Overarching Automated Weather Stations (AWS) Issues

1. Overarching Automated Weather Stations (AWS) Issues (Dr Jitze P. van der Meulen, KNMI)

2. Automatic Weather Stations History of Automatic Weather Stations • Introduced as AWS for operational practices more then 50 years ago (documented as well) • First automatic remote measurements using telegraph lines • WMO Manual on the GOS:“a station at which instruments make and either transmit or record observations automatically, the conversion to code form, if required, being made for the manual insertion of data” (provision may also be made for the manual insertion of data)

3. Automatic Weather Stations History of Automatic Weather Stations • Automatic Weather Stations (AWS) as simple extra remote weather stations • Automatic Weather Stations (AWS) as alternative for manned synoptic, climate, aeronautical, agro-meteorological stations • (AWS may be manned, but the operator is for control) • (AWS has same status [quality] as manned stations)

4. Automatic Weather Stations History of Automatic Weather Stations Positive impacts: • Data from sites which are difficult to access or inhospitable • Data from manned stations outside working hours • Standardized, uniform observations • Reduction of personnel costs • Sensors in a meteorologically favourable site independent of residence and work of the observer

5. Automatic Weather Stations First question: what do we need? We want to know for many applications the state of the atmosphere. This can be obtained by measuring physical quantities and phenomena, like variables as air temperature, relative humidity, atmospheric pressure, wind, radiation, etc. (required AWS variables: see Guide to the GOS) Second question: how to get it? There are many observation technologies available • surface and satellite based • in situ and remotely sensed (all explained in detail: see CIMO Guide)

6. Composite Observing System Surface measurements 6

7. Automatic Weather Stations • AWS (networks) are part of the composite observing system, so • In case Manual  Automatic Weather Stations we have to consider: • all other available techniques • aspects of redundancy • 100% transition is impossible: • visual observations (phenomena, ‘in the vicinity’) • technical limitations (type of cloud) • (in fact tradional variables P, T, U, R, Q were already automated)

8. Automatic Weather Stations • Relevant: • What we measure is Level I data (instrument delivered) • What we require is Level II data (user specified): information  Different types of data  Data reported to be Level II data, not Level I (Level I and Level II data defined in Manual on the GDPFS)

9. Automatic Weather Stations Introducing • appropriate models describing the present state of the atmosphere • sophisticated algorithms, linking various variables convert the data into information ‘easy’:uniform ‘complex’:divers

10. Automatic Weather Stations Conversion matrix (example): (see E-PWS-SCI)INPUT: Data PhysicalVariables Weather via database

11. Automatic Weather Stations • Outmost important for automatic observations is to define first the functional specifications and in particular the MANAUT issues like visual and subjective observations (for transformation into objective quantitative variables) • ET/IPET-AUT meetings in 1998 and 1999  all WMO programmes involved  recommendations • ET-AWS (2000-2012)  recommendations  Guide to the GOS • All recommendation covering multifunctionality and standardisation for all WMO Programmes • Many, many, many documents, IOM reports on AWS; also dedicated AWS Chapter in the CIMO Guide (‘Howto’) • Papers presented at the CIMO Technical Conferences (TECO), every two years, published as IOM Reports • 4 Int. Conf. Experiences on AWS (ICEAWS) (http://projects.knmi.nl/geoss/ICEAWS)

12. Automatic Weather Stations Camera

13. Automatic Weather Stations • Spatial representativenessof visibility • VFR in case ofunavailability of sensor information • Cloud information

14. Automatic Weather Stations informatie Obs. Systems i USERS data i algoritmes i i COS PROCESSING Concepten  zoeken naar integratie

15. Automatic Weather Stations informatie Obs. Systems i USERS data i algoritmes i i COS PROCESSING QEv QEv Concepts  Search for optimalisation

16. Automatic Weather Stations However, requests for support remains; why? Management issues: • Misunderstanding: MANAUT does not mean • “unattended operations” • “less or no maintenance” • Extra, well skilled personnel is required (training, co-operation, exchange of expertise) • New technologies, like sophisticated optical present weather sensors, require more knowledge and care • Remote validation of data is required • Frequent inspection on site is required • Choosing appropriate equipment need 1. R&D, 2. tests, and finally approval (NMHS is responsible, not industry) • Well calibrated systems, traceable to SI, conform to RIC

17. Automatic Weather Stations

18. Automatic Weather Stations Methods of Observations

19. Automatic Weather Stations

20. Automatic Weather Stations However, requests for support remains; why? In fact we have to be aware that any AWS network … is a living animal: it must be every day controlled, maintained (preventive as well against any trouble shooting) , quality assessed (calibration and traceability issues), ITC connected, real time available, with standard data delivery and further products delivery… this is our everyday business, but by far the most  resources’ demanding issue, with key performance indicators (data real-time on line, data availability, QA QC data, ..) that are easy to define, and so much to maintain

21. Automatic Weather Stations However, requests for support remains; why? Typically, the whole data communication chain has to be regarded: • Data generation (on site) • Data transport/communication • Data format • Data base • Data processing • Data control • Meta data } Data management, with QMS How to deliver data appropriate for OBSINFOBUFR

22. Automatic Weather Stations Opportunity: AWS Network design • Density • Location (area representativity) • Siting (measurement uncertainty) • Multifunctionality (Synoptic, Climate, other applications, like aeronautical observations)

23. Automatic Weather Stations Opportunity: AWS Network design • Density • Location (area representativity) • Siting (measurement uncertainty) • Multifunctionality (Synoptic, Climate, other applications, like aeronautical observations) Saba

24. Automatic Weather Stations Opportunity: AWS Network design • Density • Location (area representativity) • Siting (measurement uncertainty) • Multifunctionality (Synoptic, Climate, other applications, like aeronautical observations) CIMO Guide

25. Automatic Weather Stations Opportunity: AWS Network design • Density • Location (area representativity) • Siting (measurement uncertainty) • Multifunctionality (Synoptic, Climate, other applications, like aeronautical observations) St Eustatius