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Fog forecasting at FMI - forecaster’s view. Vesa Nietosvaara. Photo: Jenni Teittinen. Contents. Introduction Climatology Rules of thumb Localizing fog Satellite information Model support Observations Real life Case studies. Introduction. At FMI fog is forecasted for:

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Fog forecasting at fmi forecaster s view

Fog forecasting at FMI- forecaster’s view

Vesa Nietosvaara

Photo: Jenni Teittinen


Contents
Contents

  • Introduction

  • Climatology

  • Rules of thumb

  • Localizing fog

  • Satellite information

  • Model support

  • Observations

  • Real life

  • Case studies


Introduction
Introduction

  • At FMI fog is forecasted for:

    • General public in the media (overview)

    • Aviation forecasts (as precisely as possible)

    • Marine forecasts (overview -precise)

  • The central service at 6th floor takes care of most of the general and marine weather forecasting

  • Regional services: especially aviation forecasting


Climatology
Climatology

  • Stratus and fog very common in Finland. The probability of St/fog is at its maximum during late spring and winter.

  • For example, in southern and central Finland fog is observed roughly 15-25% of the days in winter.

  • In summer the fog probability is only 5-10%.


More climatology
More climatology

  • “Fog axis” at upslopes of Salpauselkä (100 km north of south coast)

  • Northern Finland hills: often foggy (Rovaniemi airport)

  • Duration of fog:

    • advection fog Oct-Dec: even days

    • Radiation fog: early summer vs. late summer: in early summer not so frequent, but more persistent!


Rules of thumb
Rules of thumb

  • The basic rules known to each forecaster:

    • For example, the requirements for the formation of radiation fog

    • Advection fog rules

    • Plus a lot of silent knowledge, especially at the aviation forecasting !

  • Local knowledge and experience (based on climatology):

    - Air streams, wind directions favourable for fog formation


Wind direction vs fog probability
Wind direction vs. fog probability

  • A climatological study within COST 722 is being done currently at FMI (Jukka Julkunen, Rovaniemi)

  • Fog climatology for ~10 selected airports in Finland.


Localizing the fog
Localizing the fog

  • Less and less manual SYNOPs and METARs.

  • More automatization.

    ► fog mapping purely based on observations is very coarse and unreliable.

  • Satellite observations are crucial:

    • MSG used, but not yet as effectively as we wish

    • AVHRR traditionally the most used instrument

  • Our experience is that even few observations combined with satellite images allow a satisfactory start for fog analysis

  • A good mesoanalysis of the fog is needed!!


Satellite information
Satellite information

  • Some examples of available satellite products at FMI:

  • AVHRR:

    • daytime 0.6 + 0.9 + 10.8 μm combinations

    • daytime 0.6 + 1.6 + 10.8 μm combinations,

    • night-time 3.7 + 10.8 + 12.0 μm combinations,

    • night-time 3.7 – 10.8 μm difference images.

  • Meteosat-8:

    • As NOAA, but difference images not yet implemented

    • Individual images: very little use

    • MTP still the most used source of information !?


General experiences
General experiences

  • AVHRR superb in northern latitudes even in Seviri era

  • Gaps in passes during the afternoon and night are problematic

  • The 15-minute-interval for Meteosat-8 is extremely valuable for nowcasting purposes


Some other examples of the use of satellite information
Some other examples of the use of satellite information

  • Fog sheets and their relation to daytime convection..

  • Dissipation of fog




Model support
Model support

  • The general forecasters work mostly with this kind of model output when forecasting fog…


Model support1
Model support

  • Or with this kind of products…


Model support2
Model support

  • …or with anything they find from the internet ...

http://meteo.icm.edu.pl


Model support3
Model support

  • But no real fog model is currently available.


Surface observations
Surface observations

  • While classical observations have decreased, new observational data has become available

    • Ceilometers

    • Mast obs

  • Sounding data not adequate for fog analysis purposes

  • Even weather radar network can be used in some cases


Real life
Real life

  • Forecasters are aware of especially difficult fog forecasting issues:

    • Spring/early summer fog banks at sea

    • Fog forming or not forming just prior to sunrise?

    • How to actually forecast the dissipation of the fog?

    • Evaluating visibility is very very difficult.


Case studies
Case studies

  • Irene Suomi: local fog case 8.9.2002 at Gulf of Finland

  • Leena Upola: fog case in northern Lapland 4.9.2003


Photo: Jenni Teittinen 8.9.02, Kruunuvuorenselkä

Case Study:

Marine Fog on the Coast of Helsinki 8.9.2002

``Yhtä sakeaa sumua olen kohdannut 35 vuoden aikana vain kolmasti.'‘ PORKKALANNIEMI 8.9.02

``Kun laiva oli miekassa, lähestyi tutkalla tehdyn havainnon mukaan etelästä purjevene, joka väisti kohti rantaa (kadotti paikan todennäköisesti). Meiltä ei sitä optisesti sumun takia nähty, vain tutkalta. Jälkeenpäin kuultiin, että kaksi purjevenettä oli harhautunut rantaan. Molemmissa oli ollut pakolaisia!!!! Auttamaan tullut polisiisvene ajoi sekin kivikkoon! '' KUSTAANMIEKKA 8.9.02

``Koko Kruunuvuorenselkä oli paksussa sumussa, näkyvyys 50 m. Itse etenin kohti Hevossalmea tutkaa hyväksi käyttäen. Koko Kruunuvuorenselkä oli täynnä veneitä, noin 20. Osa oli neljän, viiden veneen ryhmissä paikallaan'' KRUUNIVUORENSELKÄ 8.9.02


Introduction

Case Study: Marine Fog on the Coast of Helsinki, 8.9.2002

Dramatic and unexpected marine fog

  • the inlet of Kustaanmiekka was closed by Helsinki Vessel Traffic Service for about an hour in 8.9.2002 afternoon because several boats had got lost in the ship lane

  • tens of boaters were caught by the fog during the day: several alarm notices to the Maritime Rescue Co-ordination Centre in Helsinki (MRCC Helsinki)

  • weather conditions inland: warm and sunny late summer day

  • fogs fairly rare on Finnish coastal seas in autumn because surface waters are typically warm after the summer


Introduction

Case Study: Marine Fog on the Coast of Helsinki, 8.9.2002

Methods and Goals of the Study

  • Where and when? – mapping of the evolution of fog

    • satellite data

    • “in situ” observations (synoptic weather stations, boats, ships, the Coast Guard, etc.)

  • Why? – determination of physical conditions

    • sea surface temperature (from satellite images processed by SYKE)

    • meteorological factors (synoptic weather stations, soundings, mast observations)

    • origin of the foggy air: reversed model run with SILAM dispersion model

  • Predictability? – consideration of the forecasting aspects

    • as a meteorologist

    • from the viewpoint of HIRLAM (HIgh Resolution Limited Area Model)


Synoptic Weather Conditions

Case Study: Marine Fog on the Coast of Helsinki, 8.9.2002


Observed Fog Areas

8.9.2002 at 9 a.m. ± 1 h (local time) 8.9.2002 at 3 p.m. ± 1 h (local time)

Case Study: Marine Fog on the Coast of Helsinki, 8.9.2002


Observed Fog Areas

8.9.2002 at 6 p.m. ± 1 h (local time) 9.9.2002 at 9.09 a.m. (local time)

Case Study: Marine Fog on the Coast of Helsinki, 8.9.2002


Sea Surface Temperature

Wind roses:

Inkoo Bågaskär Helsinki Harmaja

28.8.-2.9.02

2.9.-8.9.02

(Source: Suomen ympäristökeskus, SYKE)

Case Study: Marine Fog on the Coast of Helsinki, 8.9.2002


The Fog at Helsinki

  • at 4 p.m.: fog patches on a narrow zone parallel to the coast (yellow)

  • later in the evening the fog area becomes wider

  • at 6 p.m.: fog is observed also at Harmaja and Isosaari (grey)

  • wind direction changes ca 360 degrees within a day:

    • land/sea breeze phenomenon

    • strengthening high pressure

  • there is correlation between the width of the foggy area and the wind direction

Case Study: Marine Fog on the Coast of Helsinki, 8.9.2002


Forecasting as a Meteorologist

Significant factors in fog formation:

  • sea surface temperature pattern: due to upwelling, the sea surface temperature was lower at the Finnish coast compared to the other parts of the Gulf of Finland and Baltic Sea

  • the air had travelled a long distance above warm sea before arriving in the Finnish Coast

  • strengthening high pressure and related weakening of wind

Difficulties in forecasting the afternoon fog:

  • marine fogs fairly rare on autumn

  • sunny and warm weather was expected inland => difficult to estimate what will happen to the fog during the day on a narrow zone of cold sea surface

  • no indications of a dramatic fog situation on satellite images

  • between 9 a.m. and 9 p.m. (local time) only one synoptic weather station (Kirkkonummi-Mäkiluoto) reported fog at observation times

Case Study: Marine Fog on the Coast of Helsinki, 8.9.2002


Conclusions

  • Fog evolution 7.-9.9.2002

    • initial formation at night 7.-8.9.2002

    • partial dissipation around noon

    • movement and extent of fog patches in the afternoon determined by the local changes in wind field

    • gradual dissipation at Finnish coast during the following night as the wind turned to north

  • Typical features of marine fog formation

    • sea surface temperature pattern: sharp change in horizontal because of upwelling in the northern Gulf of Finland

    • foggy air travelling a long distance over warm water before arriving in the area of cold sea surface

    • strengthening of high pressure and related weakening of wind


Conclusions

  • Forecasting: meteorologist

    • sea surface temperature in major role in this case but also in any marine fog case

    • meteorologists need more tools to observe the fog => more cooperation with other authorities?

  • Forecasting: HIRLAM

    • progress from ENO to the new version

    • results fairly good with climatological sea surface temperature => how about the effects of upwelling?



Satellitepicture noaa 345 the first picture in this morning
Satellitepicture (NOAA 345), (the first picture in this morning)

  • Western Lapland is cleared up.

    In north-western part some upper clouds, developing showers? (near the upper-through)

  • In eastern Lapland the cloudcover is thinning, but low stratus can be distinguished in black colour.

  • Green dots are EFRO and EFPU

4.9. 02.18z


Wind forecast
Wind forecast

  • almost the same wind speed at EFRO and at EFPU

  • weak wind helps the fog formation


Military metars in pudasj rvi
Military Metars in Pudasjärvi

  • 040250z 24004kt 4000 br few005 sct058 08/08 Q1007=

  • 040320z 24004kt 4000 br sct006 bkn062 08/08 Q1007=

  • 040350z 29005kt 5000 br bkn004 sct060 08/08 Q1008=

  • 040400z 29006kt 2000 dz ovc003 08/08 Q1008=

  • 040410z 29006kt 0800 fg ovc003 08/08 Q1008=

  • 040420z 28004kt 0500 45fg ovc002 09/08 Q1008=

  • 040450Z 27005kt 0300 fg ovc002 09/08 Q1008=

  • 040520z 28004kt 0400 fg vv002 09/08 Q1008=

  • 040550z 28005kt 0400 fg vv002 09/08 Q1009=

  • 040602Z 26004kt 3000 br OVC002 08/08 Q1009=

  • 040620Z 26004kt 8000 VV001 08/08 Q 1009=

  • 040650Z 23003kt 9999 sct003 bkn010 09/07 Q1009=

  • 040720z 21004kt 9999 sct010 10/9 Q 1009=


Conclusions
Conclusions

  • The model and radar products are displayed in a way which does not help in forecasting stratus.

  • Infrared or interpreted satellitepictures and surface observations are useful in large scale: cloud edge is approaching, a rough estimate of cloudbase.

  • Metar-observations twice in hour: almost realtime data, but air temperature and dewpoint temperatures are rounded off and information is lost, which is troublesome when we are near saturation.

  • In this particular case we were able to say, that fog/stratus is coming and dispersing in the accuracy of (+/- )3 hours

  • It isn’t nearly enough, what we are promised to do:

    Limits for amending are very near each other in poor weather!


Summary
Summary

  • better realtime observations from ground to 1500ft (significant cloud base): masts and profilers, low level soudings?

  • Serviceable, practical tool especially for radiation fog situation, where fog is developing at the same time in the whole district, not advecting.

  • Practical = visual, graphic, easy to outline. It also tries to warn in situations learned with experience (”rule of thumb”):

  • Rapid temperature change, change in radiation balance, (scattering/coming upper cloud), changing separation between air temperature and dew-point temperature.


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