p. bourgouin, c. landry, j.-f. deschênes, j. marcoux, d. talbot, m. verville meteorological systems...

P. Bourgouin, C. Landry, J.-F. Deschênes,J. Marcoux, D. Talbot, M. Verville

Meteorological Systems SectionCanadian Meteorological Centre

Meteorological Service of Canada Dorval, Québec, Canada

Great Lakes Operational Meteorology WorkshopApril 2013

Overview

1. Introduction

2. The context

3. Prototype description

4. Future development

• There is a need for a gridded nowcasting prediction system to support the public, marine and aviation forecast programs.

• Some of the requirements:

– real time observations and nowcasting weather elements on a grid

– deterministic or probabilistic weather elements

– optimum use of all types of observations

– high resolution (temporal, grid)

– reliable and totally automated

– optimum interpolation techniques

– efficient extrapolation techniques

– high resolution model

– weighted variable approach as a function of forecast time

1. Introduction

• MSC operates a point based nowcasting system called : Integrated NowCasting System (INCS)

• INCS supports Scribe the forecast production tool used to prepare Public, Marine & Air Quality forecasts.

• INCS provides weather elements only for the Public program

• MSC is currently working on planning the Next Generation Forecast System (Concept of Operation: ConOps) and a gridded weather elements approach is envisioned

• A Meso-scale Analysis and Nowcasting prototype has been developped by Pierre Bourgouin

• An event base extrapolation technique is needed to improve two nowcasting statistical modules: TAFTools & PubTools

2. The context

Matrices Generator

MODELS& other data

sources

Concepts Generato

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MATRICES

Products Generato

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PRODUCTS Weather Elements

NowcastingUpdated

Weather Elements

Scribe InterfaceObservations METAR, Radar

Lightning

Very short range Forecast System

NWP - UMOS

National Production CMC SPC Wx Office + …

Scribe: Point based Forecast production tool

Nowcasting HourlyMATRICESR

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1. Observations from surface stations are converted into analyses using the Kriging interpolation method (50 km grid):– Extraction of hourly surface observations over Canada (all),

northern US (selection) and western Greenland (selection)– Transformation of the different elements (TT, ET, TD, VS, UU, VV, VIT, PN, VE,

HB, INT, TYP, CVC, CL, PLF, TOB, OPA, ECI, ZR) into analyses at a 50-km resolution using Kriging

– Consistency check using a rule-based module– Resulting first-guess meso-scale analysis are done for precipitation

types (occurrence), convection and cloud cover

2. These preliminary analysis are then improve by using data from other sources:• Precipitation type analysis is refined using data from radar, satellite,

NWP model (Côté et al. 1998).• Convection analysis uses the Canadian lightning detector network

(Orville et al. 2002) and NWP model.• Cloud cover analysis is improved using a mid-level cloud analysis

produced using GOES satellite data (Garand 1993).

3. Prototype description

3. The sequence in the production of the meso-scale analysis is important:1. Cloud cover

2. Precipitation occurrences

3. Precipitation types

( Convective analysis is independent)

4. The final analysis are extrapolated by a forward scheme using the NWP wind field (50% of 500 hPa or 100% 700 hPa)

1. Cloud cover

• Coast + oceans : AVG [SAT + RDPS + TRIAL + Interpol.]

• Continent : AVG [SAT + TRIAL + 2*Interpol.]

CLOUD COVER ANALYSIS KRIGING

RDPS - NT

TRIAL: NC ET T+1H

SAT – CF

2. Precipitation occurrence

• Produce by combining the following information:– model data (over ocean & lakes)

– radars composite

– integrated cloud analysis (sfc obs + sattelite + model)

– interpolated precipitation occurrence analysis (sfc obs + Kriging)

– one hour forecast of precipitiation occurrence (trial field)

• A weight is given to each value. If the sommation of the weighted values exceeds a threshold, precipitation are diagnose at this grid point.

RDPS - Amounts

RADAR

Cloud CoverKRIGGING OCC. TRIAL OCC T+1H

PRECIP. OCC. ANALYSIS

INITIAL PRECIPITATION ANALYSIS (COLOR) AND NWP PRECIPITATION (BLUE)

FINAL PRECIPITATION ANALYSIS (COLOR) AND OBSERVATIONS (RED)

3. Precipitation Type

• Produced by combining the following information:– Interpolated types analysis (Kriging)

– Final Precipitations occurrences analysis

– RDPS model type analysis

– Diagnostics temperature

• If precipitation occurrence is diagnosed at a point, the type associoated will be selected from the first non “nil” of:

– Result of the Kriging analysis at that point

– Near by analysis

– Model diagnostic

– rain if T > 3o C, otherwise snow

– Type = “nil”

Types are:1. No precip

2. Liquid

3. Solid

4. Fresing

PRECIPITATION TYPES ANALYSISSUMMER CASE

Interp. TYP + OCC

RDPS TYP LIQ

FRZGSOL

Temperatures

PRECIPITATION TYPES ANALYSISWINTER CASE

20060222 1500UTC

4. Convection

• Produced by combining the following information:– Interpolation analysis of convection (Kriging)– Lightning data– Lifted index from RDPS (only for showers)

Types are:1. Stable

2. CU

3. TCU/ACC

4. CB

CONVECTION ANALYSIS (COLOR) AND OBSERVATIONS (BLUE)

THUNDERSTORMTYPE

SHOWER TYPE

CUMULUS TYPE

5. Extrapolation

• Extrapolation module using NWP wind fields to advect precipitation type, convection and cloud cover.

• Current version uses 50% of 500 hPa RDPS winds.

ADVECTION USING 50% OF THE 500hPa WIND FROM A NWP MODEL

PRECIPITATION TYPE ANALYSIS (BLUE CONT.) AND FORECAST AT T+01H (COLOR)

ADVECTION USING 50% OF THE 500hPa WIND FROM A NWP MODEL

PRECIPITATION TYPE ANALYSIS (BLUE)PRECIPITATION TYPE T+03H (MAGENTA) CONVECTION T+03H (RED Contours)

20:00 UTC 11-04-201321:00 UTC 11-04-201322:00 UTC 11-04-201323:00 UTC 11-04-201300:00 UTC 12-04-201301:00 UTC 12-04-2013

Meso-Scale Analysis Precipitation Types

Nowcasting module Precipitation types, Convection & Clouds

Verification– This evaluation was performed over two periods: a warm season

April to September and a cold season November to March.– The occurrence of precipitations was verified for the values

produced by:▪ Meso-Analysis extrapolated (Sampling)

▪ 00Z and 12Z run RDPS (Sampling)

▪ PubTools (Nowcasting statistical forecast system bases on METAR)

– Scores▪ RPSS (Ranked Probability Skill Score)

▪ HSS (Heidky Skill Score)

▪ PC (Percent Correct)

PRÉSENCE DE PRÉCIPITATION - RPSS00UTC 20070401-20070930

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PRÉSENCE DE PRÉCIPITATION - RPSS12UTC 20071001 - 20080331

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Cold Season (November to March) 00 & 12UTC

• Finalise the « operationability » of system (Ops. Standards)

• Increase resolution to current Regional Model (10km)

• Evaluate possible replacement of the Kriging method with the optimal interpolation scheme MIST (Moteur d'Interpolation Statistique : Statistical Interpolation Engine)

• Verify forecasts produced with the extrapolation technique and with INCS outputs.

• Integrate the Meso-scale Analysis & Extrapolation into INCS

• Compare extrapolation with motion vectors from MAPLE (McGill Algorithm for Precipitation Lagrangien Advection, Turner et Al. 2004) with the winds of Canadian Regional NWP model (RDPS)

• Define the best way of choosing the appropriate wind field level for the extrapolation

• Explore a vertical differential extrapolation approach based on more than one level (Ex. low, mid and high levels)

4- Future of development

p. bourgouin, c. landry, j.-f. deschênes, j. marcoux, d. talbot, m. verville meteorological systems...

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