Download PWS-SCI

E-PWS-SCI
Exploratory actions on automatic
present weather observations
Jitze P. van der Meulen,
KNMI, the Netherlands
PB-OBS 8, 12-13 June 2003
Observing systems
Experiences






thunderstorms, lightning
snow OR rain OR hail OR mixture
rain OR drizzle
types of fog, obscuration to vision
cloud amount (type)
under cooled precipitation (icing)
?
?
?
?
?
?
Alternative policy on Observing system
developments
- convert the data into information
data
physical
quantities
information

various types   atmospheric 
of data
models
sources
 algorithms
temperature,
wind, etc.
weather
information
Conversion matrix:
INPUT: Data
ptu
fd
OUTPUT:
remote
atm.
prec.
Information (real
sensing models
time)

ptu

fd
(etc)










icing, slipperiness
cloud information

phenomena
Project rationale and objectives


To establish to what extent the information
requested by a subset of the ET/AWS Table (related
to present weather) can be obtained by using or
combining current and future automatic observing
techniques
To identify the needs for R&D work to progress
towards a complete solution.
Prioritize
 PW-reports are of significance in case of
(expected) dangerous weather
 A number of variables can selected to be
nominated as primary PW variables with a high
degrees of priority:
1. The existence and rate of solid precipitation (esp.
snow)
2. Icing (freezing of liquid precip) and its intensity
 Other types of dangerous weather: current
observing techniques are based on measuring
the traditional physical quantities automatically
and remote sensing techniques: low priority
Principle requirements (with recommended
high performance, quality and without false
alarms):
1. The ability to discriminate between solid and liquid
precipitation (i.e. around freezing point).
2. The ability to measure precipitation rates (all types)
accurately from a high level down to a very low level
of intensity (in particular with respect to icing).
3. The ability to detect icing conditions, freezing
precipitation and the accretion of ice.
4. The ability to have numerical data on cloudiness,
radiation budget and state of the ground
variables are identified as with high
relevance (1):
1. Temperature. Around freezing point: Air, dew point
and surface temperature
2. Clouds: Position of cloud layers and cloud
amount/coverage (coverage measured by
satellites might be already accurate enough, so
ground based measurements might be overredundant)
3. Precipitation: Type, intensity and rate of ice
accretion (typically the variables presented by
PW-systems or weather identifiers)
4. Radiation: Net radiation (combination of global
and long wave radiation; can be performed by the
traditional surface based techniques in
combination with satellite observations)
variables are identified as with high
relevance (2):
1. Obscuration: MOR (or horizontal extinction
coefficient; measurement is traditionally based on
point measurement of the extinction coefficient)
2. Lightning: Intensity, polarity, type (can be
performed by a regional network, therefore not
relevant as output by an AWS to avoid confusion)
3. Other surface variables: Typically state-of-theground and snow depth
Promising technologies:




Light scattering
Radar signal reflection
(Ultra)sounding and vibrating sensors to detect
hail, icing, etc
Other types like simple detectors
Determination methods:

Objective

Subjective (acceptable?)
(currently) identified group/institutes






Swiss Federal Institute of Technology (ETH), Zurich:
Development of a cloud mapping system using ground-based
imagers
Institute für Meteorology und Klimaforschung, Karlsruhe:
Development of an optical disdrometer and measurement of
snow size spectra using radar.
Meteorologisches Institut, Universität Hamburg: Development
of a vertically looking Micro Rain Radar (MRR)
Helsinki University of Technology and the Espoo-Vantaa
Institute of Technology (Finland). Studies to enhance the
performance of PWS by improving their algorithms.
Meteo France (Direction des Systèmes d'Observation de la
Météorologie (DSO), Trappes): development of specific PW
related instruments within the Solfege project
UKMO and Muir Matteson to determine cloud-types and
visibility ranges based on digital image analyses.
Merge upper-air / satellite data
Upper air
1. Radiosondes
2. AMDAR (from ascending and descending
aircraft)
3. Profilers using (combined) RADAR, LIDAR
or SODAR technologies
4. Doppler Weather Radar systems, also
providing upper-air winds.
Satellites (EU projects)
1. SATREP
2. CLOUDMAP
Targeted R&D for improving automatic
PW observations/determinations


R&D TARGET #1, surface measurements:
Improved precipitation detection, discrimination
and intensity (range: 0.02 mm/h - 2000 mm/h), with
the ability to detect with high accuracy solid
precipitation.
R&D TARGET #2, upper air measurements:
Cloudiness: classification and amount. (using
satellite / ground based remote sensing)
Recommendations (General )
1. to continue the exploratory actions on automatic present
weather observations
2. R&D activities to obtain more appropriate in situ observing
technologies based on an integral concept of a set of
observation technologies (using algorithms) should be further
traced and stimulated.
3. Centers of R&D contributing today to the development of
PWS should be contacted to stimulate a better understanding
of the target requirements
4. Co-operation with the NMHS of USA & Canada on PWS
development is recommended.
5. Tests on primary or additional sensors, which determine hail,
freezing rain, etc., should be initiated.
Recommendations (General 2)
6. Decisions should be made if determining present weather
using algorithms, more or less based on estimations or on
pure empirical correlations are acceptable
7. A “performance” parameter (verification score or index),
should be introduced and quantified for a better description of
the functional specifications of PWS.
8. Developments in reporting weather from satellite
observations should be considered seriously. Satellite
weather reports should be accepted as an integral part of the
synoptic observation network.
9. Surface measurements and upper-air measurements should
be combined more effectively. The concept of a synoptic
network with stand-alone AWS should be reconsidered by
taking into account the ability of remote sensing the upper air
by LIDAR and RADAR generating a 3D “image” of the
atmosphere.
Recommendations (General 3)
10. As a result of new alternative sources informing the state of
the atmosphere, the measurement of some specific
parameters related to the present weather by PWS at AWS
should be considered to discontinue.
11. In cases where automation is extremely costly (e.g.
observation of specific phenomena) it should be considered
to introduce camera systems for controlling the weather at a
central location by human beings.
Proposals for action (1)
1. To endorse by Eumetnet the two R&D targets on:
a. Precipitation: Improved detection,
discrimination and intensity
b. Cloudiness: classification and amount
2. To organize a management for the initiation, coordination and stimulation of these targeted R&D
activities within the Eumetnet countries.
3. To build up a relationship with R&D groups, in
particular those nominated in this report, and with
recognized experts on R&D to arrange new
initiatives for R&D to meet the two recommended
R&D targets.
Proposals for action (2)
1. To indicate proposals for R&D projects which meet
the R&D targets
2. To draft proposals for the introduction of PW
observing technologies and PW determination
practices to be considered as standards.
3. To stimulate or initiate (in)formal meetings to
exchange experiences, suggestions for new
technologies and other relevant ideas on PWS.