O-B for V (m/s) Mean RMSE of O-B for t (k) Mean 6 NCMRWF PrepBufr 5 NCMRWF PrepBufr 2.5 NCMRWF PrepBufr RMSE of 5 4 2 U (m/s) V(m/s 4 t (k) OBS-FG 3 3 1.5 2 1 2 1 1 0.5 sound pilot airep synop ship sound pilot airep synop ship sound airep synop ship Types of Obs. Types of Obs. Types of Obs. Mean RMSE of O-A for V (m/s) Mean RMSE of O-A for t (k) Mean RMSE of O-A for U (m/s) 5 1.5 NCMRWF PrepBufr Mean 6 5 NCMRWF PrepBufr 1.3 NCMRWF PrepBufr 4 1.1 RMSE of U (m/s) 4 0.9 V (m/s) t (k) 3 OBS-ANA 3 0.7 2 0.5 2 0.3 1 sound pilot airep synop ship 1 0.1 sound pilot airep synop ship sound airep synop ship Types of Obs. Types of Obs. Types Obs. SARR Pilot Phase Experiments (i) with various Physics Options Dynamic Downscaling using WRF (ii) with various Physics Options Assimilation using WRF & WRF-VAR Most of the experiments are for July 1999 using NCEP & NCMRWF observation datasets SARR Pilot Phase Sensitivity Experiments All Experiments were done for July 01- 31 1999. With Assimilation- Cyclic, Four times a day (6-hourly) No Assimilation- only Model run Four-times a day (6-hourly). (Similar to downscaling experiments) Precipitation in July 1999 CMAP, TRMM (3B42) and IMD Observed Rain Precipitation from Global Reanalysis datasets for July 1999 As can be seen, the global reanalysis has failed to bring out details of rainfall distribution over India and higher rainfall amounts are placed at incorrect locations EXPERIMENTAL DESIGN CU schemes PBL Schemes SFC Schemes Expt. Names Kain-Fritsch (KF) KF-YSU-Noah Betts-Miller-Janjic Yonsei University BMJ-YSU-Noah (BMJ) (YSU) Grell Devenyi (GD) Noah Land surface GD-YSU-Noah KF KF-YSU-Noah BMJ Mellor-Yamada- BMJ-YSU-Noah Janjic (MYJ) GD GD-YSU-Noah KF KF-YSU-TD BMJ YSU BMJ-YSU-TD GD GD-YSU-TD Thermal Diffusion KF (TD) KF-MYJ-TD BMJ MYJ BMJ-MYJ-TD GD GD-MYJ-TD SARR Pilot phase Sensitivity Experiments No Assimilation With Assimilation It has been shown that just downscaling of coarse resolution global reanalysis (No Assimilation runs) is not sufficient for accurate representation of the Indian monsoon hydroclimate. When regional assimilation is carried out, such representation is improved. SARR Pilot Phase Sensitivity Experiments Experiments have been carried out using ISRO derived vegetation data instead of USGS climatological vegetation available with the WRF model. Results indicate that hydroclimate representation over India is sensitive to such specifications. Impact of Field phase Experiments- BOBMEX data 25 Bay of Bengal 20 Paradip Monsoon Experiment DS4 TS2 (SK) (BOBMEX) latitude (N) 15 Chennai DS3, TS1 (SD) July-August 1999 10 5 0 70 75 80 85 90 95 longitude (E) Figure 1. Cruise track and time series (TS) observation positions. Period: 16 July - 30 August 1999. TS1 - 13N,87E; TS2 - 17.5N, 89E. SK - ORV Sagar Kanya, SD - INS Sagardhwani, DS3 & DS4 - met ocean b uoys Impact of Field phase Experiments- BOBMEX data (00Z 12 August 1999) Assim- Control Assim- with BOBMEX Difference Parallel Assimilation from May 2001 to Sept 2001. Need of Overlapping period Pilot phase Assimilation U at 850hPa with conventional data has been completed from 1999- 2003. Assimilation with Radiance data and conventional data is being carried out for the same period. T at 850hPa Parallel run period is also being extended. Comparison between CFSR, SARR and Observation (1-31 July 2000) OBS CFSR SARR SARR Production Runs Five simultaneous Streams Jan. 1979 - Dec. 1985 7 years Apr. 1985 - Dec. 1991 7 years Apr. 1991 - Dec. 1997 7 years Apr. 1997 - Dec. 2003 7 years Apr. 2003 - Dec. 2009 7 years 9-month overlap for each stream Total 35 years of Reanalysis Computation SARR Products Archival and Distribution Archival Format (Reanalysis): IEEE (suitable for GrADS) NetCDF GRIB2 Archival Format (Observed data): ASCII (GTS) PrepBUFR little-R Original format of data Archival online/nearline disk, Tapes Available to Partner Organizations: Immediately Aug Dec Apr Aug Dec Apr Aug Dec Apr Aug Tasks 2010 2010 2011 2011 2011 2012 2012 2012 2013 2013 Pilot phase reanalysis production (1999-2003) Evaluation of pilot phase reanalysis data Refinement of assimilation techniques Collection of data from countries in SARR domain Level-I SARR Production for 1979-2009 period Evaluation of Level-I reanalysis data Final SARR Production for 1979-2009 period Reanalysis data- public SARR – What next? SARR -II After the successful completion of SARR’s present project, We propose to carryout SARR-60 SARR-60 From 1950 to 2009 at 9 km resolution Regional Ocean-Atmosphere coupling - shall be the comprehensive dataset for climate studies in South Asia. IMPACT of BACKGROUND ERRORS (BE) & ASSIMILATION Numerical Experiments • The objective of the study is to evaluate the impact of the different back ground errors (Global and Regional) towards simulation of four Monsoon Depressions (MDs) over Indian region during SARR pilot phase period. • 27-29 July 1999 (Case-1) • 17-18 June 1999 (Case-2) • 11-12 June 1999 (Case-3) • 6-8 August 1999 (Case-4) • For this purpose three numerical experiments are carried with WRF-3DVAR as follows: 1) CNTL: Without data assimilation using NCEP re- analyses as IC and BC. 2) BG-3DV: Data assimilation using NCEP global Background Error (BE). 3) BR-3DV: Data assimilation using own calculated BE over SARR region. • The additional observations viz. SYNOP, SHIP, TEMP, BUOYS, PILOT, GEOMV, AIREP etc. are used to improve the model initial condition derived from coarse resolution large scale global analysis. a) Mean RMSE of O-A for U (m/s) b) Mean RMSE of O-A for V (m/s) 6 6 BG-3DV BR-3DV 5 5 BG-3DV BR-3DV 4 U (m/s)) 4 V (m/s)) 3 3 2 2 1 1 0 0 Sound Synop Geoamv Airep Pilot Metar Ships Sound Synop Geoamv Airep Pilot Metar Ships Types of OBS Types of OBS c) Mean RMSE of O-A for Temperature (k) 2 BG-3DV BR-3DV Temperature (k))) 1.5 1 0.5 0 Sound Synop Airep Metar Ships Types of OBS Mean RMSE from BR-3DV and BG-3DV of O-A for a) U (m/s), b) V (m/s) and c) T (K). NCEP ANA BG-3DV ANA BR-3DV ANA Case-1 OBS: 21.0/89.0 CNTL:21.8/89.8 BG-3DV:21.6/88.8 BR-3DV:20.8/89.5 Case-2 NCEP ANA BG-3DV ANA BR-3DV ANA OBS:18.5/86.0 CNTL:18.5/87.0 BG-3DV:18.9/87.1 BR-3DV:19.2/86.5 Model Initial time wind fields at 850 hPa and MSLP Case-1 Case-2 Track Error Track Errors 900 700 CNTL 800 CNTL 600 BG-3DV Errors (kms) 700 BG-3DV Errors (km) 500 BR-3DV 600 500 BR-3DV 400 400 300 300 200 200 100 100 0 0 0 12 24 36 48 0 12 24 Forecast hours Forecast hours Case-3 Case-4 Track Eorrors (km) Track Errors (km) CNTL CNTL 250 BG-3DV 400 BG-3DV Errors (km) Errors (km) 200 BR-3DV 300 BR-3DV 150 200 100 50 100 0 0 0 12 24 0 12 24 36 48 Forecast hrs Forecast hrs Spatial RMSE (mm) and Correlation Co-efficient (CC) of rainfall over the area (Lat=150-250N; Lon=750-900E) for all cases. Cases RMSE CC CNTL BG-3DV BR-3DV CNTL BG-3DV BR-3DV Case-1 29. 62 26. 24 22.15 0.23 0.36 0.52 (27-29 July 99) Case-2 24. 32 22. 31 18. 38 0.21 0.33 0.46 (17-18 June 99) Case-3 26. 93 22. 54 18. 68 0.26 0.35 0.46 (11-12 Jun 99) Case-4 40. 59 34. 25 29. 41 0.33 0.46 0.52 (6-8 Aug 99) Mean 30. 37 26. 34 22. 16 0.26 0.38 0.49 CNTL BG-3DV BR-3DV 400 CNTL vs. BG CNTL vs. BR BG vs BR 60 Skill of Expts.(%) Mean VDEs(kms) 300 40 200 20 100 0 0 00 12 24 Forecast hours (UTC) Mean VDEs (km) and gain skill of experiments Impact of Radiance data Temperature (oC) at 850 hPa GTS GTS+Rad Diff. (Rad-GTS) Wind (m/s) at 850 hPa GTS+Rad GTS Diff. (Rad-GTS) Wind (m/s) at 500 hPa GTS+Rad GTS Diff. (Rad-GTS) Rainfall Climatology These are accumulated 6-hrly Rainfall from the models used for Reanalysis. Every 6-hour, observed data are inserted into the data Assimilation systems, and analyses are carried out. Assumption is that models are good enough for at least 6 hour. These studies show there are large uncertainties in the Global Reanalysis data over our Region. Model Resolution? Data Quality/Quantity? We need to carry out a Systematic Regional Reanalysis for our Region to have a consistent Hydro-climate dataset. The Global reanalysis data are utilized for studying climate change and to develop several Application models. Therefore, we should provide the users with a good quality data set for our Region. • A large part of tropical forecast Response of the Analysis Increments to errors can be represented by a single Temperature observation 10K equatorial waves. Global •These modes effectively reduce BE the mass/wind coupling at the equator. • Daley (1996) has noted that Reg. equatorial error covariance is BE weaker than higher latitude and similar to that obtained by equatorial beta plane theory. • By suppressing the erroneous a single u-wind observation 1 m/s tropical wind-height coupling, Global Daley did not find the covariance BE pattern to the south of central latitude in the tropical domain. •In our study, we find that for the Reg. BE statistics, the effect of a BE single wind observation is consistent with theoretically derived wind correlations for non-divergent flow.
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