slide1
Download
Skip this Video
Download Presentation
February, 2010

Loading in 2 Seconds...

play fullscreen
1 / 45

Genesis and Scope - PowerPoint PPT Presentation


  • 316 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Genesis and Scope' - Sharon_Dale


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

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
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.

objectives
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.

slide5

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

slide6

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

slide7

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
slide8

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

the distribution of rare extreme rainfall events
The Distribution of “Rare” Extreme Rainfall Events

The modelling platform should be able to resolve highly localized systems

hirraa model optimization gcm
HiRRAA Model Optimization (GCM)

Goswami and Gouda, MWR, 2009

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

slide13

THE MONSOON GRID

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

calibration of meso scale domains
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

slide17
Spatial distribution of 30 Hr Accumulated ensemble mean rainfall (cm) for different Domains of 30km resolution
4d var data assimilation gcm
4D-Var Data Assimilation: GCM

Goswami, Gouda and Talagrand GRL, 2005

Goswami and Mallick

slide19

Results on 4D-var Assimilation with GCM

Validation of Minimization ( Decrease of Cost Function )

slide20

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

Ui

Ui_Assim

Uf

Uf_Assim

slide23

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

telemetric reception quality control and analysis of monus data

Telemetric Reception, Quality Control and Analysis of MONUS data

G K Patra

National Physical Laboratory, Delhi

slide26

Diurnal cycle at four locations

Delhi

July 1- September 30, 2009

central telemetric reception and organization

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

quality control
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

slide30

Impact of Meso-scale Data Assimilation in High Resolution Forecast

Density of meso-scale observations

Goswami and Rakesh

slide31

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)

slide33

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

hirraa debiasing and downscaling
HiRRAA: Debiasing and Downscaling

Objective Non-linear Debiasing: Goswami and Mallick, 2009

slide35

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

slide37

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

multiple scenario visibility forecasts
Multiple Scenario Visibility Forecasts

The fog model has been now transferred to IMD for operationalization

forecasting of atmospheric pollution forecasting daily spm over delhi
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
slide41

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

slide42

5:00

12:00

17:00

Base and Top Cloud over Western Ghats

MRI NHM: (Resolution 2 km)

Base

Cloud

Top

Cloud

slide44

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

ad