© crown copyright met office new uk aws system on-going work mike molyneux and matt clark

© Crown copyright Met Office

New UK AWS SystemOn-going Work

Mike Molyneux and Matt Clark


Outline – what’s coming

•Background

•Challenges for a network

•Overview

•Sensor details

•Performance snapshots

•Quick look at MMS Windows


Who am I?

Mike Molyneux

United Kingdom - Met Office

Team Leader for Development of• Loggers• SensorsFor use in Automatic Weather Stations


Background

• The UK Met Office is refreshing the infrastructure of its network of Automatic Weather Stations.

• 200 AWS interfacing to a variety of sensors

• This is titled

• Meteorological Monitoring System - MMS project.

MMS is NOT… “Mike Molyneux’s System”


Scope of the Project

At the start of the project some decisions were made on the scope of replacement. Broadly it was chosen to:

•Retain existing sensors (but not possible at all stations)

•Retain existing communication method at site

•Retain existing signal wiring

•Benefits – uniformity – response to customers needs – improved network management – cost saving – NOT ON DAY ONE!

•Issues

• Side-by-Side testing

• Other infrastructure example surge protection


Network Challenges. Point A - Ensuring the best performance from complex instruments in remote locations

Forward scatter - precipitation type

Ultrasonic rangefinder - snow depth

Laser rangefinder – cloud height

Improved Error flagging

Direct talk through for Engineers

Link to “Tivoli” monitoring/alarm system

“ITIL” best practice in support

Older systems hard to migrate to best practice


Expectations are very high in Climate Science

Network Challenges. Point B – Quality of the measurements in many locations

Top quality loggers – improved Engineering diagnostics


Existing systems

•The UK has been using four major AWS systems.

•SAMOS - Semi-Automatic Met Observing System for manned and unmanned stations

•ESAWS - Extended Synoptic Automatic Weather Station an older unmanned system

•CDL - Climate data logger. Based on Campbell Scientific Loggers largely for simple sites

•SIESAWS – Severe Icing Environment Synoptic Automatic Weather Station for remote sites with a high icing environment


SAMOS

• SAMOS –

• ISU - Intelligent sensor units

• Interfacing, processing, transmit onto the LAN

• LAN

• Configuration change from “ring” to “star”


Network Challenges. Point C Move forward BUT control change

Re-Use existing Sensors

Largely achieved

Copy Algorithms and quality processes

Copied from our most common system but with 4 systems this can only minimise change


Project Outline


Project Outline

• System Specification

• The important parts of our existing systems

• Invitation to Tender

• Lots of high quality replies of course!

• Main Supplier CSE-Servelec

• Foundation in UK Water industry

• Campbell Scientific Limited Loggers

• Well known in meteorological industry


Project Outline continued

• Design specification – to optimise components and software to meet specific requirements

• Test plan – Functional tests + data snapshots and longer side-by-side tests

• Internal “Process change” (more later)

• Sites installed by Campbell and Met Office teams


Status in November 2008

• Rollout program well underway for unmanned sites,

• 2 Manned test sites in operation in “Continuous Operational Monitoring” – These sites have different routines – so testing is more comprehensive. Data snapshots later

• Data Quality values and coding checks by our “QC team” at Edinburgh

• Final detailed rules being applied to Observer interface


MMS overview diagram

Issues

•Manned and unmanned sites

•Secure and open sites

Components

•Firewalls

•DMZs


AWS - Sensors

Sensors interfaced to the AWSParameter details


Parameter Type Detail

Screen Temperature Platinum Resistance Thermometer

Bought to external standard. Accurate and repeatableCalibration stable over 8 years. Sampled 4 times a minute

Wind speed Cup anemometer Lightweight Vector instruments. Frequency output, sampled at 4 Hz

Wind Direction Wind vane Lightweight Vector instruments. Linear Resistance, sampled at 4 Hz

Station Level Pressure Triple cell Vaisala PTB220 for Aviation use. Serial output

Visibility Forward Scatter Meter

Belfort Instruments Sampled 2 times a minute Serial output

Present Weather Forward Scatter plus, capacitive rainmeter and thermometer

Vaisala FD12P Sampled 2 times a minute Serial output



Cloud base Laser rangefinder

Belfort and Vaisala. Sampled 2 times a minute Serial output

Cloud cover Time series cloud base strikes

Exponential Decay over 40 minutes

Rainfall Tipping Bucket Resolution of 0.2mm Area 750 sq cm15 cc per tip

Snow depth Ultra Sonic range finder

Campbell Scientific SR50

Grass, concrete and soil Temperature

Resistance Thermometer or thermistor

Calibration stable over 8 years. Sampled 4 times a minute

Screen humidity Capacitive Probe Rotronics Hygroclip



Radiation Shortwave radiometer

Kipp and Zonen CM11MilliVolt output sampled at .5 Hz

Sunshine Multiple sensor unit

Kipp and Zonen CSD1 MilliVolt output sampled at .5 Hz

Severe Icing Environment Wind speed and direction

Sonic anemometer

Heated Thies unit – serial output

Severe Icing EnvironmentTemperature

Thermistor, directly on “shaking” whip mount

Self de-icing method – assuming radiative errors are acceptable


AWS - Sensors

Sensor Performance SnapshotsData snapshots


1 minute data: MMS drybulb temperatures vs SAMOS drybulb temperature

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

17/09/200800:00

18/09/200800:00

19/09/200800:00

20/09/200800:00

21/09/200800:00

22/09/200800:00

23/09/200800:00

24/09/200800:00

date and time

(SA

MO

S -

MM

S)

dry

bu

lb d

iffe

ren

ce (

deg

rees

Cel

siu

s)

MMS extra DB vsSAMOS

Temperature

Minute data – separate and same screens

Day time variation within the screen

Bias checked – not thermometer/logger

Differential Radiative Heating?


Wet bulb TemperatureCamborne 10 minute observations wet bulb temperature time series 17-23 September 2008

7

9

11

13

15

17/09/200800:00

18/09/200800:00

19/09/200800:00

20/09/200800:00

21/09/200800:00

22/09/200800:00

23/09/200800:00

Date and time

Tem

per

atu

re (

°C)

SAMOS_WETB_TEMP

MMS_WETB_TEMP

Ten minute samples

Good agreement

PRT same measurement as dry bulb


(SAMOS - MMS): 1 minute relative humidity differences timeseries (RH sensor)

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

17/09/2008 00:00 19/09/2008 00:00 21/09/2008 00:00 23/09/2008 00:00

date and time

(SA

MO

S -

MM

S)

rela

tive

hu

mid

ity

dif

fere

nce

(%

)

RH probe values

Minute data – separate screens

Separation and probes introduce variabilty


Camborne 1 minute observations wind direction time series (17-23 Sept)

-30

30

90

150

210

270

330

390

17/09/2008 00:01

17/09/2008 12:01

18/09/2008 00:06

18/09/2008 12:06

19/09/2008 00:08

19/09/2008 12:09

20/09/2008 00:11

20/09/2008 12:11

21/09/2008 01:23

21/09/2008 13:27

22/09/2008 01:28

22/09/2008 13:30

23/09/2008 01:33

23/09/2008 13:34

Date & Time

Win

d D

irec

tio

n (

deg

rees

)SAMOS Wind Dir

MMS Wind Dir

Wind direction

Minute data

Good agreement


Camborne mean wind speed (1 minute data)

0

2

4

6

8

10

12

14

16

18

20

16/09/200800:00

17/09/200800:00

18/09/200800:00

19/09/200800:00

20/09/200800:00

21/09/200800:00

22/09/200800:00

23/09/200800:00

24/09/200800:00

25/09/200800:00

Date and time

Win

d S

pee

d (

kt)

MMS (trial)

SAMOS Mk6

Wind speed

Minute data – same mast

Rounding is visible at times

Averages and spread good


Camborne Visibilty (10 minute data)

0

5000

10000

15000

20000

25000

30000

35000

40000

16/0

9/20

08 1

2:00

16/0

9/20

08 1

8:00

17/0

9/20

08 0

0:00

17/0

9/20

08 0

6:00

17/0

9/20

08 1

2:00

17/0

9/20

08 1

8:00

18/0

9/20

08 0

0:00

18/0

9/20

08 0

6:00

18/0

9/20

08 1

2:00

18/0

9/20

08 1

8:00

19/0

9/20

08 0

0:00

19/0

9/20

08 0

6:00

19/0

9/20

08 1

2:00

19/0

9/20

08 1

8:00

20/0

9/20

08 0

0:00

20/0

9/20

08 0

6:00

20/0

9/20

08 1

2:00

20/0

9/20

08 1

8:00

21/0

9/20

08 0

0:00

21/0

9/20

08 0

6:00

21/0

9/20

08 1

2:00

21/0

9/20

08 1

8:00

22/0

9/20

08 0

0:00

22/0

9/20

08 0

6:00

22/0

9/20

08 1

2:00

22/0

9/20

08 1

8:00

23/0

9/20

08 0

0:00

23/0

9/20

08 0

6:00

23/0

9/20

08 1

2:00

23/0

9/20

08 1

8:00

Date and Time

Vis

ibili

ty (m

)SAMOS_VIS_METRES

MMS_VIS_METRES

Visibility

Different instruments

Good agreement


AWS - Sensors

Algorithms

Cloud and Present Weather


The “EDA”

• EDA - Exponential Decay Algorithm

• The EDA is a “sky condition algorithm”

• Cloud amount calculated from the LCBR

• Developed in-house

• Compared well with others


Present Weather Arbiter

• Software to control Present Weather Instrument output and provide backup using input from

• Sensitive precipitation detector

• Screen Thermometer

• Visiometer

• Anemometer

• LCBR

• Results have been studied and a scoring system developed - outcome the arbiter improves results


Process change

• Now we’ve got a new system we have to use it properly! System value will come from new ways of using it


Process changeTraining for Engineers and Network managers

Training for Observers

But also

• Identification of key processes

• Processes have to change and be documented - they will evolve

• Remember to capture new requirements

• Start using improved methods coded output to database


Examples of MMS windows

Quick Selection of

MMS User Screens

All Web access

(No easy interfaces to old systems)


Examples of MMS windows


Hard work can be fun but…


That’s all

• Any questions?


0

2

4

6

8

10

12

14

16

18

20

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131 136 141

0

0.1

0.2

0.3

0.4

0.5

0.6

MMS 10 min mean

SAMOS 10 min mean

MMS 10 min sd

SAMOS 10 min sd


-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

1 56 111 166 221 276 331 386 441 496 551 606 661 716 771 826 881 936 991 1046 1101 1156 1211 1266 1321 1376 1431

MMS DB1 - DB2

© crown copyright met office new uk aws system on-going work mike molyneux and matt clark

Documents

change slide

star slide

samos semiautomatic

mike molyneuxs system

best practice slide

meteorological industry

mms windows slide

high icing environment