High-resolution Regional Atmospheric Analysis
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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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High-resolution Regional Atmospheric Analysis The CSIR Initiative

Modelling and Implementation Issues


P Goswami

C-MMACS, Bangalore


February, 2010

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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.

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  • 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.

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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

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Global Analysis

Meso-scale observation network

3D-Var Assimilation

4D-Var Assimilation

Meso-scale Model

  • Calibration

  • Validation

Global Model

  • Calibration

  • Validation

Dynamical Fields





Organization of Model Hierarchy for HiRRAA

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CEMP: Major Modelling Activities

  • Global Model

  • Monsoon Forecasting

  • Climate Simulation

  • Meso-scale Model

  • Extreme Events

  • Cyclone Simulation



  • Process Model

  • Fog Forecast

  • Pollution Model

  • Process Studies

  • Sustainability Analysis

  • Basic Understanding

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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

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The Distribution of “Rare” Extreme Rainfall Events giants)

The modelling platform should be able to resolve highly localized systems

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HiRRAA giants)Model Optimization (GCM)

Goswami and Gouda, MWR, 2009

The GCM will provide the large-scale fields for initial and lateral boundary fields

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Horizontal Resolution : ~60kms x 50kms over Monsoon Region

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HiRRAA giants)Model Optimization (meso-scale)

Goswami and Himesh, 2009

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Calibration of Meso-scale Domains giants)

Introduction of (artificial) lateral boundaries converts a problem with homogeneous boundary forcing to one with inhomogeneous lateral boundary conditions; equivalent to a forcing

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

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4D-Var Data Assimilation: GCM rainfall (cm) for different Domains of 30km resolution

Goswami, Gouda and Talagrand GRL, 2005

Goswami and Mallick

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Results on 4D-var Assimilation with GCM rainfall (cm) for different Domains of 30km resolution

Validation of Minimization ( Decrease of Cost Function )

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Initial and forecast fields with and without 4D-Var assimilation for zonal wind (U)





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25/26-AUG-2006 assimilation for zonal wind (U)

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HiRRAA: The Observation Network assimilation for zonal wind (U)CalibrationValidation

Goswami and Patra

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CSIR Climate Monitoring Network assimilation for zonal wind (U)

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

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Telemetric Reception, Quality Control and Analysis of MONUS data

G K Patra

National Physical Laboratory, Delhi

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Diurnal cycle at four locations data


July 1- September 30, 2009

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20 m data

2 m

Central Telemetric Reception and Organization



Data Logger



30 m

Data receiver

and recorder





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Quality Control data



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



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Impact of Meso-scale Data Assimilation in High Resolution Forecast

Density of meso-scale observations

Goswami and Rakesh

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Mesoscale Model: Advanced Weather Research Forecast

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)

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Initial Wind speed difference (m/s) Valid for 05Aug 2009 from Domain 3

00 UTC

12 UTC

CNT- Without


Difference from CNT

due to four Tower data


Difference from CNT

due to single (NPL)

Tower data Assimilation

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HiRRAA: Debiasing and Downscaling from Domain 3

Objective Non-linear Debiasing: Goswami and Mallick, 2009

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Average diurnal cycle for 3 stations for the month of August 2009

(0.93, 0.98)

(0.92, 0.96)

(0.96, 0.99)


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

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Wind (m/sec) 2009

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

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Multiple Scenario Visibility Forecasts 2009

The fog model has been now transferred to IMD for operationalization

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Forecasting of Atmospheric Pollution 2009Forecasting 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

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Simulation of SPM over Delhi 2009Climatology (2000-2006) of observed and Simulated SPM

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Total Cloud Cover over Western Ghats 2009

MRI NHM: (Resolution 2 km)




17: 00

The model has been now configured for simulation at 500 meter resolution over the Western Ghats and the Himalayas

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5:00 2009



Base and Top Cloud over Western Ghats

MRI NHM: (Resolution 2 km)





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Data Assimilation: Global Vs Regional Error Covariance 2009

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

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Thank You 2009