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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

HiRRAA

P Goswami

C-MMACS, Bangalore

www.cmmacs.ernet.in

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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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.


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Components and Structure


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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

Downscaling

Validation

Debiasing

HiRRAA

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

Diagnostics

Algorithms

  • 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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HiRRAA: Modelling Requirements


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The Distribution of “Rare” Extreme Rainfall Events

The modelling platform should be able to resolve highly localized systems


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The Western Ghat orography at different resolutions


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HiRRAA 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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THE MONSOON GRID

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


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25/26-JUL-2005


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HiRRAA Model Optimization (meso-scale)

Goswami and Himesh, 2009


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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


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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

Goswami, Gouda and Talagrand GRL, 2005

Goswami and Mallick


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Results on 4D-var Assimilation with GCM

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)

Ui

Ui_Assim

Uf

Uf_Assim


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25/26-AUG-2006


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HiRRAA: The Observation NetworkCalibrationValidation

Goswami and Patra


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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


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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

Delhi

July 1- September 30, 2009


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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


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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


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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

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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Summary of the experiments


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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


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HiRRAA: Debiasing and Downscaling

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)

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


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HiRRAA: Applications


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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


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Multiple Scenario Visibility Forecasts

The fog model has been now transferred to IMD for operationalization


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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


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


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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


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5:00

12:00

17:00

Base and Top Cloud over Western Ghats

MRI NHM: (Resolution 2 km)

Base

Cloud

Top

Cloud


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Perspective


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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


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Thank You


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