Current and Future Use of Satellite Data in NWP at Environment Canada

Satellite Direct Readout Conference 2011Miami, USADavid Bradley, Gilles Verner, Mike ManoreMeteorological Service of CanadaApril 4-8, 2011

Context

Environment Canada (EC)Meteorological Services of Canada (MSC)

“providing weather and environmental predictions and services to reduce risks and contribute to the well-being of Canadians”

– Operations (e.g. 24-7 forecasts and warnings, NWP operations)– Monitoring Networks (e.g. Upper Air, Surface, Climate, Water, Space-based)– Environmental Predictions and Services (e.g. Ice, Aviation, Military, Policy)– Science (e.g. Air Quality, Climate, Meteorological)

Canadian Meteorological Centre (CMC)

Meteorological Research Division: Data Assimilation, Modeling, Cloud Physics

CMC Development Division: Data Assimilation, Numerical Weather Prediction, Weather Elements, Scientific Applications

IT Infrastructure (CIOB): Supercomputer,National Telecommunications, Network,User support

CMC Operations: Analysis & Prognosis, Env. Emergency Response (VAAC), Air Quality, Implementation and Operational Services

Role of CMC and Regions in Weather Prediction

NAVCANADA

Dept. NationalDefence

Public

Marine

Agriculture

Private sector...

CMC

Supercomputer/TelecomDecoding, QC & Databasing

Data Assimilation & ModelingPost-processing

5 EC Regions

WarningsForecastsDisseminationServicesData + Prod.

CanadianIceService

Aviation& DefenseServices

USERSCanadian Data

International Data(GTS Washington, NESDIS, Eumetsat,UKMet, etc.)

Research in NWP,Data Assimilation,Remote Sensing

and AQ

MRD

Tech. transfer

DATA ACQUISITION

COMPUTER ANALYSIS OF DATA

COMPUTER FORECAST

INTERPRETATION &DISSEMINATION

• Observations obtained from weather balloons, surface stations, ships, satellites, aircrafts, drifting buoys.

• Produce Values of atmospheric variables (temperature, winds, humidity & pressure) at mesh points.

• Run computer model of atmosphere. Provides forecast values of atmospheric variables at mesh points.

• Applications. Forecasts interpreted in terms of weather elements (e.g. sunny and cloudy periods), disseminated via media.

THE MAKING OF A WEATHER FORECAST

Data Assimilation Process

First Guess(6hr fcst)

Analysis(Spatial QC)

NWP model

Error Statistics(Observation and Forecast)

Data Acquisition

Data Quality Control

grilleptN prev

i

Nobs obs

ii Err

xP

Err

xOxJ

22 )()()(

Cost

MIN J(x) xa

J(xi-1) Use to find xi

Unified Numerical Forecasting System

Global Environmental Multiscale (GEM) Forecasting & Modelling System

2010-2020

Regional and Mesoscale Forecast ( 24-48 h, 10-15 km )

& Data assimilation

Medium-range Forecast ( 240 h, 10 to 35 km )

& Data assimilation

Middle Atmosphere Model &

Data assimilation

Regional Climate Model

Monthly Forecast

Multi- Seasonal Forecast

Ensemble Forecast

Limited-Area Model 0-24h 1-2.5km

& Data assimilation

S P A C E

S C A L E

TIME SCALE

Micro-meteorology (10m-1km)

Main Uses of Observational Data at CMC CMC is a major user of observational data, both Canadian

and foreign, main uses are:

• Data Assimilation: Blending of observations with other information to generate initial conditions (the analysis) to run the NWP forecast models. Radiosonde andRadiosonde and Satellite data are of crucial importanceSatellite data are of crucial importance.

• Forecast Verification: Observations (upper air, surface, satellite) considered as truth (after QC), and used to verify the accuracy of forecasts (both model and Scribe) and perform diagnostic studies.

• Weather Element Forecast: Observations used in generation of statistical equations which are used to produce forecasts of weather elements, important input to SCRIBE and forecast system.

• Applications: EER (volcanic ash, spills, fires, etc.), air pollution and atmospheric chemistry, nowcasting, surface fields (SST, ice, snow, etc.)

Observing Systems used in Global DA

Upper-air sites(TEMP, PILOT, DROP)

Surface stations(SYNOP, ASYNOP,METAR)

Buoys and ships

Aircraft(BUFR, AIREP, AMDAR, ADS)

Wind profilers(NOAA network)

Polar-orbiting Satellites(NOAA-15,16,17,18,METOP-A; DMSP-F15; AQUA, TERRA;) Geostationary Satellites

(GOES-11,12, Meteosat-7,9; MTSAT-1R)

Observations assimilated at CMC

200kmx200km/time step7 MW channels

U,V at 10 meter over ocean

250kmx250km/time step87 IR channels

SSM/I DMSP-13

QUIKSCAT, ASCAT

AIRS

(750 m) Vertical hourlyU,VProfiler (NOAA Network)

~180 km boxes11 layers, per time step

U,VMODIS polar winds

(Aqua, Terra, Global & DB)

1.5o x 1.5o

11 layers, per time step

U,V

(IR, WV, VI, 3.9μ channels)

AMV’s(METEOSAT E-W, GOES E-W, MTSAT-1R)

2o x 2o 3-hourlyIM3 (6.7 µm)Water vapor channel GOES 11-12

250 km x 250 km

per time step

Ocean Land

AMSU-A 4-14 6-14

AMSU-B / MHS 2-5 3-4

ATOVS

NOAA 15-16-17-18-19, AQUA, METOP

1o x 1o x 50 hPaper time stepU, V, T

Aircraft

(BUFR, AIREP, AMDAR, ADS)

1 report / 6hT, (T-Td), ps, (U, V over water)Surface report

(SYNOP, SHIP, BUOYs)

28 levelsU, V, T, (T-Td), psRadiosonde/dropsonde

ThinningVariablesType

100kmx100km/time step

GPSRO (COSMIC, GRACE, GRAS) Refractivity 830km, per time step

Conventional ObservationsRadiosondes Surface reports

Aircraft reports

Passive remote sensing observations(polar-orbiting satellites)

AIRS

SSM/I

AMSU-A/B

Passive remote sensing observations

GOES radiances

AMVs

Active remote sensing observations

GPS-RO

Wind profilers

Scatterometers

Data Quality Monitoring

• Meteorological Centres such as CMC that run Numerical Weather Prediction (NWP) models can monitor the performance of instruments (e.g. aircraft sensors used in AMDAR) on a continuous and near real-time basis

• Monitoring is based on observed minus first guess values (innovations), as well as data rejection statistics, extracted from the operational data assimilation system

• Monitoring is performed for individual platform, station, as well as by various programs (e.g. E-AMDAR, NOAA Satellites, etc).

• Time evolution of innovations, as well as their statistical distribution are extremely powerful and useful tools

Monitoring of AIRS radiance data

Analysis & Prediction at CMC

• Environmental Emergency applications – dispersion modeling

– Nuclear and volcanic ash

– Release of hazardous chemicals

– National security issues

Challenges

• Data Access– Despite numerous dissemination channels– Unique solutions for each new observation/product

• Data Timeliness– Require data less than one hour old– Weather Waits For No Man .. or Satellite .. or Data Delivery System

• Maintaining a Super-Computer facility– Many modeling programs require access– Keeping up with computing advances

• Assimilation of new data– Takes a long time to assimilate new data– Human resources - Finding, hiring and keeping operational staff,

researchers etc.