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February, 2010

High-resolution Regional Atmospheric Analysis The CSIR Initiative Modelling and Implementation Issues. HiRRAA. P Goswami C-MMACS, Bangalore www.cmmacs.ernet.in. February, 2010. Genesis and Scope. High-resolution atmospheric and land data is critical for many (industrial) applications

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February, 2010

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  1. High-resolution Regional Atmospheric Analysis The CSIR Initiative Modelling and Implementation Issues HiRRAA P Goswami C-MMACS, Bangalore www.cmmacs.ernet.in February, 2010

  2. Genesis and Scope • High-resolution atmospheric and land data is critical for many (industrial) applications • Wind energy • Geo-technical applications • Airports and Shipyards A data set homogeneous in space and time is required at spatial resolution of about 1 Km.

  3. Objectives • Phase I: Develop a high-resolution (~ 10 Km), regional (Indian sub-continent) atmospheric analysis combining • Observations • Model Hierarchy • Data assimilation • Debiasing • Downscaling Phase II: High-resolution (~ 1 Km), regional (Indian sub-continent) atmospheric and land surface analysis.

  4. Components and Structure

  5. Configuration, calibration and validation of a GCM Configuration, calibration and validation of a Limited Area Model Data Assimilation for both GCM and Limited Area Model Downscaling algorithm for calibration and validation Objective Debiasing for application Multi-scale Validation with Multi-source Data Generation of meso-scale observations High-resolution Regional Atmospheric Analysis (HiRRAA): The CSIR Initiative

  6. Global Analysis Meso-scale observation network 3D-Var Assimilation 4D-Var Assimilation Meso-scale Model • Calibration • Validation Global Model • Calibration • Validation Dynamical Fields Downscaling Validation Debiasing HiRRAA Organization of Model Hierarchy for HiRRAA

  7. CEMP: Major Modelling Activities • Global Model • Monsoon Forecasting • Climate Simulation • Meso-scale Model • Extreme Events • Cyclone Simulation Diagnostics Algorithms • Process Model • Fog Forecast • Pollution Model • Process Studies • Sustainability Analysis • Basic Understanding

  8. Global Analysis: NCEP/ERA40 (riding on the shoulders of giants) Global Model: Variable-Resolution GCM Limited Area Model: MM5/WRF Data Assimilation: 4D-VAR (GCM) and 3D-VAR (WRF) Cloud Variables (NHM, MRI) Downscaling: In-house Objective Debiasing: In-house Validation: Multi-source - IMD, TRMM, … - CSIR Network - Others High-resolution Regional Atmospheric Analysis (HiRRAA): Models and algorithms

  9. HiRRAA: Modelling Requirements

  10. The Distribution of “Rare” Extreme Rainfall Events The modelling platform should be able to resolve highly localized systems

  11. The Western Ghat orography at different resolutions

  12. HiRRAA Model Optimization (GCM) Goswami and Gouda, MWR, 2009 The GCM will provide the large-scale fields for initial and lateral boundary fields

  13. THE MONSOON GRID Horizontal Resolution : ~60kms x 50kms over Monsoon Region

  14. 25/26-JUL-2005

  15. HiRRAA Model Optimization (meso-scale) Goswami and Himesh, 2009

  16. Calibration of Meso-scale Domains Introduction of (artificial) lateral boundaries converts a problem with homogeneous boundary forcing to one with inhomogeneous lateral boundary conditions; equivalent to a forcing

  17. Spatial distribution of 30 Hr Accumulated ensemble mean rainfall (cm) for different Domains of 30km resolution

  18. 4D-Var Data Assimilation: GCM Goswami, Gouda and Talagrand GRL, 2005 Goswami and Mallick

  19. Results on 4D-var Assimilation with GCM Validation of Minimization ( Decrease of Cost Function )

  20. Initial and forecast fields with and without 4D-Var assimilation for zonal wind (U) Ui Ui_Assim Uf Uf_Assim

  21. 25/26-AUG-2006

  22. HiRRAA: The Observation NetworkCalibrationValidation Goswami and Patra

  23. CSIR Climate Monitoring Network Component 1: Meso-scale Observation Network for Urban Systems (MONUS) High-density (~ 10 Km separation) multi-level observations stations over urban area (Delhi) Component 2: Meso-scale Observation Network for Orographic Systems (MONOS) High-density (~ 10 Km separation) multi-level observations stations over orographic region (Western Ghat) Component 3: National Climate Profiler Network Multi-level observations stations over different locations All the stations are telemetrically connected to a central location and follow uniform data protocol

  24. Telemetric Reception, Quality Control and Analysis of MONUS data G K Patra National Physical Laboratory, Delhi

  25. Diurnal cycle at four locations Delhi July 1- September 30, 2009

  26. 20 m 2 m Central Telemetric Reception and Organization Rajokri NPL Data Logger Narela Internet 30 m Data receiver and recorder CIMAP GPRS/GSM Modem Hindon C-MMACS

  27. Quality Control Internet Archival Quality Control Module • Preliminary Quality Control Algorithm • Bound checking of all the parameters • NAN value checking • Data Missing Alert • Removal of data duplication • Data Size checking Feedback Analysis

  28. Impact of Meso-scale Data Assimilation in High Resolution Forecast Density of meso-scale observations Goswami and Rakesh

  29. Mesoscale Model: Advanced Weather Research and Forecasting (WRF) model (ARW) Version 3.1.1 (Latest version released in August 2009) Data Assimilation method--- WRF Three Dimensional Variational (3D-Var) scheme (Latest version released in August 2009): Global Error Covariance Data assimilated----- Multilevel data from CSIR network Towers (Pressure, Temperature, Humidity, Wind speed) Model Resolution: 36 km , 12 km, 4 km Inter-station distance: ~ 15 Km (Arial Distance)

  30. Summary of the experiments

  31. Initial Wind speed difference (m/s) Valid for 05Aug 2009 from Domain 3 00 UTC 12 UTC CNT- Without Assimilation Difference from CNT due to four Tower data Assimilation Difference from CNT due to single (NPL) Tower data Assimilation

  32. HiRRAA: Debiasing and Downscaling Objective Non-linear Debiasing: Goswami and Mallick, 2009

  33. Average diurnal cycle for 3 stations for the month of August 2009 (0.93, 0.98) (0.92, 0.96) (0.96, 0.99) Hour The numbers in the bracket in each panel represent correlation with respect to observation (OBS) for unaltered and non-linear realizable debiased forecasts, respectively. Large early morning and afternoon bias Black line: Hourly observation Blue Line: Downscaled forecasts to station location Dotted Line: Downscaled forecasts with non-linear debiasing

  34. HiRRAA: Applications

  35. Wind (m/sec) Relative Humidity (%) Foggy day Non Foggy day Foggy day Non Foggy day Foggy day Non Foggy day T-Td (oC) Time (Hours, Local Time) Advance Dynamical Fog Prediction Contrast between Foggy and Non-foggy in meso-scale simulation Foggy days are characterized By weaker winds Foggy days are characterized By higher humidity Foggy days are characterized By lower T-Td Goswami and Tyagi, 2008

  36. Multiple Scenario Visibility Forecasts The fog model has been now transferred to IMD for operationalization

  37. Forecasting of Atmospheric PollutionForecasting daily SPM over Delhi • Meteorological Fields from Meso-scale Model • Down-scaling of Meteorological Fields • SPM model developed at C-MMACS • Location-specific (Delhi) sources and sinks • Broad-spectrum sources (vehicular, dust, domestic..) • Goswami and Barua, MWR, 2008

  38. Simulation of SPM over DelhiClimatology (2000-2006) of observed and Simulated SPM

  39. Total Cloud Cover over Western Ghats MRI NHM: (Resolution 2 km) Hour 5:00 12:00 17: 00 The model has been now configured for simulation at 500 meter resolution over the Western Ghats and the Himalayas

  40. 5:00 12:00 17:00 Base and Top Cloud over Western Ghats MRI NHM: (Resolution 2 km) Base Cloud Top Cloud

  41. Perspective

  42. Data Assimilation: Global Vs Regional Error Covariance Objective Debiasing Dynamic Downscaling Ensemble Simulation: Generation of Ensemble (Informational Ensemble: Goswami, Gouda and Talagrand, GRL, 2005) Forward Modelling for Data Assimilation Land Surface Modelling and Analysis (soil moisture) High-resolution Regional Atmospheric Analysis (HiRRAA): Work Plan

  43. Thank You

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