New developments and operational experience with

surface observation technology and national

networks

Jitze P. van der Meulen,Royal Netherlands Meteorological

Institute

TECO 2006

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Historic Background 1643: Evangelista Torricelli discovers

vacuum invents mercury barometer and suggested p

1645:René Descartes suggests p = p(h)

1650: Blaise Pascal and Robert Boyle, by experiment: p & p = p(h), using a mercury barometer

All using calculus: hydrostatics & mathematics

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Historic Background 1802: Alexander von Humboldt,

explorer to discover environmental phenomena: t = t(h), later on with Joseph-Louis Gay-Lussac

< 1850: phenomenological studies with a deterministic approach, e.g. networks to measuring the intensity of Earth's magnetic field

> 1850: use of mathematics comes up and experiments produce quantitative data to prove any theory by calculus

Discovery of phenomena (qualitative), later on mathematics (quantitative)

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Historic Background > 1835: networks to measure the

geological behavior of the earth electromagnetic field in combination with barometers and themometers, to discover any correlation

Measurements of the variable behaviour of atmospheric pressure: a relation with wind (speed and direction) was discovered

Tendency of pressure useful to forecast weather (still popular today at home )

Geospatial relationship of pressure gradients and the behaviour of wind

Discovery of phenomena

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Historic Background > 1845: introduction of Telegraphy,

providing in "near real time“ and synoptic surface observations to be plotted in synoptic charts (a ‘nearly instantaneous picture of the state of the atmosphere’)

1900: postulation that hydrostatic formulas could be used to describe the current state of the atmosphere and for forecasts (‘calculating the weather’)

> 1945 introduction of operational upper air measurements and NWP

Use of geophysical data in weather charts, calculus requires “infinite amount” of arithmetics

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Historic Background

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Historic Background Today: Both science and operational services

(meteorology, climatology, etc.) are oriented on both

– discovery of weather phenomena(the classic deterministic approach), and on

– thermodynamics with plenty of mathematics (used in NWP & NCP models).

This statement not only holds for in situ measurements but also for remote sensing and especially for observations from satellites (Earth Observation).

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Systems development and functional requirements functional specifications of networks and weather

stations have a complex background A network should provide

– present weather information expressed in pre-defined parameters, like type-of-cloud, special phenomena, type-of-precipitation, icing

– numerical data, i.e. quantitative physical variables

Complex? Simple compared to the present needs of the multiform disciplines in meteorology and the available technologies of observation, both in situ and remotely sensed

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Systems development and functional requirements Development of observing systems, in

particular those systems based on satellite born remote sensing ('discovery' entity, but also producing quantities)

Geospatial differences in network densities Differences in representativety (area averaged

vs. point measurements) Differences in observation times and

frequencyin situ satellite

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Systems development and functional requirements Development of observing systems, in

particular those systems based on satellite born remote sensing ('discovery' entity, but also producing quantities)

Geospatial differences in network densities Representativety (area averaged vs. point

measurements)

wind derived from the ERS-2 scatterometer, south- east of Greenland, 2006-10-15 21:30 UTC

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Systems development and functional requirements The various disciplines in meteorology,

climatology, hydrology, etc. expressed an increasing demand on observational data and with discipline specific conditions.

Alternative or new observations, performance, representativety and frequency of observations.

Complete automation (of subjective observations, present weather, phenomena), modern AWS

Strong increasing diversity in both the users' requirements and in observation technology

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Systems development and functional requirements

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database oriented information generators

Systems development and functional requirements

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Systems development and functional requirements Defining functional specifications of physical variables to

be measured is relatively straightforward: measurement uncertainty can be expressed quantitatively and references are clearly defined.

To specify quality figures for reporting weather events, atmospheric conditions or qualitative descriptions (the assessment of the state and development of the atmosphere, and of significant weather) is complicated and affected by subjectivity:E.g., shall we use performance indicators like

Skill scores Success rates False alarms rate, or A mixture of these?

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Systems development and functional requirements A contingency matrix is most applicable, but

how to interpret?

detector

yes no

reality yes a b

no c dProbability of Detection: a / (a + b)False Alarm Ratio: c / (a + c)Equitable Skill Score: a / (a + b) – c / (c + d)BIAS = (a + b) / (a + c) [should be 1]

Event?

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Performance indicators

detector

yes no

realityyes 15% 5%

no 5% 80%

POD = 75%FAR = 25%ESS = 69%BIAS = 1

‘overall score’ ?

range ?: e.g. ‘icing’ most relevant: ‑2°C < T < +2°C

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Systems development and functional requirements New challenge: observations in

combination with a high resolution model: using the actual NWP grid-point data as

background (e.g. 11 km grid) Using a detailed orographic database

(relief map) Climate data for further fine-tuning +

knowledge of atmospheric behavior in land-water transitions

Now-casting technique with ‘downscaling’ capabilities

50 km

Virtual reality ?

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Systems development and functional requirementsMethods of observing Present

Weather, also virtual reality? Determination based on empirical

relations Direct and indirect phenomenological

en physical relationships are applied Climate data as filter tool Algorithms, tuned for optimal

performance

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Systems development and functional requirements

Methods of observing Present Weather, also virtual reality?

Examples: Undercooled precipitation using wet &

dry bulb temperature Hail detection from doppler weather

radars Precipitation determination based on

optical characteristics of the hydrometeors in combination with air temperature

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Systems development and functional requirements

Methods of observing Present Weather, also virtual reality?

Data from the true world is not available, so validation is a hazardous task

How to calibrate? What is a reliable reference?

Algorithms are not well documented and published

A quality standard for such determination is missing (may be in line with ISO 17025)

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Development and new design of instruments Clustering of sensors/instruments, e.g. on

one single statue discarding the mutual impacts

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Development and new design of instruments Clustering of sensors/instruments, e.g. on

one single statue discarding the mutual impacts

Micro-sized systems with 'multi-sensors on a single chip'

wind sensor

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Development and new design of instruments Clustering of sensors/instruments, e.g. on

one single statue discarding the mutual impacts

Micro-sized systems with 'multi-sensors on a single chip'

Pressure sensor

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Development and new design of instruments Clustering of sensors/instruments, e.g. on

one single statue discarding the mutual impacts

Micro-sized systems with 'multi-sensors on a single chip'

Introduction of ‘alternative’ techniques

holograms

monostatic radar: drop size distribution

‘Hotplate snowgauge’ Acoustic detector

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Trends and criteria for further development, a conclusion Ongoing development of new methods of

observation and measurement devices Satellite born remote sensing techniques to

observe the surface of the whole earth Ongoing automation of weather stations,

automation of observing the weather All kind of observations to be converted into

geo-physical quantities Determination of weather parameters based

on phenomenological assumptions

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Trends and criteria for further development, a conclusion Priority on efficiency and cost reduction Observation systems and networks will be

further optimized; wish to integrate observing systems; surface variables determined from in situ (surface) and remote sensing (satellites) data

Automation of subjective observations to be standardized or at least well documented in open literature; requirements on skill scores

Focus on specific, dangerous weather events, and on methods of observations to improve the performance of NWP

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Thank you